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

    Summarize the paper's key **research findings** in the style of a policy brief. Your response should:
    - Focus on the major empirical or model-based results
    - Present findings that are policy-relevant and supported by the study
    - Use concise, non-technical language with essential technical terms as needed
    - Avoid quoting directly or summarizing minor details

    Write a short, clear paragraph appropriate for the “Scientific Research Findings” section of a policy brief.

    Scientific Research Paper:
    Articles
https://doi.org/10.1038/s41560-019-0507-y
1The Ohio State University School of Environment and Natural Resources, Columbus, OH, USA. 2University of Southern California Sol Price School of Public
Policy, Los Angeles, CA, USA. 3Present address: Australian National University School of Regulation and Global Governance, Australian Capital Territory,
Canberra, Australia. *e-mail: Lee.White@anu.edu.au
T
o mitigate climate change, electricity grids need to integrate
large shares of renewable generation. Some renewables,
such as solar, are variable and cannot be generated accord-
ing to market needs, which creates challenges in matching supply
with demand. Demand-side response (DSR) measures are gain-
ing prominence as a way to align demand with non-dispatchable
supply. The residential sector accounts for 30–40% of electricity
consumption across the Organisation for Economic Co-operation
and Development countries, which makes it a prime target for
DSR1. Decision makers, such as the California Public Utilities
Commission, are enacting policies that require default enrolment in
DSR programmes2, which can yield participation rates that exceed
80% (ref. 3); other entities may follow4. Thus, DSR is poised to soon
reach millions of households, which highlights the need to under-
stand whether the costs and benefits of DSR are distributed evenly
across sociodemographic groups.
DSR measures typically use price signals to attempt to shift
demand away from high-demand ‘on-peak’ times. Static time-of-use
(TOU) rates are a common DSR measure that aim to shift electricity
use away from on-peak times using a fixed rate schedule with more
expensive on-peak times. For households that already struggle with
electricity bills, this can be detrimental5–8. Households suffering
from energy poverty are forced to make trade-offs between paying
for electricity bills versus other necessities, such as food and medi-
cine5,6,9,10. TOU and other forms of DSR may worsen this trade-off
pressure, often termed ‘the heat or eat dilemma’.
The term ‘energy poverty’ broadly refers to a confluence of fac-
tors that result in the inability to maintain a dwelling at a comfort-
able and healthy temperature, failure of which is associated with
increased mortality and morbidity, and having to make decisions
between paying for electricity or other necessities such as food7,9–11.
Energy poverty is often considered synonymous with ‘fuel pov-
erty’12, and assesses the same sets of concerns addressed by some
definitions of energy insecurity13.
Energy poverty can be considered a state of being in which
households face an inability to meet both energy and other costs
necessary to live a decent life8. In contrast, energy vulnerability is
dynamic, with energy-vulnerable households characterized as those
that “face a combination of more intense and non-negotiable energy
needs as well as a lack of social and/or financial capital”.8 Energy-
vulnerable households have a limited capacity to adapt to changing
circumstances, such as TOU assignment8.
Further, energy-vulnerable groups face energy injustices: proce-
dural injustice in limited access to information, policy participation
and legal rights; distributional injustice in inequalities in income,
energy prices and housing efficiency; and injustice in recognition,
which is a lack of recognition of the differential needs of energy-
vulnerable groups, and an unequal accordance of respect14. Walker
and Day14 provide in-depth discussions of these topics. Below, we
draw on energy poverty and energy justice literature to define vul-
nerability indicators.
Low-income households face pressure to curtail energy costs,
often with negative impacts8. For instance, during winter months
with high heating bills, low-income households curtail energy use
to thermally uncomfortable levels6. Electricity shut-offs that result
from utility debt can exacerbate both physical and mental health
conditions5, and low incomes are linked to a higher likelihood of
mortality during extreme heat events15–18. Lower incomes are linked
to distributional injustices, which include higher likelihood of liv-
ing in inefficient buildings with poor insulation and less efficient
appliances, which means these homes are more expensive to heat
(or cool) than others5,14,19–21.
Elderly people are at risk of recognition injustices. They require
a narrower band of temperatures for health22 and suffer exacerbated
mortality during both extreme heat and extreme cold if unable to
maintain the appropriate temperatures23–26. As a case in point, heat
waves in Italy and France in 2003 were associated with higher mor-
tality rates for elderly individuals17,18. Elderly people also experience
Health and financial impacts of demand-side
response measures differ across sociodemographic
groups
Lee V. White   1,2,3* and Nicole D. Sintov1
Demand-side response (DSR) measures, which facilitate the integration of high shares of intermittent renewable generation
into electric grids, are gaining prominence. DSR measures, such as time-of-use (TOU) rates, charge higher rates during high-
demand ‘on-peak’ times. These rates may disproportionately impact the energy bills and health of vulnerable households,
defined as those who face greater energy needs combined with greater social and financial pressures. Here we examine 7,487
households that took part in a randomized control TOU pilot in the southwestern United States. We found that assignment to
TOU rather than control disproportionately increases bills for households with elderly and disabled occupants, and predicts
worse health outcomes for households with disabled and ethnic minority occupants than those for non-vulnerable counter-
parts. These results suggest that vulnerable groups should be considered separately in DSR rate design, and future pilots
should seek to determine which designs most effectively avoid exacerbating existing energy injustices or creating new ones.
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greater difficulty paying for food during seasons with high heating
or cooling needs, compared to the general population27.
Families with young children experience heightened pressures
and thus are at risk of recognition injustices. Households with chil-
dren bear additional costs associated with ensuring that children
are well fed and healthy5, and thereby face greater competing (child-
care) expenses and hence more challenges to meet their energy
needs. Children living in energy poverty are more likely to face food
scarcity and to have health and developmental issues, compared to
those not in energy poverty28. Young children are also at higher risk
of morbidity in extreme heat events25,26.
People with disabilities face higher rates of energy poverty10.
Compared to the general population, people with disabilities may
need more energy to realize a range of essential capabilities24. Illness
or disability can limit freedom of movement, which raises energy
costs due to people being at home more8. Although previous work
has not found disability to predict mortality during heat events29,
financial pressure to curtail electricity use may contribute to poorer
health outcomes. The energy needs of those with disabilities vary
greatly depending on the individual and disability, but are likely
to involve a higher energy use. Yet, the needs of individuals with
disabilities are often systematically disregarded by decision makers
(recognition and procedural injustices14).
Finally, racial and ethnic minorities face procedural injustices in
the form of discrimination in areas such as housing, employment
and credit30, in addition to a lack of informed consent for energy
projects, lack of representation in the decision-making and lack of
access to information14,31. Distributional injustices arise from these
procedural injustices, and racial minorities are more likely to live
in inefficient housing that necessitates higher energy bills to con-
trol indoor temperatures compared to the non-minority counter-
parts32,33. Racial and ethnic minorities may also face greater health
impacts tied to inability to cool homes; the likelihood of death dur-
ing an extreme heat event in the United States is linked to sociode-
mographic vulnerability, defined partly by ethnic minority and
Latino immigrant status15,34.
Some argue that low-income households can save money on static
TOU rates because their existing patterns place most of their use away
from on-peak times35. However, work that examined critical peak
pricing found that, at baseline, low-income and elderly households
tend to have a lower-than-average use during on-peak times, whereas
households with chronically ill members tend to have a higher on-
peak use36. Beyond existing use patterns, demand flexibility is con-
sidered key for households to be able to take financial advantage of
TOU37. Vulnerable households may face constraints that limit the
flexibility of electricity use timing, such as poorly insulated homes
that prevent the retention of comfortable temperatures if heating or
cooling systems are turned off5,32,33,38. Some DSR trials found that
vulnerable (low-income, young children, elderly and/or chronically
ill) households can load shift on par with or to a greater extent than
non-vulnerable households36,39–41. However, other work found that
vulnerable (low-income and young children) households load shift
less and/or have a higher demand during on-peak times and limited
flexibility compared to non-vulnerable households39,42–44. Overall, it is
unclear how TOU rates will impact vulnerable households in terms
of bill changes, and responses will probably differ by group. There is a
risk that those with higher and less flexible energy needs, such as the
elderly or those with a disability, will face bill increases.
Energy poverty has been associated with a range of nega-
tive health outcomes, particularly regarding respiratory health45.
Although much work examines the links between energy poverty
and discomfort, illness and mortality in cold climates11,46–48, less
work has examined these links related to extreme heat. Prior work
identified that greater thermal discomfort associated with energy
poverty is tied to an increased likelihood of negative impacts on
both physical and mental health among households in cold cli-
mates45,49. The vulnerable groups that we focus on have been found
to suffer worse outcomes during extreme heat events15–18,25,26,34; this
may be linked to the inability to access sufficient cooling, but has
not yet been examined in the context of energy poverty.
Here we evaluate the cost and health impacts of TOU among vul-
nerable (that is, low income, elderly, disability, young children and
racial/ethnic minority; Table 1 gives the operational definitions)
versus non-vulnerable households that took part in a randomized
control TOU pilot in the southwestern United States. We found
that, although all households on TOU face bill increases relative to
controls, those vulnerable on the elderly and disability indicators
face greater bill increases on TOU versus control than their non-
vulnerable counterparts. Conversely, low-income and Hispanic
households face relatively smaller bill increases on TOU versus con-
trol than their non-vulnerable counterparts. Households vulnerable
on low-income and disability indicators face worse health outcomes
regardless of the rate. Relative to their non-vulnerable counterparts,
households vulnerable on disability and Hispanic indicators face an
increased likelihood of negative health outcomes when assigned to
TOU, and low-income households face increased discomfort. These
results suggest the need to consider vulnerable groups separately,
and the importance of a careful rate design.
Effect of TOU on electricity bills
All analyses were performed using STATA MP 14.2. The pilot
included two TOU rates and a non-TOU control group. Compared
Table 1 | Definition of vulnerability on each indicator used in the quantitative analyses
Low income
Enrolled in electric utility financial aid programme; some eligible households probably did not complete the enrolment process,
so this indicator may be imperfect
Elderly
Someone over the age of 65 resides in the household
Young children
Someone under the age of 6 resides in the household
Disability
Someone in the household has a disability or serious medical condition that requires the ‘home to be cool in the summer’, or
that requires them to ‘use more energy for medical equipment’; although this does not cover the full range of reasons for which a
person with a disability may need to use additional energy, available data preclude such a nuanced examination
Hispanic
Respondent identified as Hispanic. Respondents that identified as non-Hispanic white are considered non-vulnerable. Other
households (for example, those that identified as Asian American) could not be considered non-vulnerable in terms of ethnicity/
race. Thus, all analyses using the Hispanic vulnerability indicator use a subsample that comprises only Hispanic and non-
Hispanic white participants, and the Hispanic indicator is not included as a control in other analyses
African American
Respondent identified as African American. All analyses using the African American vulnerability indicator use a subsample that
comprises only African American and non-Hispanic white respondents, and the African American indicator is not included as a
control in other analyses
Supplementary Table 1 gives descriptive statistics. The survey question text is provided in Methods.
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to TOU Rate 1 (TOU1), TOU Rate 2 (TOU2) had higher cents per
kilowatt hour costs on-peak and fewer hours on-peak (Methods).
The pilot happened during summer in a hot climate in the south-
western United States; bills were expected to be driven by cooling
needs. We used a difference-in-difference-in-differences (triple dif-
ference) approach to examine whether assignment to TOU results
in greater electricity bill increases for vulnerable versus non-vulner-
able households. Supplementary Table 2 presents the mean monthly
bill amounts for all the study groups across the baseline and pilot
periods. Each model compares the control to either TOU1 (Table 2)
or TOU2 (Table 3). There are six models for each rate, one for each
vulnerability indicator. The mean monthly summer bill amount is
the dependent variable, and independent variables are the full set of
interaction terms and main effects required for a triple difference
model (Methods).
Both TOU rates resulted in bill increases for all participants
(P = 0.000; Fig. 1, Tables 2 and 3 and Supplementary Note 1). The
triple difference term TOU×Vulnerable×Pilot in each model gives
the estimated effect of the TOU assignment for vulnerable indi-
viduals during the pilot year (Methods). As expected, households
vulnerable on the disability indicator assigned to TOU1 (P = 0.011)
or TOU2 (P = 0.022) and households vulnerable on the elderly
indicator assigned to TOU2 (P = 0.001) saw greater baseline-to-
pilot-year bill increases compared to non-vulnerable counterparts.
Contrary to expectations, for households vulnerable on the low-
income (P = 0.012) and Hispanic indicators (P = 0.014), assignment
to TOU2 versus control is associated with a smaller increase in
bills relative to non-vulnerable households. Other groups (African
American and young children) saw no difference in TOU assign-
ment impacts versus their non-vulnerable counterparts. The
remaining model terms primarily serve as controls, and are dis-
cussed in Supplementary Note 1.
On-peak energy use reduction
In Tables 2 and 3, R2 is consistently <0.10, which suggests that fac-
tors not included in the model contribute to bill variation. Regional
fixed effects analysis confirms that changes in on-peak use predict
bill changes (Supplementary Note 2 and Supplementary Tables 1–3).
In a separate triple difference analysis (parallel to the billing analysis
(Methods)), we found that households vulnerable on the disability
indicator saw a smaller decrease in on-peak use from baseline to
pilot year when on TOU1 versus control, compared to their non-vul-
nerable counterparts; no differences were observed for other groups
(Supplementary Tables 7 and 8; the mean on-peak use reported
by group and time period is given in Supplementary Table 6).
Additionally, examining reported behavioural efforts to curtail
on-peak air conditioning (AC) use (Table 4), low-income, young
children, Hispanic and African American households reported a
greater curtailment compared to their non-vulnerable counter-
parts, whereas households with elderly members reported less cur-
tailment; no differences were observed for households with versus
without a disability (Wilcoxon rank sum tests (Methods)).
Table 2 | Triple difference estimators examining mean monthly bills (US$) for TOU1, by vulnerability group
(1)
(2)
(3)
(4)
(5)
(6)
Low income
Elderly
Young children
Disability
Hispanic
African American
TOU1×Vulnerable×Pilot
1.11 (2.53)
3.69 (2.72)
0.72 (3.89)
8.65* (3.38)
−3.79 (3.29)
−4.43 (5.94)
TOU1×Pilot
13.35*** (1.88)
12.59*** (1.71)
13.84*** (1.43)
12.42*** (1.48)
15.83*** (1.86)
15.83*** (1.86)
Vulnerable×Pilot
−5.18** (1.59)
0.36 (1.69)
1.15 (2.52)
1.25 (2.03)
2.69 (2.03)
1.76 (3.98)
Vulnerable×TOU1
−5.72 (5.28)
−2.40 (6.02)
3.33 (7.98)
3.94 (7.57)
0.02 (7.15)
2.02 (11.76)
TOU1
−3.81 (4.06)
−3.34 (3.73)
−4.53 (3.19)
−4.71 (3.23)
−2.76 (4.24)
−2.76 (4.24)
Pilot
6.18*** (1.18)
3.94*** (1.06)
3.92*** (0.88)
3.85*** (0.93)
3.32** (1.14)
3.32** (1.14)
Vulnerable
−52.72*** (3.40)
−3.69 (3.78)
5.14 (4.95)
5.11 (4.73)
−23.33*** (4.42)
−34.36*** (7.39)
R2
0.08
0.00
0.00
0.01
0.01
0.01
n
9,738
9,738
9,738
9,738
7,364
5,862
Standard errors in parentheses. *P < 0.05; **P < 0.01; ***P < 0.001.
Table 3 | Triple difference estimators examining mean monthly bills (US$) for TOU2, by vulnerability group
(1)
(2)
(3)
(4)
(5)
(6)
Low income
Elderly
Young children
Disability
Hispanic
African American
TOU2×Vulnerable×Pilot
−6.21* (2.46)
8.76*** (2.63)
5.31 (4.18)
7.49* (3.27)
−7.70* (3.12)
−6.27 (5.81)
TOU2×Pilot
26.45*** (1.92)
19.96*** (1.65)
22.98*** (1.36)
22.18*** (1.43)
27.92*** (1.84)
27.92*** (1.84)
Vulnerable×Pilot
−5.18** (1.59)
0.36 (1.69)
1.15 (2.52)
1.25 (2.03)
2.69 (2.03)
1.76 (3.98)
Vulnerable×TOU2
3.36 (4.83)
3.06 (5.34)
2.58 (7.17)
4.48 (6.63)
−3.51 (6.21)
6.99 (10.15)
TOU2
−8.03* (3.81)
−9.26** (3.44)
−8.22** (2.86)
−8.88** (2.93)
−6.51 (3.83)
−6.51 (3.83)
Pilot
6.18*** (1.18)
3.94*** (1.06)
3.92*** (0.88)
3.85*** (0.93)
3.32** (1.14)
3.32** (1.14)
Vulnerable
−52.72*** (3.40)
−3.69 (3.78)
5.14 (4.95)
5.11 (4.73)
−23.33*** (4.42)
−34.36*** (7.39)
R2
0.08
0.01
0.01
0.01
0.02
0.02
n
10,966
10,966
10,966
10,966
8,414
6,700
Standard errors in parentheses. *P < 0.05; **P < 0.01; ***P < 0.001.
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Health impacts of TOU assignment
We tested whether vulnerable versus non-vulnerable house-
holds report a higher likelihood of seeking medical attention for
heat-related reasons, using regional fixed effects regression
grouped by climate zone (Methods and Supplementary Tables 9
and 10). The dependent variable is the likelihood of seeking
–5
0
5
10
15
20
25
30
35
40
Control
TOU1
TOU2
Mean change in mean monthly bills ($US)
Rate assignment
a
–5
0
5
10
15
20
25
30
35
40
Control
TOU1
TOU2
Mean change in mean monthly bills ($US)
Rate assignment
b
–5
0
5
10
15
20
25
30
35
40
Control
TOU1
TOU2
Mean change in mean monthly bills ($US)
Rate assignment
c
–5
0
5
10
15
20
25
30
35
40
Control
TOU1
TOU2
Mean change in mean monthly bills ($US)
Rate assignment
d
–5
0
5
10
15
20
25
30
35
40
Control
TOU1
TOU2
Mean change in mean monthly bills ($US)
Rate assignment
e
–5
0
5
10
15
20
25
30
35
40
Control
TOU1
TOU2
Mean change in mean monthly bills ($US)
Rate assignment
f
Non-vulnerable
Vulnerable
Fig. 1 | Mean change in mean monthly summer bills, by vulnerability group and rate assignment. Change in mean monthly summer bills (pilot minus
baseline) for the control, TOU1 and TOU2 rate groups for each vulnerability indicator with standard error bars. a, Low income (control, n = 2,865; TOU1,
n = 2,004; TOU2, n = 2,618). b, Elderly (control, n = 2,865; TOU1, n = 2,004; TOU2, n = 2,618). c, Young children (control, n = 2,865; TOU1, n = 2,004;
TOU2, n = 2,618). d, Disability (control, n = 2,865; TOU1, n = 2,004, TOU2, n = 2,618). e, Hispanic (control, n = 2,202; TOU1, n = 1,480; TOU2, n = 2,005).
f, African American (control, n = 1,762; TOU1, n = 1,169; TOU2, n = 1,588). Solid boxes denote a positive triple difference term that indicates a greater
increase in bills for vulnerable versus non-vulnerable groups; dashed boxes denote a negative triple difference term that indicates a smaller increase in bills
for vulnerable versus non-vulnerable groups (Tables 2 and 3).
Table 4 | Reported AC curtailment by vulnerable households versus non-vulnerable counterparts
Vulnerability present (mean curtailment)
Vulnerability absent (mean curtailment)
z score
P
da
Low income
3.30
3.10
−4.85
0.000
0.16
Elderly
3.09
3.24
3.73
0.000
0.12
Young children
3.33
3.16
−2.80
0.005
0.13
Disability
3.12
3.20
1.72
0.086
0.06
Hispanic
3.29
3.02
−5.54
0.000
0.22
African American
3.45
3.02
−5.27
0.000
0.35
Main sample, n = 4,129; Hispanic subsample, n = 3,108; African American subsample, n = 2,459). Bold font shows the group that made greater efforts to curtail for each behaviour. aCohen’s d effect size.
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medical attention. Independent variables in all models are rate
assignment and vulnerability indicators for low income, elderly,
young children and disability. Hispanic and African American
indicators appear only in models that use subsamples. Interaction
terms Vulnerable×TOU are introduced individually in subsequent
models to test group-specific effects of TOU assignment. The
frequency of reported discomfort (expected precursor to more
severe problems), presence of AC (expected to impact indoor
temperature), change in on-peak use (expected to impact indoor
temperature) and home ownership (expected to impact indoor
temperature via a greater control by homeowners over efficiency
measures, such as insulation) are included as control variables in
all models. A subset of the models is presented in Table 5, which
comprises all the main effects models and models with significant
vulnerability interaction terms at P < 0.05.
Main effects indicate that households vulnerable on low-
income (P = 0.000), disability (P = 0.000) and Hispanic indicators
(0.001 < P < 0.011) are more likely to seek medical attention for
heat-related reasons (Table 5). TOU assignment alone does not pre-
dict the likelihood of seeking medical attention. A greater frequency
of discomfort predicts a higher likelihood of needing medical atten-
tion for heat-related reasons in all models (P = 0.000), whereas home
ownership predicts a lower likelihood of needing medical attention
in models (1), (2), (3) and (4) (0.011 < P < 0.032) and a reduction in
on-peak use predicts a higher likelihood of needing medical atten-
tion in models (7), (8) and (9) (0.011 < P < 0.014) (Table 5).
Considering the interaction terms, TOU versus control assign-
ment significantly alters the likelihood of seeking medical attention
among households with either young children (P = 0.045) or dis-
abled members assigned to TOU1 (P = 0.030), and Hispanic house-
holds assigned to TOU2 (P = 0.032). For significant interaction
terms, we performed post hoc tests with the conservative Scheffé’s
adjustment applied to significance testing of the contrast between
pairwise comparisons (Table 6). Among families with young chil-
dren, assignment to TOU1 versus control correlates with a lower
likelihood of needing medical attention. For households vulnerable
on the disability indicator, assignment to TOU1 is associated with
a higher likelihood of seeking medical attention relative to non-
vulnerable households on TOU1 (and non-vulnerable households
assigned to control rate). Hispanic households assigned to TOU2
face higher a likelihood of needing medical attention than non-His-
panic white households on TOU2.
Discomfort in vulnerable versus non-vulnerable households
We tested whether vulnerable versus non-vulnerable households
report more frequently experiencing discomfort due to homes
being too hot, using regional fixed effects regression grouped by
climate zone (Methods and Supplementary Tables 11 and 12). The
dependent variable is the frequency of discomfort experienced
while trying to save money on electricity. Independent variables are
rate assignment, vulnerability indicators and Vulnerability×TOU
interaction terms. The models examine the interaction terms for
each vulnerable group separately. The presence of AC, change in
on-peak use and home ownership are included as control variables
in all the models.
Regardless of the rate assignment, low-income (P = 0.000), dis-
ability (P = 0.000) and Hispanic indicators (P = 0.024, Hispanic for
TOU1 models only) predict more frequent discomfort, whereas
the elderly indicator (P < 0.042) predicts less frequent discomfort.
Two interaction terms are significant: Low income×TOU1 and
African American×TOU1. For these terms, we performed post hoc
tests with Scheffé’s adjustment (Table 7). Low-income households
assigned to TOU face a higher discomfort than their non-vulnera-
ble counterparts assigned to TOU (this is also true for the control
group). No significant differences were observed for the African
American×TOU interaction term.
Discussion
The results suggest distributional, procedural and recognition injus-
tices that differ across groups, which highlights the importance of
considering specific subpopulations in the design and rollout of DSR
programmes. The greater cost increases experienced by households
vulnerable on the disability and elderly indicators assigned to TOU,
relative to their non-vulnerable counterparts, suggest recognition
injustices14. Cost increases faced by these households probably stem
from inability to shift use times, as evidenced in our findings that
households vulnerable on the disability indicator reduced on-peak
use less than their non-vulnerable counterparts, and households
vulnerable on both disability and elderly indicators reported less AC
curtailment compared to their non-vulnerable counterparts. These
groups may be constrained in load shifting due to being home-
bound and having a greater reliance on energy for medical equip-
ment, temperature control and completing daily tasks8,23–26.
Households vulnerable on low-income and disability indicators
face worse health and comfort outcomes than the outcomes faced
by non-vulnerable counterparts, regardless of rate assignment,
which probably stems from ongoing procedural and distributional
injustices. However, TOU appears to widen the difference in health
outcomes between those vulnerable on the disability indicator and
their non-vulnerable counterparts, which suggests recognition
injustices given that this group already struggles to keep homes cool
under current distributional injustices.
TOU similarly correlates with worse health outcomes for
Hispanic households, relative to their non-vulnerable counterparts.
Hispanic households reported greater curtailment of AC compared
to non-Hispanic white households, and it is possible that this con-
tributed to negative health outcomes. Hispanic groups are more
likely to experience heat distress in extreme heat events15,34, which
raises concerns that further distributional injustice could worsen
the differentials in mortality rates. Future research should evaluate
whether this outcome is linked to inefficient homes that limit the
ability to keep cool33.
Our finding that TOU1 only shows a differential cost effect
for those with disabilities, whereas TOU2 shows differential cost
effects for those vulnerable on the low-income, elderly, disability
and Hispanic indicators, suggests that the design of TOU rates is
important in predicting outcomes for energy-vulnerable popula-
tions. Specifically, the length and kilowatt hour expense of on-peak
times appear to play an important role in the group-differentiated
financial impacts of TOU.
Given that DSR rates will probably be needed to integrate higher
shares of non-dispatchable generation, it is important for pilots to
continue trialling multiple rate designs and evaluating the impacts
on vulnerable populations, with the goal of identifying rate designs
in each locale that meet energy integration needs without worsen-
ing or creating energy injustices. The TOU rates examined here
increased electricity bills for all groups. By definition, vulnerable
groups are less able to bear cost increases than their non-vulner-
able counterparts8, which suggests that switching to the TOU rates
considered in this study probably increased hardships such as the
‘heat or eat’ dilemma. However, compared to their non-vulnera-
ble counterparts, only two groups (disability and elderly) experi-
enced greater cost increases, whereas two groups (low income and
Hispanic) experienced lower cost increases.
TOU rate design should aim to be cost neutral, and studies of
other TOU rates have found evidence that some rate designs can,
indeed, achieve cost neutrality across the general population50. If
the TOU rate in our study had achieved cost neutrality across the
general population, rather than causing increases across the board,
it is possible that only some of the vulnerable groups examined
(elderly and disability) would have been worse off, whereas some
(low income and Hispanic) may have been better off. More exten-
sive examination of potential rate designs is needed to understand if
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Table 5 | Regional fixed effects logit model grouped by climate zone, predicting likelihood of a household member needing medical attention
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Main effects,
TOU1
Young children in
interaction, TOU1
Disability in
interaction, TOU1
Main effects
(Hispanic), TOU1
Main effects
(African
American), TOU1
Main effects,
TOU2
Main effects
(Hispanic), TOU2
Hispanic in
interaction, TOU2
Main effects
(African
American), TOU2
Vulnerable×TOU
−0.71* (0.35)
0.60* (0.28)
0.38* (0.18)
Assigned to TOU
−0.07 (0.14)
0.06 (0.19)
−0.38** (0.12)
0.02 (0.21)
0.05 (0.26)
−0.06 (0.10)
−0.02 (0.15)
−0.20 (0.15)
−0.29* (0.14)
Low income
0.57*** (0.14)
0.57*** (0.14)
0.59*** (0.14)
0.58*** (0.16)
0.69*** (0.17)
0.70*** (0.14)
0.82*** (0.18)
0.81*** (0.18)
0.99*** (0.26)
Elderly
0.26 (0.16)
0.27 (0.16)
0.26 (0.16)
0.22 (0.19)
0.24 (0.23)
0.09 (0.09)
0.17 (0.09)
0.17 (0.09)
0.35 (0.22)
Young children
0.33 (0.19)
0.59** (0.21)
0.33 (0.18)
0.18 (0.24)
−0.12* (0.05)
0.47 (0.25)
0.36 (0.35)
0.37 (0.35)
0.11 (0.38)
Disability
1.58*** (0.16)
1.59*** (0.16)
1.36*** (0.15)
1.72*** (0.19)
1.69*** (0.18)
1.40*** (0.12)
1.42*** (0.19)
1.41*** (0.19)
1.17*** (0.16)
Hispanic
0.36 (0.14)
0.49** (0.15)
0.32 (0.21)
African American
0.42 (0.23)
0.30 (0.18)
Presence of AC
0.14 (0.21)
0.14 (0.21)
0.13 (0.22)
0.43* (0.22)
0.62 (0.76)
0.18 (0.39)
0.01 (0.55)
0.01 (0.56)
0.40 (0.60)
Frequency of discomfort
trying to save money
0.80*** (0.05)
0.80*** (0.05)
0.80*** (0.05)
0.76*** (0.06)
0.82*** (0.05)
0.85*** (0.06)
0.92*** (0.05)
0.92*** (0.06)
0.88*** (0.10)
Change in on-peak use
(kWh, daily average)
0.06 (0.04)
0.06 (0.04)
0.06 (0.04)
0.05 (0.03)
0.08 (0.05)
0.07+ (0.04)
0.11* (0.04)
0.11* (0.04)
0.15* (0.06)
Home owned
−0.35* (0.15)
−0.35* (0.15)
−0.33* (0.16)
−0.38* (0.15)
−0.36 (0.30)
−0.31 (0.22)
−0.25 (0.29)
−0.25 (0.28)
−0.30 (0.45)
Pseudo R2
0.20
0.20
0.20
0.21
0.22
0.19
0.22
0.22
0.20
n
4,869
4,869
4,869
3,682
2,931
5,483
4,207
4,207
3297
Standard errors in parentheses, clustered by climate zone. *P < 0.05; **P < 0.01; ***P < 0.001.
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this would be borne out in practice. Given that those vulnerable on
disability and elderly indicators have a greater need for affordable
energy compared to the general population, policy or rate design
interventions should ensure that energy costs are low enough for
these groups to maintain their health on TOU or other DSR rates
and still be able to afford other necessities24.
As opt-out DSR programmes spread, it is important that the
costs of each DSR rate relative to those of other offered rates are
clearly communicated and that the opt-out procedures be made
clear to all, particularly vulnerable groups who are at risk of unfa-
vourable outcomes on DSR. To aid households in evaluating bene-
fits and burdens of competing rates, it is important to communicate
cost information in a way that minimizes cognitive burden51,52.
Using heuristics to address common misperceptions may improve
household understanding of energy use53. Given prior findings that
households often base decisions regarding TOU enrolment on per-
ceived financial savings, but misperceive the extent of actual finan-
cial savings54, further testing of an ideal choice architecture and
information presentation for DSR is critical to facilitate all house-
holds making informed decisions about their electricity rates.
More broadly, our findings regarding the worse health and
comfort outcomes for households vulnerable on low-income and
disability indicators regardless of rate assignment suggest that
energy-vulnerable groups in hot climates globally should be the
focus of future research. These findings also suggest a need for pol-
icy intervention to support more affordable cooling, regardless of
future DSR rollout. Cooling centres may help reduce discomfort,
but often operate only during weekday business hours, so are dis-
ruptive to family routines and provide only part-time relief55. Thus,
we recommend other measures, such as improving building and
appliance energy efficiency, and carefully designed rates. Efficiency
improvement programmes can offer large cost savings and reduce
emissions, as well as decrease discomfort56,57. Future research should
directly consider the extent to which housing energy efficiency lim-
its the ability to control bills on DSR rates such as TOU, with a view
to informing the design of complementary policies to address dis-
tributional injustices.
Our results should be viewed in the light of several limitations.
First, our sample comprised individuals who opted into the pilot.
Thus, our sample may be less risk averse than the general popula-
tion58,59, or may have a greater expectation of monetary savings on
TOU60. Second, the results may not generalize to all populations.
Survey completers had higher mean baseline use and larger mean
bill increases than partial completers. Control group members were
more likely to complete the survey than those assigned to TOU1.
Several vulnerable groups (low income, elderly, disability, Hispanic
and African American) were less likely to complete the survey com-
pared to non-vulnerable counterparts. Possibly survey completers
had fewer time pressures, which suggests conservative estimates
of the impact on vulnerable groups. Third, our indicator for low-
income households relied on enrolment in a utility programme,
and thus probably underestimates the low-income households
Table 6 | Post hoc tests of Vulnerable×TOU interaction terms on the need for medical attention
Control, vulnerable versus
TOU, non-vulnerable versus
TOU, vulnerable versus
Young children×TOU1
 Control, non-vulnerable
0.59* (0.21)
0.06 (0.19)
−0.6 (0.25)
 Control, vulnerable
−0.53* (0.16)
−0.65* (0.21)
 TOU, non-vulnerable
−0.12 (0.30)
Disability×TOU
 Control, non-vulnerable
1.38* (0.16)
−0.08 (0.11)
1.34* (0.20)
 Control, vulnerable
−1.45* (0.10)
−0.03 (0.19)
 TOU, non-vulnerable
1.43* (0.17)
Hispanic×TOU2
 Control, non-vulnerable
0.32 (0.21)
−0.10 (0.15)
0.50* (0.15)
 Control, vulnerable
−0.52 (0.27)
0.18 (0.21)
 TOU, non-vulnerable
0.70* (0.14)
Pairwise comparison, contrast. Standard error in parentheses. *Scheffé test significant at the 95% level.
Table 7 | Post hoc tests of Vulnerable×TOU interaction terms on discomfort
Control, vulnerable versus
TOU, non-vulnerable versus
TOU, vulnerable versus
Low income×TOU1
 Control, non-vulnerable
0.32* (0.48)
0.06 (0.02)
0.28* (0.05)
 Control, vulnerable
−0.26* (0.04)
−0.03 (0.04)
 TOU, non-vulnerable
0.23* (0.04)
African American×TOU
 Control, non-vulnerable
−0.17 (0.10)
−0.02 (0.03)
0.08 (0.12)
 Control, vulnerable
0.15 (0.12)
0.26 (0.09)
 TOU, non-vulnerable
0.11 (0.13)
Pairwise comparison, contrast. Standard error in parentheses. *Scheffé test significant at the 95% level.
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sampled and has an imperfect separation between vulnerable and
non-vulnerable households; the associated coefficients should be
interpreted with caution. Fourth, we examined vulnerability indica-
tors in isolation, and hence the results do not capture differential
impacts faced by those bearing a double burden, such as being both
elderly and low income.
TOU raised the cost of energy for all households in our study,
but some vulnerable households (elderly and disability) face greater
bill increases on TOU compared to their non-vulnerable counter-
parts. Households vulnerable on low-income and disability indica-
tors also face more discomfort and more heat-related medical issues
regardless of rate assignment, which raises general concerns about
the health impacts of energy poverty in hot climates. TOU widens
the discomfort gap (low income) and increases the likelihood of
seeking medical attention (disability and Hispanic) of some vulner-
able groups relative to non-vulnerable counterparts on TOU. Rate
design plays an important role in the impact of price-based DSR
measures on vulnerable households, and future pilots should con-
tinue to examine multiple potential rate designs to determine which
designs most effectively avoid exacerbating existing energy injus-
tices or creating new ones.
Methods
Ethics statement. The University of Southern California’s University Park
Institutional Review Board reviewed and approved this research, and granted a
waiver of informed consent.
Participants. Data are from households that participated in a pilot programme
administered by a southwestern US electric utility. The utility sent invitations by
direct mail and email soliciting opt in to the 2016 TOU pilot to roughly 197,000
households, 14% of which opted in. Some households that accepted the offer were
not enrolled because they were ineligible (for example, were already participating
in a special rate programme). The utility randomly assigned 21,534 households
to either TOU1 (n = 4,709), TOU2 (n = 6,365), TOU3 (n = 3,746) or the control
group that opted in to TOU but was not placed on a TOU rate (n = 6,714). Low-
income households and those with elderly members were deliberately oversampled
for TOU2. TOU3 was not fully rolled out by the start of the study period, due to
additional complexities unique to the rate, and so could not be included in the
present study; we consider only TOU1, TOU2 and the control group.
TOU pilot. Households assigned to a TOU rate were shifted to this new rate in
June or July of 2016 and remained on these rates for a full year. The period of the
pilot covered by this study occurred in the summer, specifically the months July–
September 2016. Owing to the geographical region, the weather would have been
warm to hot and mostly lacking precipitation for the majority of the sample.
After the rate assignment, participants received information letters. Those
in the control group received a welcome letter informing them that they would
remain on their current rate. TOU participants received a letter containing
information on their TOU rate plan and bill protection (if customers paid more on
TOU at the end of the 12-month pilot than they would have under their previous
plan, the utility would credit back the difference after the pilot ended). They also
received TOU time-period stickers, conservation-reminder stickers and door
hangers with recommended seasonal thermostat settings.
TOU1 and TOU2 on-peak times covered different hours depending on
weekend versus weekday. On-peak hours were in the evening. Cost per kilowatt
hour varied depending on the rate and season. Summer rates per kilowatt hour
for TOU1 were c23 for super off-peak, c27.61 for off-peak and c34.51 for on-peak.
Summer rates per kilowatt hour for TOU2 were c17.33 for super off-peak, c29.3
for off-peak and c53.26 for on-peak. TOU1 had six hours on-peak from 14:00 to
20:00 for summer weekdays and no on-peak times at weekends. TOU2 had three
hours on-peak from 17:00 to 20:00 for summer weekdays and no on-peak times
at weekends.
Households could earn up to US$200 as compensation for their participation,
given as bill credits; US$100 at enrolment and US$50 after completing each of two
surveys. The second survey, which we do not have data for due to the timing of our
data request from the utility, was administered in the summer of 2017.
Survey. Customers were first surveyed between October and December 2016. At
this point, TOU participants had 3–6 months’ experience with TOU rates, solely or
primarily in the summer. Survey response rates were 82% overall, out of the 18,747
households that remained in the pilot by December 2016 after being enrolled and
not being dropped out of the pilot due to relocating, ineligibility or choosing to
leave. For the full sample examined in this manuscript (n = 7,487), 85% responded
by email, 11% by mail and 4% by phone.
Electricity use data. Our participants experienced the TOU pilot during the
summer months. Thus, the analyses use hourly electricity use data for each
household only during the summer months (July, August and September) for
the baseline years 2014 and 2015, and for the pilot year of 2016. Summer hourly
consumption data were used to form the ‘on-peak use’ variable (kilowatt hours,
daily mean).
In forming the ‘on-peak use’ variable, we took into account the different lengths
of on-peak time, that is, TOU1 on-peak sums the use during the 6 h on-peak, and
TOU2 on-peak sums the use during the 3 h on-peak. Control group households
were all coded for both hypothetical TOU1 and TOU2 rate structures, and on-peak
use was defined as use that happened during the hours designated as on-peak
by that rate. In each analysis, the full control group is compared to either TOU1
or TOU2, using the corresponding on-peak hour coding for control groups to
generate the change in on-peak use variable used for analysis. ‘Change in on-peak
use’ between the baseline and TOU pilot years is taken as the TOU pilot daily
on-peak mean use minus the mean baseline on-peak use. The following equation
describes the change in on-peak use calculation: ΔU = [U2016 – (U2014 + U2015)/2],
where U represents the mean daily on-peak use in kilowatt hours in each year.
Electricity bills. As for use, the billing data for each household were examined only
for July, August and September for the baseline years 2014 and 2015, and for the
pilot year 2016. Bill amounts used for the analysis were actual bills that customers
received. That is, they reflect the true amount customers paid each month. All the
households kept for analysis had billing data for at least two of the three summer
months. Mean monthly bill size was taken as the mean of bills across July, August
and September for a given year. A baseline bill variable was created by taking
the mean of the 2014 and 2015 bills, and the pilot bill was the 2016 mean bill. A
‘change in bills’ variable was created by subtracting the baseline bills from the pilot
bills using the equation: ΔB = [B2016 – (B2014 + B2015)/2], where B represents mean bill
amounts in US dollars each year.
Climate zone. Climate zones are defined by a government agency, and are matched
to each household based on the household’s location. The government agency
bases climate zone boundaries on the household energy use expected for heating
and cooling, local temperature and local weather, among other factors. There are
eight climate zones covered by households in our sample.
Survey and vulnerability measures. The survey assessed a number of demographic
characteristics, which included respondent age and ethnicity/race, and household
member disabilities. It also assessed homeownership status and educational
attainment, the former of which is included in regression models due to the
expectations that homeowners would more easily be able to upgrade appliances
and building insulation to increase energy efficiency. Homeownership was coded
as a dichotomous variable with 1 indicating homeownership and 0 indicating
otherwise.
We defined vulnerability indicators as follows:
•
Low income. Enrolment in an electric utility financial aid programme (that
gives households discounts on electricity bills if income falls below certain
limits based on the number of household members) serves as an indicator of
low income, with those enrolled in a financial aid programme coded 1 and
others coded 0. Enrolment status was provided by the utility.
•
Elderly. The survey assessed respondent birth year with an open entry. It
additionally asked: “How many people in each of the following age categories
lived in your home this summer, not including yourself?”, with response
categories including “Between 65 and 74 years old”, “Between 75 and 84 years
old” and “85 years or older”. Responses to this question and birth year were
aggregated to determine whether anyone over 65 resided in the household. If
someone over 65 years old lived in the household, that household was coded 1.
Otherwise, it was coded 0.
•
Young children. Respondents were asked “How many people in each of the
following age categories lived in your home this summer, not including your-
self?” with the youngest category being “Under 6 years old”. Households that
had at least one member under 6 years old were coded ‘1’ for young children,
otherwise households were coded 0.
•
Disability. If someone answered yes to either “Does anyone in your household
have a disability or serious medical condition that requires your home to be
cool in the summer?” or “Does anyone in your household have a disability or
serious medical condition that requires them to use more energy for medical
equipment?”, that household was coded 1. Otherwise, it was coded 0.
•
Hispanic. Race and ethnicity were assessed using the question “Which cat-
egories describe you?”, in response to which households could mark as many
options as they chose to out of the list provided. If a respondent identified as
“Hispanic, Latino, or Spanish origin”, their household was coded 1 for the
Hispanic indicator. If a household marked “White” as a category that
described them, and did not mark any other category (including, but not
limited to, “Hispanic, Latino, or Spanish origin”), then they were coded as 0.
That is, households were only coded 0 if they identified as white alone.
Other households (for example, those that identified as African American,
Asian American, American Indian and so on) could not be considered
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non-vulnerable in terms of ethnicity/race, so were treated as missing. Thus, all
analyses using the Hispanic vulnerability indicator use a subsample (n = 4,925),
and the Hispanic indicator is not included as a control in other analyses.
•
African American. Coding follows the procedure described for the Hispanic
indicator. If the respondent identified as “African American”, their household was
coded 1 for the African American indicator. If a household marked “White” as a
category that described them, and did not mark any other category (including,
but not limited to, “African American”), then they were coded as 0. All analyses
using the African American vulnerability indicator use a subsample (n = 3,970),
and the African American indicator is not included as a control in other analyses.
AC curtailment and ownership. AC curtailment was assessed by asking respondents,
“Since the beginning of this summer, how often, if at all, did you take the
following actions to reduce your household’s electricity use in the afternoons and
evenings?—Turned off air conditioning” on a 5-point Likert scale where 1 = Never
and 5 = Always, with an additional option of 6 = Not applicable. We retained those
who answered “Not applicable” within the sample, but coded these respondents as
missing when conducting analyses that make use of the curtailment scale.
The survey assessed both the reported behavioural curtailment of AC and the
presence of AC technology. Households were considered to have AC if they had
central AC, window AC, evaporative coolers or heat pumps (which are capable of
providing AC). Some households gave conflicting answers to their curtailment of
AC and their ownership of AC technology; for example, some households rated
their curtailment of AC rather than selecting “Not applicable”, but indicated that
they did not own AC technology. Households were coded 0 for AC ownership if
they chose “Not applicable” for AC curtailment and also indicated they owned
no AC technology, and were coded 1 for AC ownership if they rated their AC
curtailment (that is, did not choose “Not applicable”) and additionally indicated
that they owned a form of AC technology. Households that gave conflicting
answers were dropped (n = 850).
Measures for discomfort and medical needs. Discomfort was assessed with responses
to the question “Since June 2016, how often, if ever, were you or any members of
your household uncomfortably hot inside your home because you were trying to
save money on your electricity bill?” on a 5-point Likert scale with 1 = Never and
5 = Always.
The need for medical attention was assessed with responses to the question
“Since June 2016, about how many times, if ever, did you or any members of your
household need medical attention because it was too hot inside your home?”,
with respondents able to choose between options of “Never”, “1”, “2”, … to “more
than 10”. A dichotomous variable was created, with respondents coded 1 if they
answered that they had needed medical attention at least once, and 0 otherwise.
Dropped participants. By December 2016, 2,787 households had dropped out of
the pilot due to relocating, ineligibility or choosing to drop out, which left 18,747
households enrolled. Of these, 16,181 households responded to the survey. Before
receipt by the authors, the utility removed respondents who answered 5.4% or
less of the survey items. Respondents were also removed if they provided the
same rating for all items across any of the three multi-item measures in the survey
(for example, if a participant gave ratings of ‘4’ to all the items in one multi-item
question). Additionally, respondents were removed if they selected all the items in
a ‘select-all-that-apply’ question in which some categories were mutually exclusive,
for example, if when asked “What kept you from shifting use in the evening”
respondents selected both “Nothing keeps me from shifting my use” and “My
schedule doesn’t allow me to reduce my usage”.
This yielded a sample of n = 16,073 households, with 5,198 in the control
group, 3,522 on TOU1 and 4,593 on TOU2; the 2,760 households on TOU3
were not used, which left an initial sample of n = 13,313 households. Additional
households were then dropped for the reasons below.
First, households that were missing electricity use data on any days in July,
August and September in 2014, 2015 or 2016 were dropped. A total of n = 1,339
households were dropped due to incomplete use data; the missing values were
predominantly clustered across several days or weeks at the beginning of the
recorded period, which indicates that either no account was established for that
address (that is, residents moved in during the study period) or the house did not
have a smart meter at the beginning of the time period.
Second, billing outliers were removed. Customers with baseline or pilot period
use below the 1st percentile or above the 99th percentile were dropped from the
sample (n = 341 households).
Third, households with incomplete survey data were dropped. Households
were dropped if they had not answered the AC curtailment question, if they had
not answered the question assessing the presence of AC, if they had not answered
each vulnerability indicator (excepting the race/ethnicity indicators, which used
subsamples), if they had not answered both the discomfort and the medical
attention questions and if they had not included an answer to homeownership
(n = 3,296).
Finally, households that gave conflicting answers to their curtailment of AC
and their ownership of AC technology were dropped (n = 850). Our final sample
comprised 7,487 respondents.
Dropout analyses. The utility with which we partnered provided data only for
those who at least partially completed the survey, so we are unable to evaluate
presurvey opt outs. We used a two-tailed t-test to understand whether there was
a systematic difference regarding the change in bills from baseline to pilot year
between households that completed the survey and those that did not complete
the survey, restricting the sample only to those who had complete billing and
use data (n = 11,633); among those with incomplete billing and use data, this
incompleteness was due to factors beyond the households’ control, such as the
installation of smart meters. We found that survey completers (n = 7,487) saw
larger bill increases (baseline to pilot, simple difference) than non-completers
(n = 4,146); Mcomplete = 16.09, Mnon-complete = 13.35, P = 0.003, d = 0.06. We additionally
used a two-tailed t-test to examine the systematic difference in change in on-peak
use from baseline to pilot (n = 11,633) for both TOU1 peak-time survey completers
(TOU1 + control, n = 4,869) versus non-completers (TOU1 + control, n = 2,688)
and TOU2 peak-time survey completers (TOU2 + control, n = 5,483) versus
non-completers (TOU2 + control, n = 3,107), and found no difference between
completers and non-completers in usage reduction (Mcomplete,TOU1 = −0.11, Mnon-
complete,TOU1 = −0.11, P = 0.91, d = 0.002; Mcomplete,TOU2 = −0.11, Mnon-complete,TOU2 = −0.09,
P = 0.56, d = 0.01). We finally examined differences in baseline use (n = 11,633)
between those who completed the survey (n = 7,487) and those who did not
(n = 4,146), and found that completers had a higher daily baseline average use
(Mcomplete = 24.98, Mnon-complete = 22.43, P = 0.000, d = 0.18). In summary, those who
completed the survey had higher baseline use and larger bill increases during the
pilot than the non-completers. The effect size of the baseline difference in daily
average use between the survey completers and non-completers is large, whereas
the effect size difference in billing is small.
We further considered dropout across different conditions due to survey
missing data: of 4,514 in the control group, 37% were non-completers, compared
to 34% of the 3,043 on TOU1 and 36% of the 4,076 on TOU2. A simple logit model
was used to estimate the correlation of group assignment with the likelihood of
having incomplete data. We first considered all three rate types using dummy
variables (with the control as baseline), and found that, compared to the control,
TOU1 is associated with a higher likelihood of incomplete data (P < 0.034), but
TOU2 is not (P < 0.464). We then considered only TOU1 versus TOU2, with
TOU1 as the baseline, and found no difference in association with non-completion
(P < 0.155). Finally, we used a simple logit to consider whether vulnerability
predicts non-completion of the survey. We found that low-income (P < 0.001),
elderly (P < 0.001), disability (P < 0.001), Hispanic (P < 0.001) and African
American (P < 0.011) groups were all less likely to complete the survey compared
to the respective non-vulnerable counterparts.
Difference-in-difference-in-differences analyses. The difference-in-difference-in-
differences billing analyses are described by the equation:
Bst ¼ β0 þ β1TreatR1s þ β2Postt
þβ3Vulnerables þ β4 Treats ´ Postt
ð
Þ
þβ5 Vulnerables ´ Postt
ð
Þ þ β6 Vulnerables ´ Treats
ð
Þ
þβ7 Vulnerables ´ Treats ´ Postt
ð
Þ þ εst
where Bst is the mean monthly bill amount (US dollars), TreatR1s is a dichotomous
variable set to 1 if the household was on TOU1 and 0 if the household was in the
control group, Postt is a dichotomous variable set to 1 if the year was 2016 and 0
for the baseline indicator (the mean for the 2014 and 2015 mean monthly bills
amount is taken to form the baseline indicator), Vulnerables is the indicator for
vulnerability set to 1 if the household is vulnerable on a given indicator and 0 if
it is not vulnerable on that indicator (see the descriptions above for the coding
of vulnerable groups). These terms are included as controls for each individual
indicator. Subscript s refers to a term that differs across subjects, but is constant
over time for a given subject. Subscript t refers to a term that changes over time,
but is constant across subjects at any given point in time. Terms with sub-script
st vary across both subjects and time. Treats×Postt controls for the effect on
bills due to the assignment to the TOU rate during the pilot year, and takes a
value of 1 for households that were on TOU in the pilot year and 0 for all others;
Vulnerables×Postt controls for differences experienced during the pilot year by
vulnerable groups regardless of the rate assignment, and takes a value of 1 for
vulnerable households during the pilot year and 0 for all others; Vulnerables×Treats
controls for the differences of vulnerable groups assigned to the TOU condition
regardless of whether the pilot had begun or not, and takes a value of 1 for
vulnerable households assigned to a TOU rate and 0 for all others. The term of
interest is Vulnerables×Treats×Postt, which gives the effect of the TOU assignment
during the pilot year on vulnerable groups; vulnerable households assigned to a
TOU rate have a value of 1 for this term during the pilot year; all other groups, and
other time periods, take a value of 0. Errors are clustered at the household level. εst
refers to the idiosyncratic error term.
The same form is used to examine TOU2, with TreatR2s (a dichotomous variable
set to 1 if the household was on TOU2 and 0 if the household was in the control
group) substituted for TreatR1s. Likewise, this form is used to examine the on-peak
usage differences between groups and time periods, with the dependent variable
being on-peak use (kilowatt hour, mean) instead of the monthly bill amount.
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58

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Regional fixed effects regression. We used regional fixed effects analyses to
consider the impacts of predictor variables on billing, discomfort and the need for
medical attention. By using regional fixed effects grouped by climate zone, these
models control for unobserved time-invariant differences between households,
for those differences associated with living in different climate zones. That is, the
predictions of respective dependent variables in each regional fixed effects analysis
are estimated conditional on the climate zone, and each climate zone is associated
with a unique intercept.
AC curtailment analysis. We used a subsample of n = 4,129 households that were
assigned to TOU (not control) and did not answer “Not applicable” when asked
about AC curtailment (that is, we only considered households that have access to
AC in their homes). Only households assigned to TOU were examined to restrict
the comparison to households with the incentive to curtail during peak hours. For
this subsample, we examined whether the vulnerable or non-vulnerable groups
reported more frequent curtailment in the form of AC use. We used Wilcoxon rank
sum tests (due to non-normal distributions) to test for differences in the reported
frequency of evening AC curtailment among vulnerable versus non-vulnerable
households. Table 4 reports the means, z scores with associated P tests and Cohen’s
d effect sizes (d). Effect sizes indicate the importance of mean differences; a large
effect size is >0.8, a medium one >0.5 and a small one >0.2 (ref. 61). The results
provide a richer description of how households responded to TOU rates, and are
correlational rather than causal.
Online content. Any methods, additional references, Nature Research
reporting summaries, source data, extended data, supplementary information,
acknowledgements, peer review information; details of author contributions and
competing interests; and statements of data and code availability are available at
https://doi.org/10.1038/s41560-019-0507-y.
Reporting summary. Further information on research design is available in the
Nature Research Reporting Summary linked to this article.
Data availability
The processed or aggregated data that support the plots within this paper and other
findings of this study are available from the corresponding author upon reasonable
request. Authors signed a non-disclosure agreement with the utility that provided
the data analysed in this paper, and under this agreement are unable to make the
raw data publicly available. Source data for Fig. 1 are provided with the paper.
Received: 20 February 2019; Accepted: 24 October 2019;
Published online: 16 December 2019
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Acknowledgements
The authors thank their utility partner for furnishing the data, D. Mazmanian for
extensive advice and J. McPartlan for the considerable time invested in data management.
Author contributions
Both authors conceived the paper and designed the research. L.W. designed the analysis
methods, performed the analyses and wrote and revised the paper. N.S. reviewed several
drafts and made revisions.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/
s41560-019-0507-y.
Correspondence and requests for materials should be addressed to L.V.W.
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 2019
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    Scientific Research Findings:

Answer: Elderly people and those with disabilities had greater bill increases when moved to time-of-use rates (versus staying on existing rates), compared to the equivalent increase seen in their non-vulnerable counterparts assigned to time-of-use rates. Low-income and Hispanic households had lower bill increases compared to non-vulnerable counterparts. Hispanic households and those with disabilities experienced worse health outcomes on time-of-use rates, while households with young children experienced better health outcomes. Otherwise, no health and bill differences were observed between vulnerable and non-vulnerable groups. These results suggest that time-of-use rates may increase hardships faced by some groups already more likely to face energy poverty, but impacts vary by sociodemographic group and rate design. This study was conducted during the summer in the south-western United States, and we expect these results to extend to similar contexts — hot climates during summer. Findings may not generalize to different rate designs or climate conditions.