Using data collected by the U.S. Department of Energy we test how price and environmental marketing and labeling affects respondents' uncertainty about product attributes and about their purchase intentions.Consumer/Household Economics,

Levy, Alan S.

Roe, Brian E.

Rong, Huaping

Teisl, Mario F.

English

Research Papers in Economics

Environmental labeling of electricity: Effects on consumer uncertainty about product
attributes and likelihood to buy decisions
Mario F. Teisl, Assistant Professor, Department of Resource Economics and Policy, University
of Maine
Huaping Rong, Graduate Research Assistant, Department of Resource Economics and Policy,
University of Maine
Brian Roe, Assistant Professor, Department of Agricultural, Environmental and Development
Economics, The Ohio State University
Alan S. Levy, Chief, Consumer Studies Branch, U.S. Food and Drug Administration
Presented at the 2001 Meeting of the American Agricultural Economics Association
Abstract
Using data collected by the U.S. Department of Energy we test how price and environmental
marketing and labeling affects respondents’ uncertainty about product attributes and about their
purchase intentions.
Copyright 2001 held by the authors.  All rights reserved.  Readers may make verbatim copies of
this document for non-commercial purposes by any means, provided that this copyright notice
appears on all such copies.Introduction
As the electricity production market become deregulated, effective regulation of the
marketing of electricity may allow customers to make choices that clearly reflect their
preferences and thereby achieve customer-preferred outcomes.  Some of these outcomes include
the encouragement of renewable resources or the reduction of air emissions.  An open question,
however, is whether informed customer choice will lead to cleaner generating sources.  This is of
particular concern to policy makers because sulfur dioxide (SO2), nitrogen oxides (NOx) and
carbon dioxide (CO2) are all key contributors to a number of air pollution problems (acid rain,
fine particulates, ground-level ozone, and global climate change).  In addition, electricity
generation creates about two-thirds of all SO2 emissions in the U.S., and about one-third of all
NOx and CO2 emissions (USEPA 1995).
Environmental labeling and marketing may have two potential impacts on electricity
consumers.  On one hand, consumers may already have well-formed priors about the
environmental characteristics of the electricity they purchase.  On the other hand, consumers
may be relatively uncertain about these characteristics.  One would assume that presenting
environmental information to these latter consumers would have a potentially large impact on
their level of uncertainty regarding the product's environmental characteristics and thus, may
have large impacts on these consumers' purchasing decisions.  While labeling has been the focus
of major policy initiatives, little empirical research has attempted to understand the effects of
different environmental information policies; particularly with respect to changes in consumer
uncertainty.  Our research uses data collected by the U.S. Department of Energy to test how price
and environmental marketing and labeling affects respondents’ uncertainty about product
attributes and about their purchase intentions.2
Approach
With the intention of evaluating the usefulness of environmental labeling, the
Regulatory Assistance Project and the Department of Energy conducted a mall-intercept survey
during 1997.  In the survey, respondents viewed different electricity labels and were asked to
perform a series of experiments.  These experiments were designed to measure the
performance characteristics of different electricity-labeling programs.
One-thousand adult respondents were recruited from shopping malls located in eight
cities in the U.S.  Although mall-intercept studies are typically not statistically representative of
the U.S. population, the mall locations chosen for this study do provide a sample that represents
a wide variety of social and economic circumstances (Table 1).  Nearly all respondents pay the
household bills and, therefore, should be somewhat familiar with electricity pricing although
they may still be unfamiliar with the environmental characteristics of electricity.
Table 1.  Sample Demographics.
Variable
Education
High School or Less 42%
Some College or Technical Training 35%
College Degree or More 23%
Annual Household Income
Less than $20,000 26%
$20,000 - 39,999 38%
$40,000 - 64,999 23%
$65,000 or More 13%
Race
White, Not Hispanic 67%
Black, Not Hispanic 21%
Other/Multiple 12%
Average Age 37
Percent Female 51
Percent Who are Primary Handlers of Household Bills 883
We used data from one of the survey experiments to test how marketing materials, eco-
seals and labeling affect respondents' uncertainty about product attributes and about their
purchase intentions.
1  The experiment required respondents to first rate a single electricity
product on price, environmental impact and purchase likelihood after reading marketing bullet
points. Respondents were asked to indicate on a Likert scale how expensive they think the
product is, how good the product is for the environment, and how likely they would be to buy the
product.  Respondents were asked to note their answers on a scale from 1 to 10 where the scale
endpoints denoted either very inexpensive/very expensive; not very good for the
environment/very good for the environment; or very likely to buy/very unlikely to buy.  After
rating the products, the respondents were then shown the product’s accompanying label.  After
viewing the label, respondents were asked the same sequence of rating questions.
The bullet points were experimentally manipulated; participants either saw marketing
bullets that emphasized the environmental benefits of the product or the product's low price;
under some treatments they also saw an environmental seal of approval. Label conditions were
also subject to experimental manipulation.  There were four possible label treatments.
Respondents either saw a 'Full Label', which includes information on price, fuel mix, and
emissions; 'Full, No Price', includes fuel mix and emissions information; 'Fuel Mix ', only fuel
mix information is disclosed; or 'Emissions', only emissions are disclosed. All treatments contain
the same information about the contract terms.
A preliminary examination of the data revealed an interesting phenomenon; there are
differences in the proportion of individuals across treatments responding ‘Don’t Know’ to the
different perception questions (Table 2). Contingency tables confirm that similar percentages of4
people across treatments stated ‘Don’t Know’ to the first (before labels were revealed) price
(c
2
(9) = 7.56, p = 0.579) and likelihood to buy questions (c
2
(9) = 3.90, p = 0.918).  However, there
is a relatively large variation in the percent of individuals who answered ‘Don’t Know’ to the
first environmental perception question (c
2
(9) = 192.20, p = 0.001).  In addition, similar
percentages of people across treatments stated ‘Don’t Know’ to the second (after labels were
revealed) environmental (c
2
(9) = 13.80, p = 0.129) and likelihood to buy questions (c
2
(9) = 13.62,
p = 0.136).  Although there is a relatively large variation in the percent of individuals who
answered ‘Don’t Know’ to the second price perception question (c
2
(9) = 52.83, p = 0.001).
Differences in the percent of individuals that stated ‘Don’t Know’ across the first
perception questions may indicate that individuals’ have different levels of uncertainty for
different product attributes.  For example, the similarity in the number of individuals stating
‘Don’t Know’ to the first price perception question across treatments may indicate that
respondents already are relatively confident that they know the price of electricity; thus,
differences in marketing has little impact on respondent uncertainty.  However, the striking
variation in the number of individuals stating ‘Don’t Know’ to the first environmental perception
question across treatments may indicate that respondents are not confident that they know the
environmental characteristics of electricity; here differences in marketing may have large
impacts on respondent uncertainty. In addition, there are differences in the percent of individuals
that state ‘Don’t Know’ within the same treatment group before and after viewing the label
information (Table 3).  This suggests that respondents’ uncertainty is affected by the label
information. This prompts us to further analyze the relationship between information provision
and consumer uncertainty.
                                                                                                                                                      
1 See Winneg et al. (1998) for a complete description of the study.5
Table 2: Percent of Respondents Answering ‘Don’t Know’, by Treatment.
Before seeing label After seeing label
Marketing Treatment Price Environmental Likelihood
to
buy
Label
Condition
a
Price Environmental Likelihood
to
buy
1. Price 19 49 11 Full label 11 6 5
2. Environmental 20 7 10 Fuel  mix 19 8 9
3. Environmental 18 5 7 Emissions 22 6 9
4. Environmental 12 4 6 Full label 2 2 2
5. Environmental 15 7 10 Full, no price 17 5 11
6. Price 21 46 12 Fuel mix 27 11 10
7. Price 13 42 10 Emissions 19 6 6
8. Price with eco-seal 18 16 8 Full label 4 3 4
9. Environmental
     with eco-seal
11 7 7 Full label 5 4 5
10. Price 15 43 10 Full, no price 20 2 4
a 'Full Label' includes information on price, fuel mix, and emissions; 'Full, No Price',
includes fuel mix and emissions information; 'Fuel Mix ' includes only fuel mix
information; 'Emissions ' includes only emissions information.
Table 3: Chi square Results: Test of whether the Percent of Respondents Answering ‘Don’t
  Know’ Changed After Viewing Label Information, by Treatment.
c
2 - Test Statistics
Marketing Treatment Label
Condition
a
Price Environmental Likelihood to
buy
1. Price Full label 2.48 45.39*
 a 2.43
2. Environmental Fuel  mix 0.03 0.07 0.06
3. Environmental Emissions 0.50 0.09 0.27
4. Environmental Full label 7.68* 0.69 2.08
5. Environmental Full, no price 0.15 0.36 0.05
6. Price Fuel mix 0.99 30.06* 0.20
7. Price Emissions 1.34 35.64* 1.09
8. Price with eco-seal Full label 10.04* 9.85* 1.42
9. Environmental
     with eco-seal
Full label 2.45 0.87 0.36
10. Price Full, no price 0.87 48.20* 2.76*
a 'Full Label' includes information on price, fuel mix, and emissions; 'Full, No Price',
includes fuel mix and emissions information; 'Fuel Mix ' includes only fuel mix
information; 'Emissions ' includes only emissions information.
b   Denotes the test statistic is not significant at the 10 percent level.6
To determine the relationship between the number of ‘Don’t Knows’ and the different
marketing and labeling treatments, we estimate the number of ‘Don’t Know’ responses to the full
set of perception/intention questions as a function of marketing and labeling treatment variables.
Specifically we estimate:
DK_Pricei = b01PMi + b11GMi + b21PM_Certi + b31GM_Certi
+ b41PM_ Pi  + b51GM_Pi + b61PM_Fi + b71GM_Fi + b81PM_Ei + b91GM_Ei + ei
DK_Greeni = b02PMi + b12GMi + b22PM_Certi + b32GM_Certi
+ b42PM_ Pi  + b52GM_Pi + b62PM_Fi + b72GM_Fi + b82PM_Ei + b92GM_Ei + eI
DK_Buyi   = b03PMi + b13GMi + b23PM_Certi + b33GM_Certi
+ b43PM_ Pi  + b53GM_Pi + b63PM_Fi + b73GM_Fi + b83PM_Ei + b93GM_Ei + ei
where DK_Pricei, DK_Greeni and DK_Buyi denote whether individual i responds ‘Don’t Know’
to the price, environmental and likelihood to buy questions, respectively; the dependent variables
are coded as one if the respondent stated they don’t know, zero otherwise.
2  PMi is equal to one
for individuals who view price-focused marketing, 0 otherwise and GMi is equal to one for
individuals who view environment-focused marketing, 0 otherwise.  PM_Certi is equal to one for
individuals who view price-focused marketing with an eco-seal, 0 otherwise; GM_Certi is equal
to one for individuals who view environment-focused marketing with an eco-seal, 0 otherwise.
PM_Pi is equal to one for individuals who viewed price marketing followed by price labels, 0
otherwise; PM_Fi is equal to one for individuals who viewed price marketing followed by fuel
mix labels, 0 otherwise; and PM_Ei is equal to one for individuals who viewed price marketing
followed by emissions labels, 0 otherwise. GM_Pi is equal to one for individuals who viewed
environmental marketing followed by price labels, 0 otherwise; GM_Fi is equal to one for
individuals who viewed environmental marketing followed by fuel mix labels, 0 otherwise; and
                                               
2 Note that for this analysis we are combining responses to both the first and second questions.7
GM_Ei is equal to one for individuals who viewed environmental marketing followed by
emissions labels, 0 otherwise.  The models are estimated as binary logits because the dependent
variable is binary (0, 1).
Chi-square tests were performed to test the pair-wise and joint equivalence of parameter
estimates.  Specifically we are interested if the effect of price marketing alone is different than
environmental marketing alone (b0j = b1j) and whether the joint impact of price marketing with
an environmental seal is equivalent to environmental marketing alone (b0j + b2j = b1j). We are
also interested if the effect of adding price (b4j = b5j), fuel mix (b6j = b7j) or emissions (b8j = b9j)
labeling is dependent upon whether it follows price  or environmental marketing.  In addition, we
are interested in whether fuel mix or emissions labeling provides a greater impact given that it
follows price (b6j = b8j)  or environmental (b7j = b9j) marketing.   Finally, we would like to know
if the joint effect of price marketing with environmental information (environmental seal, fuel
mix and emissions labeling) is different than environmental marketing alone (b0j + b3j + b6j + b8j
= b1j).
Results
The results indicate that different marketing information has different effects across
questions (Table 4).  There is no difference between price and environmental marketing in terms
of affecting the probability that an individual is uncertain about the price of an electricity product
(c
2
(1)  = 0.41, p = 0.5230).  In addition, adding an eco-seal does not affect price perceptions; the
joint effect of price marketing with an eco-seal is no different than that of environmental
marketing alone (c
2
(1)  = 0.04, p = 0.8472).  There are differences between price and
environmental marketing in terms of affecting the probability that an individual is uncertain
about the environmental quality of electricity.  Individuals viewing environmental marketing8
information are much less likely to state that they 'Don't Know' about the environmental quality
of electricity (c
2
(1)  = 99.94, p = 0.0001).  Adding an eco-seal to price marketing information
does decrease the likelihood that an individual states that they 'Don't Know' about the
environmental quality of electricity.  However, the joint effect of price marketing with an eco-
seal is still less than that of environmental marketing alone (c
2
(1)  = 13.61, p = 0.0002).  Finally,
with respect to the intention to buy question, there is no difference between price and
environmental marketing in terms of affecting the probability that an individual is uncertain
about their decision (c
2
(1)  = 0.66, p = 0.4171).  In addition, adding an eco-seal does not affect
whether the person is uncertain about their purchase intentions; the joint effect of price
marketing with an eco-seal is no different than that of environmental marketing alone (c
2
(1)  =
0.01, p = 0.9125).
The results indicate that different labeling information has different effects across
questions and that marketing can influence the effect of labeling (Table 4).  Not surprisingly,
price labels significantly reduce the likelihood that an individual is uncertain about the product’s
price and the effects are equivalent across marketing treatments (c
2
(1)  = 0.118, p = 0.7308).
Except for fuel mix labeling following price marketing, environmental labels do not affect an
individuals price uncertainty.  Fuel mix labeling following low-price marketing actually
increases an individuals price uncertainty.
Individuals viewing fuel mix and emissions labels after low-price marketing are much
less likely to state that they 'Don't Know' about the environmental quality of electricity;
interestingly, the effect of emissions and fuel mix labels are similar (c
2
(1)  = 1.15, p = 0.2189).
Environmental labels provide no significant effect when viewed after environmental marketing
materials.  The joint effect of environmental information (eco-seal, fuel mix and emissions9
information) and price marketing on reducing environmental uncertainty is greater than that of
having environmental marketing alone (c
2
(1)  = 14.84, p = 0.0001).
Finally, with respect to the intention to buy question, there are differences between price
and environmental labeling in terms of affecting the probability that an individual is uncertain
about their decision. Adding a price label to environmental marketing information significantly
reduces the likelihood that an individual is uncertain about their intention to purchase decision.
Providing environmental labels have no effect on the intent to purchase.
Table 4: Parameter Estimates: Probability that Respondent States 'Don't Know'
Price Perception Green Perception Likelihood to Buy
PM -1.519*
a
(0.139)
-0.579*
(0.092)
-2.276*
(0.150)
GM -1.629*
(0.128)
-2.778*
(0.200)
-2.463*
(0.173)
PM_Cert -0.163
(0.263)
-1.006*
(0.267)
-0.145
(0.345)
GM_Cert -0.252
(0.299)
0.301
(0.382)
0.093
(0.357)
PM_P -1.546*
(0.473)
1.549*
(0.644)
-0.347
(0.683)
GM_P -1.773*
(0.463)
-0.874
(0.595)
-1.240*
(0.514)
PM_F 0.525*
(0.252)
-1.511*
(0.333)
0.079
(0.366)
GM_F -0.005
(0.230)
0.192
(0.361)
0.175
(0.301)
PM_E 0.082
(0.279)
-2.161*
(0.431)
-0.464
(0.447)
GM_E -0.209
(0.226)
-0.165
(0.379)
-0.175
(0.301)
a * means the parameter is significant at 10% level.
Conclusions
Policy makers (and some producers) hope that environmental marketing and labeling
restrictions imposed on a deregulated electricity production market will educate consumers about
the environmental impacts of electricity use, thereby leading to a changes in purchasing10
behavior, and ultimately, to a reduction in negative impacts.  We find that many consumers are
uncertain about the environmental characteristics of electricity.  We also find that environmental
labeling and marketing can have a profound impact on reducing consumers' uncertainty
regarding these characteristics.  One would assume that presenting environmental information to
these latter consumers would have a potentially large impact on their purchasing decisions.
Unfortunately the data preclude testing this effect.11
References
Winneg, K., M.J. Herrmann, A.S. Levy and B. Roe. 1998. Label Testing: Results of Mall
Intercept Survey Report to the National Council on Competition and the Electric
Industry. Chilton Research Services. April.
Wright, P. 1986.  Schemer Schema: Consumers’ Intuitive Theories About Marketers’ Influence
Tactics, Advances in Consumer Research, ed. Richard J. Lutes, 13, 1-3.

ENVIRONMENTAL LABELING OF ELECTRICITY: EFFECTS ON CONSUMER UNCERTAINTY ABOUT PRODUCT ATTRIBUTES AND LIKELIHOOD TO BUY DECISIONS

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