A Laboratory Study of Affirmative
and Negative Motivations for
Compliance in Emissions Trading
Programs
Leigh Raymond (Political Science)
Timothy Cason (Economics)
Purdue University
Motivation and Research Rationale
• Self-reporting and imperfect enforcement are
becoming more important in emissions trading
programs
– Continuous emissions monitoring is often infeasible
• What motivates honest reporting?
– Negative motivations: fear of punishment for violations
– Affirmative motivations: personal sense of a rule’s
legitimacy or morality determines compliance
• Experiment varied legitimacy and fairness through
manipulations of permit endowments & framing
Research Method
• Laboratory emissions permit market with voluntary
reporting of emissions each period
– “hard case” for finding influence of affirmative
motivations, since financial incentives were real but
environmental and political consequences were not
• Simple (random inspection) enforcement
mechanism, with inspection probabilities varied as a
treatment variable
• Pre- and post-experiment surveys used to assess
subjects’ normative beliefs
Specific Hypotheses
• (H1) Subjects will report emissions more honestly than
simple calculations of economic self-interest dictate.
• (H2) Subjects will rate egalitarian allocations as fairer
than grandfathering or auctions.
• (H3) Subjects rating their allocations as “unfair” will be
significantly less likely to comply.
• (H4) Subjects who express an affirmative motivation to
obey “fair” laws in general, and who assess the
experiment’s rules as “fair,” will be more likely to
comply.
• (H5) Subjects who express a general affirmative duty to
obey laws will be more likely to comply.
• (H6) Subjects who approve of emissions trading in
general as a legitimate policy will be more likely to
comply.
Experiment Design Details
• 11 rounds of trading and reporting in each session
– Step 1: permit allocation (constant within sessions)
– Step 2: computerized continuous double auction emission
permit trading
– Step 3: pollution abatement decision
– Step 4: emissions reporting decision
– Step 5: enforcement and fines (random inspection)
• 8 traders per session (2 or 3 sessions conducted
simultaneously)
• Student subject pool
– Perhaps biasing downwards the impact of framing?
Marginal Abatement Costs and Permits
• 64 permits allocated; prices in the range E$208-212
clear the market with perfect compliance.
Figure 1: Avoided Marginal Abatement Costs and Total Permits
Available
400
Cost (Experimental Dollars) .
350
Permits
Available
300
250
Avoided
Abatement
Costs
200
150
100
50
0
1
11
21
31
41
51 61 71 81 91 101 111 121 131 141 151
Quantity of Permits or Abatement
Experimental Treatments
• 2×2×2 design (8 treatments)
• High vs. Low enforcement (always E$400 fine per unit)
– 50% vs. 25% inspection probability
• Environmentally Framed vs. Neutral (“Unframed”)
– “Manager of a power plant that…burns fossil fuel to
produce electricity which pollutes the atmosphere” vs.
“choose a number…report your number choice”
• Equal vs. Unequal permit endowment
– Unequal explained in Framed as grandfathered based on
higher historical emissions and pollution control costs
• 5 sessions in each treatment cell + 1 extra session
– 328 total subjects; Ave. pay US$29, total time ~2 hours
Computerized using zTree
Any trader could
either buy or sell,
and by holding
permits they could
avoid paying
higher marginal
abatement costs.
Results: Negative Motivations
• (H1) Subjects will report emissions more honestly than
simple calculations of economic self-interest dictate.
• Table 1: percent of noncompliant emissions reports
– Noncompliance is clearly higher with low monitoring
Neutral Frame
“should” be
much higher
Environmental Frame
Unequal
Equal
Unequal
Equal
Endowments Endowments Endowments Endowments
Low
Monitoring
39.5
31.8
53.2
53.9
High
Monitoring
11.1
12.7
36.4
31.4
Over one-third of subjects reported honestly in at least 10 of 11 periods with low monitoring
Results: Fairness of Allocation Methods
• (H2) Subjects will rate egalitarian allocations as fairer
than those based on grandfathering or auctions.
• Table 2: Allocation Fairness Ratings
Very Unfair
Somewhat Unfair
Neutral
Somewhat Fair
Very Fair
Don’t Know
Which allocation
most fair?
Which allocation
most unfair?
Don’t
Know
Grandfathering
13%
32%
20%
26%
5%
5%
Equal
Shares
5%
18%
25%
36%
10%
5%
Auction
12%
54%
23%
12%
38%
7%
46%
9%
25%
22%
21%
16%
10%
6%
Results: Affirmative Motives from
Morality
• (H3) Subjects rating their allocation as “unfair” will be
significantly less likely to comply.
– Tobit models using the level of noncompliance as the
dependent variable support this hypothesis
• (H4) Subjects who express an affirmative motivation to
obey “fair” laws in general, and who assess the
experiment’s rules as “fair,” will be more likely to
comply.
– Tobit models indicate that subjects who indicate personal
beliefs as a main motivation for misrepresenting emissions
complied less; and
– Subjects who believe in importance of following fair or just
laws complied less in the neutral frame
(Dependent variable: Total amount of noncompliance)
Treatment Conditions and Endowment
Indicator=1 if environmental
context
Indicator=1 if monitoring
intensity is high
Indicator=1 if subject has a
high permit endowment
Indicator=1 if subject has a
low permit endowment
Questionnaire Responses
Indicator=1 if subject viewed own
permit endowment as unfair
Indicator=1 if subject indicated personal beliefs as main
motivation for accurately reporting emissions
Indicator=1 if subject indicated personal beliefs as main
motivation for misrepresenting emissions in reporting
Indicator=1 if subject agrees that he/she sometimes
disobeys laws when the risk or consequences are low
Indicator=1 if subject believes in importance
of following fair or just laws
Indicator=1 if subject believes in importance that
obeying the law in general is the “right thing to do”
Indicator=1 if subject viewed own permit endowment
as unfair and believes in importance of following just laws
Indicator=1 if subject is considers him/herself
an "environmentalist"
Indicator=1 if subject believes that global
warming is an important issue
Indicator=1 if subject correctly identifies a statement
describing emissions trading and supports it as a policy
All
Treatments
Neutral
Context
Environmental
Context
27.98**
(4.81)
-23.39**
(5.12)
-23.22**
(6.48)
11.84
(7.00)
-37.86**
(9.84)
-37.64**
(10.65)
4.11
(8.52)
-16.55*
(6.71)
-17.16*
(7.78)
19.43*
(9.25)
34.85**
(12.78)
-14.31
(7.71)
35.00**
(11.54)
8.61
(4.83)
2.53
(6.10)
1.85
(5.94)
-25.02
(15.78)
6.16
(5.61)
0.90
(5.73)
4.53
(10.76)
39.07*
(17.06)
-15.25
(10.23)
50.67**
(12.99)
10.74
(5.88)
14.61*
(6.15)
3.27
(7.63)
-31.00
(22.44)
3.19
(8.12)
-10.46
(6.08)
27.78*
(11.66)
41.49*
(16.45)
-11.57
(10.40)
12.81
(15.36)
9.97
(5.75)
-4.80
(9.29)
4.14
(10.58)
-29.87
(19.72)
6.62
(7.09)
14.97
(7.68)
-4.36
(16.35)
Results: Affirmative Motives from
Legitimacy
• (H5) Subjects who express a general affirmative duty
to obey laws will be more likely to comply.
– Subjects who agreed with the statement “sometimes I
disobey laws when the risks…are low” reveal weaker
motivations through legitimacy in general, but they comply
less at only marginally significant levels
• (H6) Subjects who approve of emissions trading in
general as a legitimate policy will be more likely to
comply.
– No support for this. ET supporters do not comply more
(and they actually comply less in the neutral context).
Additional Compliance Results
• Subjects with a low permit endowment, who are
typically permit buyers, comply less in the
environmentally framed treatment (as in Murphy &
Stranlund, 2007)
• Greater compliance observed among more risk
averse subjects (independent lottery choice
elicitation) and among US residents
(Dependent variable: Total amount of
noncompliance)
All
Treatments
Neutral Environmental
Context
Context
Demographic and Risk Preference Controls
Indicator=1 if
8.59
14.09
2.46
subject is male
(5.65)
(10.57)
(6.82)
Indicator=1 if
1.40
-8.56
11.63
(6.21)
(8.93)
(8.15)
-27.74**
-41.60**
-29.76**
lived in US for more than 5 years
(6.51)
(9.16)
(9.42)
Grade point average (self reported)
7.03**
-22.24**
9.86**
(2.48)
(8.29)
(2.37)
4.74
-1.14
5.44
(2.62)
(3.56)
(4.32)
Indicator=1 if subject receives
-6.55
-7.56
-9.49
need-based financial aid
(4.47)
(4.42)
(7.37)
Indicator=1 if subject's lottery choices
10.76
4.10
17.53
indicate risk seeking preferences
(8.08)
(12.71)
(10.66)
-13.95**
-6.89
-16.68**
(5.30)
(9.01)
(6.02)
subject is business major
Indicator=1 if subject has
Years of college
Indicator=1 if subject's lottery choices
indicate very risk averse preferences
Permit Market Performance
• Permit prices were greater in the high monitoring
treatment, which featured more compliance.
Figure 2: Average Median Permit Transaction Prices for Low Monitoring and High
Monitoring Treatments
220
High monitoring
200
Low Monitoring
180
Low Monitoring
Price
Prices are
significantly
higher in
sessions with
more emissions
control and
compliance.
High Monitoring
160
Full Compliance Equilibrium Range
140
120
100
0
1
2
3
4
5
6
Period
7
8
9
10
11
12
Environmental Framing
• Hypothesis (H6) depends on subjects’ environmental
policy attitudes, and other hypotheses depend on
knowledge and preferences toward environmental
regulations
• Neutral framing is much more common in
experimental economics
– Recommended by Alm (1999), for example, since it
obscures the experiment’s context and purpose—thereby
increasing experimental control
– Subjects could have very different attitudes towards the
role of environmental regulation and emissions trading
• We introduced non-neutral framing deliberately to
activate these concerns
Framing as a Treatment Variable
• Some advocates of field experiments argue that neutral
framing can reduce control if it leads subjects to invent their
own context, which is unobserved to the experimenter
– Bohm and coauthors often used environmental framing in
experiments that employed subjects with field experience
• Context framing appears most useful for expert subjects
participating in field experiments, and has a greater impact on
their behavior; so our surprising and large framing effect
could actually understate the impact of environmental
framing in the field
Neutral Frame
Environmental Frame
Percent of noncompliant
emission reports
Unequal
Equal
Unequal
Equal
Endowments Endowments Endowments Endowments
Low
Monitoring
39.5
31.8
53.2
53.9
High
Monitoring
11.1
12.7
36.4
31.4
Summary
• We confirm the importance of affirmative motivations
even in this “hard case” without real environmental
consequences
• Stronger support (for this subject pool) for egalitarian
allocations than is reflected in political proposals
• Surprisingly strong increase in noncompliance in the
environmental framing treatment (as important as the fine!)
– Environmental econ experiments should investigate further
• Emissions trading policies that rely on self-reporting
should consider legitimacy and affirmative motivations
for compliance, especially when debating alternative
allocation methods and building public support for
policies like cap and trade
Future Work
• Add real environmental consequences of
noncompliance to strengthen affirmative motivations
(e.g., buying emissions offsets)
• Include laboratory political processes (e.g., majority
voting, negotiations, or rent seeking contests) to
manipulate legitimacy of policy and allocation choice
• Extend subject pool to non-students, including
environmental managers
• Investigate intermediate frames (e.g., a “firmmanager” but not in an environmental context)
• In general, consider perceptions of the initial allocation
as part of the policy design, since they apparently
affect compliance incentives