Presentation on theme: "A Laboratory Study of Affirmative and Negative Motivations for Compliance in Emissions Trading Programs Leigh Raymond (Political Science) Timothy Cason."— Presentation transcript:
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$ clear the market with perfect compliance.
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 Unequal Endowments Equal Endowments Unequal Endowments Equal Endowments Low Monitoring High Monitoring Neutral Frame Environmental Frame “should” be much higher 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 Grand- fathering Equal Shares AuctionDon’t Know Very Unfair13%5%25% Somewhat Unfair32%18%22% Neutral20%25%21% Somewhat Fair26%36%16% Very Fair5%10% Don’t Know5% 6% Which allocation most fair? 12%54%23%12% Which allocation most unfair? 38%7%46%9%
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) All Treatments Neutral Context Environmental Context Treatment Conditions and Endowment Indicator=1 if environmental27.98** context(4.81) Indicator=1 if monitoring-23.39**-37.86**-16.55* intensity is high(5.12)(9.84)(6.71) Indicator=1 if subject has a-23.22**-37.64**-17.16* high permit endowment(6.48)(10.65)(7.78) Indicator=1 if subject has a * low permit endowment(7.00)(8.52)(9.25) Questionnaire Responses Indicator=1 if subject viewed own34.85**39.07*41.49* permit endowment as unfair(12.78)(17.06)(16.45) Indicator=1 if subject indicated personal beliefs as main motivation for accurately reporting emissions(7.71)(10.23)(10.40) Indicator=1 if subject indicated personal beliefs as main35.00**50.67**12.81 motivation for misrepresenting emissions in reporting(11.54)(12.99)(15.36) Indicator=1 if subject agrees that he/she sometimes disobeys laws when the risk or consequences are low(4.83)(5.88)(5.75) Indicator=1 if subject believes in importance *-4.80 of following fair or just laws(6.10)(6.15)(9.29) Indicator=1 if subject believes in importance that obeying the law in general is the “right thing to do”(5.94)(7.63)(10.58) Indicator=1 if subject viewed own permit endowment as unfair and believes in importance of following just laws(15.78)(22.44)(19.72) Indicator=1 if subject is considers him/herself an "environmentalist"(5.61)(8.12)(7.09) Indicator=1 if subject believes that global warming is an important issue(5.73)(6.08)(7.68) Indicator=1 if subject correctly identifies a statement *-4.36 describing emissions trading and supports it as a policy(10.76)(11.66)(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 Context Environmental Context Demographic and Risk Preference Controls Indicator=1 if subject is male(5.65)(10.57)(6.82) Indicator=1 if subject is business major(6.21)(8.93)(8.15) Indicator=1 if subject has-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) Years of college (2.62)(3.56)(4.32) Indicator=1 if subject receives need-based financial aid(4.47)(4.42)(7.37) Indicator=1 if subject's lottery choices indicate risk seeking preferences(8.08)(12.71)(10.66) Indicator=1 if subject's lottery choices-13.95** ** indicate very risk averse preferences(5.30)(9.01)(6.02)
Permit Market Performance Permit prices were greater in the high monitoring treatment, which featured more compliance. High monitoring Low Monitoring Prices are significantly higher in sessions with more emissions control and compliance.
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 Unequal Endowments Equal Endowments Unequal Endowments Equal Endowments Low Monitoring High Monitoring Neutral Frame Environmental Frame Percent of noncompliant emission reports
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 “firm- manager” 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