Subjects are sampled from the Socio-Ecological Panel (Green-SÖP, Wave 7), which is representative for the German population. Each subject completes a questionnaire, which will be published in full with the Green-SÖP Wave 7 dataset (http://fdz.rwi-essen.de/doi.html). The parts relevant for the present experiment are provided with annotations in the Docs & Materials section, both in the German original and machine translated (https://www.deepl.com/translator) form (for sake of replicability).
Subjects sampled for the experiment are randomly assigned to one of five experimental conditions: CC1 (B_Kontrolle 1 in the questionnaire), CC2 (B_Kontrolle 2), TC1 (B_Komplexität 1), TC2 (B_Komplexität 2), TC3 (B_Komplexität 3). In all conditions subjects respond to the pre-treatment questions ExpB0 and ExpB1. Depending on treatment assignment, subjects respond to question ExpB2_a (CC1), ExpB2_b (CC2), ExpB2_c (TC1), ExpB2_d (TC2), ExpB2_e (TC3). All questions involve a real decision whether - and if applicable when - to retire an allowance from the EU ETS at a personal opportunity cost. The amount of true information supplied to subjects prior to their decision is manipulated across conditions. The information describes the market stability reserve (MSR) and its implications on how the timing of retirement influences the actual amount of greenhouse gas emissions saved. The level of detail ("complexity") of the description differs across the five experimental conditions: There are two control condtions without reference to the MSR (CC1 and CC2), and three treatment conditions that supply successively more information (TC1, TC2, and TC3).
The design is motived by the following behavioral model:
Subjects have preferences for both private consumption $x$ and contributions to the public good $y$. We assume they are additive and represented by utility function $u$: $u(x,y) = x + \rho(T) \cdot v(y)$ with $v’ > 0$. $\rho(T) \geq 0$ with $T = CC1, CC2, TC1, TC2, TC3$ captures contextual aspects of the treatment specific choice situation (T) such as the degree of complexity involved. This implies that within treatment and hence subject $u(x_0,y_i) < u(x_0,y_j)$ iff $y_i < y_j$. Factually, the available $y_i$ are the same for all conditions except CC1 and TC3. However, in terms of the information available to subjects regarding the effectiveness of the options available there is also a difference between CC2 and TC1/TC2 as the latter explicitly rank the available cancellation options. Given that we hold $x$ constant across all treatments, differences in choice patterns between treatments reflect factual or perceived differences in the size of $y$ and choice context $\rho(T)$ induced by treatment variations. Note, that changes in $\rho$ only affect the choice between cancellation and private consumption, i.e. between $x$ and $y$ but not between the two cancellation options available in all conditions except CC1.
The experiment is designed to test how WC and REC are affected by revealing unexpected and potentially morally contested information on effects of voluntary climate action and the complexity of the explanations provided. To those that are willing to contribute the information provided in TC1-3 is relevant as it reveals a strict dominance in effectiveness at identical costs ($ y_1 < y_2$). Furthermore, the information is most likely surprising as the implications of the Market Stability Reserve (and indeed the mechanism’s mere existence) are known only to an expert circle. The common belief among supporters of climate actions is that (in particular at identical costs) early action is better than delaying it (UNEP 2021, Figure 3 & 6), i.e. that ($ y_1 > y_2$). Hence, at least some participants will update their beliefs and prefer late cancellations. Updating of beliefs over the relative effectiveness of early and late cancellation can be biased, because "bad news" are discounted in certain contexts (Eil & Rao 2011, Gershman 2019, Kuzmanovic et al. 2018, Yao et al. 2021). We therefore do not expect that all contributors choose the late cancellation option. The first key hypothesis is:
Hypothesis KH1: REC is larger in TC1-3 than in CC2.
However, there might be a trade-off between REC and participants’ willingness to contribute. The information might conflict with the moral belief that urgent action on climate change is a moral obligation. Hence, there might be an aversion to see this belief challenged or an urge to act against it (Bénabou & Tirole 2011). Moreover, if the dissonance or ambivalence induced is sufficiently strong, participants might be less willing to contribute at all in order to avoid having to make a choice that they are ambivalent about (Anderson 2003, Luce et al. 1997). In this case $\rho(TC1-3) < \rho(CC2 & CC1)$. The second key hypothesis is therefore:
Hypothesis KH2: WC is smaller in TC1-3 than in CC1 & CC2.
The bibliography is provided in the "Docs & Materials" section.