Starting with the results from the Tobit specification, it can be observed that variables associated with gender, age and political affiliation proved to be significant. Political affiliation has been identified as an essential variable explaining people’s willingness to pay to offset 𝐶𝑂2 emissions, and this study further enforces this observation. Other studies also note the role of gender in WTA, with females paying on average more than males, and a similar effect can also be observed in my results. Contrary to other studies, these results also highlight the role of age in the opposite direction than it was previously found. The results of the Tobit regression show that WTA increases with age controlling for other important variables such as income or education.
It is also interesting to note that the average income was not found to be significant. One of the reasons for that could be the existence of potential bias in this variable. The experiment was run using the platform Prolific, and although the platform is offering many pre-screening checks to make sure that the information provided by the participants is reliable and the pool of participants is diverse, there could be an incentive for participants to alter their data in the case of income. Presumably, people in the higher income brackets would not be as interested in these studies relative to participants with low earnings because they have a higher opportunity cost, and this may create a higher demand for the high-earning participants. For this reason, participants with lower earnings may have an incentive to provide false information about their income to have more opportunities to participate in different studies, and this may distort the actual effect of income. Although this hypothesis cannot be verified, it is crucial to consider generally with platforms similar to Prolific.
Turning to the effect of coastal flooding risk, the Tobit regressions show no significant effect for
risk of flooding showed that people who reported flooding experience expressed more concern about climate change. More importantly, their concerns directly translated into a higher willingness to pay to save energy (Spence & Butler, 2011). It is doubtful, though, to what extent this higher willingness to pay is a long-term phenomenon affected by previous flooding experience and to what extent it could have been temporarily induced by priming.
Another concern that may emerge when looking at the Tobit regression results is whether the coastal flooding risk is salient enough on its own compared to other weather events risks. For example, even though a county may have low or no coastal flooding risk, it may be subject to many other climate change risks, affecting the willingness to pay. This seems to suggest that taking the general risk rating would be more valuable than focusing on a single type of extreme weather event. I tested this hypothesis by rerunning the Tobit regressions, including the general risk rating (risk_score) as a control variable and running the Tobit regressions without cfld_risks, cfr and coastal_county but with risk_score. In both cases, the risk_score coefficient was insignificant, and the coefficients that were significant before for the other variables remained significant. A similar analysis was also conducted for PSM, where instead of using the cfr variable as the treatment variable, I used a treatment describing general risk. To do that, I created a new binary variable risk_score_bi which takes the value of 1 for high or moderate levels of general risk and the value of 0 for low levels of risk. Again, as in the Tobit regression, no significant results have been observed.
The results of both of the methods can be found in Appendix 2.
The effect of coastal flooding risk was also tested using the PSM method for externality levels of 1 and 700. For the externality level of 1, both ATE and ATET did not prove to be significant. Interestingly, the effect was negative for both of them, showing that coastal flooding risk decreased rather than increased WTA. Considering Figure 3, it is reasonable to think that the difference between the treatment and control groups is not that pronounced at this level of externality. Therefore, the treatment effect may be distorted.
Perhaps at low levels of externality, the preferences are not clearly developed.
On top of that, the externality of 1 mile driven by car is probably incurred by participants daily, and as they never put a price on this choice, it might lead to the high variability of choices. I have also
estimated ATE and ATET for higher levels of externality, and the precise results can be found in Appendix 3. I found no significant results until the level of 450, where ATET was marginally significant. Finally, at the level of 700, both ATE and ATET were significant at 5%, showing that for the entire sample and the narrowly defined treated sample, coastal flooding risk decreased WTA. This allows me to reject the null hypothesis for the externality level of 700. What can be inferred from these findings is that the relationship between cfr and switch only becomes pronounced at higher levels of externality. One of the reasons for that could be that people may not feel obliged to pay the costs for externalities at low levels because these externalities are at the level of everyday activities. People may feel more responsible at higher levels, and the reaction is more substantial for people at risk because they may be more familiar with the potential consequences.
I have also conducted another analysis using PSM, where I controlled for the level of externality.
The findings of this part showed a highly significant value of ATET, showing that for the treated sample, the risk of coastal flooding increased WTA. No such effect was found for ATE. It is helpful to combine these findings with the findings of PSM for specific externality levels because it helps understand the connection between the investigated relationship with the externality level. It seems that the effect is strong for the treated, but probably it is mainly driven by the discrepancies in choices at higher levels of externality.
It can be speculated that at higher levels, people already at risk have higher activation of System 1 thinking and react more strongly, whereas, at the low levels, no sense of urgency is ignited in any of the groups.
It could be interesting to consider what factors could contribute to a higher sense of urgency at the lower levels of the externality. One of the aspects that might be important is the specification of the cause towards which the participants are donating money by withdrawing their monetary reward. In this experiment, the researchers focused on decreasing 𝐶𝑂2emissions, but perhaps if the cause was more
It can be speculated that with a cause more specific to the coastal flooding risk, the sense of urgency would be more robust which would translate to a higher WTA for subjects living in risky areas. I believe that, on average, no such effect would be observed for subjects from non-risky areas, as this specific cause would not be closely related to their environment.
The next interesting consideration is related to one of the previously mentioned studies that looked at WTP for air travel passengers. Due to the high demand for offsetting emissions, many schemes were created, but they were not effective because they did not facilitate collective action. In general, climate change is a global problem that can only be tackled through united effort; it can be said that climate change suffers from the drop-in-the-bucket effect. However, the set-up of this experiment only allowed for individual action, so the participants were not informed about the choices of others, and no collective schemes were implemented. This lack of collective element could be critical especially considering System 1 and, more precisely, participants’ association with the local community. It can be argued that the collective element, together with a more personalized cause, could lead to even higher activation of System 1 for participants from risky areas, and the difference between participants from risky and non-risky areas would be more significant.
Nevertheless, it is still debatable whether personal or collective responsibility should be emphasized with these kinds of issues. On the one hand, personal responsibility should be highlighted as collective responsibility can diffuse a person’s incentive for individual problems and create a free-riding problem (Ostrom, 1990). On the other hand, it can be argued that collective responsibility should be emphasized as people may not believe that they can change anything as individuals. Regarding climate change, it has been shown that emphasizing collective responsibility amplifies mitigation behaviors (Obradovich & Guenther, 2016). Even though this is still up for debate, it is vital to notice that the results could be different if collective action was implemented.
All things considered, the null hypothesis can be rejected for high levels of externality, and more research should be devoted to capturing the effect at the low levels of externality. It seems that there is a connection between coastal flooding risk and WTA. One of the causes for this could be a higher sense of
urgency among the participants from risky areas, yet many other causes can be debated. It can also be argued that some survey modifications could have led to a more pronounced effect, and the observations made could be helpful in further research and future policies.