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on Discrete Choice Models |
| By: | Bartels, Lara; Kesternich, Martin; Löschel, Andreas |
| Abstract: | With the increasing recognition of the use of reforestation measures as a complement to conventional carbon emissions avoidance technologies it is important to understand the market valuation of local forest carbon sinks for climate change mitigation. We conducted a framed-field experiment among a Germany-wide sample to provide a revealed preference study on the individual willingness to pay (WTP) for carbon sequestration through forests. Our particular focus is on the role of local co-benefits of climate protection activities. In addition, we add geo-data to our experimental data to analyze the impact of spatial variation on the individual WTP. We find that the WTP for carbon removal exceeds the WTP for mitigation efforts found in previous studies. While spatial distances does affect the likelihood to contribute to a local carbon sink, it does not affect the average amount given. Additional survey data finds that trust in forest measures is higher compared to mitigation via an emissions trading scheme, whichcould explain the comparably high WTP. |
| Keywords: | voluntary provision of environmental public goods,climate change mitigation,carbon sequestration,willingness to pay,co-benefits,revealed preferences,framed-field experiment |
| JEL: | Q51 Q54 C93 Q23 H41 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:21088&r= |
| By: | Jean-Gabriel Lauzier |
| Abstract: | This article examines differentiability properties of the value function of positioning choice problems, a class of optimisation problems in finite-dimensional Euclidean spaces. We show that positioning choice problems' value function is always almost everywhere differentiable even when the objective function is discontinuous. To obtain this result we first show that the Dini superdifferential is always well-defined for the maxima of positioning choice problems. This last property allows to state first-order necessary conditions in terms of Dini supergradients. We then prove our main result, which is an ad-hoc envelope theorem for positioning choice problems. Lastly, after discussing necessity of some key assumptions, we conjecture that similar theorems might hold in other spaces as well. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2112.06815&r= |
| By: | Emerson Melo |
| Abstract: | This paper studies the Random Utility Model (RUM) in environments where the decision maker is imperfectly informed about the payoffs associated to each of the alternatives he faces. By embedding the RUM into an online decision problem, we make four contributions. First, we propose a gradient-based learning algorithm and show that a large class of RUMs are Hannan consistent (\citet{Hahn1957}); that is, the average difference between the expected payoffs generated by a RUM and that of the best fixed policy in hindsight goes to zero as the number of periods increase. Second, we show that the class of Generalized Extreme Value (GEV) models can be implemented with our learning algorithm. Examples in the GEV class include the Nested Logit, Ordered, and Product Differentiation models among many others. Third, we show that our gradient-based algorithm is the dual, in a convex analysis sense, of the Follow the Regularized Leader (FTRL) algorithm, which is widely used in the Machine Learning literature. Finally, we discuss how our approach can incorporate recency bias and be used to implement prediction markets in general environments. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2112.10993&r= |
| By: | Laffan, Kate; Sunstein, Cass; Dolan, Paul |
| Abstract: | Although there has been a proliferation of research and policy work into how nudges shape people's behaviour, most studies stop far short of consumer welfare analysis. In the current work, we critically reflect on recent efforts to provide insights into the consumer welfare impact of nudges using willingness to pay and subjective well-being reports and explore an unobtrusive approach that can speak to the immediate emotional impacts of a nudge: automatic facial expression coding. In an exploratory lab study, we use facial expression coding to assess the short-run emotional impact of being presented with calorie information about a popcorn snack in the context of a stylised ‘Cinema experience’. The results of the study indicate that calorie information has heterogeneous impacts on people's likelihood of choosing the snack and on the emotions they experience during the moment of choice which varies based on their level of health-consciousness. The information does not, however, affect the emotions people go on to experience while viewing movie clips, suggesting that the emotional effects of the information are short-lived. We conclude by emphasising the potential of automatic facial expression coding to provide new insights into the immediate emotional impacts of nudges and calling for further research into this promising technique. |
| Keywords: | nudging; emotions; automatic facial coding; calorie labelling; CUP deal |
| JEL: | L81 |
| Date: | 2021–11–11 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:112453&r= |
| By: | Jiafeng Chen |
| Abstract: | We study identification and estimation of treatment effects in common school choice settings, under unrestricted heterogeneity in individual potential outcomes. We propose two notions of identification, corresponding to design- and sampling-based uncertainty, respectively. We characterize the set of causal estimands that are identified for a large variety of school choice mechanisms, including ones that feature both random and non-random tie-breaking; we discuss their policy implications. We also study the asymptotic behavior of nonparametric estimators for these causal estimands. Lastly, we connect our approach to the propensity score approach proposed in Abdulkadiroglu, Angrist, Narita, and Pathak (2017a, forthcoming), and derive the implicit estimands of the latter approach, under fully heterogeneous treatment effects. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2112.03872&r= |