|
on Discrete Choice Models |
| By: | Santiago Burone;; Lukas Leitner; |
| Abstract: | Willingness to pay (WTP) has become an important tool in economic analysis, despite the difficulty to obtain reliable estimates. This paper investigates the occurrence of starting point bias when eliciting WTP for health, a domain where this phenomenon has received limited attention, and illustrates its effect on equivalent consumption, a preference-based well-being measure. In an online experiment, three experimental groups responded to two dichotomous choice questions, with varying initial bids. The treatment groups then provided exact estimates for their WTP in an open-ended question. We find strong evidence for the existence of the bias using both non-parametric and parametric tests, and estimate a sizeable overall effect. Different parametric specifications yield point estimates between 29 and 43 percent for the first bid, whereas the effect of the second bid, which we estimate using an instrumental variable approach, is not statistically different from zero. We propose two ex post approaches to address this effect when using WTP data for interpersonal well-being comparisons. Although the percentage of rankings reversals is relatively small across all feasible comparisons, it becomes notable when examining comparisons for individuals within the same consumption deciles. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:hdl:wpaper:2501 |
| By: | Yannik Pflugfelder; Christoph Weber (Chair for Management Sciences and Energy Economics, University of Duisburg-Essen) |
| Abstract: | The spatial distribution of future renewable capacities is a key determinant for developing appropriate grid expansion plans. This is particularly relevant for onshore wind energy. Existing studiesmostly extrapolate future installations based on existing capacities and available sites. As wind farm projects are developed mainly by private investors, the economic rationale of investing at specific sites deserves more attention. Therefore, the present contribution develops a model of economic choice for wind investments based on site-specific computations of the achievable net present value, taking into consideration the land availability at the regional level. Therefore, sitespecific investment decisions are modeled as (partly aggregated) discrete choices. The net present value is computed from investment costs and expected yields, which can be estimated based on wind speed time series and power curves. Available land can be identified by excluding settlement, infrastructure, and nature conservation areas with appropriate buffers, as well as sites with topographically unsuitable profiles. The model is formulated as a nested logit model that captures the interdependencies between choices on two levels: the probability of investment in a particular region on the first level and the probability of installing a specific turbine type on the second level. In an application for Germany with the target capacities of the German Renewable Energy Act, the model delivers a spatial distribution of the capacities at the NUTS 3 level. The model also enables the derivation of the necessary compensation level and the most frequently installed turbine types. |
| Keywords: | wind energy, regionalization models, renewable energy sources, nested logit model |
| JEL: | Q42 Q48 C35 R58 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:dui:wpaper:2501 |
| By: | Gutmann, Jerg; Brandimarte, Laura; Muehlheusser, Gerd; Weber, Franziska |
| Abstract: | We examine the trade-off between functionality and data privacy inherent in many AI products by conducting a randomized survey experiment with 1, 734 participants from the US and several European countries. Participants' willingness to adopt a hypothetical, AI-enhanced app is measured under three sets of treatments: (i) installation defaults (opt-in vs. opt-out), (ii) salience of data privacy risks, and (iii) regulatory regimes with different levels of data protection. In addition, we study how the willingness to adopt depends on individual attitudes and preferences. We find no effect of defaults or salience, while a regulatory regime with stricter privacy protection increases the likelihood that the app is adopted. Finally, greater data privacy concerns, greater risk aversion, lower levels of trust, and greater skepticism toward AI are associated with a significantly lower willingness to adopt the app. |
| Keywords: | Artificial intelligence, privacy concerns, randomized survey experiment, smart products, technology adoption |
| JEL: | D80 D90 K24 L86 Z10 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:ilewps:83 |
| By: | Matthias Plavec; Martha Lawrence; Jyoti Bisbey |
| Keywords: | Environment-Adaptation to Climate Change Environment-Climate Change Mitigation and Green House Gases Transport Urban Development-Transport in Urban Areas |
| Date: | 2024–03 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wboper:41321 |