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on Discrete Choice Models |
| By: | Maria Antonieta Cunha-e-Sa; Til Dietrich; Ana Faria; Luis Catela Nunes; Margarida Ortigao; Renato Rosa; Carina Vieira da Silva |
| Abstract: | The effect of the payment vehicle (PV) on economic valuation estimates has been addressed since the early literature on stated preferences studies. Particularly, some studies have focused on willingness to pay (WTP) sensitivity to mandatory/collective vs. voluntary/individual PVs, by comparing tax increases or redistribution based on specific taxes with donation-like contributions. These two payment schemes may induce different types of strategic behavior and eventually free riding by the economic agents involved. We conducted a choice experiment through a face-to-face survey held in 2020 for a representative sample of the Portuguese population. We investigate the national population’s WTP to invest in oil spills’ prevention along the coastline of mainland Portugal to ensure the provision of four marine and coastal ecosystem services (MCES): (1) biodiversity conservation, (2) beach use, (3) coastal protection and (4) surf. We used a split-sample design to test for differences between the two PVs considered, a mandatory income tax and a voluntary contribution collected through a crowdfunding campaign. We estimate a mixed logit model (MXL) in WTP-space. Furthermore, we control for several sociodemographic characteristics to capture the influence of respondents’ heterogeneity on the elicited WTP, and to check the robustness of our results. We find that mean WTP estimates are positive and significant for all ES except for surf. Biodiversity conservation has the highest WTP estimate. The results obtained suggest that the lack of trust in institutions, fairness concerns and disbelief in policy consequentiality seem to be intrinsic to the Portuguese population, influencing WTP regardless of the PV. However, when comparing an extra income tax with a crowdfunding campaign, respondents have a lower preference for the status quo in this latter case. Therefore, our results highlight the importance of better understanding the role that the payment vehicle may play in funding ecosystem services’ conservation. This is important since how populations respond to incentives for sustainability purposes is crucial to ensure that the targets are met in a more efficient (or cost-effective) and equitable way. |
| Keywords: | Discrete choice experiment, Oil spills, marine and costal ecosystem services (MCES), Payment vehicle |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:unl:unlfep:wp657&r=dcm |
| By: | Lanier, Joshua; Large, Jeremy; Quah, John |
| Abstract: | We present a discrete choice, random utility model and a new estimation technique for analyzing consumer demand for large numbers of products. We allow the consumer to purchase multiple units of any product and to purchase multiple products at once (think of a consumer selecting a bundle of goods in a supermarket). In our model each product has an associated unobservable vector of attributes from which the consumer derives utility. Our model allows for heterogeneous utility functions across consumers, complex patterns of substitution and complementarity across products, and nonlinear price effects. The dimension of the attribute space is, by assumption, much smaller than the number of products, which effectively reduces the size of the consumption space and simplifies estimation. Nonetheless, because the number of bundles available is massive, a new estimation technique, which is based on the practice of negative sampling in machine learning, is needed to sidestep an intractable likelihood function. We prove consistency of our estimator, validate the consistency result through simulation exercises, and estimate our model using supermarket scanner data. |
| Keywords: | discrete choice, demand estimation, negative sampling, machine learning, scanner data |
| JEL: | C13 C34 D12 L20 L66 |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:amz:wpaper:2023-01&r=dcm |
| By: | Yi-Chun Chen; Dmitry Mitrofanov |
| Abstract: | The identification of choice models is crucial for understanding consumer behavior and informing marketing or operational strategies, policy design, and product development. The identification of parametric choice-based demand models is typically straightforward. However, nonparametric models, which are highly effective and flexible in explaining customer choice, may encounter the challenge of the dimensionality curse, hindering their identification. A prominent example of a nonparametric model is the ranking-based model, which mirrors the random utility maximization (RUM) class and is known to be nonidentifiable from the collection of choice probabilities alone. Our objective in this paper is to develop a new class of nonparametric models that is not subject to the problem of nonidentifiability. Our model assumes bounded rationality of consumers, which results in symmetric demand cannibalization and intriguingly enables full identification. Additionally, our choice model demonstrates competitive prediction accuracy compared to the state-of-the-art benchmarks in a real-world case study, despite incorporating the assumption of bounded rationality which could, in theory, limit the representation power of our model. In addition, we tackle the important problem of finding the optimal assortment under the proposed choice model. We demonstrate the NP-hardness of this problem and provide a fully polynomial-time approximation scheme through dynamic programming. Additionally, we propose an efficient estimation framework using a combination of column generation and expectation-maximization algorithms, which proves to be more tractable than the estimation algorithm of the aforementioned ranking-based model. |
| Date: | 2023–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2302.04354&r=dcm |
| By: | Keisuke Hirano; Jack R. Porter |
| Abstract: | We develop asymptotic approximation results that can be applied to sequential estimation and inference problems, adaptive randomized controlled trials, and other statistical decision problems that involve multiple decision nodes with structured and possibly endogenous information sets. Our results extend the classic asymptotic representation theorem used extensively in efficiency bound theory and local power analysis. In adaptive settings where the decision at one stage can affect the observation of variables in later stages, we show that a limiting data environment characterizes all limit distributions attainable through a joint choice of an adaptive design rule and statistics applied to the adaptively generated data, under local alternatives. We illustrate how the theory can be applied to study the choice of adaptive rules and end-of-sample statistical inference in batched (groupwise) sequential adaptive experiments. |
| Date: | 2023–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2302.03117&r=dcm |