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on Discrete Choice Models |
| By: | Grilli, Gianluca; Notaro. Sandra; Raffaelli, Roberta |
| Abstract: | Using a sample of respondents interviewed face-to-face while accessing a natural park in Sardinia (Italy), we conduct a Discrete Choice Experiment to assess respondents’ willingness to pay for improved environmental quality of the site. We assess the impact of four different strategies to mitigate hypothetical bias (soft cheap talk, honesty priming, consequentiality scripts, and solemn oath) and two elicitation methods (direct and inferred evaluation methods). Results indicate that none of the strategies were significantly effective in reducing HB. Conversely, inferred valuation led to significantly lower WTP estimates. The effect was especially large for attributes of pure public nature, while attributes that include utility from indirect use are less affected by elicitation method. Overall, the study suggests that inferred valuation is more effective than other strategy in removing social desirability of respondents. |
| Keywords: | Environmental Economics and Policy |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea25:360726 |
| By: | Jiang, Qi; Vassalos, Michael; Nian, Yefan; Thayer, Anastasia; Silva, Felipe |
| Keywords: | Institutional and Behavioral Economics |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea25:360818 |
| By: | Xin, Mucong; Tang, Jianjun; Hua, Jingyi |
| Keywords: | Research Methods/Statistical Methods |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea25:360822 |
| By: | Secor, William G.; Britton, Logan L. |
| Abstract: | This study examines U.S. consumer preferences for automation in fast-food services using data from a national online survey of 1, 273 adults. The instrument collected demographic and behavioral information, followed by a discrete choice experiment varying price, speed, accuracy, and task-specific automation. Respondents also rated perceived benefits, risks, and trust in food automation. Conditional logit models and latent class segmentation were estimated. Results show strong preferences for high accuracy and faster service, with mixed acceptance of automation across tasks. Segmentation reveals three consumer types shaped by technology use and education, offering insights into targeted strategies for foodservice automation adoption. |
| Keywords: | Marketing |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea25:360832 |
| By: | Paul R. Hindsley; Owen A. Morgan; John C. Whitehead |
| Abstract: | Stated preference methods are often used to estimate benefits and costs for environmental and natural resource policy analysis, commonly relying on choice experiment questions in which respondents evaluate hypothetical scenarios with varying attributes. These applications generally assume that respondents pay attention to all attributes when making choices. However, attribute non-attendance, in which individuals ignore one or more attributes, may affect model estimates and willingness to pay. This paper critically examines the current state of research on attribute non-attendance in stated preference studies. We review the recent stated preference research to document when and how attribute non-attendance is incorporated in empirical applications. We then illustrate the implications of attribute non-attendance using marine resource choice experiment data in two case studies. We find differences in willingness to pay estimates between na•ve and attribute non-attendance models in both. Key Words: Attribute non-attendance, stated preferences, willingness to pay |
| JEL: | Q51 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:apl:wpaper:26-01 |
| By: | Ei Phyu Kyi; Tao Feng; Jieyuan Lan; Ying Liu |
| Abstract: | The potential for drone delivery services to transform logistics systems and consumer behavior has gained increasing attention. However, comprehensive empirical evidence on consumer delivery choice behavior within the context of transportation and urban air logistics remains limited, particularly in Japan. This study addresses this gap by examining Japanese consumers' preferences and behavioral intentions toward drone delivery services. Using a stated preference (SP) survey and discrete choice modeling approaches, including multinomial logit (MNL) and mixed logit (MMNL) models, the analysis evaluates how delivery cost, delivery time, drop-off location, product type, and social influence affect delivery mode choices across different demographic groups. The results indicate that although consumers express interest in drone delivery, perceived cost and concerns related to reliability continue to constrain adoption. Younger and male consumers exhibit higher preferences for drone delivery, while product type, particularly daily consumer goods and medical or healthcare items, plays a significant role in shaping preferences. Post-estimation willingness-to-pay and elasticity analyses further highlight consumers' sensitivity to delivery pricing and speed attributes. Overall, the findings provide actionable insights for logistics service providers and policymakers regarding pricing strategies, service targeting, and deployment approaches for integrating drone delivery into Japan's evolving logistics system. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.08660 |
| By: | Borengasser, Sophiea; Nalley, Lanier; McFadden, Brandon; Durand-Morat, Alvaro; Rider, Shelby |
| Keywords: | Food Consumption/Nutrition/Food Safety |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea25:360906 |
| By: | Jordan J. Norris; Martí Mestieri Ferrer |
| Abstract: | We uncover an equivalence between two opposite behavioral microfoundations for the standard multinomial logit choice model: 1) agents optimize their choice accordingly to the random utility model under Gumbel idiosyncratic shocks; 2) agents randomize over options subject to a minimum utility requirement. Both generate identical multinomial logit choice probabilities, yet have different welfare implications: wel-fare is strictly lower under randomization, since only optimizing agents select into options with favorable realizations of idiosyncratic shocks. |
| Keywords: | discrete choice, entropy, logit, Stirling's Aproximatioin |
| JEL: | C25 D61 C60 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:bge:wpaper:1547 |
| By: | Sarkar, Sampriti; Lupi, Frank |
| Keywords: | Demand and Price Analysis |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea25:361202 |
| By: | Ahmed Khwaja; Sonal Srivastava |
| Abstract: | Dynamic discrete choice (DDC) models have found widespread application in marketing. However, estimating these becomes challenging in "big data" settings with high-dimensional state-action spaces. To address this challenge, this paper develops a Reinforcement Learning (RL)-based two-step ("computationally light") Conditional Choice Simulation (CCS) estimation approach that combines the scalability of machine learning with the transparency, explainability, and interpretability of structural models, which is particularly valuable for counterfactual policy analysis. The method is premised on three insights: (1) the CCS ("forward simulation") approach is a special case of RL algorithms, (2) starting from an initial state-action pair, CCS updates the corresponding value function only after each simulation path has terminated, whereas RL algorithms may update for all the state-action pairs visited along a simulated path, and (3) RL focuses on inferring an agent's optimal policy with known reward functions, whereas DDC models focus on estimating the reward functions presupposing optimal policies. The procedure's computational efficiency over CCS estimation is demonstrated using Monte Carlo simulations with a canonical machine replacement and a consumer food purchase model. Framing CCS estimation of DDC models as an RL problem increases their applicability and scalability to high-dimensional marketing problems while retaining both interpretability and tractability. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.02069 |
| By: | Angelo Enrico Petralia |
| Abstract: | We investigate the choice of a decision maker (DM) who harms herself, by maximizing in each menu some distortion of her true preference, in which the first i alternatives are moved, in reverse order, to the bottom. This pattern has no empirical power, but it allows to define a degree of self-punishment, which measures the extent of the denial of pleasure adopted by the DM. We characterize irrational choices displaying the lowest degree of self-punishment, and we fully identify the preferences that explain the DM's picks by a minimal denial of pleasure. These datasets account for some well known selection biases, such as second-best procedures, and the handicapped avoidance. Necessary and sufficient conditions for the estimation of the degree of self-punishment of a choice are singled out. Moreover the linear orders whose harmful distortions justify choice data are partially elicited. Finally, we offer a simple characterization of the choice behavior that exhibits the highest degree of self-punishment, and we show that this subclass comprises almost all choices. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.01421 |
| By: | Zhang, Jingyuan; Melo, Grace |
| Abstract: | This study examines pecuniary (e.g., labor cost savings) and non-pecuniary (e.g., improved flexibility) labor benefits in technology adoption through a discrete choice experiment involving 212 dairy farmers in the U.S. Midwest focusing on automatic milking systems. Results reveal that farmers value flexible time 2.17 times more than hired labor savings, suggesting practitioners differentiate non-pecuniary benefit from pecuniary ones and utilize multiple methods to assess preference heterogeneity for robustness: we consistently found that farmers experienced labor difficulties favor hired labor savings, whereas those with secondary income value both benefits less. For other characteristics (e.g., herd size), preference heterogeneity is ambiguous. |
| Keywords: | Research and Development/Tech Change/Emerging Technologies |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea25:360617 |