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on Discrete Choice Models |
| By: | Runyu Wang; Haotian Zhong |
| Abstract: | Urban food delivery services have become an integral part of daily life, yet their mobility and environmental externalities remain poorly addressed by planners. Most studies neglect whether consumers pay enough to internalize the broader social costs of these services. This study quantifies the value of access to and use of food delivery services in Beijing, China, through two discrete choice experiments. The first measures willingness to accept compensation for giving up access, with a median value of CNY588 (approximately USD80). The second captures willingness to pay for reduced waiting time and improved reliability, showing valuations far exceeding typical delivery fees (e.g., CNY96.6/hour and CNY4.83/min at work). These results suggest a substantial consumer surplus and a clear underpricing problem. These findings highlight the need for urban planning to integrate digital service economies into pricing and mobility frameworks. We propose a quantity-based pricing model that targets delivery speed rather than order volume, addressing the primary source of externalities while maintaining net welfare gains. This approach offers a pragmatic, equity-conscious strategy to curb delivery-related congestion, emissions, and safety risks, especially in dense urban cores. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.26636 |
| By: | Altmann, Brianne Andrea; Wolgast, Thiemo; Bühner, Charlotte; Risius, Antje |
| Keywords: | Demand and Price Analysis |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ags:gewi24:364735 |
| By: | Markus Dertwinkel-Kalt; Hans-Theo Normann; Jan-Niklas Tiede; Tobias Werner |
| Abstract: | Consumers often face products sold as lotteries rather than fixed outcomes. A prominent case is the loot box in video games, where players pay for randomized rewards. We investigate how presentation formats shape consumer beliefs and willingness to pay. In an online experiment with 802 participants, sellers could frame lotteries using two common manipulations: censoring outcome probabilities and selectively highlighting rare successes. More than 80\% of sellers adopted such deceptive frames, particularly when both manipulations were available. These choices substantially inflated buyer beliefs and increased willingness to pay of up to six times the expected value. Sellers anticipated this effect and raised prices accordingly. Our results show how deceptive framing systematically shifts consumer beliefs and enables firms to extract additional surplus. For marketing practice, this highlights the strategic value of framing tools in probabilistic selling models; for policy, it underscores the importance of transparency requirements in protecting consumers. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.01597 |
| By: | Arnav Sood; James Best |
| Abstract: | A sender persuades a strategically naive decisionmaker (DM) by committing privately to an experiment. Sender's choice of experiment is unknown to the DM, who must form her posterior beliefs nonparametrically by applying some learning rule to an IID sample of (state, message) realizations. We show that, given mild regularity conditions, the empirical payoff functions hypo-converge to the full-information counterpart. This is sufficient to ensure that payoffs and optimal signals converge to the Bayesian benchmark. For finite sample sizes, the force of this "sampling friction" is nonmonotonic: it can induce more informative experiments than the Bayesian benchmark in settings like the classic Prosecutor-Judge game, and less revelation even in situations with perfectly aligned preferences. For many problems with state-independent preferences, we show that there is an optimal finite sample size for the DM. Although the DM would always prefer a larger sample for a fixed experiment, this result holds because the sample size affects sender's choice of experiment. Our results are robust to imperfectly informative feedback and the choice of learning rule. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.25066 |
| By: | Jason R. Blevins |
| Abstract: | This paper considers the theoretical, computational, and econometric properties of continuous time dynamic discrete choice games with stochastically sequential moves, introduced by Arcidiacono, Bayer, Blevins, and Ellickson (2016). We consider identification of the rate of move arrivals, which was assumed to be known in previous work, as well as a generalized version with heterogeneous move arrival rates. We re-establish conditions for existence of a Markov perfect equilibrium in the generalized model and consider identification of the model primitives with only discrete time data sampled at fixed intervals. Three foundational example models are considered: a single agent renewal model, a dynamic entry and exit model, and a quality ladder model. Through these examples we examine the computational and statistical properties of estimators via Monte Carlo experiments and an empirical example using data from Rust (1987). The experiments show how parameter estimates behave when moving from continuous time data to discrete time data of decreasing frequency and the computational feasibility as the number of firms grows. The empirical example highlights the impact of allowing decision rates to vary. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.02701 |
| By: | Sarah Kühn (Paderborn University); Papatya Duman (Bielefeld University); Britta Hoyer (University of Tübingen); Thomas Streck (Paderborn University); Nadja Stroh-Maraun (Paderborn University) |
| Abstract: | Preferences are central to matching markets, yet experiments typically rely on induced preferences that may not reflect real-world decisionmaking. We examine how induced versus non-induced preferences shape behavior in matching experiments, extending Chen & Sönmez (2006). Using the most frequently used school choice mechanisms (Boston, Deferred Acceptance, and Top Trading Cycles), we supplement monetary incentives with participants’ own preferences. Our results show that preference induction systematically affects truthful reporting and comprehension of mechanisms. These findings underscore that experimental design choices matter for the validity of behavioral insights and have direct implications for policy evaluation. |
| Keywords: | Non-induced Preferences, Experiments, Matching, School Choice |
| JEL: | C78 D47 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:pdn:ciepap:165 |
| By: | Van Campenhout, Bjorn; Abate, Gashaw T.; Colen, Liesbeth; Kramer, Berber |
| Abstract: | Free samples are a widely used strategy to introduce new products or technologies, offering prospective users the opportunity to gain firsthand experience and potentially facilitate diffusion through social networks. However, concerns remain that giving away products for free may reduce their perceived value, increasing the risk that recipients will underutilize, repurpose, or resell the product rather than use it for its intended purpose. We explore three mechanisms through which charging a positive price may increase uptake, intended use and subsequent adoption of a new technology: (1) a signaling effect, where a positive price conveys higher product quality; (2) a screening effect, whereby payment deters users who do not value the product and targets those more likely to use it; and (3) a sunk cost effect, where paying a positive price induces a psychological commitment to use. We test how these pricing mechanisms shape uptake, use, and subsequent adoption of recently released seed varieties of staple food crops, drawing on a field experiment with smallholder farmers in Uganda and Ethiopia. We find that willingness to pay is a reliable predictor of subsequent use of seed trial packs, pointing to the value of modest prices for targeting likely adopters. At the same time, sunk cost effects are context specific and often negative, suggesting that charging farmers can reduce their ability or willingness to experiment. These findings carry important implications for how pricing strategies can be designed to promote technology adoption in low-income settings. |
| Keywords: | technology adoption; prices; crops; seeds; costs; agricultural technology; Uganda; Ethiopia; Africa; Eastern Africa; Sub-Saharan Africa |
| Date: | 2025–10–24 |
| URL: | https://d.repec.org/n?u=RePEc:fpr:ifprid:177343 |
| By: | Fabio Bertolotti; Kyle Myers; Wei Yang Tham |
| Abstract: | We develop a method to estimate producers' productivity beliefs when output quantities and input prices are unobservable, and we use it to evaluate the market for science. Our model of researchers' labor supply shows how their willingness to pay for inputs reveals their productivity beliefs. We estimate the model's parameters using data from a nationally representative survey of researchers and find the distribution of productivity to be very skewed. Our counterfactuals indicate that a more efficient allocation of the current budget could be worth billions of dollars. There are substantial gains from developing new ways of identifying talented scientists. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.24916 |
| By: | Taveras, Elisa (The University of Texas Rio Grande Valley); Tonguc, Ozlem (State University of New York); Zhu, Maria (Syracuse University); Miller, Nicola (Binghamton University, New York) |
| Abstract: | This study investigates whether employers in an online hiring experiment exhibit discrimination based on workers’ accents that indicate English is not their primary language. To assess accent bias, we implement a randomized treatment design in which participants acting as employers are assigned to one of two conditions: a treatment where the worker’s accent is revealed (“Accent Revealed”) or a control where it is not (“Accent Blind”). Using incentive-compatible methods, we elicit employers’ beliefs about the productivity of randomly assigned workers, providing brief demographic information and audio clips that either reveal or mask accent characteristics. We evaluate worker productivity in two skills: Mathematics and Verbal reasoning. We find that employers rate accented workers as less capable than their non-accented counterparts in both skills, and this gap persists after providing employers with a signal of a worker’s test score. Employers also display lower willingness to pay, particularly in Verbal skills tasks, even when provided with performance signals. |
| Keywords: | laboratory experiments, labor market discrimination, foreign accent |
| JEL: | C91 D90 J01 J71 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18239 |
| By: | Florian Allroggen; R. John Hansman; Christopher R. Knittel; Jing Li; Xibo Wan; Juju Wang |
| Abstract: | Air transportation supports economic growth and global connectivity but imposes localized environmental costs, particularly through aircraft noise. We estimate the causal effect of aviation noise on housing prices using quasi-experimental variation from the Federal Aviation Administration's rollout of performance-based navigation (PBN) procedures and runway reconfigurations at three major U.S. airports. Combining high-resolution flight trajectory data with geocoded housing transactions, we apply a difference-in-differences hedonic framework to identify changes in exposure unanticipated by residents. A one-decibel increase in annual day-night average sound level reduces house prices by 0.6 to 1.0 percent. Among alternative noise metrics, average exposure explains property value impacts most strongly. Willingness to pay for quieter conditions varies systematically with income and race, indicating that aircraft noise externalities have meaningful distributional consequences. Our results highlight the need to incorporate localized environmental costs into aviation and urban land-use policy. |
| JEL: | L51 L62 L85 Q53 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34431 |
| By: | Ayush Sawarni; Sahasrajit Sarmasarkar; Vasilis Syrgkanis |
| Abstract: | This paper investigates the integration of response time data into human preference learning frameworks for more effective reward model elicitation. While binary preference data has become fundamental in fine-tuning foundation models, generative AI systems, and other large-scale models, the valuable temporal information inherent in user decision-making remains largely unexploited. We propose novel methodologies to incorporate response time information alongside binary choice data, leveraging the Evidence Accumulation Drift Diffusion (EZ) model, under which response time is informative of the preference strength. We develop Neyman-orthogonal loss functions that achieve oracle convergence rates for reward model learning, matching the theoretical optimal rates that would be attained if the expected response times for each query were known a priori. Our theoretical analysis demonstrates that for linear reward functions, conventional preference learning suffers from error rates that scale exponentially with reward magnitude. In contrast, our response time-augmented approach reduces this to polynomial scaling, representing a significant improvement in sample efficiency. We extend these guarantees to non-parametric reward function spaces, establishing convergence properties for more complex, realistic reward models. Our extensive experiments validate our theoretical findings in the context of preference learning over images. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.22820 |
| By: | André de Palma; Zhenyu Yang; Pietro Giardina; Nikolas Gerolimnis (CY Cergy Paris Université, THEMA) |
| Abstract: | We aim to infer commuters’ scheduling preferences from their observed arrival times, given an exogenous traffic congestion pattern. To do this, we employ a structural model that characterizes how users balance congestion costs against the penalties for arriving early or late relative to an ideal time. In this framework, each commuter selects an arrival time that minimizes her overall trip cost by considering the within-day congestion pattern along with her individual scheduling preference. By incorporating the distribution of these preferences and desired arrival times across the population, we can estimate the likelihood of observing arrivals at specific times. Using synthetic data, we then apply the maximum likelihood estimation (MLE) method to recover the parameters of the joint distribution of scheduling preferences and desired arrival times. Our numerical results demonstrate the effectiveness of the proposed method. |
| Keywords: | Bottleneck, Scheduling preferences, Traffic flow; Travel demand management |
| JEL: | C25 R41 D12 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ema:worpap:2025-15 |
| By: | Sukgyu Shin |
| Abstract: | In this paper, we examine identification in a dynamic panel logit model with state dependence, first-order Markov feedback processes, and individual unobserved heterogeneity by introducing sufficient statistics for the feedback process and unobserved heterogeneity. If a sequentially exogenous discrete covariate follows a first-order Markov process, identification of the coefficient on the covariate via conditional likelihood is infeasible, whereas identification of the coefficient on the lagged dependent variable is feasible when there are at least three periods after the initial-condition period. If the feedback depends only on the lagged dependent variable, the coefficient on the covariate is identified with at least two periods, and the coefficient on the lagged dependent variable is identified with at least three periods. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.02816 |
| By: | Christian Krekel |
| Abstract: | Using wellbeing data to assess benefits for residents. |
| Keywords: | parks, green spaces, mental health, quasi-natural experiment, compensating surplus, wellbeing |
| Date: | 2025–10–21 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepcnp:716 |