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on Discrete Choice Models |
| By: | Julien Monardo |
| Abstract: | Discrete choice demand models are commonly used to answer various economic questions. This paper develops a representation theorem that establishes the necessary and sufficient functional form and shape restrictions characterizing a large family of discrete choice demand models extending beyond the traditional additive random utility framework. The representation theorem yields three significant empirical implications. First, it provides economic intuition for (parameter) restrictions commonly imposed on some popular discrete choice models. Second, it offers a specification toolfor building demand models that satisfy mild and easily verifiable properties while being consistent with utility maximization and accommodating rich substitution patterns, including complementarity in demand. Third, it provides an efficient numerical algorithm for demand inversion, a crucial step in the demand estimation procedure. |
| Date: | 2025–04–02 |
| URL: | https://d.repec.org/n?u=RePEc:bri:uobdis:25/813 |
| By: | Peter S. Arcidiacono; Attila Gyetvai; Arnaud Maurel; Ekaterina Jardim |
| Abstract: | This paper applies some of the key insights of dynamic discrete choice models to continuous-time job search models. Our framework incorporates preference shocks into search models, resulting in a tight connection between value functions and conditional choice probabilities. In this environment, we establish constructive identification of the model parameters, including the wage offer distributions off-and on-the-job. Our framework makes it possible to estimate nonstationary search models in a simple and tractable way, without having to solve any differential equations. We apply our method using Hungarian administrative data. Longer unemployment durations are associated with lower offer arrival rates, resulting in accepted wages falling over time. Counterfactual simulations indicate that increasing unemployment benefits by 90 days results in a 14-day increase in expected unemployment duration. |
| Keywords: | job search, Identification, dynamic discrete choice |
| JEL: | J64 C31 C41 J31 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12320 |
| By: | Adam N. Elmachtoub; Kumar Goutam; Roger Lederman |
| Abstract: | We describe a novel framework for discrete choice modeling and price optimization for settings where scheduled service options (often hierarchical) are offered to customers, which is applicable across many businesses including some within Amazon. In such business settings, the customers would see multiple options, often substitutable, with their features and their prices. These options typically vary in the start and/or end time of the service requested, such as the date of service or a service time window. The costs and demand can vary widely across these different options, resulting in the need for different prices. We propose a system which allows for segmenting the marketplace (as defined by the particular business) using decision trees, while using parametric discrete choice models within each market segment to accurately estimate conversion behavior. Using parametric discrete choice models allows us to capture important behavioral aspects like reference price effects which naturally occur in scheduled service applications. In addition, we provide natural and fast heuristics to do price optimization. For one such Amazon business where we conducted a live A/B experiment, this new framework outperformed the existing pricing system in every key metric, increasing our target performance metric by 19%, while providing a robust platform to support future new services of the business. The model framework has now been in full production for this business since Q4 2023. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.22271 |
| By: | Wojciech Zawadzki (University of Warsaw, Faculty of Economic Sciences); Mikołaj Czajkowski (University of Warsaw, Faculty of Economic Sciences); Katarzyna Skrzypek (University of Warsaw, Faculty of Economic Sciences); Matylda Jędrzejewska (University of Warsaw, Faculty of Economic Sciences); Maja Żmijewska (University of Warsaw, Faculty of Economic Sciences); Jakub Ryłow (University of Warsaw, Faculty of Economic Sciences); Dominika Gadowska dos-Santos (University of Warsaw, Faculty of Economic Sciences); Gabriela Grotkowska (University of Warsaw, Faculty of Economic Sciences); Agnieszka Różycka (University of Warsaw, Faculty of Economic Sciences); Arkadiusz Filip (University of Warsaw, Faculty of Economic Sciences); Marcin Gruszczyński (University of Warsaw, Faculty of Economic Sciences); Agata Kałamucka (University of Warsaw, Faculty of Economic Sciences); Tadeusz Kowalski (University of Warsaw, Faculty of Journalism, Information and Book Studies); Waldemar Kozioł (University of Warsaw, Faculty of Management); Magdalena Olender-Skorek (University of Warsaw, Faculty of Management); Krzysztof Opolski (University of Warsaw, Faculty of Economic Sciences); Katarzyna Saczuk (University of Warsaw, Centre of Migration Research); Mateusz Szczurek (University of Warsaw, Faculty of Economic Sciences); Urszula Sztandar-Sztanderska (University of Warsaw, Faculty of Economic Sciences); Kacper Wańczyk (University of Warsaw, Centre for East European Studies); Aleksandra Wiśniewska (University of Warsaw, Faculty of Economic Sciences); Kateryna Zabarina (University of Warsaw, Faculty of Economic Science); Piotr Żoch (University of Warsaw, Faculty of Economic Sciences) |
| Abstract: | We study how university students trade off the design and demands of study against expected labor market returns. We conducted a Discrete Choice Experiment (DCE) with students of the Economics Department at the University of Warsaw. Choice alternatives varied the share of in person teaching, weekly class hours, weekly preparation time, language mix (Polish vs. English), net monthly study cost (tuition minus stipends), and expected net salary after graduation. The DCE was embedded in a broader survey measuring study experience, time use, work during studies, scheduling preferences, and perceptions of quality and reputation. The instrument and framings follow state of the art DCE guidance and are publicly documentable. Using multinomial logit and mixed logit models, we estimate compensating differentials students require to accept (i) more online teaching, (ii) more weekly effort (classes/prep), or (iii) English medium instruction in Polish language curricula. The results show large, precise utility gains from higher expected salary, disutility from higher weekly preparation time, and strong (non linear) preferences over delivery mode and language. We then simulate policy scenarios (e.g., introducing 50% online, adjusting effort) and quantify the cost equivalent or salary equivalent levers needed to maintain program attractiveness. We position our results in the international literature on DCEs in higher education and discuss external validity with respect to a large national DCE that emphasized earnings over prestige. We conclude with program design implications for universities worldwide navigating hybridization, workload calibration, and language policy in light of students’ revealed economic preferences. |
| Keywords: | discrete choice experiment, higher education, hybrid/online vs. in-person, language of instruction, student workload, willingness-to-pay |
| JEL: | I23 I21 C25 C83 D12 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:war:wpaper:2025-31 |
| By: | Jan H. R. Dressler; Peter Kurz; Winfried J. Steiner |
| Abstract: | Despite a substantial body of theoretical and empirical research in the fields of conjoint and discrete choice analysis as well as product line optimization, relatively few papers focused on the simulation of subsequent competitive dynamics employing non-cooperative game theory. Only a fraction of the existing frameworks explored competition on both product price and design, none of which used fully Bayesian choice models for simulation. Most crucially, no one has yet assessed the choice models' ability to uncover the true equilibria, let alone under different types of choice behavior. Our analysis of thousands of Nash equilibria, derived in full and numerically exact on the basis of real prices and costs, provides evidence that the capability of state-of-the-art mixed logit models to reveal the true Nash equilibria seems to be primarily contingent upon the type of choice behavior (probabilistic versus deterministic), regardless of the number of competing firms, offered products and features in the market, as well as the degree of preference heterogeneity and disturbance. Generally, the highest equilibrium recovery is achieved when applying a deterministic choice rule to estimated preferences given deterministic choice behavior in reality. It is especially in the latter setting that incorporating Bayesian (hyper)parameter uncertainty further enhances the detection rate compared to posterior means. Additionally, we investigate the influence of the above factors on other equilibrium characteristics such as product (line) differentiation. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.22864 |
| By: | Nathan Kallus |
| Abstract: | Aligning large language models to preference data is commonly implemented by assuming a known link function between the distribution of observed preferences and the unobserved rewards (e.g., a logistic link as in Bradley-Terry). If the link is wrong, however, inferred rewards can be biased and policies be misaligned. We study policy alignment to preferences under an unknown and unrestricted link. We consider an $f$-divergence-constrained reward maximization problem and show that realizability of the solution in a policy class implies a semiparametric single-index binary choice model, where a scalar-valued index determined by a policy captures the dependence on demonstrations and the rest of the preference distribution is an unrestricted function thereof. Rather than focus on estimation of identifiable finite-dimensional structural parameters in the index as in econometrics, we focus on policy learning, focusing on error to the optimal policy and allowing unidentifiable and nonparametric indices. We develop a variety of policy learners based on profiling the link function, orthogonalizing the link function, and using link-agnostic bipartite ranking objectives. We analyze these and provide finite-sample policy error bounds that depend on generic functional complexity measures of the index class. We further consider practical implementations using first-order optimization suited to neural networks and batched data. The resulting methods are robust to unknown preference noise distribution and scale, while preserving the direct optimization of policies without explicitly fitting rewards. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.21917 |
| By: | Baptiste Rigaux; Sam Hamels; Marten Ovaere (-) |
| Abstract: | We study household acceptance of flexibility contracts for electric vehicles (EVs) and heat pumps (HPs), two key technologies for the energy transition. Using a survey and choice experiment with around 3, 000 households, we analyze how contract design—particularly comfort limits such as indoor temperature or driving range— affects both the decision to participate and the flexibility households are willing to supply at different levels of remuneration. Around 70% of households in our sample are willing to participate. Discomfort affects utility nonlinearly for EVs: remaining range is valued at close to €0/km above 100 km but rises to €0.40/km below, while HP flexibility is valued at about €2 per degree of indoor temperature reduction. We derive conditions under which flexibility contracts can achieve cost-effectiveness while remaining acceptable to households. Back-of-the-envelope calculations suggest potential load reductions of up to 300 MW/event from HPs and 800 MW/event from EVs per million units. |
| Keywords: | Electricity Demand; Choice Experiment; Preferences; Thermal comfort; Range anxiety; Heat pump; Electric vehicle |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:rug:rugwps:25/1130 |
| By: | Evans, Alecia; Sesmero, Juan Pablo |
| Keywords: | Agribusiness, Agricultural and Food Policy, Industrial Organization |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea24:343956 |
| By: | Paul Hufe; Daniel Weishaar |
| Abstract: | The measurement of preferences often relies on surveys in which individuals evaluate hypothetical scenarios. This paper proposes and validates a novel factorial survey tool to measure fairness preferences. We examine whether a non-incentivized survey captures the same distributional preferences as an impartial spectator design, where choices may apply to a real person. In contrast to prior studies, our design involves high stakes, with respondents determining a real person’s monthly earnings, ranging from $500 to $5, 700. We find that the non-incentivized survey module yields nearly identical results compared to the incentivized experiment and recovers fairness preferences that are stable over time. Furthermore, we show that most respondents adopt intermediate fairness positions, with fewer exhibiting strictly egalitarian or libertarian preferences. In sum, these findings suggest that high-stake incentives do not significantly impact the measurement of fairness preferences and that non-incentivized survey questions covering realistic scenarios offer valuable insights into the nature of these preferences. |
| Date: | 2025–04–02 |
| URL: | https://d.repec.org/n?u=RePEc:bri:uobdis:25/810 |