|
on Discrete Choice Models |
| By: | Akinwehinmi, Oluwagbenga; Colen, Liesbeth; Caputo, Vincenzina |
| Keywords: | Research Methods/ Statistical Methods |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea26:404517 |
| By: | Pranjal Rawat; John Rust |
| Abstract: | This article surveys two deeply connected literatures that approach the same fundamental problem from different disciplinary traditions: dynamic discrete choice (DDC) in structural econometrics and inverse reinforcement learning (IRL) in machine learning. Both seek to infer the preferences of decision makers from observed sequential behavior, assuming that individuals act to maximize an expected reward function within a dynamic, uncertain environment formalized as a Markov decision process (MDP). Despite independent origins, the two fields have converged on similar mathematical formulations. We show that the (soft Q-learning) framework now prevalent in IRL is closely related to DDC models under additive extreme value preference shocks, yielding the same softmax (multinomial logit) choice probabilities and smooth Bellman equations that underpin structural estimation in economics. We compare the estimation and computational methods developed in each field. DDC has emphasized maximum likelihood estimation, conditional choice probability estimators, and policy iteration methods. IRL has developed scalable alternatives, including maximum entropy methods, adversarial approaches, and model-free temporal difference estimators that extend to high-dimensional state spaces using deep neural networks. Model-free IRL estimators that combine temporal difference learning with classical two-step methods from econometrics represent a promising direction for bridging the two literatures. Both fields confront shared foundational challenges: the identification problem, whereby multiple reward functions can rationalize the same observed behavior, and the curse of dimensionality in solving the underlying MDP. We believe that cross-fertilization offers substantial opportunities for methodological progress in both fields. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.24362 |
| By: | Javaudin Lucas; Araldo Andrea; Coulombel Nicolas |
| Abstract: | Intra-household joint home-based tours - trips in which household members depart together, engage in shared activities, and return together - represent a significant share of daily travel, yet are systematically ignored in transport simulations. Conflating joint and solo tours within a single mode choice framework introduces bias in preference parameter estimates. This paper proposes a three-step methodology to integrate joint tours in agent-based transport models: a Random Forest classifier to identify joint tours, a Multinomial Logit model estimating mode choice specific to joint tours, and a Penalized Logistic Regression for driver/passenger assignment. Applied to the Paris region using household travel survey data, the methodology successfully replicates observed joint tour shares and mode distributions in a synthetic population. The proposed framework enables more reliable evaluation of policies whose impacts differ between joint and solo travel, such as HOV lanes or family transit fare discounts. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.18657 |
| By: | Putri, Kadek Tania Mediana; Mahahari, Ni Wayan; Dewi, Dewa Ayu Putu Satrya; Suryaningsih, Ni Putu Aryati; Setiawan, Putu Yudhistira Budi; Darmawan, Kadek Hendra |
| Abstract: | Introduction: Balancing high efficacy against significant toxicity risks a central challenge in biologic therapy selection for chronic diseases. This systematic review evaluated discrete choice experiments (DCEs) of patient preferences for biologic therapy to identify the treatment attributes that most consistently influence patient choice. Methods: MEDLINE, Embase, Scopus, CINAHL, and Web of Science were searched (PROSPERO CRD420251271716) for Discrete Choice Experiments (DCEs) evaluating patient preferences for biologic therapies. Two reviewers independently screened eligible studies, extracted study characteristics and treatment attributes, evaluated methodological quality using an ISPOR-based quality assessment instrument, and identified the two most important treatment attributes reported in each study. Results: Twenty-four studies (2010-2025), most published after 2020, met inclusion criteria across a range of chronic diseases, most frequently psoriasis, rheumatoid arthritis, and inflammatory bowel disease. Of 173 attributes identified, 61% were outcome attributes, 34% process, and 5% cost. Patients most often prioritized the risk of serious adverse events, followed by treatment response; process features gained importance when efficacy and safety differences were minimal, and cost carried greater weight where out-of-pocket expenditure was higher. Twenty-three of 24 studies achieved the maximum ISPOR score of 9/9 Conclusions: Patient preferences in biologic selection follow a clear hierarchy determined by perceived risk, expected benefit, and treatment context. Incorporating these preferences into shared decision-making might improve treatment alignment and long-term patient adherence. This evidence supports patient-centered care and counseling at the initiation of treatment. |
| Date: | 2026–08–08 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:mhjz5_v1 |
| By: | Michael King (Trinity College Dublin); Paolina Medina (University of Houston); Benjamin Radoc, Jr. (Bangko Sentral ng Pilipinas); Roland Umanan (Trinity Impact Evaluation Unit) |
| Abstract: | Digital credit—short-term, high-interest loans offered via mobile channels—has surged over the past decade, reaching millions in the developing world. However, much like payday loans in developed economies, it remains unclear whether its liquidity benefits outweigh the risks of overindebtedness and financial distress. Using an online discrete choice experiment with digital credit users in the Philippines, we examine how disclosures about price and non-price attributes affect consumer choice. We find that standardizing contract terms across products leads consumers to choose loans with lower interest rates and higher probability of approval at the expense of longer time to disburse and higher documentation requirements. Presenting interest rates in effective (compounded) or nominal terms makes no difference but ranking by a selected attribute leads to a more favorable product choice. Typical consumers are responsive to disclosures about late payment fees but not overconfident consumers. We argue that overconfidence can reduce the effectiveness of attention-based interventions and consumers’ revealed risk profiles. |
| JEL: | D12 D14 G41 G51 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:bhd:dpaper:202509 |
| By: | Gao, Zhifeng; Duan, Dinglin |
| Keywords: | Research Methods/ Statistical Methods |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea26:404521 |
| By: | Omid Oshriyeh (University of South Carolina [Columbia]); Ercan Sirakaya-Turk; Yuksel Ekinci |
| Abstract: | Many tourists support sustainability, but sustainable tourism markets are not homogeneous. This paper reports the results of a study on whether sustainability values can be used to segment UK tourists and whether the resulting segments differ in global warming concern and stated willingness to pay for carbon offsets. Survey data were collected from 560 UK tourists at four locations. Sustainability-value domains were first identified and then used to classify respondents into value-based segments. The analysis retained four domains: respect for nature, social equity and solidarity, tolerance, and shared responsibility. Two interpretable segments were identified. Tourists in the higher-sustainability-values segment reported stronger global warming concern and greater willingness to pay for carbon offsets than tourists in the moderate segment. However, willingness to pay was not universal even among tourists with stronger sustainability values. The findings suggest that values-based segmentation can help identify more receptive audiences for carbon-offset initiatives, but values-based targeting should be supported by credible, transparent, and easy-to-evaluate offset schemes. |
| Abstract: | De nombreux touristes soutiennent la durabilité, mais les marchés du tourisme durable ne sont pas homogènes. Cet article présente les résultats d'une étude visant à déterminer si les valeurs de durabilité peuvent être utilisées pour segmenter les touristes britanniques et si les segments qui en résultent diffèrent en termes de préoccupation face au réchauffement climatique et de disposition déclarée à payer pour des compensations carbone. Des données d'enquête ont été recueillies auprès de 560 touristes britanniques dans quatre lieux. Les domaines des valeurs de durabilité ont d'abord été identifiés, puis utilisés pour classer les répondants en segments fondés sur les valeurs. L'analyse a retenu quatre domaines : le respect de la nature, l'équité sociale et la solidarité, la tolérance et la responsabilité partagée. Deux segments interprétables ont été identifiés. Les touristes du segment aux valeurs de durabilité les plus élevées ont signalé une préoccupation plus marquée pour le réchauffement climatique et une plus grande disposition à payer pour des compensations carbone que les touristes du segment modéré. Cependant, la disposition à payer n'était pas universelle, même parmi les touristes ayant des valeurs de durabilité plus fortes. Ces résultats suggèrent que la segmentation fondée sur les valeurs peut aider à identifier des publics plus récepteurs pour les initiatives de compensation carbone, mais que le ciblage basé sur les valeurs doit être soutenu par des programmes de compensation crédibles, transparents et faciles à évaluer. |
| Keywords: | climate change, United States, United Kingdom, Travel, tourism, pro-environmental behavior, carbon offsetting, market segmentation, sustainable tourism, sustainability values |
| Date: | 2026–07–28 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05706292 |
| By: | Richard Bernknopf; Leila Gonzales; Christpher Keane |
| Abstract: | Natural hazards are a nonmarket disamenity that affects an individual's search for employment resulting in a negative environmental impact that produces an economic inefficiency. We develop a seek-and-screen job search approach that uses a discrete choice simulation to examine how salary, crime, and natural hazard risk influence job choice. We model the job decision process as a series of elimination events using a Cox hazard model grounded in a Random Utility Model. We use data from the discrete choice simulation to estimate both a standard proportional hazards model and an extended specification that allows the effect of natural hazard risk to vary across decision rounds. Individuals are exposed to the dynamics of a simulated job search as they make decisions between pairs of job offers in an adaptive learning process based on income, geography, crime level, and natural hazard attributes. The results of the job choice decisions provide the input to a statistical survival analysis. The results indicate that salary and crime exert stable and economically intuitive effects on job elimination, with higher salary reducing and higher crime increasing the likelihood of removal. In contrast, natural hazard risk exhibits a time-varying effect that increases the probability of elimination in early rounds but becomes neutral or favorable in later stages of the decision process. These findings suggest that environmental risk is evaluated differently as individuals transition from initial screening to final job selection, highlighting the importance of modeling job choice as a multi-stage process. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.24811 |
| By: | Mahahari, Ni Wayan; Putri, Kadek Tania Mediana; Suryaningsih, Ni Putu Aryati; Reganata, Gde Palguna; Dewi, Dewa Ayu Putu Satrya; Darmawan, Kadek Hendra |
| Abstract: | Chronic non-cancer pain is highly prevalent, and concerns about long-term opioid harms have increased interest in non-opioid therapies. Understanding which treatment attributes patients value is essential for shared decision-making. This systematic review synthesised discrete choice experiments (DCEs) to identify the treatment attributes patients prioritise when choosing non-opioid regimens for chronic non-cancer pain, and to appraise the methodological quality of the underlying studies. Following PRISMA 2020 guidance and a prospectively registered protocol (PROSPERO CRD420251273180), we searched nine databases (MEDLINE, Embase, CINAHL, PsycINFO, Scopus, Web of Science, EconLit, PubMed and the Cochrane Library). Two reviewers independently screened records and assessed methodological quality using a modified ISPOR Conjoint Analysis Good Research Practices checklist. Attributes were classified using the Sain et al. (2020) framework into outcome, process and cost categories, and synthesised narratively. Seventeen studies (1, 540 records identified) met the inclusion criteria, encompassing 109 attributes; 88% were industry-funded. Outcome attributes predominated (67.9%), followed by process (25.7%) and cost (6.4%). Clinical effectiveness was the most influential attribute, identified as the primary priority in 57.1% of studies combining all three categories, with safety acting as a key differentiator and cost as a constraint. Most studies met ISPOR standards (scores 10.5 to 12), though visual aids (71%) and direct patient input (18%) were underused. Patients prioritise clinical benefit when choosing non-opioid pain therapies, informing preference-sensitive decision-making, clinical guidelines and health technology assessment. |
| Date: | 2026–08–08 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:m8zcs_v1 |
| By: | Asioli, Daniele; Balcombe, Kelvin; Fraser, Iain |
| Keywords: | Research Methods/ Statistical Methods |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea26:404722 |
| By: | Bhargava, Saurabh (New York University); Hyde, Timothy (Department of Economics, Oberlin College) |
| Abstract: | We study risky choice in a field setting where employees choose among goal-reward contracts resembling financial lotteries and where we observe both choices and beliefs. We find risk aversion and choice heterogeneity far exceeding expected utility predictions and unexplained by prominent behavioral motives like overconfidence, nonlinear decision weights, and loss aversion. We propose and experimentally validate a heuristic explanation for risk taking involving contingency neglect during pairwise evaluation. The heuristic fits the field and lab data better than leading alternative models, uniquely predicts the belief distortions and framing effects we document, and offers a potential explanation for empirical insurance puzzles. |
| Date: | 2026–08–01 |
| URL: | https://d.repec.org/n?u=RePEc:cxv:wpaper:2605 |
| By: | Rishabh Singh Chauhan; Mahdi Ghadimi; Lishun Liu |
| Abstract: | Objectives: While causal analysis of travel behavior is an emerging field, estimating heterogeneity in mode choice through causal modeling remains unexplored. This study demonstrates the application of a novel causal method, causal forest, to quantify the heterogeneity in travel mode choice shifts caused by the COVID-19 pandemic. Methods: We applied causal forests, a non-parametric causal machine learning method, to 802, 935 trip records from the 2017 and 2022 waves of the National Household Travel Survey. The 2017 wave serves as the pre-pandemic control group, while the 2022 wave represents the treatment condition. Within the potential outcomes framework, we estimate average treatment effects (ATE), heterogeneous treatment effects (HTE), and conditional average treatment effects (CATE) across diverse socio-demographic groups and trip characteristics. Findings: Our results reveal an estimated ATE of a 1.86 percentage point (pp) increase in car-mode share, contrasted with decreases of 0.38 pp and 1.57 pp in public transit and walking, respectively. The largest increases in car use appeared for short-distance trips (one mile or less), households with annual incomes exceeding USD 200, 000, and female travelers. Novelty: This is one of the first applications of causal forests to travel mode choice, and the first to use causal machine learning to estimate the pandemic's causal effect on mode choice analysis. Practical Applications: This study discusses methodological advantages, inherent assumptions, and limitations of causal forests within the context of transportation planning. This methodology is applied to COVID-19 travel data to illustrate how causal heterogeneity analysis can offer a deeper understanding of changes in mode choice. These insights are valuable for planners and policymakers in making policies related to mode shifts under an intervention. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.04208 |
| By: | Joern Kleinert (University of Graz, Austria) |
| Abstract: | A match in the labor market results from a two-sided discrete choice of workers and firms. On the one side, heterogeneous workers choose to apply to firms' offers which correspond well to their abilities, education, training (all closely related to job seeker's occupation), interests, and job expectations. On the other side, heterogeneous firms choose from applications received their employees depending on their occupation or work experience, special skills, and expected fit into the existing team. Each side makes discrete choices. In this way, I model labor market search and matching with mismatches as one possible result. Labor market outcomes are thereby strongly affected by non-labor market influences, such as education decisions and sector shifts in the goods market. |
| Keywords: | Labor market matching, Wages, Occupational differentiation |
| JEL: | J24 J31 J64 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:grz:wpaper:2026-16 |
| By: | Daisuke Hirata; Yusuke Kasuya |
| Abstract: | We study the properties of the cumulative offer mechanism (COM) when institutions' choice functions satisfy strong observable substitutability (strong OS). First, we show that each of the following properties of the COM characterizes strong OS: IR monotonicity, weak Maskin monotonicity, and dropping monotonicity. Dropping monotonicity is a new condition weaker than weak Maskin monotonicity, and it is interpretable as a weakening of strategy-proofness and non-bossiness. Second, we show that when choice functions are strongly OS, weak group strategy-proofness of the COM reduces to individual strategy-proofness. However, strong OS is neither necessary nor "almost necessary" for this reduction. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.27784 |
| By: | Sinan Ertemel |
| Abstract: | We study deterministic choice when the same decision maker is observed before and after a deliberative intervention that makes one of two fixed ordinal consequence dimensions more salient. Both choice modes are path independent. Ordinary choice respects two-dimensional dominance, while any binary reversal under fuller deliberation must favor the alternative that is strictly better on the emphasized dimension. We characterize exactly the admissible ordinary and full-horizon rankings. The characterization yields a protected partial order, sharp pairwise and menu-level identification, a signed restriction on menu-level choice changes, and an acyclicity test for incomplete observations. With an ordered sequence of deliberative modes, choices from any fixed menu form a monotone tradeoff path: every switch improves the emphasized dimension, worsens the other dimension, and an abandoned alternative cannot reappear. When the emphasized consequence order is strict, the admissible rankings have an equivalent Kemeny-Kendall representation. The framework is ordinal: it requires neither a cardinal tradeoff nor lexicographic priority, and it applies whenever deliberation gives greater emphasis to one of two fixed consequence dimensions. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.23781 |
| By: | Thomas Buser (University of Amsterdam) |
| Abstract: | A large experimental literature shows that when given the choice between individual per- formance incentives and competitive incentives, men are typically more likely than women to choose competition. This finding of a robust gender difference in willingness to compete has sparked much follow-up research into the causes and consequences of individual and gender differences in willingness to compete. The aim of this chapter is to summarize these different strands of research and draw conclusions on what we can learn from them. I conclude that there is strong evidence that – underlying individual differences in willingness to compete – there is a “competitiveness†trait that is not well-captured by other traits and preferences, and somewhat less conclusive evidence that there is a gender difference in this trait. Willingness to compete – and the gender gap therein – varies strongly across countries, cultures and social contexts, suggesting that it can be influenced by them. Nevertheless, while the magnitude of the gender gap varies across contexts, men are more competitive than women in most countries and settings, suggesting that biology might play a role as well. Finally, there are strong indications that individual differences in preferences for competition are economically relevant: competitiveness predicts important life choices and labor market outcomes and can statistically explain gender differences in career outcomes. |
| JEL: | D91 J16 J24 |
| Date: | 2026–09–01 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20260063 |
| By: | Wehner, Jasmin; Wolf, Chris; Zhang, Wendong |
| Keywords: | Agribusiness |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea26:404322 |
| By: | Cheu, Sungmin; Melkani, Aakanksha; Mieno, Taro |
| Keywords: | Environmental Economics and Policy |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea26:404487 |