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on Discrete Choice Models |
| By: | John Buckell; Alice Wreford; Matthew Quaife; Thomas O. Hancock |
| Abstract: | Background: Any sample of individuals has its own, unique distribution of preferences for choices that they make. Discrete choice models try to capture these distributions. Mixed logits are by far the most commonly used choice model in health. A raft of parametric model specifications for these models are available. We test a range of alternatives assumptions, and model averaging, to test if or how model outputs are impacted. Design: Scoping review of current modelling practices. Seven alternative distributions, and model averaging over all distributional assumptions, were compared on four datasets: two were stated preference, one was revealed preference, and one was simulated. Analyses examined model fit, preference distributions, willingness-to-pay, and forecasting. Results: Almost universally, using normal distributions is the standard practice in health. Alternative distributional assumptions outperformed standard practice. Preference distributions and the mean willingness-to-pay varied significantly across specifications, and were seldom comparable to those derived from normal distributions. Model averaging offered distributions allowed for greater flexibility, further gains in fit, reproduced underlying distributions in simulations, and mitigated against analyst bias arising from distribution selection. There was no evidence that distributional assumptions impacted predictions from models. Limitations: Our focus was on mixed logit models since these models are the most common in health, though latent class models are also used. Conclusions: The standard practice of using all normal distributions appears to be an inferior approach for capturing random preference heterogeneity. Implications: Researchers should test alternative assumptions to normal distributions in their models. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.14099 |
| By: | Gabriel Nova; Sander van Cranenburgh; Stephane Hess |
| Abstract: | Discrete choice modelling is a theory-driven modelling framework for understanding and forecasting choice behaviour. To obtain behavioural insights, modellers test several competing model specifications in their attempts to discover the 'true' data generation process. This trial-and-error process requires expertise, is time-consuming, and relies on subjective theoretical assumptions. Although metaheuristics have been proposed to assist choice modellers, they treat model specification as a classic optimisation problem, relying on static strategies, applying predefined rules, and neglecting outcomes from previous estimated models. As a result, current metaheuristics struggle to prioritise promising search regions, adapt exploration dynamically, and transfer knowledge to other modelling tasks. To address these limitations, we introduce a deep reinforcement learning-based framework where an 'agent' specifies models by estimating them and receiving rewards based on goodness-of-fit and parsimony. Results demonstrate the agent dynamically adapts its strategies to identify promising specifications across data generation processes, showing robustness and potential transferability, without prior domain knowledge. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.06410 |
| By: | Metcalfe, Robert; Roth, Sefi |
| Abstract: | Exposure to ambient air pollution has been shown to be detrimental to human health and productivity, and has motivated many policies to reduce such pollution. However, given that humans spend 90% of their time indoors, it is important to understand the degree of exposure to Indoor Air Pollution (IAP), and, if high, ways to reduce it. We design and implement a field experiment in London that monitors households’ IAP and then randomly reveals their IAP in real-time. At baseline, we find that IAP is worse than ambient air pollution when residents are at home and that for 38% of the time, IAP is above World Health Organization standards. Additionally, we observe a large household income-IAP gradient, larger than the income-ambient pollution gradient, highlighting large income disparities in IAP exposure. During our field experiment, we find that the randomized revelation reduces IAP by 17% (1.9 µg/m3 ) overall and 34% (5 µg/m3 ) during occupancy time. We show that the mechanism is households using more natural ventilation as a result of the feedback (i.e., opening up doors and windows). Finally, in terms of welfare, we find that: (i) households have a willingness to pay of £4.8 ($6) for every 1 µg/m3 reduction in indoor PM2.5; (ii) households have a higher willingness to pay for mitigation than for full information; (iii) households have a price elasticity of IAP monitor demand around -0.75; and (iv) a £1 subsidy for an IAP monitor or an air purifier infinite marginal value of public funds, i.e., a Pareto improvement. |
| JEL: | N0 |
| Date: | 2025–02–20 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:128514 |
| By: | Shirsho Biswas; Hema Yoganarasimhan; Haonan Zhang |
| Abstract: | The rapid rise of e-commerce has transformed consumer behavior, prompting questions about how online adoption influences offline shopping. We examine whether consumers who adopt online shopping with a retailer become more price-sensitive in their subsequent offline purchases with that retailer. Using transaction-level data from a large Brazilian pet supplies retailer operating both online and offline, we compare ''adopters'' - customers who began shopping online after a period of offline-only purchasing - with ''non-adopters'' who remained offline-only. We estimate a discrete choice logit model with individual-level heterogeneity, using a novel algorithm to handle high-dimensional fixed effects and address price endogeneity. We then apply a staggered difference-in-differences approach to the estimated price elasticities and obtain the Average Treatment Effect on the Treated (ATT). We find that offline price sensitivity increases significantly after online adoption in three out of four product categories, particularly in items with low switching costs, such as pet hygiene. Counterfactual pricing simulations show that incorporating these behavioral spillovers into pricing strategies can increase firm profits by up to 4.1%. These results underscore the importance of recognizing cross-channel effects in consumer behavior and contribute to the literature on pricing and multichannel retailing by identifying online adoption as a key driver of offline price sensitivity. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.15103 |
| By: | Björn Bartling; Krishna Srinivasan |
| Abstract: | This study investigates the determinants of individuals’ demand for and supply of paternalistic interventions - measures intended to help others avoid mistakes. Based on data from an incentivized experiment conducted with a large U.S. sample, we find that both demand and supply are higher for informational interventions than for those that restrict choice, and when targeted individuals perceive themselves or are perceived as more error-prone. Moreover, granting targets the right to withhold consent increases demand. These behavioral patterns, supported by participants’ free-text responses, suggest that both receiving and supplying interventions entail utility costs, particularly when interventions infringe upon personal autonomy. Our findings inform policy design by highlighting the importance of autonomy-preserving features such as choice options and consent rights in securing public support for paternalistic interventions. |
| Keywords: | paternalism, interventions, consent rights, policy design |
| JEL: | C91 D60 D91 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11886 |
| By: | Wang, Jingxuan; Wei, Yuchen; Galizzi, Matteo M.; Kwan, Hoi Shan; Zee, Benny Chung Ying; Fung, Hong; Yung, Tony Ka Chun; Wong, Eliza Lai Yi; Yue, Qianying; Lee, Michelle Kit Ling; Wu, Yushan; Wang, Kailu; Wu, Hongjiang; Yeoh, Eng Kiong; Chong, Ka Chun |
| Abstract: | Taxation on sugar-sweetened beverages (SSBs) is proposed as a measure to address the health consequences of excessive sugar intake, yet research on its implementation in Asian contexts is limited. This study examined the perceptions, willingness-to-pay, and associated socio-demographics of SSB taxation in Hong Kong, an affluent Asian setting. A random-sampled telephone survey was conducted with 1, 250 Hong Kong adults. We used the maximum willingness to pay (WTPM), defined as the highest accepted price that a subject willing to consume SSB products, as a measure of willingness to pay. The contingent valuation method was employed to assess the WTPM for different types of SSBs. A multiple linear regression analysis showed that, about 50% of participants were aware of negative health impacts, and over 60% being confident in reducing their intake. Even with a 30% tax, approximately 70% of individuals remained willing to continue consuming SSBs. Non-diet soft drinks had the highest WTPMs (83% of current price), while parents reported higher WTPM for their children (74%) than for themselves (66%). Full/part-time workers had higher WTPM, whereas higher income and better self-rated health correlated with lower WTPM. Full/part-time workers had higher WTPMs, while higher income and better self-rated health were associated with lower WTPMs. In summary, despite awareness of the potential health risks associated with consuming SSBs, a high tax rate was necessary to reduce SSB consumption, particularly among children and non-diet soft drinkers. Our study highlights how economic measures can influence consumer behavior and informs the implementation of such measures. |
| Keywords: | sugar-sweetened beverage; taxation; perception; health policy |
| JEL: | R14 J01 |
| Date: | 2025–06–17 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:128414 |
| By: | Majbouri, Mahdi (Babson College) |
| Abstract: | Women’s educational attainment has continuously increased across the Middle East, while fertility rates have declined substantially. Yet their labor force participation remains stubbornly low. To investigate this puzzle, I use a discrete choice experiment in Egypt that varies the gender composition of the work environment—a key but underexplored dimension. I find that men, who have final say over women’s work decisions, demand 77% higher wages for their wives if the job is in a mixed-gender setting. Since few workplaces are all-female and men can veto women’s employment, these findings help explain the persistently low female participation rate. |
| Keywords: | Middle East and North Africa, preferences toward job attributes, labor supply |
| JEL: | J21 J29 J49 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17952 |
| By: | Adachi, Daisuke; Skipper, Lars |
| Abstract: | The offshoring of manufacturing jobs has replaced low-skilled workers who often lack the relevant skills to transition to new occupations. Using Danish adult education and employer-employee data, we study how adult vocational training influences occupational choice and mitigates labor demand shocks. Despite low participation rates in training programs, we show that manufacturing workers trained in business services (BS) programs have a 0.9-3.1 percentage point higher probability of transitioning to BS occupations using dynamic difference-in-difference analysis. We then propose and estimate a life-cycle model of occupation and program choice that yields a nested logit conditional choice probability. The program take-up elasticity is lower than the occupation choice elasticity, suggesting that individuals are insensitive to the monetary value of the programs. A counterfactual wage subsidy policy tied to participation in BS-related programs supports transitions from manufacturing to BS occupations and reduces the share of low-skilled individuals leaving the labor force, especially at older ages, demonstrating the potential for a resilient labor market. |
| Date: | 2025–06–06 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:7dktj_v1 |
| By: | Giuseppe Arbia; Luca Morandini; Vincenzo Nardelli |
| Abstract: | This paper investigates Large Language Models (LLMs) ability to assess the economic soundness and theoretical consistency of empirical findings in spatial econometrics. We created original and deliberately altered "counterfactual" summaries from 28 published papers (2005-2024), which were evaluated by a diverse set of LLMs. The LLMs provided qualitative assessments and structured binary classifications on variable choice, coefficient plausibility, and publication suitability. The results indicate that while LLMs can expertly assess the coherence of variable choices (with top models like GPT-4o achieving an overall F1 score of 0.87), their performance varies significantly when evaluating deeper aspects such as coefficient plausibility and overall publication suitability. The results further revealed that the choice of LLM, the specific characteristics of the paper and the interaction between these two factors significantly influence the accuracy of the assessment, particularly for nuanced judgments. These findings highlight LLMs' current strengths in assisting with initial, more surface-level checks and their limitations in performing comprehensive, deep economic reasoning, suggesting a potential assistive role in peer review that still necessitates robust human oversight. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.06377 |
| By: | Reda, Milan Jakob; Gawel, Erik; Lehmann, Paul |
| Abstract: | This paper analyses the impact of carbon pricing on residential heating affordability using a theoretical household model with endogenous choice of a renewable heating technology. We compare two compensation policies: a renewable heating subsidy and a lump-sum transfer. The subsidy is the most effective policy to reduce the household's burden if the renewable heating technology is the optimal choice with carbon pricing alone. Otherwise, the relative effectiveness of the compensation policies depends on whether they shift the household's choice towards renewable heating. Overall, our study emphasizes the need of considering technological adjustment when analyzing how carbon pricing affects heating affordability. |
| Keywords: | residential heating, affordability, climate policy, environmental taxes and subsidies |
| JEL: | D63 H23 Q58 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:ufzdps:319624 |
| By: | Yang Xu |
| Abstract: | This paper studies a linear production model in team networks with missing links. In the model, heterogeneous workers, represented as nodes, produce jointly and repeatedly within teams, represented as links. Links are omitted when their associated outcome variables fall below a threshold, resulting in partial observability of the network. To address this, I propose a Generalized Method of Moments estimator under normally distributed errors and develop a distribution-free test for detecting link truncation. Applied to academic publication data, the estimator reveals and corrects a substantial downward bias in the estimated scaling factor that aggregates individual fixed effects into team-specific fixed effects. This finding suggests that the collaboration premium may be systematically underestimated when missing links are not properly accounted for. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.08405 |
| By: | St\'ephane Bonhomme; Kevin Dano; Bryan S. Graham |
| Abstract: | Many panel data methods, while allowing for general dependence between covariates and time-invariant agent-specific heterogeneity, place strong a priori restrictions on feedback: how past outcomes, covariates, and heterogeneity map into future covariate levels. Ruling out feedback entirely, as often occurs in practice, is unattractive in many dynamic economic settings. We provide a general characterization of all feedback and heterogeneity robust (FHR) moment conditions for nonlinear panel data models and present constructive methods to derive feasible moment-based estimators for specific models. We also use our moment characterization to compute semiparametric efficiency bounds, allowing for a quantification of the information loss associated with accommodating feedback, as well as providing insight into how to construct estimators with good efficiency properties in practice. Our results apply both to the finite dimensional parameter indexing the parametric part of the model as well as to estimands that involve averages over the distribution of unobserved heterogeneity. We illustrate our methods by providing a complete characterization of all FHR moment functions in the multi-spell mixed proportional hazards model. We compute efficient moment functions for both model parameters and average effects in this setting. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.12569 |