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on Discrete Choice Models |
| By: | Mikołaj Czajkowski (Faculty of Economic Sciences, University of Warsaw); Bartosz Jusypenko (Faculty of Economic Sciences, University of Warsaw); Ben White (SurveyEngine GmbH, Germany) |
| Abstract: | This pioneering study employs stated preference methods, specifically discrete choice experiments, to evaluate public preferences for the protection of diverse cultural heritage assets in Victoria, Australia. By analyzing responses to a series of hypothetical policy scenarios, we uncover the economic values the public assigns to various heritage attributes, including condition, accessibility, and protection measures. Our findings emphasize the importance of both use and non-use values in shaping willingness to pay for heritage conservation. These insights are critical for developing more effective, community-aligned heritage policies that reflect the public's valuation of cultural heritage. This research marks a significant advancement in the application of discrete choice experiments for general heritage valuation, offering a robust framework for future studies and policy development in cultural heritage preservation. |
| Keywords: | cultural heritage, historic preservation, built environment, non-market valuation, discrete choice experiment, public preferences |
| JEL: | C51 H43 Z18 Q51 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:war:wpaper:2025-11 |
| By: | Ewa Zawojska (Faculty of Economic Sciences, University of Warsaw); Bartosz Jusypenko (Faculty of Economic Sciences, University of Warsaw); Aleksandra Wiśniewska (Faculty of Economic Sciences, University of Warsaw) |
| Abstract: | Understanding the value of cultural goods such as performing arts is essential for designing welfare-maximizing cultural policies. However, since these goods often generate public externalities, market data alone cannot capture their full value. While primary non-market valuation studies offer robust insights, they are resource-intensive. Benefit transfer (BT)—using value estimates from existing studies to assess unstudied contexts—offers a cost-effective alternative. This study examines the accuracy of BT in cultural economics, focusing on marginal values of theater performances. Using data from a discrete choice experiment (DCE) conducted in six Polish provinces, we perform inter-provincial transfers of marginal willingness-to-pay values for four types of theater performances. By comparing transferred values with primary estimates, we assess BT’s validity and reliability. To our knowledge, this is the first BT study in cultural economics using DCE-derived marginal values. Our findings suggest that BT accuracy in culture aligns with results from more established fields, supporting the use of DCE-based estimates in cultural policy evaluation. |
| Keywords: | accuracy, benefit transfer, discrete choice experiment, performing arts, reliability, validity |
| JEL: | H43 Z11 Z18 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:war:wpaper:2025-10 |
| By: | Christophe Bruneel-Zupanc |
| Abstract: | This paper develops a general framework for dynamic models in which individuals simultaneously make both discrete and continuous choices. The framework incorporates a wide range of unobserved heterogeneity. I show that such models are nonparametrically identified. Based on constructive identification arguments, I build a novel two-step estimation method in the lineage of Hotz and Miller (1993) and Arcidiacono and Miller (2011) but extended to simultaneous discrete-continuous choice. In the first step, I recover the (type-dependent) optimal choices with an expectation-maximization algorithm and instrumental variable quantile regression. In the second step, I estimate the primitives of the model taking the estimated optimal choices as given. The method is especially attractive for complex dynamic models because it significantly reduces the computational burden associated with their estimation compared to alternative full solution methods. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.16630 |
| By: | Hannes Wallimann; Noah Balthasar |
| Abstract: | Children's travel behavior plays a critical role in shaping long-term mobility habits and public health outcomes. Despite growing global interest, little is known about the factors influencing travel mode choice of children for school journeys in Switzerland. This study addresses this gap by applying a random forest classifier - a machine learning algorithm - to data from the Swiss Mobility and Transport Microcensus, in order to identify key predictors of children's travel mode choice for school journeys. Distance consistently emerges as the most important predictor across all models, for instance when distinguishing between active vs. non-active travel or car vs. non-car usage. The models show relatively high performance, with overall classification accuracy of 87.27% (active vs. non-active) and 78.97% (car vs. non-car), respectively. The study offers empirically grounded insights that can support school mobility policies and demonstrates the potential of machine learning in uncovering behavioral patterns in complex transport datasets. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.09947 |
| 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 per- ceived as more error-prone. Moreover, granting targets the right to withhold consent increases demand. These behavioral patterns, supported by participants’ free-text re- sponses, suggest that both receiving and supplying interventions entail utility costs, particularly when interventions infringe upon personal autonomy. Our findings in- form 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: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:zur:econwp:469 |
| By: | Masahiro Tanaka |
| Abstract: | While local projections (LPs) are widely used for impulse response analysis, existing Bayesian approaches face fundamental challenges because a set of LPs does not constitute a likelihood function. Prior studies address this issue by constructing a pseudo-likelihood, either by treating LPs as a system of seemingly unrelated regressions with a multivariate normal error structure or by applying a quasi-Bayesian approach with a sandwich estimator. However, these methods lead to posterior distributions that are not "well calibrated, " preventing proper Bayesian belief updates and complicating the interpretation of posterior distributions. To resolve these issues, we propose a novel quasi-Bayesian approach for inferring LPs using the Laplace-type estimator. Specifically, we construct a quasi-likelihood based on a generalized method of moments criterion, which avoids restrictive distributional assumptions and provides well-calibrated inferences. The proposed framework enables the estimation of simultaneous credible bands and naturally extends to LPs with an instrumental variable, offering the first Bayesian treatment of this method. Furthermore, we introduce two posterior simulators capable of handling the high-dimensional parameter space of LPs with the Laplace-type estimator. We demonstrate the effectiveness of our approach through extensive Monte Carlo simulations and an empirical application to U.S. monetary policy. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.20249 |
| By: | Gosciak, Jennah; Molitor, Daniel; Lundberg, Ian (Cornell University) |
| Abstract: | Human choices are often multi-dimensional. For example, a person deciding which of two immigrants is more worthy of admission to a country might weigh the prospective immigrants' education, age, country of origin, and employment history. Conjoint experiments have rapidly generated new insight into these multidimensional choices. By independently randomizing the attributes of a pair of fictitious profiles, researchers summarize the average contribution that each attribute makes to an overall choice. But what if the effect of one attribute depends on the values of other attributes? We present a method that uses data-adaptive experimentation to search for heterogeneity in the effect of one focal attribute as a function of all other attributes. Our empirical application of this method shows that U.S. adults weigh the education of an immigrant much more heavily for certain immigrants than for others. By targeting the heterogeneous effects of a focal attribute, our approach complements conjoint designs that target the average effects of all attributes. |
| Date: | 2025–04–30 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:69y2j_v1 |
| By: | Olivier de Groote (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement) |
| Abstract: | I investigate high school tracking policies using a dynamic discrete choice model of study programs and unobserved effort. I estimate the model using data from Flanders (Belgium) and perform an ex ante evaluation of a policy that encourages underperforming students to switch to less academically oriented programs. This reduces grade retention by a third and dropout by 11%. Although it decreases college enrollment, the decrease in college graduation is small and insignificant. I also show that modeling effort is important; otherwise, smaller decreases in grade retention and dropout and larger decreases in college enrollment and graduation would be predicted. |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05027246 |
| By: | Badi H. Baltagi (Center for Policy Research, Maxwell School, Syracuse University, 426 Eggers Hall, Syracuse, NY 13244); Long Liu (Florida Atlantic University) |
| Abstract: | This paper revisits the fixed effects panel data model with AR(1) remainder disturbances and provides a bias corrected estimator for the serial correlation coefficient based on first differencing the panel regression to get rid of the fixed effects. This bias corrected estimator builds upon the estimator proposed by Han and Phillips (2010). Asymptotic properties as well as Monte Carlo results are provided that show the better performance of this new proposed bias corrected estimator. This is extended to the unbalanced panel data case and also illustrated using the empirical application in Donohue and Levitt (2001). |
| Keywords: | Panel Data, Serial Correlation, Generalized Least Squares, Fixed Effects, First Difference, Nonstationarity |
| JEL: | C23 C24 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:max:cprwps:267 |
| By: | Arno Riedl; Hans Schmeets; Peter Werner |
| Abstract: | Using an artefactual field experiment, we elicit revealed preferences for solidarity of different age groups towards the same and other age groups among a large and heterogeneous sample of the Dutch population. Preferences are elicited with a solidarity game and linked to a rich and unique administrative database, enabling us to explore demographic and socio-economic correlates of the elicited preferences. In the solidarity game a winner of a money amount is asked ex-ante how much they are willing to transfer to a loser who receives no money. We find that participants on average have a strong preference for ex-ante solidarity, as they are willing to transfer about 40% of the money they receive. At the same time, there is a mismatch between belief in solidarity and actual solidarity. Participants are overly pessimistic about what others will transfer. Moreover, we observe age-based discrimination because a significant share of participants exhibits stronger solidarity preferences with their own age group than with other age groups. Using questionnaires, we also measure stated solidarity preferences in various domains and observe that revealed solidarity preferences correlate with some self-reported attitudes about general solidarity. We also correlate revealed solidarity preferences with opinions on social security systems and self-reported field behavior involving solidarity and find some relation between them. |
| Keywords: | solidarity, age groups, group identity, social security systems, large population sample. |
| JEL: | D63 D64 D91 C93 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11841 |
| By: | Monica Billio; Roberto Casarin; Fausto Corradin; Antonio Peruzzi |
| Abstract: | Bayes Factor (BF) is one of the tools used in Bayesian analysis for model selection. The predictive BF finds application in detecting outliers, which are relevant sources of estimation and forecast errors. An efficient framework for outlier detection is provided and purposely designed for large multidimensional datasets. Online detection and analytical tractability guarantee the procedure's efficiency. The proposed sequential Bayesian monitoring extends the univariate setup to a matrix--variate one. Prior perturbation based on power discounting is applied to obtain tractable predictive BFs. This way, computationally intensive procedures used in Bayesian Analysis are not required. The conditions leading to inconclusive responses in outlier identification are derived, and some robust approaches are proposed that exploit the predictive BF's variability to improve the standard discounting method. The effectiveness of the procedure is studied using simulated data. An illustration is provided through applications to relevant benchmark datasets from macroeconomics and finance. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.19515 |
| By: | Nafisa Lohawala; Mohammad Arshad Rahman |
| Abstract: | The adoption of electric vehicles (EVs) is considered critical to achieving climate goals, yet it hinges on consumer interest. This study explores how public intent to purchase EVs relates to four unexamined factors: exposure to EV information, perceptions of EVs' environmental benefits, views on government climate policy, and confidence in future EV infrastructure; while controlling for prior EV ownership, political affiliation, and demographic characteristics (e.g., age, gender, education, and geographic location). We utilize data from three nationally representative opinion polls conducted by the Pew Research Center between 2021 and 2023, and employ Bayesian techniques to estimate the ordinal probit and ordinal quantile models. Results from ordinal probit show that respondents who are well-informed about EVs, perceive them as environmentally beneficial, or are confident in development of charging stations are more likely to express strong interest in buying an EV, with covariate effects--a metric rarely reported in EV research--of 10.2, 15.5, and 19.1 percentage points, respectively. In contrast, those skeptical of government climate initiatives are more likely to express no interest, by more than 10 percentage points. Prior EV ownership exhibits the highest covariate effect (ranging from 19.0 to 23.1 percentage points), and the impact of most demographic variables is consistent with existing studies. The ordinal quantile models demonstrate significant variation in covariate effects across the distribution of EV purchase intent, offering insights beyond the ordinal probit model. This article is the first to use quantile modeling to reveal how covariate effects differ significantly throughout the spectrum of EV purchase intent. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.09854 |
| By: | Charles I. Jones |
| Abstract: | During the Covid-19 pandemic, the United States effectively “spent” about 4 percent of GDP — via reduced economic activity — to address a mortality risk of roughly 0.3 percent. Many experts believe that catastrophic risks from advanced A.I. over the next decade are at least this large, suggesting that a comparable mitigation investment could be worthwhile. Existing lives are valued by policymakers at around $10 million each in the United States. To avoid a 1% mortality risk, this value implies a willingness to pay of $100, 000 per person — more than 100% of per capita GDP. If the risk is realized over the next two decades, an annual investment of 5% of GDP toward mitigating catastrophic risk could be justified, depending on the effectiveness of such investment. This back-of-the-envelope intuition is supported by the model developed here. In the model, for most of the scenarios and parameter combinations considered, spending at least 1% of GDP annually to mitigate AI risk can be justified even without placing any value on the welfare of future generations. |
| JEL: | O40 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33602 |
| By: | Maria Garcia-Osipenko; Nicolai V. Kuminoff; Spencer Perry; Nicholas Vreugdenhil |
| Abstract: | Utilities increasingly sell electricity using complex menus of time-constant and time-varying price schedules. We study how to design such a menu to maximize social welfare in a second best environment where the marginal private and external costs of generating electricity vary over time, institutional constraints prevent mandating time-varying pricing, and consumer behavior is distorted by frictions. We develop a model of plan choice, consumption, and intertemporal substitution with time-varying marginal social costs, and estimate it using administrative data from a large utility. We provide evidence of substantial intertemporal substitution in response to time-varying price incentives, and selection across plans based on multidimensional heterogeneity. While the current menu’s time-varying plans substantially shift consumption from high-price to low-price hours, we find that they reduce social welfare. This loss is mitigated by information frictions. We show how to redesign the menu to simultaneously improve outcomes for consumers, the utility, and the environment. |
| JEL: | Q4 Q5 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33715 |
| By: | Fiona Burlig; Amir Jina; Anant Sudarshan |
| Abstract: | Over 2 billion people lack clean drinking water. Existing solutions face high costs (piped water) or low demand (point-of-use chlorine). Using a 60, 000 household cluster-randomized experiment we test an increasingly popular alternative: decentralized treatment and home delivery of clean water to the rural poor. At low prices, take-up exceeds 90 percent, sustained throughout the experiment. High prices reduce take-up but are privately profitable. Self-reported health measures improve. We experimentally recover revealed-preference measures of valuation. Willingness-to-pay is several times higher than prior indirect estimates; willingness-to-accept is larger and exceeds marginal cost. On a cost-per-disability-adjusted-life-year basis, free water delivery regimes appear highly cost-effective. |
| JEL: | O13 Q25 Q53 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33557 |
| By: | Nikhil Kumar |
| Abstract: | This paper examines the market for AI models in which firms compete to provide accurate model predictions and consumers exhibit heterogeneous preferences for model accuracy. We develop a consumer-firm duopoly model to analyze how competition affects firms' incentives to improve model accuracy. Each firm aims to minimize its model's error, but this choice can often be suboptimal. Counterintuitively, we find that in a competitive market, firms that improve overall accuracy do not necessarily improve their profits. Rather, each firm's optimal decision is to invest further on the error dimension where it has a competitive advantage. By decomposing model errors into false positive and false negative rates, firms can reduce errors in each dimension through investments. Firms are strictly better off investing on their superior dimension and strictly worse off with investments on their inferior dimension. Profitable investments adversely affect consumers but increase overall welfare. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.13375 |
| By: | Zwetelina Iliewa; Elisabeth Kempf; Oliver Spalt |
| Abstract: | We examine nonpecuniary preferences across a broad set of corporate actions using a representative sample of the U.S. population. Our core findings, based on largescale online surveys, are that (i) self-reported nonpecuniary concerns are large both for stock market investors and non-investors; (ii) concerns about the treatment of workers and CEO pay rank highest—higher than concerns about workforce diversity and fossil energy usage; (iii) moral universalism emerges as an important driver of nonpecuniary preferences. Combined, our findings provide new evidence on the importance of moral concerns as a key determinant of nonpecuniary preferences over corporate actions. |
| Keywords: | corporate actions, non-pecuniary preferences, social responsibility |
| JEL: | G30 G40 D3 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_2025_649v2 |