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on Discrete Choice Models |
| By: | Paul L. E. Grieco; Charles Murry; Joris Pinkse; Stephan Sagl |
| Abstract: | We propose a conformant likelihood estimator with exogeneity restrictions (CLEER) for random coefficients discrete choice demand models that is applicable in a broad range of data settings. It combines the likelihoods of two mixed logit estimators—one for consumer level data, and one for product level data—with product level exogeneity restrictions. Our estimator is both efficient and conformant: its rates of convergence will be the fastest possible given the variation available in the data. The researcher does not need to pre-test or adjust the estimator and the inference procedure is valid across a wide variety of scenarios. Moreover, it can be tractably applied to large datasets. We illustrate the features of our estimator by comparing it to alternatives in the literature. |
| JEL: | C13 C18 L0 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33397 |
| By: | Bernard M. S. van Praag; J. Peter Hop; William H. Greene |
| Abstract: | In the last few decades, the study of ordinal data in which the variable of interest is not exactly observed but only known to be in a specific ordinal category has become important. In Psychometrics such variables are analysed under the heading of item response models (IRM). In Econometrics, subjective well-being (SWB) and self-assessed health (SAH) studies, and in marketing research, Ordered Probit, Ordered Logit, and Interval Regression models are common research platforms. To emphasize that the problem is not specific to a specific discipline we will use the neutral term coarsened observation. For single-equation models estimation of the latent linear model by Maximum Likelihood (ML) is routine. But, for higher -dimensional multivariate models it is computationally cumbersome as estimation requires the evaluation of multivariate normal distribution functions on a large scale. Our proposed alternative estimation method, based on the Generalized Method of Moments (GMM), circumvents this multivariate integration problem. The method is based on the assumed zero correlations between explanatory variables and generalized residuals. This is more general than ML but coincides with ML if the error distribution is multivariate normal. It can be implemented by repeated application of standard techniques. GMM provides a simpler and faster approach than the usual ML approach. It is applicable to multiple -equation models with -dimensional error correlation matrices and response categories for the equation. It also yields a simple method to estimate polyserial and polychoric correlations. Comparison of our method with the outcomes of the Stata ML procedure cmp yields estimates that are not statistically different, while estimation by our method requires only a fraction of the computing time. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.10726 |
| By: | Takuya Washio; Sota Takagi; Miki Saijo; Ken Wako; Keitaro Sato; Hiroyuki Ito; Ken-ichi Takeda; Takumi Ohashi |
| Abstract: | As global attention on sustainable and ethical food systems grows, animal welfare-friendly products (AWFP) are increasingly recognized as essential to addressing consumer and producer concerns. However, traditional research often neglects the interdependencies between production, retail, and consumption stages within the supply chain. This study examined how cross-stage interactions among producers, consumers, and retail intermediaries can promote AWFP adoption. By establishing a short value chain from production to consumption, we conducted a two-month choice experiment in the operational restaurant, employing a mixed-method approach to quantitatively and qualitatively assess stakeholder responses. The results revealed that providing information about AWFP practices significantly influenced consumer behavior, increasing both product selection and perceived value. Retailers recognized the potential for economic benefits and strengthened customer loyalty, while producers identified new revenue opportunities by re-fattening delivered cow. These coordinated changes - defined as synchronized actions and mutual reinforcement across production, retail, and consumption - generated positive feedback loops that motivated stakeholders to adopt AWFP practices. This research underscores the potential of strategically designed short value chain to foster cross-stage coordination and highlights their role as practical entry points for promoting sustainable and ethical food systems on a larger scale. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.10680 |
| By: | Ismaël Rafaï (Aix Marseille Univ, CNRS, AMSE); Bérengère Davin-Casalena (Observatoire Régional de la Santé); Dimitri Dubois (CEE-M); Bruno Ventelou (Aix Marseille Univ, CNRS, AMSE) |
| Abstract: | Background. Earlier detection of neurodegenerative diseases may help patients plan for their future, achieve a better quality of life, access clinical trials and possible future disease modifying treatments. Due to recent advances in artificial intelligence (AI), a significant help can come from the computational approaches targeting diagnosis and monitoring. Yet, detection tools are still underused. We aim to investigate the factors influencing individual valuation of AI-based prediction tools. Methods. We study individual valuation for early diagnosis tests for neurodegenerative diseases when Artificial Intelligence Diagnosis is an option. We conducted a Discrete Choice Experiment on a representative sample of the French adult public (N=1017), where we presented participants with a hypothetical risk of developing in the future a neurodegenerative disease. We ask them to repeatedly choose between two possible early diagnosis tests that differ in terms of (1) type of test (biological tests vs AI tests analyzing electronic health records); (2) identity of whom communicates tests’ results; (3) sensitivity; (4) specificity; and (5) price. We study the weight in the decision for each attribute and how socio-demographic characteristics influence them. Results. Our results are twofold: respondents indeed reveal a reduced utility value when AI testing is at stake (that is evaluated to 36.08 euros in average, IC = [22.13; 50.89]) and when results are communicated by a private company (95.15 €, IC = [82.01; 109.82]). Conclusion. We interpret these figures as the shadow price that the public attaches to medical data privacy. The general public is still reluctant to adopt AI screening on their health data, particularly when these screening tests are carried out on large sets of personal data. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:aim:wpaimx:2432 |
| By: | Laurent Davezies (CREST-ENSAE); Xavier D’Haultfoeuille (CREST-ENSAE, PSE); Louise Laage (Georgetown University) |
| Abstract: | This paper studies identification and estimation of average causal effects, such as average marginal or treatment effects, in fixed effects logit models with short panels. Relating the identified set of these effects to an extremal moment problem, we first show how to obtain sharp bounds on such effects simply, without any optimization. We also consider even simpler outer bounds, which, contrary to the sharp bounds, do not require any first-step nonparametric estimators. We build confidence intervals based on these two approaches and show their asymptotic validity. Monte Carlo simulations suggest that both approaches work well in practice, the second being typically competitive in terms of interval length. Finally, we show that our method is also useful to measure treatment effect heterogeneity. |
| Keywords: | Fixed effects logit models, panel data, partial identification. |
| JEL: | C14 C23 C25 |
| Date: | 2025–01–24 |
| URL: | https://d.repec.org/n?u=RePEc:crs:wpaper:2025-02 |
| By: | Somdeb Lahiri |
| Abstract: | We present a simple proof of a well-known axiomatic characterization of state-salient decision rules, using Weak Dominance Criterion and Global Independence of Irrelevant Alternatives. Subsequently we provide a simple axiomatic characterization of the Strict-Condorcet choice function on the domain of all preference profiles that have a strict-Condorcet winner, assuming that if the first two ranks are occupied by the same two alternatives in all states of nature, then the chosen alternative will be the one from these two that is preferred to the other with probability greater than half-provided such an alternative exists. We also show that this result is not valid if we extend the domain to the set of all preference profiles that have a unique weak-Condorcet winner. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.10986 |
| By: | Hufe, Paul (University of Bristol); Weishaar, Daniel (LMU Munich) |
| 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. |
| Keywords: | fairness preferences, survey experiment, vignette studies |
| JEL: | C90 D63 I39 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17629 |
| By: | Imke Reimers; Christoph Riedl; Joel Waldfogel |
| Abstract: | Differentiated product consumption choices made without full information can lead to welfare losses from regret and missed opportunities, but a lack of post-purchase usage data has prevented their exploration. Using novel data on individual ownership and post-purchase usage of video games, we explore both the potential welfare benefits of full information prior to purchase and the ability of contemporary prediction technology to produce these gains. We find large potential gains: Among currently owned games, fully informed consumers could achieve 90 percent of their status quo playtime with 40 percent of current expenditure; and current expenditure reallocated among all available games could double status quo playtime. We develop a tractable model of consumer choice among bundles based on hours of playtime relative to overall spending, which we implement using both a Cobb Douglas calibration and a logit model of bundle choice. Full information would raise consumer surplus by more than the value of status quo expenditure; and it would reduce expenditure by half. Consumers heeding sophisticated, personalized predictions would obtain roughly 40 percent of these welfare benefits with a fifth less spending. |
| JEL: | L15 L82 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33401 |
| By: | Zoe B. Cullen; Bobak Pakzad-Hurson; Ricardo Perez-Truglia |
| Abstract: | We estimate the value employees place on remote work using revealed preferences in a high-stakes, real-world context, focusing on U.S. tech workers. On average, employees are willing to accept a 25% pay cut for partly or fully remote roles. Our estimates are three to five times that of previous studies. We attribute this discrepancy partly to methodological differences, suggesting that existing methods may understate preferences for remote work. Because of the strong preference for remote work, we expected to find a compensating wage differential, with remote positions offering lower compensation than otherwise identical in-person positions. However, using novel data on salaries for tech jobs, we reject that hypothesis. We propose potential explanations for this puzzle, including optimization frictions and worker sorting. |
| JEL: | J24 J31 M54 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33383 |