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on Discrete Choice Models |
| By: | Mikhail Freer; Hassan Nosratabadi |
| Abstract: | In a two-stage model of choice a decision maker first shortlists a given menu and then applies her preferences. We show that a sizeable class of these models run into significant issues in terms of identification of preferences (welfare-relevance) and thus cannot be used for welfare analysis. We classify these models by their revealed preference principles and expose the principle that we deem to be the root of their identification issue. Taking our analysis to an experimental data, we observe that half of the alternatives that are revealed preferred to another under rational choice are left revealed preferred to nothing for any member of this class of models. Furthermore, the welfare-relevance of the specific models established in the literature are much worse. The model with the highest welfare-relevance produces a revealed preference relation with the average density of 2% (1 out of 45 possible comparisons revealed), while rational choice does 63% (28 out of 45 possible comparisons). We argue that the issue is not an inherent feature of two-stage models, and rather lies in the approach with which the first stage is modelled in the literature. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.08263 |
| By: | O'Dell, Dallas; Contu, Davide; Shreedhar, Ganga |
| Abstract: | Research on degrowth and its policy proposals has rapidly expanded, despite lacking empirical evidence on public perceptions. One conceptual proposition for affluent populations is that lifestyle changes, such as undertaking sufficiency-oriented behaviours, may engender degrowth policy support. Our research empirically investigated U.S. public support for degrowth policies, its relation to sufficiency behaviours, and whether a degrowth framing influenced policy support. In a pre-registered, online discrete choice experiment (N = 1012), we elicited perceptions of four commonly advocated degrowth policies - work time reductions, downscaling fossil fuel production, universal basic services, and advertising restrictions. Analyses revealed significant support for some specification of each alternative policy, especially fossil fuel caps and universal healthcare. We also found a significant positive association between sufficiency engagement and supporting fossil fuel restrictions. However, latent class analysis suggested that the link between behaviour and policy support was less consistent for socially oriented policies, and that those who supported such policies did not engage in sufficiency most frequently. Degrowth framing only significantly influenced preferences for universal healthcare. These findings suggest an appetite for advancing eco-social policies in the United States but point to a nuanced relationship between sufficiency lifestyles and degrowth policy support. |
| Keywords: | sufficiency; behaviour; degrowth; policy support; public acceptability; discrete choice experiment |
| JEL: | J1 |
| Date: | 2024–11–13 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:126084 |
| By: | Yuehao Bai; Max Tabord-Meehan |
| Abstract: | This paper studies the sharp testable implications of an additive random utility model with a discrete multi-valued treatment and a discrete multi-valued instrument, in which each value of the instrument only weakly increases the utility of one choice. Borrowing the terminology used in randomized experiments, we call such a setting an encouragement design. We derive inequalities in terms of the conditional choice probabilities that characterize when the distribution of the observed data is consistent with such a model. Through a novel constructive argument, we further show these inequalities are sharp in the sense that any distribution of the observed data that satisfies these inequalities is generated by this additive random utility model. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.09808 |
| By: | Debopam Bhattacharya; Ekaterina Oparina; Qianya Xu |
| Abstract: | We analyze demand settings where heterogeneous consumers maximize utility for product attributes subject to a nonlinear budget constraint. We develop nonparametric methods for welfare-analysis of interventions that change the constraint. Two new findings are Roy's identity for smooth, nonlinear budgets, which yields a Partial Differential Equation system, and a Slutsky-like symmetry condition for demand. Under scalar unobserved heterogeneity and single-crossing preferences, the coefficient functions in the PDEs are nonparametrically identified, and under symmetry, lead to path-independent, money-metric welfare. We illustrate our methods with welfare evaluation of a hypothetical change in relationship between property rent and neighborhood school-quality using British microdata. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.01064 |
| By: | Fan Wu; Yi Xin |
| Abstract: | We propose a new method for estimating nonseparable selection models. We show that, given the selection rule and the observed selected outcome distribution, the potential outcome distribution can be characterized as the fixed point of an operator, and we prove that this operator is a functional contraction. We propose a two-step semiparametric maximum likelihood estimator to estimate the selection model and the potential outcome distribution. The consistency and asymptotic normality of the estimator are established. Our approach performs well in Monte Carlo simulations and is applicable in a variety of empirical settings where only a selected sample of outcomes is observed. Examples include consumer demand models with only transaction prices, auctions with incomplete bid data, and Roy models with data on accepted wages. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.01799 |
| By: | Irene Botosaru; Isaac Loh; Chris Muris |
| Abstract: | We introduce a novel framework to characterize identified sets of structural and counterfactual parameters in econometric models. Our framework centers on a discrepancy function, which we construct using insights from convex analysis. The zeros of the discrepancy function determine the identified set, which may be a singleton. The discrepancy function has an adversarial game interpretation: a critic maximizes the discrepancy between data and model features, while a defender minimizes it by adjusting the probability measure of the unobserved heterogeneity. Our approach enables fast computation via linear programming. We use the sample analog of the discrepancy function as a test statistic, and show that it provides asymptotically valid inference for the identified set. Applied to nonlinear panel models with fixed effects, it offers a unified approach for identifying both structural and counterfactual parameters across exogeneity conditions, including strict and sequential, without imposing parametric restrictions on the distribution of error terms or functional form assumptions. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.04239 |
| By: | Caio Waisman |
| Abstract: | This paper outlines a Bayesian approach to estimate finite mixtures of Tobit models. The method consists of an MCMC approach that combines Gibbs sampling with data augmentation and is simple to implement. I show through simulations that the flexibility provided by this method is especially helpful when censoring is not negligible. In addition, I demonstrate the broad utility of this methodology with applications to a job training program, labor supply, and demand for medical care. I find that this approach allows for non-trivial additional flexibility that can alter results considerably and beyond improving model fit. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.09771 |
| By: | Onil Boussim |
| Abstract: | This paper generalizes the changes-in-changes (CIC) model to handle discrete treatments with more than two categories, extending the binary case of Athey and Imbens (2006). While the original CIC model is well-suited for binary treatments, it cannot accommodate multi-category discrete treatments often found in economic and policy settings. Although recent work has extended CIC to continuous treatments, there remains a gap for multi-category discrete treatments. I introduce a generalized CIC model that adapts the rank invariance assumption to multiple treatment levels, allowing for robust modeling while capturing the distinct effects of varying treatment intensities. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.01617 |