Empirical welfare analysis for discrete choice: Some general results
离散选择的实证福利分析:若干一般结果AI 解读 ↓
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离散选择的实证福利分析:若干一般结果AI 解读 ↓
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This paper develops nonparametric methods for welfare‐analysis of economic changes in the common setting of multinomial choice. The results cover (a) simultaneous price‐change of multiple alternatives, (b) introduction/elimination of an option, (c) changes in choice‐characteristics, and (d) choice among nonexclusive alternatives. In these cases, Marshallian consumer surplus becomes path‐dependent, but Hicksian welfare remains well‐defined. We demonstrate that under completely unrestricted preference‐heterogeneity and income‐effects, the distributions of Hicksian welfare are point‐identified from structural choice‐probabilities in scenarios (a), (b), and only set‐identified in (c), (d). In program‐evaluation contexts, our results enable the calculation of compensated‐effects, that is, the program's cash‐equivalent and resulting deadweight‐loss. They also facilitate a theoretically justified cost‐benefit comparison of interventions targeting different outcomes, for example, a tuition‐subsidy and a health‐product subsidy. Welfare analyses under endogeneity is briefly discussed. An application to data on choice of fishing‐mode illustrates the methods.
离散结果的工具变量模型
Single equation instrumental variable models for discrete outcomes are shown to be set identifying, not point identifying, for the structural functions that deliver the values of the discrete outcome. Bounds on identified sets are derived for a general nonparametric model and sharp set identification is demonstrated in the binary outcome case. Point identification is typically not achieved by imposing parametric restrictions. The extent of an identified set varies with the…
劳动者侧歧视:信念与偏好
When agents choose among options with unobserved attributes to maximize expected utility, their choices conflate preferences with beliefs about those attributes. We develop a novel theory-driven identification strategy for such settings, showing that compensating differentials separately identify beliefs from preferences. Noting that workers’ preferences and beliefs shape labor market outcomes, yet remain understudied, we apply our method to provide the first evidence on the…
We develop a distribution regression model under endogenous sample selection. This model is a semiparametric generalization of the Heckman selection model, which accommodates much richer patterns of heterogeneity in the effect of covariates and selection process and allows for departures from Gaussian errors while maintaining the same level of tractability. The model applies to continuous, discrete, and mixed outcomes. We provide identification, estimation, and inference…
分类结果的样本选择偏差校正
In this paper, I propose a method for correcting sample selection bias when the outcome of interest is categorical, such as occupational choice, health status, or field of study. Classical approaches to sample selection rely on strong parametric distributional assumptions, which may be restrictive in practice. I develop a local representation that decomposes each joint probability into marginal probabilities and a category-specific association parameter that captures how…
非线性面板模型中时变反事实参数的识别
We develop a general framework for the identification of counterfactual parameters in a class of nonlinear semiparametric panel models with fixed effects and time effects. Our method applies to models for discrete outcomes (e.g., two-way fixed effects binary choice) or continuous outcomes (e.g., censored regression), with discrete or continuous regressors. Our results do not require parametric assumptions on the error terms or time-homogeneity on the outcome equation. Our…
非线性面板模型中时变反事实参数的识别
We develop a general framework for the identification of counterfactual parameters in a class of nonlinear semiparametric panel models with fixed effects and time effects. Our method applies to models for discrete outcomes (e.g., two-way fixed effects binary choice) or continuous outcomes (e.g., censored regression), with discrete or continuous regressors. Our results do not require parametric assumptions on the error terms or time-homogeneity on the outcome equation. Our…