Individual Updating of Subjective Probability of Homicide Victimization: a "Natural Experiment'' on Risk Communication
个体主观凶杀受害概率的更新:风险沟通的“自然实验”AI 解读 ↓
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We investigate the dynamics of the update of subjective homicide victimization risk after an informational shock by developing two econometric models able to accommodate both optimal decisions of changing prior expectations which enable us to rationalize skeptical Bayesian agents with their disregard to new information. We apply our models to a unique household data (N = 4,030) that consists of socioeconomic and victimization expectation variables in Brazil, coupled with an informational ``natural experiment'' brought by the sample design methodology, which randomized interviewers to interviewees. The higher priors about their own subjective homicide victimization risk are set, the more likely individuals are to change their initial perceptions. In case of an update, we find that elders and females are more reluctant to change priors and choose the new response level. In addition, even though the respondents' level of education is not significant, the interviewers' level of education has a key role in changing and updating decisions. The results show that our econometric approach fits reasonable well the available empirical evidence, stressing the salient role heterogeneity represented by individual characteristics of interviewees and interviewers have on belief updating and lack of it, say, skepticism. Furthermore, we can rationalize skeptics through an informational quality/credibility argument.
高维处理的异质性因果效应估计:联合分析应用
Estimation of heterogeneous treatment effects is an active area of research. Most of the existing methods, however, focus on estimating the conditional average treatment effects of a single, binary treatment given a set of pre-treatment covariates. In this paper, we propose a method to estimate the heterogeneous causal effects of high-dimensional treatments, which poses unique challenges in terms of estimation and interpretation. The proposed approach finds maximally…
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生育、子女性别与父母迁移决策:来自中国的证据
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存在少报的有序选择模型中的变化中的变化
We develop a Difference-in-Differences framework for discrete, ordered outcomes subject to underreporting. Such outcomes commonly arise in self-reported surveys on socially undesirable or stigmatized behaviors, where respondents may conceal their true behavior. For a discrete Changes-in-Changes model that is shown to admit an equivalent threshold-crossing representation, we derive nonparametric bounds for the counterfactual and factual outcome distributions as well as for…
存在低报的有序选择模型中的变化中变化方法
We develop a Difference-in-Differences framework for discrete, ordered outcomes subject to underreporting. Such outcomes commonly arise in self-reported surveys on socially undesirable or stigmatized behaviors, where respondents may conceal their true behavior. For a discrete Changes-in-Changes model that is shown to admit an equivalent threshold-crossing representation, we derive nonparametric bounds for the counterfactual and factual outcome distributions as well as for…
组成型双重差分
Many causal questions involve outcomes distributed across mutually exclusive categories, such as votes by party, employment by status, or electricity generation by energy source, where researchers care about both the shares and the total quantity. It is well known that linear Difference-in-Differences (DiD) methods applied to category shares can produce incoherent counterfactuals and lack a discrete-choice foundation. This paper develops Compositional…