The fear of social stigma and discrimination leads many individuals worldwide to hesitate in openly disclosing their sexual orientation. Due to the large costs of concealing identity, it is crucial to understand the extent of anti-LGB sentiments and reactions to coming out. However, disclosing one’s sexual orientation is a personal choice, complicating data access and introducing endogeneity issues. This paper tackles these challenges by using an innovative data source from a popular online video game together with a natural experiment. We exploit exogenous variation in the identity of a playable character to credibly identify the effects of disclosure on players’ revealed preferences for that character. Leveraging detailed daily data, we monitor players’ preferences for the character across diverse regions globally and employ synthetic control methods to isolate the effect of the disclosure on players’ preferences. Our findings reveal a substantial and persistent negative impact of coming out. To strengthen the plausibility of social stigma as the primary explanation for the estimated effects, we systematically address and eliminate several alternative game-related channels.
Empirical studies in various social sciences often involve categorical outcomes with inherent ordering, such as self-evaluations of subjective well-being and self-assessments in health domains. While ordered choice models, such as the ordered logit and ordered probit, are popular tools for analyzing these outcomes, they may impose restrictive parametric and distributional assumptions. This paper introduces a novel estimator, the ordered correlation forest, that can naturally handle non-linearities in the data and does not assume a specific error term distribution. The proposed estimator modifies a standard random forest splitting criterion to build a collection of forests, each estimating the conditional probability of a single class. Under an “honesty” condition, predictions are consistent and asymptotically normal. The weights induced by each forest are used to obtain standard errors for the predicted probabilities and the covariates’ marginal effects. Evidence from synthetic data shows that the proposed estimator features a superior prediction performance than alternative forest-based estimators and demonstrates its ability to construct valid confidence intervals for the covariates’ marginal effects.
Uncovering the heterogeneous effects of particular policies or “treatments” is a key concern for researchers and policymakers. A common approach is to report average treatment effects across different subgroups based on observable covariates. However, there is likely to be considerable uncertainty about the appropriate grouping. This paper proposes a nonparametric approach to discovering heterogeneous subgroups in a selection-on-observables framework. The approach constructs a sequence of groupings, one for each level of granularity. Groupings are nested and feature an optimality property. An “honesty” condition allows us to construct valid confidence intervals for the average treatment effect of each group. The utility of the proposed methodology is illustrated through an empirical exercise that revisits the impact of maternal smoking on birth weight.