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Regression discontinuity (RD) models are commonly used to nonparametrically identify and estimate a local average treatment effect. Dong and Lewbel (2015) show how a derivative of this effect, called treatment effect derivative (TED) can be estimated. We argue here that TED should be employed in most RD applications, as a way to assess the stability and hence external validity of RD estimates. Closely related to TED, we define the complier probability derivative (CPD). Just as TED measures stability of the treatment effect, the CPD measures stability of the complier population in fuzzy designs. TED and CPD are numerically trivial to estimate. We provide relevant Stata code, and apply it to some real datasets.

The purpose of this paper is to investigate the role of family structure on child labor by comparing children of nuclear families headed by the father with children of single-mother families headed by the divorced mother.

This paper uses data from Brazilian urban areas provided by the Brazilian Demographic Census of 2010. The empirical approach consists of the estimation of three treatment effect models: the Average Treatment Effect, IV Treatment Effect and Two-Stage Estimator proposed by Lewbel (2012).

The main findings show that children of single-mother families headed by divorced mothers are more likely to work, compared to children living with both parents. This paper found evidence of a direct effect of family structure parents’ determinant on child participation in labor. The main hypothesis is that the absence of the father paired with exposure to family stress arising from marital dissolution is an indicator toward child labor.

This study implies that in order to combat child labor effectively, it is important to understand deeply its several causes and consider ruptures in family structure, such as divorce, as one of these factors. In addition, location and family’s characteristics also play a role on the decision of child labor. For instance, boys living at metropolis areas have less chance to work. Family’s head education and non-work income affects positively the child well-being by reducing the probability of child labor. On the other hand, the number of siblings increases the chance of child labor. Finally, the results of this study suggest policies to raise awareness among parents about the negative effects of child labor on children during both childhood and adulthood, and that social policies need to act beyond legislation and enforcement, but including family mobilization.

This paper estimates the impact of family structure on child labor using an empirical approach to deal with the endogeneity problem of the treatment.

We study the informational content of factor structures in discrete triangular systems. Factor structures have been employed in a variety of settings in cross-sectional and panel data models, and in this chapter we formally quantify their identifying power in a bivariate system often employed in the treatment effects literature. Our main findings are that imposing a factor structure yields point-identification of parameters of interest, such as the coefficient associated with the endogenous regressor in the outcome equation, under weaker assumptions than usually required in these models. In particular, we show that a “non-standard” exclusion restriction that requires an explanatory variable in the outcome equation to be excluded from the treatment equation is no longer necessary for identification, even in cases where all of the regressors from the outcome equation are discrete. We also establish identification of the coefficient of the endogenous regressor in models with more general factor structures, in situations where one has access to at least two continuous measurements of the common factor.

Capitalizing on a unique measure of takeover susceptibility principally based on the staggered implementation of state laws, this study aims to explore the takeover market’s effect on managerial ownership. The market for corporate control, often known as the takeover market, is an important external governance mechanism, whereas managerial ownership is a vital internal governance instrument. Managerial ownership brings into convergence the interests of shareholders and managers. The originality of this study arises from the usage of state-level anti-takeover legislations as a measure which is beyond the control of firms and plausibly exogenous to firm-specific characteristics.

In addition to the standard regression analysis, this study also executes a variety of robustness checks to minimize endogeneity, i.e. propensity score matching, entropy balancing, instrumental–variable analysis, Lewbel’s (2012) heteroscedastic identification and Oster’s (2019) testing for coefficient stability.

Based on a large sample of US firms, the results show that more hostile takeover threats bring about significantly lower managerial ownership. The results reinforce the prediction of the substitution hypothesis. The disciplinary function of the takeover market reduces agency conflict to the point where managerial ownership is less necessary as a governance mechanism. Specifically, a rise in takeover susceptibility by one standard deviation diminishes managerial ownership by 7.22%.

`To the best of the authors’ knowledge, this study is the first to shed light on the impact of the takeover market on managerial ownership using a novel measure mainly based on the staggered adoption of state laws, which are plausibly exogenous to individual firms’ characteristics. Consequently, unlike prior research, this study is more likely to indicate a causal effect, rather than merely a correlation.

We develop and estimate an empirical collective model with endogenous marriage formation, participation, and family labor supply. Intra-household transfers arise endogenously as the transfers that clear the marriage market. The intra-household allocation can be recovered from observations on marriage decisions. Introducing the marriage market in the collective model allows us to independently estimate transfers from labor supplies and from marriage decisions. We estimate a semiparametric version of our model using 1980, 1990, and 2000 US Census data. Estimates of the model using marriage data are much more consistent with the theoretical predictions than estimates derived from labor supply.