Previous research has estimated that food insecure children are more likely to suffer from a wide array of negative health outcomes than food secure children, leading many to claim that alleviating food insecurity would lead to better health outcomes. Identifying the causal impacts is...

Treatment analyses based on average outcomes do not immediately generalize to the case of ordered responses because the expectation of an ordinally measured variable does not exist. The proposed remedy in this paper is a shift in focus to distributional effects. Assuming a threshold crossing...

Instrumental variables can be used to make inferences about causal effects in the presence of unmeasured confounding. For a model in which the instrument, intermediate/treatment, and outcome variables are all binary, Balke and Pearl (1997, Journal of the American Statistical Association 92:...

This paper investigates Monotone Instrumental Variables (MIV) and their ability to aid in identifying treatment effects when the treatment is binary in a nonparametric bounding framework. I show that an MIV can only aid in identification beyond that of a Monotone Treatment Selection assumption...

This paper analyzes estimators based on the instrumental variable quantile regression (IVQR) model (Chernozhukov and Hansen, 2004, 2005, 2006) under the local quantile treatment effects (LQTE) framework (Abadie et al., 2002). I show that the quantile treatment effect (QTE) estimators in the IVQR...

This paper adds to the literature on the crowding out effect of tobacco expenditure in two ways. Firstly, the paper uses expenditure data from a low income sub-Saharan African country, Zambia, where most households are poor. Secondly, unlike previous studies, we relax the exclusion restriction...