We reconsider the effect of very low birth weight classification on infant mortality. We demonstrate that the estimates are highly sensitive to the exclusion of observations in the immediate vicinity of the 1,500-g threshold, weakening the confidence in the results originally reported in Almond, Doyle, Kowalski, and Williams (2010).
This study uses Monte Carlo simulations to demonstrate that regression-discontinuity designs arrive at biased estimates when attributes related to outcomes predict heaping in the running variable. After showing that our usual diagnostics are poorly suited to identifying this type of problem, we provide alternatives. We also demonstrate how the magnitude and direction of the bias varies with bandwidth choice and the location of the data heaps relative to the treatment threshold. Finally, we discuss approaches to correcting for this type of problem before considering these issues in several non-simulated environments.
This paper is the first to explore the extent to which the health effects of job displacement extend to the children of displaced workers. Using detailed work and fertility histories from the Panel Study of Income Dynamics, estimates are identified by comparing the outcomes of children born after a displacement to the outcomes of those born before. This analysis reveals that husbands' job losses have significant negative effects on infant health. They reduce birth weights by approximately four and a half percent with suggestive evidence that the effect is concentrated on the lower half of the birth weight distribution.
We use a regression discontinuity design to examine students' responses to being placed on academic probation. Consistent with a model of introducing performance standards, we find that being placed on probation at the end of the first year discourages some students from returning to school while improving the GPAs of those who do. We find heterogeneous responses across prior academic performance, gender, and native language, and discuss these results within the context of the model. We also find negative effects on graduation rates, particularly for students with the highest high school grades. (JEL I23, J16)
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.