title	author	date	output
Lecture 14 Zimmerman 2014	Nick Huntington-Klein	`r Sys.Date()`	revealjs::revealjs_presentation theme transition self_contained smart fig_caption reveal_options solarized slide true true true slideNumber true

```{r setup, include=FALSE} knitr::opts_chunk$set(echo = FALSE, warning=FALSE, message=FALSE) library(tidyverse) theme_set(theme_gray(base_size = 15)) ``` ## Zimmerman 2014 - Zimmerman 2014 uses a cutoff in the admissions process to estimate the returns to education for academically marginal students - Today we will be discussing that paper ## Zimmerman 2014 First off: - What does he look for and what does he find? - Why might we be particularly interested in the returns to education for marginal students? - How do we know that RDD gives us the return for *those* students? - What kind of RDD is this? - How can we characterize his results and any strengths/weaknesses? ## Zimmerman 2014 - Why does he check for *manipulation of the running variable* in Section V.A? - Why might this be important? - What does manipulation mean and why might it mess up an RDD result? - How does he do this check? ## Running Variable Notes - We can do these sorts of tests ourselves for manipulation using the `rddensity()` and `rdplotdensity()` functions in the **rddensity** package - Other potential issues with running variables: *granularity* - Why might it be difficult to do an RDD if the running variable is very *coarsely defined*? ## Zimmerman 2014 - What other tests does he do? - What does Figure 3 show us? - How can we get the results from the graphs and from the regression tables? - Is there anything we might want to do differently in this study?