title	author	date	output
Lecture 19b Methods Practice	Nick Huntington-Klein	March 20, 2019	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) library(dagitty) library(ggdag) library(gganimate) library(ggthemes) library(Cairo) theme_set(theme_gray(base_size = 15)) ``` ## Recap - We've been going over ways in which we can isolate causal effects - We can select similar control groups using matching or controlling (what economists call "selection on observables") - We can use a group at a different time as its own control with fixed effects - Or, "natural experiments": - When a treatment is applied at a particular time, we can select a reasonable control to account for the effects of time using difference-in-difference - When the treatment is assigned according to a cutoff in a running variable, we can use regression discontinuity ## Today - We're going to be trying to *apply* these methods - Given a real-world causal statement, how can we go about selecting a method? - We can follow the steps we've been taking all along! ## Our Approach 1. Consider the problem 2. Think about what we think the *data-generating process* is 3. Draw a diagram 4. Figure out the method (we may have to control for some things for the usable diagram to emerge!) 5. Actually implement the method ## Think about the Data-Generating Process - Our example from last time was corporate social responsibility - We think that CSR might affect stock prices, and we know that CSR resolutions are taken up by winning vote - Of course, the vote share might be related to a million different things about the company, or about the company at that time ## Draw a Diagram - `comp` is "company", `c.t` is company at a particular time ```{r, dev='CairoPNG', echo=FALSE, fig.width=6,fig.height=5} dag % tidy_dagitty() ggdag(dag,node_size=20) ``` ## Figure out a Method - What back doors do we have? (`CSR price`) - Can we measure enough variables to control/match to close them all? - Are they all individual-level or time-level variables so that we can do a diff-in-diff with panel data? - Do we have a running variable and assign the treatment with a cutoff so we can do regression discontinuity? ## Implement the Method ```{r, echo=TRUE, eval=FALSE} #I don't actually have this data but we can pretend data(CSRdata) bandwidth % #Limit to just the area around the cutoff filter(abs(vote - cutoff) < bandwidth) %>% #Then, compare winning votes to losing votes mutate(win = vote > cutoff) %>% group_by(win) %>% summarize(price = mean(price)) ``` ## Let's do More - Let's focus on the topic of real importance: - How can we build a research design based on our causal question of interest and what we know about the world? - I have five questions and topics, let's work together to build diagrams and pick a research design - Don't look ahead in the slides! ## Fishery Sustainability - We don't want to overfish the oceans! However, common economic logic dictates that fish stocks are a "common good" likely to be overharvested if without restrictions - One way of restricting fishing is to implement a transferable quota (ITQ) - a "cap and trade" basically - This limits the allowable catch, and by allowing people to trade their allotment, ensures that the most efficient boats do the catching - But does it work? Does `ITQ` affect next year's fishing `stock`? ## Fishery Sustainability Draw the diagram! To consider: - Some countries implement ITQs, others don't. We can observe countries both before and after the ITQ - Certain characteristics of the country, like size, coastline, politics, etc., might be related to the decision to implement - ITQ doesn't affect `stock` directly, but by reducing this year's `catch` - The global economy changes over time, and affects fish demand and thus `catch` ## Fishery Sustainability `coun` = country characteristics, `econ` = world economy ```{r, dev='CairoPNG', echo=FALSE, fig.width=8,fig.height=5 } dag % tidy_dagitty() ggdag(dag,node_size=20) ``` ## Fishery Sustainability - This is a clear case for applying difference-in-differences! - Do we need to worry about that `econ` back door? - Nope! Note that all back doors through `econ` either go through `time` (which we control for naturally without DID) or through `ITQ -> catch % tidy_dagitty() ggdag(dag,node_size=20) ``` ## Financial Reports - This is a case for a regression discontinuity with `time` as the running variable - When an RDD uses `time` as a running variable it's called an "interrupted time series" - Generally not considered quite as trustworthy as other RDDs, since it's more likely that other stuff changes across the before/after barrier than across the below cutoff/above cutoff barrier ## Medicare and Retirement - Does having health insurance encourage you to take more risks? Like quitting your job? - Many people in the US get health insurance through their employer and have no realistic way of paying for it otherwise - At age 65 you become eligible for Medicare - Does Medicare make people quit their jobs? ## Medicare and Retirement Draw a diagram! To consider: - You become eligible for `med`icare at exactly the day you `turn65`. - Your overall age, and your decision to `quit`, may be related in different ways to many things like `race`, `gen`der, before-age-65 `health`, and `inc`ome. Some of these things may also affect each other - Your `inc`ome may also determine whether or not you choose to use Medicare (or go with something private instead) ## Medicare and Retirement ```{r, dev='CairoPNG', echo=FALSE, fig.width=8,fig.height=6} dag % tidy_dagitty() ggdag(dag,node_size=20) ``` ## Medicare and Retirement - Regression discontinuity again, this time with age as running variable - Lots of back doors! But no need for controls, the RDD isolates just our path of interest here - As long as the treatment is "turning 65" - if the treatment is "receives Medicare" we still need to control for income - why? - Note: how can age "cause" race or gender? Why, differential mortality rates of course! ## Monetary Policy - A standard economics result is that monetary policy - putting more money into the economy, which the Federal Reserve does by buying treasury bonds ("monetary policy") - leads to more inflation - Of course, there might be other reasons why we see monetary policy linked to inflation - Perhaps, for example, the kinds of things that make the Fed respond by buying bonds happen to lead to inflation on their own? ## Monetary Policy Draw a diagram! To consider: - Buying/selling bonds (monetary policy, `MP`) changes the amount of `money` in the economy - Inflation comes from the amount of money there is relative to the amount of *stuff* there is, which comes from economic `prod`uctivity and `unemp`loyment - Money in the economy is also affected by the amount of money tied up in `inv`estments - And your `coun`try characteristics affect everything too! ## Monetary Policy ```{r, dev='CairoPNG', echo=FALSE, fig.width=8,fig.height=6} dag % tidy_dagitty() ggdag(dag,node_size=20) ``` ## Monetary Policy - For this one we need lots of controls! - We have back doors through `unemp`, `inv`, `prod`, and `coun` - So we control for all of them with controlling or matching. For `coun` we need fixed effects. ## The Minimum Wage - A classic causal question is "what is the effect of the minimum wage on employment?" - Principles of econ classes point out that raising the minimum wage (like raising the price on anything) should reduce the number of people employed - However there are other wrinkles: what if people take that money and spend it, improving the economy and increasing employment that way- - Or what if the labor market isn't competitive, meaning that increasing wages might actually encourage more employment? ## The Minimum Wage Draw a diagram! To consider: - In 1992 (i.e. in a certain `year`), New Jersey increased their `MW` from \$4.25 to \$5.05 - Neighboring Pennsylvania didn't. So the `MW` differs by `state` - We can look at fast food restaurants (most likely to be affected) just around the border - It's possible that the two states had different `trends` in terms of how their labor markets were changing - The national `econ`omy might have also had an effect on `unemp`loyment - What is the effect of the `MW` increase on `unemp`loyment? ## The Minimum Wage ```{r, dev='CairoPNG', echo=FALSE, fig.width=8,fig.height=6} dag % tidy_dagitty() ggdag(dag,node_size=20) ``` ## The Minimum Wage - A good spot for difference-in-differences! - We need to control for `trends` too - DID won't handle that on its own as it has to do with changes in the gap BETWEEN the two states over time. - No need to control for `econ` - the DID adjustment for `year` handles that back door