The dd package provides two effective helper functions to estimate and
visualize a difference-in-differences model (DD) with leads and lags.
Studies using such specifications are often referred to as (panel) event
studies in economics.
The function code_eventtime generates a time-to-treatment factor
variable which is broken out into dummies capturing leads and lags when
added to a typical model fitting function in R (such as lm or felm).
The function tidy_eventcoef uses the tidy function family from the
broom package to extract the
coefficients for the leads and lags and prepares a data frame suitable
to pass to ggplot for plotting.
To install the dd package, use:
remotes::install_github("sumtxt/dd")The command eventdd provides similar
functionality in Stata but bundled with code for model estimation (which
this R package leaves up to the user for added flexibility).
Dinas, Matakos, Xefteris and Hangartner (2019) show that the exposure to the European refugee protection crisis in 2015/16 increased support for the Golden Dawn, a radical right party in Greece.
Their data is a balanced panel of vote shares from 4 elections covering 95 municipalities on islands in the Aegean Sea. Some islands close to the Turkish border experienced a sudden and drastic increase in the number of Syrian refugees before the 2016 elections.
In code below, code_eventtime adds a time-to-event variable to the
data frame which is then used in the subsequent two-way fixed effect
regression. Next, the estimates are extracted via tidy_eventcoef and
passed to ggplot.
library(ggplot2)
library(dd)
library(lfe)
data(goldendawn)
with(goldendawn, table(year, post))
#> post
#> year 0 1
#> 2012 95 0
#> 2013 95 0
#> 2015 95 0
#> 2016 83 12
goldendawn$t <- code_eventtime(
unit=muni,
time=year,
treat=post,
data=goldendawn)
m <- felm(gd ~ t | muni + year | 0 | muni,
data=goldendawn)
summary(m)
#>
#> Call:
#> felm(formula = gd ~ t | muni + year | 0 | muni, data = goldendawn)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -4.589 -0.521 0.001 0.441 7.000
#>
#> Coefficients:
#> Estimate Cluster s.e. t value Pr(>|t|)
#> t-3 0.0155 0.6120 0.03 0.98
#> t-1 -0.0319 0.5844 -0.05 0.96
#> t0 2.0733 0.4281 4.84 0.000005
#>
#> Residual standard error: 1.11 on 279 degrees of freedom
#> Multiple R-squared(full model): 0.867 Adjusted R-squared: 0.819
#> Multiple R-squared(proj model): 0.0898 Adjusted R-squared: -0.236
#> F-statistic(full model, *iid*):18.1 on 100 and 279 DF, p-value: <0.0000000000000002
#> F-statistic(proj model): 21.7 on 3 and 94 DF, p-value: 0.0000000000887
toplot <- tidy_eventcoef(
model=m,
varname="t",
conf.int=TRUE)
ggplot(toplot, aes(eventtime,estimate)) +
geom_point() + geom_line() +
geom_linerange(aes(ymin=conf.low, ymax=conf.high)) +
geom_hline(aes(yintercept=0),lty=2) +
theme_minimal() + xlab("Years to 2016 election") +
ylab("Estimate")
This example comes from Stevenson and Wolfers (2006). The authors study how no-fault unilateral divorce reforms affect female suicide in United States. The panel includes 49 states between 1964 to 1996. Reforms in the states occur at different points in time which makes this a staggered difference-in-differences design.
By default code_eventtime generates a time-to-event variable that
leads to a saturated number of lags and leads. However, users can change
this and accumulate leads and lags as demonstrated below. Users can also
choose to use another baseline (reference period).
data(divorce)
divorce$t <- code_eventtime(
unit=stfips,
time=year,
treat=post,
data=divorce,
leads=10,
lags=20,
baseline=-1)
ff <- asmrs ~ t + pcinc + asmrh + cases | stfips + year | 0 | stfips
m <- felm(ff, data=divorce)
toplot <- tidy_eventcoef(
model=m,
varname="t",
baseline=-1,
conf.int=TRUE)
ggplot(toplot, aes(eventtime,estimate)) +
geom_point() + geom_line() +
geom_linerange(aes(ymin=conf.low, ymax=conf.high)) +
geom_hline(aes(yintercept=0),lty=2) +
theme_minimal() + xlab("Years to/from reform") +
ylab("Estimate") +
scale_x_continuous(
breaks=c(-10,0,10,20),
labels=c("-10","0","10","20+"))
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