Difference-in-differences in R

data
Author

Dan Hicks

Published

October 10, 2018

This post recreates this post with proper formatting, syntax highlighting, etc.

library(tidyverse)
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
✔ ggplot2 3.4.0      ✔ purrr   1.0.0 
✔ tibble  3.1.8      ✔ dplyr   1.0.10
✔ tidyr   1.2.1      ✔ stringr 1.5.0 
✔ readr   2.1.3      ✔ forcats 0.5.2 
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(skimr)

First we’ll load the data (download if necessary). I just started playing around with skimr::skim() to generate nice summaries. (digits doesn’t seem to do anything, though?)

data_file = '../_data/did_data.dta'
if (!file.exists(data_file)) {
    download.file(url = 'https://drive.google.com/uc?authuser=0&id=0B0iAUHM7ljQ1cUZvRWxjUmpfVXM&export=download', 
                  destfile = data_file)
}

dataf = haven::read_dta(data_file)
skim(dataf, work, year, children)
Data summary
Name dataf
Number of rows 13746
Number of columns 11
_______________________
Column type frequency:
numeric 3
________________________
Group variables None

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
work 0 1 0.51 0.50 0 0 1 1 1 ▇▁▁▁▇
year 0 1 1993.35 1.70 1991 1992 1993 1995 1996 ▇▃▃▃▃
children 0 1 1.19 1.38 0 0 1 2 9 ▇▃▁▁▁

Following the old post, we need to construct dummy variables for (a) before-and-after the EITC takes effect in 1994 and (b) the treatment group (1 or more children). We’ll keep these as logicals (rather than the numerics in the old post).

dataf = dataf %>%
    mutate(post93 = year >= 1994, 
           anykids = children >= 1)

The regression equation is

\[ work = \beta_0 + \delta_0 post93 + \beta_1 anykids + \delta_1 (anykids \times post93) + \varepsilon. \]

The “difference-in-differences coefficient” is \[\delta_1\], which indicates how the effect of kids changed after the EITC went into effect. Let’s take a second to plot this.

ggplot(dataf, aes(post93, work, color = anykids)) +
    geom_jitter() +
    theme_minimal()

Okay, so the scatterplot version, like, isn’t perspicuous. How about the un-dummied variables, and just the mean?

ggplot(dataf, aes(year, work, color = anykids)) +
    stat_summary(geom = 'line') +
    geom_vline(xintercept = 1994) +
    theme_minimal()
No summary function supplied, defaulting to `mean_se()`

The parallel trends assumption looks good, at least qualitatively. (Remember this only applies prior to the intervention.) However, the parallel trends are nonlinear, which maybe is why the example uses the dummied variable.

Anyway, on to the regression. Which, hey, is a linear probability model because econometricians are funny like that.

model = lm(work ~ anykids*post93, data = dataf)
summary(model)

Call:
lm(formula = work ~ anykids * post93, data = dataf)

Residuals:
    Min      1Q  Median      3Q     Max 
-0.5755 -0.4908  0.4245  0.5092  0.5540 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)             0.575460   0.008845  65.060  < 2e-16 ***
anykidsTRUE            -0.129498   0.011676 -11.091  < 2e-16 ***
post93TRUE             -0.002074   0.012931  -0.160  0.87261    
anykidsTRUE:post93TRUE  0.046873   0.017158   2.732  0.00631 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.4967 on 13742 degrees of freedom
Multiple R-squared:  0.0126,    Adjusted R-squared:  0.01238 
F-statistic: 58.45 on 3 and 13742 DF,  p-value: < 2.2e-16

This indicates that the EITC increased work (workforce participation? whether someone was employed?) by 5% among families with at least 1 child. In the second plot, the blue line goes from about 45% prior to 1994 to about 50% afterwards.