R Econ Visual Library

# Install and load packages ---------------
packages <- c(
  "tidyverse",
  "tidymodels",
  "haven",
  "lfe"
)

# Change to install = TRUE to install the required packages
pacman::p_load(packages, character.only = TRUE, install = FALSE)

# Load an example dataset ---------------
# Autor (2003): "Outsourcing at Will: The Contribution of Unjust Dismissal Doctrine to the Growth of Employment Outsourcing"
data <- read_dta("https://github.com/worldbank/r-econ-visual-library/raw/master/Library/Data/autor-jole-2003.dta")

admico_list <- c("admico_2", "admico_1", "admico0", "admico1", "admico2", "admico3", "mico4")
admppa_list <- c("admppa_2", "admppa_1", "admppa0", "admppa1", "admppa2", "admppa3", "mppa4")
admgfa_list <- c("admgfa_2", "admgfa_1", "admgfa0", "admgfa1", "admgfa2", "admgfa3", "mgfa4")
labels <- c("2 Years\nprior", "1 Year\nprior", "Year of\nadoption", "1 Year\nafter", "2 Years\nafter", "3 Years\nafter", "4+ Years\nafter")

data <- data %>%
  filter(year >= 79, year <= 95, state != 98) %>%
  select(state, year, annemp, lnths, all_of(admico_list), all_of(admppa_list), all_of(admgfa_list)) %>%
  mutate(
    trend = year - 78,
    state = as.factor(state), 
    year = as.factor(year)
    )

eq <- "lnths ~ log(annemp)"
for (list in list(admico_list, admppa_list, admgfa_list)){
  for (item in list){
    eq <- paste(eq, "+", item)
  }
}
eq <- paste(eq, "| state + year + state:trend | 0 | state")

res <- felm(as.formula(eq), data = data)

fig_data <- tibble(
  label = factor(labels, levels = labels),
  coef = summary(res)$coef[admico_list, "Estimate"] * 100,
  se = summary(res)$coef[admico_list, "Cluster s.e."] * 100
  )

ggplot(fig_data, aes(x = label, y = coef)) +
  geom_pointrange(aes(ymin = coef - 1.96 * se, ymax = coef + 1.96 * se)) +
  theme_classic() +
  geom_hline(yintercept = 0, alpha = 0.5, size = 0.5) +
  ylab("Log points") +
  xlab("Time relative to adoption of implied contract exception")