test.R

########### TEST ###########

library(stargazer)


source("TANF_clean.r")

csp <- read_csv("Input Data/correlatesofstatepolicyprojectv2_1.csv") %>%
  select(state, year, ranney4_control) %>% 
  filter(year > 1996) %>% 
  mutate(year = year +1) %>% 
  filter(year < 2014) %>% 
  mutate(year = as.character(year)) %>% 
  filter(state != "District of Columbia") %>% 
  arrange(year) %>% 
  mutate(state_id = rep(state.abb, 16))

test <- read.dta13("Input Data/caughey_warshaw_summary.dta") %>% 
  as.tibble() %>% 
  mutate(year = as.numeric(as.character(year))) %>% 
  mutate(stpo = as.character(stpo)) %>% 
  rename(state_id = stpo) %>% 
  filter(year > 1996) %>% 
  mutate(year = year + 1) %>% #IV t - 1 == DV t
  filter(year < 2014) %>% 
  select(1:8) %>% 
  mutate(year = as.character(year))

avg_props_pdata <- avg_props_pdata %>% 
  filter(STATE != "DIST.OF COLUMBIA") %>%
  ungroup() %>% 
  arrange(year) %>% 
  mutate(state_id = rep(state.abb, 16)) %>% 
  mutate(service = cc + pregnancy + shortben + tax + work) %>% 
  mutate(ba_dif = ba - service) %>% 
  arrange(STATE) %>% 
  group_by(STATE) %>% 
  mutate(ba_dif_before = dplyr::lag(ba_dif, n = 1)) %>% 
  mutate(ba_dif_before2 = dplyr::lag(ba_dif, n = 2)) %>% 
  ungroup()

avg_props_pdata <- left_join(test, avg_props_pdata, by = c("year", "state_id"))
anti_join(test, avg_props_pdata, by = c("year", "state_id"))

avg_props_pdata <- left_join(csp, avg_props_pdata, by = c("year", "state_id"))
anti_join(csp, avg_props_pdata, by = c("year", "state_id"))


p1 <- felm(ba_dif ~ african_americans + hispanics + ranney4_control + unemployment +
             pcpi_regional + fiscal_stability + caseload + wpr
           | STATE + year | 0 | STATE, 
           data = avg_props_pdata)

stargazer(p1,
          title = "Regression Output",
          dep.var.labels = "Basic Assistance minus WORSSI",
          covariate.labels = c("Percent of caseload that is African American", 
                                "Percent of caseload that is Hispanic",
                                "Policy liberalism",
                                "Unemployment rate", 
                                "Per capita income (in thousands)",
                                "Fiscal balance as a percent of spending",
                                "Percent change in caseload",
                                "Work participation rate",
                                "Difference in prior year"),
          header = FALSE,
          star.cutoffs = c(.05),
          notes = "*p < 0.05; SEs clustered by state",
          notes.append = FALSE,
          notes.align = "r",
          model.numbers = FALSE,
          initial.zero = FALSE,
          column.sep.width = "1pt",
          font.size = "small",
          type = "latex",
          out = "Figures and Tables/Table1_test.html")

library(plm)
pwartest(ba_dif ~ african_americans + hispanics + ranney4_control + unemployment +
           pcpi_regional + fiscal_stability + caseload + wpr + 
           ba_dif_before + factor(avg_props_pdata$state),
         data = avg_props_pdata)
detach("package:plm", unload=TRUE)












#p3 <- felm(ba_dif ~ policyeconlib_est
#           | STATE + year | 0 | STATE, 
#           data = avg_props_pdata)
#
#p4 <- felm(ba_dif ~ policyeconlib_est + african_americans
#           | STATE + year | 0 | STATE, 
#           data = avg_props_pdata)
#
#p5 <- felm(ba_dif ~ policyeconlib_est + african_americans + policyeconlib_est*african_americans
#           | STATE + year | 0 | STATE, 
#           data = avg_props_pdata)
#
#p6 <- felm(ba_dif ~ ranney4_control
#           | STATE + year | 0 | STATE, 
#           data = avg_props_pdata)
#
#p7 <- felm(ba_dif ~ ranney4_control + african_americans
#           | STATE + year | 0 | STATE, 
#           data = avg_props_pdata)
#
#p8 <- felm(ba_dif ~ ranney4_control + african_americans + ranney4_control*african_americans
#           | STATE + year | 0 | STATE, 
#           data = avg_props_pdata)
#
#stargazer(p3, p4, p5, p6, p7, p8, 
#          title = "Regression Output",
#          column.labels = NULL,
#          dep.var.labels = "Basic Assistance minus WORSSI",
#          add.lines = list(c("Time FEs", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes"),
#                           c("State FEs", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes")),
#          header = FALSE,
#          star.cutoffs = c(.05),
#          notes = "*p < 0.05; SEs clustered by state",
#          notes.append = FALSE,
#          notes.align = "r",
#          model.numbers = FALSE,
#          initial.zero = FALSE,
#          column.sep.width = "1pt",
#          font.size = "small",
#          type = "latex",
#          out = "Figures and Tables/Table2_test.html")
#
#
#
#
#
#
#
#
#boom <- spread(ann_means_vis, category, value) %>% 
#  mutate(dif = ba - service)
#
#boom %>% 
#  ggplot(aes(year, dif, group = 1)) +
#  geom_line() +
#  labs(title = "Basic assistance minus WORSSI",
#       subtitle = "FY 1998 - 2013",
#       x = NULL, 
#       y = NULL) +
#  scale_y_continuous(labels = scales::percent_format(1),
#                     limits = c(-.20,.40),
#                     breaks = seq(-.20, .40, .20),
#                     expand = c(0,0)) +
#  scale_x_discrete(breaks = c("1998", "2003", "2008", "2013"),
#                   labels = c("1998", "2003", "2008", "2013"))