Wage MSA Standart Error placebo

Plot Function

with.overlay <- function(est, s) { attr(est,'overlay') = s; est }

estimators <- function(sdid,sc,s) {
  estimator.list = list(with.overlay(sdid, s), sc)
  names(estimator.list)=c('SDID+Fixed', 'SC')
  estimator.list
}

plot.estimators <- function(ests, alpha.multiplier) {
  p = synthdid_plot(ests, se.method='none',
                    alpha.multiplier=alpha.multiplier, facet=rep(1,length(ests)),
                    trajectory.linetype = 1, effect.curvature=-.4,
                    trajectory.alpha=1, effect.alpha=.5, diagram.alpha=1)
  suppressMessages(p + scale_alpha(range=c(0,1), guide='none'))
}

#Xlabel show
x.br<-seq.Date(as.Date("2000-01-01"),as.Date("2015-01-01"),by="5 year") 
x.lb<-format(x.br,"%Y-%m")

Import Dataset

WageMSA <- read_csv("Average Weekly Wage MSA.csv")

WageMSA<-WageMSA%>%
  mutate(Date = case_when(Qtr == 1 ~ paste0(Year, "-01-01"),
                          Qtr == 2 ~ paste0(Year, "-04-01"),
                          Qtr == 3 ~ paste0(Year, "-07-01"),
                          Qtr == 4 ~ paste0(Year, "-10-01"),
                          TRUE ~ "NA"))%>%
  mutate(Date=as.Date(Date),
         treat = ifelse(State == "LA" & #Makes Louisiana as treated State
                          Date >= as.Date("2005-08-01"), 1, 0))

l.LA<-c("Alexandria, LA MSA","Baton Rouge, LA MSA","Hammond, LA MSA",
        "Houma-Thibodaux, LA MSA","Lafayette, LA MSA","Lake Charles, LA MSA",
        "Monroe, LA MSA","Shreveport-Bossier City, LA MSA")

n.WageMSA<-WageMSA%>% #Kepp only New Orleans
  filter(!AreaName %in% l.LA,Date<="2015-01-01",Date>="1998-01-01")

# Makes Ballance Dataset Prepare Date
s.t<-seq.Date(from = as.Date("1998-01-01"),
              to = as.Date("2015-01-01"),
              by="quarter")

unique(n.WageMSA$AreaName)%>%length() #Check number of unique msa

## [1] 388

## Ballance Dataset
mmsa<-data.frame(Date=rep(s.t,388))
mmsa<-arrange(mmsa,Date)
mmsa$AreaName<-rep(unique(n.WageMSA$AreaName),69)

#Joining
n.WageMSA<-left_join(mmsa,n.WageMSA,by=c("Date","AreaName"))
nwm<-data.frame(n.WageMSA)

Total Employement

nwm$lg<-log(nwm$Total.Covered.Total..all.industries)

nwmp<-nwm%>%
  group_by(AreaName)%>%
  filter(!any(is.na(Total.Covered.Total..all.industries)))%>%
  data.frame()

pnm<-panel.matrices(nwmp,
                    unit = "AreaName",
                    time = "Date",
                    outcome = "lg",
                    treatment = "treat")

sdid.x<-synthdid_estimate(pnm$Y,pnm$N0,pnm$T0)
sc.x<-sc_estimate(pnm$Y,pnm$N0,pnm$T0)

se = sqrt(vcov(sdid.x, method='placebo',replications = 25))
se

##           [,1]
## [1,] 0.0278757

p4 <- plot.estimators(estimators(sdid = sdid.x, sc = sc.x, s = 1),
                      alpha.multiplier = c(1, 1, 1))

plot.theme <- theme(legend.position="bottom",
                    legend.direction = 'horizontal',
                    legend.key = element_blank(),
                    legend.background = element_blank(),
                    plot.title = element_text(hjust = 0.5, size = 14,face = "bold"),
                    axis.text = element_text(size = 10),axis.title = element_text(size = 9),
                    )

pl.0 <- p4 + plot.theme +
  labs(title = "",
       y = "Total Employment", x = "Date")+  
  geom_vline(xintercept = as.Date("2005-08-01"),linetype=2)+
  scale_y_continuous(labels = function(x) format(x, scientific = FALSE))+
  scale_x_continuous(breaks = x.br,
                     labels = x.lb)
 
pl.0