Total Quarterly Wage

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

Wage<-read.csv("Total Quarterly Wages (State).csv")

# Get Date from Quarter,since it can direcly used for analysis
Wage <- Wage%>%
  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 == "Louisiana " & #Makes Louisiana as treated State
                        Date >= as.Date("2005-08-01"), 1, 0))

wg<-Wage%>%filter(Date>="2000-01-01",Date<="2015-01-01")
wg<-data.frame(wg)

Total Employement

wg$lg<-log(wg$Total.Covered.Total..all.industries) # makes a log

#Remove State if NA Available
wgp<-wg%>%
  group_by(State)%>%
  filter(!any(is.na(Total.Covered.Total..all.industries)))%>%
  data.frame()

pnm<-panel.matrices(panel = wgp,
                    unit = "State",      #ID Variable    
                    time = "Date",       #Time Period   
                    outcome = "lg",      #Dependent Variable   
                    treatment = "treat") #Treated Dummy 

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

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))+ ## Remove E from Hight Number
  scale_x_continuous(breaks = x.br,
                     labels = x.lb)
 
pl.0

Good Producing

wg$lg<-(wg$Private.Goods.producing)

#Remove State if NA Available
wgp<-wg%>%
  group_by(State)%>%
  filter(!any(is.na(Private.Goods.producing)))%>%
  data.frame()

pnm<-panel.matrices(panel = wgp,
                    unit = "State",      #ID Variable
                    time = "Date",       #Time Period 
                   outcome = "lg",      #Dependent Variable 
                    treatment = "treat") #Treated Dummy

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

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.1 <- p4 + plot.theme +
  labs(title = "",
       y = "Good Producing", 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.1

Natural Resources and Mining

wg$lg<-(wg$Private.Natural.resources.and.mining)

#Remove State if NA Available
wgp<-wg%>%
  group_by(State)%>%
  filter(!any(is.na(Private.Natural.resources.and.mining)))%>%
  data.frame()

pnm<-panel.matrices(panel = wgp,
                    unit = "State",      #ID Variable
                    time = "Date",       #Time Period 
                   outcome = "lg",      #Dependent Variable 
                    treatment = "treat") #Treated Dummy

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

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.2 <- p4 + plot.theme +
  labs(title = "",
       y = "Natural Resources and Mining", 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.2

Construction

wg$lg<-(wg$Private.Construction)

#Remove State if NA Available
wgp<-wg%>%
  group_by(State)%>%
  filter(!any(is.na(Private.Construction)))%>%
  data.frame()

pnm<-panel.matrices(panel = wgp,
                    unit = "State",      #ID Variable
                    time = "Date",       #Time Period 
                   outcome = "lg",      #Dependent Variable 
                    treatment = "treat") #Treated Dummy

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

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.3 <- p4 + plot.theme +
  labs(title = "",
       y = "Construction", 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.3

Manufacturing

wg$lg<-(wg$Private.Manufacturing)

#Remove State if NA Available
wgp<-wg%>%
  group_by(State)%>%
  filter(!any(is.na(Private.Manufacturing)))%>%
  data.frame()

pnm<-panel.matrices(panel = wgp,
                    unit = "State",      #ID Variable
                    time = "Date",       #Time Period 
                   outcome = "lg",      #Dependent Variable 
                    treatment = "treat") #Treated Dummy

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

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.4 <- p4 + plot.theme +
  labs(title = "",
       y = "Manufacturing", 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.4

Service providing

wg$lg<-(wg$Private.Service.providing)

#Remove State if NA Available
wgp<-wg%>%
  group_by(State)%>%
  filter(!any(is.na(Private.Service.providing)))%>%
  data.frame()

pnm<-panel.matrices(panel = wgp,
                    unit = "State",      #ID Variable
                    time = "Date",       #Time Period 
                   outcome = "lg",      #Dependent Variable 
                    treatment = "treat") #Treated Dummy

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

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.5 <- p4 + plot.theme +
  labs(title = "",
       y = "Service providing", 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.5

Trade transportation and utilities

wg$lg<-(wg$Private.Trade..transportation..and.utilities)

#Remove State if NA Available
wgp<-wg%>%
  group_by(State)%>%
  filter(!any(is.na(Private.Trade..transportation..and.utilities)))%>%
  data.frame()

pnm<-panel.matrices(panel = wgp,
                    unit = "State",      #ID Variable
                    time = "Date",       #Time Period 
                   outcome = "lg",      #Dependent Variable 
                    treatment = "treat") #Treated Dummy

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

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.6 <- p4 + plot.theme +
  labs(title = "",
       y = "Trade transportation and utilities", 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.6

Financial activities

wg$lg<-(wg$Private.Financial.activities)

#Remove State if NA Available
wgp<-wg%>%
  group_by(State)%>%
  filter(!any(is.na(Private.Financial.activities)))%>%
  data.frame()

pnm<-panel.matrices(panel = wgp,
                    unit = "State",      #ID Variable
                    time = "Date",       #Time Period 
                   outcome = "lg",      #Dependent Variable 
                    treatment = "treat") #Treated Dummy

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

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.7 <- p4 + plot.theme +
  labs(title = "",
       y = "Financial activities", 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.7

Professional and business services

wg$lg<-(wg$Private.Professional.and.business.services)

#Remove State if NA Available
wgp<-wg%>%
  group_by(State)%>%
  filter(!any(is.na(Private.Professional.and.business.services)))%>%
  data.frame()

pnm<-panel.matrices(panel = wgp,
                    unit = "State",      #ID Variable
                    time = "Date",       #Time Period 
                   outcome = "lg",      #Dependent Variable 
                    treatment = "treat") #Treated Dummy

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

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.8 <- p4 + plot.theme +
  labs(title = "",
       y = "Professional and business services", 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.8

Education and health services

wg$lg<-(wg$Private.Education.and.health.services)

#Remove State if NA Available
wgp<-wg%>%
  group_by(State)%>%
  filter(!any(is.na(Private.Education.and.health.services)))%>%
  data.frame()

pnm<-panel.matrices(panel = wgp,
                    unit = "State",      #ID Variable
                    time = "Date",       #Time Period 
                   outcome = "lg",      #Dependent Variable 
                    treatment = "treat") #Treated Dummy

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

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.9 <- p4 + plot.theme +
  labs(title = "",
       y = "Education and health services", 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.9

Leisure and hospitality

wg$lg<-(wg$Private.Leisure.and.hospitality)

#Remove State if NA Available
wgp<-wg%>%
  group_by(State)%>%
  filter(!any(is.na(Private.Leisure.and.hospitality)))%>%
  data.frame()

pnm<-panel.matrices(panel = wgp,
                    unit = "State",      #ID Variable
                    time = "Date",       #Time Period 
                   outcome = "lg",      #Dependent Variable 
                    treatment = "treat") #Treated Dummy

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

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.10 <- p4 + plot.theme +
  labs(title = "",
       y = "Leisure and hospitality", 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.10

Local Government

wg$lg<-(wg$Local.Government.Total..all.industries)

#Remove State if NA Available
wgp<-wg%>%
  group_by(State)%>%
  filter(!any(is.na(Local.Government.Total..all.industries)))%>%
  data.frame()

pnm<-panel.matrices(panel = wgp,
                    unit = "State",      #ID Variable
                    time = "Date",       #Time Period 
                   outcome = "lg",      #Dependent Variable 
                    treatment = "treat") #Treated Dummy

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

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.11 <- p4 + plot.theme +
  labs(title = "",
       y = "Local Government", 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.11

Arrange Figure

# extract legend Name
lge <- lapply(list(pl.0),
    function(p) {
  # Extract the legend from the plot
  g_legend <- function(p) {
    tmp <- ggplotGrob(p)
    leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
    legend <- tmp$grobs[[leg]]
    return(legend)
  }
  g_legend(p)
})

#Arrange The Plot
main_plot <- grid.arrange(
    pl.0 + theme(legend.position = "none") + xlab(""),
    pl.1 + theme(legend.position = "none") + xlab(""),
    pl.2 + theme(legend.position = "none") + xlab(""),
    pl.3 + theme(legend.position = "none") + xlab(""),
    pl.4 + theme(legend.position = "none") + xlab(""),
    pl.5 + theme(legend.position = "none") + xlab(""),
    pl.6 + theme(legend.position = "none") + xlab(""),
    pl.7 + theme(legend.position = "none") + xlab(""),
    pl.8 + theme(legend.position = "none") + xlab(""),
    pl.9 + theme(legend.position = "none") + xlab(""),
    pl.10 + theme(legend.position = "none") + xlab(""),
    pl.11 + theme(legend.position = "none") + xlab(""), nrow = 4,
    left = textGrob("Total Quarterly Wage", rot = 90,
        gp = gpar(fontface = "bold", fontsize = 18)),
    bottom = textGrob("Date", gp = gpar(fontface = "bold", fontsize = 18)))

grid.arrange(main_plot, lge[[1]], nrow = 2, heights = c(20, 1))

Total Quarterly Wage

2023-06-24

Plot Function

Import Dataset

Total Employement

Good Producing

Natural Resources and Mining

Construction

Manufacturing

Service providing

Trade transportation and utilities

Financial activities

Professional and business services

Education and health services

Leisure and hospitality

Local Government

Arrange Figure