Percapita SDID Estimate

Function Plot

NAME <- read_excel("V_NAME_All.xlsx")

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

estimators = function(sdid,sc,s) {
  estimator.list = list(with.overlay(sdid, s), sc, sdid)
  names(estimator.list)=c('SDID+fixed effect', 'synth. control', 'synth. diff-in-diff')
  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=.5, effect.alpha=.5, diagram.alpha=1)
  suppressMessages(p + scale_alpha(range=c(0,1), guide='none'))
}

AER

PAER <- read_excel("PAER.xlsx")

## Remove NA
dumy_date=seq.Date(from = as.Date("1998-01-01"),to = as.Date("2020-01-01"),by = "quarter")
Fix_df<-data.frame(STATE=rep(unique(countypop$abbr),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(countypop$abbr)))

AER<-left_join(Fix_df,PAER,by=c("STATE","DATE"))
AER$DATE<-as.Date(AER$DATE)
AER<-AER%>%filter(DATE>="1998-01-01",DATE<="2008-01-01")
AER<-AER%>%select(STATE,PAER,DATE)
AER<-AER%>% group_by(STATE) %>%  filter(!any(is.na(PAER)))


##PLOTING
AER$Treat<-ifelse(AER$DATE>="2005-08-10"&
                  AER$DATE<="2022-01-01"&
                  AER$STATE=="LA",1,0)
AER$STATE<-factor(AER$STATE)
AER<-as.data.frame(AER)
AER$AER_L<-log(AER$PAER)


setup<-panel.matrices(AER,unit = 1,time = 3,outcome = 5,treatment = 4)

sdid.AER = synthdid_estimate(setup$Y, setup$N0, setup$T0)
sc.AER = sc_estimate(setup$Y, setup$N0, setup$T0)

p4 = plot.estimators(estimators(sdid = sdid.AER,sc = sc.AER,s = 1),   alpha.multiplier=c(1, 1,1))

plot.theme = theme(legend.position=c(.2,.25), legend.direction='horizontal',
                   legend.key=element_blank(), legend.background=element_blank(), 
                   plot.title = element_text(hjust = 0.5,size = 9),axis.text = element_text(size=4),axis.title = element_text(size=7))

pl.AER<- p4 + plot.theme+#xlab("Year") + 
 ylab("Arts, Entertainment,\n and Recreation")

pl.AER

AF

PAF <- read_excel("PAF.xlsx")

## Remove NA
dumy_date=seq.Date(from = as.Date("1998-01-01"),to = as.Date("2020-01-01"),by = "quarter")
Fix_df<-data.frame(STATE=rep(unique(countypop$abbr),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(countypop$abbr)))

AF<-left_join(Fix_df,PAF,by=c("STATE","DATE"))
AF$DATE<-as.Date(AF$DATE)
AF<-AF%>%filter(DATE>="1998-01-01",DATE<="2008-01-01")
AF<-AF%>%select(STATE,PAF,DATE)
AF<-AF%>% group_by(STATE) %>%  filter(!any(is.na(PAF)))

##PLOTING
AF$Treat<-ifelse(AF$DATE>="2005-08-10"&
                  AF$DATE<="2022-01-01"&
                  AF$STATE=="LA",1,0)
AF$STATE<-factor(AF$STATE)
AF<-as.data.frame(AF)
AF$PAF_L<-log(AF$PAF)

setup<-panel.matrices(AF,unit = 1,time = 3,outcome = 5,treatment = 4)

sdid.AF = synthdid_estimate(setup$Y, setup$N0, setup$T0)
sc.AF = sc_estimate(setup$Y, setup$N0, setup$T0)

p4 = plot.estimators(estimators(sdid = sdid.AF,sc = sc.AF,s = 1),   alpha.multiplier=c(1, 1,1))

plot.theme = theme(legend.position=c(.2,.25), legend.direction='horizontal',
                   legend.key=element_blank(), legend.background=element_blank(), 
                   plot.title = element_text(hjust = 0.5,size = 9),axis.text = element_text(size=4),axis.title = element_text(size=7))

pl.AF<- p4 + plot.theme+#xlab("Year") + 
  ylab("Accommodation and \n Food Services")

pl.AF

ASW

PASW <- read_excel("PASW.xlsx")

## Remove NA
dumy_date=seq.Date(from = as.Date("1998-01-01"),to = as.Date("2020-01-01"),by = "quarter")
Fix_df<-data.frame(STATE=rep(unique(countypop$abbr),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(countypop$abbr)))

ASW<-left_join(Fix_df,PASW,by=c("STATE","DATE"))
ASW$DATE<-as.Date(ASW$DATE)
ASW<-ASW%>%filter(DATE>="1998-01-01",DATE<="2008-01-01")
ASW<-ASW%>%select(STATE,PASW,DATE)
ASW<-ASW%>% group_by(STATE) %>%  filter(!any(is.na(PASW)))

##PLOTING
ASW$Treat<-ifelse(ASW$DATE>="2005-08-10"&
                  ASW$DATE<="2022-01-01"&
                  ASW$STATE=="LA",1,0)
ASW$STATE<-factor(ASW$STATE)
ASW<-as.data.frame(ASW)
ASW$ASW_L<-log(ASW$PASW)
setup<-panel.matrices(ASW,unit = 1,time = 3,outcome = 5,treatment = 4)

sdid.ASW = synthdid_estimate(setup$Y, setup$N0, setup$T0)
sc.ASW = sc_estimate(setup$Y, setup$N0, setup$T0)

p4 = plot.estimators(estimators(sdid = sdid.ASW,sc = sc.ASW,s = 1),   alpha.multiplier=c(1, 1,1))

plot.theme = theme(legend.position=c(.2,.25), legend.direction='horizontal',
                   legend.key=element_blank(), legend.background=element_blank(), 
                   plot.title = element_text(hjust = 0.5,size = 9),axis.text = element_text(size=4),axis.title = element_text(size=7))

pl.ASW<- p4 + plot.theme+#xlab("Year") + 
  ylab("ASW")

pl.ASW

CON

PCON <- read_excel("PCON.xlsx")

## Remove NA
dumy_date=seq.Date(from = as.Date("1998-01-01"),to = as.Date("2020-01-01"),by = "quarter")
Fix_df<-data.frame(STATE=rep(unique(countypop$abbr),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(countypop$abbr)))

CON<-left_join(Fix_df,PCON,by=c("STATE","DATE"))
CON$DATE<-as.Date(CON$DATE)
CON<-CON%>%filter(DATE>="1998-01-01",DATE<="2008-01-01")
CON<-CON%>%select(STATE,PCON,DATE)
CON<-CON%>% group_by(STATE) %>%  filter(!any(is.na(PCON)))

##PLOTING
CON$Treat<-ifelse(CON$DATE>="2005-08-10"&
                  CON$DATE<="2022-01-01"&
                  CON$STATE=="LA",1,0)
CON$STATE<-factor(CON$STATE)
CON<-as.data.frame(CON)
CON$CON_L<-log(CON$PCON)
setup<-panel.matrices(CON,unit = 1,time = 3,outcome = 5,treatment = 4)

sdid.CON = synthdid_estimate(setup$Y, setup$N0, setup$T0)
sc.CON = sc_estimate(setup$Y, setup$N0, setup$T0)

p4 = plot.estimators(estimators(sdid = sdid.CON,sc = sc.CON,s = 1),   alpha.multiplier=c(1, 1,1))

plot.theme = theme(legend.position=c(.2,.25), legend.direction='horizontal',
                   legend.key=element_blank(), legend.background=element_blank(), 
                   plot.title = element_text(hjust = 0.5,size = 9),axis.text = element_text(size=4),axis.title = element_text(size=7))

pl.CON<- p4 + plot.theme+#xlab("Year") + 
 ylab("Construction")

pl.CON

ES

PES <- read_excel("PES.xlsx")

## Remove NA
dumy_date=seq.Date(from = as.Date("1998-01-01"),to = as.Date("2020-01-01"),by = "quarter")
Fix_df<-data.frame(STATE=rep(unique(countypop$abbr),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(countypop$abbr)))

ES<-left_join(Fix_df,PES,by=c("STATE","DATE"))
ES$DATE<-as.Date(ES$DATE)
ES<-ES%>%filter(DATE>="1998-01-01",DATE<="2008-01-01")
ES<-ES%>%select(STATE,PES,DATE)
ES<-ES%>% group_by(STATE) %>%  filter(!any(is.na(PES)))

##PLOTING
ES$Treat<-ifelse(ES$DATE>="2005-08-10"&
                  ES$DATE<="2022-01-01"&
                  ES$STATE=="LA",1,0)
ES$STATE<-factor(ES$STATE)
ES<-as.data.frame(ES)

ES$ES_L<-log(ES$PES)

setup<-panel.matrices(ES,unit = 1,time = 3,outcome = 5,treatment = 4)

sdid.ES = synthdid_estimate(setup$Y, setup$N0, setup$T0)
sc.ES = sc_estimate(setup$Y, setup$N0, setup$T0)

p4 = plot.estimators(estimators(sdid = sdid.ES,sc = sc.ES,s = 1),   alpha.multiplier=c(1, 1,1))

plot.theme = theme(legend.position=c(.2,.25), legend.direction='horizontal',
                   legend.key=element_blank(), legend.background=element_blank(), 
                   plot.title = element_text(hjust = 0.5,size = 9),axis.text = element_text(size=4),axis.title = element_text(size=7))

pl.ES<- p4 + plot.theme+#xlab("Year") + 
 ylab("Educational \n Services")

pl.ES

## FI

PFI <- read_excel("PFI.xlsx")

## Remove NA
dumy_date=seq.Date(from = as.Date("1998-01-01"),to = as.Date("2020-01-01"),by = "quarter")
Fix_df<-data.frame(STATE=rep(unique(countypop$abbr),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(countypop$abbr)))

FI<-left_join(Fix_df,PFI,by=c("STATE","DATE"))
FI$DATE<-as.Date(FI$DATE)
FI<-FI%>%filter(DATE>="1998-01-01",DATE<="2008-01-01")
FI<-FI%>%select(STATE,PFI,DATE)
FI<-FI%>% group_by(STATE) %>%  filter(!any(is.na(PFI)))

##PLOTING
FI$Treat<-ifelse(FI$DATE>="2005-08-10"&
                  FI$DATE<="2022-01-01"&
                  FI$STATE=="LA",1,0)
FI$STATE<-factor(FI$STATE)
FI<-as.data.frame(FI)
FI$FI_L<-log(FI$PFI)

setup<-panel.matrices(FI,unit = 1,time = 3,outcome = 5,treatment = 4)

sdid.FI = synthdid_estimate(setup$Y, setup$N0, setup$T0)
sc.FI = sc_estimate(setup$Y, setup$N0, setup$T0)

p4 = plot.estimators(estimators(sdid = sdid.FI,sc = sc.FI,s = 1),   alpha.multiplier=c(1, 1,1))

plot.theme = theme(legend.position=c(.2,.25), legend.direction='horizontal',
                   legend.key=element_blank(), legend.background=element_blank(), 
                   plot.title = element_text(hjust = 0.5,size = 9),axis.text = element_text(size=4),axis.title = element_text(size=7))

pl.FI<- p4 + plot.theme+#xlab("Year") + 
 ylab("Financial \n Insurance")

pl.FI

## GOV

PGOV <- read_excel("PGOV.xlsx")

## Remove NA
dumy_date=seq.Date(from = as.Date("1998-01-01"),to = as.Date("2020-01-01"),by = "quarter")
Fix_df<-data.frame(STATE=rep(unique(countypop$abbr),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(countypop$abbr)))

GOV<-left_join(Fix_df,PGOV,by=c("STATE","DATE"))
GOV$DATE<-as.Date(GOV$DATE)
GOV<-GOV%>%filter(DATE>="1998-01-01",DATE<="2008-01-01")
GOV<-GOV%>%select(STATE,PGOV,DATE)
GOV<-GOV%>% group_by(STATE) %>%  filter(!any(is.na(PGOV)))

##PLOTING
GOV$Treat<-ifelse(GOV$DATE>="2005-08-10"&
                  GOV$DATE<="2022-01-01"&
                  GOV$STATE=="LA",1,0)
GOV$STATE<-factor(GOV$STATE)
GOV<-as.data.frame(GOV)

GOV$GOV_L<-log(GOV$PGOV)
setup<-panel.matrices(GOV,unit = 1,time = 3,outcome = 5,treatment = 4)

sdid.GOV = synthdid_estimate(setup$Y, setup$N0, setup$T0)
sc.GOV = sc_estimate(setup$Y, setup$N0, setup$T0)

p4 = plot.estimators(estimators(sdid = sdid.GOV,sc = sc.GOV,s = 1),   alpha.multiplier=c(1, 1,1))

plot.theme = theme(legend.position=c(.2,.25), legend.direction='horizontal',
                   legend.key=element_blank(), legend.background=element_blank(), 
                   plot.title = element_text(hjust = 0.5,size = 9),axis.text = element_text(size=4),axis.title = element_text(size=7))

pl.GOV<- p4 + plot.theme+#xlab("Year") + 
  ylab("Government")

pl.GOV

## HCS

PHCS <- read_excel("PHCS.xlsx")

## Remove NA
dumy_date=seq.Date(from = as.Date("1998-01-01"),to = as.Date("2020-01-01"),by = "quarter")
Fix_df<-data.frame(STATE=rep(unique(countypop$abbr),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(countypop$abbr)))

HCS<-left_join(Fix_df,PHCS,by=c("STATE","DATE"))
HCS$DATE<-as.Date(HCS$DATE)
HCS<-HCS%>%filter(DATE>="1998-01-01",DATE<="2008-01-01")
HCS<-HCS%>%select(STATE,PHCS,DATE)
HCS<-HCS%>% group_by(STATE) %>%  filter(!any(is.na(PHCS)))

##PLOTING
HCS$Treat<-ifelse(HCS$DATE>="2005-08-10"&
                  HCS$DATE<="2022-01-01"&
                  HCS$STATE=="LA",1,0)
HCS$STATE<-factor(HCS$STATE)
HCS<-as.data.frame(HCS)

HCS$HCS_L<-log(HCS$PHCS)
setup<-panel.matrices(HCS,unit = 1,time = 3,outcome = 5,treatment = 4)

sdid.HCS = synthdid_estimate(setup$Y, setup$N0, setup$T0)
sc.HCS = sc_estimate(setup$Y, setup$N0, setup$T0)

p4 = plot.estimators(estimators(sdid = sdid.HCS,sc = sc.HCS,s = 1),   alpha.multiplier=c(1, 1,1))

plot.theme = theme(legend.position=c(.2,.25), legend.direction='horizontal',
                   legend.key=element_blank(), legend.background=element_blank(), 
                   plot.title = element_text(hjust = 0.5,size = 9),axis.text = element_text(size=4),axis.title = element_text(size=7))

pl.HCS<- p4 + plot.theme+#xlab("Year") + 
 ylab("Health Care and \n Social Assistance")

pl.HCS

## INF

PINF <- read_excel("PINF.xlsx")

## Remove NA
dumy_date=seq.Date(from = as.Date("1998-01-01"),to = as.Date("2020-01-01"),by = "quarter")
Fix_df<-data.frame(STATE=rep(unique(countypop$abbr),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(countypop$abbr)))

INF<-left_join(Fix_df,PINF,by=c("STATE","DATE"))
INF$DATE<-as.Date(INF$DATE)
INF<-INF%>%filter(DATE>="1998-01-01",DATE<="2008-01-01")
INF<-INF%>%select(STATE,PINF,DATE)
INF<-INF%>% group_by(STATE) %>%  filter(!any(is.na(PINF)))

##PLOTING
INF$Treat<-ifelse(INF$DATE>="2005-08-10"&
                  INF$DATE<="2022-01-01"&
                  INF$STATE=="LA",1,0)
INF$STATE<-factor(INF$STATE)
INF<-as.data.frame(INF)

INF$INF_L<-log(INF$PINF)
setup<-panel.matrices(INF,unit = 1,time = 3,outcome = 5,treatment = 4)

sdid.INF = synthdid_estimate(setup$Y, setup$N0, setup$T0)
sc.INF = sc_estimate(setup$Y, setup$N0, setup$T0)

p4 = plot.estimators(estimators(sdid = sdid.INF,sc = sc.INF,s = 1),   alpha.multiplier=c(1, 1,1))

plot.theme = theme(legend.position=c(.2,.25), legend.direction='horizontal',
                   legend.key=element_blank(), legend.background=element_blank(), 
                   plot.title = element_text(hjust = 0.5,size = 9),axis.text = element_text(size=4),axis.title = element_text(size=7))

pl.INF<- p4 + plot.theme+#xlab("Year") + 
  ylab("Information")

pl.INF

MCE

PMCE <- read_excel("PMCE.xlsx")

## Remove NA
dumy_date=seq.Date(from = as.Date("1998-01-01"),to = as.Date("2020-01-01"),by = "quarter")
Fix_df<-data.frame(STATE=rep(unique(countypop$abbr),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(countypop$abbr)))

MCE<-left_join(Fix_df,PMCE,by=c("STATE","DATE"))
MCE$DATE<-as.Date(MCE$DATE)
MCE<-MCE%>%filter(DATE>="1998-01-01",DATE<="2008-01-01")
MCE<-MCE%>%select(STATE,PMCE,DATE)
MCE<-MCE%>% group_by(STATE) %>%  filter(!any(is.na(PMCE)))

##PLOTING
MCE$Treat<-ifelse(MCE$DATE>="2005-08-10"&
                  MCE$DATE<="2022-01-01"&
                  MCE$STATE=="LA",1,0)
MCE$STATE<-factor(MCE$STATE)
MCE<-as.data.frame(MCE)

MCE$MCE_L<-log(MCE$PMCE)
setup<-panel.matrices(MCE,unit = 1,time = 3,outcome = 5,treatment = 4)

sdid.MCE = synthdid_estimate(setup$Y, setup$N0, setup$T0)
sc.MCE = sc_estimate(setup$Y, setup$N0, setup$T0)

p4 = plot.estimators(estimators(sdid = sdid.MCE,sc = sc.MCE,s = 1),   alpha.multiplier=c(1, 1,1))

plot.theme = theme(legend.position=c(.2,.25), legend.direction='horizontal',
                   legend.key=element_blank(), legend.background=element_blank(), 
                   plot.title = element_text(hjust = 0.5,size = 9),axis.text = element_text(size=4),axis.title = element_text(size=7))

pl.MCE<- p4 + plot.theme+#xlab("Year") + 
  ylab("Management of Companies \n and Enterprises")

pl.MCE

## MF

PMF <- read_excel("PMF.xlsx")

## Remove NA
dumy_date=seq.Date(from = as.Date("1998-01-01"),to = as.Date("2020-01-01"),by = "quarter")
Fix_df<-data.frame(STATE=rep(unique(countypop$abbr),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(countypop$abbr)))

MF<-left_join(Fix_df,PMF,by=c("STATE","DATE"))
MF$DATE<-as.Date(MF$DATE)
MF<-MF%>%filter(DATE>="1998-01-01",DATE<="2008-01-01")
MF<-MF%>%select(STATE,PMF,DATE)
MF<-MF%>% group_by(STATE) %>%  filter(!any(is.na(PMF)))

##PLOTING
MF$Treat<-ifelse(MF$DATE>="2005-08-10"&
                  MF$DATE<="2022-01-01"&
                  MF$STATE=="LA",1,0)
MF$STATE<-factor(MF$STATE)
MF<-as.data.frame(MF)
MF$MF_L<-log(MF$PMF)
setup<-panel.matrices(MF,unit = 1,time = 3,outcome = 5,treatment = 4)

sdid.MF = synthdid_estimate(setup$Y, setup$N0, setup$T0)
sc.MF = sc_estimate(setup$Y, setup$N0, setup$T0)

p4 = plot.estimators(estimators(sdid = sdid.MF,sc = sc.MF,s = 1),   alpha.multiplier=c(1, 1,1))

plot.theme = theme(legend.position=c(.2,.25), legend.direction='horizontal',
                   legend.key=element_blank(), legend.background=element_blank(), 
                   plot.title = element_text(hjust = 0.5,size = 9),axis.text = element_text(size=4),axis.title = element_text(size=7))

pl.MF<- p4 + plot.theme+#xlab("Year") + 
  ylab("Manufacturing")

pl.MF

## OS

POS <- read_excel("POS.xlsx")

## Remove NA
dumy_date=seq.Date(from = as.Date("1998-01-01"),to = as.Date("2020-01-01"),by = "quarter")
Fix_df<-data.frame(STATE=rep(unique(countypop$abbr),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(countypop$abbr)))

OS<-left_join(Fix_df,POS,by=c("STATE","DATE"))
OS$DATE<-as.Date(OS$DATE)
OS<-OS%>%filter(DATE>="1998-01-01",DATE<="2008-01-01")
OS<-OS%>%select(STATE,POS,DATE)
OS<-OS%>% group_by(STATE) %>%  filter(!any(is.na(POS)))

##PLOTING
OS$Treat<-ifelse(OS$DATE>="2005-08-10"&
                  OS$DATE<="2022-01-01"&
                  OS$STATE=="LA",1,0)
OS$STATE<-factor(OS$STATE)
OS<-as.data.frame(OS)
OS$OS_L<-log(OS$POS)
setup<-panel.matrices(OS,unit = 1,time = 3,outcome = 5,treatment = 4)

sdid.OS = synthdid_estimate(setup$Y, setup$N0, setup$T0)
sc.OS = sc_estimate(setup$Y, setup$N0, setup$T0)

p4 = plot.estimators(estimators(sdid = sdid.OS,sc = sc.OS,s = 1),   alpha.multiplier=c(1, 1,1))

plot.theme = theme(legend.position=c(.2,.25), legend.direction='horizontal',
                   legend.key=element_blank(), legend.background=element_blank(), 
                   plot.title = element_text(hjust = 0.5,size = 9),axis.text = element_text(size=4),axis.title = element_text(size=7))

pl.OS<- p4 + plot.theme+#xlab("Year") + 
  ylab("Other \n Services")

pl.OS

## RE

PRE <- read_excel("PRE.xlsx")

## Remove NA
dumy_date=seq.Date(from = as.Date("1998-01-01"),to = as.Date("2020-01-01"),by = "quarter")
Fix_df<-data.frame(STATE=rep(unique(countypop$abbr),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(countypop$abbr)))

RE<-left_join(Fix_df,PRE,by=c("STATE","DATE"))
RE$DATE<-as.Date(RE$DATE)
RE<-RE%>%filter(DATE>="1998-01-01",DATE<="2008-01-01")
RE<-RE%>%select(STATE,PRE,DATE)
RE<-RE%>% group_by(STATE) %>%  filter(!any(is.na(PRE)))

##PLOTING
RE$Treat<-ifelse(RE$DATE>="2005-08-10"&
                  RE$DATE<="2022-01-01"&
                  RE$STATE=="LA",1,0)
RE$STATE<-factor(RE$STATE)
RE<-as.data.frame(RE)
RE$RE_L<-log(RE$PRE)
setup<-panel.matrices(RE,unit = 1,time = 3,outcome = 5,treatment = 4)

sdid.RE = synthdid_estimate(setup$Y, setup$N0, setup$T0)
sc.RE = sc_estimate(setup$Y, setup$N0, setup$T0)

p4 = plot.estimators(estimators(sdid = sdid.RE,sc = sc.RE,s = 1),   alpha.multiplier=c(1, 1,1))

plot.theme = theme(legend.position=c(.2,.25), legend.direction='horizontal',
                   legend.key=element_blank(), legend.background=element_blank(), 
                   plot.title = element_text(hjust = 0.5,size = 9),axis.text = element_text(size=4),axis.title = element_text(size=7))

pl.RE<- p4 + plot.theme+#xlab("Year") + 
  ylab("Real Estate, Rental \n and Leasing")

pl.RE

## RT

PRT <- read_excel("PRT.xlsx")

## Remove NA
dumy_date=seq.Date(from = as.Date("1998-01-01"),to = as.Date("2020-01-01"),by = "quarter")
Fix_df<-data.frame(STATE=rep(unique(countypop$abbr),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(countypop$abbr)))

RT<-left_join(Fix_df,PRT,by=c("STATE","DATE"))
RT$DATE<-as.Date(RT$DATE)
RT<-RT%>%filter(DATE>="1998-01-01",DATE<="2008-01-01")
RT<-RT%>%select(STATE,PRT,DATE)
RT<-RT%>% group_by(STATE) %>%  filter(!any(is.na(PRT)))

##PLOTING
RT$Treat<-ifelse(RT$DATE>="2005-08-10"&
                  RT$DATE<="2022-01-01"&
                  RT$STATE=="LA",1,0)
RT$STATE<-factor(RT$STATE)
RT<-as.data.frame(RT)
RT$RT_L<-log(RT$PRT)

setup<-panel.matrices(RT,unit = 1,time = 3,outcome = 5,treatment = 4)

sdid.RT = synthdid_estimate(setup$Y, setup$N0, setup$T0)
sc.RT = sc_estimate(setup$Y, setup$N0, setup$T0)

p4 = plot.estimators(estimators(sdid = sdid.RT,sc = sc.RT,s = 1),   alpha.multiplier=c(1, 1,1))

plot.theme = theme(legend.position=c(.2,.25), legend.direction='horizontal',
                   legend.key=element_blank(), legend.background=element_blank(), 
                   plot.title = element_text(hjust = 0.5,size = 9),axis.text = element_text(size=4),axis.title = element_text(size=7))

pl.RT<- p4 + plot.theme+#xlab("Year") + 
  ylab("Retail \n Trade")

pl.RT

## SPS

PSPS <- read_excel("PSPS.xlsx")

## Remove NA
dumy_date=seq.Date(from = as.Date("1998-01-01"),to = as.Date("2020-01-01"),by = "quarter")
Fix_df<-data.frame(STATE=rep(unique(countypop$abbr),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(countypop$abbr)))

SPS<-left_join(Fix_df,PSPS,by=c("STATE","DATE"))
SPS$DATE<-as.Date(SPS$DATE)
SPS<-SPS%>%filter(DATE>="1998-01-01",DATE<="2008-01-01")
SPS<-SPS%>%select(STATE,PSPS,DATE)
SPS<-SPS%>% group_by(STATE) %>%  filter(!any(is.na(PSPS)))

##PLOTING
SPS$Treat<-ifelse(SPS$DATE>="2005-08-10"&
                  SPS$DATE<="2022-01-01"&
                  SPS$STATE=="LA",1,0)
SPS$STATE<-factor(SPS$STATE)
SPS<-as.data.frame(SPS)
SPS$SPS_L<-log(SPS$PSPS)
setup<-panel.matrices(SPS,unit = 1,time = 3,outcome = 5,treatment = 4)

sdid.SPS = synthdid_estimate(setup$Y, setup$N0, setup$T0)
sc.SPS = sc_estimate(setup$Y, setup$N0, setup$T0)

p4 = plot.estimators(estimators(sdid = sdid.SPS,sc = sc.SPS,s = 1),   alpha.multiplier=c(1, 1,1))

plot.theme = theme(legend.position=c(.2,.25), legend.direction='horizontal',
                   legend.key=element_blank(), legend.background=element_blank(), 
                   plot.title = element_text(hjust = 0.5,size = 9),axis.text = element_text(size=4),axis.title = element_text(size=7))

pl.SPS<- p4 + plot.theme+#xlab("Year") + 
  ylab("Professional,Scientific & \n  Technical Services")

pl.SPS

## TNF

PTNF <- read_excel("PTNF.xlsx")

## Remove NA
dumy_date=seq.Date(from = as.Date("1998-01-01"),to = as.Date("2020-01-01"),by = "quarter")
Fix_df<-data.frame(STATE=rep(unique(countypop$abbr),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(countypop$abbr)))

TNF<-left_join(Fix_df,PTNF,by=c("STATE","DATE"))
TNF$DATE<-as.Date(TNF$DATE)
TNF<-TNF%>%filter(DATE>="1998-01-01",DATE<="2008-01-01")
TNF<-TNF%>%select(STATE,PTNF,DATE)
TNF<-TNF%>% group_by(STATE) %>%  filter(!any(is.na(PTNF)))

##PLOTING
TNF$Treat<-ifelse(TNF$DATE>="2005-08-10"&
                  TNF$DATE<="2022-01-01"&
                  TNF$STATE=="LA",1,0)
TNF$STATE<-factor(TNF$STATE)
TNF<-as.data.frame(TNF)
TNF$TNF_L<-log(TNF$PTNF)

setup<-panel.matrices(TNF,unit = 1,time = 3,outcome = 5,treatment = 4)

sdid.TNF = synthdid_estimate(setup$Y, setup$N0, setup$T0)
sc.TNF = sc_estimate(setup$Y, setup$N0, setup$T0)

p4 = plot.estimators(estimators(sdid = sdid.TNF,sc = sc.TNF,s = 1),   alpha.multiplier=c(1, 1,1))

plot.theme = theme(legend.position=c(.2,.25), legend.direction='horizontal',
                   legend.key=element_blank(), legend.background=element_blank(), 
                   plot.title = element_text(hjust = 0.5,size = 9),axis.text = element_text(size=4),axis.title = element_text(size=7))

pl.TNF<- p4 + plot.theme+#xlab("Year") + 
 ylab("Total\n Nonfarm")

pl.TNF

## TWU

PTWU <- read_excel("PTWU.xlsx")

## Remove NA
dumy_date=seq.Date(from = as.Date("1998-01-01"),to = as.Date("2020-01-01"),by = "quarter")
Fix_df<-data.frame(STATE=rep(unique(countypop$abbr),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(countypop$abbr)))

TWU<-left_join(Fix_df,PTWU,by=c("STATE","DATE"))
TWU$DATE<-as.Date(TWU$DATE)
TWU<-TWU%>%filter(DATE>="1998-01-01",DATE<="2008-01-01")
TWU<-TWU%>%select(STATE,PTWU,DATE)
TWU<-TWU%>% group_by(STATE) %>%  filter(!any(is.na(PTWU)))

##PLOTING
TWU$Treat<-ifelse(TWU$DATE>="2005-08-10"&
                  TWU$DATE<="2022-01-01"&
                  TWU$STATE=="LA",1,0)
TWU$STATE<-factor(TWU$STATE)
TWU<-as.data.frame(TWU)
TWU$TWU_L<-log(TWU$PTWU)

setup<-panel.matrices(TWU,unit = 1,time = 3,outcome = 5,treatment = 4)

sdid.TWU = synthdid_estimate(setup$Y, setup$N0, setup$T0)
sc.TWU = sc_estimate(setup$Y, setup$N0, setup$T0)

p4 = plot.estimators(estimators(sdid = sdid.TWU,sc = sc.TWU,s = 1),   alpha.multiplier=c(1, 1,1))

plot.theme = theme(legend.position=c(.2,.25), legend.direction='horizontal',
                   legend.key=element_blank(), legend.background=element_blank(), 
                   plot.title = element_text(hjust = 0.5,size = 9),axis.text = element_text(size=4),axis.title = element_text(size=7))

pl.TWU<- p4 + plot.theme+#xlab("Year") + 
  ylab("Transportation, \n Warehousing, and Utilities")

pl.TWU

## WT

PWT <- read_excel("PWT.xlsx")

## Remove NA
dumy_date=seq.Date(from = as.Date("1998-01-01"),to = as.Date("2020-01-01"),by = "quarter")
Fix_df<-data.frame(STATE=rep(unique(countypop$abbr),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(countypop$abbr)))

WT<-left_join(Fix_df,PWT,by=c("STATE","DATE"))
WT$DATE<-as.Date(WT$DATE)
WT<-WT%>%filter(DATE>="1998-01-01",DATE<="2008-01-01")
WT<-WT%>%select(STATE,PWT,DATE)
WT<-WT%>% group_by(STATE) %>%  filter(!any(is.na(PWT)))

##PLOTING
WT$Treat<-ifelse(WT$DATE>="2005-08-10"&
                  WT$DATE<="2022-01-01"&
                  WT$STATE=="LA",1,0)
WT$STATE<-factor(WT$STATE)
WT<-as.data.frame(WT)
WT$WT_L<-log(WT$PWT)

setup<-panel.matrices(WT,unit = 1,time = 3,outcome = 5,treatment = 4)

sdid.WT = synthdid_estimate(setup$Y, setup$N0, setup$T0)
sc.WT = sc_estimate(setup$Y, setup$N0, setup$T0)

p4 = plot.estimators(estimators(sdid = sdid.WT,sc = sc.WT,s = 1),   alpha.multiplier=c(1, 1,1))

plot.theme = theme(legend.position=c(.2,.25), legend.direction='horizontal',
                   legend.key=element_blank(), legend.background=element_blank(), 
                   plot.title = element_text(hjust = 0.5,size = 9),axis.text = element_text(size=4),axis.title = element_text(size=7))

pl.WT<- p4 + plot.theme+#xlab("Year") + 
 ylab("Wholesale \n Trade")

pl.WT

Append All

plot2<-ggarrange(pl.TNF,pl.GOV,pl.HCS,
                 pl.AF,pl.RT,pl.CON,
                 pl.OS,pl.ES,pl.MF,
                 pl.ASW,pl.AER,pl.TWU,
                 pl.WT,pl.FI,pl.INF,
                 pl.RE,pl.MCE,pl.SPS,
                 nrow = 6,ncol = 3,legend = "bottom",common.legend = TRUE)%>%
        annotate_figure(bottom = "Date",left = "Change in Percentage")
plot2

#plot3<-ggarrange(pl.ASW,pl.AER,pl.TWU,
#                 pl.WT,pl.FI,pl.INF,
#                 pl.RE,pl.MCE,pl.SPS,
#                 nrow = 3,ncol = 3,legend = "bottom",common.legend = TRUE)%>%
#        annotate_figure(bottom = "Date",left = "Change in Percentage")
#plot3

png("SDID Percapita Append v7.png",width = 7.00,height = 8.05,units = "in",res = 400)
plot2
dev.off()

## png 
##   2

#png("SDID Percapita9p Append v2.png",width = 7.00,height = 8.05,units = "in",res = 400)
##plot3
#dev.off()

Percapita SDID Estimate

2023-05-31

Function Plot

AER

AF

ASW

CON

ES

MCE

Append All