Control State Map For LA

TNF

TNF <- read_excel("TNF_State.xls", sheet = "TNF")

#DATE CLEANS
TNF$DATE<-as.Date(TNF$DATE)
TNF<-arrange(TNF,STATE)
TNF<-filter(TNF,DATE>="2000-01-01")

#VariablTNF Clean
TNF<-select(TNF,STATE,TNF,DATE)

dumy_date=seq.Date(from = as.Date("2000-01-01"),to = as.Date("2022-01-01"),by = "month")
Fix_df<-data_frame(STATE=rep(unique(TNF$STATE),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(TNF$STATE)))

TNF<-left_join(Fix_df,TNF,by=c("STATE","DATE"))

TNF<-TNF %>% # Remove All NA STATE
  group_by(STATE) %>%  
  filter(DATE<="2008-01-01",!any(is.na(TNF)))

TNF$Treat<-ifelse(between(TNF$DATE,as.Date("2005-08-01"),as.Date("2022-06-01"))
                        &TNF$STATE=="LA",1,0)
TNF<-as.data.frame(TNF)
TNF$STATE<-factor(TNF$STATE)
TNF$L_TNF<-log(TNF$TNF)

# SDID PAN
matx<-panel.matrices(TNF,unit = 1,time = 2,outcome = 5,treatment = 4)

sdid.TNF<-synthdid_estimate(matx$Y, matx$N0, matx$T0)
sc.TNF = sc_estimate(matx$Y, matx$N0, matx$T0)

wg<-summary(sdid.TNF)$controls
wg<-data.frame(State=rownames(wg),Controls=wg[,1])
rownames(wg)<-NULL

wg<-left_join(statepop,wg,by=c("abbr"="State"))

pl.TNF<-plot_usmap(regions = "state",data = wg,values = "Controls")+
  scale_fill_gradient2(na.value = "white")+
  ggtitle("Total NonFarm")

AER

AER <- read_excel("AER_State.xls", sheet = "AER")

#DATE CLEANS
AER$DATE<-as.Date(AER$DATE)
AER<-arrange(AER,STATE)
AER<-filter(AER,DATE>="2000-01-01")

#VariablAER Clean
AER<-select(AER,STATE,AER,DATE)

dumy_date=seq.Date(from = as.Date("2000-01-01"),to = as.Date("2022-01-01"),by = "month")
Fix_df<-data_frame(STATE=rep(unique(AER$STATE),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(AER$STATE)))

AER<-left_join(Fix_df,AER,by=c("STATE","DATE"))

AER<-AER %>% # Remove All NA STATE
  group_by(STATE) %>%  
  filter(DATE<="2008-01-01",!any(is.na(AER)))

AER$Treat<-ifelse(between(AER$DATE,as.Date("2005-08-01"),as.Date("2022-06-01"))
                        &AER$STATE=="LA",1,0)
AER<-as.data.frame(AER)
AER$STATE<-factor(AER$STATE)
AER$L_AER<-log(AER$AER)

# SDID PAN
matx<-panel.matrices(AER,unit = 1,time = 2,outcome = 5,treatment = 4)

sdid.AER<-synthdid_estimate(matx$Y, matx$N0, matx$T0)
sc.AER = sc_estimate(matx$Y, matx$N0, matx$T0)

wg<-summary(sdid.AER)$controls
wg<-data.frame(State=rownames(wg),Controls=wg[,1])
rownames(wg)<-NULL

wg<-left_join(statepop,wg,by=c("abbr"="State"))

pl.AER<-plot_usmap(regions = "state",data = wg,values = "Controls")+
  scale_fill_gradient2(na.value = "white")+
  ggtitle("Arts, Entertainment,\n and Recreation")

AF

AF <- read_excel("AF_State.xls", sheet = "AF")

#DATE CLEANS
AF$DATE<-as.Date(AF$DATE)
AF<-arrange(AF,STATE)
AF<-filter(AF,DATE>="2000-01-01")

#VariablAF Clean
AF<-select(AF,STATE,AF,DATE)

dumy_date=seq.Date(from = as.Date("2000-01-01"),to = as.Date("2022-01-01"),by = "month")
Fix_df<-data_frame(STATE=rep(unique(AF$STATE),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(AF$STATE)))

AF<-left_join(Fix_df,AF,by=c("STATE","DATE"))

AF<-AF %>% # Remove All NA STATE
  group_by(STATE) %>%  
  filter(DATE<="2008-01-01",!any(is.na(AF)))

AF$Treat<-ifelse(between(AF$DATE,as.Date("2005-08-01"),as.Date("2022-06-01"))
                        &AF$STATE=="LA",1,0)
AF<-as.data.frame(AF)
AF$STATE<-factor(AF$STATE)
AF$L_AF<-log(AF$AF)

# SDID PAN
matx<-panel.matrices(AF,unit = 1,time = 2,outcome = 5,treatment = 4)

sdid.AF<-synthdid_estimate(matx$Y, matx$N0, matx$T0)
sc.AF = sc_estimate(matx$Y, matx$N0, matx$T0)

wg<-summary(sdid.AF)$controls
wg<-data.frame(State=rownames(wg),Controls=wg[,1])
rownames(wg)<-NULL

wg<-left_join(statepop,wg,by=c("abbr"="State"))

pl.AF<-plot_usmap(regions = "state",data = wg,values = "Controls")+
  scale_fill_gradient2(na.value = "white")+
  ggtitle("Accommodation and \n Food Services")

ASW

ASW <- read_excel("ASW_State.xls", sheet = "ASW")

#DATE CLEANS
ASW$DATE<-as.Date(ASW$DATE)
ASW<-arrange(ASW,STATE)
ASW<-filter(ASW,DATE>="2000-01-01")

#VariablASW Clean
ASW<-select(ASW,STATE,ASW,DATE)

dumy_date=seq.Date(from = as.Date("2000-01-01"),to = as.Date("2022-01-01"),by = "month")
Fix_df<-data_frame(STATE=rep(unique(ASW$STATE),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(ASW$STATE)))

ASW<-left_join(Fix_df,ASW,by=c("STATE","DATE"))

ASW<-ASW %>% # Remove All NA STATE
  group_by(STATE) %>%  
  filter(DATE<="2008-01-01",!any(is.na(ASW)))

ASW$Treat<-ifelse(between(ASW$DATE,as.Date("2005-08-01"),as.Date("2022-06-01"))
                        &ASW$STATE=="LA",1,0)
ASW<-as.data.frame(ASW)
ASW$STATE<-factor(ASW$STATE)
ASW$L_ASW<-log(ASW$ASW)

# SDID PAN
matx<-panel.matrices(ASW,unit = 1,time = 2,outcome = 5,treatment = 4)

sdid.ASW<-synthdid_estimate(matx$Y, matx$N0, matx$T0)
sc.ASW = sc_estimate(matx$Y, matx$N0, matx$T0)

wg<-summary(sdid.ASW)$controls
wg<-data.frame(State=rownames(wg),Controls=wg[,1])
rownames(wg)<-NULL

wg<-left_join(statepop,wg,by=c("abbr"="State"))

pl.ASW<-plot_usmap(regions = "state",data = wg,values = "Controls")+
  scale_fill_gradient2(na.value = "white")+
  ggtitle("ASW")

CON

CON <- read_excel("CON_State.xls", sheet = "CON")

#DATE CLEANS
CON$DATE<-as.Date(CON$DATE)
CON<-arrange(CON,STATE)
CON<-filter(CON,DATE>="2000-01-01")

#VariablCON Clean
CON<-select(CON,STATE,CON,DATE)

dumy_date=seq.Date(from = as.Date("2000-01-01"),to = as.Date("2022-01-01"),by = "month")
Fix_df<-data_frame(STATE=rep(unique(CON$STATE),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(CON$STATE)))

CON<-left_join(Fix_df,CON,by=c("STATE","DATE"))

CON<-CON %>% # Remove All NA STATE
  group_by(STATE) %>%  
  filter(DATE<="2008-01-01",!any(is.na(CON)))

CON$Treat<-ifelse(between(CON$DATE,as.Date("2005-08-01"),as.Date("2022-06-01"))
                        &CON$STATE=="LA",1,0)
CON<-as.data.frame(CON)
CON$STATE<-factor(CON$STATE)
CON$L_CON<-log(CON$CON)

# SDID PAN
matx<-panel.matrices(CON,unit = 1,time = 2,outcome = 5,treatment = 4)

sdid.CON<-synthdid_estimate(matx$Y, matx$N0, matx$T0)
sc.CON = sc_estimate(matx$Y, matx$N0, matx$T0)

wg<-summary(sdid.CON)$controls
wg<-data.frame(State=rownames(wg),Controls=wg[,1])
rownames(wg)<-NULL

wg<-left_join(statepop,wg,by=c("abbr"="State"))

pl.CON<-plot_usmap(regions = "state",data = wg,values = "Controls")+
  scale_fill_gradient2(na.value = "white")+
  ggtitle("Construction")

ES

ES <- read_excel("ES_State.xls", sheet = "ES")

#DATE CLEANS
ES$DATE<-as.Date(ES$DATE)
ES<-arrange(ES,STATE)
ES<-filter(ES,DATE>="2000-01-01")

#VariablES Clean
ES<-select(ES,STATE,ES,DATE)

dumy_date=seq.Date(from = as.Date("2000-01-01"),to = as.Date("2022-01-01"),by = "month")
Fix_df<-data_frame(STATE=rep(unique(ES$STATE),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(ES$STATE)))

ES<-left_join(Fix_df,ES,by=c("STATE","DATE"))

ES<-ES %>% # Remove All NA STATE
  group_by(STATE) %>%  
  filter(DATE<="2008-01-01",!any(is.na(ES)))

ES$Treat<-ifelse(between(ES$DATE,as.Date("2005-08-01"),as.Date("2022-06-01"))
                        &ES$STATE=="LA",1,0)
ES<-as.data.frame(ES)
ES$STATE<-factor(ES$STATE)
ES$L_ES<-log(ES$ES)

# SDID PAN
matx<-panel.matrices(ES,unit = 1,time = 2,outcome = 5,treatment = 4)

sdid.ES<-synthdid_estimate(matx$Y, matx$N0, matx$T0)
sc.ES = sc_estimate(matx$Y, matx$N0, matx$T0)

wg<-summary(sdid.ES)$controls
wg<-data.frame(State=rownames(wg),Controls=wg[,1])
rownames(wg)<-NULL

wg<-left_join(statepop,wg,by=c("abbr"="State"))

pl.ES<-plot_usmap(regions = "state",data = wg,values = "Controls")+
  scale_fill_gradient2(na.value = "white")+
  ggtitle("Educational \n Services")

FI

FI <- read_excel("FI_State.xls", sheet = "FI")

#DATE CLEANS
FI$DATE<-as.Date(FI$DATE)
FI<-arrange(FI,STATE)
FI<-filter(FI,DATE>="2000-01-01")

#VariablFI Clean
FI<-select(FI,STATE,FI,DATE)

dumy_date=seq.Date(from = as.Date("2000-01-01"),to = as.Date("2022-01-01"),by = "month")
Fix_df<-data_frame(STATE=rep(unique(FI$STATE),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(FI$STATE)))

FI<-left_join(Fix_df,FI,by=c("STATE","DATE"))

FI<-FI %>% # Remove All NA STATE
  group_by(STATE) %>%  
  filter(DATE<="2008-01-01",!any(is.na(FI)))

FI$Treat<-ifelse(between(FI$DATE,as.Date("2005-08-01"),as.Date("2022-06-01"))
                        &FI$STATE=="LA",1,0)
FI<-as.data.frame(FI)
FI$STATE<-factor(FI$STATE)
FI$L_FI<-log(FI$FI)

# SDID PAN
matx<-panel.matrices(FI,unit = 1,time = 2,outcome = 5,treatment = 4)

sdid.FI<-synthdid_estimate(matx$Y, matx$N0, matx$T0)
sc.FI = sc_estimate(matx$Y, matx$N0, matx$T0)

wg<-summary(sdid.FI)$controls
wg<-data.frame(State=rownames(wg),Controls=wg[,1])
rownames(wg)<-NULL

wg<-left_join(statepop,wg,by=c("abbr"="State"))

pl.FI<-plot_usmap(regions = "state",data = wg,values = "Controls")+
  scale_fill_gradient2(na.value = "white")+
  ggtitle("Financial \n Insurance")

GOV

GOV <- read_excel("GOV_State.xls", sheet = "GOV")

#DATE CLEANS
GOV$DATE<-as.Date(GOV$DATE)
GOV<-arrange(GOV,STATE)
GOV<-filter(GOV,DATE>="2000-01-01")

#VariablGOV Clean
GOV<-select(GOV,STATE,GOV,DATE)

dumy_date=seq.Date(from = as.Date("2000-01-01"),to = as.Date("2022-01-01"),by = "month")
Fix_df<-data_frame(STATE=rep(unique(GOV$STATE),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(GOV$STATE)))

GOV<-left_join(Fix_df,GOV,by=c("STATE","DATE"))

GOV<-GOV %>% # Remove All NA STATE
  group_by(STATE) %>%  
  filter(DATE<="2008-01-01",!any(is.na(GOV)))

GOV$Treat<-ifelse(between(GOV$DATE,as.Date("2005-08-01"),as.Date("2022-06-01"))
                        &GOV$STATE=="LA",1,0)
GOV<-as.data.frame(GOV)
GOV$STATE<-factor(GOV$STATE)
GOV$L_GOV<-log(GOV$GOV)

# SDID PAN
matx<-panel.matrices(GOV,unit = 1,time = 2,outcome = 5,treatment = 4)

sdid.GOV<-synthdid_estimate(matx$Y, matx$N0, matx$T0)
sc.GOV = sc_estimate(matx$Y, matx$N0, matx$T0)

wg<-summary(sdid.GOV)$controls
wg<-data.frame(State=rownames(wg),Controls=wg[,1])
rownames(wg)<-NULL

wg<-left_join(statepop,wg,by=c("abbr"="State"))

pl.GOV<-plot_usmap(regions = "state",data = wg,values = "Controls")+
  scale_fill_gradient2(na.value = "white")+
  ggtitle("Government")

HCS

HCS <- read_excel("HCS_State.xls", sheet = "HCS")

#DATE CLEANS
HCS$DATE<-as.Date(HCS$DATE)
HCS<-arrange(HCS,STATE)
HCS<-filter(HCS,DATE>="2000-01-01")

#VariablHCS Clean
HCS<-select(HCS,STATE,HCS,DATE)

dumy_date=seq.Date(from = as.Date("2000-01-01"),to = as.Date("2022-01-01"),by = "month")
Fix_df<-data_frame(STATE=rep(unique(HCS$STATE),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(HCS$STATE)))

HCS<-left_join(Fix_df,HCS,by=c("STATE","DATE"))

HCS<-HCS %>% # Remove All NA STATE
  group_by(STATE) %>%  
  filter(DATE<="2008-01-01",!any(is.na(HCS)))

HCS$Treat<-ifelse(between(HCS$DATE,as.Date("2005-08-01"),as.Date("2022-06-01"))
                        &HCS$STATE=="LA",1,0)
HCS<-as.data.frame(HCS)
HCS$STATE<-factor(HCS$STATE)
HCS$L_HCS<-log(HCS$HCS)

# SDID PAN
matx<-panel.matrices(HCS,unit = 1,time = 2,outcome = 5,treatment = 4)

sdid.HCS<-synthdid_estimate(matx$Y, matx$N0, matx$T0)
sc.HCS = sc_estimate(matx$Y, matx$N0, matx$T0)

wg<-summary(sdid.HCS)$controls
wg<-data.frame(State=rownames(wg),Controls=wg[,1])
rownames(wg)<-NULL

wg<-left_join(statepop,wg,by=c("abbr"="State"))

pl.HCS<-plot_usmap(regions = "state",data = wg,values = "Controls")+
  scale_fill_gradient2(na.value = "white")+
  ggtitle("Health Care and \n Social Assistance")

INF

INF <- read_excel("INF_State.xls", sheet = "INF")

#DATE CLEANS
INF$DATE<-as.Date(INF$DATE)
INF<-arrange(INF,STATE)
INF<-filter(INF,DATE>="2000-01-01")

#VariablINF Clean
INF<-select(INF,STATE,INF,DATE)

dumy_date=seq.Date(from = as.Date("2000-01-01"),to = as.Date("2022-01-01"),by = "month")
Fix_df<-data_frame(STATE=rep(unique(INF$STATE),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(INF$STATE)))

INF<-left_join(Fix_df,INF,by=c("STATE","DATE"))

INF<-INF %>% # Remove All NA STATE
  group_by(STATE) %>%  
  filter(DATE<="2008-01-01",!any(is.na(INF)))

INF$Treat<-ifelse(between(INF$DATE,as.Date("2005-08-01"),as.Date("2022-06-01"))
                        &INF$STATE=="LA",1,0)
INF<-as.data.frame(INF)
INF$STATE<-factor(INF$STATE)
INF$L_INF<-log(INF$INF)

# SDID PAN
matx<-panel.matrices(INF,unit = 1,time = 2,outcome = 5,treatment = 4)

sdid.INF<-synthdid_estimate(matx$Y, matx$N0, matx$T0)
sc.INF = sc_estimate(matx$Y, matx$N0, matx$T0)

wg<-summary(sdid.INF)$controls
wg<-data.frame(State=rownames(wg),Controls=wg[,1])
rownames(wg)<-NULL

wg<-left_join(statepop,wg,by=c("abbr"="State"))

pl.INF<-plot_usmap(regions = "state",data = wg,values = "Controls")+
  scale_fill_gradient2(na.value = "white")+
  ggtitle("Information")

MCE

MCE <- read_excel("MCE_State.xls", sheet = "MCE")

#DATE CLEANS
MCE$DATE<-as.Date(MCE$DATE)
MCE<-arrange(MCE,STATE)
MCE<-filter(MCE,DATE>="2000-01-01")

#VariablMCE Clean
MCE<-select(MCE,STATE,MCE,DATE)

dumy_date=seq.Date(from = as.Date("2000-01-01"),to = as.Date("2022-01-01"),by = "month")
Fix_df<-data_frame(STATE=rep(unique(MCE$STATE),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(MCE$STATE)))

MCE<-left_join(Fix_df,MCE,by=c("STATE","DATE"))

MCE<-MCE %>% # Remove All NA STATE
  group_by(STATE) %>%  
  filter(DATE<="2008-01-01",!any(is.na(MCE)))

MCE$Treat<-ifelse(between(MCE$DATE,as.Date("2005-08-01"),as.Date("2022-06-01"))
                        &MCE$STATE=="LA",1,0)
MCE<-as.data.frame(MCE)
MCE$STATE<-factor(MCE$STATE)
MCE$L_MCE<-log(MCE$MCE)

# SDID PAN
matx<-panel.matrices(MCE,unit = 1,time = 2,outcome = 5,treatment = 4)

sdid.MCE<-synthdid_estimate(matx$Y, matx$N0, matx$T0)
sc.MCE = sc_estimate(matx$Y, matx$N0, matx$T0)

wg<-summary(sdid.MCE)$controls
wg<-data.frame(State=rownames(wg),Controls=wg[,1])
rownames(wg)<-NULL

wg<-left_join(statepop,wg,by=c("abbr"="State"))

pl.MCE<-plot_usmap(regions = "state",data = wg,values = "Controls")+
  scale_fill_gradient2(na.value = "white")+
  ggtitle("Management of Companies \n and Enterprises")

MAN

MAN <- read_excel("MAN_State.xls", sheet = "MAN")

#DATE CLEANS
MAN$DATE<-as.Date(MAN$DATE)
MAN<-arrange(MAN,STATE)
MAN<-filter(MAN,DATE>="2000-01-01")

#VariablMAN Clean
MAN<-select(MAN,STATE,MAN,DATE)

dumy_date=seq.Date(from = as.Date("2000-01-01"),to = as.Date("2022-01-01"),by = "month")
Fix_df<-data_frame(STATE=rep(unique(MAN$STATE),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(MAN$STATE)))

MAN<-left_join(Fix_df,MAN,by=c("STATE","DATE"))

MAN<-MAN %>% # Remove All NA STATE
  group_by(STATE) %>%  
  filter(DATE<="2008-01-01",!any(is.na(MAN)))

MAN$Treat<-ifelse(between(MAN$DATE,as.Date("2005-08-01"),as.Date("2022-06-01"))
                        &MAN$STATE=="LA",1,0)
MAN<-as.data.frame(MAN)
MAN$STATE<-factor(MAN$STATE)
MAN$L_MAN<-log(MAN$MAN)

# SDID PAN
matx<-panel.matrices(MAN,unit = 1,time = 2,outcome = 5,treatment = 4)

sdid.MAN<-synthdid_estimate(matx$Y, matx$N0, matx$T0)
sc.MAN = sc_estimate(matx$Y, matx$N0, matx$T0)

wg<-summary(sdid.MAN)$controls
wg<-data.frame(State=rownames(wg),Controls=wg[,1])
rownames(wg)<-NULL

wg<-left_join(statepop,wg,by=c("abbr"="State"))

pl.MAN<-plot_usmap(regions = "state",data = wg,values = "Controls")+
  scale_fill_gradient2(na.value = "white")+
  ggtitle("Manufacturing")

OS

OS <- read_excel("OS_State.xls", sheet = "OS")

#DATE CLEANS
OS$DATE<-as.Date(OS$DATE)
OS<-arrange(OS,STATE)
OS<-filter(OS,DATE>="2000-01-01")

#VariablOS Clean
OS<-select(OS,STATE,OS,DATE)

dumy_date=seq.Date(from = as.Date("2000-01-01"),to = as.Date("2022-01-01"),by = "month")
Fix_df<-data_frame(STATE=rep(unique(OS$STATE),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(OS$STATE)))

OS<-left_join(Fix_df,OS,by=c("STATE","DATE"))

OS<-OS %>% # Remove All NA STATE
  group_by(STATE) %>%  
  filter(DATE<="2008-01-01",!any(is.na(OS)))

OS$Treat<-ifelse(between(OS$DATE,as.Date("2005-08-01"),as.Date("2022-06-01"))
                        &OS$STATE=="LA",1,0)
OS<-as.data.frame(OS)
OS$STATE<-factor(OS$STATE)
OS$L_OS<-log(OS$OS)

# SDID PAN
matx<-panel.matrices(OS,unit = 1,time = 2,outcome = 5,treatment = 4)

sdid.OS<-synthdid_estimate(matx$Y, matx$N0, matx$T0)
sc.OS = sc_estimate(matx$Y, matx$N0, matx$T0)

wg<-summary(sdid.OS)$controls
wg<-data.frame(State=rownames(wg),Controls=wg[,1])
rownames(wg)<-NULL

wg<-left_join(statepop,wg,by=c("abbr"="State"))

pl.OS<-plot_usmap(regions = "state",data = wg,values = "Controls")+
  scale_fill_gradient2(na.value = "white")+
  ggtitle("Other \n Services")

RE

RE <- read_excel("RE_State.xls", sheet = "RE")

#DATE CLEANS
RE$DATE<-as.Date(RE$DATE)
RE<-arrange(RE,STATE)
RE<-filter(RE,DATE>="2000-01-01")

#VariablRE Clean
RE<-select(RE,STATE,RE,DATE)

dumy_date=seq.Date(from = as.Date("2000-01-01"),to = as.Date("2022-01-01"),by = "month")
Fix_df<-data_frame(STATE=rep(unique(RE$STATE),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(RE$STATE)))

RE<-left_join(Fix_df,RE,by=c("STATE","DATE"))

RE<-RE %>% # Remove All NA STATE
  group_by(STATE) %>%  
  filter(DATE<="2008-01-01",!any(is.na(RE)))

RE$Treat<-ifelse(between(RE$DATE,as.Date("2005-08-01"),as.Date("2022-06-01"))
                        &RE$STATE=="LA",1,0)
RE<-as.data.frame(RE)
RE$STATE<-factor(RE$STATE)
RE$L_RE<-log(RE$RE)

# SDID PAN
matx<-panel.matrices(RE,unit = 1,time = 2,outcome = 5,treatment = 4)

sdid.RE<-synthdid_estimate(matx$Y, matx$N0, matx$T0)
sc.RE = sc_estimate(matx$Y, matx$N0, matx$T0)

wg<-summary(sdid.RE)$controls
wg<-data.frame(State=rownames(wg),Controls=wg[,1])
rownames(wg)<-NULL

wg<-left_join(statepop,wg,by=c("abbr"="State"))

pl.RE<-plot_usmap(regions = "state",data = wg,values = "Controls")+
  scale_fill_gradient2(na.value = "white")+
  ggtitle("Real Estate, Rental \n and Leasing")

RT

RT <- read_excel("RT_State.xls", sheet = "RT")

#DATE CLEANS
RT$DATE<-as.Date(RT$DATE)
RT<-arrange(RT,STATE)
RT<-filter(RT,DATE>="2000-01-01")

#VariablRT Clean
RT<-select(RT,STATE,RT,DATE)

dumy_date=seq.Date(from = as.Date("2000-01-01"),to = as.Date("2022-01-01"),by = "month")
Fix_df<-data_frame(STATE=rep(unique(RT$STATE),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(RT$STATE)))

RT<-left_join(Fix_df,RT,by=c("STATE","DATE"))

RT<-RT %>% # Remove All NA STATE
  group_by(STATE) %>%  
  filter(DATE<="2008-01-01",!any(is.na(RT)))

RT$Treat<-ifelse(between(RT$DATE,as.Date("2005-08-01"),as.Date("2022-06-01"))
                        &RT$STATE=="LA",1,0)
RT<-as.data.frame(RT)
RT$STATE<-factor(RT$STATE)
RT$L_RT<-log(RT$RT)

# SDID PAN
matx<-panel.matrices(RT,unit = 1,time = 2,outcome = 5,treatment = 4)

sdid.RT<-synthdid_estimate(matx$Y, matx$N0, matx$T0)
sc.RT = sc_estimate(matx$Y, matx$N0, matx$T0)

wg<-summary(sdid.RT)$controls
wg<-data.frame(State=rownames(wg),Controls=wg[,1])
rownames(wg)<-NULL

wg<-left_join(statepop,wg,by=c("abbr"="State"))

pl.RT<-plot_usmap(regions = "state",data = wg,values = "Controls")+
  scale_fill_gradient2(na.value = "white")+
  ggtitle("Retail \n Trade")

TWU

TWU <- read_excel("TWU_State.xls", sheet = "TWU")

#DATE CLEANS
TWU$DATE<-as.Date(TWU$DATE)
TWU<-arrange(TWU,STATE)
TWU<-filter(TWU,DATE>="2000-01-01")

#VariablTWU Clean
TWU<-select(TWU,STATE,TWU,DATE)

dumy_date=seq.Date(from = as.Date("2000-01-01"),to = as.Date("2022-01-01"),by = "month")
Fix_df<-data_frame(STATE=rep(unique(TWU$STATE),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(TWU$STATE)))

TWU<-left_join(Fix_df,TWU,by=c("STATE","DATE"))

TWU<-TWU %>% # Remove All NA STATE
  group_by(STATE) %>%  
  filter(DATE<="2008-01-01",!any(is.na(TWU)))

TWU$Treat<-ifelse(between(TWU$DATE,as.Date("2005-08-01"),as.Date("2022-06-01"))
                        &TWU$STATE=="LA",1,0)
TWU<-as.data.frame(TWU)
TWU$STATE<-factor(TWU$STATE)
TWU$L_TWU<-log(TWU$TWU)

# SDID PAN
matx<-panel.matrices(TWU,unit = 1,time = 2,outcome = 5,treatment = 4)

sdid.TWU<-synthdid_estimate(matx$Y, matx$N0, matx$T0)
sc.TWU = sc_estimate(matx$Y, matx$N0, matx$T0)

wg<-summary(sdid.TWU)$controls
wg<-data.frame(State=rownames(wg),Controls=wg[,1])
rownames(wg)<-NULL

wg<-left_join(statepop,wg,by=c("abbr"="State"))

pl.TWU<-plot_usmap(regions = "state",data = wg,values = "Controls")+
  scale_fill_gradient2(na.value = "white")+
  ggtitle("Transportation, \n Warehousing, and Utilities")

WT

WT <- read_excel("WT_State.xls", sheet = "WT")

#DATE CLEANS
WT$DATE<-as.Date(WT$DATE)
WT<-arrange(WT,STATE)
WT<-filter(WT,DATE>="2000-01-01")

#VariablWT Clean
WT<-select(WT,STATE,WT,DATE)

dumy_date=seq.Date(from = as.Date("2000-01-01"),to = as.Date("2022-01-01"),by = "month")
Fix_df<-data_frame(STATE=rep(unique(WT$STATE),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(WT$STATE)))

WT<-left_join(Fix_df,WT,by=c("STATE","DATE"))

WT<-WT %>% # Remove All NA STATE
  group_by(STATE) %>%  
  filter(DATE<="2008-01-01",!any(is.na(WT)))

WT$Treat<-ifelse(between(WT$DATE,as.Date("2005-08-01"),as.Date("2022-06-01"))
                        &WT$STATE=="LA",1,0)
WT<-as.data.frame(WT)
WT$STATE<-factor(WT$STATE)
WT$L_WT<-log(WT$WT)

# SDID PAN
matx<-panel.matrices(WT,unit = 1,time = 2,outcome = 5,treatment = 4)

sdid.WT<-synthdid_estimate(matx$Y, matx$N0, matx$T0)
sc.WT = sc_estimate(matx$Y, matx$N0, matx$T0)

wg<-summary(sdid.WT)$controls
wg<-data.frame(State=rownames(wg),Controls=wg[,1])
rownames(wg)<-NULL

wg<-left_join(statepop,wg,by=c("abbr"="State"))

pl.WT<-plot_usmap(regions = "state",data = wg,values = "Controls")+
  scale_fill_gradient2(na.value = "white")+
  ggtitle("Wholesale \n Trade")

SPS

SPS <- read_excel("SPS_State.xls", sheet = "SPS")

#DATE CLEANS
SPS$DATE<-as.Date(SPS$DATE)
SPS<-arrange(SPS,STATE)
SPS<-filter(SPS,DATE>="2000-01-01")

#VariablSPS Clean
SPS<-select(SPS,STATE,SPS,DATE)

dumy_date=seq.Date(from = as.Date("2000-01-01"),to = as.Date("2022-01-01"),by = "month")
Fix_df<-data_frame(STATE=rep(unique(SPS$STATE),length(dumy_date)))
Fix_df<-arrange(Fix_df,STATE)
Fix_df$DATE<-rep(dumy_date,length(unique(SPS$STATE)))

SPS<-left_join(Fix_df,SPS,by=c("STATE","DATE"))

SPS<-SPS %>% # Remove All NA STATE
  group_by(STATE) %>%  
  filter(DATE<="2008-01-01",!any(is.na(SPS)))

SPS$Treat<-ifelse(between(SPS$DATE,as.Date("2005-08-01"),as.Date("2022-06-01"))
                        &SPS$STATE=="LA",1,0)
SPS<-as.data.frame(SPS)
SPS$STATE<-factor(SPS$STATE)
SPS$L_SPS<-log(SPS$SPS)

# SDID PAN
matx<-panel.matrices(SPS,unit = 1,time = 2,outcome = 5,treatment = 4)

sdid.SPS<-synthdid_estimate(matx$Y, matx$N0, matx$T0)
sc.SPS = sc_estimate(matx$Y, matx$N0, matx$T0)

wg<-summary(sdid.SPS)$controls
wg<-data.frame(State=rownames(wg),Controls=wg[,1])
rownames(wg)<-NULL

wg<-left_join(statepop,wg,by=c("abbr"="State"))

pl.SPS<-plot_usmap(regions = "state",data = wg,values = "Controls")+
  scale_fill_gradient2(na.value = "white")+
  ggtitle("Professional,Scientific, \n and Technical Services")

plot2<-ggarrange(pl.TNF,pl.GOV,pl.HCS,
                 pl.AF,pl.RT,pl.CON,
                 pl.OS,pl.ES,pl.MAN,
                 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)
plot2

png("SDID Employee Weight Control2.png",width = 7.00,height = 8.05,units = "in",res = 400)
plot2
dev.off()

## png 
##   2

Control State Map For LA

2023-06-05

TNF

AER

AF

ASW

CON

ES

FI

GOV

HCS

INF

MCE

MAN

OS

RE

RT

TWU

WT

SPS