Civil Labor
df2 <- read_excel("ALLCOUNTY/Merge Sectors Civil Labor Ballance.xlsx")
df2 <- df2 %>%
mutate(
DATE = as.Date(DATE)
)
df2 <- df2 %>%
mutate(
treat = as.numeric(STATE == "LA"),
t = as.numeric(DATE > as.Date("2005-08-01"))
)
df2 <- df2 %>%
mutate(
year = as.numeric(format(DATE, "%Y"))
)
## SUbset Data, because we want focus on before Feb 2010
df3 <- df2 %>% filter(DATE <"2010-02-01")
## Makes Date Dummy
dloop<-data.frame(DATE=unique(df3$DATE))
dloop<-dloop%>%
mutate(year=format(DATE,"%Y"),
month=str_pad(string = format(DATE,"%m"),width = 2,side = "left",pad = 0))
## YEAR DUMMY
for (i in 1:nrow(dloop)) {
df3[paste0("d.", dloop$year[i],".",dloop$month[i])] <- as.numeric(df3$DATE == unique(df3$DATE)[i])
}
## RUN MODEL
did.reg <- plm(log(Civil.Labor) ~ treat+Population+Median.Income+
treat:(d.2000.01+d.2000.02+d.2000.03+d.2000.04+d.2000.05+d.2000.06+
d.2000.07+d.2000.08+d.2000.09+d.2000.10+d.2000.11+d.2000.12+
d.2001.01+d.2001.02+d.2001.03+d.2001.04+d.2001.05+d.2001.06+d.2001.07+d.2001.08+d.2001.09+d.2001.10+d.2001.11+d.2001.12+
d.2002.01+d.2002.02+d.2002.03+d.2002.04+d.2002.05+d.2002.06+d.2002.07+d.2002.08+d.2002.09+d.2002.10+d.2002.11+d.2002.12+
d.2003.01+d.2003.02+d.2003.03+d.2003.04+d.2003.05+d.2003.06+d.2003.07+d.2003.08+d.2003.09+d.2003.10+d.2003.11+d.2003.12+
d.2004.01+d.2004.02+d.2004.03+d.2004.04+d.2004.05+d.2004.06+d.2004.07+d.2004.08+d.2004.09+d.2004.10+d.2004.11+d.2004.12+
d.2005.01+d.2005.02+d.2005.03+d.2005.04+d.2005.05+d.2005.06+d.2005.07+d.2005.09+d.2005.10+d.2005.11+d.2005.12+
d.2006.01+d.2006.02+d.2006.03+d.2006.04+d.2006.05+d.2006.06+d.2006.07+d.2006.08+d.2006.09+d.2006.10+d.2006.11+d.2006.12+
d.2007.01+d.2007.02+d.2007.03+d.2007.04+d.2007.05+d.2007.06+d.2007.07+d.2007.08+d.2007.09+d.2007.10+d.2007.11+d.2007.12+
d.2008.01+d.2008.02+d.2008.03+d.2008.04+d.2008.05+d.2008.06+d.2008.07+d.2008.08+d.2008.09+d.2008.10+d.2008.11+d.2008.12+
d.2009.01+d.2009.02+d.2009.03+d.2009.04+d.2009.05+d.2009.06+d.2009.07+d.2009.08+d.2009.09+d.2009.10+d.2009.11+d.2009.12+
d.2010.01),
data = df3, model = "within", index=c("COUNTY", "DATE"),cluster="COUNTY")
## Export Coeffiecent and test for Heteroscedasticity SE
coef_df <- coeftest(did.reg,vcov. = vcovHC(did.reg,type = "HC1"))
coef_df <- coef_df[,]%>%as_tibble()%>%mutate(variables=rownames(coef_df))
coef_df<-coef_df[3:122,]
coef_df$date<-as.Date(paste0(coef_df$variables,".01"),format="treat:d.%Y.%m.%d")
event_df<-data.frame(date=seq.Date(as.Date("2000-01-01"),as.Date("2010-01-01"),by = "month"))
event_df<-left_join(event_df,coef_df[,c(1,2,6)])
event_df[is.na(event_df)]<-0
colnames(event_df)<-c("time","coef","se")
## Makes Confident Interval
event_df$ci_upper <- event_df$coef + 1.96 * event_df$se
event_df$ci_lower <- event_df$coef - 1.96 * event_df$se
head(event_df,3)
## time coef se ci_upper ci_lower
## 1 2000-01-01 0.09357687 0.01041698 0.1139942 0.07315959
## 2 2000-02-01 0.08876098 0.01014788 0.1086508 0.06887114
## 3 2000-03-01 0.09642221 0.01016976 0.1163549 0.07648948
dates<-seq.Date(as.Date("2000-01-01"),as.Date("2010-01-01"),by='month')
# Define start and end dates for plot
start_date <- as.Date("2004-08-01")
end_date <- as.Date("2010-01-01")
# Create sequence of dates for x-axis
dates_seq <- seq.Date(from = start_date, to = end_date, by = "month")
# Subset event_df to only include dates after or equal to start_date
event_df_sub <- event_df[dates >= start_date, ]
pl<-ggplot(event_df, aes(x = dates, y = coef)) +
geom_point() +
geom_line()+
#geom_errorbar(aes(ymin = ci_lower, ymax = ci_upper), width = 0.2) +
geom_ribbon(aes(ymin = ci_lower, ymax = ci_upper), alpha = 0.2) +
geom_hline(yintercept = 0, linetype = "dashed", color = "red") +
geom_vline(xintercept = as.Date("2005-08-01"), linetype = "dashed", color = "blue") +
scale_x_date(date_labels = "%b %Y", limits = c(start_date, end_date), breaks = "1 year") +
labs(x = "Date", y = "Coefficient", title = "") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))+theme_bw()
pl
Employee Person
df2 <- read_excel("Employee Person/Merge Sectors Employee Person Full-Location Ballance.xlsx")
df2 <- df2 %>%
mutate(
DATE = as.Date(DATE)
)
df2 <- df2 %>%
mutate(
treat = as.numeric(STATE == "LA"),
t = as.numeric(DATE > as.Date("2005-08-01"))
)
df2 <- df2 %>%
mutate(
year = as.numeric(format(DATE, "%Y"))
)
## SUbset Data, because we want focus on before Feb 2010
df3 <- df2 %>% filter(DATE <"2010-02-01")
## Makes Date Dummy
dloop<-data.frame(DATE=unique(df3$DATE))
dloop<-dloop%>%
mutate(year=format(DATE,"%Y"),
month=str_pad(string = format(DATE,"%m"),width = 2,side = "left",pad = 0))
## YEAR DUMMY
for (i in 1:nrow(dloop)) {
df3[paste0("d.", dloop$year[i],".",dloop$month[i])] <- as.numeric(df3$DATE == unique(df3$DATE)[i])
}
## RUN MODEL
did.reg <- plm(log(Employee) ~ treat+Population+Median.Income+
treat:(d.2000.01+d.2000.02+d.2000.03+d.2000.04+d.2000.05+d.2000.06+
d.2000.07+d.2000.08+d.2000.09+d.2000.10+d.2000.11+d.2000.12+
d.2001.01+d.2001.02+d.2001.03+d.2001.04+d.2001.05+d.2001.06+d.2001.07+d.2001.08+d.2001.09+d.2001.10+d.2001.11+d.2001.12+
d.2002.01+d.2002.02+d.2002.03+d.2002.04+d.2002.05+d.2002.06+d.2002.07+d.2002.08+d.2002.09+d.2002.10+d.2002.11+d.2002.12+
d.2003.01+d.2003.02+d.2003.03+d.2003.04+d.2003.05+d.2003.06+d.2003.07+d.2003.08+d.2003.09+d.2003.10+d.2003.11+d.2003.12+
d.2004.01+d.2004.02+d.2004.03+d.2004.04+d.2004.05+d.2004.06+d.2004.07+d.2004.08+d.2004.09+d.2004.10+d.2004.11+d.2004.12+
d.2005.01+d.2005.02+d.2005.03+d.2005.04+d.2005.05+d.2005.06+d.2005.07+d.2005.09+d.2005.10+d.2005.11+d.2005.12+
d.2006.01+d.2006.02+d.2006.03+d.2006.04+d.2006.05+d.2006.06+d.2006.07+d.2006.08+d.2006.09+d.2006.10+d.2006.11+d.2006.12+
d.2007.01+d.2007.02+d.2007.03+d.2007.04+d.2007.05+d.2007.06+d.2007.07+d.2007.08+d.2007.09+d.2007.10+d.2007.11+d.2007.12+
d.2008.01+d.2008.02+d.2008.03+d.2008.04+d.2008.05+d.2008.06+d.2008.07+d.2008.08+d.2008.09+d.2008.10+d.2008.11+d.2008.12+
d.2009.01+d.2009.02+d.2009.03+d.2009.04+d.2009.05+d.2009.06+d.2009.07+d.2009.08+d.2009.09+d.2009.10+d.2009.11+d.2009.12+
d.2010.01),
data = df3, model = "within", index=c("COUNTY", "DATE"),cluster="COUNTY")
## Export Coeffiecent and test for Heteroscedasticity SE
coef_df <- coeftest(did.reg,vcov. = vcovHC(did.reg,type = "HC1"))
coef_df <- coef_df[,]%>%as_tibble()%>%mutate(variables=rownames(coef_df))
coef_df<-coef_df[3:122,]
coef_df$date<-as.Date(paste0(coef_df$variables,".01"),format="treat:d.%Y.%m.%d")
event_df<-data.frame(date=seq.Date(as.Date("2000-01-01"),as.Date("2010-01-01"),by = "month"))
event_df<-left_join(event_df,coef_df[,c(1,2,6)])
event_df[is.na(event_df)]<-0
colnames(event_df)<-c("time","coef","se")
## Makes Confident Interval
event_df$ci_upper <- event_df$coef + 1.96 * event_df$se
event_df$ci_lower <- event_df$coef - 1.96 * event_df$se
head(event_df,3)
## time coef se ci_upper ci_lower
## 1 2000-01-01 0.09022138 0.01090413 0.1115935 0.06884928
## 2 2000-02-01 0.09220049 0.01056306 0.1129041 0.07149689
## 3 2000-03-01 0.09785490 0.01066583 0.1187599 0.07694987
dates<-seq.Date(as.Date("2000-01-01"),as.Date("2010-01-01"),by='month')
# Define start and end dates for plot
start_date <- as.Date("2004-08-01")
end_date <- as.Date("2010-01-01")
# Create sequence of dates for x-axis
dates_seq <- seq.Date(from = start_date, to = end_date, by = "month")
# Subset event_df to only include dates after or equal to start_date
event_df_sub <- event_df[dates >= start_date, ]
pl<-ggplot(event_df, aes(x = dates, y = coef)) +
geom_point() +
geom_line()+
#geom_errorbar(aes(ymin = ci_lower, ymax = ci_upper), width = 0.2) +
geom_ribbon(aes(ymin = ci_lower, ymax = ci_upper), alpha = 0.2) +
geom_hline(yintercept = 0, linetype = "dashed", color = "red") +
geom_vline(xintercept = as.Date("2005-08-01"), linetype = "dashed", color = "blue") +
scale_x_date(date_labels = "%b %Y", limits = c(start_date, end_date), breaks = "1 year") +
labs(x = "Date", y = "Coefficient", title = "") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))+theme_bw()
pl
Unemployee Rate
df2 <- read_excel("UnemRate/Merge Sectors UnemRate ballance V2 FULL-Location.xlsx")
df2 <- df2 %>%
mutate(
DATE = as.Date(DATE)
)
df2 <- df2 %>%
mutate(
treat = as.numeric(STATE == "LA"),
t = as.numeric(DATE > as.Date("2005-08-01"))
)
df2 <- df2 %>%
mutate(
year = as.numeric(format(DATE, "%Y"))
)
## SUbset Data, because we want focus on before Feb 2010
df3 <- df2 %>% filter(DATE <"2010-02-01")
## Makes Date Dummy
dloop<-data.frame(DATE=unique(df3$DATE))
dloop<-dloop%>%
mutate(year=format(DATE,"%Y"),
month=str_pad(string = format(DATE,"%m"),width = 2,side = "left",pad = 0))
## YEAR DUMMY
for (i in 1:nrow(dloop)) {
df3[paste0("d.", dloop$year[i],".",dloop$month[i])] <- as.numeric(df3$DATE == unique(df3$DATE)[i])
}
## RUN MODEL
did.reg <- plm(Unemployee.Rate ~treat+Population+Median.Income+
treat:(d.2000.01+d.2000.02+d.2000.03+d.2000.04+d.2000.05+d.2000.06+
d.2000.07+d.2000.08+d.2000.09+d.2000.10+d.2000.11+d.2000.12+
d.2001.01+d.2001.02+d.2001.03+d.2001.04+d.2001.05+d.2001.06+d.2001.07+d.2001.08+d.2001.09+d.2001.10+d.2001.11+d.2001.12+
d.2002.01+d.2002.02+d.2002.03+d.2002.04+d.2002.05+d.2002.06+d.2002.07+d.2002.08+d.2002.09+d.2002.10+d.2002.11+d.2002.12+
d.2003.01+d.2003.02+d.2003.03+d.2003.04+d.2003.05+d.2003.06+d.2003.07+d.2003.08+d.2003.09+d.2003.10+d.2003.11+d.2003.12+
d.2004.01+d.2004.02+d.2004.03+d.2004.04+d.2004.05+d.2004.06+d.2004.07+d.2004.08+d.2004.09+d.2004.10+d.2004.11+d.2004.12+
d.2005.01+d.2005.02+d.2005.03+d.2005.04+d.2005.05+d.2005.06+d.2005.07+d.2005.09+d.2005.10+d.2005.11+d.2005.12+
d.2006.01+d.2006.02+d.2006.03+d.2006.04+d.2006.05+d.2006.06+d.2006.07+d.2006.08+d.2006.09+d.2006.10+d.2006.11+d.2006.12+
d.2007.01+d.2007.02+d.2007.03+d.2007.04+d.2007.05+d.2007.06+d.2007.07+d.2007.08+d.2007.09+d.2007.10+d.2007.11+d.2007.12+
d.2008.01+d.2008.02+d.2008.03+d.2008.04+d.2008.05+d.2008.06+d.2008.07+d.2008.08+d.2008.09+d.2008.10+d.2008.11+d.2008.12+
d.2009.01+d.2009.02+d.2009.03+d.2009.04+d.2009.05+d.2009.06+d.2009.07+d.2009.08+d.2009.09+d.2009.10+d.2009.11+d.2009.12+
d.2010.01),
data = df3, model = "within", index=c("COUNTY", "DATE"),cluster="COUNTY")
## Export Coeffiecent and test for Heteroscedasticity SE
coef_df <- coeftest(did.reg,vcov. = vcovHC(did.reg,type = "HC1"))
coef_df <- coef_df[,]%>%as_tibble()%>%mutate(variables=rownames(coef_df))
coef_df<-coef_df[3:122,]
coef_df$date<-as.Date(paste0(coef_df$variables,".01"),format="treat:d.%Y.%m.%d")
event_df<-data.frame(date=seq.Date(as.Date("2000-01-01"),as.Date("2010-01-01"),by = "month"))
event_df<-left_join(event_df,coef_df[,c(1,2,6)])
event_df[is.na(event_df)]<-0
colnames(event_df)<-c("time","coef","se")
## Makes Confident Interval
event_df$ci_upper <- event_df$coef + 1.96 * event_df$se
event_df$ci_lower <- event_df$coef - 1.96 * event_df$se
head(event_df,3)
## time coef se ci_upper ci_lower
## 1 2000-01-01 -0.3230158 0.1448893 -0.0390328 -0.6069988
## 2 2000-02-01 -1.0444444 0.1486567 -0.7530772 -1.3358116
## 3 2000-03-01 -0.8551587 0.1473370 -0.5663781 -1.1439393
dates<-seq.Date(as.Date("2000-01-01"),as.Date("2010-01-01"),by='month')
# Define start and end dates for plot
start_date <- as.Date("2004-08-01")
end_date <- as.Date("2010-01-01")
# Create sequence of dates for x-axis
dates_seq <- seq.Date(from = start_date, to = end_date, by = "month")
# Subset event_df to only include dates after or equal to start_date
event_df_sub <- event_df[dates >= start_date, ]
pl<-ggplot(event_df, aes(x = dates, y = coef)) +
geom_point() +
geom_line()+
#geom_errorbar(aes(ymin = ci_lower, ymax = ci_upper), width = 0.2) +
geom_ribbon(aes(ymin = ci_lower, ymax = ci_upper), alpha = 0.2) +
geom_hline(yintercept = 0, linetype = "dashed", color = "red") +
geom_vline(xintercept = as.Date("2005-08-01"), linetype = "dashed", color = "blue") +
scale_x_date(date_labels = "%b %Y", limits = c(start_date, end_date), breaks = "1 year") +
labs(x = "Date", y = "Coefficient", title = "") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))+theme_bw()
pl
