Deposits
DR VY
2023-07-03
# # 2000 data: NAICS Based Measure
#
# # NAICS is available in CBP data post 1997.
#
#
# # cbp employment raw data
# cbp <- fread(paste0("C:/Users/dratnadiwakara2/Documents/OneDrive - Louisiana State University/Raw Data/CBP/county/cbp",substr(st_yr,3,4),"co.txt"))
# cbp[,naics:=as.numeric(naics)]
# cbp <- cbp[!is.na(naics)]
#
# cbp[,imp_emp:=n1_4*2.5+n5_9*7+n10_19*15+n20_49*35+n50_99*75+n100_249*175+n250_499*375+n500_999*750+n1000_1*1250+n1000_2*2000+n1000_3*3750+n1000_4*10000]
#
# cbp[,imp_emp:=ifelse(!is.na(emp) & emp>0,emp,imp_emp)]
#
# cbp[,fips:=paste0(str_pad(fipstate,2,"left","0"),str_pad(fipscty,3,"left","0"))]
#
# cbp <- cbp[,c("fips","naics","imp_emp")]
#
# naics_sic <- fread("David Dorn/NAICS97_6_to_SIC87_4.csv")
# cbp <- merge(cbp,naics_sic,by.x="naics",by.y="naics6",allow.cartesian = T)
# cbp[,imp_emp:=imp_emp*weight]
#
# cbp[,emp_sic_n:=sum(imp_emp),by=sic4]
# cbp[,emp_fips:=sum(imp_emp),by=fips]
#
#
#
#
# us_imports <- fread("David Dorn/Imports_from_China_by_sic87dd_1991_2014.csv")
# us_imports <- us_imports[,c("sic87dd","l_import_usch_2000","l_import_usch_2007")]
# us_imports[,chg_st_ed:=l_import_usch_2007-l_import_usch_2000]
#
#
#
# emp_naics <- merge(cbp,us_imports[,c("chg_st_ed","sic87dd")],by.x="sic4",by.y="sic87dd",all.x=T)
#
# emp_naics[,chg_per_employee:=chg_st_ed/emp_sic_n]
#
# emp_naics[,w_chg_per_employee:=chg_per_employee*imp_emp/emp_fips]
#
# # d_tradeusch_pw_dr is our approximation of d_tradeusch_pw in ADH
# emp_czone <- emp_naics[,.(import_sensitivity=sum(w_chg_per_employee,na.rm=T)),by=fips]
#
#
#
#
# # Other country IV
# imports_oth <- fread("David Dorn/Imports_from_China_by_sic87dd_1991_2014.csv")
# imports_oth <- imports_oth[,c("sic87dd","l_import_otch_2000","l_import_otch_2007")]
# imports_oth[,chg_st_ed:=l_import_otch_2007-l_import_otch_2000]
#
# cbp <- fread(paste0("C:/Users/dratnadiwakara2/Documents/OneDrive - Louisiana State University/Raw Data/CBP/county/cbp",substr(st_yr-2,3,4),"co.txt"))
# cbp[,naics:=as.numeric(naics)]
# cbp <- cbp[!is.na(naics)]
#
# cbp[,imp_emp:=n1_4*2.5+n5_9*7+n10_19*15+n20_49*35+n50_99*75+n100_249*175+n250_499*375+n500_999*750+n1000_1*1250+n1000_2*2000+n1000_3*3750+n1000_4*10000]
#
# cbp[,imp_emp:=ifelse(!is.na(emp) & emp>0,emp,imp_emp)]
#
# cbp[,fips:=paste0(str_pad(fipstate,2,"left","0"),str_pad(fipscty,3,"left","0"))]
#
# cbp <- cbp[,c("fips","naics","imp_emp")]
#
# naics_sic <- fread("David Dorn/NAICS97_6_to_SIC87_4.csv")
# cbp <- merge(cbp,naics_sic,by.x="naics",by.y="naics6",allow.cartesian = T)
# cbp[,imp_emp:=imp_emp*weight]
#
# cbp[,emp_sic_n:=sum(imp_emp),by=sic4]
# cbp[,emp_fips:=sum(imp_emp),by=fips]
#
#
# emp_naics_oc <- merge(cbp,imports_oth[,c("chg_st_ed","sic87dd")],by.x="sic4",by.y="sic87dd",all.x=T)
#
# emp_naics_oc[,chg_per_employee:=chg_st_ed/emp_sic_n]
#
# emp_naics_oc[,w_chg_per_employee:=chg_per_employee*imp_emp/emp_fips]
#
# # tradeotch_pw_lag_dr is our approximation of tradeotch_pw_lag in ADH
# emp_naics_oc <- emp_naics_oc[,.(iv_import_sensitivity=sum(w_chg_per_employee,na.rm=T)),by=fips]
#
# measures <- merge(emp_czone,emp_naics_oc,by="fips")
#
# measures[,x_bin:=ntile(import_sensitivity,3)]
# measures[,iv_bin:=ntile(iv_import_sensitivity,100)]
#
# saveRDS(measures,"measures_county_levels_sic87dd.rds")# pop_data <- list()
#
# pop_2000 <- get_decennial(geography = "county", variables = "P001001",year = 2000)
# pop_2000 <- pop_2000[,c("GEOID","value")]
# names(pop_2000) <- c("fips","population")
# pop_2000 <- data.table(pop_2000)
# pop_2000[,fips:=as.numeric(fips)]
# pop_2000[,year:=2000]
# pop_data[[1]] <- pop_2000
#
# #https://repository.duke.edu/catalog/f49b199b-1496-4636-91f3-36226c8e7f80
# pop_csv <- fread("county_population_2000_2010.csv")
# pop_csv <- pop_csv[year %in% 2001:2009, c("fips","tot_pop","year")]
# names(pop_csv) <- c("fips","population","year")
# pop_data[[2]] <- pop_csv
#
# i=3
# for(yr in 2010:2019){
# pop <- get_acs(geography = "county", variables = c("B01003_001"), year = yr)
# pop <- data.table(pop)
# pop <- dcast(pop,GEOID~variable,value.var = "estimate",fun.aggregate = sum)
# names(pop) <- c("fips","population")
# pop[,year:=yr]
# pop_data[[i]] <- pop
# i=i+1
# }
#
# pop_data <- do.call(rbind,pop_data)
#
# pop_data[,fips:=str_pad(as.character(fips),width = 5,side = "left",pad = "0")]
#
#
#
# #https://apps.bea.gov/regional/downloadzip.cfm
# county_data <- fread("bea_data_1969_2021.csv")
#
# county_data[,variable:=ifelse(Description=="Personal income (thousands of dollars)","total_income",
# ifelse(Description=="Population (persons) 1/","population",
# ifelse(Description=="Per capita personal income (dollars) 2/","income_per_capita","")))]
#
# county_data <- county_data[variable %in% c("income_per_capita")]
# county_data[,c("GeoName","Region","TableName","LineCode","IndustryClassification","Unit","Description"):=list(NULL)]
# setnames(county_data,"GeoFIPS","fips")
#
# county_data <- melt(county_data,id.vars = c("fips","variable"))
# county_data[,value:=as.numeric(value)]
# county_data <- dcast(county_data,fips+variable.1~variable,value.var = "value",fun.aggregate = sum)
# county_data[,variable.1:=as.numeric(as.character(variable.1))]
#
# setnames(county_data,"variable.1","year")
#
# unemp <- fread("la.data.64.County.txt")
# unemp <- unemp[substr(series_id,1,5)=="LAUCN"]
# unemp[,fips:=substr(series_id,6,10)]
# unemp <- unemp[substr(series_id,19,20)=="03"]
# unemp[,value:=as.numeric(value)]
# unemp <- unemp[,.(unemp_rate=mean(value,na.rm=T)),by=.(fips,year)]
# unemp[,c("series_id","period","footnote_codes"):=list(NULL)]
#
# county_data <- merge(county_data,unemp,by=c("fips","year"))
# county_data <- merge(county_data,pop_data,by=c("fips","year"))
#
# saveRDS(county_data,"county_data.rds")# sod <- list()
# i=1
# for(fl in list.files(path="C:/Users/dratnadiwakara2/Documents/OneDrive - Louisiana State University/Raw Data/SOD/data",full.names = T)){
# sod[[i]] <- fread(fl,select = c("YEAR","CERT","DEPSUMBR","DEPSUM","RSSDHCR","RSSDID","STCNTYBR","ASSET","UNINUMBR","SIMS_ESTABLISHED_DATE","SIMS_LATITUDE","SIMS_LONGITUDE","ADDRESBR"))
# i=i+1
# }
#
# sod <- rbindlist(sod,fill=T)
#
# sod[,DEPSUMBR:=str_remove_all(DEPSUMBR,",")]
# sod[,DEPSUM:=str_remove_all(DEPSUM,",")]
# sod[,ASSET:=str_remove_all(ASSET,",")]
#
# sod[,DEPSUMBR:= as.numeric(DEPSUMBR)]
# sod[,DEPSUM:= as.numeric(DEPSUM)]
# sod[,ASSET:= as.numeric(ASSET)]
#
# sod[,fips:=str_pad(STCNTYBR,5,"left","0")]
# sod[,SIMS_ESTABLISHED_DATE:=as.Date(SIMS_ESTABLISHED_DATE,"%m/%d/%Y")]
#
# sod[,br_id:=paste0(tolower(ADDRESBR),round(SIMS_LATITUDE,1),round(SIMS_LONGITUDE,1))]
#
#
#
#
# br <- list()
# i <- 1
# for(yr in 2001:2020){
# temp <- sod[YEAR==yr]
# temp_p <- sod[YEAR==(yr-1)]
# temp_a <- sod[YEAR==(yr+1)]
#
# # openings <- temp[year(SIMS_ESTABLISHED_DATE)==yr]
# openings <- temp[DEPSUMBR> 0 & !UNINUMBR %in% temp_p[DEPSUMBR> 0]$UNINUMBR]
# openings <- rbind(openings,temp[DEPSUMBR> 0 & !br_id %in% temp_p[DEPSUMBR> 0]$br_id])
# openings <- openings[!duplicated(openings)]
# openings <- openings[,.(openings=.N),by=fips]
#
#
# closings <- temp[DEPSUMBR> 0 & !UNINUMBR %in% temp_a[DEPSUMBR> 0]$UNINUMBR]
# closings <- rbind(closings,temp[DEPSUMBR> 0 & !br_id %in% temp_a[DEPSUMBR> 0]$br_id])
# closings <- closings[!duplicated(closings)]
# closings <- closings[,.(closings=.N),by=fips]
#
# temp<- data.table(fips=unique(sod$fips),YEAR=yr)
# temp <- merge(temp,openings,by="fips",all.x = T)
# temp <- merge(temp,closings,by="fips",all.x = T)
# temp[is.na(temp)] <- 0
#
# br[[i]] <- temp
# i=i+1
# }
#
# br <- do.call(rbind,br)
# setorder(br,fips,YEAR)
#
#
# saveRDS(sod,"sod_sum.rds")
# saveRDS(br,"br_open_close.rds")measures <- readRDS("measures_county_levels_sic87dd.rds")
measures[,import_sensitivity:=(import_sensitivity-min(measures$import_sensitivity,na.rm=T)+1)]
measures[,iv_import_sensitivity:=(iv_import_sensitivity-min(measures$iv_import_sensitivity,na.rm=T)+1)]
measures[,high_x:=ntile(import_sensitivity,2)]
measures[,is_q:=ntile(import_sensitivity,4)]
measures[,is_top_q:=ifelse(is_q==4,1,0)]
measures[,iv_q:=ntile(iv_import_sensitivity,4)]
measures[,iv_top_q:=ifelse(iv_q==4,1,0)]
county_data <- readRDS("county_data.rds")
sod <- readRDS("sod_sum.rds")1 Growth of US imports from China
us_imports_china <- fread("us_imports_china.csv")
ggplot(us_imports_china,aes(x=year,y=ch_imports_bn))+
geom_rect(mapping=aes(xmin=2000, xmax=2008, ymin=0, ymax=550),fill="gray90", alpha=0.9)+
geom_line()+geom_point()+
theme_minimal()+labs(x="",y="USD billions",title="U.S. imports of trade goods from China ")
2 Change in number of branches, deposits, and deposit concentration
Import sensitivity for each county was calculated by following
Autor,Dorn, and Hanson (2013). Data period 2000 to 2007.
Counties are categorized in to three groups: 1 - lowest third
sensitivity, 2 - middle third senstivitiy, 3 - top third sensitivity
County-level per capita number of branches and per capita deposits were normalized using the year 2000 values.
sod_county_year <- sod[,.(no_branches=.N,total_deposits=sum(DEPSUMBR,na.rm=T)),by=.(fips,YEAR)]
sod_county_year <- merge(sod_county_year,measures,by="fips")
sod_county_year <- merge(sod_county_year,county_data,by.y=c("fips","year"),by.x=c("fips","YEAR"))
sod_county_year[,no_branches:=no_branches/population]
sod_county_year[,total_deposits:=total_deposits/population]
sod_cy_sum <- sod_county_year[,.(no_branches=mean(no_branches),total_deposits=median(total_deposits)),by=.(YEAR,x_bin)]
temp <-sod_cy_sum[YEAR==2000,c("x_bin","no_branches","total_deposits")]
names(temp) <- c("x_bin","no_branches_st","total_deposits_st")
sod_cy_sum <- merge(sod_cy_sum,temp,by="x_bin")
sod_cy_sum[,no_branches:=no_branches/no_branches_st]
sod_cy_sum[,total_deposits:=total_deposits/total_deposits_st]ggplot(sod_cy_sum[YEAR>=2000],aes(x=YEAR,y=no_branches,color=factor(x_bin)))+geom_line()+geom_point()+
theme_minimal()+
theme(legend.position = "bottom")+
ggtitle("Per capita number of branches")
ggplot(sod_cy_sum[YEAR>=2000 & YEAR<2020],aes(x=YEAR,y=total_deposits,color=factor(x_bin)))+geom_line()+geom_point()+
theme_minimal()+
theme(legend.position = "bottom")+
ggtitle("Per capita deposits")
sod_county_year <- sod[,.(bank_deposits=sum(DEPSUMBR,na.rm=T)),by=.(fips,YEAR,RSSDID)]
sod_county_year[,total_deposits:=sum(bank_deposits,na.rm=T),by=.(fips,YEAR)]
sod_county_year[,mkt_share2:=bank_deposits/total_deposits]
sod_county_year[,mkt_share2:=mkt_share2*mkt_share2]
sod_hhi <- sod_county_year[,.(hhi=sum(mkt_share2,na.rm = T)),by=.(fips,YEAR)]
sod_hhi <- merge(sod_hhi,measures,by="fips")
sod_hhi[, hhi := Winsorize(hhi, probs = c(0.025,0.975), na.rm = TRUE)]
sod_hhi <- sod_hhi[,.(hhi_mean=mean(hhi),hhi_median=median(hhi),hhi_q1=quantile(hhi,0.25),hhi_q3=quantile(hhi,0.75)),by=.(YEAR,x_bin)]
temp <-sod_hhi[YEAR==2000,c("x_bin","hhi_mean","hhi_median")]
names(temp) <- c("x_bin","hhi_mean_st","hhi_median_st")
sod_hhi <- merge(sod_hhi,temp,by="x_bin")
sod_hhi[,hhi_mean:=hhi_mean/hhi_mean_st]
sod_hhi[,hhi_median:=hhi_median/hhi_median_st]ggplot(sod_hhi[YEAR>=2000 & YEAR<2020],aes(x=YEAR,y=hhi_mean,color=factor(x_bin)))+geom_line()+geom_point()+
theme_minimal()+
theme(legend.position = "bottom")+
ggtitle("Deposit concentration")
sod_county_year <- sod[,.(bank_deposits=sum(DEPSUMBR,na.rm=T)),by=.(fips,YEAR,RSSDID)]
sod_county_year[,total_deposits:=sum(bank_deposits,na.rm=T),by=.(fips,YEAR)]
sod_county_year[,mkt_share2:=bank_deposits/total_deposits]
sod_county_year[,mkt_share2:=mkt_share2*mkt_share2]
sod_hhi <- sod_county_year[,.(hhi=sum(mkt_share2,na.rm = T)),by=.(fips,YEAR)]
sod_chg <- sod[,.(no_branches=.N,total_deposits=sum(DEPSUMBR,na.rm=T)),by=.(fips,YEAR)]
sod_chg <- merge(sod_chg,county_data,by.x=c("fips","YEAR"),by.y = c("fips","year"))
sod_chg <- merge(sod_chg,sod_hhi,by= c("fips","YEAR"))
setorder(sod_chg,fips,YEAR)
sod_chg[,no_branches:=no_branches/population]
sod_chg[,total_deposits:=total_deposits/population]
sod_chg[,hhi_lag:=shift(hhi),by=fips]
sod_chg[,no_branches_lag:=shift(no_branches),by=fips]
sod_chg[,total_deposits_lag:=shift(total_deposits),by=fips]
sod_chg[,delta_hhi:=hhi/hhi_lag]
sod_chg[,delta_no_branches:=no_branches/no_branches_lag]
sod_chg[,delta_total_deposits:=total_deposits/total_deposits_lag]
sod_chg <- merge(sod_chg,measures,by="fips")
sod_chg[,state:=substr(fips,1,2)]
sod_chg[,state_yr:=paste(state,YEAR)]r <- list()
r[[1]] <- felm(log(delta_no_branches)~is_top_q|YEAR|0|fips,data=sod_chg[YEAR %in% 2000:2007])
r[[2]] <- felm(log(delta_total_deposits)~is_top_q|YEAR|0|fips,data=sod_chg[YEAR %in% 2000:2007])
r[[3]] <- felm(log(delta_hhi)~is_top_q|YEAR|0|fips,data=sod_chg[YEAR %in% 2000:2007])
r[[4]] <- felm(log(delta_no_branches)~is_top_q|YEAR|0|fips,data=sod_chg[YEAR %in% 2008:2019])
r[[5]] <- felm(log(delta_total_deposits)~is_top_q|YEAR|0|fips,data=sod_chg[YEAR %in% 2008:2019])
r[[6]] <- felm(log(delta_hhi)~is_top_q|YEAR|0|fips,data=sod_chg[YEAR %in% 2008:2019])
stargazer(r,type="text",omit.stat = "ser",column.labels = c("2000-2007","2008-2019"),column.separate = c(3,3))##
## ============================================================================================================================================
## Dependent variable:
## -------------------------------------------------------------------------------------------------------------------------------
## log(delta_no_branches) log(delta_total_deposits) log(delta_hhi) log(delta_no_branches) log(delta_total_deposits) log(delta_hhi)
## 2000-2007 2008-2019
## (1) (2) (3) (4) (5) (6)
## --------------------------------------------------------------------------------------------------------------------------------------------
## is_top_q -0.004*** -0.005*** 0.002* -0.001* -0.005*** -0.001
## (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)
##
## --------------------------------------------------------------------------------------------------------------------------------------------
## Observations 19,277 19,277 19,277 33,019 33,019 33,019
## R2 0.002 0.013 0.002 0.015 0.007 0.003
## Adjusted R2 0.002 0.013 0.001 0.014 0.007 0.002
## ============================================================================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.013 Deposit and Branch Change from 2008 to 2019
main_st <- 2008
main_ed <- 2019
cont_st <- 2000
cont_ed <- 2007
# all 2008-2019
sod_st <- sod[YEAR==main_st,.(no_branches_st=.N,total_deposits_st=sum(DEPSUMBR,na.rm=T)),by=fips]
sod_st <- merge(sod_st,county_data[year==main_st],by="fips")
sod_st[,no_branches_pc_st:=no_branches_st/population]
sod_st[,total_deposits_pc_st:=total_deposits_st/population]
setnames(sod_hhi,"hhi","hhi_st")
sod_st <- merge(sod_st,sod_hhi[YEAR==main_st],by=c("fips"))
sod_ed <- sod[YEAR==main_ed,.(no_branches_ed=.N,total_deposits_ed=sum(DEPSUMBR,na.rm=T)),by=fips]
sod_ed <- merge(sod_ed,county_data[year==main_ed],by="fips")
sod_ed[,no_branches_pc_ed:=no_branches_ed/population]
sod_ed[,total_deposits_pc_ed:=total_deposits_ed/population]
setnames(sod_ed,c("income_per_capita","unemp_rate","population"),c("income_per_capita_ed","unemp_rate_ed","population_ed"))
setnames(sod_hhi,"hhi_st","hhi_ed")
sod_st <- merge(sod_st,sod_hhi[YEAR==main_ed],by=c("fips"))
sod_chg <- merge(sod_st,sod_ed,by="fips")
sod_chg[,sod_br_change:=no_branches_ed*100/no_branches_st-1]
sod_chg[,sod_deposit_change:=total_deposits_ed*100/total_deposits_st-1]
sod_chg[,sod_br_change_pc:=no_branches_pc_ed*100/no_branches_pc_st-1]
sod_chg[,sod_deposit_change_pc:=total_deposits_pc_ed*100/total_deposits_pc_st-1]
sod_chg[,hhi_change:=hhi_ed*100/hhi_st-1]
# all banks 2000-2008
sod_st <- sod[YEAR==cont_st,.(no_branches_st=.N,total_deposits_st=sum(DEPSUMBR,na.rm=T)),by=fips]
setnames(sod_hhi,"hhi_ed","hhi_st")
sod_st <- merge(sod_st,sod_hhi[YEAR==cont_st],by=c("fips"))
sod_ed <- sod[YEAR==cont_ed,.(no_branches_ed=.N,total_deposits_ed=sum(DEPSUMBR,na.rm=T)),by=fips]
setnames(sod_hhi,"hhi_st","hhi_ed")
sod_st <- merge(sod_st,sod_hhi[YEAR==cont_ed],by=c("fips"))
sod_chg_s <- merge(sod_st,sod_ed)
sod_chg_s[,sod_br_change_cont:=no_branches_ed*100/no_branches_st-1]
sod_chg_s[,sod_deposit_change_cont:=total_deposits_ed*100/total_deposits_st-1]
sod_chg_s[,hhi_change_cont:=hhi_ed*100/hhi_st-1]
sod_chg_s <- sod_chg_s[,c("fips","sod_br_change_cont","sod_deposit_change_cont","hhi_change_cont")]
reg_sample <- merge(sod_chg,sod_chg_s,by="fips",all.x=T)
reg_sample <- merge(reg_sample,measures,by="fips")
reg_sample[,import_sensitivity:=(import_sensitivity-min(reg_sample$import_sensitivity,na.rm=T)+1)]
reg_sample[,iv_import_sensitivity:=(iv_import_sensitivity-min(reg_sample$iv_import_sensitivity,na.rm=T)+1)]
reg_sample[,statefips:=substr(fips,1,2)]This is similar to main results of Autor,Dorn, and Hanson
(2013), but at county-level.
log(import sensitivity) is instrumented using
log(iv import sensitivity) which is based on the import
growth in other developed countries. (same as Autor,Dorn, and Hanson
(2013))
3.1 First Stage
log-log specification has more first stage power.
ggplot(reg_sample[log(iv_import_sensitivity)>1 & log(iv_import_sensitivity)<2.5],aes(x=log(iv_import_sensitivity),y=log(import_sensitivity)))+geom_point()+geom_smooth(method="lm")
3.2 Descriptive statistics
var_list <- c("sod_br_change","sod_br_change_pc","sod_deposit_change","sod_deposit_change_pc","hhi_change","population","income_per_capita","import_sensitivity","iv_import_sensitivity")
stargazer(reg_sample[,..var_list],type="text",summary.stat = c("mean","sd","p25","median","p75","N"))##
## ============================================================================
## Statistic Mean St. Dev. Pctl(25) Median Pctl(75) N
## ----------------------------------------------------------------------------
## sod_br_change 88.593 17.176 78.851 89.476 99.000 2,747
## sod_br_change_pc 87.814 18.186 76.413 87.751 99.908 2,747
## sod_deposit_change 133.560 46.108 111.904 127.371 146.880 2,747
## sod_deposit_change_pc 131.313 39.927 112.738 127.168 143.827 2,747
## hhi_change 107.430 27.173 93.006 102.559 117.198 2,747
## population 86,658.340 228,507.200 11,103 25,981 64,391 2,747
## income_per_capita 33,771.630 8,775.446 28,247.5 32,400 37,330 2,747
## import_sensitivity 14.522 4.979 12.747 13.561 14.940 2,747
## iv_import_sensitivity 4.659 3.436 3.162 3.915 5.150 2,747
## ----------------------------------------------------------------------------3.3 Impact on county-level branches
The first table below regresses the change in number of branches at
county level from 2008 to 2019. Column 4 uses
per capita
branches change
controls = "log(income_per_capita)+log(population)"3.3.1 OLS
r <- list()
r[[1]] <- felm(sod_br_change_cont~is_top_q+log(income_per_capita)+log(population)|statefips|0|statefips,data=reg_sample)
r[[2]] <- felm(sod_deposit_change_cont~is_top_q+log(income_per_capita)+log(population)|statefips|0|statefips,data=reg_sample)
r[[3]] <- felm(hhi_change_cont~is_top_q+log(income_per_capita)+log(population)|statefips|0|statefips,data=reg_sample)
r[[4]] <- felm(sod_br_change_pc~is_top_q+log(income_per_capita)+log(population)|statefips|0|statefips,data=reg_sample)
r[[5]] <- felm(sod_deposit_change_pc~is_top_q+log(income_per_capita)+log(population)|statefips|0|statefips,data=reg_sample)
r[[6]] <- felm(hhi_change~is_top_q+log(income_per_capita)+log(population)|statefips|0|statefips,data=reg_sample)
stargazer(r,type="text",omit.stat = "ser",no.space = T,column.labels = c("2000-2007","2008-2019"),column.separate = c(3,3))##
## ===================================================================================================================================
## Dependent variable:
## ------------------------------------------------------------------------------------------------------------
## sod_br_change_cont sod_deposit_change_cont hhi_change_cont sod_br_change_pc sod_deposit_change_pc hhi_change
## 2000-2007 2008-2019
## (1) (2) (3) (4) (5) (6)
## -----------------------------------------------------------------------------------------------------------------------------------
## is_top_q -0.893 0.061 2.452** 0.410 -0.785 -1.317
## (0.924) (3.326) (1.173) (0.747) (2.192) (1.512)
## log(income_per_capita) 13.269*** 37.849*** -0.423 -0.756 25.548*** -8.547**
## (2.781) (8.484) (2.752) (2.110) (4.639) (4.052)
## log(population) 3.950*** 10.122*** 0.594 -3.642*** 1.313 0.557
## (0.921) (2.345) (0.870) (0.373) (1.177) (0.596)
## -----------------------------------------------------------------------------------------------------------------------------------
## Observations 2,745 2,745 2,745 2,747 2,747 2,747
## R2 0.127 0.168 0.038 0.235 0.142 0.043
## Adjusted R2 0.113 0.155 0.022 0.223 0.127 0.027
## ===================================================================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.013.3.2 IV - 1
r <- list()
r[[1]] <- felm(sod_br_change_cont~log(income_per_capita)+log(population)|statefips|(log(import_sensitivity)~log(iv_import_sensitivity))|statefips,data=reg_sample)
r[[2]] <- felm(sod_deposit_change_cont~log(income_per_capita)+log(population)|statefips|(log(import_sensitivity)~log(iv_import_sensitivity))|statefips,data=reg_sample)
r[[3]] <- felm(hhi_change_cont~log(income_per_capita)+log(population)|statefips|(log(import_sensitivity)~log(iv_import_sensitivity))|statefips,data=reg_sample)
r[[4]] <- felm(sod_br_change_pc~log(income_per_capita)+log(population)|statefips|(log(import_sensitivity)~log(iv_import_sensitivity))|statefips,data=reg_sample)
r[[5]] <- felm(sod_deposit_change_pc~log(income_per_capita)+log(population)|statefips|(log(import_sensitivity)~log(iv_import_sensitivity))|statefips,data=reg_sample)
r[[6]] <- felm(hhi_change~log(income_per_capita)+log(population)|statefips|(log(import_sensitivity)~log(iv_import_sensitivity))|statefips,data=reg_sample)
stargazer(r,type="text",omit.stat = "ser",no.space = T,column.labels = c("2000-2007","2008-2019"),column.separate = c(3,3))##
## ===========================================================================================================================================
## Dependent variable:
## ------------------------------------------------------------------------------------------------------------
## sod_br_change_cont sod_deposit_change_cont hhi_change_cont sod_br_change_pc sod_deposit_change_pc hhi_change
## 2000-2007 2008-2019
## (1) (2) (3) (4) (5) (6)
## -------------------------------------------------------------------------------------------------------------------------------------------
## log(income_per_capita) 12.996*** 36.985*** -0.709 -1.197 25.455*** -7.652*
## (2.702) (8.236) (2.656) (2.023) (4.715) (3.797)
## log(population) 4.051*** 10.352*** 0.595 -3.536*** 1.362 0.357
## (0.923) (2.366) (0.863) (0.369) (1.096) (0.594)
## `log(import_sensitivity)(fit)` -7.755** -14.992* 3.094 -6.368*** -4.231 11.325**
## (3.605) (7.861) (3.117) (2.089) (9.581) (4.526)
## -------------------------------------------------------------------------------------------------------------------------------------------
## Observations 2,745 2,745 2,745 2,747 2,747 2,747
## R2 0.129 0.168 0.036 0.236 0.141 0.043
## Adjusted R2 0.115 0.154 0.020 0.223 0.127 0.027
## ===========================================================================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.013.3.3 IV - 2
r <- list()
r[[1]] <- felm(sod_br_change_cont~log(income_per_capita)+log(population)|statefips|(is_top_q~log(iv_import_sensitivity))|statefips,data=reg_sample)
r[[2]] <- felm(sod_deposit_change_cont~log(income_per_capita)+log(population)|statefips|(is_top_q~log(iv_import_sensitivity))|statefips,data=reg_sample)
r[[3]] <- felm(hhi_change_cont~log(income_per_capita)+log(population)|statefips|(is_top_q~log(iv_import_sensitivity))|statefips,data=reg_sample)
r[[4]] <- felm(sod_br_change_pc~log(income_per_capita)+log(population)|statefips|(is_top_q~log(iv_import_sensitivity))|statefips,data=reg_sample)
r[[5]] <- felm(sod_deposit_change_pc~log(income_per_capita)+log(population)|statefips|(is_top_q~log(iv_import_sensitivity))|statefips,data=reg_sample)
r[[6]] <- felm(hhi_change~log(income_per_capita)+log(population)|statefips|(is_top_q~log(iv_import_sensitivity))|statefips,data=reg_sample)
stargazer(r,type="text",omit.stat = "ser",no.space = T,column.labels = c("2000-2007","2008-2019"),column.separate = c(3,3))##
## ===================================================================================================================================
## Dependent variable:
## ------------------------------------------------------------------------------------------------------------
## sod_br_change_cont sod_deposit_change_cont hhi_change_cont sod_br_change_pc sod_deposit_change_pc hhi_change
## 2000-2007 2008-2019
## (1) (2) (3) (4) (5) (6)
## -----------------------------------------------------------------------------------------------------------------------------------
## log(income_per_capita) 12.676*** 36.366*** -0.581 -1.460 25.281*** -7.185*
## (2.728) (8.202) (2.681) (1.981) (4.766) (3.842)
## log(population) 4.012*** 10.277*** 0.610 -3.568*** 1.341 0.414
## (0.919) (2.375) (0.866) (0.370) (1.133) (0.594)
## `is_top_q(fit)` -4.043* -7.815** 1.613 -3.320*** -2.206 5.904**
## (2.013) (3.692) (1.651) (1.114) (4.879) (2.506)
## -----------------------------------------------------------------------------------------------------------------------------------
## Observations 2,745 2,745 2,745 2,747 2,747 2,747
## R2 0.124 0.166 0.038 0.229 0.141 0.032
## Adjusted R2 0.110 0.152 0.022 0.216 0.127 0.016
## ===================================================================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.014 Deposit Rates
measures <- readRDS("measures_county_levels_sic87dd.rds")
measures[,import_sensitivity:=(import_sensitivity-min(measures$import_sensitivity,na.rm=T)+1)]
measures[,iv_import_sensitivity:=(iv_import_sensitivity-min(measures$iv_import_sensitivity,na.rm=T)+1)]
measures[,high_x:=ntile(import_sensitivity,2)-1]
measures[,low_x:=2-high_x]
measures[,rev_x_bin:=3-x_bin]
measures[,is_q:=ntile(import_sensitivity,4)]
measures[,is_top_q:=ifelse(is_q==4,1,0)]sod <- readRDS("sod_sum.rds")
asset_w <- sod[,c("CERT","YEAR","ASSET")]
asset_w <- asset_w[!duplicated(asset_w)]
asset_w[,tot_assets:=sum(ASSET,na.rm=T),by=YEAR]
asset_w[,w_asset:=ASSET/tot_assets]
asset_w[,q80:=quantile(ASSET,0.8,na.rm=T),by=YEAR]
asset_w[,q95:=quantile(ASSET,0.95,na.rm=T),by=YEAR]
asset_w[,size:=ifelse(ASSET <q80,1,ifelse(ASSET<q95,2,0))]
bank_import_exposure <- sod[YEAR %in% 1994:2000]
bank_import_exposure <- bank_import_exposure[,.(county_deposits=sum(DEPSUMBR,na.rm=T)),by=.(CERT,fips)]
bank_import_exposure <- merge( bank_import_exposure,measures,by="fips")
bank_import_exposure[,total_deposits:=sum(county_deposits,na.rm=T),by=CERT]
bank_import_exposure[,wa_exposure:=county_deposits*import_sensitivity/total_deposits]
bank_import_exposure <- bank_import_exposure[,.(wa_exposure=sum(wa_exposure,na.rm=T)),by=CERT]
bank_import_exposure[,exp_q:=ntile(wa_exposure,4)]
bank_import_exposure[,exp_top_q:=ifelse(exp_q==4,1,0)]
bank_import_exposure[,exp_bin:=ntile(bank_import_exposure,3)]rw_inst <- fread("C:/Users/dratnadiwakara2/Documents/OneDrive - Louisiana State University/Raw Data/RateWatch/Deposit_InstitutionDetails.txt")
rw_inst <- rw_inst[,c("ACCT_NBR","RSSD_ID","CERT_NBR","STATE_FPS","CNTY_FPS")]
rw_inst[,fips:=paste0(str_pad(STATE_FPS,2,"left","0"),str_pad(CNTY_FPS,3,"left","0"))]
rw_inst[,c("STATE_FPS","CNTY_FPS"):=list(NULL)]
rw_summary <- readRDS("C:/Users/dratnadiwakara2/Documents/OneDrive - Louisiana State University/Raw Data/RateWatch/rate_watch_summary_12MCD10K_weekly.rds")
rw_summary[,year:=year(qtr)]
# rw_summary <- rw_summary[,.(mean_apy=mean(mean_apy,na.rm=T),median_apy=median(median_apy,na.rm=T)),by=.(year,ACCOUNTNUMBER,PRD_TYP_JOIN)]
rw_summary <- merge(rw_summary,rw_inst,by.x="ACCOUNTNUMBER",by.y="ACCT_NBR")
# rw_bank <- rw_summary[,.(mean_apy=mean(mean_apy,na.rm=T),median_apy=median(median_apy,na.rm = T)),by=.(year,PRD_TYP_JOIN,CERT_NBR)]
# rw_county <- rw_summary[,.(mean_apy=mean(mean_apy,na.rm=T),median_apy=median(median_apy,na.rm = T)),by=.(year,PRD_TYP_JOIN,fips)]
# rw_bank_county <- rw_summary[,.(mean_apy=mean(mean_apy,na.rm=T),median_apy=median(median_apy,na.rm = T)),by=.(year,PRD_TYP_JOIN,CERT_NBR,fips)]reg_sample <- merge(measures,rw_summary,by="fips")
reg_sample <- merge(bank_import_exposure,reg_sample,by.x="CERT",by.y="CERT_NBR")
reg_sample[,fips_qt:=paste(fips,qtr)]
reg_sample[,bank_qt:=paste(CERT,qtr)]
reg_sample[,state:=substr(fips,1,2)]
reg_sample[,state_qt:=paste(state,year)]
reg_sample <- reg_sample[log(1+wa_exposure)>=2 & log(1+wa_exposure)<=4 & log(import_sensitivity)>=2 & log(import_sensitivity)<=4]
reg_sample <- merge(reg_sample,asset_w[,c("CERT","YEAR","w_asset","ASSET","size")],by.x=c("CERT","year"),by.y=c("CERT","YEAR"))
reg_sample[,is_top_q_exp_top_q:=ifelse(is_top_q==1 & exp_top_q==1,1,0)]
reg_sample[,mn:=mean(mean_apy),by=qtr]
reg_sample[,rate_diff:=mean_apy-mn]Data: The following results are based on RateWatch data. RateWatch data starts in 2001, and provides deposit rates for different products offered by each branch weekly.
Sample: Bank-county-year-product level. For each, bank-county-year-product, mean_apy, was calculated and was used as the dependent variable. Key product types are CD, SAV, MM, and INTCK (checking).
Key variables:
import_sensitivity: County-level sensitivity to Chinese imports based on Autor et al, 2013
is_top_q: This is a dummy variable that takes the value 1 if the import_sensitivity is in the forth quartile
wa_exposure: This is a bank-level variable, which calculates the weighted average exposure to chinese imports based on the county-level deposit composition
exp_top_q: takes the value 1 if wa_exposure is in the forth quartile
Specification: County, Year, Bank, and Product type fixed effects. Standard errors clustered at bank level
Main takeaway: Banks that are more exposed to Chinese imports (based on wa_exposure) offer higher rates in counties that are less exposed to Chinese imports?
4.1 12 Month CD rates overtime
cd_rates <- reg_sample[,.(mean_apy=mean(mean_apy),sd_apy=sd(mean_apy),count=.N),by=qtr]
cd_rates <- cd_rates %>%
mutate(
lower_ci = mean_apy - 1.96 * sd_apy ,
upper_ci = mean_apy + 1.96 * sd_apy
)ggplot(cd_rates, aes(x = qtr, y = mean_apy)) +
geom_line() +
geom_ribbon(aes(ymin = lower_ci, ymax = upper_ci), alpha = 0.3) +
labs(x = "", y = "12 Month 10k CD Rate") +
theme_minimal()
4.2 12 Month CD rates overtime - by exposure
cd_rates <- reg_sample[,.(mean_apy=mean(rate_diff),sd_apy=sd(mean_apy),count=.N),by=.(year,is_top_q_exp_top_q)]
cd_rates[,mean_apy_min:=mean_apy[which.min(year)],by=is_top_q_exp_top_q]
cd_rates[,mean_apy:=mean_apy-mean_apy_min]ggplot(cd_rates, aes(x = year, y = mean_apy,color=factor(is_top_q_exp_top_q))) +
geom_line() +
labs(x = "", y = "12 Month 10k CD Rate") +
theme_minimal()
4.3 Effect of County Exposure on deposit rates
\[ apy=log(import.sensitivity) + State*Week FE+Bank*WeekFE\]
Q: “Before going to the interaction of county exposure and bank exposure, did you first examine the effect of county exposure alone on deposit rates? Basically the same regression with county, year, bank and product type fixed effects.”
A: Not able to use county fixed effects since import sensitivity is not time varying. Used state*yr fixed effects instead
4.3.1 All Banks
min_yr = 2000
max_yr = 2007
r <- list()
reg_formula <- as.formula("mean_apy~log(import_sensitivity)|state_qt+bank_qt|0|CERT")
r[[1]] <- felm(reg_formula,data=reg_sample[year>=min_yr & year<=max_yr])
reg_formula <- as.formula("mean_apy~factor(is_q)|state_qt+bank_qt|0|CERT")
r[[2]] <- felm(reg_formula,data=reg_sample[ year>=min_yr & year<=max_yr])
reg_formula <- as.formula("mean_apy~is_top_q|state_qt+bank_qt|0|CERT")
r[[3]] <- felm(reg_formula,data=reg_sample[ year>=min_yr & year<=max_yr])
min_yr = 2008
max_yr = 2019
reg_formula <- as.formula("mean_apy~log(import_sensitivity)|state_qt+bank_qt|0|CERT")
r[[4]] <- felm(reg_formula,data=reg_sample[year>=min_yr & year<=max_yr])
reg_formula <- as.formula("mean_apy~factor(is_q)|state_qt+bank_qt|0|CERT")
r[[5]] <- felm(reg_formula,data=reg_sample[ year>=min_yr & year<=max_yr])
reg_formula <- as.formula("mean_apy~is_top_q|state_qt+bank_qt|0|CERT")
r[[6]] <- felm(reg_formula,data=reg_sample[ year>=min_yr & year<=max_yr])
stargazer(r,type="text",column.labels = c("2000-2007","2008-2019"),column.separate = c(3,3),omit.stat = "ser")##
## ===================================================================================
## Dependent variable:
## -----------------------------------------------------------
## mean_apy
## 2000-2007 2008-2019
## (1) (2) (3) (4) (5) (6)
## -----------------------------------------------------------------------------------
## log(import_sensitivity) 0.030 -0.047**
## (0.029) (0.019)
##
## factor(is_q)2 0.002 0.002
## (0.013) (0.009)
##
## factor(is_q)3 -0.002 -0.018*
## (0.015) (0.010)
##
## factor(is_q)4 -0.003 -0.021**
## (0.016) (0.010)
##
## is_top_q -0.003 -0.012**
## (0.010) (0.006)
##
## -----------------------------------------------------------------------------------
## Observations 3,187,263 3,187,263 3,187,263 4,316,471 4,316,471 4,316,471
## R2 0.935 0.935 0.935 0.960 0.960 0.960
## Adjusted R2 0.883 0.883 0.883 0.907 0.907 0.907
## ===================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.014.3.2 By Bank Size
Size=1 => Small Bank
Size=2 => Medium Bank
Size=0
=> Large Bank
min_yr = 2000
max_yr = 2007
r <- list()
reg_formula <- as.formula("mean_apy~log(import_sensitivity)*factor(size)|state_qt+bank_qt|0|CERT")
r[[1]] <- felm(reg_formula,data=reg_sample[year>=min_yr & year<=max_yr])
reg_formula <- as.formula("mean_apy~is_top_q*factor(size)|state_qt+bank_qt|0|CERT")
r[[2]] <- felm(reg_formula,data=reg_sample[ year>=min_yr & year<=max_yr])
min_yr = 2008
max_yr = 2019
reg_formula <- as.formula("mean_apy~log(import_sensitivity)*factor(size)|state_qt+bank_qt|0|CERT")
r[[3]] <- felm(reg_formula,data=reg_sample[year>=min_yr & year<=max_yr ])
reg_formula <- as.formula("mean_apy~is_top_q*factor(size)|state_qt+bank_qt|0|CERT")
r[[4]] <- felm(reg_formula,data=reg_sample[ year>=min_yr & year<=max_yr])
stargazer(r,type="text",column.labels = c("2000-2007","2008-2019"),column.separate = c(2,2),omit.stat = "ser")##
## =============================================================================
## Dependent variable:
## ---------------------------------------
## mean_apy
## 2000-2007 2008-2019
## (1) (2) (3) (4)
## -----------------------------------------------------------------------------
## log(import_sensitivity) -0.006 -0.066***
## (0.042) (0.024)
##
## is_top_q -0.024 -0.023***
## (0.015) (0.008)
##
## factor(size)1
## (0.000) (0.000) (0.000) (0.000)
##
## factor(size)2
## (0.000) (0.000) (0.000) (0.000)
##
## log(import_sensitivity):factor(size)1 0.074 0.079
## (0.066) (0.052)
##
## log(import_sensitivity):factor(size)2 0.063 0.011
## (0.068) (0.051)
##
## is_top_q:factor(size)1 0.040* 0.042**
## (0.023) (0.017)
##
## is_top_q:factor(size)2 0.044* 0.009
## (0.024) (0.013)
##
## -----------------------------------------------------------------------------
## Observations 3,187,263 3,187,263 4,316,471 4,316,471
## R2 0.935 0.935 0.960 0.960
## Adjusted R2 0.883 0.883 0.907 0.907
## =============================================================================
## Note: *p<0.1; **p<0.05; ***p<0.014.4 Effect of Bank and County Exposure on deposit rates
\[ apy=import.sensitivity.top.q \times bank.exposure.top.q + County*Week FE+Bank*Week FE+ ProductTypeFE\]
min_yr = 2000
max_yr = 2007
r <- list()
reg_formula <- as.formula("mean_apy~is_top_q*exp_top_q|state_qt+bank_qt|0|CERT")
r[[1]] <- felm(reg_formula,data=reg_sample[year>=min_yr & year<=max_yr])
reg_formula <- as.formula("mean_apy~factor(is_q)*exp_top_q|state_qt+bank_qt|0|CERT")
r[[2]] <- felm(reg_formula,data=reg_sample[year>=min_yr & year<=max_yr])
reg_formula <- as.formula("mean_apy~log(import_sensitivity)*exp_top_q|state_qt+bank_qt|0|CERT")
r[[3]] <- felm(reg_formula,data=reg_sample[year>=min_yr & year<=max_yr])
min_yr = 2008
max_yr = 2019
reg_formula <- as.formula("mean_apy~is_top_q*exp_top_q|state_qt+bank_qt|0|CERT")
r[[4]] <- felm(reg_formula,data=reg_sample[year>=min_yr & year<=max_yr])
reg_formula <- as.formula("mean_apy~factor(is_q)*exp_top_q|state_qt+bank_qt|0|CERT")
r[[5]] <- felm(reg_formula,data=reg_sample[year>=min_yr & year<=max_yr])
reg_formula <- as.formula("mean_apy~log(import_sensitivity)*exp_top_q|state_qt+bank_qt|0|CERT")
r[[6]] <- felm(reg_formula,data=reg_sample[year>=min_yr & year<=max_yr])
stargazer(r,type="text",column.labels = c("2000-2007","2008-2019"),column.separate = c(3,3),omit.stat = "ser")##
## =============================================================================================
## Dependent variable:
## -----------------------------------------------------------
## mean_apy
## 2000-2007 2008-2019
## (1) (2) (3) (4) (5) (6)
## ---------------------------------------------------------------------------------------------
## is_top_q 0.009 -0.001
## (0.012) (0.007)
##
## factor(is_q)2 0.008 0.005
## (0.014) (0.009)
##
## factor(is_q)3 0.004 -0.016
## (0.017) (0.010)
##
## factor(is_q)4 0.013 -0.007
## (0.018) (0.011)
##
## log(import_sensitivity) 0.075* -0.011
## (0.039) (0.022)
##
## exp_top_q
## (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
##
## is_top_q:exp_top_q -0.032 -0.036***
## (0.021) (0.014)
##
## factor(is_q)2:exp_top_q -0.030 -0.020
## (0.040) (0.026)
##
## factor(is_q)3:exp_top_q -0.034 -0.017
## (0.045) (0.033)
##
## factor(is_q)4:exp_top_q -0.062 -0.054*
## (0.048) (0.032)
##
## log(import_sensitivity):exp_top_q -0.112* -0.098**
## (0.058) (0.044)
##
## ---------------------------------------------------------------------------------------------
## Observations 3,187,263 3,187,263 3,187,263 4,316,471 4,316,471 4,316,471
## R2 0.935 0.935 0.935 0.960 0.960 0.960
## Adjusted R2 0.883 0.883 0.883 0.907 0.907 0.907
## =============================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.014.4.1 By Bank Size
Size=1 => Small Bank
Size=2 => Medium Bank
Size=0
=> Large Bank
min_yr = 2000
max_yr = 2007
r <- list()
reg_formula <- as.formula("mean_apy~is_top_q*exp_top_q*factor(size)|state_qt+bank_qt|0|CERT")
r[[1]] <- felm(reg_formula,data=reg_sample[year>=min_yr & year<=max_yr])
reg_formula <- as.formula("mean_apy~log(import_sensitivity)*log(wa_exposure)*factor(size)|state_qt+bank_qt|0|CERT")
r[[2]] <- felm(reg_formula,data=reg_sample[year>=min_yr & year<=max_yr])
min_yr = 2008
max_yr = 2019
reg_formula <- as.formula("mean_apy~is_top_q*exp_top_q*factor(size)|state_qt+bank_qt|0|CERT")
r[[3]] <- felm(reg_formula,data=reg_sample[year>=min_yr & year<=max_yr])
reg_formula <- as.formula("mean_apy~log(import_sensitivity)*log(wa_exposure)*factor(size)|state_qt+bank_qt|0|CERT")
r[[4]] <- felm(reg_formula,data=reg_sample[year>=min_yr & year<=max_yr])
stargazer(r,type="text",column.labels = c("2000-2007","2008-2019"),column.separate = c(2,2),omit.stat = "ser")##
## ==============================================================================================
## Dependent variable:
## ---------------------------------------
## mean_apy
## 2000-2007 2008-2019
## (1) (2) (3) (4)
## ----------------------------------------------------------------------------------------------
## is_top_q -0.012 -0.017*
## (0.017) (0.008)
##
## exp_top_q
## (0.000) (0.000)
##
## log(import_sensitivity) 3.576* 1.113
## (1.881) (0.770)
##
## log(wa_exposure)
## (0.000) (0.000)
##
## factor(size)1
## (0.000) (0.000) (0.000) (0.000)
##
## factor(size)2
## (0.000) (0.000) (0.000) (0.000)
##
## is_top_q:exp_top_q -0.042 -0.026
## (0.035) (0.020)
##
## is_top_q:factor(size)1 0.021 0.062***
## (0.029) (0.020)
##
## is_top_q:factor(size)2 0.062** 0.025*
## (0.029) (0.015)
##
## exp_top_q:factor(size)1
## (0.000) (0.000)
##
## exp_top_q:factor(size)2
## (0.000) (0.000)
##
## is_top_q:exp_top_q:factor(size)1 0.060 -0.036
## (0.050) (0.035)
##
## is_top_q:exp_top_q:factor(size)2 -0.040 -0.030
## (0.053) (0.031)
##
## log(import_sensitivity):log(wa_exposure) -1.333* -0.439
## (0.700) (0.288)
##
## log(import_sensitivity):factor(size)1 -3.506* -0.316
## (1.925) (0.876)
##
## log(import_sensitivity):factor(size)2 -2.350 -0.372
## (2.008) (0.897)
##
## log(wa_exposure):factor(size)1
## (0.000) (0.000)
##
## log(wa_exposure):factor(size)2
## (0.000) (0.000)
##
## log(import_sensitivity):log(wa_exposure):factor(size)1 1.332* 0.156
## (0.715) (0.323)
##
## log(import_sensitivity):log(wa_exposure):factor(size)2 0.908 0.150
## (0.746) (0.332)
##
## ----------------------------------------------------------------------------------------------
## Observations 3,187,263 3,187,263 4,316,471 4,316,471
## R2 0.935 0.935 0.960 0.960
## Adjusted R2 0.883 0.883 0.907 0.907
## ==============================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.015 Deposit Growth
sod <- readRDS("sod_sum.rds")
sod <- sod[!is.na(UNINUMBR)]
setorder(sod,UNINUMBR,YEAR)
sod[,lagged_DEPSUMBR:= shift(DEPSUMBR,n=1,type="lag"),by=.(UNINUMBR)]
sod[,deposit_growth:= DEPSUMBR/(lagged_DEPSUMBR+1)-1]
sod <- sod[deposit_growth<1 & deposit_growth > - 0.25]
# sod <- sod[deposit_growth<2 & deposit_growth > - 0.5]
sod[,state:=substr(fips,1,2)]
sod[,state_yr:=paste(state,YEAR)]
sod[,county_yr:=paste(fips,YEAR)]
sod[,bank_yr:=paste(CERT,YEAR)]sod <- readRDS("sod_sum.rds")
sod <- sod[,.(deposits_bank_fips=sum(DEPSUMBR,na.rm=T)),by=.(YEAR,CERT,fips)]
setorder(sod,fips,CERT,YEAR)
sod[,lagged_deposits_bank_fips:= shift(deposits_bank_fips,n=1,type="lag"),by=.(fips,CERT)]
sod[,deposit_growth:= deposits_bank_fips/(lagged_deposits_bank_fips+1)-1]
sod <- sod[deposit_growth<10 & deposit_growth > - 0.2]
sod[,state:=substr(fips,1,2)]
sod[,state_yr:=paste(state,YEAR)]
sod[,county_yr:=paste(fips,YEAR)]
sod[,bank_yr:=paste(CERT,YEAR)]reg_sample <- merge(sod,measures,by="fips")
reg_sample <- merge(bank_import_exposure,reg_sample,by="CERT")
reg_sample[,is_top_q_exp_top_q:=ifelse(exp_top_q==1 & is_top_q==1,1,0)]plot_summary <- reg_sample[YEAR<=2019,.(deposit_growth=mean(deposit_growth,na.rm=T)),by=.(YEAR,is_top_q_exp_top_q)]
ggplot(plot_summary,aes(x=YEAR,y=deposit_growth,color=factor(is_top_q_exp_top_q)))+geom_line()
reg_formula_2 <- as.formula("deposit_growth~is_top_q*exp_top_q|bank_yr+state_yr|0|CERT")
reg_formula_1 <- as.formula("deposit_growth~is_top_q|bank_yr+state_yr|0|CERT")
r_t <- list()
r_t[[1]] <- felm(reg_formula_1,data=reg_sample[YEAR %in% 1996:1999])
r_t[[2]] <- felm(reg_formula_2,data=reg_sample[YEAR %in% 1996:1999 ])
r_t[[3]] <- felm(reg_formula_1,data=reg_sample[YEAR %in% 2000:2007])
r_t[[4]] <- felm(reg_formula_2,data=reg_sample[YEAR %in% 2000:2007])
r_t[[5]] <- felm(reg_formula_1,data=reg_sample[YEAR %in% 2008:2019])
r_t[[6]] <- felm(reg_formula_2,data=reg_sample[YEAR %in% 2008:2019])
stargazer(r_t,type="text",no.space = T,omit.stat = "ser",column.labels = c("1996-1999","2000-2007","2008-2019"),column.separate = c(2,2,2),
add.lines=list(c("Bank*Yr",rep("Y",6)),c("State*Yr",rep("Y",6))))##
## =============================================================================
## Dependent variable:
## ----------------------------------------------------------
## deposit_growth
## 1996-1999 2000-2007 2008-2019
## (1) (2) (3) (4) (5) (6)
## -----------------------------------------------------------------------------
## is_top_q -0.015** 0.027*** -0.024*** 0.023*** -0.011*** 0.010**
## (0.008) (0.009) (0.005) (0.008) (0.003) (0.004)
## exp_top_q
## (0.000) (0.000) (0.000)
## is_top_q:exp_top_q -0.127*** -0.150*** -0.066***
## (0.017) (0.012) (0.007)
## -----------------------------------------------------------------------------
## Bank*Yr Y Y Y Y Y Y
## State*Yr Y Y Y Y Y Y
## Observations 85,082 85,082 183,561 183,561 278,289 278,289
## R2 0.466 0.467 0.349 0.351 0.246 0.247
## Adjusted R2 -0.071 -0.069 -0.044 -0.041 -0.014 -0.014
## =============================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01