# # 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)-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("RSSDID","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=.(RSSDID,fips)]
bank_import_exposure <- merge( bank_import_exposure,measures,by="fips")
bank_import_exposure[,total_deposits:=sum(county_deposits,na.rm=T),by=RSSDID]
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=RSSDID]
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)]
cra_ts <- read_fst("C:/Users/dratnadiwakara2/Documents/OneDrive - Louisiana State University/Raw Data/CRA/Transmittal/cra_transmittal_1997_2021")
cra_ts <- cra_ts[,c("respondentid","agencycode","activityyear","id_rssd","taxid")]
cra_disc <- read_fst("C:/Users/dratnadiwakara2/Documents/OneDrive - Louisiana State University/Raw Data/CRA/Disclosure/cra_disc_1997_2021.fst")
cra_disc <- data.table(cra_disc)
cra_disc <- cra_disc[reportlevel=="040"]
cra_disc[,fips:=paste0(state,county)]
cra_disc <- merge(cra_disc,cra_ts,by=c("respondentid","agencycode","activityyear"))
cra_disc[,RSSD:=as.numeric(id_rssd)]
cra <- merge(cra_disc,bank_import_exposure,by.x="RSSD",by.y="RSSDID")
cra <- merge(cra,measures,by="fips")
cra[,bank_fips:=paste(agencycode,respondentid,fips)]
cra[,hmda_id:=paste(agencycode,respondentid)]
gc()
## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 3372599 180.2 5595518 298.9 3930354 210.0
## Vcells 181686121 1386.2 1044705914 7970.5 1632305687 12453.6
cra <- cra[log(1+wa_exposure)>=2 & log(1+wa_exposure)<=4 & log(import_sensitivity)>=2 & log(import_sensitivity)<=4]
cra[,no_total:=no_sba_lt_100k+no_sba_100_250k+no_sba_250_1000k]
cra[,amount_total:=amount_sba_lt_100k+amount_sba_100_250k+amount_sba_250_1000k]
Effect of County
Exposure on Number of small business loans
based on county-year panel
cra_summary <- cra[activityyear>=1999,
.(no_loans=sum(no_total,na.rm=T),
total_loan_amount =sum(amount_total,na.rm=T)
),by=.(activityyear,fips)]
cra_summary <- merge(cra_summary,measures,by="fips")
County-level change
in small business loans
\[
log(chg\_loans\_from\_2000\_2007)_{county} =
import\_sensitivity\_quartile\_dummy_{county}
\] ### From 2000 to 2007
styr= 2000
edyr = 2007
sod_2000 <- cra_summary[activityyear==styr,c("fips","no_loans","total_loan_amount")]
setnames(sod_2000,c("no_loans","total_loan_amount"),c("no_loans_st","total_loan_amount_st"))
sod_2007 <- cra_summary[activityyear==edyr,c("fips","no_loans","total_loan_amount")]
setnames(sod_2007,c("no_loans","total_loan_amount"),c("no_loans_ed","total_loan_amount_ed"))
sod_2000_2007 <- merge(sod_2000,sod_2007,by=c("fips"))
sod_2000_2007[,chg_no_loans:=(no_loans_ed/no_loans_st)]
sod_2000_2007[,chg_total_loan_amount:=(total_loan_amount_ed/total_loan_amount_st)]
reg_sample <- merge(sod_2000_2007,measures,by="fips")
reg_sample[,state:=substr(fips,1,2)]
stargazer(reg_sample[,c("chg_no_loans","chg_total_loan_amount")],summary = T,type="text")
##
## ========================================================
## Statistic N Mean St. Dev. Min Max
## --------------------------------------------------------
## chg_no_loans 3,131 2.009 0.825 0.267 23.000
## chg_total_loan_amount 3,131 1.999 2.698 0.085 108.000
## --------------------------------------------------------
reg_formula_1 <- as.formula("log(1+chg_no_loans)~factor(is_q)")
reg_formula_2 <- as.formula("log(1+chg_total_loan_amount)~factor(is_q)")
r <- list()
r[[1]] <- felm(reg_formula_1,data=reg_sample)
r[[2]] <- felm(reg_formula_2,data=reg_sample)
From 2007 to
2019
styr= 2007
edyr = 2019
sod_2000 <- cra_summary[activityyear==styr,c("fips","no_loans","total_loan_amount")]
setnames(sod_2000,c("no_loans","total_loan_amount"),c("no_loans_st","total_loan_amount_st"))
sod_2007 <- cra_summary[activityyear==edyr,c("fips","no_loans","total_loan_amount")]
setnames(sod_2007,c("no_loans","total_loan_amount"),c("no_loans_ed","total_loan_amount_ed"))
sod_2000_2007 <- merge(sod_2000,sod_2007,by=c("fips"))
sod_2000_2007[,chg_no_loans:=(no_loans_ed/no_loans_st)]
sod_2000_2007[,chg_total_loan_amount:=(total_loan_amount_ed/total_loan_amount_st)]
reg_sample <- merge(sod_2000_2007,measures,by="fips")
reg_sample[,state:=substr(fips,1,2)]
stargazer(reg_sample[,c("chg_no_loans","chg_total_loan_amount")],summary = T,type="text")
##
## =======================================================
## Statistic N Mean St. Dev. Min Max
## -------------------------------------------------------
## chg_no_loans 3,126 0.571 0.224 0.107 6.000
## chg_total_loan_amount 3,126 0.927 0.998 0.024 36.667
## -------------------------------------------------------
r[[3]] <- felm(reg_formula_1,data=reg_sample)
r[[4]] <- felm(reg_formula_2,data=reg_sample)
stargazer(r,type="text",omit.stat = "ser",column.labels = c("From 2000 to 2007","From 2007 to 2019"),column.separate = c(2,2))
##
## =======================================================================================================================
## Dependent variable:
## ---------------------------------------------------------------------------------------------------------
## log(1 + chg_no_loans) log(1 + chg_total_loan_amount) log(1 + chg_no_loans) log(1 + chg_total_loan_amount)
## From 2000 to 2007 From 2007 to 2019
## (1) (2) (3) (4)
## -----------------------------------------------------------------------------------------------------------------------
## factor(is_q)2 -0.019* -0.059*** 0.017*** -0.044***
## (0.012) (0.019) (0.006) (0.014)
##
## factor(is_q)3 -0.066*** -0.133*** 0.016*** -0.073***
## (0.012) (0.019) (0.006) (0.014)
##
## factor(is_q)4 -0.088*** -0.151*** 0.001 -0.071***
## (0.012) (0.019) (0.006) (0.014)
##
## Constant 1.117*** 1.086*** 0.435*** 0.654***
## (0.008) (0.014) (0.004) (0.010)
##
## -----------------------------------------------------------------------------------------------------------------------
## Observations 3,131 3,131 3,126 3,126
## R2 0.023 0.024 0.005 0.011
## Adjusted R2 0.022 0.023 0.004 0.010
## =======================================================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
Over time
\[
log(depvar) = is\_top\_q \times times Year Dummies+CountyFE+YearFE
\]
min_yr = 2000
reg_formula <- as.formula("log(1+no_loans)~is_top_q*factor(activityyear)|fips+activityyear|0|fips")
reg_formula2 <- as.formula("log(1+no_loans)~log(import_sensitivity)*factor(activityyear)|fips+activityyear|0|fips")
r <- list()
r[[1]] <- felm(reg_formula,data=cra_summary)
r[[2]] <- felm(reg_formula2,data=cra_summary)
reg_formula <- as.formula("log(1+total_loan_amount)~is_top_q*factor(activityyear)|fips+activityyear|0|fips")
reg_formula2 <- as.formula("log(1+total_loan_amount)~log(import_sensitivity)*factor(activityyear)|fips+activityyear|0|fips")
r[[3]] <- felm(reg_formula,data=cra_summary)
r[[4]] <- felm(reg_formula2,data=cra_summary)
# stargazer(r,type="text",column.labels = c("log(loans)","log(loan amount)"),column.separate = rep(2,2),omit.stat = "ser")
coef_plot_1reg(r[[1]],"is_top_q:factor(activityyear)",1999)+ggtitle("Dep var:log(no of loans)")
coef_plot_1reg(r[[3]],"is_top_q:factor(activityyear)",1999)+ggtitle("Dep var:log(loan amount)")
# coef_plot_1reg(r[[2]],"log(import_sensitivity):log(wa_exposure):factor(activityyear)",1997)+ggtitle("Dep var:log(no of loans)")
# coef_plot_1reg(r[[4]],"log(import_sensitivity):log(wa_exposure):factor(activityyear)",1997)+ggtitle("Dep var:log(total loan amount)")
Loan Composition at
bank-county level
cra_summary <- cra[activityyear>=1999,
.(no_loans=sum(no_total,na.rm=T),
total_loan_amount =sum(amount_total,na.rm=T)
),by=.(activityyear,RSSD,fips)]
cra_summary <- merge(cra_summary,bank_import_exposure,by.x="RSSD",by.y="RSSDID")
cra_summary <- merge(cra_summary,measures,by="fips")
cra_summary[,bank_fips:=paste(RSSD,fips)]
cra_summary[,fips_year:=paste(fips,activityyear)]
cra_summary[,tot_loans:=sum(no_loans,na.rm=T),by=.(activityyear,RSSD)]
cra_summary[,loan_frac:=no_loans/tot_loans]
cra_summary[,tot_loan_amount_bk_yr:=sum(total_loan_amount,na.rm=T),by=.(activityyear,RSSD)]
cra_summary[,loan_amt_frac:=total_loan_amount/tot_loan_amount_bk_yr]
cra_summary[,activityyear:=as.numeric(activityyear)]
sod <- readRDS("sod_sum.rds")
sod <- sod[,c("ASSET","RSSDID","YEAR")]
sod <- sod[!duplicated(sod)]
sod[,q80:=quantile(ASSET,0.8,na.rm=T),by=YEAR]
sod[,q95:=quantile(ASSET,0.95,na.rm=T),by=YEAR]
sod[,size:=ifelse(ASSET <q80,1,ifelse(ASSET<q95,2,0))]
sod[,c("q80","q95"):=list(NULL)]
cra_summary <- merge(cra_summary,sod,by.x=c("RSSD","activityyear"),by.y=c("RSSDID","YEAR"),all.x=T)
styr= 2000
edyr = 2007
sod_2000 <- cra_summary[activityyear==styr,c("fips","RSSD","loan_frac","loan_amt_frac")]
setnames(sod_2000,c("loan_frac","loan_amt_frac"),c("loan_frac_st","loan_amt_frac_st"))
sod_2000 <- sod_2000[loan_frac_st<1]
sod_2007 <- cra_summary[activityyear==edyr,c("fips","RSSD","loan_frac","loan_amt_frac")]
setnames(sod_2007,c("loan_frac","loan_amt_frac"),c("loan_frac_ed","loan_amt_frac_ed"))
sod_2000_2007 <- merge(sod_2000,sod_2007,by=c("fips","RSSD"),all.x=T,all.y=T)
sod_2000_2007[,loan_frac_st:=ifelse(is.na(loan_frac_st),0,loan_frac_st)]
sod_2000_2007[,loan_amt_frac_st:=ifelse(is.na(loan_amt_frac_st),0,loan_amt_frac_st)]
sod_2000_2007[,loan_frac_ed:=ifelse(is.na(loan_frac_ed),0,loan_frac_ed)]
sod_2000_2007[,loan_amt_frac_ed:=ifelse(is.na(loan_amt_frac_ed),0,loan_amt_frac_ed)]
sod_2000_2007[,chg_loan_amt_frac:=(loan_amt_frac_ed-loan_amt_frac_st)]
sod_2000_2007[,chg_loan_no_frac:=(loan_frac_ed-loan_frac_st)]
reg_sample1 <- merge(sod_2000_2007,measures,by="fips")
reg_sample1 <- merge(bank_import_exposure,reg_sample1,by.x="RSSDID",by.y="RSSD")
reg_sample1 <- merge(reg_sample1,asset_w[YEAR==2000,c("RSSDID","w_asset","ASSET","size")],by.x=c("RSSDID"),by.y=c("RSSDID"))
styr= 2007
edyr = 2019
sod_2000 <- cra_summary[activityyear==styr,c("fips","RSSD","loan_frac","loan_amt_frac")]
setnames(sod_2000,c("loan_frac","loan_amt_frac"),c("loan_frac_st","loan_amt_frac_st"))
sod_2000 <- sod_2000[loan_frac_st<1]
sod_2007 <- cra_summary[activityyear==edyr,c("fips","RSSD","loan_frac","loan_amt_frac")]
setnames(sod_2007,c("loan_frac","loan_amt_frac"),c("loan_frac_ed","loan_amt_frac_ed"))
sod_2000_2007 <- merge(sod_2000,sod_2007,by=c("fips","RSSD"),all.x=T,all.y=T)
sod_2000_2007[,loan_frac_st:=ifelse(is.na(loan_frac_st),0,loan_frac_st)]
sod_2000_2007[,loan_amt_frac_st:=ifelse(is.na(loan_amt_frac_st),0,loan_amt_frac_st)]
sod_2000_2007[,loan_frac_ed:=ifelse(is.na(loan_frac_ed),0,loan_frac_ed)]
sod_2000_2007[,loan_amt_frac_ed:=ifelse(is.na(loan_amt_frac_ed),0,loan_amt_frac_ed)]
sod_2000_2007[,chg_loan_amt_frac:=(loan_amt_frac_ed-loan_amt_frac_st)]
sod_2000_2007[,chg_loan_no_frac:=(loan_frac_ed-loan_frac_st)]
reg_sample2 <- merge(sod_2000_2007,measures,by="fips")
reg_sample2 <- merge(bank_import_exposure,reg_sample2,by.x="RSSDID",by.y="RSSD")
reg_sample2 <- merge(reg_sample2,asset_w[YEAR==2000,c("RSSDID","w_asset","ASSET","size")],by.x=c("RSSDID"),by.y=c("RSSDID"))
Change from 2000 to
2007
stargazer(reg_sample1[,c("chg_loan_no_frac","chg_loan_amt_frac")],summary = T,type="text")
##
## ======================================================
## Statistic N Mean St. Dev. Min Max
## ------------------------------------------------------
## chg_loan_no_frac 130,591 -0.006 0.069 -0.999 1.000
## chg_loan_amt_frac 130,591 -0.007 0.068 -0.998 1.000
## ------------------------------------------------------
Change from 2007 to
2019
stargazer(reg_sample2[,c("chg_loan_no_frac","chg_loan_amt_frac")],summary = T,type="text")
##
## ======================================================
## Statistic N Mean St. Dev. Min Max
## ------------------------------------------------------
## chg_loan_no_frac 147,770 -0.002 0.039 -0.984 1.000
## chg_loan_amt_frac 147,770 -0.002 0.039 -0.987 1.000
## ------------------------------------------------------
\[
change\text{ }in\text{ }craloan\text{ }fraction\text{ }from\text{
}2000\text{ }to\text{ }2007=
\frac{craloans_{b,c,2007}}{craloans_{b,2007}}-\frac{craloans_{b,c,2000}}{craloans_{b,2000}}
\]
Specification
\[
Y\text{ }from\text{ }2000\text{ }to\text{ }2007_{bank,county} =
import.sensitivitity.quartile.dumies \times top\_exp_q+BankFE+County FE
\]
Main
Specification
reg_formula1 <- as.formula("chg_loan_no_frac~factor(is_q)*exp_top_q|fips+RSSDID|0|RSSDID")
reg_formula2 <- as.formula("chg_loan_amt_frac~factor(is_q)*exp_top_q|fips+RSSDID|0|RSSDID")
r <- list()
r[[1]] <- felm(reg_formula1,data=reg_sample1)
r[[2]] <- felm(reg_formula2,data=reg_sample1)
r[[3]] <- felm(reg_formula1,data=reg_sample2)
r[[4]] <- felm(reg_formula2,data=reg_sample2)
stargazer(r,type="text",no.space = T,column.labels = c("From 2000 to 2007","From 2007 to 2019"),column.separate = c(2,2),omit.stat = "ser")
##
## =============================================================================================
## Dependent variable:
## ---------------------------------------------------------------------
## chg_loan_no_frac chg_loan_amt_frac chg_loan_no_frac chg_loan_amt_frac
## From 2000 to 2007 From 2007 to 2019
## (1) (2) (3) (4)
## ---------------------------------------------------------------------------------------------
## factor(is_q)2
## (0.000) (0.000) (0.000) (0.000)
## factor(is_q)3
## (0.000) (0.000) (0.000) (0.000)
## factor(is_q)4
## (0.000) (0.000) (0.000) (0.000)
## exp_top_q
## (0.000) (0.000) (0.000) (0.000)
## factor(is_q)2:exp_top_q 0.004*** 0.004*** 0.001*** 0.001**
## (0.001) (0.001) (0.0005) (0.0005)
## factor(is_q)3:exp_top_q 0.006*** 0.006*** 0.001** 0.001**
## (0.001) (0.001) (0.001) (0.001)
## factor(is_q)4:exp_top_q -0.019*** -0.017*** -0.005*** -0.005***
## (0.004) (0.004) (0.001) (0.001)
## ---------------------------------------------------------------------------------------------
## Observations 130,591 130,591 147,770 147,770
## R2 0.268 0.275 0.213 0.217
## Adjusted R2 0.238 0.245 0.191 0.194
## =============================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
By Size
reg_formula1 <- as.formula("chg_loan_no_frac~factor(is_q)*exp_top_q*factor(size)|fips+RSSDID|0|RSSDID")
reg_formula2 <- as.formula("chg_loan_amt_frac~factor(is_q)*exp_top_q*factor(size)|fips+RSSDID|0|RSSDID")
r <- list()
r[[1]] <- felm(reg_formula1,data=reg_sample1)
r[[2]] <- felm(reg_formula2,data=reg_sample1)
r[[3]] <- felm(reg_formula1,data=reg_sample2)
r[[4]] <- felm(reg_formula2,data=reg_sample2)
stargazer(r,type="text",no.space = T,column.labels = c("From 2000 to 2007","From 2007 to 2019"),column.separate = c(2,2),omit.stat = "ser")
##
## ===========================================================================================================
## Dependent variable:
## ---------------------------------------------------------------------
## chg_loan_no_frac chg_loan_amt_frac chg_loan_no_frac chg_loan_amt_frac
## From 2000 to 2007 From 2007 to 2019
## (1) (2) (3) (4)
## -----------------------------------------------------------------------------------------------------------
## factor(is_q)2
## (0.000) (0.000) (0.000) (0.000)
## factor(is_q)3
## (0.000) (0.000) (0.000) (0.000)
## factor(is_q)4
## (0.000) (0.000) (0.000) (0.000)
## exp_top_q
## (0.000) (0.000) (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)
## factor(is_q)2:exp_top_q 0.002*** 0.002** 0.0004 0.0004
## (0.001) (0.001) (0.0003) (0.0004)
## factor(is_q)3:exp_top_q 0.002*** 0.002*** 0.001* 0.001**
## (0.001) (0.001) (0.0004) (0.0004)
## factor(is_q)4:exp_top_q -0.006*** -0.005*** -0.001** -0.001**
## (0.002) (0.002) (0.001) (0.001)
## factor(is_q)2:factor(size)1 -0.002 -0.002 0.001 0.002
## (0.005) (0.005) (0.001) (0.001)
## factor(is_q)3:factor(size)1 -0.005 -0.004 -0.001 -0.001
## (0.006) (0.005) (0.001) (0.002)
## factor(is_q)4:factor(size)1 0.008 0.007 0.002 0.001
## (0.005) (0.005) (0.001) (0.001)
## factor(is_q)2:factor(size)2 -0.006*** -0.006*** -0.001 -0.001
## (0.002) (0.002) (0.001) (0.001)
## factor(is_q)3:factor(size)2 -0.008*** -0.008*** -0.002 -0.002*
## (0.002) (0.002) (0.001) (0.001)
## factor(is_q)4:factor(size)2 0.008*** 0.006*** 0.002*** 0.002**
## (0.002) (0.002) (0.001) (0.001)
## exp_top_q:factor(size)1
## (0.000) (0.000) (0.000) (0.000)
## exp_top_q:factor(size)2
## (0.000) (0.000) (0.000) (0.000)
## factor(is_q)2:exp_top_q:factor(size)1 0.002 -0.001 -0.0003 -0.001
## (0.007) (0.008) (0.003) (0.003)
## factor(is_q)3:exp_top_q:factor(size)1 0.015* 0.015* -0.001 -0.001
## (0.008) (0.008) (0.003) (0.003)
## factor(is_q)4:exp_top_q:factor(size)1 -0.035** -0.028** -0.012* -0.010*
## (0.015) (0.013) (0.007) (0.006)
## factor(is_q)2:exp_top_q:factor(size)2 0.007*** 0.006** 0.003** 0.003**
## (0.003) (0.003) (0.001) (0.001)
## factor(is_q)3:exp_top_q:factor(size)2 0.008*** 0.008** 0.002 0.002
## (0.003) (0.003) (0.001) (0.001)
## factor(is_q)4:exp_top_q:factor(size)2 -0.030*** -0.028*** -0.009*** -0.009***
## (0.007) (0.007) (0.003) (0.003)
## -----------------------------------------------------------------------------------------------------------
## Observations 130,591 130,591 147,770 147,770
## R2 0.270 0.277 0.214 0.217
## Adjusted R2 0.241 0.247 0.191 0.195
## ===========================================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
Over time
\[
no.loans.fips.frac = is\_top\_q\times exp\_top\_q \times
Year.Dummies+Year*County FE+BankFE
\]
All banks
r <- list()
r[[1]] <- felm(loan_frac~is_top_q*exp_top_q*factor(activityyear)+log(ASSET)+log(1+tot_loan_amount_bk_yr)|RSSD+fips_year+size|0|RSSD,
data=cra_summary)
r[[2]] <- felm(loan_amt_frac~is_top_q*exp_top_q*factor(activityyear)+log(ASSET)+log(1+tot_loan_amount_bk_yr)|RSSD+fips_year+size|0|RSSD,
data=cra_summary)
coef_plot_1reg(r[[1]],"is_top_q:exp_top_q:factor(activityyear)",1999)+ggtitle("Dep. var: no_loan_frac")
coef_plot_1reg(r[[2]],"is_top_q:exp_top_q:factor(activityyear)",1999)+ggtitle("Dep. var: total_loan_amount_frac")
Excluding large
banks
r <- list()
r[[1]] <- felm(loan_frac~is_top_q*exp_top_q*factor(activityyear)|RSSD+fips_year|0|RSSD,
data=cra_summary[size!=0])
r[[2]] <- felm(loan_amt_frac~is_top_q*exp_top_q*factor(activityyear)|RSSD+fips_year|0|RSSD,
data=cra_summary[size!=0])
coef_plot_1reg(r[[1]],"is_top_q:exp_top_q:factor(activityyear)",1999)+ggtitle("Dep. var: no_loan_frac")
coef_plot_1reg(r[[2]],"is_top_q:exp_top_q:factor(activityyear)",1999)+ggtitle("Dep. var: total_loan_amount_frac")
Large banks
r <- list()
r[[1]] <- felm(loan_frac~is_top_q*exp_top_q*factor(activityyear)|RSSD+fips_year|0|RSSD,
data=cra_summary[size==0])
r[[2]] <- felm(loan_amt_frac~is_top_q*exp_top_q*factor(activityyear)|RSSD+fips_year|0|RSSD,
data=cra_summary[size==0])
coef_plot_1reg(r[[1]],"is_top_q:exp_top_q:factor(activityyear)",1999)+ggtitle("Dep. var: no_loan_frac")
coef_plot_1reg(r[[2]],"is_top_q:exp_top_q:factor(activityyear)",1999)+ggtitle("Dep. var: total_loan_amount_frac")
