#### Data adaptions ####
# Set your working directory
setwd("C:/your/directory/")
# 1) File miansufieconometrica_countylevel.dta:
library(haven)
library(dplyr)
countylevel = read_dta("miansufieconometrica_countylevel.dta")
countylevel = countylevel%>%
rename(
# Rename variables.
hnw = netwp_h,
households = total,
lf_0709 = Clf_0709,
emp_2007 = Cemp2_2007,
wage_2007 = Cwage_2007,
emp_0709 = Cemp2_0709,
wage_0709 = Cwage_0709,
emp_food_0709 = CFemp2_0709,
const_2007 = C_const,
empshare1 = C2D06share1,
empshare2 = C2D06share2,
empshare3 = C2D06share3,
empshare4 = C2D06share4,
empshare5 = C2D06share5,
empshare6 = C2D06share6,
empshare7 = C2D06share7,
empshare8 = C2D06share8,
empshare9 = C2D06share9,
empshare10 = C2D06share10,
empshare11 = C2D06share11,
empshare12 = C2D06share12,
empshare13 = C2D06share13,
empshare14 = C2D06share14,
empshare15 = C2D06share15,
empshare16 = C2D06share16,
empshare17 = C2D06share17,
empshare18 = C2D06share18,
empshare19 = C2D06share19,
empshare20 = C2D06share20,
empshare21 = C2D06share21,
empshare22 = C2D06share22,
empshare23 = C2D06share23,
emp_other_rwt = CCemp2_0709_0,
emp_other_rwt_2007 = CCemp2_2007_0,
emp_other_rwt_2009 = CCemp2_2009_0,
emp_other_rwt_1_4 = CCemp2_1_4_0709_0,
emp_other_rwt_5_9 = CCemp2_5_9_0709_0,
emp_other_rwt_10_19 = CCemp2_10_19_0709_0,
emp_other_rwt_20_49 = CCemp2_20_49_0709_0,
emp_other_rwt_50_99= CCemp2_50_99_0709_0,
emp_other_rwt_100plus = CCemp2_100plus_0709_0,
tremp_rwt = CCemp2_0709_1,
tremp_rwt_2007 = CCemp2_2007_1,
tremp_rwt_2009 = CCemp2_2009_1,
tremp_rwt_1_4 = CCemp2_1_4_0709_1,
tremp_rwt_5_9 = CCemp2_5_9_0709_1,
tremp_rwt_10_19 = CCemp2_10_19_0709_1,
tremp_rwt_20_49 = CCemp2_20_49_0709_1,
tremp_rwt_50_99 = CCemp2_50_99_0709_1,
tremp_rwt_100plus = CCemp2_100plus_0709_1,
ntremp_rwt = CCemp2_0709_2,
ntremp_rwt_2007 = CCemp2_2007_2,
ntremp_rwt_2009 = CCemp2_2009_2,
ntremp_rwt_1_4 = CCemp2_1_4_0709_2,
ntremp_rwt_5_9 = CCemp2_5_9_0709_2,
ntremp_rwt_10_19 = CCemp2_10_19_0709_2,
ntremp_rwt_20_49 = CCemp2_20_49_0709_2,
ntremp_rwt_50_99 = CCemp2_50_99_0709_2,
ntremp_rwt_100plus = CCemp2_100plus_0709_2,
emp_const_rwt = CCemp2_0709_3,
emp_const_rwt_2007 = CCemp2_2007_3,
emp_const_rwt_2009 = CCemp2_2009_3,
emp_const_rwt_1_4 = CCemp2_1_4_0709_3,
emp_const_rwt_5_9 = CCemp2_5_9_0709_3,
emp_const_rwt_10_19 = CCemp2_10_19_0709_3,
emp_const_rwt_20_49 = CCemp2_20_49_0709_3,
emp_const_rwt_50_99 = CCemp2_50_99_0709_3,
emp_const_rwt_100plus = CCemp2_100plus_0709_3,
ntremp_geog = CH2emp2_0709_1,
ntremp_geog_2007 = CH2emp2_2007_1,
ntremp_geog_2009 = CH2emp2_2009_1,
emp_2_geog = CH2emp2_0709_2,
emp_2_geog_2007 = CH2emp2_2007_2,
emp_2_geog_2009 = CH2emp2_2009_2,
emp_3_geog = CH2emp2_0709_3,
emp_3_geog_2007 = CH2emp2_2007_3,
emp_3_geog_2009 = CH2emp2_2009_3,
tremp_geog = CH2emp2_0709_4,
tremp_geog_2007 = CH2emp2_2007_4,
tremp_geog_2009 = CH2emp2_2009_4,
deposits = Cdeposits,
localshare = Clocalshare,
defshock = Cdefshock,
netincome = Cnetincome,
chargeoff = Cchargeoff,
movest0709 = Cmovest0709,
wagehr_Wmean2007 = Cwagehr_Wmean2007,
wagehr_Wmean0709 = Cwagehr_Wmean0709,
wagehr_median2007 = Cwagehr_median2007,
wagehr_median0709 = Cwagehr_median0709,
wagehr_p102007 = Cwagehr_p102007,
wagehr_p100709 = Cwagehr_p100709,
wagehr_p252007 = Cwagehr_p252007,
wagehr_p250709 = Cwagehr_p250709,
wagehr_p752007 = Cwagehr_p752007,
wagehr_p750709 = Cwagehr_p750709,
wagehr_p902007 = Cwagehr_p902007,
wagehr_p900709 = Cwagehr_p900709
)%>%
mutate(
# Change data format of labeled variables into factors.
countyname = as_factor(countyname),
statename = as_factor(statename),
# Create the new variable pop0709.
pop0709 = log(pop2009)- log(pop2007)
)%>%
# Reorder variables.
relocate(fips, countyname, statename)%>%
relocate(lf_0709, .after = wage_0709)%>%
relocate(nontradable_strict, .before = ntremp_rwt)%>%
relocate(white, .after = homevalmed)
# Store the result in countylevel.Rds.
saveRDS(countylevel, file = "countylevel.Rds")
# 2) File miansufieconometrica_countyindustrylevel.dta:
countyindustrylevel = read_dta("miansufieconometrica_countyindustrylevel.dta")
countyindustrylevel = countyindustrylevel%>%
# Rename variables.
rename(
hnw = netwp_h,
households = total,
CIemp_2007 = CIemp2_2007,
CIemp_2009 = CIemp2_2009,
Iemp_2007 = Iemp2_2007,
CIemp_0709 = CIemp2_0709,
Ihcat = Ihcat2
)%>%
mutate(
# Change data format of labeled variables into factors.
countyname = as_factor(countyname),
statename = as_factor(statename),
industry = as_factor(industry),
indcat = as_factor(indcat),
# Remove slashes.
naics = substr(naics,1,nchar(naics)-2),
# Create the new variables ntr_rwt, tr_rwt, ntr_geog, tr_geog.
ntr_rwt = ifelse(indcat == "non-tradable", 1, 0),
tr_rwt = ifelse (indcat == "tradable", 1, 0),
ntr_geog = ifelse(Ihcat == 1, 1 ,0),
tr_geog = ifelse(Ihcat == 4, 1 ,0)
)%>%
# Reorder variables.
relocate(fips, countyname, statename, hnw, elasticity, households, naics,
industry, CIemp_2007, CIemp_2009, CIemp_0709, Iemp_2007, nontradable_strict,
indcat, Iherf, Ihcat, export_worker, ntr_rwt, tr_rwt, ntr_geog, tr_geog)
# Store the result in countyindustrylevel.Rds.
saveRDS(countyindustrylevel, file = "countyindustrylevel.Rds")
# 3) Files emppop.dta and Qnfib.dta:
# 3.1) Modify emppop.dta.
library(zoo)
library(stataXml)
emppop = read_dta("emppop.dta")
emppop = emppop%>%
# Twelvefold the rows of the data set.
slice(rep(1:n(), each = 12))%>%
# Add column month.
group_by(year)%>%
mutate(
month = 1:12,
# Add column emppop.
emppop = case_when(
month == 1 ~ jan,
month == 2 ~ feb,
month == 3 ~ mar,
month == 4 ~ apr,
month == 5 ~ may,
month == 6 ~ jun,
month == 7 ~ jul,
month == 8 ~ aug,
month == 9 ~ sep,
month == 10 ~ oct,
month == 11 ~ nov,
month == 12 ~ dec
)
)%>%
# Keep columns year, month, emppop.
select(year, month, emppop)
# Rename column "month" to "m".
names(emppop)[2] ="m"
# Add column month.
emppop$month <- as.yearmon(paste(emppop$year, emppop$m), "%Y %m")
# Add column quarter.
emppop$quarter = as.yearqtr(emppop$month, "%m %Y")
emppop = emppop %>%
# Keep the last month of quarter.
group_by(year, quarter)%>%
filter (m == max(m))%>%
ungroup()%>%
# Keep columns emppop, quarter.
select(quarter, emppop)
# 3.2) Modify Qnfib.dta.
Qnfib = read_dta("Qnfib.dta")
# Change format of column quarter.
Qnfib$quarter = fromStataTime(Qnfib$quarter, '%tq')
Qnfib$quarter = as.yearqtr(Qnfib$quarter,"%Y-%m-%d")
# Add column govtax.
Qnfib = Qnfib%>%
mutate(govtax = taxes + gov)
# 3.3) Merge data sets empopp and Qnfib and store the result in variable bc.
bc = merge(Qnfib, emppop, by = "quarter", all.y = TRUE)
saveRDS(bc, file = "bc.Rds")
# 4) File nfibcs.dta:
nfibcs = read_dta("nfibcs.dta")
# Rename variable netwp_h.
nfibcs = nfibcs%>%
rename(hnw = netwp_h)
# Store the result in nfibcs.Rds.
saveRDS(nfibcs, file = "nfibcs.Rds")
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