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FAFZones_no_cbp.R
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#################################################################################
rm(list = ls())
options(scipen = '10')
list.of.packages <-
c("dplyr",
"tidyr",
"data.table",
"sf",
"tmap",
"tmaptools",
"base",
"bit64")
new.packages <-
list.of.packages[!(list.of.packages %in% installed.packages()[, "Package"])]
if (length(new.packages))
install.packages(new.packages)
lapply(list.of.packages, require, character = TRUE)
#################################################################################
path2file <-
"/Users/xiaodanxu/Documents/SynthFirm.nosync"
setwd(path2file)
# define inputs
region_name = 'Austin'
naics_code <-
data.table::fread('RawData/corresp_naics6_n6io_sctg_revised.csv', h = T)
cbp_data <-
data.table::fread(paste0('inputs_', region_name, '/data_emp_cbp.csv'), h = T)
region_file_name = paste0('inputs_', region_name, '/', region_name, '_FAFCNTY.csv')
faf_county_lookup <-
data.table::fread(region_file_name, h = T)
# generate list of industries to fill (not in CBP data)
list_of_naics_in_cbp <- unique(cbp_data$Industry_NAICS6_CBP)
naics_code_to_fill <-naics_code %>% filter(!Industry_NAICS6_CBP %in% list_of_naics_in_cbp) %>% as_tibble()
list_of_naics_to_add <- as.character(unique(naics_code_to_fill$Industry_NAICS6_CBP))
#download from U.S. department of labor, QCEW by industry, https://www.bls.gov/cew/downloadable-data-files.htm
list_of_qcew_files <- list.files('RawData/2017_annual_by_industry')
qcew_files <- as.data.table(list_of_qcew_files)
qcew_files_sep <- separate(qcew_files, sep = ' ', col = c('list_of_qcew_files'),
into = c('s1', 's2', 's3', 's4', 's5', 's6'))
qcew_files_sep <- cbind(qcew_files_sep, qcew_files)
qcew_files_to_add <- qcew_files_sep %>% filter(s2 %in% list_of_naics_to_add) %>% as_tibble() # match with 6 digit
cbp_filled_out <- NULL
for (row in 1:nrow(qcew_files_to_add)) {
print(qcew_files_to_add[row, 's2'])
link <- paste0('RawData/', '2017_annual_by_industry/', qcew_files_to_add[row, 'list_of_qcew_files'])
firm_emp_by_naics <- data.table::fread(link, h = T)
firm_emp_by_naics <- firm_emp_by_naics %>% filter(agglvl_code == 78, annual_avg_estabs_count > 0) %>% as_tibble()
firm_emp_by_naics_summary <- firm_emp_by_naics %>%
group_by(area_fips, industry_code) %>%
summarise(employment = sum(annual_avg_emplvl), establishment = sum(annual_avg_estabs_count))
firm_emp_by_naics_summary <- firm_emp_by_naics_summary %>% mutate(ST_CNTY = as.integer(area_fips))
firm_emp_by_naics_summary <- merge(firm_emp_by_naics_summary, faf_county_lookup, by = 'ST_CNTY', all.x = TRUE, all.y = FALSE)
firm_emp_by_naics_out <- firm_emp_by_naics_summary %>%
group_by(industry_code, FAFID, CBPZONE1) %>%
summarise(emp = sum(employment), est = sum(establishment))
firm_emp_by_naics_out <- firm_emp_by_naics_out %>% mutate(emp = ifelse(emp == 0, est, emp)) # if not employment found, fill with 1 as a ghost worker (self-employment)
firm_emp_by_naics_out <- firm_emp_by_naics_out %>% mutate(firm_size = emp/est)
firm_emp_by_naics_out <- firm_emp_by_naics_out %>% na.exclude()
# assign firm size indicator
#1 = '1-19',2 = '20-99',3 ='100-499',4 = '500-999',5 = '1,000-2,499',6 = '2,500-4,999',7 = 'Over 5,000'
firm_emp_by_naics_out <- firm_emp_by_naics_out %>% mutate(e1 = ifelse(firm_size < 20, est, 0))
firm_emp_by_naics_out <- firm_emp_by_naics_out %>% mutate(e2 = ifelse((firm_size < 100) & (firm_size>=20), est, 0))
firm_emp_by_naics_out <- firm_emp_by_naics_out %>% mutate(e3 = ifelse((firm_size < 500) & (firm_size>=100), est, 0))
firm_emp_by_naics_out <- firm_emp_by_naics_out %>% mutate(e4 = ifelse((firm_size < 1000) & (firm_size>=500), est, 0))
firm_emp_by_naics_out <- firm_emp_by_naics_out %>% mutate(e5 = ifelse((firm_size < 2500) & (firm_size>=1000), est, 0))
firm_emp_by_naics_out <- firm_emp_by_naics_out %>% mutate(e6 = ifelse((firm_size < 5000) & (firm_size>=2500), est, 0))
firm_emp_by_naics_out <- firm_emp_by_naics_out %>% mutate(e7 = ifelse(firm_size >= 5000, est, 0))
firm_emp_by_naics_out <- firm_emp_by_naics_out %>% select(industry_code,
FAFID,
CBPZONE1,
emp,
est,
e1,
e2,
e3,
e4,
e5,
e6,
e7)
colnames(firm_emp_by_naics_out) <- c('Industry_NAICS6_CBP', 'FAFZONE', 'CBPZONE',
'employment', 'establishment', 'e1', 'e2', 'e3', 'e4', 'e5', 'e6', 'e7')
cbp_filled_out <- rbind(cbp_filled_out, firm_emp_by_naics_out)
# break
}
industry_code_added <- unique(cbp_filled_out$Industry_NAICS6_CBP)
naics_code_to_fill_remaining <- naics_code_to_fill[(! naics_code_to_fill$Industry_NAICS6_CBP %in% industry_code_added),]
additional_cbp_filled_out <- NULL
list_of_naics_with_data <- unique(as.numeric(qcew_files_sep$s2))
for (row in 1:nrow(naics_code_to_fill_remaining)) {
raw_naics <- as.numeric(naics_code_to_fill_remaining[row, 'Industry_NAICS6_CBP'])
print(raw_naics)
# find closest level of aggregation to fetch the employment data
if (raw_naics %in% list_of_naics_with_data){ # 6digit
s2_value <- as.character(raw_naics)
agg_lev_code <- 78
} else if (floor(raw_naics/10) %in% list_of_naics_with_data){ # 5digit
s2_value <- as.character(floor(raw_naics/10))
agg_lev_code <- 77
}else if (floor(raw_naics/100) %in% list_of_naics_with_data){ # 4digit
s2_value <- as.character(floor(raw_naics/100))
agg_lev_code <- 76
}else if (floor(raw_naics/1000) %in% list_of_naics_with_data){ # 3digit
s2_value <- as.character(floor(raw_naics/1000))
agg_lev_code <- 75
}else if (floor(raw_naics/10000) %in% list_of_naics_with_data){ # 2digit
s2_value <- as.character(floor(raw_naics/10000))
agg_lev_code <- 74
}else{
print(paste(raw_naics, 'file not found!'))
next}
file_name <- qcew_files_sep[qcew_files_sep$s2 == s2_value, 'list_of_qcew_files']
selected_naics <- qcew_files_sep[qcew_files_sep$s2 == s2_value, 's2']
link <- paste0('RawData/', '2017_annual_by_industry/', file_name)
firm_emp_by_naics <- data.table::fread(link, h = T)
firm_emp_by_naics <- firm_emp_by_naics %>% filter(agglvl_code == agg_lev_code, annual_avg_estabs_count > 0) %>% as_tibble()
firm_emp_by_naics_summary <- firm_emp_by_naics %>%
group_by(area_fips, industry_code) %>%
summarise(employment = sum(annual_avg_emplvl), establishment = sum(annual_avg_estabs_count))
firm_emp_by_naics_summary <- firm_emp_by_naics_summary %>% mutate(ST_CNTY = as.integer(area_fips))
firm_emp_by_naics_summary <- merge(firm_emp_by_naics_summary, faf_county_lookup, by = 'ST_CNTY', all.x = TRUE, all.y = FALSE)
firm_emp_by_naics_out <- firm_emp_by_naics_summary %>%
group_by(industry_code, FAFID, CBPZONE1) %>%
summarise(emp = sum(employment), est = sum(establishment))
firm_emp_by_naics_out <- firm_emp_by_naics_out %>% mutate(emp = ifelse(emp == 0, est, emp)) # if not employment found, fill with 1 as a ghost worker (self-employment)
firm_emp_by_naics_out <- firm_emp_by_naics_out %>% mutate(firm_size = emp/est)
firm_emp_by_naics_out <- firm_emp_by_naics_out %>% na.exclude()
# assign firm size indicator
#1 = '1-19',2 = '20-99',3 ='100-499',4 = '500-999',5 = '1,000-2,499',6 = '2,500-4,999',7 = 'Over 5,000'
firm_emp_by_naics_out <- firm_emp_by_naics_out %>% mutate(e1 = ifelse(firm_size < 20, est, 0))
firm_emp_by_naics_out <- firm_emp_by_naics_out %>% mutate(e2 = ifelse((firm_size < 100) & (firm_size>=20), est, 0))
firm_emp_by_naics_out <- firm_emp_by_naics_out %>% mutate(e3 = ifelse((firm_size < 500) & (firm_size>=100), est, 0))
firm_emp_by_naics_out <- firm_emp_by_naics_out %>% mutate(e4 = ifelse((firm_size < 1000) & (firm_size>=500), est, 0))
firm_emp_by_naics_out <- firm_emp_by_naics_out %>% mutate(e5 = ifelse((firm_size < 2500) & (firm_size>=1000), est, 0))
firm_emp_by_naics_out <- firm_emp_by_naics_out %>% mutate(e6 = ifelse((firm_size < 5000) & (firm_size>=2500), est, 0))
firm_emp_by_naics_out <- firm_emp_by_naics_out %>% mutate(e7 = ifelse(firm_size >= 5000, est, 0))
firm_emp_by_naics_out <- firm_emp_by_naics_out %>% select(industry_code,
FAFID,
CBPZONE1,
emp,
est,
e1,
e2,
e3,
e4,
e5,
e6,
e7)
colnames(firm_emp_by_naics_out) <- c('Industry_NAICS_selected', 'FAFZONE', 'CBPZONE',
'employment', 'establishment', 'e1', 'e2', 'e3', 'e4', 'e5', 'e6', 'e7')
firm_emp_by_naics_out$Industry_NAICS6_CBP = raw_naics
additional_cbp_filled_out <- rbind(additional_cbp_filled_out, firm_emp_by_naics_out)
}
# output_path <- paste0('inputs_', region_name, '/gap_filling_cbp_data.csv')
# write.csv(additional_cbp_filled_out, output_path)
additional_cbp_out <- additional_cbp_filled_out %>%
ungroup() %>%
select(Industry_NAICS6_CBP, FAFZONE, CBPZONE, employment, establishment, e1, e2, e3, e4, e5, e6, e7)
final_cbp_with_gap_filling <- rbind(cbp_data, cbp_filled_out, additional_cbp_out)
output_path <- paste0('inputs_', region_name, '/data_emp_cbp_imputed.csv')
write.csv(final_cbp_with_gap_filling, output_path)
total_emp <- sum(final_cbp_with_gap_filling$employment)
total_est <- sum(final_cbp_with_gap_filling$establishment)
old_emp <- sum(cbp_data$employment)
old_est <- sum(cbp_data$establishment)
print(paste('firms before imputation', old_est))
print(paste('firms after imputation', total_est))
print(paste('employees before imputation', old_emp))
print(paste('employees after imputation', total_emp))