employment-data

Data Sources

The main data source for employment data is the U.S. Bureau of Labor Statistics (BLS). BLS provides Local Area Unemployment Statistics (LAUS) which provides us with estimates on employment, labor force, and unemployment. These data points are major indicators of local economic conditions and help inform the demand for housing as local economies add more jobs.

Data Collection and Preparation

The blscrapeR package is used to pull LAUS data on employment and unemployment for all Virginia localities. All data is presented without season adjustments

library('blscrapeR')
library('tidyverse')
library('tigris')

# The following pulls all localities in Virginia to help iterate through BLS series.
# The TIGRIS package allows us to do this and pull FIPS codes.

virginia <- list_counties("VA")

# The following creates an additional column of series ids based on BLS labels. The
# mutate() function creates a new column called 'seriesid' while the paste0() function
# concatenates the necessary pieces of the series id without spaces.

virginia <- virginia %>% mutate(seriesid = paste0("LAUCN51", county_code, "0000000005"), #employment
                                rateseries = paste0("LAUCN51", county_code, "0000000003"), #unemployment rate
                                lfseries = paste0("LAUCN51", county_code, "0000000006"), #labor force
                                unemployedseries = paste0("LAUCN51", county_code, "0000000004") # unemployment
                                )

# Only the 'seriesid' field is needed to pull from the BLS API, therefore we need to
# create a list of series ids for all 136 Virginia localities. 

list <- list(virginia$seriesid)
ratelist <- list(virginia$rateseries)
lflist <- list(virginia$lfseries)
unemploymentlist <- list(virginia$unemployedseries)

# The blscrapeR does not accept a list in its query pull and requires a vector. Therefore, 
# unlist() is used in order to simplifies the list to a vector.

list <- unlist(list)   
ratelist <- unlist(ratelist)
lflist <- unlist(lflist)
unemploymentlist <- unlist(unemploymentlist)

# The BLS API has a series query limit of 50, but there are 136 localities (series ids)
# to pull. So we need to split the vector into chunks using the split() function.

list <- split(list, ceiling(seq_along(list)/50))
ratelist <- split(ratelist, ceiling(seq_along(ratelist)/50))
lflist <- split(lflist, ceiling(seq_along(lflist)/50))
unemploymentlist <- split(unemploymentlist, ceiling(seq_along(unemploymentlist)/50))


# A function is needed that will run through the bls_api() using the chunked vector we
# created previously. This function will be utilized in map_dfr() to combine the three 
# iterations together into a single table

bls_employment_pull <- function(list){
  bls_api(list,
          startyear = 2008, endyear = 2022, Sys.getenv("BLS_KEY")) %>%
    dateCast()
}

va_employed <- map_dfr(list, bls_employment_pull)

bls_unemployment_rate_pull <- function(ratelist){
  bls_api(ratelist,
          startyear = 2008, endyear = 2022, Sys.getenv("BLS_KEY")) %>%
    dateCast()
}

va_unemployment_rate <- map_dfr(ratelist, bls_unemployment_rate_pull)

bls_laborforce_pull <- function(lflist){
  bls_api(lflist,
          startyear = 2008, endyear = 2022, Sys.getenv("BLS_KEY")) %>%
    dateCast()
}

va_laborforce <- map_dfr(lflist, bls_laborforce_pull)

bls_unemployment_pull <- function(unemploymentlist){
  bls_api(unemploymentlist,
          startyear = 2008, endyear = 2022, Sys.getenv("BLS_KEY")) %>%
    dateCast()
}

va_unemployment <- map_dfr(unemploymentlist, bls_unemployment_pull)



# Now that a data frame is created with all localities and years needed, we need to create
# an additional column for the FIPS code of each locality, so that we can join to a table 
# of locality names later.

fip_function <- function(x) {
  x %>%
    mutate(fips = substr(seriesID, 6, 10))
}

result <- list(va_employed, va_unemployment_rate, va_laborforce, va_unemployment) %>% 
  lapply(fip_function)

names(result) = c('va_employed', 'va_unemployment_rate', 'va_laborforce', 'va_unemployment')

list2env(result, envir = .GlobalEnv)


# Write to csv file.

write_csv(va_employed, "data/va_employment_2008_2021.csv")
write_csv(va_unemployment_rate, "data/va_unemployment_rate_2008_2021.csv")
write_csv(va_laborforce, "data/va_laborforce_2008_2021.csv")
write_csv(va_unemployment, "data/va_unemployment_2008_2021.csv")