Top 0.5% Percentile Descriptive Stats

Top 0.5% Micro

moodys_employ_micro <- moodys_employ %>%
  dplyr::filter(year == 2020) %>%
  dplyr::group_by(geoid_co) %>%
  dplyr::summarise(
    employment = sum(value, na.rm = TRUE)
  ) %>%
  dplyr::left_join(
    cbsa,
    by = c("geoid_co"="geoid")
  ) %>%
  dplyr::filter(!is.na(metropolitan_designation)) %>%
  dplyr::filter(metropolitan_designation == "Micro")

moodys_employ_micro$percentile <- dplyr::percent_rank(moodys_employ_micro$employment)

micro_stats <- moodys_employ_micro %>% 
  dplyr::filter(percentile > .95) %>%
  dplyr::arrange(desc(percentile)) %>%
  dplyr::left_join(
    county_state_crosswalk,
    by = "geoid_co"
  )

rmarkdown::paged_table(micro_stats)

Top 0.5% Metro

moodys_employ_metro <- moodys_employ %>%
  dplyr::filter(year == 2020) %>%
  dplyr::group_by(geoid_co) %>%
  dplyr::summarise(
    employment = sum(value, na.rm = TRUE)
  ) %>%
  dplyr::left_join(
    cbsa,
    by = c("geoid_co"="geoid")
  ) %>%
  dplyr::filter(!is.na(metropolitan_designation)) %>%
  dplyr::filter(metropolitan_designation == "Metro")

moodys_employ_metro$percentile <- dplyr::percent_rank(moodys_employ_metro$employment)

metro_stats <- moodys_employ_metro %>% 
  dplyr::filter(percentile > .95) %>%
  dplyr::arrange(desc(percentile)) %>%
  dplyr::left_join(
    county_state_crosswalk,
    by = "geoid_co"
  )

rmarkdown::paged_table(metro_stats)