Census of Governments (COG)

Location, employment, and payroll for state and local (but not federal) government agencies in the U.S.

One record per agency, one per agency function, plus the government units master address file.
Complete enumeration of civilian employment in state and local governments in the 50 states + D.C.
The Annual Survey of Public Employment & Payroll becomes a census in years ending with 2 and 7.
Administered and financed by the US Census Bureau.

Please skim before you begin:

2022 Census of Governments, Survey of Public Employment & Payroll Methodology
Government Units Survey Methodology
This human-composed haiku or a bouquet of artificial intelligence-generated limericks

# courthouse steps wedding
# schools police fire water
# no fed mail invite

Download, Import, Preparation

Download, import, and stack the government units listing file:

library(readxl)

tf_gus <- tempfile()

gus_url <- "https://www2.census.gov/programs-surveys/gus/datasets/2022/govt_units_2022.ZIP"

download.file( gus_url , tf_gus , mode = 'wb' )

unzipped_files_gus <- unzip( tf_gus , exdir = tempdir() )

xlsx_gus_fn <- grep( "\\.xlsx$" , unzipped_files_gus , value = TRUE )

xlsx_sheets <- excel_sheets( xlsx_gus_fn )

# read all sheets into a list of tibbles
gus_tbl_list <- lapply( xlsx_sheets , function( w ) read_excel( xlsx_gus_fn , sheet = w ) )

# convert all tibbles to data.frame objects
gus_df_list <- lapply( gus_tbl_list , data.frame )

# lowercase all column names
gus_df_list <-
    lapply( 
        gus_df_list , 
        function( w ){ names( w ) <- tolower( names( w ) ) ; w } 
    )

# add the excel tab source to each data.frame
for( i in seq( xlsx_sheets ) ) gus_df_list[[ i ]][ , 'source_tab' ] <- xlsx_sheets[ i ]

# determine which columns are in all tables
column_intersect <- Reduce( intersect , lapply( gus_df_list , names ) )

# determine which columns are in some but not all tables
column_union <- unique( unlist( lapply( gus_df_list , names ) ) )

# these columns will be discarded by stacking:
unique(
    unlist(
        lapply(
            lapply( gus_df_list , names ) , 
            function( w ) column_union[ !column_union %in% w ]
        )
    )
)

# stack all excel sheets, keeping only the columns that all tables have in common
gus_df <- Reduce( rbind , lapply( gus_df_list , function( w ) w[ column_intersect ] ) )

Download and import the survey of public employment & payroll, one record per function (not per unit):

tf_apes <- tempfile()

apes_url <-
    paste0(
        "https://www2.census.gov/programs-surveys/apes/datasets/" ,
        "2022/2022%20COG-E%20Individual%20Unit%20Files.zip"
    )

download.file( apes_url , tf_apes , mode = 'wb' )

unzipped_files_apes <- unzip( tf_apes , exdir = tempdir() )

xlsx_apes_fn <- grep( "\\.xlsx$" , unzipped_files_apes , value = TRUE )

apes_tbl <- read_excel( xlsx_apes_fn )

apes_df <- data.frame( apes_tbl )

names( apes_df ) <- tolower( names( apes_df ) )

Review the non-matching records between these two tables, then merge:

# all DEP School Districts and a third of Special Districts are not in the `apes_df`
table(
    gus_df[ , 'census_id_gidid' ] %in% apes_df[ , 'individual.unit.id' ] ,
    gus_df[ , 'source_tab' ] ,
    useNA = 'always'
)

# state governments are not in the `gus_df`
table(
    apes_df[ , 'individual.unit.id' ] %in% gus_df[ , 'census_id_gidid' ] ,
    apes_df[ , 'type.of.government' ] ,
    useNA = 'always'
)

# check for overlapping field names:
( overlapping_names <- intersect( names( apes_df ) , names( gus_df ) ) )

# rename the state column in `gus_df` to state abbreviation
names( gus_df )[ names( gus_df ) == 'state' ] <- 'stateab'

double_df <-
    merge(
        apes_df ,
        gus_df ,
        by.x = 'individual.unit.id' ,
        by.y = 'census_id_gidid' ,
        all.x = TRUE
    )

stopifnot( nrow( double_df ) == nrow( apes_df ) )

# replace dots with underscores
names( double_df ) <- gsub( "\\." , "_" , names( double_df ) )

Keep either the one record per agency rows or the one record per function rows:

# `Total - All Government Employment Functions` records sum to the same as all other records:
with( double_df , tapply( full_time_employees , grepl( "Total" , government_function ) , sum ) )

with( double_df , tapply( part_time_payroll , grepl( "Total" , government_function ) , sum ) )

# keep one record per government function (multiple records per agency):
cog_df <- subset( double_df , !grepl( "Total" , government_function ) )

# keep one record per government agency:
# cog_df <- subset( double_df , grepl( "Total" , government_function ) )

Save locally

Save the object at any point:

# cog_fn <- file.path( path.expand( "~" ) , "COG" , "this_file.rds" )
# saveRDS( cog_df , file = cog_fn , compress = FALSE )

Load the same object:

# cog_df <- readRDS( cog_fn )

Variable Recoding

Add new columns to the data set:

cog_df <- 
    transform( 
        cog_df , 
        
        one = 1 ,
        
        total_payroll = full_time_payroll + part_time_payroll ,
        
        total_employees = full_time_employees + part_time_employees ,

        any_full_time_employees = full_time_employees > 0
    )

Analysis Examples with base R

Unweighted Counts

Count the unweighted number of records in the table, overall and by groups:

nrow( cog_df )

table( cog_df[ , "type_of_government" ] , useNA = "always" )

Descriptive Statistics

Calculate the mean (average) of a linear variable, overall and by groups:

mean( cog_df[ , "full_time_employees" ] )

tapply(
    cog_df[ , "full_time_employees" ] ,
    cog_df[ , "type_of_government" ] ,
    mean 
)

Calculate the distribution of a categorical variable, overall and by groups:

prop.table( table( cog_df[ , "census_region" ] ) )

prop.table(
    table( cog_df[ , c( "census_region" , "type_of_government" ) ] ) ,
    margin = 2
)

Calculate the sum of a linear variable, overall and by groups:

sum( cog_df[ , "full_time_employees" ] )

tapply(
    cog_df[ , "full_time_employees" ] ,
    cog_df[ , "type_of_government" ] ,
    sum 
)

Calculate the median (50th percentile) of a linear variable, overall and by groups:

quantile( cog_df[ , "full_time_employees" ] , 0.5 )

tapply(
    cog_df[ , "full_time_employees" ] ,
    cog_df[ , "type_of_government" ] ,
    quantile ,
    0.5 
)

Subsetting

Limit your data.frame to Elementary, Secondary, Higher, and Other Educational Government Agencies:

sub_cog_df <- subset( cog_df , grepl( 'Education' , government_function ) )

Calculate the mean (average) of this subset:

mean( sub_cog_df[ , "full_time_employees" ] )

Measures of Uncertainty

Calculate the variance, overall and by groups:

var( cog_df[ , "full_time_employees" ] )

tapply(
    cog_df[ , "full_time_employees" ] ,
    cog_df[ , "type_of_government" ] ,
    var 
)

Regression Models and Tests of Association

Perform a t-test:

t.test( full_time_employees ~ any_full_time_employees , cog_df )

Perform a chi-squared test of association:

this_table <- table( cog_df[ , c( "any_full_time_employees" , "census_region" ) ] )

chisq.test( this_table )

Perform a generalized linear model:

glm_result <- 
    glm( 
        full_time_employees ~ any_full_time_employees + census_region , 
        data = cog_df
    )

summary( glm_result )

Replication Example

This example matches excel cell “C17” of Employment & Payroll Data by State and by Function:

financial_admin_df <- subset( cog_df , government_function == 'Financial Administration' )

stopifnot( sum( financial_admin_df[ , 'full_time_employees' ] ) == 404228 )

Analysis Examples with `dplyr`

The R dplyr library offers an alternative grammar of data manipulation to base R and SQL syntax. dplyr offers many verbs, such as summarize, group_by, and mutate, the convenience of pipe-able functions, and the tidyverse style of non-standard evaluation. This vignette details the available features. As a starting point for COG users, this code replicates previously-presented examples:

library(dplyr)
cog_tbl <- as_tibble( cog_df )

Calculate the mean (average) of a linear variable, overall and by groups:

cog_tbl %>%
    summarize( mean = mean( full_time_employees ) )

cog_tbl %>%
    group_by( type_of_government ) %>%
    summarize( mean = mean( full_time_employees ) )

Analysis Examples with `data.table`

The R data.table library provides a high-performance version of base R’s data.frame with syntax and feature enhancements for ease of use, convenience and programming speed. data.table offers concise syntax: fast to type, fast to read, fast speed, memory efficiency, a careful API lifecycle management, an active community, and a rich set of features. This vignette details the available features. As a starting point for COG users, this code replicates previously-presented examples:

library(data.table)
cog_dt <- data.table( cog_df )

Calculate the mean (average) of a linear variable, overall and by groups:

cog_dt[ , mean( full_time_employees ) ]

cog_dt[ , mean( full_time_employees ) , by = type_of_government ]

Analysis Examples with `duckdb`

The R duckdb library provides an embedded analytical data management system with support for the Structured Query Language (SQL). duckdb offers a simple, feature-rich, fast, and free SQL OLAP management system. This vignette details the available features. As a starting point for COG users, this code replicates previously-presented examples:

library(duckdb)
con <- dbConnect( duckdb::duckdb() , dbdir = 'my-db.duckdb' )
dbWriteTable( con , 'cog' , cog_df )

Calculate the mean (average) of a linear variable, overall and by groups:

dbGetQuery( con , 'SELECT AVG( full_time_employees ) FROM cog' )

dbGetQuery(
    con ,
    'SELECT
        type_of_government ,
        AVG( full_time_employees )
    FROM
        cog
    GROUP BY
        type_of_government'
)

Census of Governments (COG)

Download, Import, Preparation

Save locally

Variable Recoding

Analysis Examples with base R

Unweighted Counts

Descriptive Statistics

Subsetting

Measures of Uncertainty

Regression Models and Tests of Association

Replication Example

Analysis Examples with dplyr

Analysis Examples with data.table

Analysis Examples with duckdb

Analysis Examples with `dplyr`

Analysis Examples with `data.table`

Analysis Examples with `duckdb`