Research Question

How are government workforce composition and the distribution of government employment across public functions associated across U.S. states and Washington, D.C. in 2025?

Hypotheses

H1: Geographic areas with a higher proportion of full-time government employees will tend to have higher average monthly government payroll per employee.

H2: Geographic areas with a higher proportion of government employment devoted to education will tend to have a lower proportion devoted to police protection.

Data

The data used for this analysis come from the U.S. Census Bureau’s Annual Survey of Public Employment & Payroll (ASPEP), specifically the 2025 State and Local Government Employment and Payroll Data. The dataset provides information on public employment and payroll across geographic areas and government functions.

Dataset Selection

The dataset meets the five criteria established for this course.

1. Relevant documentation

The U.S. Census Bureau provides documentation for the Annual Survey of Public Employment & Payroll, including information about the survey methodology, variables, data files, and government classifications. This documentation provides information needed to interpret the variables and understand how the data were collected and organized.

2. Protection of personal and sensitive information

The dataset contains aggregate government employment and payroll information rather than individual-level records. It does not contain personally identifying information, personal health information, or other individual-level sensitive information.

3. Authorization for public distribution

The data are publicly distributed by the U.S. Census Bureau as part of its public government employment and payroll data products.

4. Sufficient observations and variables

The dataset contains 1,976 observations and 20 variables. The observations represent combinations of geographic areas and government functions for 2025. The dataset includes employment and payroll measures that provide sufficient information to conduct descriptive and correlational analyses.

5. Relevance to Public Administration and Nonprofit Administration

The dataset is directly relevant to Public Administration because it measures employment and payroll within state and local governments. Government workforce composition and the distribution of public employees across functions are important aspects of public-sector administration and resource allocation.

Data Preparation

data <- read.csv("GOVSEMPTIMESERIES.GS00EMP01-2026-09-22T170141.csv")

data$Full.Time.Employment..FT_EMP. <- as.numeric(data$Full.Time.Employment..FT_EMP.)
## Warning: NAs introduced by coercion
data$Total.Full.Time.and.Part.Time.Employment..TOT_EMP. <- as.numeric(data$Total.Full.Time.and.Part.Time.Employment..TOT_EMP.)
## Warning: NAs introduced by coercion
data$Total.Full.Time.and.Part.Time.Payroll..TOT_PAY. <- as.numeric(data$Total.Full.Time.and.Part.Time.Payroll..TOT_PAY.)
## Warning: NAs introduced by coercion
dim(data)
## [1] 1976   20
names(data)
##  [1] "Year..time."                                                                   
##  [2] "Geographic.Area.Name..NAME."                                                   
##  [3] "Meaning.of.Aggregate.Description..AGG_DESC_LABEL."                             
##  [4] "Meaning.of.Type.of.Government..GOVTYPE_LABEL."                                 
##  [5] "Full.Time.Employment..FT_EMP."                                                 
##  [6] "Full.Time.Payroll..FT_PAY."                                                    
##  [7] "Part.Time.Employment..PT_EMP."                                                 
##  [8] "Part.Time.Payroll..PT_PAY."                                                    
##  [9] "Part.Time.Hours..PT_HRS."                                                      
## [10] "Full.Time.Equivalent.Employment..FTE."                                         
## [11] "Total.Full.Time.and.Part.Time.Employment..TOT_EMP."                            
## [12] "Total.Full.Time.and.Part.Time.Payroll..TOT_PAY."                               
## [13] "Full.Time.Employment.Coefficient.of.Variation..FT_EMP_CV."                     
## [14] "Full.Time.Payroll.Coefficient.of.Variation..FT_PAY_CV."                        
## [15] "Part.Time.Employment.Coefficient.of.Variation..PT_EMP_CV."                     
## [16] "Part.Time.Payroll.Coefficient.of.Variation..PT_PAY_CV."                        
## [17] "Part.Time.Hours.Coefficient.of.Variation..PT_HRS_CV."                          
## [18] "Full.Time.Equivalent.Employment.Coefficient.of.Variation..FTE_CV."             
## [19] "Total.Full.Time.and.Part.Time.Employment.Coefficient.of.Variation..TOT_EMP_CV."
## [20] "Total.Full.Time.and.Part.Time.Payroll.Coefficient.of.Variation..TOT_PAY_CV."

Exploratory Statistics

Summary Statistics

summary(data$Full.Time.Employment..FT_EMP.)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.     NAs 
##     0.0    67.0   311.0   361.6   599.0   995.0    1479
summary(data$Total.Full.Time.and.Part.Time.Employment..TOT_EMP.)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.     NAs 
##     0.0    59.0   313.5   365.4   622.0   995.0    1534
hist(data$Full.Time.Employment..FT_EMP.)

plot(data$Full.Time.Employment..FT_EMP.,data$Total.Full.Time.and.Part.Time.Payroll..TOT_PAY.)

data$Full.Time.Equivalent.Employment..FTE. <- as.numeric(data$Full.Time.Equivalent.Employment..FTE.)
## Warning: NAs introduced by coercion
cor(data$Full.Time.Employment..FT_EMP.,data$Full.Time.Equivalent.Employment..FTE., use="complete.obs")
## [1] 0.9868869