Estimating the Nonprofit and Philanthropic Sector's Payroll Taxes, 2026

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```{r, message=FALSE, warning=FALSE} # Load libraries library( knitr ) library( tidyverse ) library( pander ) ``` ```{r, message=FALSE, warning=FALSE} # Read data pc.2022 <- readr::read_csv( "22eoextract990.csv" ) pc.2023 <- readr::read_csv( "23eoextract990.csv" ) pc.2024 <- readr::read_csv( "24eoextract990.csv" ) pf.2022 <- readr::read_csv( "22eoextract990pf.csv" ) pf.2023 <- readr::read_csv( "23eoextract990pf.csv" ) pf.2024 <- readr::read_csv( "24eoextract990pf.csv" ) ``` ```{r, echo=TRUE} # Add year variables pc.2022$year <- pc.2022$tax_pd %>% substr( 1, 4 ) pc.2023$year <- pc.2023$tax_pd %>% substr( 1, 4 ) pc.2024$year <- pc.2024$tax_pd %>% substr( 1, 4 ) pf.2022$year <- pf.2022$TAX_PRD %>% substr( 1, 4 ) pf.2023$year <- pf.2023$TAX_PRD %>% substr( 1, 4 ) pf.2024$year <- pf.2024$TAX_PRD %>% substr( 1, 4 ) ``` ```{r, echo=TRUE} # Create tax period tables pc.2024 %>% group_by( year ) %>% summarise( n() ) %>% kable( col.names = c( "Tax Period", "Frequency" ), caption = "Tax Periods in the 2024 Form 990 Dataset" ) pf.2024 %>% group_by( year ) %>% summarise( n() ) %>% kable( col.names = c( "Tax Period", "Frequency" ), caption = "Tax Periods in the 2024 Form 990-PF Dataset" ) ``` ```{r, echo=TRUE} # Subset to tax period 2022 pc.2024.subset <- filter( pc.2024, year=="2022" ) pc.2023.subset <- filter( pc.2023, year=="2022" ) pc.2022.subset <- filter( pc.2022, year=="2022" ) pf.2024.subset <- filter( pf.2024, year=="2022" ) pf.2023.subset <- filter( pf.2023, year=="2022" ) pf.2022.subset <- filter( pf.2022, year=="2022" ) ``` ```{r, echo=TRUE} # Check subsets pc.2024.subset %>% group_by( year ) %>% summarise( n() ) %>% kable( col.names = c( "Tax Period", "Frequency" ), caption = "Tax Periods in the Subsetted 2024 Form 990 Dataset" ) pf.2024.subset %>% group_by( year ) %>% summarise( n() ) %>% kable( col.names = c( "Tax Period", "Frequency" ), caption = "Tax Periods in the Subsetted 2024 Form 990-PF Dataset" ) ``` ```{r, echo=TRUE} # Standardize names names( pc.2024.subset ) <- tolower( names( pc.2024.subset ) ) names( pc.2023.subset ) <- tolower( names( pc.2023.subset ) ) names( pc.2022.subset ) <- tolower( names( pc.2022.subset ) ) names( pf.2024.subset ) <- tolower( names( pf.2024.subset ) ) names( pf.2023.subset ) <- tolower( names( pf.2023.subset ) ) names( pf.2022.subset ) <- tolower( names( pf.2022.subset ) ) ``` ```{r, echo=TRUE} # Select relevant variables pc.2024.subset2 <- pc.2024.subset %>% select( ein, payrolltx, compnsatncurrofcr, compnsatnandothr, othrsalwages, pensionplancontrb, othremplyeebenef ) names( pc.2024.subset2 ) head( pc.2024.subset2 ) pc.2023.subset2 <- pc.2023.subset %>% select( ein, payrolltx, compnsatncurrofcr, compnsatnandothr, othrsalwages, pensionplancontrb, othremplyeebenef ) names( pc.2023.subset2 ) head( pc.2023.subset2 ) pc.2022.subset2 <- pc.2022.subset %>% select( ein, payrolltx, compnsatncurrofcr, compnsatnandothr, othrsalwages, pensionplancontrb, othremplyeebenef ) names( pc.2022.subset2 ) head( pc.2022.subset2 ) pf.2024.subset2 <- pf.2024.subset %>% select( ein, compofficers, pensplemplbenf ) names( pf.2024.subset2 ) head( pf.2024.subset2 ) pf.2023.subset2 <- pf.2023.subset %>% select( ein, compofficers, pensplemplbenf ) names( pf.2023.subset2 ) head( pf.2023.subset2 ) pf.2022.subset2 <- pf.2022.subset %>% select( ein, compofficers, pensplemplbenf ) names( pf.2022.subset2 ) head( pf.2022.subset2 ) ``` ```{r, echo=TRUE} # Combine years pc <- bind_rows( pc.2024.subset2, pc.2023.subset2, pc.2022.subset2 ) pf <- bind_rows( pf.2024.subset2, pf.2023.subset2, pf.2022.subset2) ``` ```{r, echo=TRUE} # Check for duplicate EINs n_distinct( pc$ein ) nrow( pc ) n_distinct( pf$ein ) nrow( pf ) ``` ```{r, echo=TRUE} # Deduplicate EINs pc <- pc %>% distinct( ein, .keep_all = TRUE ) pf <- pf %>% distinct( ein, .keep_all = TRUE ) ``` ```{r, echo=TRUE} # Check deduplication n_distinct( pc$ein ) nrow( pc ) n_distinct( pf$ein ) nrow( pf ) ``` ```{r, echo=TRUE} # Create helper function dollarize <- function(x) { paste0( "$", format( round(x,0), big.mark="," ) ) } ``` ```{r, echo=TRUE} # Calculate payroll taxes prtax <- sum( pc$payrolltx, na.rm = T ) dollarize( prtax ) salaries <- sum( pc$compnsatncurrofcr, pc$compnsatnandothr, pc$othrsalwages, pc$pensionplancontrb, pc$othremplyeebenef, na.rm = T ) dollarize( salaries ) ratio <- prtax / salaries paste0( 100*round( ratio, 4 ), "%" ) salaries.pf <- sum( pf$compofficers, pf$pensplemplbenf, na.rm = T ) dollarize( salaries.pf ) prtax.pf <- ratio * salaries.pf dollarize( prtax.pf ) tax.total <- prtax + prtax.pf dollarize( tax.total ) ``` ```{r, echo=TRUE} # Adjust for inflation # According to https://www.bls.gov/data/inflation_calculator.htm, a dollar in # January 2022 has the same buying power as $1.15 in December 2025, which is # the latest available data. ( tax.total * 1.15 ) %>% dollarize()