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#######Background Setup######library(httr)
library(tidyverse)
library(stringr)
library(RCurl)
library(reshape2)
library(RColorBrewer)
library(extrafont)
library(knitr)
library(foreign)
library(kableExtra)
library(urbnthemes)
library(grid)
library(gridExtra)
#set_urban_defaults()set_urban_defaults()
#######Download Raw NCCS Data#######This code will use the following NCCS data sets, so import separately using defined functions, and save in the "Data" folder#Retrieve NCCS Data Archive download functionssource("NCCS_Code/Prep IRS BMF.R")
source("NCCS_Code/Prep NCCS Core File.R")
#The following code will retrieve the stated data sets from the NCCS Data Archive.#This code is commented out in final to avoid repeated (and bandwidth intensive) downloads#IRS Business Master Files:#bm0601#bm0601 <- getbmffile("2006", "01")##bm1106#bm1106 <- getbmffile("2011", "06")##bm1502#bm1502 <- getbmffile("2015", "02")##bm1602#bm1602 <- getbmffile("2016", "02")####core2005pf#core2005pf <- getcorefile(2005, "pf")##core2005pc#core2005pc <- getcorefile(2005, "pc")##core2005co#core2005co <- getcorefile(2005, "co")#####core2010pf#core2010pf <- getcorefile(2010, "pf")##core2010pc#core2010pc <- getcorefile(2010, "pc")##core2010co#core2010co <- getcorefile(2010, "co")#####core2014pf#core2014pf <- getcorefile(2014, "pf")##core2014pc#core2014pc <- getcorefile(2014, "pc")##core2014co#core2014co <- getcorefile(2014, "co")#####core2015pf#core2015pf <- getcorefile(2015, "pf")##core2015pc#core2015pc <- getcorefile(2015, "pc")##core2015co#core2015co <- getcorefile(2015, "co")#######Import Index Tables#######The NTEE Lookup file can be downloaded from: http://nccs-data.urban.org/data/misc/nccs.nteedocAllEins.csv#The following code assumes that it has been saved in the local "Data" folder#retrieve from CSV:nteedocalleins <- read_csv("Data/nteedocalleins.csv",
col_types = cols_only(EIN = col_character(),
NTEEFINAL = col_character()
))
#Inflation Index#Load Inflation index table#Based on information from Consumer Price Index Table 24: "Historical Consumer Price Index for All Urban Consumers (CPI-U): U.S. city average, all items"#Updated April 2018, available at https://www.bls.gov/cpi/tables/supplemental-files/home.htm (Historical CPI-U)inflindex <- read.csv("External_Data/Inflation Index.csv", row.names =1, header = TRUE)
#Create function to prepare and import selected BMF fields for analysisprepbmffile <- function(bmffilepath) {
output <- read_csv(bmffilepath,
col_types = cols_only(EIN = col_character(),
NTEECC = col_character(),
STATE = col_character(),
OUTNCCS = col_character(),
SUBSECCD = col_character(),
FNDNCD = col_character(),
CFILER = col_character(),
CZFILER = col_character(),
CTAXPER = col_character(),
CTOTREV = col_double(),
CASSETS = col_double()
))
names(output) <- toupper(names(output))
return(output)
}#Create function to prepare and import selected NCCS Core PC/CO fields for analysisprepcorepcfile <- function(corefilepath) {
output <- read_csv(corefilepath,
col_types = cols_only(EIN = col_character(),
OUTNCCS = col_character(),
SUBSECCD = col_character(),
FNDNCD = col_character(),
TOTREV = col_double(),
EXPS = col_double(),
ASS_EOY = col_double(),
GRREC = col_double()
))
names(output) <- toupper(names(output))
return(output)
}#Create function to prepare and import selected NCCS Core PF fields for analysisprepcorepffile <- function(corefilepath) {
output <- read_csv(corefilepath,
col_types = cols_only(EIN = col_character(),
OUTNCCS = col_character(),
SUBSECCD = col_character(),
FNDNCD = col_character(),
P1TOTREV = col_double(),
P1TOTEXP = col_double(),
P2TOTAST = col_double()
))
names(output) <- toupper(names(output))
return(output)
}#######Import and Prepare NCCS Data files#Note: data has already been saved locally using above code##########BMF Data####2005 BMF Databmf2005 <-prepbmffile("Data/bm0601.csv")
#2010 BMF Databmf2010 <-prepbmffile("Data/bm1106.csv")
#2014 BMF Databmf2014 <-prepbmffile("Data/bm1502.csv")
#2015 BMF Databmf2015 <-prepbmffile("Data/bm1602.csv")
####Core Data#####Core 2005 Data##PCcore2005pc <- prepcorepcfile("Data/core2005pc.csv")
#COcore2005co <- prepcorepcfile("Data/core2005co.csv")
#PFcore2005pf <- prepcorepffile("Data/core2005pf.csv")
##Core 2010 Data##PCcore2010pc <- prepcorepcfile("Data/core2010pc.csv")
#COcore2010co <- prepcorepcfile("Data/core2010co.csv")
#PFcore2010pf <- prepcorepffile("Data/core2010pf.csv")
##Core 2014 Data##PCcore2014pc <- prepcorepcfile("Data/core2014pc.csv")
#COcore2014co <- prepcorepcfile("Data/core2014co.csv")
#PFcore2014pf <- prepcorepffile("Data/core2014pf.csv")
##Core 2015 Data##PCcore2015pc <- prepcorepcfile("Data/core2015pc.csv")
#COcore2015co <- prepcorepcfile("Data/core2015co.csv")
#PFcore2015pf <- prepcorepffile("Data/core2015pf.csv")
#######Create Grouping Categories for Analysis by NTEE and Size##########NTEE Groupings####Create NTEE grouping categoriesarts <- c("A")
highered <- c("B4", "B5")
othered <- c("B")
envanimals <- c("C", "D")
hospitals <- c('E20','E21','E22','E23','E24','F31','E30','E31','E32')
otherhlth <- c("E", "F", "G", "H")
humanserv <- c("I", "J", "K", "L", "M", "N", "O", "P")
intl <- c("Q")
pubben <- c("R", "S", "T", "U", "V", "W", "Y", "Z")
relig <- c("X")
#define function to join NTEE Master list and categorize organizations accordinglyNTEEclassify <- function(dataset) {
#merge in Master NTEE look up filedataset <- dataset %>%
left_join(nteedocalleins, by = "EIN")
#create NTEEGRP classificationsdataset$NTEEGRP <- " "
dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1) %in% arts ] <- "Arts"
dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1) %in% othered ] <- "Other education"
dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,2) %in% highered ] <- "Higher education"
dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1) %in% envanimals] <- "Environment and animals"
dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1) %in% otherhlth] <- "Other health care"
dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,3) %in% hospitals] <- "Hospitals and primary care facilities"
dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1) %in% humanserv] <- "Human services"
dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1) %in% intl] <- "International"
dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1) %in% pubben] <- "Other public and social benefit"
dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1) %in% relig] <- "Religion related"
dataset$NTEEGRP[is.na(dataset$NTEEFINAL)] <- "Other public and social benefit"
return(dataset)
}####Expense Groupings####define function to classify organizations by expenses sizeEXPclassify <-function(dataset) {
dataset$EXPCAT <- " "
dataset$EXPCAT[dataset$EXPS<100000] <- "a. Under $100,000"
dataset$EXPCAT[dataset$EXPS >= 100000 & dataset$EXPS< 500000] <- "b. $100,000 to $499,999"
dataset$EXPCAT[dataset$EXPS >= 500000 & dataset$EXPS< 1000000] <- "c. $500,000 to $999,999"
dataset$EXPCAT[dataset$EXPS >= 1000000 & dataset$EXPS< 5000000] <- "d. $1 million to $4.99 million"
dataset$EXPCAT[dataset$EXPS >= 5000000 & dataset$EXPS< 10000000] <- "e. $5 million to $9.99 million"
dataset$EXPCAT[dataset$EXPS >= 10000000] <- "f. $10 million or more"
return(dataset)
}####Apply Groupings to relevant data sets####NTEEcore2005pc <- NTEEclassify(core2005pc)
core2010pc <- NTEEclassify(core2010pc)
core2014pc <- NTEEclassify(core2014pc)
core2015pc <- NTEEclassify(core2015pc)
#Expensescore2005pc <-EXPclassify(core2005pc)
core2010pc <-EXPclassify(core2010pc)
core2014pc <-EXPclassify(core2014pc)
core2015pc <-EXPclassify(core2015pc)
The Nonprofit Sector in Brief 2018: Public Charites, Giving, and Volunteering
by Brice S. McKeever
November 2018
This brief discusses trends in the number and finances of 501(c)(3) public charities and key findings on two important resources for the nonprofit sector: private charitable contributions and volunteering.
Back to topHighlights
- Approximately 1.56 million nonprofits were registered with the Internal Revenue Service (IRS) in 2015, an increase of 10.4 percent from 2005.
- The nonprofit sector contributed an estimated $985.4 billion to the US economy in 2015, composing 5.4 percent of the country's gross domestic product (GDP).[1]
- Of the nonprofit organizations registered with the IRS, 501(c)(3) public charities accounted for just over three-quarters of revenue and expenses for the nonprofit sector as a whole ($1.98 trillion and $1.84 trillion, respectively) and just under two-thirds of the nonprofit sector's total assets ($3.67 trillion).
- In 2017, total private giving from individuals, foundations, and businesses totaled $410.02 billion (Giving USA Foundation 2018), an increase of 3 percent from 2016 (after adjusting for inflation). According to Giving USA (2018) total charitable giving rose for the fourth consecutive year in 2017, making 2017 the largest single year for private charitable giving, even after adjusting for inflation.
- An estimated 25.1 percent of US adults volunteered in 2017, contributing an estimated 8.8 billion hours. This is a 1.6 percent increase from 2016. The value of these hours is approximately $195.0 billion.
Size and Scope of the Nonprofit Sector
#Define Table 1 FunctionTable1 <- function(datayear) {
####Step1: Pull from raw bmf data to get Number of registered organizations####Step1a: Create function to pull in BMF databyear <- function(datayear) {
#get BMF file names:bmf1 <- as.character(paste("bmf", (datayear -10), sep =""))
bmf2 <- as.character(paste("bmf", (datayear -5), sep =""))
bmf3 <- as.character(paste("bmf", (datayear), sep =""))
#for each BMF file name, run the following:bcomponent <- function(bmfnum, year_of_int){
#get datasetbmf <- get(bmfnum)
#calculate all registered nonprofitsall <- bmf %>%
filter((OUTNCCS != "OUT")) %>%
summarize(year = as.character(year_of_int),
"All registered nonprofits" = n()
)#calculate all public charitiespc <- bmf %>%
filter((FNDNCD != "02" & FNDNCD!= "03" & FNDNCD != "04"), (SUBSECCD == "03"|SUBSECCD== "3"), (OUTNCCS != "OUT")) %>%
summarize(year = as.character(year_of_int),
"501(c)(3) public charities" = n()
)#combine registered nonprofits and public charitiescombined <- all %>%
left_join(pc, by = "year")
#return combined filereturn(combined)
}#run function for each yearbcomp1 <-bcomponent(bmf1, (datayear -10))
bcomp2 <-bcomponent(bmf2, (datayear -5))
bcomp3 <-bcomponent(bmf3, datayear)
#merge yearstotal <- rbind(bcomp1, bcomp2, bcomp3)
#return finalreturn(total)
}#Step 1b: run against year of interest:btest<- byear(datayear)
####Step 2: pull correct core file years####Step 2a: function to pull correct years starting from base year:T1grab = function(yr) {
output <- c(yr-10,
yr-5,
yr)return(list(output))
}#Step 2b: pull the right years:T1years <-T1grab(datayear)
#Step 2c: Function for individual years of core filesT1Fin<- function(datayear) {
pcname <- as.character(paste("core", datayear, "pc", sep =""))
coname <- as.character(paste("core", datayear, "co", sep =""))
pfname <- as.character(paste("core", datayear, "pf", sep =""))
pcfile <- get(pcname)
cofile <- get(coname)
pffile <- get(pfname)
pcfile <- if(datayear < 2010) filter(pcfile, (GRREC >= 25000)) else filter(pcfile, ((GRREC >= 50000)|(TOTREV>50000)))
cofile <- if(datayear < 2010) filter(cofile, ((GRREC >= 25000)|(TOTREV>25000))) else filter(cofile, ((GRREC >= 50000)|(TOTREV>50000)))
pc <-pcfile %>%
filter((is.na(OUTNCCS)|OUTNCCS != "OUT"), (FNDNCD != "02" & FNDNCD!= "03" & FNDNCD != "04")) %>%
summarize(Reporting = n(),
"Revenue ($ billions)" = round((sum(as.numeric(TOTREV), na.rm =TRUE))/1000000000, digits =2),
"Expenses ($ billions)" = round((sum(as.numeric(EXPS), na.rm =TRUE))/1000000000, digits =2),
"Assets ($ billions)" = round((sum(as.numeric(ASS_EOY), na.rm =TRUE))/1000000000, digits=2))
pc <- melt(pc)
colnames(pc)[2] <- "PC"
co <- cofile %>%
filter((OUTNCCS != "OUT")) %>%
summarize(Reporting = n(),
"Revenue ($ billions)" = round((sum(as.numeric(TOTREV), na.rm =TRUE))/1000000000, digits =2),
"Expenses ($ billions)" = round((sum(as.numeric(EXPS), na.rm =TRUE))/1000000000, digits =2),
"Assets ($ billions)" = round((sum(as.numeric(ASS_EOY), na.rm =TRUE))/1000000000, digits=2))
co <- melt(co)
colnames(co)[2] <- "CO"
pf <- pffile %>%
filter(OUTNCCS != "OUT") %>%
summarize(Reporting = n(),
"Revenue ($ billions)" = round((sum(as.numeric(P1TOTREV), na.rm =TRUE))/1000000000, digits =2),
"Expenses ($ billions)" = round((sum(as.numeric(P1TOTEXP), na.rm =TRUE))/1000000000, digits =2),
"Assets ($ billions)" = round((sum(as.numeric(P2TOTAST), na.rm =TRUE))/1000000000, digits=2))
pf <- melt(pf)
colnames(pf)[2] <- "PF"
Table1 <- pc %>%
left_join(co, by = "variable") %>%
left_join(pf, by = "variable") %>%
transmute(variable = variable,"Reporting nonprofits" = (PC+CO+PF),
"Reporting public charities" = PC)
Table1 <- melt(Table1)
colnames(Table1)[2]= "Type"
colnames(Table1)[3]= as.character(datayear)
Table1$variable <-ifelse(Table1$variable == "Reporting" & Table1$Type == "Reporting nonprofits",
"Reporting nonprofits", as.character(Table1$variable))
Table1$variable <-ifelse(Table1$variable == "Reporting" & Table1$Type == "Reporting public charities",
"Reporting public charities", as.character(Table1$variable))
return(Table1)
}#Step 2d: run core file function for each core file year:comp1 <- T1Fin(T1years[[1]][1])
comp2 <- T1Fin(T1years[[1]][2])
comp3 <- T1Fin(T1years[[1]][3])
#Step 2e: join multiple core file years togetherTable1All <- comp1 %>%
left_join(comp2, by = c("Type", "variable")) %>%
left_join(comp3, by = c("Type", "variable"))
#Step 2f: drop intermediary columnTable1All <- Table1All[-2]
####Step 3 Merge with BMF data###AllRegNonprofits<- data.frame("All registered nonprofits", btest[[2]][1], btest[[2]][2], btest[[2]][3])
names(AllRegNonprofits) <- names(Table1All)
AllPCs<- data.frame("501(c)(3) public charities", btest[[3]][1], btest[[3]][2], btest[[3]][3])
names(AllPCs) <- names(Table1All)
Table1All <- rbind(Table1All, AllRegNonprofits, AllPCs)
####Step 4: Calculate change over time###Table1All <- Table1All %>%
mutate(ChangeA = round(((Table1All[, as.character(datayear-5)] - Table1All[, as.character(datayear-10)])/(Table1All[, as.character(datayear-10)]))
*100, digits=1),
ChangeB = round(((Table1All[, as.character(datayear)] - Table1All[, as.character(datayear-10)])/(Table1All[, as.character(datayear-10)]))
*100, digits=1)
)####Step 5: calculate inflation adjustments###Table1All <- Table1All %>%
mutate(Y1 = round(((Table1All[, as.character(datayear-10)] * inflindex[as.character(datayear),])/(inflindex[as.character(datayear-10),])), digits=3),
Y2 = round(((Table1All[, as.character(datayear-5)] * inflindex[as.character(datayear),])/(inflindex[as.character(datayear-5),])), digits=3),
Y3 = round(((Table1All[, as.character(datayear)] * inflindex[as.character(datayear),])/(inflindex[as.character(datayear),])), digits=3),
ChangeAInfl = round(((Y2-Y1)/Y1)*100, digits = 1),
ChangeBInfl = round(((Y3-Y1)/Y1)*100, digits = 1)
)####Step 6: Format and prepare final table####Step 6a: remove intermediary columnsTable1All[7:9] <- list(NULL)
#Step 6b: reorder columns to fit Nonprofit Sector in BriefTable1All <- Table1All[, c(1,2,3,5,7,4,6,8)]
#Step 6c: omit numerical count columns from inflation adjustmentsTable1All[[5]][1] <- "--"
Table1All[[5]][5] <- "--"
Table1All[[8]][1] <- "--"
Table1All[[8]][5] <- "--"
Table1All[[5]][9] <- "--"
Table1All[[5]][10] <- "--"
Table1All[[8]][9] <- "--"
Table1All[[8]][10] <- "--"
#Step 6d: rename columnscolnames(Table1All)[1] <- ""
colnames(Table1All)[4] <- paste("% change, ", as.character(datayear -10), "\u2013", as.character(datayear - 5), sep = "")
colnames(Table1All)[5] <- paste("% change, ", as.character(datayear -10), "\u2013", as.character(datayear - 5), " (inflation adjusted)", sep = "")
colnames(Table1All)[7] <- paste("% change, ", as.character(datayear -10), "\u2013", as.character(datayear ), sep = "")
colnames(Table1All)[8] <- paste("% change, ", as.character(datayear -10), "\u2013", as.character(datayear ), " (inflation adjusted)", sep = "")
#Step6e: reorder rowsTable1All <- Table1All[c(9,1,2,3,4,10,5,6,7,8),]
#Step 6f: return final outputreturn(Table1All)
}#Create Table 1 based on 2015 dataTable1_2015 <-Table1(params$NCCSDataYr)
write.csv(Table1_2015, "Tables/NSiB_Table1.csv")
#Define Table 1 Current Growth Function (Appendix Table Showing only most recent growth)Table1CurGrowth <- function(datayear) {
####Step1: Pull from raw BMF data to get Number of registered organizations####Step1a: Create functionbyear <- function(datayear) {
#get BMF file names:bmf1 <- as.character(paste("bmf", (datayear -1), sep =""))
bmf2 <- as.character(paste("bmf", (datayear), sep =""))
#for each BMF file name, run the following:bcomponent <- function(bmfnum, year_of_int){
#get datasetbmf <- get(bmfnum)
#calculate all registered nonprofitsall <- bmf %>%
filter((OUTNCCS != "OUT")) %>%
summarize(year = as.character(year_of_int),
"All registered nonprofits" = n()
)#calculate all public charitiespc <- bmf %>%
filter((FNDNCD != "02" & FNDNCD!= "03" & FNDNCD != "04"), (SUBSECCD == "03"|SUBSECCD== "3"), (OUTNCCS != "OUT")) %>%
summarize(year = as.character(year_of_int),
"501(c)(3) public charities" = n()
)#combine registered nonprofits and public charitiescombined <- all %>%
left_join(pc, by = "year")
#return combined filereturn(combined)
}#run function for each yearbcomp1 <-bcomponent(bmf1, (datayear -1))
bcomp2 <-bcomponent(bmf2, (datayear))
#merge yearstotal <- rbind(bcomp1, bcomp2)
#return finalreturn(total)
}#Step 1b: run against year of interest:btest<- byear(datayear)
####Step 2: Pull NCCS Core File data####Step 2a: function to pull correct years starting from base year:T1grab = function(yr) {
output <- c(yr-1,
yr)return(list(output))
}#Step 2b: pull the right years:T1years <-T1grab(datayear)
#Step 2c: Function for individual years of core filesT1Fin<- function(datayear) {
pcname <- as.character(paste("core", datayear, "pc", sep =""))
coname <- as.character(paste("core", datayear, "co", sep =""))
pfname <- as.character(paste("core", datayear, "pf", sep =""))
pcfile <- get(pcname)
cofile <- get(coname)
pffile <- get(pfname)
pcfile <- if(datayear < 2010) filter(pcfile, (GRREC >= 25000)) else filter(pcfile, ((GRREC >= 50000)|(TOTREV>50000)))
cofile <- if(datayear < 2010) filter(cofile, ((GRREC >= 25000)|(TOTREV>25000))) else filter(cofile, ((GRREC >= 50000)|(TOTREV>50000)))
pc <-pcfile %>%
filter((is.na(OUTNCCS)|OUTNCCS != "OUT"), (FNDNCD != "02" & FNDNCD!= "03" & FNDNCD != "04")) %>%
summarize(Reporting = n(),
"Revenue ($ billions)" = round((sum(as.numeric(TOTREV), na.rm =TRUE))/1000000000, digits =2),
"Expenses ($ billions)" = round((sum(as.numeric(EXPS), na.rm =TRUE))/1000000000, digits =2),
"Assets ($ billions)" = round((sum(as.numeric(ASS_EOY), na.rm =TRUE))/1000000000, digits=2))
pc <- melt(pc)
colnames(pc)[2] <- "PC"
co <- cofile %>%
filter((OUTNCCS != "OUT")) %>%
summarize(Reporting = n(),
"Revenue ($ billions)" = round((sum(as.numeric(TOTREV), na.rm =TRUE))/1000000000, digits =2),
"Expenses ($ billions)" = round((sum(as.numeric(EXPS), na.rm =TRUE))/1000000000, digits =2),
"Assets ($ billions)" = round((sum(as.numeric(ASS_EOY), na.rm =TRUE))/1000000000, digits=2))
co <- melt(co)
colnames(co)[2] <- "CO"
pf <- pffile %>%
filter(OUTNCCS != "OUT") %>%
summarize(Reporting = n(),
"Revenue ($ billions)" = round((sum(as.numeric(P1TOTREV), na.rm =TRUE))/1000000000, digits =2),
"Expenses ($ billions)" = round((sum(as.numeric(P1TOTEXP), na.rm =TRUE))/1000000000, digits =2),
"Assets ($ billions)" = round((sum(as.numeric(P2TOTAST), na.rm =TRUE))/1000000000, digits=2))
pf <- melt(pf)
colnames(pf)[2] <- "PF"
Table1 <- pc %>%
left_join(co, by = "variable") %>%
left_join(pf, by = "variable") %>%
transmute(variable = variable,"Reporting nonprofits" = (PC+CO+PF),
"Reporting public charities" = PC)
Table1 <- melt(Table1)
colnames(Table1)[2]= "Type"
colnames(Table1)[3]= as.character(datayear)
Table1$variable <-ifelse(Table1$variable == "Reporting" & Table1$Type == "Reporting nonprofits",
"Reporting nonprofits", as.character(Table1$variable))
Table1$variable <-ifelse(Table1$variable == "Reporting" & Table1$Type == "Reporting public charities",
"Reporting public charities", as.character(Table1$variable))
return(Table1)
}#Step 2d: run core file function for each core file year:comp1 <- T1Fin(T1years[[1]][1])
comp2 <- T1Fin(T1years[[1]][2])
#Setp 2e: join multiple core file years togetherTable1CG <- comp1 %>%
left_join(comp2, by = c("Type", "variable"))
#Step 2f: drop intermediary columnTable1CG <- Table1CG[-2]
#####Step 3: Merge with BMF data###AllRegNonprofits<- data.frame("All registered nonprofits", btest[[2]][1], btest[[2]][2])
names(AllRegNonprofits) <- names(Table1CG)
AllPCs<- data.frame("501(c)(3) public charities", btest[[3]][1], btest[[3]][2])
names(AllPCs) <- names(Table1CG)
Table1CG <- rbind(Table1CG, AllRegNonprofits, AllPCs)
####Step 4: Calculate change over time###Table1CG <- Table1CG %>%
mutate(Change = round(((Table1CG[, as.character(datayear)] - Table1CG[, as.character(datayear-1)])/(Table1CG[, as.character(datayear-1)]))
*100, digits=1)
)####Step 5: calculate inflation adjustments###Table1CG <- Table1CG %>%
mutate(Y1_InflAdj = round(((Table1CG[, as.character(datayear-1)] * inflindex[as.character(datayear),])/(inflindex[as.character(datayear-1),])), digits=3),
Y2_InflAdj = round(((Table1CG[, as.character(datayear)] * inflindex[as.character(datayear),])/(inflindex[as.character(datayear),])), digits=3),
ChangeInfl = round(((Y2_InflAdj-Y1_InflAdj)/Y1_InflAdj)*100, digits = 1)
)####Step 6: Format and prepare final table####Step 6a: omit numerical count columns from inflation adjustmentsTable1CG[[5]][1] <- "--"
Table1CG[[5]][5] <- "--"
Table1CG[[5]][9] <- "--"
Table1CG[[5]][10] <- "--"
Table1CG[[6]][1] <- "--"
Table1CG[[6]][5] <- "--"
Table1CG[[6]][9] <- "--"
Table1CG[[6]][10] <- "--"
Table1CG[[7]][1] <- "--"
Table1CG[[7]][5] <- "--"
Table1CG[[7]][9] <- "--"
Table1CG[[7]][10] <- "--"
#Step 6b: rename columnscolnames(Table1CG)[1] <- ""
colnames(Table1CG)[4] <- paste("% change, ", as.character(datayear -1), "\u2013", as.character(datayear), sep = "")
colnames(Table1CG)[7] <- paste("% change, ", as.character(datayear -1), "\u2013", as.character(datayear), " (inflation adjusted)", sep = "")
#Step 6c: reorder rowsTable1CG <- Table1CG[c(9,1,2,3,4,10,5,6,7,8),]
#Step 6d: return final outputreturn(Table1CG)
}#Create Table 1 Current Growth (2014-2015) based on 2015 dataTable1CG_2015 <- Table1CurGrowth(params$NCCSDataYr)
write.csv(Table1CG_2015, "Tables/NSiB_Table1_Appendix_Current_Growth.csv")
All Nonprofit Organizations
Number
From 2005 to 2015, the number of nonprofit organizations registered with the IRS rose from 1.41 million to 1.56 million, an increase of 10.4 percent. These 1.56 million organizations comprise a diverse range of nonprofits, including art, health, education, and advocacy nonprofits; labor unions; and business and professional associations. This broad spectrum, however, only includes registered nonprofit organizations; the total number of nonprofit organizations operating in the United States is unknown. Religious congregations and organizations with less than $5,000 in gross receipts are not required to register with the IRS, although many do.[2] These unregistered organizations expand the scope of the nonprofit sector beyond the 1.56 million organizations this brief focuses on.
Finances
Approximately 34 percent of nonprofits registered with the IRS in 2015 were required to file a Form 990, Form 990-EZ, or Form 990-PF.[3] These reporting nonprofits identified $2.54 trillion in revenues and $5.79 trillion in assets (table 1).[4] Between 2005 and 2015, reporting nonprofits experienced positive financial growth. Both revenues and assets grew faster than GDP; after adjusting for inflation revenues grew 28.4 percent and assets grew 36.2 percent, compared with 13.6 percent growth for national GDP during the same period. Expenses grew 31.8 percent between2005 and 2015. In the short term, after adjusting for inflation, revenues grew 4.1 percent from $ 2.44 trillion in 2014 to $2.54 in 2015; assets increased 3.2 percent from $5.61 trillion to $5.79. Expenses also grew from $2.25 trillion in 2014 to $2.36 in 2015, an increase of 5 percent.
TABLE 1
Size and Scope of the Nonprofit Sector, 2005–2015
#Display Table 1options(knitr.kable.NA ="")
kable(Table1_2015, format.args = list(decimal.mark = '.', big.mark = ","),
"html",row.names = FALSE,
align = "lccccccc") %>%
kable_styling("hover", full_width = F) %>%
row_spec(c(1,6), bold = T ) %>%
row_spec(3:5, italic = T) %>%
row_spec(8:10, italic = T) %>%
add_indent(c(3,4,5,8,9,10))
| 2005 | 2010 | % change, 2005–2010 | % change, 2005–2010 (inflation adjusted) | 2015 | % change, 2005–2015 | % change, 2005–2015 (inflation adjusted) | |
|---|---|---|---|---|---|---|---|
| All registered nonprofits | 1,414,343.00 | 1,493,407.00 | 5.6 | -- | 1,561,616.00 | 10.4 | -- |
| Reporting nonprofits | 552,115.00 | 514,494.00 | -6.8 | -- | 531,026.00 | -3.8 | -- |
| Revenue ($ billions) | 1,632.58 | 2,052.79 | 25.7 | 12.6 | 2,544.52 | 55.9 | 28.4 |
| Expenses ($ billions) | 1,476.80 | 1,931.02 | 30.8 | 17.1 | 2,361.45 | 59.9 | 31.8 |
| Assets ($ billions) | 3,500.91 | 4,441.45 | 26.9 | 13.6 | 5,785.56 | 65.3 | 36.2 |
| 501(c)(3) public charities | 847,946.00 | 979,883.00 | 15.6 | -- | 1,088,447.00 | 28.4 | -- |
| Reporting public charities | 312,778.00 | 293,265.00 | -6.2 | -- | 314,744.00 | 0.6 | -- |
| Revenue ($ billions) | 1,173.21 | 1,509.43 | 28.7 | 15.2 | 1,978.52 | 68.6 | 39 |
| Expenses ($ billions) | 1,077.37 | 1,450.74 | 34.7 | 20.6 | 1,838.81 | 70.7 | 40.6 |
| Assets ($ billions) | 2,065.18 | 2,671.86 | 29.4 | 15.9 | 3,668.59 | 77.6 | 46.4 |
Sources: Urban Institute, National Center for Charitable Statistics, Core Files (2005, 2010, and 2015); and the Internal Revenue Service Business Master Files, Exempt Organizations (2006–16).
Notes: Reporting public charities include only organizations that both reported (filed IRS Forms 990) and were required to do so (had $25,000 or more in gross receipts in 2005 and more than $50,000 in gross receipts in 2010 and 2015). Organizations that had their tax-exempt status revoked for failing to file a financial return for three consecutive years have been removed from the 2015 nonprofit total. Foreign organizations, government-associated organizations, and organizations without state identifiers have also been excluded. Unless noted, all amounts are in current dollars and are not adjusted for inflation.
Public Charities
Number
Public charities are the largest category of the more than 30 types of tax-exempt nonprofit organizations defined by the Internal Revenue Code. Classified under section 501(c)(3) (along with private foundations), public charities include arts, culture, and humanities organizations; education organizations; health care organizations; human services organizations; and other types of organizations to which donors can make tax-deductible donations. In 2015, about 1.09 million organizations were classified as public charities, composing about two-thirds of all registered nonprofits. Between 2005 and 2015, the number of public charities grew 28.4 percent, faster than the growth of all registered nonprofits (10.4 percent). The number of registered public charities also grew faster than other nonprofit subgroups during the decade, including private foundations, which grew by only 0.1 percent, and 501(c)(4) organizations, which declined 28 percent. Consequently, public charities made up a larger share of the nonprofit sector in 2015 (69.7 percent) than in 2005 (60 percent).
The number of reporting public charities required to file a Form 990 or Form 990-EZ grew slightly between 2014 and 2015, showing an increase of 2.2 percent.
Finances
Almost three-fifths (59.3 percent) of all nonprofit organizations reporting to the IRS in 2015 were public charities. Accounting for more than three-quarters of revenue and expenses for the nonprofit sector, public charities reported $1.98 trillion in revenues and $1.84 trillion in expenses. Assets held by public charities accounted for just under two-thirds of the sector's total ($3.67 trillion).
Size
#create Figure 1 Underlying tableFig1Table <- function(datayear) {
#select core file by yearfile <- c(paste("core", datayear, "pc", sep =""))
#get core filedataset <- get(file)
#filter out organizations below minimum filing threshold for 990-EZdataset <- if(datayear < 2010) filter(dataset, ((GRREC >= 25000)|(TOTREV>25000))) else filter(dataset, ((GRREC >= 50000)|(TOTREV>50000)))
#create tableexpstable <- dataset %>%
#filter by GRREC over threshold, not out, and FNDNCD != 2,3,4filter(((GRREC >= 50000)|(TOTREV>50000)), (OUTNCCS != "OUT"), (FNDNCD != "02" & FNDNCD!= "03" & FNDNCD != "04")) %>%
#group by exps catgroup_by(EXPCAT) %>%
#create summary valuessummarize(number_orgs = n(),
total_expenses = round((sum(EXPS, na.rm =TRUE)/1000000000), digits =2)
) %>%
#drop old variables, keep only categories and proportionsmutate(year_of_data = as.character(datayear),
EXPCAT = EXPCAT,"Public charities" = round(((number_orgs/sum(number_orgs))*100),digits=1),
"Total expenses" = round(((total_expenses/sum(total_expenses))*100),digits=1)
)#return outputreturn(expstable)}
#Create figure 1 Based on 2015 dataFigure1_2015 <- Fig1Table(params$NCCSDataYr)
write.csv(Figure1_2015, "Figures/NSiB_Figure1_Table.csv")
Even after excluding organizations with gross receipts below the $50,000 filing threshold, small organizations composed the majority of public charities in 2015. As shown in figure 1 below, 66.9 percent had less than $500,000 in expenses (210,670 organizations); they composed less than 2 percent of total public charity expenditures ($32.3 billion). Though organizations with $10 million or more included just 5.3 percent of total public charities (16,556 organizations), they accounted for 87.7 percent of public charity expenditures ($1.6 trillion).
FIGURE 1
Number and Expenses of Reporting Public Charities as a Percentage of All Reporting Public Charities and Expenses
#Create and Display Figure For 2015 DataFig1Plot <- function(expstable) {
#select relevant fieldsexpstable <- expstable[,c("year_of_data", "EXPCAT", "Public charities", "Total expenses")]
#plot graphFig1<- expstable %>%
#shift from wide to longmelt() %>%
#create graphggplot(aes(EXPCAT, value, fill=variable))+
geom_bar(stat="identity", position="dodge") +
geom_text(aes(EXPCAT, value, label=formatC(round(value,1), format = 'f', digits =1)),
vjust=-1,
position = position_dodge(width=1),
size =3) +
#labs(#title = "Figure 1",#subtitle = paste("Number and Expenses of Reporting Public Charities as a Percentage \nof All Reporting Public Charities and Expenses, ", expstable$year_of_data[1], sep =""),#caption = paste("Urban Institute, National Center for Charitable Statistics, Core Files (Public Charities, "#, expstable$year_of_data[1], ")", sep ="")) +theme(axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_blank(),
panel.grid = element_blank()) +
scale_y_continuous(expand = c(0, 0), limits = c(0,105)) +
scale_x_discrete(labels = c("Under $100,00", "$100,000 to \n$499,999", "$500,000 to \n$999,999", "$1 million to \n$4.99 million",
"$5 million to \n$9.99 million", "$10 million \nor more"))
UrbCaption <- grobTree(
gp = gpar(fontsize = 8, hjust = 1),
textGrob(label = "I N S T I T U T E",
name = "caption1",
x = unit(1, "npc"),
y = unit(0, "npc"),
hjust = 1,
vjust = 0),
textGrob(label = "U R B A N ",
x = unit(1, "npc") - grobWidth("caption1") - unit(0.01, "lines"),
y = unit(0, "npc"),
hjust = 1,
vjust = 0,
gp = gpar(col = "#1696d2")))
grid.arrange(Fig1, UrbCaption, ncol = 1, heights = c(30, 1))
}Fig1Plot(Figure1_2015)
Source: Urban Institute, National Center for Charitable Statistics, Core Files (Public Charities, 2015)
Type
#Create Table 2 FunctionTable2 <- function(datayear) {
#select core file based on yearfile <- c(paste("core", datayear, "pc", sep =""))
#get core filedataset <- get(file)
#filter out organizations below minimum filing threshold for 990-EZdataset <- if(datayear < 2010) filter(dataset, ((GRREC >= 25000)|(TOTREV>25000))) else filter(dataset, ((GRREC >= 50000)|(TOTREV>50000)))
#create tableTable2<- dataset %>%
filter((OUTNCCS != "OUT"), (FNDNCD != "02" & FNDNCD!= "03" & FNDNCD != "04")) %>%
group_by(NTEEGRP) %>%
summarize(Number_of_Orgs = n(),
Revenue = round((sum(TOTREV, na.rm =TRUE))/1000000000, digits =1),
Expenses = round((sum(EXPS, na.rm =TRUE))/1000000000, digits =1),
Assets = round((sum(ASS_EOY, na.rm =TRUE))/1000000000, digits=1)) %>%
mutate(Revenue_PCT = round((Revenue/sum(Revenue)) *100, digits =1),
Expenses_PCT = round((Expenses/sum(Expenses)) *100, digits =1),
Assets_PCT = round((Assets/sum(Assets)) *100, digits =1),
Numbers_PCT = round((Number_of_Orgs/sum(Number_of_Orgs)) *100, digits =1)
)#reorder columnsTable2 <- Table2[,c("NTEEGRP", "Number_of_Orgs","Numbers_PCT","Revenue","Expenses", "Assets", "Revenue_PCT", "Expenses_PCT","Assets_PCT")]
#Add total rowmyNumCols <- which(unlist(lapply(Table2, is.numeric)))
Table2[(nrow(Table2) + 1), myNumCols] <- colSums(Table2[, myNumCols], na.rm=TRUE)
Table2$NTEEGRP[11] = "All public charities"
#add All Ed and All health rowsTable2[12,1] = "Education"
Table2[12,2:9] <- Table2[3,2:9] + Table2[7,2:9]
Table2[13,1] = "Health"
Table2[13,2:9] <- Table2[4,2:9] + Table2[8,2:9]
#reorder table with new rowst2order <- c("All public charities", "Arts", "Education", "Higher education", "Other education", "Environment and animals",
"Health", "Hospitals and primary care facilities", "Other health care", "Human services",
"International", "Other public and social benefit", "Religion related")
Table2 <- Table2 %>%
slice(match(t2order, NTEEGRP))
#add year of data columnTable2 <- cbind(year_of_data = as.character(datayear), Table2)
return(Table2)
}#Run for Table 2 for 2015 dataTable2_2015 <- Table2(params$NCCSDataYr)
write.csv(Table2_2015, "Tables/NSiB_Table2.csv")
Table 2 below displays the 2015 distribution of public charities by type of organization. Human services groups—such as food banks, homeless shelters, youth services, sports organizations, and family or legal services—composed over one-third of all public charities (35.2 percent). They were more than twice as numerous as education organizations, the next-most prolific type of organization, which accounted for 17.2 percent of all public charities. Education organizations include booster clubs, parent-teacher associations, and financial aid groups, as well as academic institutions, schools, and universities. Health care organizations, though accounting for only 12.4 percent of reporting public charities, accounted for nearly three-fifths of public charity revenues and expenses in 2015. Education organizations accounted for 17.9 percent of revenues and 17.2 percent of expenses; human services, despite being more numerous, accounted for comparatively less revenue (11.8 percent of the total) and expenses (12.2 percent of the total). Hospitals, despite representing only 2.3 percent of total public charities (7,113 organizations), accounted for about half of all public charity revenues and expenses (49.4 and 50.4 percent, respectively).
TABLE 2
Number and Finances of Reporting Public Charities by Subsector, 2015
#Display Table 2kable(Table2_2015[c(2:10)], format.args = list(decimal.mark = '.', big.mark = ","),
"html",align = "lcccccccc",
col.names = c("", "Number", "% of total", "Revenues", "Expenses", "Assets", "Revenues", "Expenses", "Assets")) %>%
kable_styling("hover", full_width = F) %>%
row_spec(c(4,5,8,9), italic = T ) %>%
row_spec(1, bold = T ) %>%
add_indent(c(4,5,8,9)) %>%
add_header_above(c(" " = 3, "Dollar Total ($ billions)" = 3, "Percentage of Total" = 3))
| Dollar Total ($ billions) | Percentage of Total | |||||||
|---|---|---|---|---|---|---|---|---|
| Number | % of total | Revenues | Expenses | Assets | Revenues | Expenses | Assets | |
| All public charities | 314,744 | 100.0 | 1,978.6 | 1,838.9 | 3,668.6 | 100.0 | 100.0 | 100.1 |
| Arts | 31,429 | 10.0 | 40.6 | 35.7 | 127.9 | 2.1 | 1.9 | 3.5 |
| Education | 54,214 | 17.2 | 354.3 | 315.5 | 1,128.8 | 17.9 | 17.2 | 30.8 |
| Higher education | 2,153 | 0.7 | 230.9 | 207.4 | 736.3 | 11.7 | 11.3 | 20.1 |
| Other education | 52,061 | 16.5 | 123.4 | 108.1 | 392.5 | 6.2 | 5.9 | 10.7 |
| Environment and animals | 14,591 | 4.6 | 19.7 | 16.5 | 47.8 | 1.0 | 0.9 | 1.3 |
| Health | 38,861 | 12.4 | 1,160.5 | 1,102.3 | 1,574.1 | 58.7 | 59.9 | 42.9 |
| Hospitals and primary care facilities | 7,113 | 2.3 | 977.1 | 926.7 | 1,281.5 | 49.4 | 50.4 | 34.9 |
| Other health care | 31,748 | 10.1 | 183.4 | 175.6 | 292.6 | 9.3 | 9.5 | 8.0 |
| Human services | 110,801 | 35.2 | 234.1 | 224.0 | 357.1 | 11.8 | 12.2 | 9.7 |
| International | 6,927 | 2.2 | 38.5 | 34.5 | 43.2 | 1.9 | 1.9 | 1.2 |
| Other public and social benefit | 37,478 | 11.9 | 111.3 | 93.3 | 347.1 | 5.6 | 5.1 | 9.5 |
| Religion related | 20,443 | 6.5 | 19.6 | 17.1 | 42.6 | 1.0 | 0.9 | 1.2 |
Source: Urban Institute, National Center for Charitable Statistics, Core Files (Public Charities, 2015).
Note: Subtotals may not sum to totals because of rounding.
Growth
#Create Table 3 functionTable3 <- function(datayear) {
#define years of interestT3grab = function(yr) {
output <- c(paste("core", yr-10, "pc", sep = ""),
paste("core", yr-5, "pc", sep =""),
paste("core", yr, "pc", sep =""))
return(list(output))
}#define financial summarizerT3Fin <- function(dataset, year) {
df <- get(dataset)
#filter out organizations below minimum filing threshold for 990-EZdf <- if(year < 2010) filter(df, ((GRREC >= 25000)|(TOTREV>25000))) else filter(df, ((GRREC >= 50000)|(TOTREV>50000)))
output <- df %>%
filter((OUTNCCS != "OUT"), (FNDNCD != "02" & FNDNCD!= "03" & FNDNCD != "04")) %>%
group_by(NTEEGRP) %>%
summarize(Number_of_Orgs = n(),
Revenue = round((sum(as.numeric(TOTREV), na.rm =TRUE)/1000000000), digits =1),
Expenses = round((sum(as.numeric(EXPS), na.rm =TRUE)/1000000000), digits=1),
Assets = round((sum(as.numeric(ASS_EOY), na.rm =TRUE)/1000000000), digits=1)
) %>%
mutate(Revenue = round((Revenue * inflindex[as.character(datayear),])/(inflindex[as.character(year),]), digits =1),
Expenses = round((Expenses * inflindex[as.character(datayear),])/(inflindex[as.character(year),]), digits =1),
Assets = round((Assets * inflindex[as.character(datayear),])/(inflindex[as.character(year),]), digits =1)
)colnames(output)[2:5] <- paste(colnames(output)[2:5], year, sep = "_")
return(output)
}#run grabber for years of interestT3years <-T3grab(datayear)
#pull each yearcomp1 <- T3Fin(T3years[[1]][1], (datayear-10))
comp2 <- T3Fin(T3years[[1]][2], (datayear-5))
comp3 <- T3Fin(T3years[[1]][3], datayear)
#merge tablesTable3 <- comp1 %>%
left_join(comp2, by = "NTEEGRP") %>%
left_join(comp3, by = "NTEEGRP")
#reorder columnsTable3IA <- Table3[, c(1,2,6,10,3,7,11,4,8,12,5,9,13)]
#Add total rowmyNumCols <- which(unlist(lapply(Table3IA, is.numeric)))
Table3IA[(nrow(Table3IA) + 1), myNumCols] <- colSums(Table3IA[, myNumCols], na.rm=TRUE)
Table3IA$NTEEGRP[11] = "All public charities"
#add All Ed and All health rowsTable3IA[12,1] = "Education"
Table3IA[12,2:13] <- Table3IA[3,2:13] + Table3IA[7,2:13]
Table3IA[13,1] = "Health"
Table3IA[13,2:13] <- Table3IA[4,2:13] + Table3IA[8,2:13]
#reorder table with new rowst3order <- c("All public charities", "Arts", "Education", "Higher education", "Other education", "Environment and animals",
"Health", "Hospitals and primary care facilities", "Other health care", "Human services",
"International", "Other public and social benefit", "Religion related")
Table3IA <- Table3IA %>%
slice(match(t3order, NTEEGRP))
#add year of data columnTable3IA <- cbind(year_of_data = as.character(datayear), Table3IA)
return(Table3IA)
}#Run Table 3 for 2015 dataTable3_2015 <- Table3(params$NCCSDataYr)
write.csv(Table3_2015, "Tables/NSiB_Table3.csv")
#####################################################Create Table 4 functionTable4 <- function(datayear) {
#start with table 3 dataTable4 <- Table3(datayear)
#calculate percentage change fieldsTable4 <- Table4 %>%
mutate(RevAtoC = round(((Table4[,8] - Table4[,6] )/(Table4[,6] )) *100,1),
RevAtoB = round(((Table4[,7]- Table4[,6] )/(Table4[,6] )) *100,1),
RevBtoC = round(((Table4[,8]- Table4[,7] )/(Table4[,7] )) *100,1),
ExpsAtoC =round(((Table4[,11] - Table4[,9] )/(Table4[,9] )) *100,1),
ExpsAtoB =round(((Table4[,10]- Table4[,9] )/(Table4[,9] )) *100,1),
ExpsBtoC =round(((Table4[,11]- Table4[,10] )/(Table4[,10] )) *100,1),
AssAtoC = round(((Table4[,14] -Table4[,12] )/(Table4[,12] )) *100,1),
AssAtoB = round(((Table4[,13]- Table4[,12] )/(Table4[,12] )) *100,1),
AssBtoC = round(((Table4[,14]- Table4[,13])/(Table4[,13] ))*100,1)
)#drop intermediary raw number columnsTable4 <- Table4[-(3:14)]
#rename columns by yearcolnames(Table4)[3] <- paste("Revenue", datayear-10, "\u2013", datayear, sep = "_")
colnames(Table4)[4] <- paste("Revenue", datayear-10, "\u2013", datayear-5, sep = "_")
colnames(Table4)[5] <- paste("Revenue", datayear-5, "\u2013", datayear, sep = "_")
colnames(Table4)[6] <- paste("Expenses", datayear-10, "\u2013", datayear, sep = "_")
colnames(Table4)[7] <- paste("Expenses", datayear-10, "\u2013", datayear-5, sep = "_")
colnames(Table4)[8] <- paste("Expenses", datayear-5, "\u2013", datayear, sep = "_")
colnames(Table4)[9] <- paste("Assets", datayear-10, "\u2013", datayear, sep = "_")
colnames(Table4)[10] <- paste("Assets", datayear-10, "\u2013", datayear-5, sep = "_")
colnames(Table4)[11] <- paste("Assets", datayear-5, "\u2013", datayear, sep = "_")
#return outputreturn(Table4)
}#Run Table 4 for 2015 dataTable4_2015 <- Table4(params$NCCSDataYr)
write.csv(Table4_2015,"Tables/NSiB_Table4.csv")
The number of reporting public charities in 2015 was approximately 2.2 percent higher than the number in 2014. The total revenues, expenses, and assets for reporting public charities all increased between 2014 and 2015; after adjusting for inflation, revenues rose 5.6 percent, expenses rose 5.7 percent, and assets rose 4.2 percent.
These trends are indicative of larger growth in the sector: both the number and finances of organizations in the nonprofit sector have grown over the past 10 years. But this growth has differed by subsector and period (table 3). Subsectors experienced varying degrees of financial expansion: although all subsectors reported increases in revenue in 2015 compared with 2005 (even after adjusting for inflation), a few decreased in number of nonprofits, including arts, education (excluding higher education), health, and other public and social benefit organizations. Consequently, these organizations accounted for a slightly lower proportion of the total sector in 2015 (50.8 percent) than they did in 2005 (53.6 percent). The smallest subsectors (international and foreign affairs organizations and environment and animals organizations) saw the largest growth rates in the number of organizations, increasing 21.7 and 14.7 percent, respectively, from 2005 to 2015.
Financially, religion-related organizations had the largest proportional increase in both revenue and expenses, growing from $12.3 billion in revenue in 2005 to $19.6 billion in 2015 after adjusting for inflation (a change of 59.3 percent). Environment and animals organizations experienced similar growth, growing from $13 billion in revenue in 2005 to $19.7 billion in 2015 after adjusting for inflation (a change of 51.5 percent). Both types of organizations, however, still account for a very small proportion of overall nonprofit sector revenue in 2015, at just about 1 percent each. Health-related organizations, which account for a much larger proportion of overall sector finances (58.7, 59.9 and 42.9 percent, respectively, of revenues, expenses, and assets), also experienced considerable growth between 2005 and 2015. Revenues for hospitals and primary care facilities, in particular, increased from $689.3 billion in 2005 to $977.1 billion in 2015 after adjusting for inflation, by far the largest dollar growth of any subsector during this period. The growth for the health sector, $343.3 billion, accounts for over three-fifths of the growth of the entire nonprofit sector between 2005 and 2015 ($554.6 billion).
TABLE 3
Number, Revenues, and Assets of Reporting Public Charities by Subsector, 2005–2015 (adjusted for inflation)
#Display Table 3kable(Table3_2015[c(2:14)], format.args = list(decimal.mark = '.', big.mark = ","),
"html",col.names = c("", "2005", "2010", "2015", "2005", "2010", "2015", "2005", "2010", "2015", "2005", "2010", "2015"),
align = "lcccccccccccc"#,
)%>%
kable_styling("hover", full_width = F) %>%
row_spec(c(4,5,8,9), italic = T ) %>%
row_spec(1, bold = T ) %>%
add_indent(c(4,5,8,9)) %>%
add_header_above(c(" ", "Number of Organizations" = 3, "Revenue ($ billions)" = 3, "Expenses ($ billions)" = 3, "Assets ($ billions)" = 3))
| Number of Organizations | Revenue ($ billions) | Expenses ($ billions) | Assets ($ billions) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2005 | 2010 | 2015 | 2005 | 2010 | 2015 | 2005 | 2010 | 2015 | 2005 | 2010 | 2015 | |
| All public charities | 312,779 | 293,265 | 314,744 | 1,424.0 | 1,641.0 | 1,978.6 | 1,307.7 | 1,576.9 | 1,838.9 | 2,506.4 | 2,904.1 | 3,668.6 |
| Arts | 34,483 | 29,409 | 31,429 | 31.7 | 31.5 | 40.6 | 27.7 | 29.9 | 35.7 | 98.3 | 105.3 | 127.9 |
| Education | 56,030 | 50,387 | 54,214 | 251.7 | 268.4 | 354.3 | 210.5 | 261.5 | 315.5 | 783.1 | 846.1 | 1,128.8 |
| Higher education | 1,869 | 2,040 | 2,153 | 166.3 | 173.3 | 230.9 | 140.2 | 169.3 | 207.4 | 527.6 | 549.7 | 736.3 |
| Other education | 54,161 | 48,347 | 52,061 | 85.4 | 95.1 | 123.4 | 70.3 | 92.2 | 108.1 | 255.5 | 296.4 | 392.5 |
| Environment and animals | 12,721 | 12,715 | 14,591 | 13.0 | 14.7 | 19.7 | 11.0 | 13.7 | 16.5 | 32.0 | 37.7 | 47.8 |
| Health | 40,774 | 38,840 | 38,861 | 817.2 | 986.0 | 1,160.5 | 775.8 | 944.6 | 1,102.3 | 1,000.8 | 1,238.9 | 1,574.1 |
| Hospitals and primary care facilities | 7,150 | 7,229 | 7,113 | 689.3 | 838.3 | 977.1 | 658.4 | 802.8 | 926.7 | 790.1 | 1,004.2 | 1,281.5 |
| Other health care | 33,624 | 31,611 | 31,748 | 127.9 | 147.7 | 183.4 | 117.4 | 141.8 | 175.6 | 210.7 | 234.7 | 292.6 |
| Human services | 105,938 | 103,451 | 110,801 | 185.3 | 213.3 | 234.1 | 176.8 | 206.2 | 224.0 | 274.3 | 321.2 | 357.1 |
| International | 5,691 | 6,066 | 6,927 | 30.5 | 32.0 | 38.5 | 27.1 | 31.1 | 34.5 | 28.5 | 31.3 | 43.2 |
| Other public and social benefit | 38,381 | 34,595 | 37,478 | 82.3 | 81.2 | 111.3 | 68.2 | 77.0 | 93.3 | 262.5 | 292.6 | 347.1 |
| Religion related | 18,761 | 17,802 | 20,443 | 12.3 | 13.9 | 19.6 | 10.6 | 12.9 | 17.1 | 26.9 | 31.0 | 42.6 |
Sources: Urban Institute, National Center for Charitable Statistics, Core Files (Public Charities, 2005, 2010, and 2015).
Note: Subtotals may not sum to totals because of rounding.
Public charities' financial growth within the given span largely occurred within the second half (table 4). From 2005 to 2010, revenue and assets for all public charities increased 15.2 and 15.9 percent, respectively, but both grew much more quickly in the years following: 20.6 percent for revenues and 26.3 percent for assets, after adjusting for inflation. Further, expenses grew much faster than revenues between 2005 and 2010, with expenses increasing 20.6 percent (compared with revenues increasing 15.2 percent). But between 2010 and 2015 growth in expenses (16.6 percent) was outpaced by the growth in revenues (20.6 percent).
These periods of growth varied by subsector, however. Two subsectors experienced declining revenue between 2005 and 2010: arts, culture, and humanities organizations and other public and social benefit organizations. Of the two, other public and social benefit organizations experienced the larger decline, falling $1.1 billion in revenue from 2005 to 2010, a decline of 1.3 percent. However, both subsectors experienced substantial revenue increases from 2010 to 2015: revenue for other public and social benefit organizations grew 37.1 percent during those five years, while revenue for arts, culture and humanities organizations grew 28.9 percent. Both revenue growth rates were well above the growth rate for human services organizations, which at 9.8 percent was the lowest for any subsector within that period.
TABLE 4
Percent Change in Revenue, Expenses, and Assets of Reporting Public Charities by Subsector, 2005–2015 (adjusted for inflation)
#Display Table 4 Datakable(Table4_2015[c(2:11)], format.args = list(decimal.mark = '.', big.mark = ","),
"html",col.names = c("", paste("2005", "\u2013", "15", sep = ""), paste("2005", "\u2013", "10", sep = ""), paste("2010", "\u2013", "15", sep = ""), paste("2005", "\u2013", "15", sep = ""), paste("2005", "\u2013", "10", sep = ""), paste("2010", "\u2013", "15", sep = ""), paste("2005", "\u2013", "15", sep = ""), paste("2005", "\u2013", "10", sep = ""), paste("2010", "\u2013", "15", sep = "")),
align = "lccccccccc") %>%
kable_styling("hover", full_width = F) %>%
row_spec(c(4,5,8,9), italic = T ) %>%
row_spec(1, bold = T ) %>%
add_indent(c(4,5,8,9)) %>%
add_header_above(c(" ", "Change in Revenues" = 3, "Change in Expenses" = 3,"Change in Assets" = 3))
| Change in Revenues | Change in Expenses | Change in Assets | |||||||
|---|---|---|---|---|---|---|---|---|---|
| 2005–15 | 2005–10 | 2010–15 | 2005–15 | 2005–10 | 2010–15 | 2005–15 | 2005–10 | 2010–15 | |
| All public charities | 38.9 | 15.2 | 20.6 | 40.6 | 20.6 | 16.6 | 46.4 | 15.9 | 26.3 |
| Arts | 28.1 | -0.6 | 28.9 | 28.9 | 7.9 | 19.4 | 30.1 | 7.1 | 21.5 |
| Education | 40.8 | 6.6 | 32.0 | 49.9 | 24.2 | 20.7 | 44.1 | 8.0 | 33.4 |
| Higher education | 38.8 | 4.2 | 33.2 | 47.9 | 20.8 | 22.5 | 39.6 | 4.2 | 33.9 |
| Other education | 44.5 | 11.4 | 29.8 | 53.8 | 31.2 | 17.2 | 53.6 | 16.0 | 32.4 |
| Environment and animals | 51.5 | 13.1 | 34.0 | 50.0 | 24.5 | 20.4 | 49.4 | 17.8 | 26.8 |
| Health | 42.0 | 20.7 | 17.7 | 42.1 | 21.8 | 16.7 | 57.3 | 23.8 | 27.1 |
| Hospitals and primary care facilities | 41.8 | 21.6 | 16.6 | 40.8 | 21.9 | 15.4 | 62.2 | 27.1 | 27.6 |
| Other health care | 43.4 | 15.5 | 24.2 | 49.6 | 20.8 | 23.8 | 38.9 | 11.4 | 24.7 |
| Human services | 26.3 | 15.1 | 9.8 | 26.7 | 16.6 | 8.6 | 30.2 | 17.1 | 11.2 |
| International | 26.2 | 4.9 | 20.3 | 27.3 | 14.8 | 10.9 | 51.6 | 9.8 | 38.0 |
| Other public and social benefit | 35.2 | -1.3 | 37.1 | 36.8 | 12.9 | 21.2 | 32.2 | 11.5 | 18.6 |
| Religion related | 59.3 | 13.0 | 41.0 | 61.3 | 21.7 | 32.6 | 58.4 | 15.2 | 37.4 |
Sources: Urban Institute, National Center for Charitable Statistics, Core Files (Public Charities, 2005, 2010, and 2015).
Note: Subtotals may not sum to totals because of rounding.
Giving
Giving Amounts
#Create Figure 2 underlying table#Import Figure 2 raw data (available from Giving USA 2018, https://givingusa.org/)Figure2 <- read_csv("External_Data/GivingUSACont.csv",
col_types = cols_only(Years = col_integer(),
Current_Dollars = col_double()
))
#Adjust for inflationFigure2 <- Figure2 %>%
mutate('Constant (2017) Dollars' = round((Current_Dollars * inflindex[as.character(2017),])/(inflindex[as.character(Years),]), digits =2)
)#Add Column Namescolnames(Figure2)<- c("Year", "Current dollars", "Constant (2017) dollars")
Figure2 <- Figure2 %>%
melt(id = "Year")
colnames(Figure2)[2] <- "Contributions"
#Write final table to CSVwrite.csv(Figure2, "Figures/NSiB_Figure2_Table.csv")
Private charitable contributions reached an estimated $410.02 billion in 2017, as shown in figure 2 below (Giving USA Foundation 2018). Total charitable giving has been increasing for four consecutive years, beginning with 2014. Since 2007, private giving has increased 11.5 percent, adjusting for inflation.
FIGURE 2
Private Charitable Contributions 2000-2017
#Create Figure 2Fig2Plot <- function(Fig2Table) {
Fig2 <- Fig2Table %>%
ggplot(aes(x=Year, y =value, fill = Contributions)) +
geom_bar(position = "dodge", stat = "identity") +
geom_text(aes(label = formatC(round(value,2), format = 'f', digits =2)),
position= position_dodge(width=1),
hjust =-.1,size=3) +
scale_y_continuous(expand = c(0, 0), limits = c(0,450)) +
scale_x_continuous(breaks = 2000:2017)+
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
panel.grid.major = element_blank()#,
# axis.title.y = element_text(angle=0)) +
labs(#title = "Figure 2",
#subtitle = "Private Charitable Contributions, 2000-2016",#caption = "Giving USA Foundation (2018)",x = "Year",
y = "") +
coord_flip()
UrbCaption <- grobTree(
gp = gpar(fontsize = 8, hjust = 1),
textGrob(label = "I N S T I T U T E",
name = "caption1",
x = unit(1, "npc"),
y = unit(0, "npc"),
hjust = 1,
vjust = 0),
textGrob(label = "U R B A N ",
x = unit(1, "npc") - grobWidth("caption1") - unit(0.01, "lines"),
y = unit(0, "npc"),
hjust = 1,
vjust = 0,
gp = gpar(col = "#1696d2")))
grid.arrange(Fig2, UrbCaption, ncol = 1, heights = c(30, 1))
}Fig2Plot(Figure2)
Source: Giving USA Foundation (2018).
Recipients
#Create Table 5#Import raw Table 5 data (available from Giving USA 2018, https://givingusa.org/)Table5 <- read_csv("External_Data/GivingUSAType.csv",
col_types= cols_only(Type = col_character(),
Year2012 = col_double(),
Year2017 = col_double()))
#Calculate percentage changeTable5 <- Table5 %>%
mutate(PCt_change = Year2017 - Year2012)
#Rename Columnscolnames(Table5)<- c("Charity type", "% of all contributions, 2012", "% of all contributions, 2017", paste("% point change, 2012", "\u2013", "17", sep =""))
#Write final table to CSVwrite.csv(Table5, "Tables/NSiB_Table5.csv")
Congregations and religious organizations received just under a third (30.9 percent) of all charitable contributions in 2017 (table 5), a slightly lower proportion than they received five years earlier in 2012 (32.3 percent). Education organizations received the next-highest share of private charitable contributions (14.3 percent), which is the same proportion received in 2012 (also 14.3 percent of all donations). Human services organizations received the third-highest pro portion of all contributions in 2017 (12.1 percent), but this is a slight decline from their 2012 proportion (12.6 percent). Gifts to individuals made up the smallest proportion of total contributions in 2017: 1.8 percent.
TABLE 5
Charitable Contributions by Type of Recipient Organizations, 2017
#Display Table 5kable(Table5, format.args = list(decimal.mark = '.', big.mark = ","),
"html",align = "lccc") %>%
kable_styling("hover", full_width = F)
| Charity type | % of all contributions, 2012 | % of all contributions, 2017 | % point change, 2012–17 |
|---|---|---|---|
| Religion | 32.3 | 30.9 | -1.4 |
| Education | 14.3 | 14.3 | 0.0 |
| Human services | 12.6 | 12.1 | -0.5 |
| Gifts to foundations | 12.2 | 11.1 | -1.1 |
| Health | 8.0 | 9.3 | 1.3 |
| International affairs | 4.9 | 5.6 | 0.7 |
| Public-society benefit | 7.0 | 7.2 | 0.2 |
| Arts, culture, and humanities | 4.2 | 4.7 | 0.5 |
| Environment and animals | 2.7 | 2.9 | 0.2 |
| Gifts to individuals | 1.8 | 1.9 | 0.1 |
Source: Giving USA Foundation (2018).
Foundations
#Import Raw Figure 3 data (available from the Foundation Center Foundation Stats, http://data.foundationcenter.org/)Figure3 <- read_csv("External_Data/FoundationCenter.csv",
col_types = cols_only(Year = col_integer(),
Foundations = col_integer(),
Grants = col_double(),
Assets = col_double()
))
#Adjust for inflationFigure3 <- Figure3 %>%
mutate(Constant_Grants = round((Grants * inflindex[as.character(2015),])/(inflindex[as.character(Year),]), digits =1),
Constant_Assets = round((Assets * inflindex[as.character(2015),])/(inflindex[as.character(Year),]), digits =1)
)#write final table to csvwrite.csv(Figure3, "Figures/NSiB_Figure3_Table.csv")
The Foundation Center (2018) estimates there were more than 86,203 grantmaking foundations in the United States in 2015. Their grants, a component of private charitable contributions, totaled $62.8 billion in 2015, up 4.1 percent from 2014 after adjusting for inflation (figure 3). Between 2005 and 2015, foundation grantmaking increased 42.1 percent after adjusting for inflation. Foundation assets also grew over the same period, increasing 33.2 percent from $668.2 billion in 2005 to $890.1 billion in 2015 after adjusting for inflation.
FIGURE 3
Number of Foundations and Amount of Grants Made by Year, 2005-2015
#Graph Figure 3 TableFig3Plot <- function(Fig3Table) {
Fig3 <- Fig3Table %>%
ggplot(aes(x=Year)) +
geom_bar(aes(y=Foundations, fill= "Foundations"), stat = "identity") +
geom_line(aes(y=Constant_Grants*1000, color = "Grants made"), size = 2) +
scale_y_continuous(expand = c(0, 0), limits = c(0,100000),
sec.axis = sec_axis(~./1000, name = "Grants made ($ billions)"),
labels = scales::comma) +
scale_x_continuous(breaks = 2005:2015)+
labs(#caption = "The Foundation Center, Foundation Stats (2018)",
x = "Year",
y = "Number of foundations") +
scale_color_manual("", values = c("Foundations" = "#1696d2", "Grants made" = "black")) +
scale_fill_manual(" ", values = "#1696d2")
UrbCaption <- grobTree(
gp = gpar(fontsize = 8, hjust = 1),
textGrob(label = "I N S T I T U T E",
name = "caption1",
x = unit(1, "npc"),
y = unit(0, "npc"),
hjust = 1,
vjust = 0),
textGrob(label = "U R B A N ",
x = unit(1, "npc") - grobWidth("caption1") - unit(0.01, "lines"),
y = unit(0, "npc"),
hjust = 1,
vjust = 0,
gp = gpar(col = "#1696d2")))
grid.arrange(Fig3, UrbCaption, ncol = 1, heights = c(30, 1))
}Fig3Plot(Figure3)
Source: The Foundation Center, Foundation Stats (2018).
Volunteering
#Calculate proportion of volunteering hours#Data taken from Bureau of Labor Statistics: American Time Use Survey 2017 (https://www.bls.gov/tus/datafiles_2017.htm)#Data downloaded and saved locally, read in files:respondent17 <- read_csv("External_Data/atusresp_2017.dat", na = "-1")
activity17 <- read_csv("External_Data/atussum_2017.dat", na = "-1")
#Code to analyze American Time Use Survey Data#Step 1: change variable names to lowercasenames(respondent17) <- tolower(names(respondent17))
names(activity17) <- tolower(names(activity17))
#Step 2: join respondent and activity dataatus17 <- left_join(respondent17, activity17, by = "tucaseid")
#Step 3: Create volunteering subset by filtering cases without any volunteering hoursatus17vol <- atus17 %>%
filter(t150101>0 |
t150102>0 |
t150103>0 |
t150104>0 |
t150105>0 |
t150106>0 |
t150199>0 |
t150201>0 |
t150202>0 |
t150203>0 |
t150204>0 |
t150299>0 |
t150301>0 |
t150302>0 |
t150399>0 |
t150401>0 |
t150402>0 |
t150499>0 |
t150501>0 |
t150599>0 |
t150601>0 |
t150602>0 |
t150699>0 |
t150701>0 |
t150799>0 |
#t150801>0 | #(note: commented out because not available in 2017 ATUS)#t150899>0 | #(note: commented out because not available in 2017 ATUS)t159999>0 |
t181501>0 |
t181599>0)
#Step 4: calculate weighted volunteering hoursatus17vol <- atus17vol %>%
mutate(t150101w = tufinlwgt.x* t150101,
t150102w = tufinlwgt.x* t150102,
t150103w = tufinlwgt.x* t150103,
t150104w = tufinlwgt.x* t150104,
t150105w = tufinlwgt.x* t150105,
t150106w = tufinlwgt.x* t150106,
t150199w = tufinlwgt.x* t150199,
t150201w = tufinlwgt.x* t150201,
t150202w = tufinlwgt.x* t150202,
t150203w = tufinlwgt.x* t150203,
t150204w = tufinlwgt.x* t150204,
t150299w = tufinlwgt.x* t150299,
t150301w = tufinlwgt.x* t150301,
t150302w = tufinlwgt.x* t150302,
t150399w = tufinlwgt.x* t150399,
t150401w = tufinlwgt.x* t150401,
t150402w = tufinlwgt.x* t150402,
t150499w = tufinlwgt.x* t150499,
t150501w = tufinlwgt.x* t150501,
t150599w = tufinlwgt.x* t150599,
t150601w = tufinlwgt.x* t150601,
t150602w = tufinlwgt.x* t150602,
t150699w = tufinlwgt.x* t150699,
t150701w = tufinlwgt.x* t150701,
t150799w = tufinlwgt.x* t150799,
#t150801w = tufinlwgt.x* t150801, (note: commented out because not available in 2017 ATUS)#t150899w = tufinlwgt.x* t150899, (note: commented out because not available in 2017 ATUS)t159999w = tufinlwgt.x* t159999,
t181501w = tufinlwgt.x* t181501,
t181599w = tufinlwgt.x* t181599
)#Step 5: Create reduced file of only weighted dataatus17vol <- atus17vol %>%
select(tucaseid,t150101w,
t150102w,
t150103w,
t150104w,
t150105w,
t150106w,
t150199w,
t150201w,
t150202w,
t150203w,
t150204w,
t150299w,
t150301w,
t150302w,
t150399w,
t150401w,
t150402w,
t150499w,
t150501w,
t150599w,
t150601w,
t150602w,
t150699w,
t150701w,
t150799w,
#t150801w, (note: commented out because not available in 2017 ATUS)#t150899w, (note: commented out because not available in 2017 ATUS)t159999w,
t181501w,
t181599w,
tufinlwgt.x)
#Step 6: Create categorical groupings, number of volunteer hoursatus17vol <- atus17vol %>%
mutate(adminsupport = t150101w + t150102w + t150103w + t150104w + t150105w + t150106w +t150199w,
socialservice = t150201w + t150202w + t150203w + t150204w + t150299w,
maintenance = t150301w + t150302w+ t150399w,
performculture = t150401w + t150402w + t150499w,
attendmeet = t150501w + t150599w,
pubhealth = t150601w + t150602w + t150699w,
waiting = t150701w + t150799w,
#security = t150801w,travel = t181501w + t181599w,
othervol = t159999w)#Step 7: Calculate proprotion of weighted individuals involved in each category#Step 7a: Administrative/Supportatus17vol$adminsupportprop <- ifelse((atus17vol$t150101w +
atus17vol$t150102w +atus17vol$t150103w +atus17vol$t150104w +atus17vol$t150105w +atus17vol$t150106w +atus17vol$t150199w) >0,
atus17vol$tufinlwgt.x,0)
#Step 7b: Social serviceatus17vol$socialserviceprop <- ifelse((atus17vol$t150201w +
atus17vol$t150202w +atus17vol$t150203w +atus17vol$t150204w +atus17vol$t150299w) >0,
atus17vol$tufinlwgt.x,0)
#Step 7c: Maintenanceatus17vol$maintenanceprop <- ifelse((atus17vol$t150301w +
atus17vol$t150302w +atus17vol$t150399w) >0,
atus17vol$tufinlwgt.x,0)
#Step 7d: Perform cultureatus17vol$performcultureprop <- ifelse((atus17vol$t150401w +
atus17vol$t150402w +atus17vol$t150499w) >0,
atus17vol$tufinlwgt.x,0)
#Step 7e: Attend meetingsatus17vol$attendmeetprop <- ifelse((atus17vol$t150501w+
atus17vol$t150599w) >0,
atus17vol$tufinlwgt.x,0)
#Step 7f: Public healthatus17vol$pubhealthprop <- ifelse((atus17vol$t150601w +
atus17vol$t150602w +atus17vol$t150699w) >0,
atus17vol$tufinlwgt.x,0)
#Step 7g: Waitingatus17vol$waitingprop <- ifelse((atus17vol$t150701w +
atus17vol$t150799w) >0,
atus17vol$tufinlwgt.x,0)
#Step 7h: Security#atus17vol$securityprop <- ifelse((atus17vol$t150801w) >0,#atus17vol$tufinlwgt.x,#0)#Step 7i: Travelatus17vol$travelprop <- ifelse((atus17vol$t181501w +
atus17vol$t181599w) >0,
atus17vol$tufinlwgt.x,0)
#Step 7j: Otheratus17vol$othervolprop <- ifelse((atus17vol$t159999w) >0,
atus17vol$tufinlwgt.x,0)
#Step 8: Summarize number of hours/volunteers in each categoryatus17volsum<- atus17vol %>%
summarise(adminsupportprop = sum(adminsupportprop),
socialserviceprop = sum(socialserviceprop),
maintenanceprop = sum(maintenanceprop),
performcultureprop = sum(performcultureprop),
attendmeetprop = sum(attendmeetprop),
pubhealthprop = sum(pubhealthprop),
waitingprop = sum(waitingprop),
#securityprop = sum(securityprop),travelprop = sum(travelprop),
othervolprop = sum(othervolprop),
adminsupport = sum(adminsupport),
socialservice = sum(socialservice),
maintenance= sum(maintenance),
performculture = sum(performculture),
attendmeet = sum(attendmeet),
pubhealth = sum(pubhealth),
waiting = sum(waiting),
#security = sum(security),travel = sum(travel),
othervol = sum(othervol)
)#Step 9: Reduce to number of volunteer hoursatus17volhours<- atus17volsum %>%
select(adminsupport, socialservice, maintenance, performculture, attendmeet, pubhealth, waiting,#security,travel, othervol) %>%
gather(adminsupport, socialservice, maintenance, performculture, attendmeet, pubhealth, waiting,#security,travel, othervol,
key = "type",
value = "hours")
#Step 10: rename columnsatus17volhours$type[grepl("adminsupport",atus17volhours$type )] <- "Administrative and support"
atus17volhours$type[grepl("socialservice",atus17volhours$type )] <- "Social service and care"
atus17volhours$type[grepl("maintenance",atus17volhours$type )] <- "Maintenance, building, and cleanup"
atus17volhours$type[grepl("performculture",atus17volhours$type )] <- "Performing and cultural activities"
atus17volhours$type[grepl("attendmeet",atus17volhours$type )] <- "Meetings, conferences, and training"
atus17volhours$type[grepl("pubhealth",atus17volhours$type )] <- "Public health and safety"
atus17volhours$type[grepl("waiting",atus17volhours$type )] <- "Waiting"
#atus17volhours$type[grepl("security",atus17volhours$type )] <- "Security procedures"atus17volhours$type[grepl("travel",atus17volhours$type )] <- "Travel"
atus17volhours$type[grepl("othervol",atus17volhours$type )] <- "Other"
atus17volhours$type[grepl("adminsupport",atus17volhours$type )] <- "Administrative and support"
#Step 11: Calculate totalatus17volhours[10,2] <-sum(atus17volhours$hours)
atus17volhours$type[10] = "Total"
#Step 12: Calculate proportional number of hours per categoryatus17volhours <-atus17volhours %>%
mutate(AsPct = round(((hours/hours[10])*100),1)
)#Step 12: Remane final underlying table and write to CSVFigure4 <- atus17volhourswrite.csv(Figure4, "Figures/NSiB_Figure4_Table.csv")
#Read in Table 6 raw data#Based on US Department of Labor, Bureau of Labor Statistics, Current Population Survey, Volunteer Supplement (2007-2015) (https://www.bls.gov/cps/home.htm),#US Department of Labor, Bureau of Labor Statistics, American Time Use Survey (2008-2017) (https://www.bls.gov/tus/home.htm),#US Department of Labor, Bureau of Labor Statistics, Current Employment Statistics (2017) (https://www.bls.gov/ces/), and#US Census Bureau "Annual Estimates of the Resident Population by Sex, Age, Race, and Hispanic Origin for the United States and States: April 1,2010 to July 1, 2017", (https://factfinder.census.gov/)#Read in raw data, and write to CSVTable6 <- read_csv("D:/Users/BMcKeeve/Documents/Nonprofit-Sector-in-Brief/External_Data/Volunteering Data.csv")
write.csv(Table6, "Tables/NSiB_Table6.csv")
Volunteering is an important component of the nonprofit sector: over two-fifths of public charities rely on volunteers.[5] Using data from the Current Population Survey and the American Time Use Survey, this section discusses the distribution of average volunteer time by activity (figure 4), as well as the number of volunteers, the amount of hours volunteered, and the economic value of volunteer time (table 6).[6]
Number of Volunteers
An estimated 25.1 adults, NA percent of the population volunteered at least once in 2017. The highest volunteer rate reported in the decade spanning from 2008 to 2017 was NA percent, which was reported in 2009 and 2011. The lowest volunteer rate was reported in 2015: NA percent.
The percentage of the population volunteering on a given day increased slightly in 2017, rising to -- percent from -- percent in 2016. This rise occurs after 2016 saw the lowest proportion of the population volunteering on an average day within the previous 10 years: however, the NA people volunteering on a given day represents an increase of over 1 million daily volunteers from 2016. In the past decade, the highest proportion of Americans volunteering on a given day was in 2009, when 52 percent of the population volunteered (NA people).
Hours Volunteered
Americans volunteered an estimated 64.4 hours in 2017, a slight increase from 63.9 hours in 2016. This amounts to about 8.8 hours per volunteer, slightly more than in 2016.
Volunteer Activities
Figure 4 provides more information on how volunteers spent their time in 2017. The largest use of volunteer hours in 2017 was on social service and care activities (22 percent). These activities include such tasks as preparing food, collecting and delivering clothing or other goods, providing care, and teaching, counseling, or mentoring. Administrative and support activities made up the next-largest proportion of volunteer time (24.8 percent); this category includes things like computer use, telephone calls (except hotline counseling), writing, fundraising, and the like. These two categories of activities also led volunteer hours in 2016, although the proportion of time spent in social service and care activities has decreased slightly (from 23.5 percent) while the proportion of time spent in social administrative and support activities increased slightly (from 25.3 percent). Volunteers spent a larger proportion of their time in performing or cultural activities and meetings, conferenecs, and trainings in 2017 than in 2016, while they spent less time in maintenance, building, and cleanup activities.
FIGURE 4
Distribution of Volunteer Time by Activity, 2017 (percent)
#Display Figure 4Fig4Plot <- function(Fig4Table) {
Fig4<- Fig4Table %>%
filter(type != "Total") %>%
#filter(type != "Security procedures") %>% #Filtered out because equals 0%ggplot(aes(x=reorder(type, -AsPct), y =AsPct)) +
geom_bar(stat = "identity") +
geom_text(aes(label=formatC(round(AsPct,1), format = 'f', digits =1)),
position= position_dodge(width=1),
vjust =-.3,size=3) +
scale_y_continuous(expand = c(0, 0), limits = c(0,30)) +
labs(y = "Percent of total volunteer time") +
theme(axis.title = element_blank(),
panel.grid.major = element_blank(),
axis.text.y = element_blank()) +
scale_x_discrete(labels = function(type) str_wrap(type, width=10))
UrbCaption <- grobTree(
gp = gpar(fontsize = 8, hjust = 1),
textGrob(label = "I N S T I T U T E",
name = "caption1",
x = unit(1, "npc"),
y = unit(0, "npc"),
hjust = 1,
vjust = 0),
textGrob(label = "U R B A N ",
x = unit(1, "npc") - grobWidth("caption1") - unit(0.01, "lines"),
y = unit(0, "npc"),
hjust = 1,
vjust = 0,
gp = gpar(col = "#1696d2")))
grid.arrange(Fig4, UrbCaption, ncol = 1, heights = c(30, 1))
}Fig4Plot(Figure4)
Source: Author's calculations based on US Department of Labor, Bureau of Labor Statistics, American Time Use Survey 2017.
Value of Volunteering
The time volunteers spent in 2017 was worth an estimated 256 (table 6). The value of volunteer time combined with private giving accounted for over half a trillion dollars ($444.91 billion); volunteer time represents 12.6 percent of that total.
TABLE 6
Number, Hours, and Dollar Value of Volunteers, 2008-2017
#Display Table 6kable(Table6,"html",format.args = list(decimal.mark = '.', big.mark = ","),
align = "lcccccccccc",
col.names = c("", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017")) %>%
kable_styling("hover", full_width = F) %>%
row_spec(c(1,7,11), bold = T, hline_after = T )
| 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | |
|---|---|---|---|---|---|---|---|---|---|---|
| Per year | ||||||||||
| Percent of population volunteering | 26.4 | 26.8 | 26.3 | 26.8 | 26.5 | 25.4 | 25.3 | 24.9 | 25.2 | 25.1 |
| Number of volunteers (millions) | 61.8 | 63.4 | 62.8 | 64.3 | 64.5 | 62.6 | 62.8 | 62.6 | 63.9 | 64.4 |
| Hours volunteered (billions) | 8 | 8.1 | 8.1 | 8.5 | 8.5 | 8.3 | 8.7 | 8.5 | 8.7 | 8.8 |
| Average hours per volunteer | 130 | 128 | 129 | 132 | 132 | 133 | 139 | 136 | 136 | 137 |
| Median hours per volunteer | 52 | 52 | 52 | 51 | 50 | 50 | 50 | 52 | -- | -- |
| Per average day | ||||||||||
| Percent of population volunteering | 6.8 | 7.1 | 6.8 | 6 | 5.8 | 6.1 | 6.4 | 6.4 | 5.6 | 6 |
| Number of volunteers (millions) | 16.2 | 17.1 | 16.6 | 14.6 | 14.3 | 15.1 | 16 | 16.3 | 14.4 | 15.6 |
| Hours per day per volunteer | 2.43 | 2.39 | 2.46 | 2.84 | 2.48 | 2.57 | 2.41 | 2.49 | 2.39 | 2.86 |
| Value of volunteers | ||||||||||
| Population age 16 and over (millions) | 234.4 | 236.3 | 238.3 | 240 | 243.8 | 246.2 | 248.4 | 251.3 | 253.6 | 256 |
| Full-time-equivalent employees (millions) | 4.7 | 4.8 | 4.8 | 5 | 5 | 4.9 | 5.1 | 5 | 5.1 | 5.2 |
| Assigned hourly wages for volunteers | $18.08 | $18.63 | $19.07 | $19.47 | $19.75 | $20.16 | $20.59 | $21.08 | $21.63 | $22.13 |
| Assigned value of volunteer time ($ billions) | $144.70 | $150.70 | $154.10 | $164.80 | $168.30 | $167.20 | $179.20 | $179.00 | $187.40 | $195.00 |
Sources: Author's calculations based on data from US Department of Labor, Bureau of Labor Statistics, Current Population Survey, Volunteer Supplement (2007–16); US Department of Labor, Bureau of Labor Statistics, American Time Use Survey (2007–16); and US Department of Labor, Bureau of Labor Statistics, Current Employment Statistics (2016).
Notes: Median hours per volunteer not available for 2016 – 17. Percent of population volunteering and hours volunteered for 2016 – 17 estimated based on previous years.
Conclusion
In 2015, public charities composed over two-thirds of all registered nonprofit organizations and accounted for over three-quarters of the revenue and expenses of the nonprofit sector in the United States. While the nonprofit sector as a whole did see steady financial growth even throughout the heart of the recent recession (with revenue, expenses, and assets all growing more than 15 percent during those years), the recession impacted the various subsectors differently. Arts organizations and public and social benefit organizations both saw slight downturns in revenue between 2005 and 2010; on the other hand, health organizations experienced above-average nonprofit growth during those years. However, from 2010 to 2015, all nonprofit sectors experienced renewed growth: revenue growth has outpaced the growth in expenses (20.6 percent compared with 16.6 percent, respectively) while assets have also grown considerably (26.3 percent).
Private charitable giving has also increased for several straight years (since 2014), even after adjusting for inflation: 2017's peak of $410.02 billion is the largest single-year contribution total reported on file and is the first time annual charitable giving has exceeded $400 billion. The total number of volunteers also increased in 2017 to an estimated NA percent of the US adult population. The share of the population volunteering on any given day also rose to -- percent, a rebound from 2016, which had the lowest proportion of the population volunteering within the past 10 years (--). Volunteers contributed an estimated 64.4 hours in 2017.
References
Foundation Center. 2018. Foundation Stats (2014). New York: Foundation Center. http://data.foundationcenter.org/#/foundations/all/nationwide/total/list/2014
Giving USA Foundation. 2018. Giving USA 2018: The Annual Report on Philanthropy for the Year 2017. Bloomington, IN: Giving USA Foundation.
About the Author
Brice McKeever is a research associate in the Center on Nonprofits and Philanthropy at the Urban Institute, where he performs quantitative social science research across a wide spectrum of public policy initiatives related to the nonprofit sector, civic participation, and the impact of technology on social interaction. He is the lead author of The Nonprofit Almanac: Ninth Edition and multiple research reports related to nonprofit-sector growth and diversity.
Acknowledgments
*This brief was funded by the Urban Institute. The views expressed are those of the author and should not be attributed to the Urban Institute, its trustees, or its funders. Funders do not determine research findings or the insights and recommendations of Urban experts. Further information on the Urban Institute's funding principles is available at urban.org/fundingprinciples. *
Notes
[1] GDP estimates are from the Bureau of Economic Analysis and include nonprofit institutions serving households. They exclude nonprofit institutions serving government or business. See table 1.3.5: Gross Value Added by Sector at “National Income and Product Accounts: National Data: Section 1 - Domestic Product and Income,” Bureau of Economic Analysis, accessed May 1, 2018, https://www.bea.gov/iTable/iTable.cfm?reqid=19&step=2#reqid=19&step=2&isuri=1&1921.
[2] The standard source for estimates of religious congregations is American Church lists, a company that provides marketing data using phone listings and other sources. The most recent estimates from American Church lists suggest that there are about 345,000 houses of worship in the United States. Of these, approximately 240,000 are registered with the IRS, according to National Center for Charitable Statistics's analysis of the February 2016 IRS Business Master File. See “Churches Mailing Lists and Sales Leads,” InfoUSA, accessed May 1, 2018, https://www.infousa.com/product/church-list/.
[3] All private foundations, regardless of size, are required to file a Form 990-PF. Before tax year 2010, nonprofits with gross receipts of $25,000 or more (excluding religious congregations) were required to file a Form 990 or Form 990-EZ. Beginning in 2010, only organizations with $50,000 or more in gross receipts (excluding religious congregations) are required to file a Form 990 or Form 990-EZ. Organizations with less than $50,000 in gross receipts are required to file an information return known as the Form 990-N (e-Postcard). Filing requirements by year are available at “Form 990 Series Which Forms Do Exempt Organizations File Filing Phase In,” Internal Revenue Service, accessed May 1, 2018, https://www.irs.gov/charities-non-profits/form-990-series-which-forms-do-exempt-organizations-file-filing-phase-in.
[4] Table 1 lists little change in the number of reporting charities between 2005 and 2015, with a growth rate of only 0.6 percent; it also lists the number of reporting charities falling 6.2 percent between 2005 and 2010. As noted in previous editions of “The Nonprofit Sector in Brief,” the 2012 National Center for Charitable Statistics Core File showed a substantial decrease from previous years. Because of IRS changes in data processing, we cannot conclusively verify the reason for this change. However, National Center for Charitable Statistics analysis suggests that this is likely caused by the introduction of new IRS filing guidelines and database management. With the introduction of the Form 990-N (e-Postcard), many smaller organizations were no longer required to file the full Form 990 or Form 990-EZ. Many organizations that had opted to file a Form 990 or Form 990-EZ (despite being under the minimum filing threshold) instead filed a Form 990-N for the 2012 tax period and afterward, and those Form 990-N filers are not counted as reporting organizations in table 1. The number of reporting charities increased 6.8 percent between 2010 and 2015—and, indeed, increased 2.3 percent between 2012 and 2013—suggesting that the sector continues to grow. Thus, the decline in number of reporting organizations between 2005 and 2010 reported here compared with editions of “The Nonprofit Sector in Brief” before 2014 should be understood as a reflection of the change in IRS filing guidelines.
[5] National Center for Charitable Statistics calculations of IRS Statistics of Income Division Exempt Organizations Sample (2012).
[6] Estimates of number of hours volunteered are based on data from the Current Population Survey (CPS). Volunteer statistics from the CPS Volunteer Supplement are not available after September 2015: 2016 figures for total hours volunteered and total number of volunteers are estimated from historical CPS data. Three years of data (2013–2015) were used to estimates 2016 volunteer rate and hours per capita. These rates were applied to Census Bureau estimates of the 16-and-over population (which includes people in the military or living in institutionalized housing) to project the sizes of the 2016 CPS 16-and-over population from the 2014 and 2015 estimates. For more on the CPS, see “Labor Force Statistics from the Current Population Survey,” Bureau of Labor Statistics, accessed May 1, 2018, https://www.bls.gov/cps/home.htm. For more on the American Time Use Survey, see “American Time Use Survey,” Bureau of Labor Statistics, accessed May 1, 2018, https://www.bls.gov/tus/home.htm. For more on the Census Bureau population estimates, see “Annual Estimates of the Resident Population by Sex, Age, Race, and Hispanic Origin for the United States and States: April 1,2010 to July 1, 2016,” US Census Bureau, available at American Fact Finder, accessed May 10, 2018, https://factfinder.census.gov/.
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