How Many Nonprofits Are Registered With The Internal Revenue Service (Irs) In 2016
The Nonprofit Sector in Brief 2019
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###### -
#Groundwork Setup -
###### -
library (httr)
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library (tidyverse)
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library (stringr)
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library (RCurl)
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library (reshape2)
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library (RColorBrewer)
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library (extrafont)
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library (knitr)
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library (strange)
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library (kableExtra)
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library (urbnthemes)
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library ( grid )
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library (gridExtra)
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library (rmarkdown)
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set_urbn_defaults( )
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###### -
#Download Raw NCCS Information -
###### -
#This code will utilize the post-obit NCCS data sets, so import separately using defined functions, and save in the "Information" folder -
#Think NCCS Data Archive download functions -
source ( "NCCS_Code/Prep IRS BMF.R" )
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source ( "NCCS_Code/Prep NCCS Core File.R" )
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#The following lawmaking will think 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 Main Files: -
#bm0601 -
#bm0601 <- getbmffile("2006", "01") -
#bm0701 -
#bm0701 <- getbmffile("2007", "01") -
##bm1106 -
#bm1106 <- getbmffile("2011", "06") -
##bm1206 -
#bm1206 <- getbmffile("2012", "06") -
##bm1502 -
#bm1502 <- getbmffile("2015", "02") -
##bm1602 -
#bm1602 <- getbmffile("2016", "02") -
##bm1709 -
#bm1709 <- getbmffile("2017", "09") -
## -
##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-information.urban.org/data/misc/nccs.nteedocAllEins.csv -
#The post-obit lawmaking 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( ),
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NteeFinal = col_character( )
-
) ) %>%
-
rename(NTEEFINAL = NteeFinal)
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#Inflation Index -
#Load Inflation index table -
#Based on information from Consumer Price Index Tabular array 24: "Historical Consumer Price Index for All Urban Consumers (CPI-U): U.Due south. 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 = ane, header = TRUE)
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#Create function to prepare and import selected BMF fields for assay -
prepbmffile <- function (bmffilepath) {
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output <- read_csv(bmffilepath,
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col_types = cols_only(EIN = col_character( ),
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NTEECC = col_character( ),
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STATE = col_character( ),
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OUTNCCS = col_character( ),
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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) )
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return (output)
-
} -
#Create function to prepare and import selected NCCS Core PC/CO fields for assay -
prepcorepcfile <- part (corefilepath) {
-
output <- read_csv(corefilepath,
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col_types = cols_only(EIN = col_character( ),
-
OUTNCCS = col_character( ),
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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 set up and import selected NCCS Core PF fields for analysis -
prepcorepffile <- role (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) )
-
render (output)
-
} -
###### -
#Import and Prepare NCCS Data files -
#Note: information has already been saved locally using above code -
###### -
### -
#BMF Data -
### -
#2005 BMF Data -
bmf2005 <-prepbmffile( "Information/bm0601.csv" )
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#2006 BMF Data -
bmf2006 <-prepbmffile( "Data/bm0701.csv" )
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#2010 BMF Information -
bmf2010 <-prepbmffile( "Data/bm1106.csv" )
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#2011 BMF Data -
bmf2011 <-prepbmffile( "Data/bm1206.csv" )
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#2014 BMF Data -
bmf2014 <-prepbmffile( "Data/bm1502.csv" )
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#2015 BMF Information -
bmf2015 <-prepbmffile( "Data/bm1602.csv" )
-
#2016 BMF Information -
bmf2016 <-prepbmffile( "Information/bm1709.csv" )
-
### -
#Cadre Data -
### -
# -
#Core 2005 Information -
# -
#PC -
core2005pc <- prepcorepcfile( "Data/core2005pc.csv" )
-
#CO -
core2005co <- prepcorepcfile( "Data/core2005co.csv" )
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#PF -
core2005pf <- prepcorepffile( "Data/core2005pf.csv" )
-
# -
#Core 2006 Data -
# -
#PC -
core2006pc <- prepcorepcfile( "Information/core2006pc.csv" )
-
#CO -
core2006co <- prepcorepcfile( "Data/core2006co.csv" )
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#PF -
core2006pf <- prepcorepffile( "Data/core2006pf.csv" )
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# -
#Core 2010 Information -
# -
#PC -
core2010pc <- prepcorepcfile( "Data/core2010pc.csv" )
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#CO -
core2010co <- prepcorepcfile( "Data/core2010co.csv" )
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#PF -
core2010pf <- prepcorepffile( "Information/core2010pf.csv" )
-
# -
#Cadre 2011 Information -
# -
#PC -
core2011pc <- prepcorepcfile( "Data/core2011pc.csv" )
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#CO -
core2011co <- prepcorepcfile( "Data/core2011co.csv" )
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#PF -
core2011pf <- prepcorepffile( "Data/core2011pf.csv" )
-
# -
#Cadre 2014 Data -
# -
#PC -
core2014pc <- prepcorepcfile( "Data/core2014pc.csv" )
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#CO -
core2014co <- prepcorepcfile( "Data/core2014co.csv" )
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#PF -
core2014pf <- prepcorepffile( "Data/core2014pf.csv" )
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# -
#Core 2015 Data -
# -
#PC -
core2015pc <- prepcorepcfile( "Data/core2015pc.csv" )
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#CO -
core2015co <- prepcorepcfile( "Information/core2015co.csv" )
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#PF -
core2015pf <- prepcorepffile( "Data/core2015pf.csv" )
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# -
#Cadre 2016 Data -
# -
#PC -
core2016pc <- prepcorepcfile( "Data/core2016pc.csv" )
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#CO -
core2016co <- prepcorepcfile( "Data/core2016co.csv" )
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# Annotation there is no PF file for 2016 swapping in 2015 instead -
core2016pf <- prepcorepffile( "Data/core2015pf.csv" )
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###### -
#Create Group Categories for Analysis by NTEE and Size -
###### -
### -
#NTEE Groupings -
### -
#Create NTEE grouping categories -
arts <- c ( "A" )
-
highered <- c ( "B4", "B5" )
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othered <- c ( "B" )
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envanimals <- c ( "C", "D" )
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hospitals <- c ( 'E20','E21','E22','E23','E24','F31','E30','E31','E32' )
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otherhlth <- c ( "Eastward", "F", "Chiliad", "H" )
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humanserv <- c ( "I", "J", "K", "L", "Thousand", "N", "O", "P" )
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intl <- c ( "Q" )
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pubben <- c ( "R", "Southward", "T", "U", "V", "West", "Y", "Z" )
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relig <- c ( "X" )
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#define office to join NTEE Master listing and categorize organizations appropriately -
NTEEclassify <- function (dataset) {
-
#merge in Master NTEE wait up file -
dataset <- dataset %>%
-
left_join(nteedocalleins, by = "EIN" )
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#create NTEEGRP classifications -
dataset$NTEEGRP <- " "
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dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1 ) %in% arts ] <- "Arts"
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dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,i,1 ) %in% othered ] <- "Other education"
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dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,i,ii ) %in% highered ] <- "Higher educational activity"
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dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,i,1 ) %in% envanimals] <- "Environment and animals"
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dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1 ) %in% otherhlth] <- "Other health care"
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dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,3 ) %in% hospitals] <- "Hospitals and primary intendance facilities"
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dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,i,one ) %in% humanserv] <- "Human services"
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dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1 ) %in% intl] <- "International"
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dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,ane ) %in% pubben] <- "Other public and social do good"
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dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,i,i ) %in% relig] <- "Organized religion related"
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dataset$NTEEGRP[ is.na (dataset$NTEEFINAL) ] <- "Other public and social benefit"
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return (dataset)
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} -
### -
#Expense Groupings -
### -
#define function to allocate organizations by expenses size -
EXPclassify <- function (dataset) {
-
dataset$EXPCAT <- " "
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dataset$EXPCAT[dataset$EXPS< 100000 ] <- "a. Under $100,000"
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dataset$EXPCAT[dataset$EXPS >= 100000 & dataset$EXPS< 500000 ] <- "b. $100,000 to $499,999"
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dataset$EXPCAT[dataset$EXPS >= 500000 & dataset$EXPS< 1000000 ] <- "c. $500,000 to $999,999"
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dataset$EXPCAT[dataset$EXPS >= million & dataset$EXPS< 5000000 ] <- "d. $1 million to $4.99 million"
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dataset$EXPCAT[dataset$EXPS >= 5000000 & dataset$EXPS< 10000000 ] <- "e. $v million to $9.99 meg"
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dataset$EXPCAT[dataset$EXPS >= 10000000 ] <- "f. $10 million or more"
-
return (dataset)
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} -
### -
#Employ Groupings to relevant information sets -
### -
#NTEE -
core2005pc <- NTEEclassify(core2005pc)
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core2006pc <- NTEEclassify(core2006pc)
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core2010pc <- NTEEclassify(core2010pc)
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core2011pc <- NTEEclassify(core2011pc)
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core2014pc <- NTEEclassify(core2014pc)
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core2015pc <- NTEEclassify(core2015pc)
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core2016pc <- NTEEclassify(core2016pc)
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#Expenses -
core2005pc <-EXPclassify(core2005pc)
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core2006pc <-EXPclassify(core2006pc)
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core2010pc <-EXPclassify(core2010pc)
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core2011pc <-EXPclassify(core2011pc)
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core2014pc <-EXPclassify(core2014pc)
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core2015pc <-EXPclassify(core2015pc)
-
core2016pc <-EXPclassify(core2016pc)
Dorsum to top
The Nonprofit Sector in Brief 2019
by NCCS Project Team
June 2020
This brief discusses trends in the number and finances of 501(c)(three) public charities and key information insights on important resources for the nonprofit sector, such as: private charitable contributions and grantmaking past foundations.
Dorsum to top
Highlights
- Approximately one.54 meg nonprofits were registered with the Internal Revenue Service (IRS) in 2016, an increase of iv.5 per centum from 2006.
- The nonprofit sector contributed an estimated $ane.047.2 trillion to the United states of america economy in 2016, composing 5.6 percent of the country's gross domestic product (Gross domestic product).1
- Of the nonprofit organizations registered with the IRS, 501(c)(3) public charities accounted for simply over 3-quarters of acquirement and expenses for the nonprofit sector as a whole ($two.04 trillion and $ane.94 trillion, respectively) and just under 2-thirds of the nonprofit sector'southward total assets ($3.79 trillion).
- In 2018, total private giving from individuals, foundations, and businesses totaled $427.71 billion (Giving USA Foundation 2019), a decrease of -i.7 per centum from 2017 (after adjusting for inflation). According to Giving USA (2018) full charitable giving rose for sequent years from 2014 to 2017, making 2017 the largest single twelvemonth for private charitable giving, fifty-fifty after adjusting for inflation.
- An estimated 25.1 percent of U.s. adults volunteered in 2017, contributing an estimated 8.8 billion hours. This is a 1.half-dozen percent increment from 2016. The value of these hours is approximately $195.0 billion.
Dorsum to peak
Size and Scope of the Nonprofit Sector
-
#Ascertain Table 1 Function -
Table1 <- office (datayear) {
-
### -
#Step1: Pull from raw bmf data to get Number of registered organizations -
### -
#Step1a: Create function to pull in BMF data -
byear <- office (datayear) {
-
#get BMF file names: -
bmf1 <- as.character ( paste ( "bmf", (datayear - 10 ), sep = "" ) )
-
bmf2 <- equally.character ( paste ( "bmf", (datayear - five ), sep = "" ) )
-
bmf3 <- as.graphic symbol ( paste ( "bmf", (datayear), sep = "" ) )
-
#for each BMF file name, run the following: -
bcomponent <- function (bmfnum, year_of_int) {
-
#get dataset -
bmf <- go (bmfnum)
-
#calculate all registered nonprofits -
all <- bmf %>%
-
filter ( (OUTNCCS != "OUT" ) ) %>%
-
summarize( -
yr = as.graphic symbol (year_of_int),
-
"All registered nonprofits" = n( )
-
) -
#calculate all public charities -
pc <- bmf %>%
-
filter ( (FNDNCD != "02" & FNDNCD!= "03" & FNDNCD != "04" ), (SUBSECCD == "03" |SUBSECCD== "3" ), (OUTNCCS != "OUT" ) ) %>%
-
summarize( -
year = as.grapheme (year_of_int),
-
"501(c)(three) public charities" = n( )
-
) -
#combine registered nonprofits and public charities -
combined <- all %>%
-
left_join(pc, by = "year" )
-
#render combined file -
return (combined)
-
} -
#run office for each year -
bcomp1 <-bcomponent(bmf1, (datayear - 10 ) )
-
bcomp2 <-bcomponent(bmf2, (datayear - 5 ) )
-
bcomp3 <-bcomponent(bmf3, datayear)
-
#merge years -
total <- rbind (bcomp1, bcomp2, bcomp3)
-
#return final -
return (full)
-
} -
#Step 1b: run confronting year of interest: -
btest<- byear(datayear)
-
### -
#Step 2: pull correct core file years -
### -
#Stride 2a: function to pull right years starting from base year: -
T1grab = function (yr) {
-
output <- c (yr- 10,
-
yr- 5,
-
twelvemonth) -
return ( list (output) )
-
} -
#Footstep 2b: pull the right years: -
T1years <-T1grab(datayear)
-
#Step 2c: Function for individual years of core files -
T1Fin<- function (datayear) {
-
pcname <- equally.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 <- become (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 =Truthful) ) / 1000000000, digits = two ),
-
"Expenses ($ billions)" = round ( ( sum ( as.numeric (EXPS), na.rm =TRUE) ) / 1000000000, digits = ii ),
-
"Assets ($ billions)" = round ( ( sum ( as.numeric (ASS_EOY), na.rm =TRUE) ) / 1000000000, digits= two ) )
-
pc <- melt(pc)
-
colnames (pc) [ 2 ] <- "PC"
-
co <- cofile %>%
-
filter ( (OUTNCCS != "OUT" ) ) %>%
-
summarize( -
Reporting = north( ),
-
"Acquirement ($ billions)" = round ( ( sum ( as.numeric (TOTREV), na.rm =TRUE) ) / 1000000000, digits = two ),
-
"Expenses ($ billions)" = round ( ( sum ( as.numeric (EXPS), na.rm =TRUE) ) / 1000000000, digits = 2 ),
-
"Assets ($ billions)" = round ( ( sum ( every bit.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 ( equally.numeric (P1TOTEXP), na.rm =TRUE) ) / 1000000000, digits = ii ),
-
"Assets ($ billions)" = round ( ( sum ( as.numeric (P2TOTAST), na.rm =TRUE) ) / 1000000000, digits= two ) )
-
pf <- melt( pf )
-
colnames ( pf ) [ ii ] <- "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 <- cook(Table1)
-
colnames (Table1) [ 2 ] = "Type"
-
colnames (Table1) [ iii ] = 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 cadre file part for each core file year: -
comp1 <- T1Fin(T1years[ [ i ] ] [ one ] )
-
comp2 <- T1Fin(T1years[ [ 1 ] ] [ 2 ] )
-
comp3 <- T1Fin(T1years[ [ 1 ] ] [ 3 ] )
-
#Footstep 2e: join multiple core file years together -
Table1All <- comp1 %>%
-
left_join(comp2, by = c ( "Blazon", "variable" ) ) %>%
-
left_join(comp3, by = c ( "Type", "variable" ) )
-
#Step 2f: drop intermediary column -
Table1All <- Table1All[ - 2 ]
-
### -
#Pace 3 Merge with BMF data -
### -
AllRegNonprofits<- data.frame ( "All registered nonprofits", btest[ [ 2 ] ] [ i ], btest[ [ 2 ] ] [ ii ], btest[ [ 2 ] ] [ 3 ] )
-
names (AllRegNonprofits) <- names (Table1All)
-
AllPCs<- data.frame ( "501(c)(3) public charities", btest[ [ three ] ] [ 1 ], btest[ [ 3 ] ] [ 2 ], btest[ [ 3 ] ] [ 3 ] )
-
names (AllPCs) <- names (Table1All)
-
Table1All <- rbind (Table1All, AllRegNonprofits, AllPCs)
-
### -
#Pace 4: Summate alter over time -
### -
Table1All <- Table1All %>%
-
mutate( -
ChangeA = circular ( ( (Table1All[, equally.character (datayear- 5 ) ] - Table1All[, every bit.character (datayear- x ) ] ) / (Table1All[, as.character (datayear- ten ) ] ) )
-
* 100, digits= 1 ),
-
ChangeB = round ( ( (Table1All[, equally.character (datayear) ] - Table1All[, equally.grapheme (datayear- ten ) ] ) / (Table1All[, as.character (datayear- 10 ) ] ) )
-
* 100, digits= 1 )
-
) -
### -
#Step 5: calculate aggrandizement adjustments -
### -
Table1All <- Table1All %>%
-
mutate( -
Y1 = circular ( ( (Table1All[, as.grapheme (datayear- 10 ) ] * inflindex[ equally.graphic symbol (datayear),] ) / (inflindex[ as.character (datayear- 10 ),] ) ), digits= 3 ),
-
Y2 = round ( ( (Table1All[, as.character (datayear- five ) ] * inflindex[ as.character (datayear),] ) / (inflindex[ as.graphic symbol (datayear- 5 ),] ) ), digits= 3 ),
-
Y3 = round ( ( (Table1All[, every bit.grapheme (datayear) ] * inflindex[ as.grapheme (datayear),] ) / (inflindex[ every bit.grapheme (datayear),] ) ), digits= 3 ),
-
ChangeAInfl = circular ( ( (Y2-Y1) /Y1) * 100, digits = 1 ),
-
ChangeBInfl = circular ( ( (Y3-Y1) /Y1) * 100, digits = 1 )
-
) -
### -
#Step 6: Format and prepare final table -
### -
#Stride 6a: remove intermediary columns -
Table1All[ 7 : nine ] <- list (NULL)
-
#Step 6b: reorder columns to fit Nonprofit Sector in Brief -
Table1All <- Table1All[, c ( ane,2,3,five,7,four,six,8 ) ]
-
#Pace 6c: omit numerical count columns from inflation adjustments -
Table1All[ [ 5 ] ] [ 1 ] <- "--"
-
Table1All[ [ 5 ] ] [ 5 ] <- "--"
-
Table1All[ [ 8 ] ] [ 1 ] <- "--"
-
Table1All[ [ 8 ] ] [ v ] <- "--"
-
Table1All[ [ 5 ] ] [ 9 ] <- "--"
-
Table1All[ [ five ] ] [ 10 ] <- "--"
-
Table1All[ [ viii ] ] [ 9 ] <- "--"
-
Table1All[ [ 8 ] ] [ 10 ] <- "--"
-
#Footstep 6d: rename columns -
colnames (Table1All) [ ane ] <- ""
-
colnames (Table1All) [ iv ] <- paste ( "% change, ", equally.character (datayear - 10 ), "\u2013", as.character (datayear - 5 ), sep = "" )
-
colnames (Table1All) [ 5 ] <- paste ( "% alter, ", equally.character (datayear - x ), "\u2013", as.character (datayear - 5 ), " (inflation adjusted)", sep = "" )
-
colnames (Table1All) [ 7 ] <- paste ( "% modify, ", as.graphic symbol (datayear - 10 ), "\u2013", as.character (datayear ), sep = "" )
-
colnames (Table1All) [ viii ] <- paste ( "% change, ", as.character (datayear - 10 ), "\u2013", as.character (datayear ), " (aggrandizement adapted)", sep = "" )
-
#Step6e: reorder rows -
Table1All <- Table1All[ c ( 9,1,2,3,iv,10,5,6,vii,8 ),]
-
#Step 6f: return final output -
return (Table1All)
-
} -
#Create Table i based on 2015 information -
Table1_2016 <-Table1(params$NCCSDataYr)
-
write.csv (Table1_2016, "Tables/NSiB_Table1.csv" )
-
#Define Table 1 Current Growth Role (Appendix Table Showing only most contempo growth) -
Table1CurGrowth <- function (datayear) {
-
### -
#Step1: Pull from raw BMF data to get Number of registered organizations -
### -
#Step1a: Create function -
byear <- role (datayear) {
-
#get BMF file names: -
bmf1 <- equally.graphic symbol ( paste ( "bmf", (datayear - 1 ), sep = "" ) )
-
bmf2 <- as.character ( paste ( "bmf", (datayear), sep = "" ) )
-
#for each BMF file proper noun, run the following: -
bcomponent <- function (bmfnum, year_of_int) {
-
#get dataset -
bmf <- become (bmfnum)
-
#summate all registered nonprofits -
all <- bmf %>%
-
filter ( (OUTNCCS != "OUT" ) ) %>%
-
summarize( -
yr = equally.character (year_of_int),
-
"All registered nonprofits" = n( )
-
) -
#calculate all public charities -
pc <- bmf %>%
-
filter ( (FNDNCD != "02" & FNDNCD!= "03" & FNDNCD != "04" ), (SUBSECCD == "03" |SUBSECCD== "3" ), (OUTNCCS != "OUT" ) ) %>%
-
summarize( -
twelvemonth = every bit.character (year_of_int),
-
"501(c)(3) public charities" = n( )
-
) -
#combine registered nonprofits and public charities -
combined <- all %>%
-
left_join(pc, past = "yr" )
-
#render combined file -
return (combined)
-
} -
#run role for each yr -
bcomp1 <-bcomponent(bmf1, (datayear - 1 ) )
-
bcomp2 <-bcomponent(bmf2, (datayear) )
-
#merge years -
total <- rbind (bcomp1, bcomp2)
-
#return concluding -
render (full)
-
} -
#Pace 1b: run confronting yr of interest: -
btest<- byear(datayear)
-
### -
#Step two: Pull NCCS Core File information -
### -
#Step 2a: role to pull right years starting from base of operations yr: -
T1grab = function (year) {
-
output <- c (yr- 1,
-
yr) -
return ( list (output) )
-
} -
#Step 2b: pull the correct years: -
T1years <-T1grab(datayear)
-
#Step 2c: Office for private years of core files -
T1Fin<- function (datayear) {
-
pcname <- equally.character ( paste ( "core", datayear, "pc", sep = "" ) )
-
coname <- as.character ( paste ( "cadre", datayear, "co", sep = "" ) )
-
pfname <- equally.graphic symbol ( paste ( "cadre", datayear, "pf", sep = "" ) )
-
pcfile <- get (pcname)
-
cofile <- get (coname)
-
pffile <- go (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 = due north( ),
-
"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 = ii ),
-
"Assets ($ billions)" = circular ( ( sum ( as.numeric (ASS_EOY), na.rm =Truthful) ) / 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 = two ),
-
"Expenses ($ billions)" = round ( ( sum ( as.numeric (P1TOTEXP), na.rm =TRUE) ) / 1000000000, digits = 2 ),
-
"Assets ($ billions)" = round ( ( sum ( as.numeric (P2TOTAST), na.rm =Truthful) ) / 1000000000, digits= 2 ) )
-
pf <- cook( pf )
-
colnames ( pf ) [ two ] <- "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) [ iii ] = equally.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) )
-
render (Table1)
-
} -
#Step 2nd: run core file function for each cadre file year: -
comp1 <- T1Fin(T1years[ [ 1 ] ] [ 1 ] )
-
comp2 <- T1Fin(T1years[ [ 1 ] ] [ ii ] )
-
#Setp 2e: join multiple cadre file years together -
Table1CG <- comp1 %>%
-
left_join(comp2, by = c ( "Blazon", "variable" ) )
-
#Step 2f: drop intermediary column -
Table1CG <- Table1CG[ - two ]
-
#### -
#Step three: Merge with BMF data -
### -
AllRegNonprofits<- information.frame ( "All registered nonprofits", btest[ [ 2 ] ] [ 1 ], btest[ [ 2 ] ] [ 2 ] )
-
names (AllRegNonprofits) <- names (Table1CG)
-
AllPCs<- data.frame ( "501(c)(iii) public charities", btest[ [ 3 ] ] [ 1 ], btest[ [ 3 ] ] [ 2 ] )
-
names (AllPCs) <- names (Table1CG)
-
Table1CG <- rbind (Table1CG, AllRegNonprofits, AllPCs)
-
### -
#Footstep four: Calculate change over time -
### -
Table1CG <- Table1CG %>%
-
mutate( -
Change = circular ( ( (Table1CG[, as.character (datayear) ] - Table1CG[, as.graphic symbol (datayear- 1 ) ] ) / (Table1CG[, every bit.character (datayear- 1 ) ] ) )
-
* 100, digits= 1 )
-
) -
### -
#Step v: summate inflation adjustments -
### -
Table1CG <- Table1CG %>%
-
mutate( -
Y1_InflAdj = circular ( ( (Table1CG[, as.character (datayear- one ) ] * inflindex[ as.grapheme (datayear),] ) / (inflindex[ as.character (datayear- 1 ),] ) ), digits= three ),
-
Y2_InflAdj = round ( ( (Table1CG[, as.character (datayear) ] * inflindex[ as.character (datayear),] ) / (inflindex[ every bit.grapheme (datayear),] ) ), digits= 3 ),
-
ChangeInfl = round ( ( (Y2_InflAdj-Y1_InflAdj) /Y1_InflAdj) * 100, digits = 1 )
-
) -
### -
#Footstep 6: Format and fix final table -
### -
#Step 6a: omit numerical count columns from inflation adjustments -
Table1CG[ [ 5 ] ] [ 1 ] <- "--"
-
Table1CG[ [ 5 ] ] [ 5 ] <- "--"
-
Table1CG[ [ v ] ] [ 9 ] <- "--"
-
Table1CG[ [ 5 ] ] [ ten ] <- "--"
-
Table1CG[ [ vi ] ] [ 1 ] <- "--"
-
Table1CG[ [ 6 ] ] [ 5 ] <- "--"
-
Table1CG[ [ 6 ] ] [ ix ] <- "--"
-
Table1CG[ [ half-dozen ] ] [ ten ] <- "--"
-
Table1CG[ [ 7 ] ] [ one ] <- "--"
-
Table1CG[ [ 7 ] ] [ 5 ] <- "--"
-
Table1CG[ [ 7 ] ] [ 9 ] <- "--"
-
Table1CG[ [ 7 ] ] [ x ] <- "--"
-
#Pace 6b: rename columns -
colnames (Table1CG) [ 1 ] <- ""
-
colnames (Table1CG) [ 4 ] <- paste ( "% change, ", as.character (datayear - 1 ), "\u2013", as.character (datayear), sep = "" )
-
colnames (Table1CG) [ vii ] <- paste ( "% change, ", every bit.character (datayear - 1 ), "\u2013", as.character (datayear), " (inflation adjusted)", sep = "" )
-
#Stride 6c: reorder rows -
Table1CG <- Table1CG[ c ( 9,one,ii,3,4,x,5,6,7,viii ),]
-
#Stride 6d: return final output -
return (Table1CG)
-
} -
#Create Table 1 Current Growth (2015-2016) based on 2016 data -
Table1CG_2016 <- Table1CurGrowth(params$NCCSDataYr)
-
write.csv (Table1CG_2016, "Tables/NSiB_Table1_Appendix_Current_Growth.csv" )
All Nonprofit Organizations
Number
From 2006 to 2016, the number of nonprofit organizations registered with the IRS rose from 1.48 million to ane.54 million, an increase of four.5 pct. These one.54 one thousand thousand organizations incorporate a diverse range of nonprofits, including art, health, education, and advocacy nonprofits; labor unions; and business and professional associations. This broad spectrum, yet, 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 $five,000 in gross receipts are not required to register with the IRS, although many do.2 These unregistered organizations aggrandize the telescopic of the nonprofit sector across the 1.54 meg organizations this brief focuses on.
Finances
Approximately 35 percent of nonprofits registered with the IRS in 2016 were required to file a Form 990, Form 990-EZ, or Form 990-PF.iii These reporting nonprofits identified $2.62 trillion in revenues and $5.99 trillion in assets (table 1).four Between 2006 and 2016, reporting nonprofits experienced positive financial growth. Both revenues and assets grew faster than Gdp; after adjusting for aggrandizement revenues grew 24.2 percent and assets grew 30.9 percent, compared with 13.6 percent growth for national Gross domestic product during the aforementioned menstruation. Expenses grew xxx.7 percent between 2006 and 2016. In the short term, after adjusting for inflation, revenues grew 1.8 percent from $two.58 trillion in 2015 to $two.62 in 2016; assets increased 2.iii percent from $five.86 trillion to $5.99. Expenses besides grew from $2.39 trillion in 2015 to $2.48 in 2016, an increase of 3.half-dozen percentage.
Table i
Size and Scope of the Nonprofit Sector, 2006–2016
-
#Display Table 1 -
options (knitr.kable.NA = "" )
-
kable(Table1_2016, format.args = list (decimal.marker = '.', big.mark = "," ),
-
"html", -
row.names = False,
-
align = "lccccccc" ) %>%
-
kable_styling( "hover", full_width = F ) %>%
-
row_spec( c ( 1,half dozen ), bold = T ) %>%
-
row_spec( 3 : 5, italic = T ) %>%
-
row_spec( eight : ten, italic = T ) %>%
-
add_indent( c ( three,4,5,8,9,10 ) )
| 2006 | 2011 | % change, 2006–2011 | % change, 2006–2011 (aggrandizement adjusted) | 2016 | % alter, 2006–2016 | % change, 2006–2016 (aggrandizement adjusted) | |
|---|---|---|---|---|---|---|---|
| All registered nonprofits | 1,478,553.00 | i,447,471.00 | -2.ane | -- | one,544,812.00 | 4.5 | -- |
| Reporting nonprofits | 560,352.00 | 511,658.00 | -8.7 | -- | 533,112.00 | -4.9 | -- |
| Revenue ($ billions) | 1,773.29 | 2,099.69 | eighteen.4 | 6.i | ii,621.85 | 47.9 | 24.ii |
| Expenses ($ billions) | 1,591.87 | 1,987.fifteen | 24.8 | 11.9 | 2,476.83 | 55.6 | 30.7 |
| Assets ($ billions) | 3,845.30 | 4,611.08 | 19.9 | 7.5 | v,990.47 | 55.viii | 30.ix |
| 501(c)(three) public charities | 904,672.00 | 966,573.00 | 6.viii | -- | 1,081,969.00 | 19.half dozen | -- |
| Reporting public charities | 326,246.00 | 287,318.00 | -eleven.9 | -- | 318,015.00 | -2.five | -- |
| Revenue ($ billions) | 1,290.46 | 1,592.07 | 23.4 | x.6 | ii,041.50 | 58.2 | 32.9 |
| Expenses ($ billions) | 1,171.30 | ane,496.44 | 27.8 | fourteen.5 | 1,937.31 | 65.4 | 38.9 |
| Avails ($ billions) | 2,272.14 | ii,826.34 | 24.4 | 11.5 | 3,793.70 | 67.0 | 40.ii |
Sources: Urban Institute, National Center for Charitable Statistics, Core Files (2006, 2011, and 2016); 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 exercise so (had $25,000 or more than in gross receipts in 2006 and more $50,000 in gross receipts in 2011 and 2016). Organizations that had their revenue enhancement-exempt status revoked for failing to file a financial return for iii sequent years have been removed from the 2016 nonprofit full. Foreign organizations, government-associated organizations, and organizations without country identifiers have also been excluded. Unless noted, all amounts are in electric current dollars and are not adjusted for inflation.
Public Charities
Number
Public charities are the largest category of the more than thirty types of tax-exempt nonprofit organizations defined by the Internal Revenue Code. Classified nether 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 revenue enhancement-deductible donations. In 2016, well-nigh one.08 million organizations were classified every bit public charities, composing about two-thirds of all registered nonprofits. Between 2006 and 2016, the number of public charities grew 19.6 percent, faster than the growth of all registered nonprofits (4.5 percentage). Consequently, public charities made up a larger share of the nonprofit sector in 2015 (69.7 percent) than in 2005 (60 percentage).
The number of reporting public charities required to file a Form 990 or Grade 990-EZ grew slightly betwixt 2015 and 2016, showing an increase of i percent.
Finances
Almost three-fifths (59.7 percent) of all nonprofit organizations reporting to the IRS in 2016 were public charities. Accounting for more than three-quarters of revenue and expenses for the nonprofit sector, public charities reported $2.04 trillion in revenues and $1.94 trillion in expenses. Avails held by public charities accounted for just nether two-thirds of the sector'south full ($3.79 trillion).
Size
-
#create Figure ane Underlying table -
Fig1Table <- function (datayear) {
-
#select cadre file by yr -
file <- c ( paste ( "core", datayear, "pc", sep = "" ) )
-
#go core file -
dataset <- get ( file )
-
#filter out organizations below minimum filing threshold for 990-EZ -
dataset <- if (datayear < 2010 ) filter (dataset, ( (GRREC >= 25000 ) | (TOTREV> 25000 ) ) ) else filter (dataset, ( (GRREC >= 50000 ) | (TOTREV> 50000 ) ) )
-
#create table -
expstable <- dataset %>%
-
#filter past GRREC over threshold, not out, and FNDNCD != two,3,4 -
filter ( ( (GRREC >= 50000 ) | (TOTREV> 50000 ) ), (OUTNCCS != "OUT" ), (FNDNCD != "02" & FNDNCD!= "03" & FNDNCD != "04" ) ) %>%
-
#group past exps cat -
group_by(EXPCAT) %>%
-
#create summary values -
summarize( -
number_orgs = northward( ),
-
total_expenses = round ( ( sum (EXPS, na.rm =TRUE) / 1000000000 ), digits = two )
-
) %>%
-
#drop former variables, continue but categories and proportions -
mutate( -
year_of_data = every bit.character (datayear),
-
EXPCAT = EXPCAT, -
"Public charities" = circular ( ( (number_orgs/ sum (number_orgs) ) * 100 ),digits= i ),
-
"Full expenses" = round ( ( (total_expenses/ sum (total_expenses) ) * 100 ),digits= ane )
-
) -
#render output -
return (expstable) }
-
#Create figure 1 Based on 2015 data -
Figure1_2016 <- Fig1Table(params$NCCSDataYr)
-
write.csv (Figure1_2016, "Figures/NSiB_Figure1_Table.csv" )
Even after excluding organizations with gross receipts beneath the $l,000 filing threshold, small organizations composed the majority of public charities in 2016. Every bit shown in figure i beneath, 66.6 percent had less than $500,000 in expenses (211,782 organizations); they composed less than 2 per centum of full public charity expenditures ($32.8 billion). Though organizations with $10 million or more included but five.four percent of total public charities (17,063 organizations), they accounted for 88.1 percent of public charity expenditures ($ane.7 trillion).
FIGURE 1
Number and Expenses of Reporting Public Charities every bit a Percentage of All Reporting Public Charities and Expenses
-
#Create and Display Figure For 2016 Data -
Fig1Plot <- office (expstable) {
-
#select relevant fields -
expstable <- expstable[,c ( "year_of_data", "EXPCAT", "Public charities", "Total expenses" ) ]
-
#plot graph -
Fig1<- expstable %>%
-
#shift from broad to long -
melt( ) %>%
-
#create graph -
ggplot(aes(EXPCAT, value, fill=variable) ) +
-
geom_bar(stat= "identity", position= "dodge" ) +
-
geom_text(aes(EXPCAT, value, characterization= formatC ( round (value,1 ), format = 'f', digits = 1 ) ),
-
vjust=- 1,
-
position = position_dodge(width= 1 ),
-
size = 3 ) +
-
#labs( -
#championship = "Figure ane", -
#subtitle = paste("Number and Expenses of Reporting Public Charities as a Percent of All Reporting Public Charities and Expenses, ", expstable$year_of_data[one], sep =""), -
#caption = paste("Urban Found, National Center for Charitable Statistics, Cadre Files (Public Charities, " -
#, expstable$year_of_data[1], ")", sep ="")) + -
theme(centrality.title.y = element_blank( ),
-
centrality.text.y = element_blank( ),
-
axis.ticks.y = element_blank( ),
-
axis.championship.x = element_blank( ),
-
console.grid = element_blank( ) ) +
-
scale_y_continuous(expand = c ( 0, 0 ), limits = c ( 0,105 ) ) +
-
scale_x_discrete( labels = c ( "Nether $100,00", "$100,000 to $499,999", "$500,000 to $999,999", "$1 million to $4.99 million",
-
"$5 million to $ix.99 million", "$ten million or more" ) )
-
UrbCaption <- grobTree(
-
gp = gpar(fontsize = 8, hjust = ane ),
-
textGrob(label = "I N South T I T U T E",
-
name = "caption1",
-
x = unit of measurement( 1, "npc" ),
-
y = unit of measurement( 0, "npc" ),
-
hjust = one,
-
vjust = 0 ),
-
textGrob(characterization = "U R B A N ",
-
10 = unit( i, "npc" ) - grobWidth( "caption1" ) - unit( 0.01, "lines" ),
-
y = unit( 0, "npc" ),
-
hjust = i,
-
vjust = 0,
-
gp = gpar( col = "#1696d2" ) ) )
-
filigree.arrange (Fig1, UrbCaption, ncol = i, heights = c ( thirty, one ) )
-
} -
Fig1Plot(Figure1_2016)
Source: Urban Institute, National Center for Charitable Statistics, Core Files (Public Charities, 2016)
Type
-
#Create Table 2 Function -
Table2 <- function (datayear) {
-
#select core file based on year -
file <- c ( paste ( "cadre", datayear, "pc", sep = "" ) )
-
#get core file -
dataset <- get ( file )
-
#filter out organizations below minimum filing threshold for 990-EZ -
dataset <- if (datayear < 2010 ) filter (dataset, ( (GRREC >= 25000 ) | (TOTREV> 25000 ) ) ) else filter (dataset, ( (GRREC >= 50000 ) | (TOTREV> 50000 ) ) )
-
#create table -
Table2<- dataset %>%
-
filter ( (OUTNCCS != "OUT" ), (FNDNCD != "02" & FNDNCD!= "03" & FNDNCD != "04" ) ) %>%
-
group_by(NTEEGRP) %>%
-
summarize( -
Number_of_Orgs = north( ),
-
Revenue = round ( ( sum (TOTREV, na.rm =TRUE) ) / 1000000000, digits = one ),
-
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 = ane ),
-
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 columns -
Table2 <- Table2[,c ( "NTEEGRP", "Number_of_Orgs","Numbers_PCT","Revenue","Expenses", "Assets", "Revenue_PCT", "Expenses_PCT","Assets_PCT" ) ]
-
#Add total row -
myNumCols <- which ( unlist ( lapply (Table2, is.numeric ) ) )
-
Table2[ ( nrow (Table2) + 1 ), myNumCols] <- colSums (Table2[, myNumCols], na.rm =Truthful)
-
Table2$NTEEGRP[ xi ] = "All public charities"
-
#add All Ed and All health rows -
Table2[ 12,one ] = "Education"
-
Table2[ 12,2 : 9 ] <- Table2[ 3,2 : 9 ] + Table2[ 7,2 : 9 ]
-
Table2[ 13,1 ] = "Health"
-
Table2[ xiii,2 : 9 ] <- Table2[ 4,two : 9 ] + Table2[ eight,2 : 9 ]
-
#reorder tabular array with new rows -
t2order <- c ( "All public charities", "Arts", "Pedagogy", "College education", "Other didactics", "Surroundings and animals",
-
"Wellness", "Hospitals and principal care facilities", "Other health care", "Human services",
-
"International", "Other public and social benefit", "Religion related" )
-
Table2 <- Table2 %>%
-
slice( friction match (t2order, NTEEGRP) )
-
#add yr of data cavalcade -
Table2 <- cbind (year_of_data = as.grapheme (datayear), Table2)
-
return (Table2)
-
} -
#Run for Table 2 for 2015 data -
Table2_2016 <- Table2(params$NCCSDataYr)
-
write.csv (Table2_2016, "Tables/NSiB_Table2.csv" )
Tabular array 2 beneath displays the 2016 distribution of public charities by type of arrangement. Homo services groups—such as food banks, homeless shelters, youth services, sports organizations, and family or legal services—equanimous over one-tertiary of all public charities (35.2 percent). They were more than twice as numerous as pedagogy organizations, the next-almost prolific type of organization, which deemed for 17.ii pct of all public charities. Education organizations include booster clubs, parent-teacher associations, and financial aid groups, equally well as academic institutions, schools, and universities. Health intendance organizations, though accounting for only 12.2 per centum of reporting public charities, deemed for nigh three-fifths of public charity revenues and expenses in 2016. Education organizations deemed for 17.three percentage of revenues and 16.9 percent of expenses; human services, despite being more numerous, accounted for comparatively less revenue (eleven.9 percent of the full) and expenses (12.one percent of the total). Hospitals, despite representing only two.2 percent of total public charities (7,054 organizations), accounted for about one-half of all public charity revenues and expenses (49.8 and 50.vi pct, respectively).
TABLE ii
Number and Finances of Reporting Public Charities by Subsector, 2016
-
#Display Tabular array two -
kable(Table2_2016[ c ( 2 : 10 ) ], format.args = listing (decimal.mark = '.', big.marker = "," ),
-
"html", -
marshal = "lcccccccc",
-
col.names = c ( "", "Number", "% of total", "Revenues", "Expenses", "Assets", "Revenues", "Expenses", "Assets" ) ) %>%
-
kable_styling( "hover", full_width = F ) %>%
-
row_spec( c ( iv,5,8,9 ), italic = T ) %>%
-
row_spec( 1, bold = T ) %>%
-
add_indent( c ( 4,5,eight,nine ) ) %>%
-
add_header_above( c ( " " = iii, "Dollar Full ($ billions)" = 3, "Percentage of Total" = 3 ) )
| Dollar Total ($ billions) | Percentage of Total | |||||||
|---|---|---|---|---|---|---|---|---|
| Number | % of total | Revenues | Expenses | Assets | Revenues | Expenses | Assets | |
| All public charities | 318,015 | 100.1 | ii,041.five | i,937.3 | three,793.7 | 100.0 | 100.0 | 100.0 |
| Arts | 31,894 | 10.0 | 40.2 | 36.9 | 132.9 | 2.0 | i.ix | 3.v |
| Teaching | 54,632 | 17.ii | 353.viii | 327.9 | ane,144.8 | 17.iii | xvi.nine | 30.2 |
| Higher education | two,161 | 0.7 | 226.four | 213.4 | 740.6 | 11.i | 11.0 | xix.5 |
| Other education | 52,471 | sixteen.5 | 127.four | 114.v | 404.2 | half dozen.2 | 5.9 | 10.vii |
| Surroundings and animals | 14,932 | iv.7 | nineteen.viii | 17.two | l.8 | 1.0 | 0.9 | 1.3 |
| Health | 38,853 | 12.2 | 1,208.5 | 1,167.eight | 1,643.ane | 59.2 | 60.3 | 43.iii |
| Hospitals and main care facilities | 7,054 | 2.2 | 1,016.0 | 980.1 | i,339.1 | 49.viii | 50.6 | 35.3 |
| Other health intendance | 31,799 | 10.0 | 192.5 | 187.7 | 304.0 | 9.iv | 9.7 | viii.0 |
| Human services | 111,797 | 35.two | 243.0 | 234.five | 371.4 | 11.9 | 12.ane | nine.eight |
| International | 6,956 | 2.ii | 39.vii | 35.9 | 44.6 | 1.9 | 1.ix | 1.two |
| Other public and social benefit | 38,071 | 12.0 | 117.1 | 99.3 | 369.0 | 5.7 | 5.1 | 9.7 |
| Religion related | 20,880 | 6.6 | nineteen.4 | 17.8 | 37.1 | 1.0 | 0.9 | i.0 |
Source: Urban Institute, National Centre for Charitable Statistics, Core Files (Public Charities, 2016).
Note: Subtotals may non sum to totals because of rounding.
Growth
-
#Create Table 3 part -
Table3 <- function (datayear) {
-
#define years of interest -
T3grab = function (yr) {
-
output <- c ( paste ( "cadre", yr- 10, "pc", sep = "" ),
-
paste ( "cadre", yr- five, "pc", sep = "" ),
-
paste ( "core", yr, "pc", sep = "" ) )
-
return ( list (output) )
-
} -
#define financial summarizer -
T3Fin <- function (dataset, yr) {
-
df <- get (dataset)
-
#filter out organizations below minimum filing threshold for 990-EZ -
df <- 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( ),
-
Acquirement = round ( ( sum ( equally.numeric (TOTREV), na.rm =True) / 1000000000 ), digits = 1 ),
-
Expenses = round ( ( sum ( every bit.numeric (EXPS), na.rm =TRUE) / 1000000000 ), digits= ane ),
-
Avails = circular ( ( sum ( as.numeric (ASS_EOY), na.rm =TRUE) / 1000000000 ), digits= 1 )
-
) %>%
-
mutate( -
Acquirement = round ( (Acquirement * inflindex[ as.character (datayear),] ) / (inflindex[ as.character (twelvemonth),] ), digits = 1 ),
-
Expenses = round ( (Expenses * inflindex[ every bit.character (datayear),] ) / (inflindex[ as.character (year),] ), digits = 1 ),
-
Assets = round ( (Assets * inflindex[ equally.character (datayear),] ) / (inflindex[ as.character (twelvemonth),] ), digits = i )
-
) -
colnames (output) [ two : 5 ] <- paste ( colnames (output) [ 2 : 5 ], twelvemonth, sep = "_" )
-
return (output)
-
} -
#run grabber for years of interest -
T3years <-T3grab(datayear)
-
#pull each twelvemonth -
comp1 <- T3Fin(T3years[ [ 1 ] ] [ 1 ], (datayear- 10 ) )
-
comp2 <- T3Fin(T3years[ [ ane ] ] [ 2 ], (datayear- 5 ) )
-
comp3 <- T3Fin(T3years[ [ 1 ] ] [ three ], datayear)
-
#merge tables -
Table3 <- comp1 %>%
-
left_join(comp2, by = "NTEEGRP" ) %>%
-
left_join(comp3, by = "NTEEGRP" )
-
#reorder columns -
Table3IA <- Table3[, c ( i,2,6,ten,iii,7,eleven,4,viii,12,5,9,13 ) ]
-
#Add total row -
myNumCols <- which ( unlist ( lapply (Table3IA, is.numeric ) ) )
-
Table3IA[ ( nrow (Table3IA) + 1 ), myNumCols] <- colSums (Table3IA[, myNumCols], na.rm =Truthful)
-
Table3IA$NTEEGRP[ 11 ] = "All public charities"
-
#add together All Ed and All health rows -
Table3IA[ 12,1 ] = "Education"
-
Table3IA[ 12,ii : thirteen ] <- Table3IA[ 3,2 : 13 ] + Table3IA[ 7,two : 13 ]
-
Table3IA[ 13,one ] = "Health"
-
Table3IA[ 13,2 : xiii ] <- Table3IA[ 4,2 : 13 ] + Table3IA[ viii,2 : 13 ]
-
#reorder tabular array with new rows -
t3order <- c ( "All public charities", "Arts", "Education", "Higher education", "Other education", "Environment and animals",
-
"Wellness", "Hospitals and primary intendance facilities", "Other health intendance", "Homo services",
-
"International", "Other public and social benefit", "Religion related" )
-
Table3IA <- Table3IA %>%
-
slice( friction match (t3order, NTEEGRP) )
-
#add year of data column -
Table3IA <- cbind (year_of_data = as.character (datayear), Table3IA)
-
return (Table3IA)
-
} -
#Run Table 3 for 2016 data -
Table3_2016 <- Table3(params$NCCSDataYr)
-
write.csv (Table3_2016, "Tables/NSiB_Table3.csv" )
-
#################################################### -
#Create Table 4 office -
Table4 <- function (datayear) {
-
#start with table 3 data -
Table4 <- Table3(datayear)
-
#calculate percentage change fields -
Table4 <- Table4 %>%
-
mutate( -
RevAtoC = round ( ( (Table4[,8 ] - Table4[,6 ] ) / (Table4[,6 ] ) ) * 100,1 ),
-
RevAtoB = round ( ( (Table4[,seven ] - Table4[,6 ] ) / (Table4[,half-dozen ] ) ) * 100,ane ),
-
RevBtoC = circular ( ( (Table4[,8 ] - Table4[,7 ] ) / (Table4[,7 ] ) ) * 100,ane ),
-
ExpsAtoC = circular ( ( (Table4[,xi ] - Table4[,nine ] ) / (Table4[,9 ] ) ) * 100,ane ),
-
ExpsAtoB = round ( ( (Table4[,ten ] - Table4[,9 ] ) / (Table4[,9 ] ) ) * 100,1 ),
-
ExpsBtoC = round ( ( (Table4[,11 ] - Table4[,ten ] ) / (Table4[,ten ] ) ) * 100,1 ),
-
AssAtoC = circular ( ( (Table4[,14 ] -Table4[,12 ] ) / (Table4[,12 ] ) ) * 100,i ),
-
AssAtoB = round ( ( (Table4[,13 ] - Table4[,12 ] ) / (Table4[,12 ] ) ) * 100,1 ),
-
AssBtoC = round ( ( (Table4[,14 ] - Table4[,xiii ] ) / (Table4[,13 ] ) ) * 100,ane )
-
) -
#driblet intermediary raw number columns -
Table4 <- Table4[ - ( 3 : 14 ) ]
-
#rename columns by year -
colnames (Table4) [ three ] <- paste ( "Revenue", datayear- 10, "\u2013", datayear, sep = "_" )
-
colnames (Table4) [ 4 ] <- paste ( "Revenue", datayear- 10, "\u2013", datayear- v, sep = "_" )
-
colnames (Table4) [ five ] <- paste ( "Revenue", datayear- 5, "\u2013", datayear, sep = "_" )
-
colnames (Table4) [ 6 ] <- paste ( "Expenses", datayear- 10, "\u2013", datayear, sep = "_" )
-
colnames (Table4) [ vii ] <- paste ( "Expenses", datayear- x, "\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- x, "\u2013", datayear- five, sep = "_" )
-
colnames (Table4) [ xi ] <- paste ( "Assets", datayear- 5, "\u2013", datayear, sep = "_" )
-
#return output -
return (Table4)
-
} -
#Run Tabular array 4 for 2016 data -
Table4_2016 <- Table4(params$NCCSDataYr)
-
write.csv (Table4_2016,"Tables/NSiB_Table4.csv" )
The number of reporting public charities in 2016 was approximately 1 pct college than the number in 2015. The total revenues, expenses, and assets for reporting public charities all increased between 2015 and 2016; after adjusting for inflation, revenues rose 1.9 percent, expenses rose 4 percent, and avails rose 2.1 percentage.
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. Simply this growth has differed past subsector and period (table iii). Subsectors experienced varying degrees of financial expansion: although all subsectors reported increases in acquirement in 2016 compared with 2006 (even after adjusting for inflation), a few decreased in number of nonprofits, including arts, pedagogy (excluding college education), wellness, and other public and social do good organizations. Consequently, these organizations deemed for a slightly lower proportion of the full sector in 2016 (l.vii percent) than they did in 2006 (53.5 percent). The smallest subsectors (international and foreign affairs organizations and environs and animals organizations) saw the largest growth rates in the number of organizations, increasing 16 and 10.i percentage, respectively, from 2006 to 2016.
Financially, religion-related organizations had the largest proportional increase in both acquirement and expenses, growing from $13.2 billion in revenue in 2006 to $19.4 billion in 2016 after adjusting for inflation (a change of 47 percent). Environment and animals organizations experienced similar growth, growing from $14.half dozen billion in revenue in 2006 to $19.8 billion in 2016 subsequently adjusting for aggrandizement (a change of 35.half-dozen percent). Both types of organizations, all the same, still account for a very pocket-size proportion of overall nonprofit sector revenue in 2016, at just about ane percent each. Wellness-related organizations, which account for a much larger proportion of overall sector finances (59.two, 60.3 and 43.iii percent, respectively, of revenues, expenses, and avails), also experienced considerable growth between 2006 and 2016. Revenues for hospitals and primary care facilities, in particular, increased from $739.seven billion in 2006 to $1016 billion in 2016 after adjusting for inflation, past far the largest dollar growth of whatever subsector during this period. The growth for the health sector, $331.4 billion, accounts for over three-fifths of the growth of the entire nonprofit sector between 2006 and 2016 ($505.ane billion).
Table 3
Number, Revenues, and Assets of Reporting Public Charities past Subsector, 2006–2016 (adjusted for aggrandizement)
-
#Display Tabular array iii -
kable(Table3_2016[ c ( 2 : 14 ) ], format.args = list (decimal.marker = '.', big.mark = "," ),
-
"html", -
col.names = c ( "", "2006", "2011", "2016", "2006", "2011", "2016", "2006", "2011", "2016", "2006", "2011", "2016" ),
-
align = "lcccccccccccc" #,
-
) %>%
-
kable_styling( "hover", full_width = F ) %>%
-
row_spec( c ( 4,five,8,9 ), italic = T ) %>%
-
row_spec( 1, assuming = T ) %>%
-
add_indent( c ( 4,5,viii,ix ) ) %>%
-
add_header_above( c ( " ", "Number of Organizations" = three, "Acquirement ($ billions)" = 3, "Expenses ($ billions)" = iii, "Assets ($ billions)" = 3 ) )
| Number of Organizations | Revenue ($ billions) | Expenses ($ billions) | Assets ($ billions) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2006 | 2011 | 2016 | 2006 | 2011 | 2016 | 2006 | 2011 | 2016 | 2006 | 2011 | 2016 | |
| All public charities | 326,246 | 287,318 | 318,015 | 1,536.4 | 1,698.5 | 2,041.5 | i,394.5 | 1,596.7 | one,937.3 | 2,705.ii | three,015.seven | 3,793.vii |
| Arts | 36,065 | 28,579 | 31,894 | 35.four | 32.5 | 40.2 | 29.0 | 29.8 | 36.9 | 106.1 | 107.6 | 132.9 |
| Instruction | 58,663 | 49,223 | 54,632 | 273.vii | 286.8 | 353.viii | 225.ane | 259.8 | 327.9 | 835.viii | 906.viii | one,144.eight |
| College education | 1,933 | 2,013 | ii,161 | 179.1 | 186.5 | 226.4 | 147.seven | 169.four | 213.4 | 557.2 | 585.half dozen | 740.6 |
| Other pedagogy | 56,730 | 47,210 | 52,471 | 94.vi | 100.iii | 127.4 | 77.iv | ninety.4 | 114.v | 278.6 | 321.2 | 404.2 |
| Environment and animals | 13,565 | 12,547 | 14,932 | fourteen.6 | xv.eight | nineteen.eight | xi.9 | fourteen.2 | 17.ii | 34.9 | 38.ii | l.8 |
| Health | 41,753 | 37,828 | 38,853 | 877.1 | i,008.0 | 1,208.5 | 826.7 | 957.8 | ane,167.8 | 1,083.half dozen | 1,284.3 | ane,643.ane |
| Hospitals and principal care facilities | 7,266 | vii,093 | vii,054 | 739.7 | 854.5 | one,016.0 | 702.3 | 811.vii | 980.1 | 858.half-dozen | i,040.i | 1,339.1 |
| Other health intendance | 34,487 | 30,735 | 31,799 | 137.four | 153.5 | 192.five | 124.iv | 146.1 | 187.vii | 225.0 | 244.2 | 304.0 |
| Man services | 110,226 | 102,321 | 111,797 | 198.3 | 215.4 | 243.0 | 187.3 | 208.4 | 234.5 | 291.3 | 322.3 | 371.4 |
| International | 5,999 | half dozen,047 | 6,956 | 31.0 | 30.8 | 39.7 | 28.1 | xxx.0 | 35.nine | 31.7 | 31.nine | 44.6 |
| Other public and social do good | 40,029 | 33,365 | 38,071 | 93.1 | 94.7 | 117.one | 74.9 | 83.5 | 99.iii | 292.5 | 292.7 | 369.0 |
| Religion related | 19,946 | 17,408 | 20,880 | thirteen.ii | fourteen.5 | 19.4 | 11.5 | xiii.2 | 17.8 | 29.three | 31.ix | 37.one |
Source: Urban Institute, National Center for Charitable Statistics, Cadre Files (Public Charities, 2006, 2011, and 2016).
Notation: Subtotals may not sum to totals because of rounding.
Public charities' financial growth within the given bridge largely occurred within the second half (tabular array 4). From 2006 to 2011, revenue and assets for all public charities increased 10.half dozen and eleven.5 percent, respectively, but both grew much more quickly in the years following: 20.two percent for revenues and 25.eight percent for avails, later adjusting for inflation. Further, expenses grew much faster than revenues betwixt 2006 and 2011, with expenses increasing 14.5 per centum (compared with revenues increasing 10.6 per centum). Merely between 2011 and 2016 growth in expenses (21.3 percent) was outpaced past the growth in revenues (20.ii percent).
These periods of growth varied by subsector, however. Two subsectors experienced declining revenue betwixt 2006 and 2011: 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 $-1.6 billion in revenue from 2006 to 2011, a decline of -1.7 pct. However, both subsectors experienced substantial revenue increases from 2011 to 2016: revenue for other public and social do good organizations grew 23.7 percentage during those v years, while revenue for arts, civilization and humanities organizations grew 23.7 percent. Both revenue growth rates were well higher up the growth charge per unit for human services organizations, which at 12.8 percent was the lowest for any subsector within that menstruation.
Table 4
Per centum Modify in Revenue, Expenses, and Assets of Reporting Public Charities by Subsector, 2006–2016 (adjusted for inflation)
-
#Display Table 4 Information -
kable(Table4_2016[ c ( two : eleven ) ], format.args = list (decimal.mark = '.', large.mark = "," ),
-
"html", -
col.names = c ( "", paste ( "2006", "\u2014", "16", sep = "" ), paste ( "2006", "\u2014", "eleven", sep = "" ), paste ( "2011", "\u2014", "16", sep = "" ), paste ( "2006", "\u2014", "16", sep = "" ), paste ( "2006", "\u2014", "11", sep = "" ), paste ( "2011", "\u2014", "16", sep = "" ), paste ( "2006", "\u2014", "xvi", sep = "" ), paste ( "2006", "\u2014", "11", sep = "" ), paste ( "2011", "\u2014", "16", sep = "" ) ),
-
align = "lccccccccc" ) %>%
-
kable_styling( "hover", full_width = F ) %>%
-
row_spec( c ( four,5,8,9 ), italic = T ) %>%
-
row_spec( 1, bold = T ) %>%
-
add_indent( c ( 4,five,8,9 ) ) %>%
-
add_header_above( c ( " ", "Modify in Revenues" = 3, "Change in Expenses" = iii,"Change in Avails" = iii ) )
| Alter in Revenues | Change in Expenses | Change in Assets | |||||||
|---|---|---|---|---|---|---|---|---|---|
| 2006—16 | 2006—11 | 2011—16 | 2006—16 | 2006—xi | 2011—16 | 2006—sixteen | 2006—11 | 2011—16 | |
| All public charities | 32.nine | 10.6 | twenty.two | 38.9 | 14.5 | 21.three | 40.2 | 11.v | 25.8 |
| Arts | thirteen.6 | -8.2 | 23.7 | 27.2 | 2.8 | 23.8 | 25.3 | 1.4 | 23.5 |
| Education | 29.three | 4.8 | 23.4 | 45.seven | fifteen.4 | 26.ii | 37.0 | 8.5 | 26.2 |
| College education | 26.4 | iv.1 | 21.four | 44.five | fourteen.vii | 26.0 | 32.ix | five.1 | 26.five |
| Other educational activity | 34.vii | half dozen.0 | 27.0 | 47.9 | 16.8 | 26.vii | 45.1 | xv.iii | 25.eight |
| Environs and animals | 35.vi | viii.2 | 25.iii | 44.5 | 19.3 | 21.1 | 45.half dozen | ix.v | 33.0 |
| Wellness | 37.viii | 14.9 | nineteen.ix | 41.3 | 15.nine | 21.ix | 51.6 | 18.5 | 27.9 |
| Hospitals and primary intendance facilities | 37.iv | xv.5 | 18.9 | 39.half-dozen | 15.half dozen | 20.7 | 56.0 | 21.1 | 28.7 |
| Other health care | forty.1 | 11.vii | 25.iv | 50.9 | 17.4 | 28.five | 35.1 | eight.5 | 24.5 |
| Human being services | 22.5 | 8.6 | 12.8 | 25.2 | eleven.3 | 12.5 | 27.5 | ten.six | 15.2 |
| International | 28.1 | -0.6 | 28.9 | 27.8 | half dozen.8 | 19.7 | 40.7 | 0.vi | 39.viii |
| Other public and social benefit | 25.8 | 1.7 | 23.vii | 32.6 | 11.v | xviii.ix | 26.2 | 0.1 | 26.1 |
| Organized religion related | 47.0 | 9.8 | 33.8 | 54.8 | 14.8 | 34.8 | 26.6 | 8.nine | 16.three |
Source: Urban Establish, National Center for Charitable Statistics, Core Files (Public Charities, 2006, 2011, and 2016).
Note: Subtotals may not sum to totals because of rounding.
Back to pinnacle
Giving
Giving Amounts
-
#Create Figure 2 underlying table -
#Import Figure ii 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 aggrandizement -
Figure2 <- Figure2 %>%
-
mutate( -
'Abiding (2017) Dollars' = round ( (Current_Dollars * inflindex[ as.character ( 2018 ),] ) / (inflindex[ equally.character (Years),] ), digits = 2 )
-
) -
#Add Column Names -
colnames (Figure2) <- c ( "Yr", "Current dollars", "Constant (2018) dollars" )
-
Figure2 <- Figure2 %>%
-
melt(id = "Year" )
-
colnames (Figure2) [ 2 ] <- "Contributions"
-
#Write final table to CSV -
write.csv (Figure2, "Figures/NSiB_Figure2_Table.csv" )
Private charitable contributions reached an estimated $427.71 billion in 2018, as shown in figure 2 below (Giving United states Foundation 2019). Although total charitable giving has been increasing for four consecutive years, first with 2014. In 2018, total charitable giving decreased -i.vii percent from 2017.
FIGURE 2
Private Charitable Contributions 2000-2018
-
#Create Figure ii -
Fig2Plot <- role (Fig2Table) {
-
Fig2 <- Fig2Table %>%
-
ggplot(aes(10=Twelvemonth, y =value, make full = Contributions) ) +
-
geom_bar(position = "contrivance", stat = "identity" ) +
-
geom_text(aes(label = formatC ( round (value,ii ), format = 'f', digits = 2 ) ),
-
position= position_dodge(width= 1 ),
-
hjust =-.one, -
size= three ) +
-
scale_y_continuous(expand = c ( 0, 0 ), limits = c ( 0,460 ) ) +
-
scale_x_continuous(breaks = 2000 : 2018 ) +
-
theme(axis.text.x = element_blank( ),
-
axis.ticks.x = element_blank( ),
-
panel.filigree.major = element_blank( ) #,
-
# axis.title.y = element_text(angle=0) -
) +
-
labs( #title = "Effigy 2",
-
#subtitle = "Private Charitable Contributions, 2000-2016", -
#explanation = "Giving USA Foundation (2018)", -
x = "Year",
-
y = "" ) +
-
coord_flip( )
-
UrbCaption <- grobTree(
-
gp = gpar(fontsize = 8, hjust = ane ),
-
textGrob(label = "I Due north 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 Northward ",
-
10 = unit( 1, "npc" ) - grobWidth( "caption1" ) - unit( 0.01, "lines" ),
-
y = unit of measurement( 0, "npc" ),
-
hjust = 1,
-
vjust = 0,
-
gp = gpar( col = "#1696d2" ) ) )
-
grid.adapt (Fig2, UrbCaption, ncol = 1, heights = c ( 30, 1 ) )
-
} -
Fig2Plot(Figure2)
Source: Giving USA Foundation (2019).
Recipients
-
#Create Table 5 -
#Import raw Table five data (available from Giving Us 2018, https://givingusa.org/) -
Table5 <- read_csv( "External_Data/GivingUSAType.csv",
-
col_types= cols_only(Type = col_character( ),
-
Year2013 = col_double( ),
-
Year2018 = col_double( ) ) )
-
#Calculate percentage change -
Table5 <- Table5 %>%
-
mutate(PCt_change = Year2018 - Year2013)
-
#Rename Columns -
colnames (Table5) <- c ( "Charity type", "% of all contributions, 2013", "% of all contributions, 2018", paste ( "% bespeak change, 2013", "\u2013", "18", sep = "" ) )
-
#Write final table to CSV -
write.csv (Table5, "Tables/NSiB_Table5.csv" )
Congregations and religious organizations received just under a tertiary (29.6 percentage) of all charitable contributions in 2018 (table five), a lower proportion than they received five years earlier in 2013 (32.two percent). Instruction organizations received the next-highest share of private charitable contributions (13.ix per centum), which is the same proportion received in 2013 (also 13 percent of all donations). Man services organizations received the third-highest pro portion of all contributions in 2018 (12.2 percent), but this is a slight pass up from their 2013 proportion (12 percent). Gifts to individuals fabricated up the smallest proportion of full contributions in 2018: two.one percent.
Table 5
Charitable Contributions past Type of Recipient Organizations, 2018
-
#Display Tabular array five -
kable(Table5, format.args = list (decimal.mark = '.', big.mark = "," ),
-
"html", -
align = "lccc" ) %>%
-
kable_styling( "hover", full_width = F )
| Charity blazon | % of all contributions, 2013 | % of all contributions, 2018 | % signal change, 2013–xviii |
|---|---|---|---|
| Religion | 32.2 | 29.half-dozen | -two.6 |
| Education | 13.0 | 13.nine | 0.9 |
| Homo services | 12.0 | 12.ii | 0.two |
| Gifts to foundations | eleven.9 | xi.ix | 0.0 |
| Health | 9.four | 9.7 | 0.three |
| International diplomacy | five.vii | 5.4 | -0.three |
| Public-society benefit | 7.ane | seven.4 | 0.three |
| Arts, culture, and humanities | 4.3 | 4.6 | 0.three |
| Environment and animals | ii.5 | 3.0 | 0.v |
| Gifts to individuals | 2.1 | ii.2 | 0.1 |
Source: Giving USA Foundation (2019).
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(Twelvemonth = col_integer( ),
-
Foundations = col_integer( ),
-
Grants = col_double( ),
-
Avails = col_double( )
-
) )
-
#Suit for inflation -
Figure3 <- Figure3 %>%
-
mutate( -
Constant_Grants = round ( (Grants * inflindex[ as.character ( 2017 ),] ) / (inflindex[ as.character (Twelvemonth),] ), digits = 1 ),
-
Constant_Assets = circular ( (Assets * inflindex[ every bit.grapheme ( 2017 ),] ) / (inflindex[ every bit.character (Year),] ), digits = 1 )
-
) -
#write concluding tabular array to csv -
write.csv (Figure3, "Figures/NSiB_Figure3_Table.csv" )
The Foundation Heart (2019) estimates there were more than 86,125 grantmaking foundations in the United States in 2017. Their grants, a component of individual charitable contributions, totaled $77.7 billion in 2017, up 10.4 percent from 2016 after adjusting for aggrandizement (effigy 3). Between 2005 and 2017, foundation grantmaking increased 70 percent afterwards adjusting for inflation. Foundation avails also grew over the same period, increasing 46.6 percent from $691 billion in 2005 to $1012.9 billion in 2017 after adjusting for inflation.
FIGURE iii
Number of Foundations and Corporeality of Grants Made by Twelvemonth, 2005-2017
-
#Graph Figure three Table -
Fig3Plot <- part (Fig3Table) {
-
Fig3 <- Fig3Table %>%
-
ggplot(aes(x=Yr) ) +
-
geom_bar(aes(y=Foundations, fill up= "Foundations" ), stat = "identity" ) +
-
geom_line(aes(y=Constant_Grants* 1000, color = "Grants made" ), size = ii ) +
-
scale_y_continuous(aggrandize = c ( 0, 0 ), limits = c ( 0,100000 ),
-
sec.centrality = sec_axis(~./ 1000, name = "Grants made ($ billions)" ),
-
labels = scales:: comma ) +
-
scale_x_continuous(breaks = 2005 : 2017 ) +
-
labs( #caption = "The Foundation Middle, Foundation Stats (2019)",
-
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 = viii, hjust = 1 ),
-
textGrob(characterization = "I N S T I T U T E",
-
name = "caption1",
-
10 = unit of measurement( 1, "npc" ),
-
y = unit( 0, "npc" ),
-
hjust = 1,
-
vjust = 0 ),
-
textGrob(label = "U R B A North ",
-
x = unit of measurement( 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 = one, heights = c ( xxx, 1 ) )
-
} -
Fig3Plot(Figure3)
Source: The Foundation Eye, Foundation Stats (2019).
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Volunteering
-
#Calculate proportion of volunteering hours -
#Data taken from Bureau of Labor Statistics: American Time Apply Survey 2018 (https://www.bls.gov/tus/datafiles_2018.htm) -
#Data downloaded and saved locally, read in files: -
respondent18 <- read_csv( "External_Data/atusresp_2017.dat", na = "-1" )
-
activity18 <- read_csv( "External_Data/atussum_2017.dat", na = "-1" )
-
#Code to analyze American Fourth dimension Utilise Survey Information -
#Step 1: alter variable names to lowercase -
names (respondent18) <- tolower ( names (respondent18) )
-
names (activity18) <- tolower ( names (activity18) )
-
#Stride ii: join respondent and activity data -
atus18 <- left_join(respondent18, activity18, by = "tucaseid" )
-
#Step 3: Create volunteering subset by filtering cases without any volunteering hours -
atus18vol <- atus18 %>%
-
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 bachelor in 2017 ATUS) -
t159999> 0 |
-
t181501> 0 |
-
t181599> 0 )
-
#Step 4: summate weighted volunteering hours -
atus18vol <- atus18vol %>%
-
mutate( -
t150101w = tufinlwgt.x * t150101,
-
t150102w = tufinlwgt.ten * t150102,
-
t150103w = tufinlwgt.10 * t150103,
-
t150104w = tufinlwgt.x * t150104,
-
t150105w = tufinlwgt.x * t150105,
-
t150106w = tufinlwgt.x * t150106,
-
t150199w = tufinlwgt.x * t150199,
-
t150201w = tufinlwgt.10 * t150201,
-
t150202w = tufinlwgt.x * t150202,
-
t150203w = tufinlwgt.ten * t150203,
-
t150204w = tufinlwgt.x * t150204,
-
t150299w = tufinlwgt.x * t150299,
-
t150301w = tufinlwgt.x * t150301,
-
t150302w = tufinlwgt.ten * t150302,
-
t150399w = tufinlwgt.x * t150399,
-
t150401w = tufinlwgt.10 * 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, (annotation: commented out because not available in 2017 ATUS) -
#t150899w = tufinlwgt.x* t150899, (note: commented out because not available in 2017 ATUS) -
t159999w = tufinlwgt.10 * t159999,
-
t181501w = tufinlwgt.x * t181501,
-
t181599w = tufinlwgt.x * t181599
-
) -
#Pace 5: Create reduced file of merely weighted data -
atus18vol <- atus18vol %>%
-
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 non bachelor in 2017 ATUS) -
#t150899w, (notation: commented out because not bachelor in 2017 ATUS) -
t159999w,
-
t181501w,
-
t181599w,
-
tufinlwgt.x )
-
#Step 6: Create categorical groupings, number of volunteer hours -
atus18vol <- atus18vol %>%
-
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: Authoritative/Back up -
atus18vol$adminsupportprop <- ifelse ( (atus18vol$t150101w +
-
atus18vol$t150102w + -
atus18vol$t150103w + -
atus18vol$t150104w + -
atus18vol$t150105w + -
atus18vol$t150106w + -
atus18vol$t150199w) > 0,
-
atus18vol$tufinlwgt.x, -
0 )
-
#Stride 7b: Social service -
atus18vol$socialserviceprop <- ifelse ( (atus18vol$t150201w +
-
atus18vol$t150202w + -
atus18vol$t150203w + -
atus18vol$t150204w + -
atus18vol$t150299w) > 0,
-
atus18vol$tufinlwgt.x, -
0 )
-
#Step 7c: Maintenance -
atus18vol$maintenanceprop <- ifelse ( (atus18vol$t150301w +
-
atus18vol$t150302w + -
atus18vol$t150399w) > 0,
-
atus18vol$tufinlwgt.x, -
0 )
-
#Step 7d: Perform culture -
atus18vol$performcultureprop <- ifelse ( (atus18vol$t150401w +
-
atus18vol$t150402w + -
atus18vol$t150499w) > 0,
-
atus18vol$tufinlwgt.x, -
0 )
-
#Step 7e: Attend meetings -
atus18vol$attendmeetprop <- ifelse ( (atus18vol$t150501w+
-
atus18vol$t150599w) > 0,
-
atus18vol$tufinlwgt.x, -
0 )
-
#Step 7f: Public health -
atus18vol$pubhealthprop <- ifelse ( (atus18vol$t150601w +
-
atus18vol$t150602w + -
atus18vol$t150699w) > 0,
-
atus18vol$tufinlwgt.10, -
0 )
-
#Stride 7g: Waiting -
atus18vol$waitingprop <- ifelse ( (atus18vol$t150701w +
-
atus18vol$t150799w) > 0,
-
atus18vol$tufinlwgt.x, -
0 )
-
#Step 7h: Security -
#atus18vol$securityprop <- ifelse((atus18vol$t150801w) >0, -
#atus18vol$tufinlwgt.x, -
#0) -
#Step 7i: Travel -
atus18vol$travelprop <- ifelse ( (atus18vol$t181501w +
-
atus18vol$t181599w) > 0,
-
atus18vol$tufinlwgt.10, -
0 )
-
#Step 7j: Other -
atus18vol$othervolprop <- ifelse ( (atus18vol$t159999w) > 0,
-
atus18vol$tufinlwgt.x, -
0 )
-
#Step 8: Summarize number of hours/volunteers in each category -
atus18volsum<- atus18vol %>%
-
summarise( -
adminsupportprop = sum (adminsupportprop),
-
socialserviceprop = sum (socialserviceprop),
-
maintenanceprop = sum (maintenanceprop),
-
performcultureprop = sum (performcultureprop),
-
attendmeetprop = sum (attendmeetprop),
-
pubhealthprop = sum (pubhealthprop),
-
waitingprop = sum (waitingprop),
-
#securityprop = sum(securityprop), -
travelprop = sum (travelprop),
-
othervolprop = sum (othervolprop),
-
adminsupport = sum (adminsupport),
-
socialservice = sum (socialservice),
-
maintenance= sum (maintenance),
-
performculture = sum (performculture),
-
attendmeet = sum (attendmeet),
-
pubhealth = sum (pubhealth),
-
waiting = sum (waiting),
-
#security = sum(security), -
travel = sum (travel),
-
othervol = sum (othervol)
-
) -
#Step 9: Reduce to number of volunteer hours -
atus18volhours<- atus18volsum %>%
-
select(adminsupport, socialservice, maintenance, performculture, attendmeet, pubhealth, waiting, -
#security, -
travel, othervol) %>%
-
assemble(adminsupport, socialservice, maintenance, performculture, attendmeet, pubhealth, waiting, -
#security, -
travel, othervol,
-
key = "blazon",
-
value = "hours" )
-
#Step 10: rename columns -
atus18volhours$blazon[ grepl ( "adminsupport",atus18volhours$type ) ] <- "Administrative and back up"
-
atus18volhours$blazon[ grepl ( "socialservice",atus18volhours$type ) ] <- "Social service and care"
-
atus18volhours$type[ grepl ( "maintenance",atus18volhours$type ) ] <- "Maintenance, building, and cleanup"
-
atus18volhours$type[ grepl ( "performculture",atus18volhours$type ) ] <- "Performing and cultural activities"
-
atus18volhours$type[ grepl ( "attendmeet",atus18volhours$blazon ) ] <- "Meetings, conferences, and grooming"
-
atus18volhours$type[ grepl ( "pubhealth",atus18volhours$type ) ] <- "Public health and safety"
-
atus18volhours$blazon[ grepl ( "waiting",atus18volhours$type ) ] <- "Waiting"
-
#atus18volhours$blazon[grepl("security",atus18volhours$type )] <- "Security procedures" -
atus18volhours$blazon[ grepl ( "travel",atus18volhours$type ) ] <- "Travel"
-
atus18volhours$type[ grepl ( "othervol",atus18volhours$type ) ] <- "Other"
-
atus18volhours$blazon[ grepl ( "adminsupport",atus18volhours$type ) ] <- "Authoritative and support"
-
#Step 11: Calculate total -
atus18volhours[ ten,2 ] <- sum (atus18volhours$hours)
-
atus18volhours$type[ ten ] = "Total"
-
#Step 12: Calculate proportional number of hours per category -
atus18volhours <-atus18volhours %>%
-
mutate( -
AsPct = circular ( ( (hours/hours[ 10 ] ) * 100 ),i )
-
) -
#Step 12: Remane final underlying table and write to CSV -
Figure4 <- atus18volhours -
write.csv (Figure4, "Figures/NSiB_Figure4_Table.csv" )
-
#Read in Table 6 raw data -
#Based on United states of america Department of Labor, Bureau of Labor Statistics, Current Population Survey, Volunteer Supplement (2007-2015) (https://www.bls.gov/cps/dwelling.htm), -
#US Department of Labor, Agency of Labor Statistics, American Time Use Survey (2008-2017) (https://world wide web.bls.gov/tus/home.htm), -
#US Department of Labor, Agency of Labor Statistics, Current Employment Statistics (2017) (https://world wide web.bls.gov/ces/), and -
#US Census Bureau "Almanac Estimates of the Resident Population past Sex, Age, Race, and Hispanic Origin for the United States and States: April i,2010 to July 1, 2017", (https://factfinder.census.gov/) -
#Read in raw data, and write to CSV -
Table6 <- read_csv( "External_Data/Volunteering Data.csv" )
-
write.csv (Table6, "Tables/NSiB_Table6.csv" )
Volunteering is an of import component of the nonprofit sector: over two-fifths of public charities rely on volunteers. 5 In previous nonprofit sector briefs, volunteering estimates were based on data from the Electric current Population Survey (CPS). Volunteer statistics from the CPS Volunteer Supplement are not available after September 2015: electric current figures shown here for total hours volunteered and total number of volunteers are based on previous estimates. For ongoing volunteering information updates, please visit https://www.nationalservice.gov/serve/via 6
Number of Volunteers
An estimated 64.iv one thousand thousand adults, 25.1 percentage of the population volunteered at least once in 2017. The highest volunteer rate reported in the decade spanning from 2008 to 2017 was 26.8 percentage, which was reported in 2009 and 2011. The lowest volunteer rate was reported in 2015: 24.ix percentage.
The percentage of the population volunteering on a given day increased slightly in 2017, rising to vi percent from five.6 percentage in 2016. This rise occurs after 2016 saw the lowest proportion of the population volunteering on an average 24-hour interval inside the previous ten years: however, the 15.6 people volunteering on a given 24-hour interval represents an increase of over 1 meg daily volunteers from 2016. In the past decade, the highest proportion of Americans volunteering on a given 24-hour interval was in 2009, when 7.1 percentage of the population volunteered (17.1 people).
Hours Volunteered
Americans volunteered an estimated 64.iv hours in 2017, a slight increment from 63.ix hours in 2016. This amounts to nearly viii.8 hours per volunteer, slightly more than in 2016.
Volunteer Activities
Effigy 4 provides more information on how volunteers spent their time in 2018. The largest use of volunteer hours in 2018 was on social service and care activities (22 percent). These activities include such tasks equally preparing nutrient, collecting and delivering clothing or other goods, providing intendance, and teaching, counseling, or mentoring. Authoritative and support activities made upwards the next-largest proportion of volunteer fourth dimension (24.viii percent); this category includes things like computer use, telephone calls (except hotline counseling), writing, fundraising, and the similar. These ii categories of activities also led volunteer hours in 2017, although the proportion of time spent in social service and care activities has decreased slightly (from 24.8 percent) while the proportion of time spent in social administrative and support activities increased slightly (from 22 percent). Volunteers spent a larger proportion of their fourth dimension in performing or cultural activities and meetings, conferenecs, and trainings in 2018 than in 2017, while they spent less time in maintenance, building, and cleanup activities.
FIGURE 4
Distribution of Volunteer Fourth dimension by Acitivty, 2018 (percent)
-
#Brandish Figure 4 -
Fig4Plot <- part (Fig4Table) {
-
Fig4<- Fig4Table %>%
-
filter (type != "Full" ) %>%
-
#filter(blazon != "Security procedures") %>% #Filtered out considering equals 0% -
ggplot(aes(ten= reorder (type, -AsPct), y =AsPct) ) +
-
geom_bar(stat = "identity" ) +
-
geom_text(aes(label= formatC ( round (AsPct,one ), format = 'f', digits = ane ) ),
-
position= position_dodge(width= 1 ),
-
vjust =-.3, -
size= 3 ) +
-
scale_y_continuous(aggrandize = c ( 0, 0 ), limits = c ( 0,30 ) ) +
-
labs(y = "Percentage of total volunteer fourth dimension" ) +
-
theme(centrality.title = element_blank( ),
-
panel.filigree.major = element_blank( ),
-
centrality.text.y = element_blank( ) ) +
-
scale_x_discrete( labels = function (blazon) str_wrap(blazon, width= 10 ) )
-
UrbCaption <- grobTree(
-
gp = gpar(fontsize = 8, hjust = 1 ),
-
textGrob(label = "I Northward Southward T I T U T E",
-
proper noun = "caption1",
-
x = unit of measurement( 1, "npc" ),
-
y = unit( 0, "npc" ),
-
hjust = 1,
-
vjust = 0 ),
-
textGrob(characterization = "U R B A N ",
-
ten = unit( 1, "npc" ) - grobWidth( "caption1" ) - unit( 0.01, "lines" ),
-
y = unit( 0, "npc" ),
-
hjust = one,
-
vjust = 0,
-
gp = gpar( col = "#1696d2" ) ) )
-
grid.arrange (Fig4, UrbCaption, ncol = 1, heights = c ( xxx, 1 ) )
-
} -
Fig4Plot(Figure4)
Source: Author'due south calculations based on US Department of Labor, Bureau of Labor Statistics, American Time Employ Survey 2018.
Value of Volunteering
The time volunteers spent in 2017 was worth an estimated 256 (table 6). The value of volunteer fourth dimension combined with private giving deemed for over half a trillion dollars ($435.31 billion); volunteer time represents 12.9 pct of that full.
Tabular array 6
Number, Hours, and Dollar Value of Volunteers, 2008-2017
-
#Display Tabular array 6 -
kable(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 ( ane,7,11 ), assuming = T, hline_after = T )
| 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | |
|---|---|---|---|---|---|---|---|---|---|---|
| Per year | ||||||||||
| Percent of population volunteering | 26.iv | 26.eight | 26.3 | 26.8 | 26.5 | 25.4 | 25.three | 24.nine | 25.2 | 25.1 |
| Number of volunteers (millions) | 61.8 | 63.4 | 62.8 | 64.3 | 64.5 | 62.six | 62.8 | 62.6 | 63.nine | 64.4 |
| Hours volunteered (billions) | 8 | 8.one | 8.1 | 8.5 | 8.five | 8.three | 8.vii | 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 | fifty | l | 52 | -- | -- |
| Per average day | ||||||||||
| Percent of population volunteering | 6.8 | vii.one | 6.viii | 6 | v.eight | 6.1 | 6.four | 6.iv | 5.half-dozen | 6 |
| Number of volunteers (millions) | 16.ii | 17.1 | xvi.6 | 14.6 | 14.3 | 15.1 | 16 | xvi.three | 14.4 | 15.6 |
| Hours per day per volunteer | 2.43 | ii.39 | 2.46 | 2.84 | 2.48 | 2.57 | 2.41 | two.49 | two.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) | four.seven | 4.viii | 4.8 | 5 | 5 | 4.9 | five.i | five | 5.1 | 5.two |
| Assigned hourly wages for volunteers | $18.08 | $18.63 | $nineteen.07 | $19.47 | $xix.75 | $20.16 | $20.59 | $21.08 | $21.63 | $22.13 |
| Assigned value of volunteer time ($ billions) | $144.70 | $150.seventy | $154.10 | $164.80 | $168.30 | $167.20 | $179.20 | $179.00 | $187.40 | $195.00 |
Sources: Author's calculations based on data from United states 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 U.s.a. Section of Labor, Bureau of Labor Statistics, Current Employment Statistics (2016).
Notes: Median hours per volunteer not bachelor for 2016 – 17. Percent of population volunteering and hours volunteered for 2016 – 17 estimated based on previous years.
Back to superlative
Conclusion
Overall, in 2018, the nonprofit sector remained relatively salubrious with continuous financial growth and increases in the number of nonprofits throughout various subsectors. However, new data in charitable giving trends point to nuances worthof further exploration. Public charities composed over 2-thirds of all registered nonprofit organizations and deemed for over 3-quarters of the revenue andexpenses of the nonprofit sector in the United states. From 2011 to 2016, the number of nonprofit organizations registered with the IRS rose by four.five percent. Nonprofit revenues grew 1.viii percent; assets increased ii.3 percent; and expenses grew past 3.vi per centum.
While the nonprofit sector as a whole continues to see steady financial growth (with revenue, expenses, and assets all standing to grow)," U.S. giving data points to new nuances in charitable giving worth disentangling. While annual levels of giving remained relatively steady since the Great Recession, pointing to an overall increment of 0.7 percentage in giving between 2017 and 2018, after adjusting for inflation, individual charitable giving shows a down trend in full charitable giving. Disentangling this dash will aid better inform the overall state of nonprofit sector in the Usa.
References
Foundation Center. 2018. Foundation Stats (2014). New York: Foundation Center. http://data.foundationcenter.org/#/foundations/all/nationwide/full/list/2014
Giving United states of america Foundation. 2018. Giving USA 2018: The Almanac Report on Philanthropy for the Twelvemonth 2017. Bloomington, IN: Giving United states of america Foundation.
Acknowledgments
*This cursory was funded by the Urban Institute. The views expressed are those of the author and should non exist attributed to the Urban Plant, its trustees, or its funders. Funders exercise non make up one's mind 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
Dorsum to pinnacle
How Many Nonprofits Are Registered With The Internal Revenue Service (Irs) In 2016,
Source: https://nccs.urban.org/publication/nonprofit-sector-brief-2019
Posted by: samswortatuslege.blogspot.com
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