The Nonprofit Sector in Brief 2018

12.13.2018
Brice McKeever

More from this project:

  1. ######
  2. #Background Setup
  3. ######
  4.  
  5.  
  6. library(httr)
  7. library(tidyverse)
  8. library(stringr)
  9. library(RCurl)
  10. library(reshape2)
  11. library(RColorBrewer)
  12. library(extrafont)
  13. library(knitr)
  14. library(foreign)
  15. library(kableExtra)
  16. library(urbnthemes)
  17. library(grid)
  18. library(gridExtra)
  19.  
  20. #set_urban_defaults()
  21.  
  22. set_urban_defaults()
  23.  
  24. ######
  25. #Download Raw NCCS Data
  26. ######
  27.  
  28. #This code will use the following NCCS data sets, so import separately using defined functions, and save in the "Data" folder
  29.  
  30. #Retrieve NCCS Data Archive download functions
  31. source("NCCS_Code/Prep IRS BMF.R")
  32. source("NCCS_Code/Prep NCCS Core File.R")
  33.  
  34.  
  35. #The following code will retrieve the stated data sets from the NCCS Data Archive.
  36. #This code is commented out in final to avoid repeated (and bandwidth intensive) downloads
  37.  
  38. #IRS Business Master Files:
  39. #bm0601
  40. #bm0601 <- getbmffile("2006", "01")
  41. ##bm1106
  42. #bm1106 <- getbmffile("2011", "06")
  43. ##bm1502
  44. #bm1502 <- getbmffile("2015", "02")
  45. ##bm1602
  46. #bm1602 <- getbmffile("2016", "02")
  47. ##
  48. ##core2005pf
  49. #core2005pf <- getcorefile(2005, "pf")
  50. ##core2005pc
  51. #core2005pc <- getcorefile(2005, "pc")
  52. ##core2005co
  53. #core2005co <- getcorefile(2005, "co")
  54. #
  55. ##
  56. ##core2010pf
  57. #core2010pf <- getcorefile(2010, "pf")
  58. ##core2010pc
  59. #core2010pc <- getcorefile(2010, "pc")
  60. ##core2010co
  61. #core2010co <- getcorefile(2010, "co")
  62. #
  63. ##
  64. ##core2014pf
  65. #core2014pf <- getcorefile(2014, "pf")
  66. ##core2014pc
  67. #core2014pc <- getcorefile(2014, "pc")
  68. ##core2014co
  69. #core2014co <- getcorefile(2014, "co")
  70. #
  71. ##
  72. ##core2015pf
  73. #core2015pf <- getcorefile(2015, "pf")
  74. ##core2015pc
  75. #core2015pc <- getcorefile(2015, "pc")
  76. ##core2015co
  77. #core2015co <- getcorefile(2015, "co")
  78.  
  79.  
  80.  
  81. ######
  82. #Import Index Tables
  83. ######
  84.  
  85.  
  86. #The NTEE Lookup file can be downloaded from: http://nccs-data.urban.org/data/misc/nccs.nteedocAllEins.csv
  87. #The following code assumes that it has been saved in the local "Data" folder
  88.  
  89. #retrieve from CSV:
  90. nteedocalleins <- read_csv("Data/nteedocalleins.csv",
  91.                            col_types = cols_only(EIN = col_character(),
  92.                                                  NTEEFINAL = col_character()
  93.                                                  ))
  94.  
  95. #Inflation Index
  96.  
  97. #Load Inflation index table
  98. #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"
  99. #Updated April 2018, available at https://www.bls.gov/cpi/tables/supplemental-files/home.htm (Historical CPI-U)
  100. inflindex <- read.csv("External_Data/Inflation Index.csv", row.names =1, header = TRUE)
  101.  
  102.  
  103. #Create function to prepare and import selected BMF fields for analysis
  104. prepbmffile <- function(bmffilepath) {
  105.   output <- read_csv(bmffilepath,
  106.                      col_types = cols_only(EIN = col_character(),
  107.                                            NTEECC = col_character(),
  108.                                            STATE = col_character(),         
  109.                                            OUTNCCS = col_character(), 
  110.                                            SUBSECCD = col_character(),
  111.                                            FNDNCD = col_character(),
  112.                                            CFILER = col_character(),
  113.                                            CZFILER = col_character(),
  114.                                            CTAXPER = col_character(),
  115.                                            CTOTREV = col_double(),
  116.                                            CASSETS = col_double()
  117.                      ))
  118.   names(output) <- toupper(names(output))
  119.   return(output)
  120. }
  121.  
  122. #Create function to prepare and import selected NCCS Core PC/CO fields for analysis
  123. prepcorepcfile <- function(corefilepath) {
  124.   output <- read_csv(corefilepath,
  125.                      col_types = cols_only(EIN = col_character(),
  126.                                            OUTNCCS = col_character(),
  127.                                            SUBSECCD = col_character(),
  128.                                            FNDNCD = col_character(),
  129.                                            TOTREV = col_double(),
  130.                                            EXPS = col_double(),
  131.                                            ASS_EOY = col_double(),
  132.                                            GRREC = col_double()
  133.                      ))
  134.   names(output) <- toupper(names(output))
  135.   return(output)
  136. }
  137.  
  138.  
  139. #Create function to prepare and import selected NCCS Core PF fields for analysis
  140. prepcorepffile <- function(corefilepath) {
  141.   output <- read_csv(corefilepath,
  142.                      col_types = cols_only(EIN = col_character(),
  143.                                           OUTNCCS = col_character(),
  144.                                           SUBSECCD = col_character(),
  145.                                           FNDNCD = col_character(),
  146.                                           P1TOTREV = col_double(),
  147.                                           P1TOTEXP = col_double(),
  148.                                           P2TOTAST = col_double()
  149.                     ))
  150.     names(output) <- toupper(names(output))
  151.     return(output)
  152. }
  153.  
  154.  
  155. ######
  156. #Import and Prepare NCCS Data files
  157. #Note: data has already been saved locally using above code
  158. ######
  159.  
  160.  
  161. ###
  162. #BMF Data
  163. ###
  164.  
  165. #2005 BMF Data
  166. bmf2005 <-prepbmffile("Data/bm0601.csv")
  167.  
  168. #2010 BMF Data
  169. bmf2010 <-prepbmffile("Data/bm1106.csv")
  170.  
  171. #2014 BMF Data
  172. bmf2014 <-prepbmffile("Data/bm1502.csv")
  173.  
  174. #2015 BMF Data
  175. bmf2015 <-prepbmffile("Data/bm1602.csv")
  176.  
  177.  
  178. ###
  179. #Core Data
  180. ###
  181.  
  182. #
  183. #Core 2005 Data
  184. #
  185.  
  186. #PC
  187. core2005pc <- prepcorepcfile("Data/core2005pc.csv")
  188. #CO
  189. core2005co <- prepcorepcfile("Data/core2005co.csv")
  190. #PF
  191. core2005pf <- prepcorepffile("Data/core2005pf.csv")
  192.  
  193.  
  194. #
  195. #Core 2010 Data
  196. #
  197.  
  198. #PC
  199. core2010pc <- prepcorepcfile("Data/core2010pc.csv")
  200. #CO
  201. core2010co <- prepcorepcfile("Data/core2010co.csv")
  202. #PF
  203. core2010pf <- prepcorepffile("Data/core2010pf.csv")
  204.  
  205.  
  206. #
  207. #Core 2014 Data
  208. #
  209.  
  210. #PC
  211. core2014pc <- prepcorepcfile("Data/core2014pc.csv")
  212. #CO
  213. core2014co <- prepcorepcfile("Data/core2014co.csv")
  214. #PF
  215. core2014pf <- prepcorepffile("Data/core2014pf.csv")
  216.  
  217.  
  218. #
  219. #Core 2015 Data
  220. #
  221.  
  222. #PC
  223. core2015pc <- prepcorepcfile("Data/core2015pc.csv")
  224. #CO
  225. core2015co <- prepcorepcfile("Data/core2015co.csv")
  226. #PF
  227. core2015pf <- prepcorepffile("Data/core2015pf.csv")
  228.  
  229.  
  230. ######
  231. #Create Grouping Categories for Analysis by NTEE and Size
  232. ######
  233.  
  234. ###
  235. #NTEE Groupings
  236. ###
  237.  
  238. #Create NTEE grouping categories
  239. arts <- c("A")
  240. highered <- c("B4", "B5")
  241. othered <- c("B")
  242. envanimals <- c("C", "D")
  243. hospitals <- c('E20','E21','E22','E23','E24','F31','E30','E31','E32')
  244. otherhlth <- c("E", "F", "G", "H")
  245. humanserv <- c("I", "J", "K", "L", "M", "N", "O", "P")
  246. intl <- c("Q")
  247. pubben <- c("R", "S", "T", "U", "V", "W", "Y", "Z")
  248. relig <- c("X")
  249.  
  250. #define function to join NTEE Master list and categorize organizations accordingly
  251. NTEEclassify <- function(dataset) {
  252.   #merge in Master NTEE look up file
  253.   dataset <- dataset %>%
  254.     left_join(nteedocalleins, by = "EIN")
  255.   #create NTEEGRP classifications
  256.   dataset$NTEEGRP <- "  "
  257.   dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1) %in% arts ] <- "Arts"
  258.   dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1) %in% othered ] <- "Other education"
  259.   dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,2) %in% highered ] <- "Higher education"
  260.   dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1) %in% envanimals] <- "Environment and animals"
  261.   dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1) %in% otherhlth] <- "Other health care"
  262.   dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,3) %in% hospitals] <- "Hospitals and primary care facilities"
  263.   dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1) %in% humanserv] <- "Human services"
  264.   dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1) %in% intl] <- "International"
  265.   dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1) %in% pubben] <- "Other public and social benefit"
  266.   dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1) %in% relig] <- "Religion related"
  267.   dataset$NTEEGRP[is.na(dataset$NTEEFINAL)] <- "Other public and social benefit"
  268.   return(dataset)
  269. }
  270.  
  271. ###
  272. #Expense Groupings
  273. ###
  274.  
  275. #define function to classify organizations by expenses size
  276. EXPclassify <-function(dataset) {
  277.   dataset$EXPCAT <- " "
  278.   dataset$EXPCAT[dataset$EXPS<100000] <- "a. Under $100,000"
  279.   dataset$EXPCAT[dataset$EXPS >= 100000 & dataset$EXPS< 500000] <- "b. $100,000 to $499,999"
  280.   dataset$EXPCAT[dataset$EXPS >= 500000 & dataset$EXPS< 1000000] <- "c. $500,000 to $999,999"
  281.   dataset$EXPCAT[dataset$EXPS >= 1000000 & dataset$EXPS< 5000000] <- "d. $1 million to $4.99 million"
  282.   dataset$EXPCAT[dataset$EXPS >= 5000000 & dataset$EXPS< 10000000] <- "e. $5 million to $9.99 million"
  283.   dataset$EXPCAT[dataset$EXPS >= 10000000] <- "f. $10 million or more"
  284.   return(dataset)
  285.  
  286. }
  287.  
  288. ###
  289. #Apply Groupings to relevant data sets
  290. ###
  291.  
  292. #NTEE
  293. core2005pc <- NTEEclassify(core2005pc)
  294. core2010pc <- NTEEclassify(core2010pc)
  295. core2014pc <- NTEEclassify(core2014pc)
  296. core2015pc <- NTEEclassify(core2015pc)
  297.  
  298. #Expenses
  299.  
  300. core2005pc <-EXPclassify(core2005pc)
  301. core2010pc <-EXPclassify(core2010pc)
  302. core2014pc <-EXPclassify(core2014pc)
  303. core2015pc <-EXPclassify(core2015pc)
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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.

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Highlights

  • 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.


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Size and Scope of the Nonprofit Sector

  1. #Define Table 1 Function
  2. Table1 <- function(datayear) {
  3.  
  4.   ###
  5.   #Step1: Pull from raw bmf data to get Number of registered organizations
  6.   ###
  7.  
  8.   #Step1a: Create function to pull in BMF data
  9.   byear <- function(datayear) {
  10.  
  11.     #get BMF file names:
  12.     bmf1 <- as.character(paste("bmf", (datayear -10), sep =""))
  13.     bmf2 <- as.character(paste("bmf", (datayear -5), sep =""))
  14.     bmf3 <- as.character(paste("bmf", (datayear), sep =""))
  15.  
  16.     #for each BMF file name, run the following:
  17.     bcomponent <- function(bmfnum, year_of_int){
  18.  
  19.       #get dataset  
  20.       bmf <- get(bmfnum)
  21.  
  22.       #calculate all registered nonprofits
  23.       all <- bmf %>%
  24.         filter((OUTNCCS != "OUT")) %>%
  25.         summarize(
  26.           year = as.character(year_of_int),
  27.           "All registered nonprofits" = n()
  28.         ) 
  29.  
  30.       #calculate all public charities
  31.       pc <- bmf %>%  
  32.         filter((FNDNCD != "02" & FNDNCD!= "03" & FNDNCD != "04"), (SUBSECCD == "03"|SUBSECCD== "3"), (OUTNCCS != "OUT")) %>%
  33.         summarize(
  34.           year = as.character(year_of_int),
  35.           "501(c)(3) public charities" = n()
  36.         )
  37.  
  38.       #combine registered nonprofits and public charities
  39.       combined <- all %>%
  40.         left_join(pc, by = "year")
  41.  
  42.       #return combined file
  43.       return(combined)
  44.     }
  45.  
  46.     #run function for each year
  47.     bcomp1 <-bcomponent(bmf1, (datayear -10))
  48.     bcomp2 <-bcomponent(bmf2, (datayear -5))
  49.     bcomp3 <-bcomponent(bmf3, datayear)
  50.  
  51.     #merge years
  52.     total <- rbind(bcomp1, bcomp2, bcomp3)
  53.  
  54.     #return final
  55.     return(total)
  56.   }
  57.  
  58.   #Step 1b: run against year of interest:
  59.   btest<- byear(datayear)
  60.  
  61.   ###
  62.   #Step 2: pull correct core file years
  63.   ###
  64.  
  65.   #Step 2a: function to pull correct years starting from base year:
  66.   T1grab = function(yr) {
  67.     output <- c(yr-10, 
  68.                 yr-5, 
  69.                 yr)
  70.     return(list(output))
  71.   }
  72.  
  73.   #Step 2b: pull the right years:
  74.   T1years <-T1grab(datayear)
  75.  
  76.   #Step 2c: Function for individual years of core files
  77.  
  78.   T1Fin<-  function(datayear) {
  79.  
  80.     pcname <- as.character(paste("core", datayear, "pc", sep =""))
  81.     coname <- as.character(paste("core", datayear, "co", sep =""))
  82.     pfname <- as.character(paste("core", datayear, "pf", sep =""))
  83.  
  84.     pcfile <- get(pcname)
  85.     cofile <- get(coname)
  86.     pffile <- get(pfname)
  87.  
  88.     pcfile <- if(datayear < 2010) filter(pcfile, (GRREC >= 25000)) else filter(pcfile, ((GRREC >= 50000)|(TOTREV>50000)))
  89.     cofile <- if(datayear < 2010) filter(cofile, ((GRREC >= 25000)|(TOTREV>25000))) else filter(cofile, ((GRREC >= 50000)|(TOTREV>50000)))
  90.  
  91.     pc <-pcfile %>%
  92.       filter((is.na(OUTNCCS)|OUTNCCS != "OUT"), (FNDNCD != "02" & FNDNCD!= "03" & FNDNCD != "04")) %>%
  93.       summarize(
  94.         Reporting = n(),
  95.         "Revenue ($ billions)" = round((sum(as.numeric(TOTREV), na.rm =TRUE))/1000000000, digits =2),
  96.         "Expenses ($ billions)" = round((sum(as.numeric(EXPS), na.rm =TRUE))/1000000000, digits =2),
  97.         "Assets ($ billions)" = round((sum(as.numeric(ASS_EOY), na.rm =TRUE))/1000000000, digits=2))
  98.     pc <- melt(pc)
  99.     colnames(pc)[2] <- "PC"
  100.  
  101.     co <- cofile %>%
  102.       filter((OUTNCCS != "OUT")) %>%
  103.       summarize(
  104.         Reporting = n(),
  105.         "Revenue ($ billions)" = round((sum(as.numeric(TOTREV), na.rm =TRUE))/1000000000, digits =2),
  106.         "Expenses ($ billions)" = round((sum(as.numeric(EXPS), na.rm =TRUE))/1000000000, digits =2),
  107.         "Assets ($ billions)" = round((sum(as.numeric(ASS_EOY), na.rm =TRUE))/1000000000, digits=2))
  108.     co <- melt(co)
  109.     colnames(co)[2] <- "CO"
  110.  
  111.     pf <- pffile %>%
  112.       filter(OUTNCCS != "OUT") %>%
  113.       summarize(
  114.         Reporting = n(),
  115.         "Revenue ($ billions)" = round((sum(as.numeric(P1TOTREV), na.rm =TRUE))/1000000000, digits =2),
  116.         "Expenses ($ billions)" = round((sum(as.numeric(P1TOTEXP), na.rm =TRUE))/1000000000, digits =2),
  117.         "Assets ($ billions)" = round((sum(as.numeric(P2TOTAST), na.rm =TRUE))/1000000000, digits=2))
  118.     pf <- melt(pf)
  119.     colnames(pf)[2] <- "PF"
  120.  
  121.     Table1 <- pc %>%
  122.       left_join(co, by = "variable") %>%
  123.       left_join(pf, by = "variable") %>%
  124.       transmute(
  125.         variable = variable,
  126.         "Reporting nonprofits" = (PC+CO+PF),
  127.         "Reporting public charities" = PC)
  128.     Table1 <- melt(Table1)
  129.     colnames(Table1)[2]= "Type"
  130.     colnames(Table1)[3]= as.character(datayear)
  131.     Table1$variable <-ifelse(Table1$variable == "Reporting" & Table1$Type == "Reporting nonprofits", 
  132.                              "Reporting nonprofits", as.character(Table1$variable))
  133.     Table1$variable <-ifelse(Table1$variable == "Reporting" & Table1$Type == "Reporting public charities", 
  134.                              "Reporting public charities", as.character(Table1$variable))
  135.     return(Table1)
  136.   }
  137.  
  138.   #Step 2d: run core file function for each core file year:
  139.   comp1 <- T1Fin(T1years[[1]][1])
  140.   comp2 <- T1Fin(T1years[[1]][2])
  141.   comp3 <- T1Fin(T1years[[1]][3])
  142.  
  143.   #Step 2e: join multiple core file years together
  144.   Table1All <- comp1 %>%
  145.     left_join(comp2, by = c("Type", "variable")) %>%
  146.     left_join(comp3, by = c("Type", "variable")) 
  147.  
  148.   #Step 2f: drop intermediary column
  149.   Table1All <- Table1All[-2]
  150.  
  151.   ###
  152.   #Step 3 Merge with BMF data
  153.   ###
  154.  
  155.   AllRegNonprofits<- data.frame("All registered nonprofits", btest[[2]][1], btest[[2]][2], btest[[2]][3])
  156.   names(AllRegNonprofits) <- names(Table1All)
  157.   AllPCs<- data.frame("501(c)(3) public charities", btest[[3]][1], btest[[3]][2], btest[[3]][3])
  158.   names(AllPCs) <- names(Table1All)
  159.  
  160.   Table1All <- rbind(Table1All, AllRegNonprofits, AllPCs)
  161.  
  162.   ###
  163.   #Step 4: Calculate change over time
  164.   ###
  165.   Table1All <- Table1All %>%
  166.     mutate(
  167.       ChangeA = round(((Table1All[, as.character(datayear-5)] - Table1All[, as.character(datayear-10)])/(Table1All[, as.character(datayear-10)]))
  168.                       *100, digits=1),
  169.       ChangeB = round(((Table1All[, as.character(datayear)] - Table1All[, as.character(datayear-10)])/(Table1All[, as.character(datayear-10)]))
  170.                       *100, digits=1)
  171.     )
  172.  
  173.   ###
  174.   #Step 5: calculate inflation adjustments
  175.   ###
  176.   Table1All <- Table1All %>%
  177.     mutate(
  178.       Y1 = round(((Table1All[, as.character(datayear-10)] * inflindex[as.character(datayear),])/(inflindex[as.character(datayear-10),])), digits=3),
  179.       Y2 = round(((Table1All[, as.character(datayear-5)] * inflindex[as.character(datayear),])/(inflindex[as.character(datayear-5),])), digits=3),
  180.       Y3 = round(((Table1All[, as.character(datayear)] * inflindex[as.character(datayear),])/(inflindex[as.character(datayear),])), digits=3),
  181.       ChangeAInfl = round(((Y2-Y1)/Y1)*100, digits = 1),
  182.       ChangeBInfl = round(((Y3-Y1)/Y1)*100, digits = 1)
  183.     )
  184.  
  185.   ###
  186.   #Step 6: Format and prepare final table
  187.   ###
  188.  
  189.   #Step 6a: remove intermediary columns
  190.   Table1All[7:9] <- list(NULL)
  191.  
  192.   #Step 6b: reorder columns to fit Nonprofit Sector in Brief
  193.   Table1All <- Table1All[, c(1,2,3,5,7,4,6,8)]
  194.  
  195.   #Step 6c: omit numerical count columns from inflation adjustments
  196.   Table1All[[5]][1] <- "--"
  197.   Table1All[[5]][5] <- "--"
  198.   Table1All[[8]][1] <- "--"
  199.   Table1All[[8]][5] <- "--"
  200.   Table1All[[5]][9] <- "--"
  201.   Table1All[[5]][10] <- "--"
  202.   Table1All[[8]][9] <- "--"
  203.   Table1All[[8]][10] <- "--"
  204.  
  205.   #Step 6d: rename columns
  206.   colnames(Table1All)[1] <- ""
  207.   colnames(Table1All)[4] <- paste("% change, ", as.character(datayear -10), "\u2013", as.character(datayear - 5), sep = "")
  208.   colnames(Table1All)[5] <- paste("% change, ", as.character(datayear -10), "\u2013", as.character(datayear - 5), " (inflation adjusted)", sep = "")
  209.   colnames(Table1All)[7] <- paste("% change, ", as.character(datayear -10), "\u2013", as.character(datayear ), sep = "")
  210.   colnames(Table1All)[8] <- paste("% change, ", as.character(datayear -10), "\u2013", as.character(datayear ), " (inflation adjusted)", sep = "")
  211.  
  212.   #Step6e: reorder rows
  213.   Table1All <- Table1All[c(9,1,2,3,4,10,5,6,7,8),]
  214.  
  215.   #Step 6f: return final output
  216.   return(Table1All)
  217. }
  218.  
  219. #Create Table 1 based on 2015 data
  220. Table1_2015 <-Table1(params$NCCSDataYr)
  221. write.csv(Table1_2015, "Tables/NSiB_Table1.csv")
  222.  
  223.  
  224.  
  225. #Define Table 1 Current Growth Function (Appendix Table Showing only most recent growth)
  226. Table1CurGrowth <- function(datayear) {
  227.   ###
  228.   #Step1: Pull from raw BMF data to get Number of registered organizations
  229.   ###
  230.  
  231.   #Step1a: Create function
  232.   byear <- function(datayear) {
  233.  
  234.     #get BMF file names:
  235.     bmf1 <- as.character(paste("bmf", (datayear -1), sep =""))
  236.     bmf2 <- as.character(paste("bmf", (datayear), sep =""))
  237.  
  238.     #for each BMF file name, run the following:
  239.     bcomponent <- function(bmfnum, year_of_int){
  240.  
  241.       #get dataset  
  242.       bmf <- get(bmfnum)
  243.  
  244.       #calculate all registered nonprofits
  245.       all <- bmf %>%
  246.         filter((OUTNCCS != "OUT")) %>%
  247.         summarize(
  248.           year = as.character(year_of_int),
  249.           "All registered nonprofits" = n()
  250.         ) 
  251.  
  252.       #calculate all public charities
  253.       pc <- bmf %>%  
  254.         filter((FNDNCD != "02" & FNDNCD!= "03" & FNDNCD != "04"), (SUBSECCD == "03"|SUBSECCD== "3"), (OUTNCCS != "OUT")) %>%
  255.         summarize(
  256.           year = as.character(year_of_int),
  257.           "501(c)(3) public charities" = n()
  258.         )
  259.  
  260.       #combine registered nonprofits and public charities
  261.       combined <- all %>%
  262.         left_join(pc, by = "year")
  263.  
  264.       #return combined file
  265.       return(combined)
  266.     }
  267.  
  268.     #run function for each year
  269.     bcomp1 <-bcomponent(bmf1, (datayear -1))
  270.     bcomp2 <-bcomponent(bmf2, (datayear))
  271.  
  272.     #merge years
  273.     total <- rbind(bcomp1, bcomp2)
  274.  
  275.     #return final
  276.     return(total)
  277.   }
  278.  
  279.   #Step 1b: run against year of interest:
  280.   btest<- byear(datayear) 
  281.  
  282.   ###
  283.   #Step 2: Pull NCCS Core File data
  284.   ###
  285.  
  286.   #Step 2a: function to pull correct years starting from base year:
  287.   T1grab = function(yr) {
  288.     output <- c(yr-1, 
  289.                 yr)
  290.     return(list(output))
  291.   }
  292.  
  293.   #Step 2b: pull the right years:
  294.   T1years <-T1grab(datayear)  
  295.  
  296.   #Step 2c: Function for individual years of core files
  297.  
  298.   T1Fin<-  function(datayear) {  
  299.  
  300.     pcname <- as.character(paste("core", datayear, "pc", sep =""))
  301.     coname <- as.character(paste("core", datayear, "co", sep =""))
  302.     pfname <- as.character(paste("core", datayear, "pf", sep =""))
  303.  
  304.     pcfile <- get(pcname)
  305.     cofile <- get(coname)
  306.     pffile <- get(pfname)
  307.  
  308.     pcfile <- if(datayear < 2010) filter(pcfile, (GRREC >= 25000)) else filter(pcfile, ((GRREC >= 50000)|(TOTREV>50000)))
  309.     cofile <- if(datayear < 2010) filter(cofile, ((GRREC >= 25000)|(TOTREV>25000))) else filter(cofile, ((GRREC >= 50000)|(TOTREV>50000)))
  310.  
  311.     pc <-pcfile %>%
  312.       filter((is.na(OUTNCCS)|OUTNCCS != "OUT"), (FNDNCD != "02" & FNDNCD!= "03" & FNDNCD != "04")) %>%
  313.       summarize(
  314.         Reporting = n(),
  315.         "Revenue ($ billions)" = round((sum(as.numeric(TOTREV), na.rm =TRUE))/1000000000, digits =2),
  316.         "Expenses ($ billions)" = round((sum(as.numeric(EXPS), na.rm =TRUE))/1000000000, digits =2),
  317.         "Assets ($ billions)" = round((sum(as.numeric(ASS_EOY), na.rm =TRUE))/1000000000, digits=2))
  318.     pc <- melt(pc)
  319.     colnames(pc)[2] <- "PC"
  320.  
  321.     co <- cofile %>%
  322.       filter((OUTNCCS != "OUT")) %>%
  323.       summarize(
  324.         Reporting = n(),
  325.         "Revenue ($ billions)" = round((sum(as.numeric(TOTREV), na.rm =TRUE))/1000000000, digits =2),
  326.         "Expenses ($ billions)" = round((sum(as.numeric(EXPS), na.rm =TRUE))/1000000000, digits =2),
  327.         "Assets ($ billions)" = round((sum(as.numeric(ASS_EOY), na.rm =TRUE))/1000000000, digits=2))
  328.     co <- melt(co)
  329.     colnames(co)[2] <- "CO"
  330.  
  331.     pf <- pffile %>%
  332.       filter(OUTNCCS != "OUT") %>%
  333.       summarize(
  334.         Reporting = n(),
  335.         "Revenue ($ billions)" = round((sum(as.numeric(P1TOTREV), na.rm =TRUE))/1000000000, digits =2),
  336.         "Expenses ($ billions)" = round((sum(as.numeric(P1TOTEXP), na.rm =TRUE))/1000000000, digits =2),
  337.         "Assets ($ billions)" = round((sum(as.numeric(P2TOTAST), na.rm =TRUE))/1000000000, digits=2))
  338.     pf <- melt(pf)
  339.     colnames(pf)[2] <- "PF"
  340.  
  341.     Table1 <- pc %>%
  342.       left_join(co, by = "variable") %>%
  343.       left_join(pf, by = "variable") %>%
  344.       transmute(
  345.         variable = variable,
  346.         "Reporting nonprofits" = (PC+CO+PF),
  347.         "Reporting public charities" = PC)
  348.     Table1 <- melt(Table1)
  349.     colnames(Table1)[2]= "Type"
  350.     colnames(Table1)[3]= as.character(datayear)
  351.     Table1$variable <-ifelse(Table1$variable == "Reporting" & Table1$Type == "Reporting nonprofits", 
  352.                              "Reporting nonprofits", as.character(Table1$variable))
  353.     Table1$variable <-ifelse(Table1$variable == "Reporting" & Table1$Type == "Reporting public charities", 
  354.                              "Reporting public charities", as.character(Table1$variable))
  355.     return(Table1)
  356.   }
  357.  
  358.   #Step 2d: run core file function for each core file year:
  359.   comp1 <- T1Fin(T1years[[1]][1])
  360.   comp2 <- T1Fin(T1years[[1]][2])
  361.  
  362.   #Setp 2e: join multiple core file years together
  363.   Table1CG <- comp1 %>%
  364.     left_join(comp2, by = c("Type", "variable"))
  365.  
  366.   #Step 2f: drop intermediary column
  367.   Table1CG <- Table1CG[-2]
  368.  
  369.   ####
  370.   #Step 3: Merge with BMF data
  371.   ###
  372.  
  373.   AllRegNonprofits<- data.frame("All registered nonprofits", btest[[2]][1], btest[[2]][2])
  374.   names(AllRegNonprofits) <- names(Table1CG)
  375.   AllPCs<- data.frame("501(c)(3) public charities", btest[[3]][1], btest[[3]][2])
  376.   names(AllPCs) <- names(Table1CG)
  377.  
  378.   Table1CG <- rbind(Table1CG, AllRegNonprofits, AllPCs)
  379.  
  380.   ###
  381.   #Step 4: Calculate change over time
  382.   ###
  383.   Table1CG <- Table1CG %>%
  384.     mutate(
  385.       Change = round(((Table1CG[, as.character(datayear)] - Table1CG[, as.character(datayear-1)])/(Table1CG[, as.character(datayear-1)]))
  386.                       *100, digits=1)
  387.     )  
  388.  
  389.   ###
  390.   #Step 5: calculate inflation adjustments
  391.   ###
  392.   Table1CG <- Table1CG %>%
  393.     mutate(
  394.       Y1_InflAdj = round(((Table1CG[, as.character(datayear-1)] * inflindex[as.character(datayear),])/(inflindex[as.character(datayear-1),])), digits=3),  
  395.       Y2_InflAdj = round(((Table1CG[, as.character(datayear)] * inflindex[as.character(datayear),])/(inflindex[as.character(datayear),])), digits=3),
  396.       ChangeInfl = round(((Y2_InflAdj-Y1_InflAdj)/Y1_InflAdj)*100, digits = 1)
  397.     )
  398.  
  399.   ###
  400.   #Step 6: Format and prepare final table
  401.   ###
  402.  
  403.   #Step 6a: omit numerical count columns from inflation adjustments
  404.   Table1CG[[5]][1] <- "--"
  405.   Table1CG[[5]][5] <- "--"
  406.   Table1CG[[5]][9] <- "--"
  407.   Table1CG[[5]][10] <- "--"
  408.   Table1CG[[6]][1] <- "--"
  409.   Table1CG[[6]][5] <- "--"
  410.   Table1CG[[6]][9] <- "--"
  411.   Table1CG[[6]][10] <- "--"
  412.   Table1CG[[7]][1] <- "--"
  413.   Table1CG[[7]][5] <- "--"
  414.   Table1CG[[7]][9] <- "--"
  415.   Table1CG[[7]][10] <- "--"
  416.  
  417.   #Step 6b: rename columns
  418.   colnames(Table1CG)[1] <- ""
  419.   colnames(Table1CG)[4] <- paste("% change, ", as.character(datayear -1), "\u2013", as.character(datayear), sep = "")
  420.   colnames(Table1CG)[7] <- paste("% change, ", as.character(datayear -1), "\u2013", as.character(datayear), " (inflation adjusted)", sep = "")
  421.  
  422.   #Step 6c: reorder rows
  423.   Table1CG <- Table1CG[c(9,1,2,3,4,10,5,6,7,8),]
  424.  
  425.   #Step 6d: return final output
  426.   return(Table1CG)
  427. }
  428.  
  429. #Create Table 1 Current Growth (2014-2015) based on 2015 data
  430. Table1CG_2015 <- Table1CurGrowth(params$NCCSDataYr)
  431. 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

  1. #Display Table 1
  2. options(knitr.kable.NA ="")
  3. kable(Table1_2015, format.args = list(decimal.mark = '.', big.mark = ","), 
  4.       "html", 
  5.       row.names = FALSE,
  6.       align = "lccccccc") %>%
  7.   kable_styling("hover", full_width = F) %>%
  8.   row_spec(c(1,6), bold = T ) %>%
  9.   row_spec(3:5, italic = T) %>%
  10.   row_spec(8:10, italic = T) %>%
  11.   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

  1. #create Figure 1 Underlying table
  2.  
  3. Fig1Table <- function(datayear) { 
  4.   #select core file by year
  5.   file <-  c(paste("core", datayear, "pc", sep ="")) 
  6.  
  7.   #get core file
  8.   dataset <- get(file)
  9.  
  10.   #filter out organizations below minimum filing threshold for 990-EZ
  11.   dataset <- if(datayear < 2010) filter(dataset, ((GRREC >= 25000)|(TOTREV>25000))) else filter(dataset, ((GRREC >= 50000)|(TOTREV>50000))) 
  12.  
  13.   #create table
  14.   expstable <- dataset %>% 
  15.     #filter by GRREC over threshold, not out, and FNDNCD != 2,3,4
  16.     filter(((GRREC >= 50000)|(TOTREV>50000)), (OUTNCCS != "OUT"), (FNDNCD != "02" & FNDNCD!= "03" & FNDNCD != "04")) %>% 
  17.     #group by exps cat
  18.     group_by(EXPCAT) %>%
  19.     #create summary values
  20.     summarize( 
  21.       number_orgs = n(),
  22.       total_expenses = round((sum(EXPS, na.rm =TRUE)/1000000000), digits =2)
  23.     ) %>% 
  24.     #drop old variables, keep only categories and proportions
  25.     mutate(
  26.       year_of_data = as.character(datayear),
  27.       EXPCAT = EXPCAT,
  28.       "Public charities" = round(((number_orgs/sum(number_orgs))*100),digits=1),
  29.       "Total expenses" = round(((total_expenses/sum(total_expenses))*100),digits=1)
  30.     )  
  31.   #return output
  32.   return(expstable)}
  33.  
  34. #Create figure 1 Based on 2015 data
  35. Figure1_2015 <- Fig1Table(params$NCCSDataYr)
  36. 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

  1. #Create and Display Figure For 2015 Data 
  2. Fig1Plot <- function(expstable) {
  3.  
  4.   #select relevant fields
  5.   expstable <- expstable[,c("year_of_data", "EXPCAT", "Public charities", "Total expenses")]
  6.  
  7.   #plot graph
  8.   Fig1<- expstable %>%  
  9.     #shift from wide to long
  10.     melt() %>% 
  11.     #create graph
  12.     ggplot(aes(EXPCAT, value, fill=variable))+
  13.     geom_bar(stat="identity", position="dodge") +
  14.     geom_text(aes(EXPCAT, value, label=formatC(round(value,1), format = 'f', digits =1)),
  15.               vjust=-1, 
  16.               position = position_dodge(width=1),
  17.               size =3) +
  18.     #labs(
  19.       #title = "Figure 1",
  20.       #subtitle =  paste("Number and Expenses of Reporting Public Charities as a Percentage \nof All Reporting Public Charities and Expenses, ", expstable$year_of_data[1], sep =""),
  21.       #caption = paste("Urban Institute, National Center for Charitable Statistics, Core Files (Public Charities, "
  22.                          #, expstable$year_of_data[1], ")", sep ="")) +
  23.     theme(axis.title.y = element_blank(),
  24.           axis.text.y = element_blank(),
  25.           axis.ticks.y = element_blank(),
  26.           axis.title.x = element_blank(),
  27.           panel.grid = element_blank()) +
  28.     scale_y_continuous(expand = c(0, 0), limits = c(0,105)) +
  29.     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",
  30.                                "$5 million to \n$9.99 million", "$10 million \nor more")) 
  31.  
  32.  
  33.   UrbCaption <- grobTree(
  34.     gp = gpar(fontsize = 8, hjust = 1), 
  35.     textGrob(label = "I N S T I T U T E", 
  36.              name = "caption1",
  37.              x = unit(1, "npc"),  
  38.              y = unit(0, "npc"),
  39.              hjust = 1, 
  40.              vjust = 0),
  41.     textGrob(label = "U R B A N  ", 
  42.              x = unit(1, "npc") - grobWidth("caption1") - unit(0.01, "lines"),         
  43.              y = unit(0, "npc"), 
  44.              hjust = 1, 
  45.              vjust = 0, 
  46.              gp = gpar(col = "#1696d2")))
  47.  
  48.  
  49.   grid.arrange(Fig1, UrbCaption, ncol = 1, heights = c(30, 1))
  50.  
  51.  
  52. }
  53.  
  54. Fig1Plot(Figure1_2015)

Figure_1

Source: Urban Institute, National Center for Charitable Statistics, Core Files (Public Charities, 2015)

Type

  1. #Create Table 2 Function
  2. Table2 <- function(datayear) {
  3.   #select core file based on year
  4.   file <-  c(paste("core", datayear, "pc", sep =""))
  5.  
  6.   #get core file
  7.   dataset <- get(file)
  8.  
  9.   #filter out organizations below minimum filing threshold for 990-EZ
  10.   dataset <- if(datayear < 2010) filter(dataset, ((GRREC >= 25000)|(TOTREV>25000))) else filter(dataset, ((GRREC >= 50000)|(TOTREV>50000)))
  11.  
  12.   #create table
  13.   Table2<- dataset %>%
  14.     filter((OUTNCCS != "OUT"), (FNDNCD != "02" & FNDNCD!= "03" & FNDNCD != "04")) %>%
  15.     group_by(NTEEGRP) %>%
  16.     summarize( 
  17.       Number_of_Orgs = n(),
  18.       Revenue = round((sum(TOTREV, na.rm =TRUE))/1000000000, digits =1),
  19.       Expenses = round((sum(EXPS, na.rm =TRUE))/1000000000, digits =1),
  20.       Assets = round((sum(ASS_EOY, na.rm =TRUE))/1000000000, digits=1)) %>%
  21.     mutate(
  22.       Revenue_PCT = round((Revenue/sum(Revenue)) *100, digits =1),
  23.       Expenses_PCT = round((Expenses/sum(Expenses)) *100, digits =1),
  24.       Assets_PCT = round((Assets/sum(Assets)) *100, digits =1),
  25.       Numbers_PCT = round((Number_of_Orgs/sum(Number_of_Orgs)) *100, digits =1)
  26.     )
  27.  
  28.   #reorder columns
  29.   Table2 <- Table2[,c("NTEEGRP", "Number_of_Orgs","Numbers_PCT","Revenue","Expenses", "Assets", "Revenue_PCT", "Expenses_PCT","Assets_PCT")]
  30.  
  31.   #Add total row
  32.   myNumCols <- which(unlist(lapply(Table2, is.numeric)))
  33.   Table2[(nrow(Table2) + 1), myNumCols] <- colSums(Table2[, myNumCols], na.rm=TRUE)
  34.   Table2$NTEEGRP[11] = "All public charities"
  35.  
  36.   #add All Ed and All health rows
  37.   Table2[12,1] = "Education"
  38.   Table2[12,2:9] <- Table2[3,2:9] + Table2[7,2:9]
  39.   Table2[13,1] = "Health"
  40.   Table2[13,2:9] <- Table2[4,2:9] + Table2[8,2:9]
  41.  
  42.   #reorder table with new rows
  43.   t2order <- c("All public charities", "Arts", "Education", "Higher education", "Other education", "Environment and animals",
  44.                "Health", "Hospitals and primary care facilities", "Other health care", "Human services",
  45.                "International", "Other public and social benefit", "Religion related")
  46.  
  47.   Table2 <- Table2 %>%
  48.     slice(match(t2order, NTEEGRP))
  49.  
  50.   #add year of data column
  51.   Table2 <- cbind(year_of_data = as.character(datayear), Table2)
  52.  
  53.   return(Table2)
  54.  
  55. }
  56.  
  57. #Run for Table 2 for 2015 data
  58. Table2_2015 <- Table2(params$NCCSDataYr)
  59. 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

  1. #Display Table 2
  2. kable(Table2_2015[c(2:10)], format.args = list(decimal.mark = '.', big.mark = ","), 
  3.      "html",
  4.       align = "lcccccccc",
  5.       col.names = c("", "Number", "% of total", "Revenues", "Expenses", "Assets", "Revenues", "Expenses", "Assets")) %>%
  6.   kable_styling("hover", full_width = F) %>%
  7.   row_spec(c(4,5,8,9), italic = T ) %>%
  8.   row_spec(1, bold = T ) %>%
  9.   add_indent(c(4,5,8,9)) %>%
  10.   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

  1. #Create Table 3 function
  2. Table3 <- function(datayear) {
  3.  
  4.   #define years of interest
  5.   T3grab = function(yr) {
  6.     output <- c(paste("core", yr-10, "pc", sep = ""), 
  7.                 paste("core", yr-5, "pc", sep =""),
  8.                 paste("core", yr, "pc", sep =""))
  9.     return(list(output))
  10.   }
  11.  
  12.  
  13.   #define financial summarizer
  14.   T3Fin <- function(dataset, year) {
  15.     df <- get(dataset)
  16.  
  17.     #filter out organizations below minimum filing threshold for 990-EZ
  18.     df <- if(year < 2010) filter(df, ((GRREC >= 25000)|(TOTREV>25000))) else filter(df, ((GRREC >= 50000)|(TOTREV>50000)))
  19.  
  20.     output <- df %>%
  21.       filter((OUTNCCS != "OUT"), (FNDNCD != "02" & FNDNCD!= "03" & FNDNCD != "04")) %>%
  22.       group_by(NTEEGRP) %>%
  23.       summarize( 
  24.         Number_of_Orgs = n(),
  25.         Revenue = round((sum(as.numeric(TOTREV), na.rm =TRUE)/1000000000), digits =1),
  26.         Expenses = round((sum(as.numeric(EXPS), na.rm =TRUE)/1000000000), digits=1),
  27.         Assets = round((sum(as.numeric(ASS_EOY), na.rm =TRUE)/1000000000), digits=1)
  28. ) %>%
  29.       mutate(
  30.         Revenue = round((Revenue * inflindex[as.character(datayear),])/(inflindex[as.character(year),]), digits =1),
  31.                 Expenses = round((Expenses * inflindex[as.character(datayear),])/(inflindex[as.character(year),]), digits =1),
  32.         Assets = round((Assets * inflindex[as.character(datayear),])/(inflindex[as.character(year),]), digits =1)
  33.       )  
  34.     colnames(output)[2:5] <- paste(colnames(output)[2:5], year, sep = "_")
  35.     return(output)
  36.   }
  37.  
  38.   #run grabber for years of interest
  39.   T3years <-T3grab(datayear)
  40.  
  41.   #pull each year
  42.   comp1 <- T3Fin(T3years[[1]][1], (datayear-10))
  43.   comp2 <- T3Fin(T3years[[1]][2], (datayear-5))
  44.   comp3 <- T3Fin(T3years[[1]][3], datayear)
  45.  
  46.   #merge tables
  47.   Table3 <- comp1 %>%
  48.     left_join(comp2, by = "NTEEGRP") %>%
  49.     left_join(comp3, by = "NTEEGRP")
  50.  
  51.   #reorder columns
  52.   Table3IA <- Table3[, c(1,2,6,10,3,7,11,4,8,12,5,9,13)]
  53.  
  54.   #Add total row
  55.   myNumCols <- which(unlist(lapply(Table3IA, is.numeric)))
  56.   Table3IA[(nrow(Table3IA) + 1), myNumCols] <- colSums(Table3IA[, myNumCols], na.rm=TRUE)
  57.   Table3IA$NTEEGRP[11] = "All public charities"
  58.  
  59.   #add All Ed and All health rows
  60.   Table3IA[12,1] = "Education"
  61.   Table3IA[12,2:13] <- Table3IA[3,2:13] + Table3IA[7,2:13]
  62.   Table3IA[13,1] = "Health"
  63.   Table3IA[13,2:13] <- Table3IA[4,2:13] + Table3IA[8,2:13]
  64.  
  65.   #reorder table with new rows
  66.   t3order <- c("All public charities", "Arts", "Education", "Higher education", "Other education", "Environment and animals",
  67.                "Health", "Hospitals and primary care facilities", "Other health care", "Human services",
  68.                "International", "Other public and social benefit", "Religion related")
  69.  
  70.   Table3IA <- Table3IA %>%
  71.     slice(match(t3order, NTEEGRP))
  72.  
  73.   #add year of data column
  74.   Table3IA <- cbind(year_of_data = as.character(datayear), Table3IA)
  75.  
  76.   return(Table3IA)
  77. }
  78.  
  79. #Run Table 3 for 2015 data
  80. Table3_2015 <- Table3(params$NCCSDataYr)
  81. write.csv(Table3_2015, "Tables/NSiB_Table3.csv")
  82.  
  83. ####################################################
  84.  
  85. #Create Table 4 function
  86. Table4 <- function(datayear) {
  87.  
  88.   #start with table 3 data
  89.   Table4 <- Table3(datayear)
  90.  
  91.   #calculate percentage change fields
  92.   Table4 <- Table4 %>%
  93.     mutate(
  94.       RevAtoC = round(((Table4[,8] - Table4[,6] )/(Table4[,6] )) *100,1),
  95.       RevAtoB = round(((Table4[,7]-  Table4[,6] )/(Table4[,6] )) *100,1),
  96.       RevBtoC = round(((Table4[,8]-  Table4[,7] )/(Table4[,7] )) *100,1),
  97.       ExpsAtoC =round(((Table4[,11] - Table4[,9] )/(Table4[,9] )) *100,1),
  98.       ExpsAtoB =round(((Table4[,10]-  Table4[,9] )/(Table4[,9] )) *100,1),
  99.       ExpsBtoC =round(((Table4[,11]-  Table4[,10] )/(Table4[,10] )) *100,1),
  100.       AssAtoC = round(((Table4[,14] -Table4[,12] )/(Table4[,12] )) *100,1),
  101.       AssAtoB = round(((Table4[,13]- Table4[,12] )/(Table4[,12] )) *100,1),
  102.       AssBtoC = round(((Table4[,14]- Table4[,13])/(Table4[,13] ))*100,1)
  103.  
  104.     )
  105.  
  106.   #drop intermediary raw number columns
  107.   Table4 <- Table4[-(3:14)]
  108.  
  109.   #rename columns by year
  110.   colnames(Table4)[3] <- paste("Revenue", datayear-10, "\u2013", datayear, sep = "_")
  111.   colnames(Table4)[4] <- paste("Revenue", datayear-10, "\u2013", datayear-5, sep = "_")
  112.   colnames(Table4)[5] <- paste("Revenue", datayear-5, "\u2013", datayear, sep = "_")
  113.   colnames(Table4)[6] <- paste("Expenses", datayear-10, "\u2013", datayear, sep = "_")
  114.   colnames(Table4)[7] <- paste("Expenses", datayear-10, "\u2013", datayear-5, sep = "_")
  115.   colnames(Table4)[8] <- paste("Expenses", datayear-5, "\u2013", datayear, sep = "_")
  116.   colnames(Table4)[9] <- paste("Assets", datayear-10, "\u2013", datayear, sep = "_")
  117.   colnames(Table4)[10] <- paste("Assets", datayear-10, "\u2013", datayear-5, sep = "_")
  118.   colnames(Table4)[11] <- paste("Assets", datayear-5, "\u2013", datayear, sep = "_")
  119.  
  120.   #return output
  121.   return(Table4)
  122.  
  123. }
  124.  
  125. #Run Table 4 for 2015 data
  126. Table4_2015 <- Table4(params$NCCSDataYr)
  127. 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)

  1. #Display Table 3
  2. kable(Table3_2015[c(2:14)], format.args = list(decimal.mark = '.', big.mark = ","), 
  3.       "html",
  4.       col.names = c("", "2005", "2010", "2015", "2005", "2010", "2015", "2005", "2010", "2015", "2005", "2010", "2015"),
  5.       align = "lcccccccccccc"#,
  6.           )%>%
  7.   kable_styling("hover", full_width = F) %>%
  8.   row_spec(c(4,5,8,9), italic = T ) %>%
  9.   row_spec(1, bold = T ) %>%
  10.   add_indent(c(4,5,8,9)) %>%
  11.   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)

  1. #Display Table 4 Data
  2. kable(Table4_2015[c(2:11)], format.args = list(decimal.mark = '.', big.mark = ","), 
  3.       "html",
  4.       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 = "")),
  5.       align = "lccccccccc") %>%
  6.   kable_styling("hover", full_width = F) %>%
  7.   row_spec(c(4,5,8,9), italic = T ) %>%
  8.   row_spec(1, bold = T ) %>%
  9.   add_indent(c(4,5,8,9)) %>%
  10.   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.


Back to top

Giving

Giving Amounts

  1. #Create Figure 2 underlying table
  2.  
  3. #Import Figure 2 raw data (available from Giving USA 2018, https://givingusa.org/)
  4. Figure2 <- read_csv("External_Data/GivingUSACont.csv",
  5.                           col_types = cols_only(Years = col_integer(),
  6.                                                 Current_Dollars = col_double()
  7.                                                                           ))
  8. #Adjust for inflation
  9. Figure2 <- Figure2 %>%
  10.   mutate(
  11.     'Constant (2017) Dollars' = round((Current_Dollars * inflindex[as.character(2017),])/(inflindex[as.character(Years),]), digits =2)
  12.   )
  13. #Add Column Names
  14. colnames(Figure2)<- c("Year", "Current dollars", "Constant (2017) dollars")
  15.  
  16. Figure2 <- Figure2 %>%
  17.   melt(id = "Year")
  18.  
  19. colnames(Figure2)[2] <- "Contributions"
  20.  
  21. #Write final table to CSV
  22. write.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

  1. #Create Figure 2
  2. Fig2Plot <- function(Fig2Table) {
  3.  
  4.   Fig2 <- Fig2Table %>%
  5.     ggplot(aes(x=Year, y =value, fill = Contributions)) +
  6.     geom_bar(position = "dodge", stat = "identity") +
  7.     geom_text(aes(label = formatC(round(value,2), format = 'f', digits =2)),
  8.               position= position_dodge(width=1), 
  9.               hjust =-.1,
  10.               size=3) +
  11.     scale_y_continuous(expand = c(0, 0), limits = c(0,450)) +
  12.     scale_x_continuous(breaks = 2000:2017)+
  13.     theme(axis.text.x = element_blank(),
  14.           axis.ticks.x = element_blank(),
  15.           panel.grid.major = element_blank()#,
  16.          # axis.title.y = element_text(angle=0)
  17.           ) +
  18.     labs(#title = "Figure 2",
  19.          #subtitle = "Private Charitable Contributions, 2000-2016",
  20.          #caption = "Giving USA Foundation (2018)",
  21.          x = "Year",
  22.          y = "") +
  23.     coord_flip()
  24.  
  25.   UrbCaption <- grobTree(
  26.     gp = gpar(fontsize = 8, hjust = 1), 
  27.     textGrob(label = "I N S T I T U T E", 
  28.              name = "caption1",
  29.              x = unit(1, "npc"),  
  30.              y = unit(0, "npc"),
  31.              hjust = 1, 
  32.              vjust = 0),
  33.     textGrob(label = "U R B A N  ", 
  34.              x = unit(1, "npc") - grobWidth("caption1") - unit(0.01, "lines"),         
  35.              y = unit(0, "npc"), 
  36.              hjust = 1, 
  37.              vjust = 0, 
  38.              gp = gpar(col = "#1696d2")))
  39.  
  40.  
  41.   grid.arrange(Fig2, UrbCaption, ncol = 1, heights = c(30, 1))
  42. }
  43.  
  44. Fig2Plot(Figure2)

NSiB Figure 2
Source: Giving USA Foundation (2018).

Recipients

  1. #Create Table 5
  2.  
  3. #Import raw Table 5 data (available from Giving USA 2018, https://givingusa.org/)
  4. Table5 <- read_csv("External_Data/GivingUSAType.csv",
  5.                    col_types= cols_only(Type = col_character(),
  6.                                         Year2012 = col_double(),
  7.                                         Year2017 = col_double()))
  8. #Calculate percentage change
  9. Table5 <- Table5 %>%
  10.   mutate(PCt_change = Year2017 - Year2012)
  11.  
  12. #Rename Columns
  13. colnames(Table5)<- c("Charity type", "% of all contributions, 2012", "% of all contributions, 2017", paste("% point change, 2012", "\u2013", "17", sep =""))
  14.  
  15. #Write final table to CSV
  16. write.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

  1. #Display Table 5
  2. kable(Table5, format.args = list(decimal.mark = '.', big.mark = ","), 
  3.       "html",
  4.       align = "lccc") %>%
  5.   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

  1. #Import Raw Figure 3 data (available from the Foundation Center Foundation Stats, http://data.foundationcenter.org/)
  2. Figure3 <- read_csv("External_Data/FoundationCenter.csv",
  3.                     col_types = cols_only(Year = col_integer(),
  4.                                           Foundations = col_integer(),
  5.                                           Grants = col_double(),
  6.                                           Assets = col_double()
  7.                     ))
  8. #Adjust for inflation
  9. Figure3 <- Figure3 %>%
  10.   mutate(
  11.     Constant_Grants = round((Grants * inflindex[as.character(2015),])/(inflindex[as.character(Year),]), digits =1),
  12.     Constant_Assets = round((Assets * inflindex[as.character(2015),])/(inflindex[as.character(Year),]), digits =1) 
  13.   )
  14.  
  15. #write final table to csv
  16. write.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

  1. #Graph Figure 3 Table
  2. Fig3Plot <- function(Fig3Table) {
  3.  
  4.   Fig3 <- Fig3Table %>%
  5.     ggplot(aes(x=Year)) +
  6.     geom_bar(aes(y=Foundations, fill= "Foundations"), stat = "identity") +
  7.     geom_line(aes(y=Constant_Grants*1000, color = "Grants made"), size = 2) +
  8.     scale_y_continuous(expand = c(0, 0), limits = c(0,100000),
  9.                        sec.axis = sec_axis(~./1000, name = "Grants made ($ billions)"),
  10.                        labels = scales::comma) +
  11.     scale_x_continuous(breaks = 2005:2015)+
  12.     labs(#caption = "The Foundation Center, Foundation Stats (2018)",
  13.          x = "Year",
  14.          y = "Number of foundations") +
  15.     scale_color_manual("", values = c("Foundations" = "#1696d2", "Grants made" = "black")) +
  16.     scale_fill_manual("  ", values = "#1696d2")
  17.  
  18.   UrbCaption <- grobTree(
  19.     gp = gpar(fontsize = 8, hjust = 1), 
  20.     textGrob(label = "I N S T I T U T E", 
  21.              name = "caption1",
  22.              x = unit(1, "npc"),  
  23.              y = unit(0, "npc"),
  24.              hjust = 1, 
  25.              vjust = 0),
  26.     textGrob(label = "U R B A N  ", 
  27.              x = unit(1, "npc") - grobWidth("caption1") - unit(0.01, "lines"),         
  28.              y = unit(0, "npc"), 
  29.              hjust = 1, 
  30.              vjust = 0, 
  31.              gp = gpar(col = "#1696d2")))
  32.  
  33.   grid.arrange(Fig3, UrbCaption, ncol = 1, heights = c(30, 1))
  34. }
  35.  
  36. Fig3Plot(Figure3)

Figure_3
Source: The Foundation Center, Foundation Stats (2018).


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Volunteering

  1. #Calculate proportion of volunteering hours
  2. #Data taken from Bureau of Labor Statistics: American Time Use Survey 2017 (https://www.bls.gov/tus/datafiles_2017.htm)
  3.  
  4. #Data downloaded and saved locally, read in files:
  5. respondent17 <- read_csv("External_Data/atusresp_2017.dat", na = "-1")
  6. activity17 <- read_csv("External_Data/atussum_2017.dat", na = "-1")
  1. #Code to analyze American Time Use Survey Data
  2.  
  3. #Step 1: change variable names to lowercase
  4. names(respondent17) <- tolower(names(respondent17))
  5. names(activity17) <- tolower(names(activity17))
  6.  
  7. #Step 2: join respondent and activity data
  8. atus17 <- left_join(respondent17, activity17, by = "tucaseid")
  9.  
  10. #Step 3: Create volunteering subset by filtering cases without any volunteering hours
  11. atus17vol <- atus17 %>%
  12.   filter(t150101>0 | 
  13.            t150102>0 | 
  14.            t150103>0 | 
  15.            t150104>0 | 
  16.            t150105>0 | 
  17.            t150106>0 | 
  18.            t150199>0 | 
  19.            t150201>0 | 
  20.            t150202>0 | 
  21.            t150203>0 | 
  22.            t150204>0 | 
  23.            t150299>0 | 
  24.            t150301>0 | 
  25.            t150302>0 | 
  26.            t150399>0 | 
  27.            t150401>0 | 
  28.            t150402>0 | 
  29.            t150499>0 | 
  30.            t150501>0 | 
  31.            t150599>0 | 
  32.            t150601>0 | 
  33.            t150602>0 | 
  34.            t150699>0 | 
  35.            t150701>0 | 
  36.            t150799>0 | 
  37.            #t150801>0 | #(note: commented out because not available in 2017 ATUS)
  38.            #t150899>0 |  #(note: commented out because not available in 2017 ATUS)
  39.            t159999>0 | 
  40.            t181501>0 | 
  41.            t181599>0)
  42.  
  43. #Step 4: calculate weighted volunteering hours
  44. atus17vol <- atus17vol %>%
  45.   mutate(
  46.     t150101w = tufinlwgt.x* t150101,
  47.     t150102w = tufinlwgt.x* t150102, 
  48.     t150103w = tufinlwgt.x* t150103, 
  49.     t150104w = tufinlwgt.x* t150104, 
  50.     t150105w = tufinlwgt.x* t150105, 
  51.     t150106w = tufinlwgt.x* t150106, 
  52.     t150199w = tufinlwgt.x* t150199, 
  53.     t150201w = tufinlwgt.x* t150201, 
  54.     t150202w = tufinlwgt.x* t150202, 
  55.     t150203w = tufinlwgt.x* t150203, 
  56.     t150204w = tufinlwgt.x* t150204, 
  57.     t150299w = tufinlwgt.x* t150299, 
  58.     t150301w = tufinlwgt.x* t150301, 
  59.     t150302w = tufinlwgt.x* t150302, 
  60.     t150399w = tufinlwgt.x* t150399, 
  61.     t150401w = tufinlwgt.x* t150401, 
  62.     t150402w = tufinlwgt.x* t150402, 
  63.     t150499w = tufinlwgt.x* t150499, 
  64.     t150501w = tufinlwgt.x* t150501, 
  65.     t150599w = tufinlwgt.x* t150599, 
  66.     t150601w = tufinlwgt.x* t150601, 
  67.     t150602w = tufinlwgt.x* t150602, 
  68.     t150699w = tufinlwgt.x* t150699, 
  69.     t150701w = tufinlwgt.x* t150701, 
  70.     t150799w = tufinlwgt.x* t150799, 
  71.     #t150801w = tufinlwgt.x* t150801, (note: commented out because not available in 2017 ATUS)
  72.     #t150899w = tufinlwgt.x* t150899, (note: commented out because not available in 2017 ATUS)
  73.     t159999w = tufinlwgt.x* t159999, 
  74.     t181501w = tufinlwgt.x* t181501, 
  75.     t181599w = tufinlwgt.x* t181599
  76.   )
  77.  
  78. #Step 5: Create reduced file of only weighted data
  79. atus17vol <- atus17vol %>%
  80.   select(tucaseid, 
  81.          t150101w,
  82.          t150102w,
  83.          t150103w,
  84.          t150104w,
  85.          t150105w,
  86.          t150106w,
  87.          t150199w,
  88.          t150201w,
  89.          t150202w,
  90.          t150203w,
  91.          t150204w,
  92.          t150299w,
  93.          t150301w,
  94.          t150302w,
  95.          t150399w,
  96.          t150401w,
  97.          t150402w,
  98.          t150499w,
  99.          t150501w,
  100.          t150599w,
  101.          t150601w,
  102.          t150602w,
  103.          t150699w,
  104.          t150701w,
  105.          t150799w,
  106.          #t150801w, (note: commented out because not available in 2017 ATUS)
  107.          #t150899w, (note: commented out because not available in 2017 ATUS)
  108.          t159999w,
  109.          t181501w,
  110.          t181599w,
  111.          tufinlwgt.x)
  112.  
  113.  
  114.  
  115. #Step 6: Create categorical groupings, number of volunteer hours
  116. atus17vol <- atus17vol %>%
  117.   mutate( 
  118.     adminsupport = t150101w + t150102w + t150103w + t150104w + t150105w + t150106w +t150199w,
  119.     socialservice = t150201w + t150202w + t150203w + t150204w + t150299w,
  120.     maintenance = t150301w + t150302w+ t150399w,
  121.     performculture = t150401w + t150402w + t150499w,
  122.     attendmeet = t150501w + t150599w,
  123.     pubhealth = t150601w + t150602w + t150699w,
  124.     waiting = t150701w + t150799w,
  125.     #security = t150801w,
  126.     travel = t181501w + t181599w,
  127.     othervol = t159999w
  128.   )
  129.  
  130. #Step 7: Calculate proprotion of weighted individuals involved in each category
  131.  
  132. #Step 7a: Administrative/Support
  133. atus17vol$adminsupportprop <- ifelse((atus17vol$t150101w + 
  134.                                         atus17vol$t150102w + 
  135.                                         atus17vol$t150103w + 
  136.                                         atus17vol$t150104w +
  137.                                         atus17vol$t150105w + 
  138.                                         atus17vol$t150106w + 
  139.                                         atus17vol$t150199w) >0, 
  140.                                      atus17vol$tufinlwgt.x, 
  141.                                      0)
  142.  
  143. #Step 7b: Social service
  144. atus17vol$socialserviceprop <- ifelse((atus17vol$t150201w + 
  145.                                          atus17vol$t150202w + 
  146.                                          atus17vol$t150203w + 
  147.                                          atus17vol$t150204w +
  148.                                          atus17vol$t150299w) >0, 
  149.                                       atus17vol$tufinlwgt.x, 
  150.                                       0)
  151.  
  152. #Step 7c: Maintenance
  153. atus17vol$maintenanceprop <- ifelse((atus17vol$t150301w + 
  154.                                        atus17vol$t150302w +
  155.                                        atus17vol$t150399w) >0, 
  156.                                     atus17vol$tufinlwgt.x, 
  157.                                     0)
  158.  
  159. #Step 7d: Perform culture
  160. atus17vol$performcultureprop <- ifelse((atus17vol$t150401w + 
  161.                                           atus17vol$t150402w +
  162.                                           atus17vol$t150499w) >0, 
  163.                                        atus17vol$tufinlwgt.x, 
  164.                                        0)
  165.  
  166. #Step 7e: Attend meetings
  167. atus17vol$attendmeetprop <- ifelse((atus17vol$t150501w+
  168.                                       atus17vol$t150599w) >0, 
  169.                                    atus17vol$tufinlwgt.x, 
  170.                                    0)
  171.  
  172. #Step 7f: Public health
  173. atus17vol$pubhealthprop <- ifelse((atus17vol$t150601w + 
  174.                                      atus17vol$t150602w +
  175.                                      atus17vol$t150699w) >0, 
  176.                                   atus17vol$tufinlwgt.x, 
  177.                                   0)
  178.  
  179. #Step 7g: Waiting
  180. atus17vol$waitingprop <- ifelse((atus17vol$t150701w + 
  181.                                    atus17vol$t150799w) >0, 
  182.                                 atus17vol$tufinlwgt.x, 
  183.                                 0)
  184.  
  185. #Step 7h: Security
  186. #atus17vol$securityprop <- ifelse((atus17vol$t150801w) >0, 
  187.                                  #atus17vol$tufinlwgt.x, 
  188.                                  #0)
  189.  
  190. #Step 7i: Travel
  191. atus17vol$travelprop <- ifelse((atus17vol$t181501w + 
  192.                                   atus17vol$t181599w) >0, 
  193.                                atus17vol$tufinlwgt.x, 
  194.                                0)
  195.  
  196. #Step 7j: Other
  197. atus17vol$othervolprop <- ifelse((atus17vol$t159999w) >0, 
  198.                                  atus17vol$tufinlwgt.x, 
  199.                                  0)
  200.  
  201. #Step 8: Summarize number of hours/volunteers in each category
  202. atus17volsum<- atus17vol %>%
  203.   summarise(
  204.     adminsupportprop = sum(adminsupportprop),
  205.     socialserviceprop = sum(socialserviceprop),
  206.     maintenanceprop = sum(maintenanceprop),
  207.     performcultureprop = sum(performcultureprop),
  208.     attendmeetprop = sum(attendmeetprop),
  209.     pubhealthprop = sum(pubhealthprop),
  210.     waitingprop = sum(waitingprop),
  211.     #securityprop = sum(securityprop),
  212.     travelprop = sum(travelprop),
  213.     othervolprop = sum(othervolprop),
  214.     adminsupport = sum(adminsupport),
  215.     socialservice = sum(socialservice),
  216.     maintenance= sum(maintenance),
  217.     performculture = sum(performculture),
  218.     attendmeet = sum(attendmeet),
  219.     pubhealth = sum(pubhealth),
  220.     waiting = sum(waiting),
  221.     #security = sum(security),
  222.     travel = sum(travel),
  223.     othervol = sum(othervol)
  224.   ) 
  225.  
  226. #Step 9: Reduce to number of volunteer hours
  227. atus17volhours<- atus17volsum %>%
  228.   select(adminsupport, socialservice, maintenance,  performculture, attendmeet, pubhealth, waiting, 
  229.          #security, 
  230.          travel, othervol) %>%
  231.   gather(adminsupport, socialservice, maintenance,  performculture, attendmeet, pubhealth, waiting, 
  232.          #security, 
  233.          travel, othervol,
  234.          key = "type",
  235.          value = "hours")
  236.  
  237.  
  238. #Step 10: rename columns
  239. atus17volhours$type[grepl("adminsupport",atus17volhours$type )] <- "Administrative and support"
  240. atus17volhours$type[grepl("socialservice",atus17volhours$type )] <- "Social service and care"
  241. atus17volhours$type[grepl("maintenance",atus17volhours$type )] <- "Maintenance, building, and cleanup"
  242. atus17volhours$type[grepl("performculture",atus17volhours$type )] <- "Performing and cultural activities"
  243. atus17volhours$type[grepl("attendmeet",atus17volhours$type )] <- "Meetings, conferences, and training"
  244. atus17volhours$type[grepl("pubhealth",atus17volhours$type )] <- "Public health and safety"
  245. atus17volhours$type[grepl("waiting",atus17volhours$type )] <- "Waiting"
  246. #atus17volhours$type[grepl("security",atus17volhours$type )] <- "Security procedures"
  247. atus17volhours$type[grepl("travel",atus17volhours$type )] <- "Travel"
  248. atus17volhours$type[grepl("othervol",atus17volhours$type )] <- "Other"
  249. atus17volhours$type[grepl("adminsupport",atus17volhours$type )] <- "Administrative and support"
  250.  
  251. #Step 11: Calculate total
  252. atus17volhours[10,2] <-sum(atus17volhours$hours)
  253. atus17volhours$type[10] = "Total"
  254.  
  255. #Step 12: Calculate proportional number of hours per category
  256. atus17volhours <-atus17volhours %>%
  257.   mutate(
  258.     AsPct = round(((hours/hours[10])*100),1)
  259.   )
  260.  
  261. #Step 12: Remane final underlying table and write to CSV
  262. Figure4 <- atus17volhours
  263. write.csv(Figure4, "Figures/NSiB_Figure4_Table.csv")
  1. #Read in Table 6 raw data
  2. #Based on US Department of Labor, Bureau of Labor Statistics, Current Population Survey, Volunteer Supplement (2007-2015) (https://www.bls.gov/cps/home.htm),
  3. #US Department of Labor, Bureau of Labor Statistics, American Time Use Survey (2008-2017) (https://www.bls.gov/tus/home.htm), 
  4. #US Department of Labor, Bureau of Labor Statistics, Current Employment Statistics (2017) (https://www.bls.gov/ces/), and
  5. #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/)
  6.  
  7. #Read in raw data, and write to CSV
  8. Table6 <- read_csv("D:/Users/BMcKeeve/Documents/Nonprofit-Sector-in-Brief/External_Data/Volunteering Data.csv")
  9. 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)

  1. #Display Figure 4
  2. Fig4Plot <- function(Fig4Table) {
  3.  
  4.   Fig4<- Fig4Table %>%
  5.     filter(type != "Total") %>%
  6.     #filter(type != "Security procedures") %>% #Filtered out because equals 0%
  7.     ggplot(aes(x=reorder(type, -AsPct), y =AsPct)) +
  8.     geom_bar(stat = "identity") +
  9.     geom_text(aes(label=formatC(round(AsPct,1), format = 'f', digits =1)),
  10.               position= position_dodge(width=1), 
  11.               vjust =-.3,
  12.               size=3) +
  13.     scale_y_continuous(expand = c(0, 0), limits = c(0,30)) +
  14.     labs(y = "Percent of total volunteer time") +
  15.     theme(axis.title = element_blank(),
  16.           panel.grid.major = element_blank(),
  17.           axis.text.y = element_blank()) +
  18.     scale_x_discrete(labels = function(type) str_wrap(type, width=10))
  19.  
  20.   UrbCaption <- grobTree(
  21.     gp = gpar(fontsize = 8, hjust = 1), 
  22.     textGrob(label = "I N S T I T U T E", 
  23.              name = "caption1",
  24.              x = unit(1, "npc"),  
  25.              y = unit(0, "npc"),
  26.              hjust = 1, 
  27.              vjust = 0),
  28.     textGrob(label = "U R B A N  ", 
  29.              x = unit(1, "npc") - grobWidth("caption1") - unit(0.01, "lines"),         
  30.              y = unit(0, "npc"), 
  31.              hjust = 1, 
  32.              vjust = 0, 
  33.              gp = gpar(col = "#1696d2")))
  34.  
  35.   grid.arrange(Fig4, UrbCaption, ncol = 1, heights = c(30, 1))
  36. }
  37.  
  38. Fig4Plot(Figure4)

Figure_4
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

  1. #Display Table 6
  2. kable(Table6,  
  3.       "html", 
  4.       format.args = list(decimal.mark = '.', big.mark = ","),
  5.       align = "lcccccccccc",
  6.       col.names = c("", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017")) %>%
  7.   kable_styling("hover", full_width = F) %>%
  8.   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.


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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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