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irs990efile

R package for building a research database from IRS 990 nonprofit efiler tax returns.

The full set of tables is available in the DATA DICTIONARY.

The Master Concordance File provides the crosswalk architecture for moving from XML files to rectangular tables.

https://github.com/Nonprofit-Open-Data-Collective/irs-efile-master-concordance-file

Several dozen one-to-many tables exist on Form 990 (one unique 990 filing to a table with many entries, such as many board members serving a single nonprofit). Documentation about the XML table structure can be useful when attempting to parse XML nodes into well-behaved relational tables.

https://nonprofit-open-data-collective.github.io/efile-rdb-tables/

Installation

Note that xmltools is not available on CRAN so has to be installed remotely before installing the irs990efiler package.

# install.packages( 'devtools' )  
devtools::install_github( 'ultinomics/xmltools' )
devtools::install_github( 'nonprofit-open-data-collective/irs990efile' )

Use

library( irs990efile )
library( dplyr )

index <- build_index( tax.years=2018 )
index.small <- dplyr::sample_n( index, 1000 )
build_database( index.small )

# download the 2018 index file
# build all one-to-one tables
# for a sample of 1,000 nonprofits

IRS Efile XML Docs

Update: The IRS is no longer hosting efile data on AWS. Files must be downloaded from the IRS site directly.

The irs990efile package now pulls from the Data Commons Data Lake:

Download their full efile index file (check their site for newer versions):

DataCommons Efile Index (5,598,994 records)

Index Data Dictionary

c("BuildTs", "DAF", "DateSigned", "DocStatus", "EIN", "FileSha256", 
"FileSizeBytes", "FormType", "GrossReceipts", "GroupAffiliatesIncluded", 
"GroupExemptionNumber", "GroupReturnForAffiliates", "IndexedOn", 
"LegalDomicileCountry", "LegalDomicileState", "ObjectId", "OrgType", 
"OrganizationName", "ReturnTs", "ReturnVersion", "SubmittedOn", 
"TaxPeriod", "TaxPeriodBeginDate", "TaxPeriodEndDate", "TaxStatus", 
"TaxYear", "TotalAssetsBkEOY", "TotalExpensesCY", "TotalLiabilitiesBkEOY", 
"TotalNetAssetsBkEOY", "TotalRevenueCY", "URL", "Website", "YearFormed", 
"ZipFile")

URL <- "https://nccs-efile.s3.us-east-1.amazonaws.com/index/index_all_years_efiledata_xmls_created_on_2024-01-19.csv"
download.file( URL, destfile="index.csv" )
d <- data.table::fread( "index.csv", colClasses=c( "ObjectId"="character" ) )
table( d$TaxYear, d$FormType ) |> knitr::kable()
d |>dplyr::filter( TaxYear == 2021 ) |> dplyr::count( TaxStatus ) |> knitr::kable()

990 Efile Returns by FormType and TaxYear

990 990EZ 990PF 990T
2007 17 17 0 0
2008 87 114 20 0
2009 33311 15470 2345 0
2010 123026 63326 25249 0
2011 159504 82048 34597 0
2012 179688 93750 39933 0
2013 198856 104375 45887 0
2014 218620 116417 53442 0
2015 233520 124894 58815 0
2016 243903 130485 62988 0
2017 261612 139146 68950 0
2018 271442 149384 80138 0
2019 283649 152579 87773 0
2020 318850 169296 114605 22616
2021 319445 192642 116404 22469
2022 158730 135775 81631 7154

TaxStatus for 2021 Cases

Var1 Freq
138873
501c2 2578
501c3 391557
501c4 23639
501c5 16901
501c6 32008
501c7 16548
501c8 6577
501c9 4803
501c10 1808
501c11 5
501c12 3687
501c13 2917
501c14 1920
501c15 139
501c16 11
501c17 71
501c18 4
501c19 6265
501c20 1
501c23 4
501c25 358
501c26 6
501c27 3
501c29 14
4947a1 263

More Examples

library( irs990efile )
library( dplyr )

### Preview the index file:
index <- build_index( tax.years=2018 )
head( index3[ , c(1:2) ] ) %>% knitr::kable()
head( index3[ , c(3:4,6:7) ] ) %>% knitr::kable()
OrganizationName EIN
ANCIENT & ACCEPTED SCOTTISH RITE OF FREEMASONRY VALLEY OF MONROE 237144224
THE EDUCATIONAL AND SCIENTIFIC RESEARCH ESR FOUNDATION INC 814680405
THE LITERARY CLASSICS OF THE US INC D/B/A LIBRARY OF AMERICA 132986916
RAPID CITY SOFTBALL LEAGUE ASSOS 460410637
CENTRO CAMPESINO FARMWORKER CENTER INC 591460598
HAITIAN PEOPLES SUPPORT PROJECT INC 141755401
FormType TaxYear OrgType TaxStatus
990 2018 Corp 501c10
990PF 2018 ExemptPF NA
990 2018 Corp 501c3
990 2018 Corp 501c4
990 2018 Corp 501c3
990EZ 2018 Corp 501c3
# FULL INDEX LOAD TIME 
tictoc::tic( )  #---------------------------
index <- build_index( tax.years=2008:2022 )
tictoc::toc()   #---------------------------
# 798.84 sec elapsed
# ~ 15 minutes to read full index

table( index$FormType ) %>% knitr::kable()

# |Var1  |    Freq|
# |:-----|-------:|
# |990   | 2886557|
# |990EZ | 1597025|
# |990PF |  836034|
# |990T  |   46023|
###   BUILD THE DATABASE
# test on random sample of 10,000 cases
index <- tinyindex  
build_database( index )
###   BUILD THE FULL DATABASE (~4.5 million 990 & 990EZ filers)
###   (note: this can take days) 
###   (test on a sample first) 

# build the Data Commons index 
index <- build_index( tax.years=2008:2022 )
build_database( index ) 
###
###  WORKING WITH SPECIFIC TABLES OR SAMPLES
###

# pre-loaded demo index of 10,000 random efilers from AWS:
tinyindex %>% 
  select( OrganizationName, EIN, TaxYear, FormType ) %>% 
  head()

#                              OrganizationName       EIN TaxYear FormType
# 1                    MASTOCYTOSIS SOCIETY INC 521959601    2018      990
# 2                            MCKINLEY III INC 364165018    2018      990
# 3                           REAL SERVICES INC 351157606    2013      990
# 4 GREATER KANSAS CITY FRIENDS OF FISHER HOUSE 842359546    2019    990EZ
# 5                           THUMBNAIL THEATER 510563980    2014    990EZ
# 6     BERNARD M AND CARYL H SUSMAN FOUNDATION 208068788    2010    990PF


# index files from 2009 to 2020 are preloaded: 
data( index2009 )
head( index2009 )

# combine index files for all years 2009-2020 ehre forms available: 
index <- build_index()  # build_index( tax.years=2009:2020 )

# create index of 10 organizations from 2018  
index.2018 <-
  index2018 %>% 
  filter( FormType %in% c("990","990EZ") ) %>%
  sample_n( 10 )

# build all one-to-one tables for the sample
dir.create( "EFILE" )
setwd( "./EFILE" )
build_tables( url=index.2018$URL, year=2018 )


###  TEST SPECIFIC TABLES

index <- tinyindex  # random sample of 10,000 cases

# split index file into smaller chunks (for parallelization) and build tables 

years <- 2017:2019

tables <- c( "F9-P00-T00-HEADER","F9-P01-T00-SUMMARY",
             "F9-P08-T00-REVENUE","F9-P09-T00-EXPENSES",
             "F9-P11-T00-ASSETS" )

# TABLE NAME:          'F9-P00-T00-HEADER' 
# FUNCTION NAME: 'BUILD_F9_P00_T00_HEADER'

tables <- gsub( "-", "_", tables )
tables <- paste0( "BUILD_", tables )


for( i in years )
{
  dir.create( as.character(i) )
  setwd( as.character(i) )                               # creates folders for each year
  index.i <- dplyr::filter( index, TaxYear == i )        # create index for one year 
  groups <- split_index( index.i, group.size = 100 )     # parser builds temporary tables then combines them at the end
  build_tables_parallel( 
    groups=groups, year=i, table.names=tables )          # processing many small groups keep memory usage low
  setwd( ".." ) # return to main directory 
}

bind_data( years )    # compile all temp tables into one table

Details

The full set of tables is available in the DATA DICTIONARY.

Background

The IRS started processing electronic filings for nonprofit 990 tax forms in 2010 and releasing 990 efile returns via AWS in 2016. For more details on the history and current status of nonprofit efiling see this recent report.

All electronic tax returns have been released as XML documents currently stored in an AWS bucket (though soon migrating to the IRS website).

XML forms can be rendered using an efile viewer so that they look the same as a PDF of a regular 990 filing (you can see examples on ProPublica's Nonprofit Explorer). They are NOT, however, in a convenient format for statistical analysis.

The irs990efile package was created to convert XML files into a relational database: normal rectangular data tables linked by a set of keys.

RDB Key Fiels:

All of the files share the following meta-fields:

  • OBJECTID
  • URL
  • ORG_EIN
  • ORG_NAME_L1
  • ORG_NAME_L2
  • RETURN_TYPE
  • RETURN_VERSION
  • TAX_YEAR

Intuitively we would expect that the EIN-TAX_YEAR combination could serve as a unique key for table joins. That is not the case because the same nonprofit can file multiple 990 returns in a single year due to (1) ammendments, (2) group filings where the parent organization files a single 990 for themselves and another group filing for all of the related entities, (3) a change in fiscal year results in a partial return.

In theory the ObjectID should work as the join key but it is error prone because of a well-known precision loss problem that occurs when reading or recasting large numbers.

options( scipen=16)
x <- c(  "202301529349200315", 
         "202301529349200316", 
         "202301529349200317"   )
y <- x |> as.numeric()
cbind(x,y) |> knitr::kable()

|x                  |y                  |
|:------------------|:------------------|
|202301529349200315 |202301529349200320 |
|202301529349200316 |202301529349200320 |
|202301529349200317 |202301529349200320 |

As a result the ObjectId may get corrupted unexpectedly and the joins are no longer valid. The problem is specific to columns that contain only numbers, as many functions that read CSV formats or other file types that lack explicit data types will automatically cast them as numeric variable types, at which time the precision loss occurs during the conversion. You can assert more control over the default behavior of some functions:

d <- read.csv( filename, colClasses = "character" )
d <- readr::read_csv( filename, col_types = cols(.default = col_character()) )
d <- data.table::fread( filename, colClasses=c( "ObjectId"="character" ) )

Alternatively, the URL field includes characters so it is by default loaded as a character vector. Note that the URL contains the original ObjectId:

x <- head(d$ObjectId, 3 )
y <- head(d$URL, 3 )
cbind(x,y) |> knitr::kable()

|x                  |y                                                                                               |
|:------------------|:-----------------------------------------------------------------------------------------------|
|202301529349200315 |https://gt990datalake-rawdata.s3.amazonaws.com/EfileData/XmlFiles/202301529349200315_public.xml |
|201921359349311872 |https://gt990datalake-rawdata.s3.amazonaws.com/EfileData/XmlFiles/201921359349311872_public.xml |
|201410859349300511 |https://gt990datalake-rawdata.s3.amazonaws.com/EfileData/XmlFiles/201410859349300511_public.xml |

You will find it to be a safe and reliable key for table joins.

Legacy XML Files from the Original IRS S3 Bucket:

All of the XML efile returns have been archived on the Data Commons.

If you need to access the original legacy IRS S3 bucket (previously at https://www.irs.gov/charities-non-profits/form-990-series-downloads) for reproducibility purposes, it is available at: https://nccs-efile.s3.us-east-1.amazonaws.com/xml/

The Index files are available at:

https://nccs-efile.s3.us-east-1.amazonaws.com/index/index-PX.csv  # 990 and 999EZ returns
https://nccs-efile.s3.us-east-1.amazonaws.com/index/index-PF.csv  # 990PF returns 

The URLs can be constructed with the object ID and will thus look like: https://nccs-efile.s3.us-east-1.amazonaws.com/xml/201020793492001120_public.xml

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R package for building a research database from IRS 990 efiler tax returns.

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