From 1e72c4d3ae749d68fcce541335d9731c9b4be4b0 Mon Sep 17 00:00:00 2001 From: donboyd5 Date: Tue, 29 Oct 2024 15:23:46 -0400 Subject: [PATCH 1/4] add initial prepare files --- tmd/areas/targets/prepare/.gitignore | 17 ++ tmd/areas/targets/prepare/_quarto.yml | 77 +++++ .../cd_construct_long_soi_data_file.qmd | 196 +++++++++++++ .../cd_construct_variable_documentation.qmd | 138 +++++++++ ...cd_create_basefile_for_cd_target_files.qmd | 270 ++++++++++++++++++ ...eate_crosswalk_from_cd117th_to_cd118th.qmd | 226 +++++++++++++++ .../targets/prepare/cd_download_soi_data.qmd | 43 +++ .../prepare/cd_get_census_population.qmd | 94 ++++++ .../targets/prepare/cd_issues_and_TODOs.qmd | 37 +++ ...cd_map_tcvars_and_extract_target_files.qmd | 116 ++++++++ .../prepare/cd_overall_documentation.qmd | 139 +++++++++ .../prepare/cds/raw_data/21incddocguide.docx | Bin 0 -> 57513 bytes ...on_extracted_from_21incddocguide.docx.xlsx | Bin 0 -> 457684 bytes .../cds/raw_data/congressional2021.zip | Bin 0 -> 9041081 bytes .../Image 2024-10-20 at 5.23.32 PM.jpeg | Bin 0 -> 60906 bytes .../prepare/images/clipboard-719051713.png | Bin 0 -> 3560 bytes .../targets/prepare/images/raw_data.jpeg | Bin 0 -> 15415 bytes tmd/areas/targets/prepare/index.qmd | 15 + tmd/areas/targets/prepare/prepare.Rproj | 13 + tmd/areas/targets/prepare/usage.qmd | 50 ++++ 20 files changed, 1431 insertions(+) create mode 100644 tmd/areas/targets/prepare/.gitignore create mode 100644 tmd/areas/targets/prepare/_quarto.yml create mode 100644 tmd/areas/targets/prepare/cd_construct_long_soi_data_file.qmd create mode 100644 tmd/areas/targets/prepare/cd_construct_variable_documentation.qmd create mode 100644 tmd/areas/targets/prepare/cd_create_basefile_for_cd_target_files.qmd create mode 100644 tmd/areas/targets/prepare/cd_create_crosswalk_from_cd117th_to_cd118th.qmd create mode 100644 tmd/areas/targets/prepare/cd_download_soi_data.qmd create mode 100644 tmd/areas/targets/prepare/cd_get_census_population.qmd create mode 100644 tmd/areas/targets/prepare/cd_issues_and_TODOs.qmd create mode 100644 tmd/areas/targets/prepare/cd_map_tcvars_and_extract_target_files.qmd create mode 100644 tmd/areas/targets/prepare/cd_overall_documentation.qmd create mode 100644 tmd/areas/targets/prepare/cds/raw_data/21incddocguide.docx create mode 100644 tmd/areas/targets/prepare/cds/raw_data/cd_documentation_extracted_from_21incddocguide.docx.xlsx create mode 100644 tmd/areas/targets/prepare/cds/raw_data/congressional2021.zip create mode 100644 tmd/areas/targets/prepare/images/Image 2024-10-20 at 5.23.32 PM.jpeg create mode 100644 tmd/areas/targets/prepare/images/clipboard-719051713.png create mode 100644 tmd/areas/targets/prepare/images/raw_data.jpeg create mode 100644 tmd/areas/targets/prepare/index.qmd create mode 100644 tmd/areas/targets/prepare/prepare.Rproj create mode 100644 tmd/areas/targets/prepare/usage.qmd diff --git a/tmd/areas/targets/prepare/.gitignore b/tmd/areas/targets/prepare/.gitignore new file mode 100644 index 00000000..c423ad96 --- /dev/null +++ b/tmd/areas/targets/prepare/.gitignore @@ -0,0 +1,17 @@ +# prepare + +# folders to ignore +.quarto/ +.Rproj.user/ +_docs/ +_targetprep/ +_freeze/ +intermediate/ + +# files to ignore +.Rhistory + +# Local Netlify folder +.netlify + +/.quarto/ diff --git a/tmd/areas/targets/prepare/_quarto.yml b/tmd/areas/targets/prepare/_quarto.yml new file mode 100644 index 00000000..ce2bd4e6 --- /dev/null +++ b/tmd/areas/targets/prepare/_quarto.yml @@ -0,0 +1,77 @@ +project: + type: book + output-dir: _targetprep + +# https://prerelease.quarto.org/ # quarto documentation at this link + + +# site info: + # OLD id: 4d646266-9d1f-4d69-acb4-b9a17b63a5ff + # Unique deploy URL: https://671e13320a7e7cfb68b1ba7d--tmd-areas-prepare-targets.netlify.app + # url: https://tmd-areas-targets-prepare.netlify.app + +# publishing with netlify cli: +# open terminal in prepare +# quarto render && netlify deploy --prod --dir=_targetprep + +# quarto render # inspect to be sure it is as desired +# netlify deploy --prod --dir=_targetprep + +# or step by step +# netlify deploy # to test it, give _examine as publish directory +# netlify deploy --prod # to deploy, give _docs as publish directory + +execute: + eval: true + echo: true + output: true + freeze: auto # auto: during global project renders, re-render only when source changes + +book: + title: "Develop targets for subnational areas" + subtitle: "Create csv target files for use by area targeting routies" + # author: "Don Boyd" + date: today + date-format: long + chapters: + - index.qmd + - part: "Usage and notes" + chapters: + - usage.qmd + - cd_issues_and_TODOs.qmd + - part: "IRS Congressional District data" + chapters: + - cd_overall_documentation.qmd + - cd_get_census_population.qmd + - cd_download_soi_data.qmd + - cd_construct_variable_documentation.qmd + - cd_construct_long_soi_data_file.qmd + - cd_create_basefile_for_cd_target_files.qmd + - cd_create_crosswalk_from_cd117th_to_cd118th.qmd + - cd_map_tcvars_and_extract_target_files.qmd + +format: + html: + theme: cosmo + code-fold: true + +editor_options: + chunk_output_type: console + +# R packages using old 209 libxml +# gt, + + +# rendering commands +# quarto render +# quarto publish netlify --no-prompt --no-render --no-browser + +# possibly use this at start of each doc +# --- +# output: html_document +# editor_options: +# chunk_output_type: console +# --- + + + \ No newline at end of file diff --git a/tmd/areas/targets/prepare/cd_construct_long_soi_data_file.qmd b/tmd/areas/targets/prepare/cd_construct_long_soi_data_file.qmd new file mode 100644 index 00000000..f981cbb7 --- /dev/null +++ b/tmd/areas/targets/prepare/cd_construct_long_soi_data_file.qmd @@ -0,0 +1,196 @@ +--- +output: html_document +editor_options: + chunk_output_type: console +--- + +# Parse the Congressional District data + +Clean the CD data and save. + +## Setup + +```{r} +#| label: setup + +source(here::here("R", "libraries.R")) +source(here::here("R", "constants.R")) + +# 334283385.27000004 national pop + +``` + +## Background + +> AREA prefix for state areas are the two lower-case character postal codes. AREA prefix for congressional districts are the state prefix followed by two digits (with a leading zero) identifying the district. There are no district files for states with only one congressional district. + +Here is an example of the first few rows of a targets file: + +![](images/Image 2024-10-20 at 5.23.32 PM.jpeg) + +## Create AGI stub information + +```{r} +#| label: agi-bins + +# example of targets file +# varname,count,scope,agilo,agihi,fstatus,target +# XTOT, 0, 0,-9e99, 9e99, 0, 33e6 +# e00300, 0, 1,-9e99, 9e99, 0, 20e9 +# e00900, 0, 1,-9e99, 9e99, 0, 30e9 +# e00200, 0, 1,-9e99, 9e99, 0,1000e9 +# e02000, 0, 1,-9e99, 9e99, 0, 30e9 +# e02400, 0, 1,-9e99, 9e99, 0, 60e9 +# c00100, 0, 1,-9e99, 9e99, 0,1200e9 +# XTOT, 1, 1, 1e6, 9e99, 0, 10e3 +# e00400, 0, 1,-9e99, 9e99, 0, 2e9 +# e00600, 0, 1,-9e99, 9e99, 0, 8e9 +# e00650, 0, 1,-9e99, 9e99, 0, 7e9 +# e01700, 0, 1,-9e99, 9e99, 0, 12e9 +# e02300, 0, 1,-9e99, 9e99, 0, 10e9 +# e17500, 0, 1,-9e99, 9e99, 0, 5e9 +# e18400, 0, 1,-9e99, 9e99, 0, 10e9 +# e18500, 0, 1,-9e99, 9e99, 0, 10e9 + +# in_bin = (vardf.c00100 >= row.agilo) & (vardf.c00100 < row.agihi) + +# 0 = Total +# 1 = Under $1 +# 2 = $1 under $10,000 +# 3 = $10,000 under $25,000 +# 4 = $25,000 under $50,000 +# 5 = $50,000 under $75,000 +# 6 = $75,000 under $100,000 +# 7 = $100,000 under $200,000 +# 8 = $200,000 under $500,000 +# 9 = $500,000 or more + +agibins <- read_delim( +delim=";", +trim_ws = TRUE, +file="AGI_STUB; agirange; agilo; agihi +0; Total; -9e99; 9e99 +1; Under $1; -9e99; 1 +2; $1 under $10,000; 1; 10e3 +3; $10,000 under $25,000; 10e3; 25e3 +4; $25,000 under $50,000; 25e3; 50e3 +5; $50,000 under $75,000; 50e3; 75e3 +6; $75,000 under $100,000; 75e3; 100e3 +7; $100,000 under $200,000; 100e3; 200e3 +8; $200,000 under $500,000; 200e3; 500e3 +9; $500,000 or more; 500e3; 9e99 +") + +write_csv(agibins, fs::path(CDINTERMEDIATE, "cd_agi_bins.csv")) + +# agibins |> kable() +agibins |> + gt() |> + tab_header( + title = html("Congressional District AGI bins"), + subtitle = html("in_bin = (vardf.c00100 >= row.agilo) & (vardf.c00100 < row.agihi)")) |> + fmt_number(columns=c(agilo, agihi), + rows=3:9, + # scale=1e-9, + decimals=0) + +``` + +## Prepare, clean, and save wide data file + +Set eval: to true for these chunks to recreate the data file. + +```{r} +#| label: parse-cddata +#| eval: true + +# read the csv file from the zip archive that contains it +zpath <- fs::path(CDRAW, fs::path_file(CDZIPURL)) +con <- unz(zpath, "21incd.csv") +data <- read_csv(con) +rm(con) + +count(data, STATE) # US, DC, and 50 states +count(data, CONG_DISTRICT) # max is 53 + +``` + +```{r} +#| label: clean-save-cddata-wide +#| eval: true + +# cleaning and reshaping: +# - determine record type + +agibins <- read_csv(fs::path(CDINTERMEDIATE, "cd_agi_bins.csv")) + +data2 <- data |> + rename_with(toupper) |> # agi_stub becomes upper case + mutate(nstub0 = sum(AGI_STUB == 0), + .by = STATE) |> + mutate(rectype = case_when( + STATE == "US" ~ "US", + STATE == "DC" ~ "DC", + nstub0 == 1 ~ "cdstate", # the cd and state record for 8 states with only 1 cd + nstub0 > 1 & CONG_DISTRICT == "00" ~ "state", + nstub0 > 1 & CONG_DISTRICT != "00" ~ "cd", # cd records for multi-cd states + .default = "ERROR" + )) |> + mutate(ndist = sum(AGI_STUB ==0 & rectype %in% c("cdstate", "cd")), .by=STATE) |> + left_join(agibins, by = join_by(AGI_STUB)) |> + select(-nstub0) |> + relocate(rectype, ndist) |> + relocate(agirange, agilo, agihi, .after=AGI_STUB) + +glimpse(data2) +count(data2, STATE, ndist) + +cdnums <- data2 |> + select(STATE, ndist) |> + distinct() |> + janitor::adorn_totals() + +# single-CD states +cdnums |> + filter(ndist==1) + +data2 |> + filter(AGI_STUB == 0) |> + count(rectype) + +write_csv(data2, fs::path(CDINTERMEDIATE, "cddata_wide_clean.csv")) + +rm(data, data2, cdnums) + +``` + +## Create long SOI data file + +```{r} +#| label: create-save-soi-cddata-long +#| eval: false + +cdwide <- read_csv(fs::path(CDINTERMEDIATE, "cddata_wide_clean.csv")) +doc <- read_csv(fs::path(CDINTERMEDIATE, "variable_documentation.csv")) + +glimpse(cdwide) +glimpse(doc) + +idvars <- c("rectype", "ndist", "STATEFIPS", "STATE", "CONG_DISTRICT", + "AGI_STUB", "agirange", "agilo", "agihi") + +# TODO: put amount units in dollars!! + +dlong1 <- cdwide |> + pivot_longer(cols = -all_of(idvars), + names_to = "vname") |> + left_join(doc |> + select(vname, description, reference, vtype, basevname), + by = join_by(vname)) + +count(dlong1, vname) +count(dlong1, vtype) + +write_csv(dlong1, fs::path(CDINTERMEDIATE, "cddata_long_clean.csv")) + +``` diff --git a/tmd/areas/targets/prepare/cd_construct_variable_documentation.qmd b/tmd/areas/targets/prepare/cd_construct_variable_documentation.qmd new file mode 100644 index 00000000..9ffe8466 --- /dev/null +++ b/tmd/areas/targets/prepare/cd_construct_variable_documentation.qmd @@ -0,0 +1,138 @@ +--- +output: html_document +editor_options: + chunk_output_type: console +--- + + +# Parse and show Congressional District data documentation + +Note that we do not have number of exemptions, but we do have: + +- N1 -- number of returns + +- N2 -- number of individuals + +Footnote 5 in the IRS documentation file (21incddocguide.docx), pertaining to N2, says: + +> Beginning in 2018, personal exemption deductions were suspended for the primary, secondary, and dependent taxpayers. However, the data used to create the “Number of individuals”—filing status, dependent status indicator, and identifying dependent information—are still available on the Form 1040. This field is based on these data. + + +## Setup + +```{r} +#| label: setup + +source(here::here("R", "libraries.R")) +source(here::here("R", "constants.R")) + +``` + + +## Parse and save variable documentation + +```{r} +#| label: parse-save-doc +#| output: false + +doc1 <- read_excel(fs::path(CDRAW, CDDOCEXTRACT), sheet = "cleaned", range = "A2:D169") + +doc2 <- doc1 |> + mutate( + across(everything(), + \(x) str_trim(x)), + # clean reference field so that we can show line breaks in datatable + reference = str_replace_all(reference, + coll("\r\n"), + "
" + )) |> + # determine whether variable is count, amount, or other + mutate(vtype = case_when( + vname %in% c("STATEFIPS", "STATE", "CONG_DISTRICT", "AGI_STUB") ~ "other", + str_detect(description, + coll("number of", ignore_case = TRUE)) ~ "count", + str_detect(description, coll("amount", ignore_case = TRUE)) ~ "amount", + # dollar amounts where description does not contain "amount" + vname %in% c("A00100", "A02910", "A04475", "A11520", "A11530", "A18460") ~ "amount", + .default = "ERROR")) |> + mutate(description = ifelse(vname == "A00100", + str_replace(description, "Adjust gross", "Adjusted gross"), + description), + row = row_number()) |> + relocate(row) + +# determine which variable names are part of a count-amount pair and get the base variable name +doc3 <- doc2 |> + mutate(suffix = ifelse(nchar(vname) == 6, + str_sub(vname, 2, -1), + NA_character_), + suffix = ifelse(!is.na(as.integer(suffix)), + suffix, + NA_character_)) |> + arrange(suffix, vname) |> + mutate(nvars = n(), .by=suffix) |> + mutate(basevname= ifelse(nvars==2, + paste0("v", suffix), + NA_character_)) |> + select(-c(nvars, suffix)) |> + arrange(row) + +write_csv(doc3, fs::path(CDINTERMEDIATE, "variable_documentation.csv")) + +rm(doc1, doc2, doc3) + +``` + + +```{r} +#| label: check-doc +#| eval: false +#| output: false + +# run some checks on the data interactively + +doc2 <- read_csv(fs::path(CDINTERMEDIATE, "variable_documentation.csv")) +doc2 |> + filter(type != "Num") + +count(doc2, vtype) + +doc2 |> + filter(vtype=="ERROR") + +doc2 |> + filter(vtype=="other") + +# check these next 2 to be sure they are appropriate +countvars <- doc2 |> + filter(vtype=="count") + +amountvars <- doc2 |> + filter(vtype=="amount") + +rm(amountvars, countvars) + +``` + + +## Show variable documentation + +Show documentation table for variables in the Congressional District csv data. + +```{r} +#| label: show-doc + +vdoc <- read_csv(fs::path(CDINTERMEDIATE, "variable_documentation.csv")) + +vdoc |> + select(-type) |> + DT::datatable(rownames = FALSE, + options = list(order = list(0, "asc"), # use 1st column (0) for sorting + scrollX = TRUE, scrollY = TRUE, paging = TRUE, pageLength = 20, + autoWidth = TRUE), + filter="top", + escape = FALSE) + +rm(vdoc) + +``` diff --git a/tmd/areas/targets/prepare/cd_create_basefile_for_cd_target_files.qmd b/tmd/areas/targets/prepare/cd_create_basefile_for_cd_target_files.qmd new file mode 100644 index 00000000..ced4c05b --- /dev/null +++ b/tmd/areas/targets/prepare/cd_create_basefile_for_cd_target_files.qmd @@ -0,0 +1,270 @@ +--- +output: html_document +editor_options: + chunk_output_type: console +--- + +# Create base file for final CD target files + +This section creates one long file that is a superset of what we need for individual CD target files. This long file has everything needed to extract and save a target file for any CD. It also has additional convenience variables that will not be included in individual CD target files such as variable descriptions, human-friendly AGI-range labels, state fips codes, and a sort code for ordering records within a CD. + +## Documentation for target files for individual CDs + +### Target file name + +Congressional District target files follow the naming convention **xxxx_targets.csv**, where **xxxx** is a 4 character CD identifier. + +- The first two characters are the state postal abbreviation or, in the case of the District of Columbia, "DC". (DC does not have a voting representative but does have a non-voting member. The SOI data have information for DC and so it is in the data. Thus, we have data for 435 voting districts, plus data for DC.) + +- The next 2 characters identify the Congressional District within the state, with a leading zero. For states that have more than one district, these range from 01 to the number of districts (for example, 53 in the case of California). For the 7 states and DC that have only one CD, these 2 characters are 00, following the SOI convention. + +- Thus, the filename for California's 3rd Congressional District would be CA03_targets.csv and allowable file names would range from CA01_targets.csv to CA53_targets.csv. There is no CA00_targets.csv. The filename for any of the 7 states (or DC) that have only one CD would be WY00_targets.csv. + +### Target file variables + +Each target file will have the following variables: + +- **varname**: This is a PUF-based filename, as used in Tax-Calculator. Thus, examples of allowable names are XTOT (but see below), e00200 (wages), c00100 (AGI, calculated), and e00900. + +- **count**: Indicates whether the target is a count or a dollar amount. Allowable values are 0 for dollar amount and 1 for count. + +- **scope**: Indicates which kinds of records the target applies to. Allowable values are 0 for all records, 1 for tax filers, and 2 for nonfilers. + +- **agilo**, **agihi**: Lower and upper bounds for the AGI range. The interval is of the form \[agilo, agihi) -- that is, it includes all values \>= agilo and \< agihi. + +- **fstatus**: Filing status, following the PUF MARS definition. Allowable values are integers 0-5, where 0 = all records, 1 = single, 2 = married filing joint, 3 = married filing separately, 4 = head of household, and 5 = surviving spouse. **\[?? VERIFY WITH MARTIN\]** + +- **target**: The SOI value (or other target, if the user overrides the SOI value) for this variable, scope, agi range, and filing status. Counts and dollar amounts are "raw" values - neither is scaled to be in thousands or millions, for example. (Because SOI reported dollar values usually are in \$ thousands, we have multipled them by 1,000 so that they are unscaled.) + +### The special first data row of a CD target file + +The area targeting software needs a value for total population in the area. It uses this to scale initial weights prior to optimization so that they sum to the area population. To assist in this, the target file must contain in its first data row a value for the total area population. This special row must have the following values: + +- **varname**: XTOT +- **count**: 0 +- **scope**: 0 +- **agilo**: must be \< -8e99 +- **agihi**: must be \> 8e99 +- **fstatus**: 0 +- **target**: area population + +For example, here is the first data row of an area that has population of 33 million: + +varname,count,scope,agilo,agihi,fstatus,target + +XTOT, 0, 0,-9e99, 9e99, 0, 33e6 + +## Setup + +```{r} +#| label: setup + +source(here::here("R", "libraries.R")) +source(here::here("R", "constants.R")) + +# 334283385.27000004 national pop + +# varname,count,scope,agilo,agihi,fstatus,target +# XTOT, 0, 0,-9e99, 9e99, 0, 33e6 +# e00300, 0, 1,-9e99, 9e99, 0, 20e9 +# e00900, 0, 1,-9e99, 9e99, 0, 30e9 +# e00200, 0, 1,-9e99, 9e99, 0,1000e9 +# e02000, 0, 1,-9e99, 9e99, 0, 30e9 +# e02400, 0, 1,-9e99, 9e99, 0, 60e9 + +``` + +## Get needed data + +```{r} +#| label: get-soi-based-data + +cdlong <- read_csv(fs::path(CDINTERMEDIATE, "cddata_long_clean.csv")) + +``` + + +## Create streamlined long CD-only file + +- Drop non-CD records. +- Drop variables we would never want. +- Construct fstatus 0 records and records with counts by filing status. + +### Drop non-CD records and variables we won't want + +```{r} +#| label: drop-records-and-variables + +cdlong1 <- cdlong |> + filter(rectype %in% c("cd", "cdstate", "DC")) + +# quick data checks +cdlong1 |> filter(AGI_STUB==0) |> count(STATE) +cdlong1 |> filter(AGI_STUB==0) |> select(STATE, CONG_DISTRICT) |> distinct() |> nrow() +count(cdlong1, vtype) + +# winnow data to variables we might consider targeting +# show candidates for dropping in a nice order +cdlong1 |> + filter(is.na(basevname)) |> + select(vname, description) |> + distinct() |> + arrange(vname) + +dropvars <- c("CPREP", "DIR_DEP", "ELDERLY", "ELF", "PREP", "RAC", "TCE", + "TOTAL_VITA", "VITA", "VITA_EIC", "VRTCRIND") + +cdlong2 <- cdlong1 |> + filter(!vname %in% dropvars) + +rm(dropvars) + +``` + + +### Address agi issue + +We have an issue with AGI: for Congressional Districts IRS does NOT report the number of returns with AGI. See the discussion on the introduction page. + +We are going to create new rows for N00100 (Number of returns with AGI (estimated)), equal to N1, add it to the data. + +```{r} +#| label: address-agi + +nagi <- cdlong2 |> + filter(vname == "N1") |> + mutate(vname = "N00100", + basevname = "v00100", + description = "Number of returns with Adjusted Gross Income - AGI (estimated)") + +cdlong3 <- bind_rows(cdlong2, nagi) |> + mutate(basevname = ifelse(vname == "A00100", + "v00100", + basevname)) + +# check +check <- cdlong3 |> + filter(basevname=="v00100", STATE=="WY") |> + arrange(STATE, CONG_DISTRICT, basevname, desc(vtype), AGI_STUB) + +rm(nagi, check) + +``` + + +### Define statecd, fstatus, scope, count; put amounts in dollars; sort + +```{r} +#| label: fstatus-misc + +cdlong4 <- cdlong3 |> + mutate( + fstatus = case_when( + vname == "MARS1" ~ 1, + vname == "MARS2" ~ 2, + vname == "MARS4" ~ 4, # VERIFY WITH MARTIN + .default = 0), + + count = case_when( + vtype == "count" ~ 1, + vtype == "amount" ~ 0, + .default = -99), + + scope = 1, + value = ifelse(vtype == "amount", + value * 1000, + value) + ) + +summary(cdlong4) +# skim(cdlong4) +count(cdlong4, fstatus) +count(cdlong4, count, vtype) +count(cdlong4, scope) + +``` + +### Add Census population records to data + +Prepare the Census population data. + +```{r} +#| label: prepare-census-pop + +# - **varname**: XTOT +# - **count**: 0 +# - **scope**: 0 +# - **agilo**: must be \< -8e99 +# - **agihi**: must be \> 8e99 +# - **fstatus**: 0 +# - **target**: area population + +cdpop <- read_csv(fs::path(CDINTERMEDIATE, "cdpop1year.csv")) +glimpse(cdpop) + +cdpop1 <- cdpop |> + select(STATEFIPS, STATE, CONG_DISTRICT, target=pop2021) |> + mutate(vname="XTOT", + basevname="XTOT", + description = "CD population in 2021 per Census ACS", + AGI_STUB = 0, + agirange = "Total", + agilo = -9e99, + agihi = 9e99, + count = 0, + scope = 0, + fstatus = 0) + +# how well does cdpop merge against the soi data? +soistubs <- cdlong4 |> + select(STATEFIPS, STATE, CONG_DISTRICT, rectype, ndist) |> + distinct() + +fmatch <- soistubs |> + left_join(cdpop1, + by = join_by(STATEFIPS, STATE, CONG_DISTRICT)) + +fmatch |> filter(is.na(target)) # good, we matched on all CDs + +# put rectype and ndist on the cdpop file +cdpop2 <- cdpop1 |> + left_join(fmatch |> + select(STATE, CONG_DISTRICT, rectype, ndist), + by = join_by(STATE, CONG_DISTRICT))# statecd = paste0(STATE, CONG_DISTRICT), + +rm(soistubs, fmatch) + +``` + + +```{r} +#| label: create-cdbasefile + +cdbasefile <- bind_rows(cdlong4 |> + rename(target=value) |> + mutate(src="soi"), + cdpop2 |> mutate(src="census")) |> + mutate(statecd = paste0(STATE, CONG_DISTRICT), + basevname=case_when( + is.na(basevname) & vname=="A00101" ~ "v00101", # special handling + is.na(basevname) ~ vname, + .default = basevname)) |> # THINK ABOUT THIS CAREFULLY + select(src, rectype, stabbr=STATE, cd=CONG_DISTRICT, statecd, + agistub=AGI_STUB, agilo, agihi, basevname, scope, fstatus, count, target, + vname, description, agirange) |> + arrange(statecd, src, scope, fstatus, basevname, count, agistub) + +glimpse(cdbasefile) +summary(cdbasefile) +skim(cdbasefile) + +cdbasefile |> count(basevname) + +cdbasefile |> filter(statecd=="WY00", agistub==0, basevname=="v00100") +cdbasefile |> filter(statecd=="WY00", agistub==0, basevname=="v00101") + +write_csv(cdbasefile, fs::path(CDINTERMEDIATE, "cdbasefile.csv")) + +``` + + diff --git a/tmd/areas/targets/prepare/cd_create_crosswalk_from_cd117th_to_cd118th.qmd b/tmd/areas/targets/prepare/cd_create_crosswalk_from_cd117th_to_cd118th.qmd new file mode 100644 index 00000000..b0196f17 --- /dev/null +++ b/tmd/areas/targets/prepare/cd_create_crosswalk_from_cd117th_to_cd118th.qmd @@ -0,0 +1,226 @@ +--- +output: html_document +editor_options: + chunk_output_type: console +--- + +# Prepare crosswalk from Congressional district boundaries for the 117th Congressional session to those for the 118th session + +IRS SOI data currently available for Congressional districts is based on 117th Congressional session boundaries, which were drawn using information from the 2010 decennial census. These sometimes differ signficantly from current district boundaries, for the 118th Congressional session, which were drawn based on data from the 2020 decennial census. + +To address this, we develop estimates for 118th session districts by allocating targets for 117th session districts to 118th session districts based on the fraction of each 117th district's 2020 population that is present in different 118th session districts. + +For example, California Congressional district 13 (CA-13) as defined in the 118th session includes 3.6% of the 2020 population of 117th session CA-09, 30.2% of the 2020 population of 117th session CA-10, 54.5% of the population of CA-16, and 13.7% of CA-21. To construct estimated targets for 118th session CA-13, we allocate these percentages of each target of the four 117th session districts (CA-09, CA-10, CA-16, and CA-21) to 118th session CA-13. We repeat this process for every 118th session Congressional district, for all potential targets. + +Doing this requires a crosswalk that shows what fraction of the 2020 population of each 117th district appears in each 118th session district. Fortunately, the Missouri Census Data Center (MCDC) has an online tool, [*Geocorr 2022: Geographic Correspondence Engine*](https://mcdc.missouri.edu/applications/geocorr2022.html), that can create such a crosswalk. On October 15, 2024 we used *Geocorr 2022* to create the crosswalk file "geocorr2022_2428906586.csv", in the cds/raw_data folder of this project. We use this file to construct population-weighted targets for 118th session Congressional districts from the 117th session targets. + +## Setup + +```{r} +#| label: setup +#| output: false + +source(here::here("R", "libraries.R")) +source(here::here("R", "constants.R")) + +# phase4_statecds <- c("AK00", "DE00", "ID01", "ID02", "ME02", "MT00", "ND00", "PA08", "SD00", "WY00") + +``` + + +## Get data +```{r} +#| label: get-data +#| output: false + +cd117 <- read_csv(fs::path(CDINTERMEDIATE, "cdbasefile.csv")) + +df <- read_csv(fs::path(CDRAW, "geocorr2022_2428906586.csv")) +glimpse(df) + +(xwlabs <- unlist(df[1, ], use.names = TRUE)) # variable names and labels + +xwalk1 <- df |> + filter(row_number() != 1) |> + rename_with(str_to_lower) |> + rename(stabbr=stab, pop2020=pop20) |> + mutate(across(pop2020:afact, as.numeric), + statecd117=paste0(stabbr, cd117), + statecd118=paste0(stabbr, cd118)) |> + rename(af118to117=afact2, + af117to118=afact) + +``` + + +```{r} +#| label: data-checks +#| output: false + +count(xwalk1, stabbr) # 52 including DC and PR +count(xwalk1, stabbr) |> filter(n==1) +xwalk1 |> filter(stabbr=="PR") + +# check numbers of districts +cd117codes <- unique(xwalk1$statecd117) |> sort() # 438 -- why? +cd118codes <- unique(xwalk1$statecd118) |> sort() # 437 -- why? + +cd117codes # DC98 instead of DC00; PR98; NC seems to have bad codes < 10 +cd118codes # DC98 instead of DC00; PR98; NC codes look ok here + +# do the shares of statecd117 given to various statecd118s add to 1? +xwalk1 |> + summarise(af117to118=sum(af117to118), .by=statecd117) |> + filter(af117to118 != 1) |> + arrange(desc(abs(af117to118 - 1))) # minimal differences from 1 + +# do the shares of statecd118 given to various statecd117s add to 1? +xwalk1 |> + summarise(af118to117=sum(af118to117), .by=statecd118) |> + filter(af118to117 != 1) |> + arrange(desc(abs(af118to117 - 1))) # minimal differences from 1 + +# do the individual shares of af117to118 match what we get with population? +xwalk1 |> + mutate(share117to118=pop2020 / sum(pop2020), .by=statecd117) |> + mutate(diff=share117to118 - af117to118) |> + relocate(af117to118, .before=share117to118) |> + arrange(desc(abs(diff))) # good, they match within small tolerances +# use our calculated amounts + +# do the individual shares of af118to117 match what we get with population? +xwalk1 |> + mutate(share118to117=pop2020 / sum(pop2020), .by=statecd118) |> + mutate(diff=share118to117 - af118to117) |> + relocate(af118to117, .before=share118to117) |> + arrange(desc(abs(diff))) # good, they match within small tolerances + +# how well do the cds match against our 117th cd data? +xwalk2 <- xwalk1 |> + filter(stabbr != "PR") |> + filter(cd117 != "-") |> # not sure what this is and pop2020 is only 13 + # redo codes + mutate( + oldcd117 = cd117, + cd117 = case_when(stabbr=="NC" & nchar(cd117) != 2 ~ + str_pad(as.integer(cd117), width=2, side="left", pad="0"), + .default = cd117), + statecd117=paste0(stabbr, cd117), + statecd118=paste0(stabbr, cd118), + statecd117 = case_when( + statecd117 == "DC98" ~ "DC00", + .default = statecd117), + statecd118 = case_when( + statecd118 == "DC98" ~ "DC00", + .default = statecd118)) + +xwalk2 |> + filter(cd117 != oldcd117) |> + relocate(oldcd117, .after=cd117) + +xwalk2 |> + filter(statecd118 == "NC14") + +# how do the 117th CDs match up? +usoi <- cd117$statecd |> unique() +ugeo <- xwalk2$statecd117 |> unique() + +usoi +ugeo + +setdiff(usoi, ugeo) # none missing +setdiff(ugeo, usoi) # none missing + +check <- xwalk2 |> + filter(stabbr=="NC") + +``` + + +```{r} +#| label: make-save-final-xwalk +#| output: false + +# calc pop shares (so we have more precision than in the source data) and save +xwalk3 <- xwalk2 |> + mutate(share117to118=pop2020 / sum(pop2020), .by=statecd117) + +xwalk3 |> + mutate(diff=share117to118 - af117to118) |> + relocate(af117to118, .before=share117to118) |> + arrange(desc(abs(diff))) # good, they match within small tolerances + +xwalk_final <- xwalk3 |> + select(stabbr, cd117, cd118, statecd117, statecd118, share117to118) + +write_csv(xwalk_final, fs::path(CDINTERMEDIATE, "xwalk_final.csv")) + +``` + + +## Create 118th Session Congressional Districts +```{r} +#| label: create-cd118 +#| output: false + +xwalk <- read_csv(fs::path(CDINTERMEDIATE, "xwalk_final.csv")) + +cd118v1 <- xwalk |> + rename(statecd=statecd118, + cd=cd118) |> + left_join(cd117 |> + select(-cd) |> + rename(statecd117=statecd), + by = join_by(stabbr, statecd117), + relationship = "many-to-many") + +# collapse the file to statecd +cd118v2 <- cd118v1 |> + mutate(target=target * share117to118) |> + summarise(target=sum(target), + .by=c(stabbr, cd, statecd, src, rectype, + agistub, agilo, agihi, basevname, + scope, fstatus, count, vname, description, agirange)) + +glimpse(cd118v2) +summary(cd118v2) +write_csv(cd118v2, fs::path(CDINTERMEDIATE, "cdbasefile_118.csv")) + + +``` + + +```{r} + + +cd117 <- read_csv(fs::path(CDINTERMEDIATE, "cdbasefile.csv")) +cd118 <- read_csv(fs::path(CDINTERMEDIATE, "cdbasefile_118.csv")) + +stack <- bind_rows( + cd117 |> mutate(session="s117"), + cd118 |> mutate(session="s118") +) + +glimpse(stack) +states <- stack |> + summarise(target=sum(target), + .by=c(session, stabbr, src, rectype, + agistub, agilo, agihi, basevname, + scope, fstatus, count, vname, description, agirange)) + +states |> + pivot_wider(names_from = session, + values_from = target) |> + mutate(diff=s118 - s117, + pdiff=diff / s117) |> + arrange(desc(abs(pdiff))) # good all the state sums work + +write_csv(stack, fs::path(CDINTERMEDIATE, "cdbasefile_sessions.csv")) + +``` + + + + + + diff --git a/tmd/areas/targets/prepare/cd_download_soi_data.qmd b/tmd/areas/targets/prepare/cd_download_soi_data.qmd new file mode 100644 index 00000000..e6b38e1a --- /dev/null +++ b/tmd/areas/targets/prepare/cd_download_soi_data.qmd @@ -0,0 +1,43 @@ +--- +output: html_document +editor_options: + chunk_output_type: console +--- + +# Download Congressional District data and documentation files + +NOTE: To download data, change eval: to true in the header of the downloads chunk. Otherwise, this program assumes data already have been downloaded. + +## Setup + +```{r} +#| label: setup + +source(here::here("R", "libraries.R")) +source(here::here("R", "constants.R")) + +``` + + +## Download CD data and documentation if not already downloaded + +- [SOI Congressional Districts landing page](https://www.irs.gov/statistics/soi-tax-stats-data-by-congressional-district) + + - [2021 folder](https://www.irs.gov/statistics/soi-tax-stats-data-by-congressional-district-2021) + + +```{r} +#| label: downloads +#| eval: false + +# data +fname <- fs::path_file(CDZIPURL) +download.file(url = CDZIPURL, destfile = fs::path(CDRAW, fname), mode = "wb") + +# documentation +fname <- fs::path_file(CDDOCURL) +download.file(url = CDDOCURL, destfile = fs::path(CDRAW, fname), mode = "wb") + +``` + + diff --git a/tmd/areas/targets/prepare/cd_get_census_population.qmd b/tmd/areas/targets/prepare/cd_get_census_population.qmd new file mode 100644 index 00000000..cbec8c94 --- /dev/null +++ b/tmd/areas/targets/prepare/cd_get_census_population.qmd @@ -0,0 +1,94 @@ +--- +output: html_document +editor_options: + chunk_output_type: console +--- + +# Get Congressional District Census population data + +## Setup + +```{r} +#| label: setup + +source(here::here("R", "libraries.R")) +source(here::here("R", "constants.R")) + +# 334283385.27000004 national pop + +``` + + +## Get Congressional District population + +```{r} +#| label: cdpop-download +#| eval: false + +# Note that 2021 gets data from the 116th Congress and 2022 gets the 218th Congress + +cdpop5year <- get_acs( + geography = "congressional district", + variables = "B01003_001", # Total population variable + year = 2021, + survey = "acs5" # Use 5-year estimates for better coverage, especially in smaller areas +) +write_csv(cdpop5year, fs::path(CDRAW, "cdpop5year_acs.csv")) + +cdpop1year <- get_acs( + geography = "congressional district", + variables = "B01003_001", # Total population variable + year = 2021, + survey = "acs1" # Use 5-year estimates for better coverage, especially in smaller areas +) +write_csv(cdpop1year, fs::path(CDRAW, "cdpop1year_acs.csv")) + +``` + +## Clean Congressional District population + +```{r} +#| label: cdpop-clean + +cdpop1year <- read_csv(fs::path(CDRAW, "cdpop1year_acs.csv")) +cdpop5year <- read_csv(fs::path(CDRAW, "cdpop5year_acs.csv")) + +cdpop1year |> summarise(estimate=sum(estimate)) # 335157329 +cdpop5year |> summarise(estimate=sum(estimate)) # 333036755 + + +stcodes <- tigris::states() |> + as.data.frame() |> + select(STATEFIPS=STATEFP, STATE=STUSPS) + +cdpop1 <- cdpop1year |> + mutate(STATEFIPS = str_sub(GEOID, 1, 2), + CONG_DISTRICT = str_sub(GEOID, 3, 4)) |> + left_join(stcodes, by = join_by(STATEFIPS)) |> + filter(STATE != "PR") |> # we're not using Puerto Rico + mutate(CONG_DISTRICT = ifelse(STATE == "DC", + "00", # Census data has 98 for DC + CONG_DISTRICT)) |> + select(STATEFIPS, STATE, CONG_DISTRICT, cdname=NAME, pop2021=estimate) + +count(cdpop1, STATEFIPS, STATE) + +cdpop1 |> filter(STATE=="NY") + +cdpop1 |> filter(STATE=="AK") +cdpop1 |> filter(STATE=="DC") +cdpop1 |> filter(STATE=="WY") + +count(cdpop1, CONG_DISTRICT) +count(cdpop1, STATEFIPS, STATE) + +glimpse(cdpop1) +sum(cdpop1$pop2021) # 331,893,745 compared to Martin's 334,283,385 + +write_csv(cdpop1, fs::path(CDINTERMEDIATE, "cdpop1year.csv")) + + +``` + + + diff --git a/tmd/areas/targets/prepare/cd_issues_and_TODOs.qmd b/tmd/areas/targets/prepare/cd_issues_and_TODOs.qmd new file mode 100644 index 00000000..418a8c5c --- /dev/null +++ b/tmd/areas/targets/prepare/cd_issues_and_TODOs.qmd @@ -0,0 +1,37 @@ +--- +output: html_document +editor_options: + chunk_output_type: console +--- + + +# Issues and TODOs + +### Investigate collapsed AGI ranges in the SOI data + +- Per IRS documentation: "Income and tax items with less than 20 returns for a particular AGI class were combined with another AGI class within the same congressional district. Collapsed AGI classes are identified with a double asterisk (dropped) in the Excel files." **This will require attention soon.** + +### Defining number of returns with AGI + +- We have an issue with AGI: for Congressional Districts IRS does NOT report the number of returns with AGI. They report two variables that should be close in concept: N1 (Number of returns), and N02650 (Number of returns with total income). For all CDs in the U.S. N1 was 157,375,370 in 2021 and N02650 was 155,283,590. Since N1 is larger and is probably a better indicator of total number of filers, we'll use that as the number-of-returns counterpart to AGI (c00100). We address this late in the process because for most of the data development we try to keep the data faithful to what IRS reports and because the solution chosen here may be suboptimal and we may want to change it later. + +### Census population + +Used to create the row 0 "XTOT" (population) target as a way to develop an initial scaling ratio: `initial_weights_scale = row.target / national_population`. + +- Project currently uses 2021 population by Congressional District obtained from 1-year ACS via R package tidycensus. tidycensus reports that these are from the 116th Congressional session, not 117th (as I had expected). Seems like they must be appropriate, but requires verification. + +- 2021 was a strange year for population data because of Covid. It might be better to use the 5-year ACS ending in 2021 for more stable and representative data. + +### Estimate targets for 118th Congress district boundaries weighting by population weights + +- Use already-created population crosswalk from Missouri Census Data Center to allocate 117th Congress district targets to 118th Congress districts. See [MCDC site](https://mcdc.missouri.edu/applications/geocorr2022.html). + +### Address SALT deductions + +Two possible approaches: + +- Calculate total relevant 2021 deductions in the TMD 2021 file ("e" variables), by AGI range. Allocate that to CDs based on their share of corresponding SOI amounts among all CDs. Easy, straightforward, does not require changes to optimization cache variables. + +- Add TMD 2021 "c" variables to the optimization cache. Map these variables to SOI variables based on their share of corresponding SOI amounts among all CDs. Requires modifying the cache in create_area_weights.py. + diff --git a/tmd/areas/targets/prepare/cd_map_tcvars_and_extract_target_files.qmd b/tmd/areas/targets/prepare/cd_map_tcvars_and_extract_target_files.qmd new file mode 100644 index 00000000..8b37fab5 --- /dev/null +++ b/tmd/areas/targets/prepare/cd_map_tcvars_and_extract_target_files.qmd @@ -0,0 +1,116 @@ +--- +output: html_document +editor_options: + chunk_output_type: console +--- + +# Map tax calculator vars to soi vars and extract targets + + +```{r} +#| label: setup + +source(here::here("R", "libraries.R")) +source(here::here("R", "constants.R")) + +# 334283385.27000004 national pop + + +``` + +## Get needed data + +```{r} +#| label: get-cdbasefile + +cdbase <- read_csv(fs::path(CDINTERMEDIATE, "cdbasefile.csv")) + +``` + +## Create variable mapping + +```{r} +#| label: tc-soi-variablemap + +soivars <- count(cdbase, basevname) +soivars$basevname + +# the MARS mappings let us get counts by filing status by agi range +vmap <- read_csv(file=" +tcvar, soivar +XTOT, XTOT +c00100, MARS1 +c00100, MARS2 +c00100, MARS4 +c00100, v00100 +e00200, v00200 +e00300, v00300 +e01700, v01700 +e26270, v26270 +") + +``` + +```{r} +#| label: mapped-file + +mapped <- cdbase |> + filter(basevname %in% vmap$soivar) |> + mutate(varname=factor(basevname, levels=vmap$soivar, labels=vmap$tcvar)) + +count(mapped, varname, vname) + +``` + + +```{r} +#| label: extracts + +# varname,count,scope,agilo,agihi,fstatus,target +# XTOT, 0, 0,-9e99, 9e99, 0, 33e6 +# e00300, 0, 1,-9e99, 9e99, 0, 20e9 + +# define extracts we want + +phase4cds <- c("AK00", "DE00", "ID01", "ID02", "ME02", "MT00", "ND00", "PA08", "SD00", "WY00") +# statecds <- "NY21" + +statecds <- phase4cds + +extracted <- mapped |> + filter(statecd %in% statecds) |> + arrange(statecd, src, scope, fstatus, basevname, count, agistub) # to be safe + +count(extracted, statecd) +count(extracted, basevname, varname) + +targets <- extracted |> + filter(varname %in% c("XTOT", "c00100", "e00200", "e26270")) |> + filter(varname == "XTOT" | (agistub != 0)) |> + filter(!(count == 1 & (varname != "c00100"))) |> + select(statecd, varname, count, scope, agilo, agihi, fstatus, target) + +check <- targets |> + filter(statecd == "AK00") + +f <- function(data, group){ + cd <- group$statecd |> + str_to_lower() + fname <- paste0(cd, "_targets.csv") + fpath <- fs::path(CDFINAL, fname) + print(fpath) + write_csv(data, fpath) +} + +targets |> + group_by(statecd) |> + group_walk(~f(.x, .y)) + + +# write_csv(targets, fs::path(CDFINAL, "ny21_targets.csv")) + + +``` + + + diff --git a/tmd/areas/targets/prepare/cd_overall_documentation.qmd b/tmd/areas/targets/prepare/cd_overall_documentation.qmd new file mode 100644 index 00000000..48058751 --- /dev/null +++ b/tmd/areas/targets/prepare/cd_overall_documentation.qmd @@ -0,0 +1,139 @@ +--- +output: html_document +editor_options: + chunk_output_type: console +--- + +# About the data + +This chapter has two sections: + +- IRS documentation: Copied verbatim from the IRS SOI data documentation (21incddocguide.docx), with no substantive edits and no commentary. + +- Comments on the data: Notes about selected issues, quirks, and pitfalls in the data discovered from working with the data. + +## IRS documentation + +All text in this section is a direct quote from IRS documentation. + +### Time period + +The Statistics of Income (SOI) Division’s Congressional district data is tabulated using individual income tax returns (Forms 1040) filed with the Internal Revenue Service (IRS) during the 12-month period, January 1, 2022 to December 31, 2022. While the bulk of returns filed during this 12-month period are primarily for Tax Year 2021, the IRS received a limited number of returns for tax years before 2021. These prior-year returns are used as a proxy for returns that are typically filed beyond the 12-month period and have been included within the congressional district data. + +### Population Definitions and Tax Return Addresses + +- Congressional data are based on the population of individual income tax returns processed by the IRS during the 2022 calendar year. + +- Returns filed for the purpose of receiving an Economic Impact Payment, due to COVID-19, were excluded from the data. + +- State totals within the Congressional data may not be comparable to State totals published elsewhere by SOI because of disclosure protection procedures or the exclusion of returns that did not match based on the ZIP code. See footnote for complete State totals. \[2\] + +- Data do not represent the full U.S. population because many individuals are not required to file an individual income tax return. + +- The address shown on the tax return may differ from the taxpayer’s actual residence. + +- Congressional districts were based on the ZIP code shown on the return. + +- Tax returns filed without a ZIP code and returns filed with a ZIP code that did not match the State code shown on the return were excluded. + +- Tax returns filed using Army Post Office (APO) and Fleet Post Office addresses, foreign addresses, and addresses in Puerto Rico, Guam, Virgin Islands, American Samoa, Marshall Islands, Northern Marianas, and Palau were excluded. + +### Congressional District and ZIP Code Matching Procedures + +SOI uses a commercial file to match ZIP codes to congressional districts. Congressional districts cover the 435 congressional districts in the 50 states and the District of Columbia. District boundaries are based on the 117th Congress. + +The matching process first utilizes the 9-digit ZIP code, if present on the return, to determine the proper congressional district for that return. Nearly 97 percent of the returns match on the 9-digit ZIP code. When the 9-digit ZIP code is not available, the matching process uses the 5-digit ZIP code to determine the proper congressional district. Returns that do not match on ZIP code, or where a ZIP code is not present, are excluded from the data. + +Eight states (AK, DC, DE, MT, ND, SD, VT, and WY) have only one congressional district, therefore the matching procedures are not performed on these states. Returns with only one congressional district represent 2 percent of the total number of returns. + +### Disclosure Protection Procedures + +SOI did not attempt to correct any ZIP codes listed on the tax returns; however, it did take the following precautions to avoid disclosing information about specific taxpayers: + +- Income and tax items with less than 20 returns for a particular AGI class were combined with another AGI class within the same congressional district. Collapsed AGI classes are identified with a double asterisk (\*\*) in the Excel files. + +- Income and tax items with less than 20 returns for a congressional district were excluded. + +- If an income or tax item from one return constitutes more than a specified percentage of the total of any particular cell, the specific data item for that return is excluded from that cell. For example, if the amount for wages from one return represents 75 percent of the value of the total for that cell, the data item will be suppressed. The actual threshold percentage used cannot be released. + +### IRS Endnotes + +[1] The use of prior-year returns as a proxy for returns that are filed beyond the current processing year is consistent with SOI’s national, state, county, and ZIP code tabulations. A description of SOI’s sample, which is used as an input for the geographic data, and the use of prior-year returns, can be found at https://www.irs.gov/statistics/soi-tax-stats-individual-income-tax-returns-publication-1304-complete-report#_sec2. + +[2] For complete individual income tax tabulations at the State level, see the historic table posted to Tax Stats at http://www.irs.gov/uac/SOI-Tax-Stats---Historic-Table-2. + +[3] The State Federal Information Processing System (FIPS) codes used for these statistics were derived from the U.S. Census Bureau. A complete list of codes can be obtained from https://www.census.gov/geo/reference/codes/cou.html. + +[4] "Number," here, and elsewhere represents number of returns, unless otherwise specified. The number of returns have been rounded to the nearest ten. + +[5] Beginning in 2018, personal exemption deductions were suspended for the primary, secondary, and dependent taxpayers. However, the data used to create the “Number of individuals”—filing status, dependent status indicator, and identifying dependent information—are still available on the Form 1040. This field is based on these data. + +[6] The "Number of volunteer prepared returns" shows counts of returns prepared by IRS-certified volunteers to taxpayers with limited income, persons with disabilities, limited English speaking taxpayers, current and former members of the military, and taxpayers who are 60 years of age and older. + +[7] These data do not distinguish between refund anticipation loans and refund advances. + +[8] Includes primary taxpayers 60 years of age and older. + +[9] Less deficit. + +[10] “Qualified dividends” are ordinary dividends received in tax years beginning after 2002 that meet certain conditions and receive preferential tax rates. + +[11] Includes the Alaskan permanent fund, reported by residents of Alaska on Forms 1040A and 1040EZ's. +This fund only applies to statistics in the totals, and the state of Alaska. + +[12] “Total tax credits" represent the summation of taxpayer reported credit items made up in the calculation of the total credits line of Form 1040. It does not include the "earned income credit" and "refundable education credit," which are shown separately below in the table. + +[13] Earned income credit includes both the refundable and non-refundable portions. The non-refundable portion could reduce income tax and certain related taxes to zero. The earned income credit amounts in excess of total tax liability, or amounts when there was no tax liability at all, were refundable. See footnote 14 below for explanation of the refundable portion of the earned income credit. + +[14] The refundable portion of the earned income credit equals total income tax minus the earned income credit. If the result is negative, this amount is considered the refundable portion. No other refundable credits were taken into account for this calculation. + +[15] The "refundable education credit" can partially or totally offset tax liability as well as be totally refundable. + +[16] The 2021 recovery rebate credit was created by the American Rescue Plan Act of 2021, Public Law 117-2, 135 Stat. 4 (March 11, 2021). The recovery rebate credit was a credit against income tax for tax year 2021, but the American Rescue Plan Act of 2021 directed the IRS to make advance refunds of the recovery rebate credit “as rapidly as possible,” using information from tax year 2020 returns. + +An advance refund of the 2021 recovery rebate credit made under section 6428B of the Internal Revenue Code (Code), which was added by the American Rescue Plan Act of 2021, is referred to as a third round Economic Impact Payment (EIP). The dollar amount of the 2021 recovery rebate credit received by the taxpayer when they file their 2021 return is equal to a tentative amount, calculated based on the taxpayer’s 2021 return information, which is then reduced (but not below zero) by the amount of the third round EIP received by the taxpayer. The number of returns reported here for the 2021 recovery rebate credit is the number of returns that claimed some portion of the recovery rebate credit on their tax year 2021 return. +(A taxpayer would want to claim the recovery rebate credit if they did not receive the full amount of the credit as an EIP, which could happen, for example, if their income or number of dependent children on their 2021 return was different than on the 2020 return used as the basis for determining their EIP amounts). The amount reported here for the 2021 recovery rebate credit is the amount of the recovery rebate credit claimed by taxpayers on 2021 returns, which does not include the amount received as EIPs. + +[17] This table includes only payments issued to taxpayers who filed tax year 2021 returns. Individuals who received a third round Economic Impact Payment but did not file a 2021 return are excluded from these tabulations. For tabulations that include all recipients of third round EIPs, see: https://www.irs.gov/statistics/soi-tax-stats-coronavirus-aid-relief-and-economic-security-act-cares-act-statistics + + +[18] Section 6428B of the Internal Revenue Code (Code) directed the IRS to use information from tax year 2020 returns to determine eligibility for and the amount of the third round EIP. In contrast, this table is based primarily on information from tax year 2021 returns. Income, location, and household composition may have changed between the 2020 return used for the third round EIP and the 2021 return used for this table. Many taxpayers who would have been ineligible to claim the 2021 recovery rebate credit on their +2021 return because their 2021 income exceeded the phaseout region for the credit were, nevertheless, eligible for a third round EIP on the basis of their 2020 income. Section 6428B of the Code did not include any provisions for otherwise eligible taxpayers who had income in the eligible range in 2020 but whose income exceeded the eligible range in 2021 to pay back any of the third round EIP. For details on eligibility criteria, amount, and phaseout structure of the third round EIP, see: https://www.irs.gov/statistics/soi-tax-stats-coronavirus-aid-relief-and-economic-security-act-cares-act-statistics + +[19] “Total tax liability” differs from “Income tax”, in that “Total tax liability” includes the taxes from recapture of certain prior-year credits, tax applicable to individual retirement arrangements (IRA's), social security taxes on self-employment income and on certain tip income, advanced earned income payments, household employment taxes, and certain other taxes listed in the Form 1040 instructions. + +[20] Reflects payments to or withholdings made to "Total tax liability". This is the amount the tax filer owes when the income tax return is filed. + +[21] The amount of overpayments the tax filer requested to have refunded. + + +## Comments on the data + +### Determining which records are Congressional District records + +- Calculate nstub0 -- number of records by state where AGI_STUB == 0 (the totals record) +- Note that CONG_DISTRICT == "00" is a totals record for the state. There are 8 states that only have 1 CD (see IRS documentation above), and for those states this record doubles as a CD record and as the state record. +- Determine type of record: + - US -- STATE == "US" + - DC -- STATE == "DC" + - state -- nstub0 \> 1 & CONG_DISTRICT == "00" + - cdstate -- nstub0 == 1 (this is both a state record and a CD record, for 8 states) + - cd -- nstub0 \> 1 & CONG_DISTRICT != "00" + +The cd and cdstate records have data for Congressional Districts. There are 435 of these for AGI_STUB == 0 - one for each voting Congressional District (not including the District of Columbia). SOI data also have records for the nonvoting DC district. It is not included in the 435 Congressional Districts . + +The state and cdstate records have data for states. There are 51 of these (4) + +![](images/clipboard-719051713.png) + +To verify that this produces a proper calculation of the number of districts by state, I asked ChatGPT (4o) the following question, and compared the results by state to the calculation above. They are the same. + +> Please give me a table of the number of congressional districts by state (plus the District of Columbia), based on the 117th Congress, ideally as a google sheet or exportable to a spreadsheet. It should have 3 columns: state postal abbreviation, state name, and number of districts. It should add to 435 districts, I believe. + +### Exemptions + +Note that there are no data on exemptions but we do have total number of individuals (N2). + +When run on 2024-10-12 tmd national population was 334,283,385 (\`national_population = (vardf.s006 \* vardf.XTOT).sum()\`). By contrast, the sum of N2 for the U.S. was 289,054,220, or 13.5% less, according to 21incdall.xlsx. + +FWIW, IRS total number of returns in was 160,824,340 per 21in14ar.xls. When run on 2024-10-12 tmd sum of s006 was 184,024,657, or 14.4% more. 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