SNAP_Amber

SNAP

The request: “Ask Coda if we can get numbers on how many more people this would scoop up.”

Results: Analysis of 2023 American Community Survey Public Use Microdata (ACS PUMS) estimates that approximately 1,073,313 Texans currently fall within the income band between 165% and 185% of the federal poverty level, meaning they are ineligible for SNAP under Texas’s current threshold but would qualify if the state aligned its standard with WIC and NSLP at 185% FPL. Of this population, 334,808 are children under 18 (31%), 536,586 are working-age adults (50%), and 201,919 are seniors aged 60 and older (19%). Applying Texas-specific participation rate assumptions, estimated new SNAP enrollment ranges from approximately 697,653 under a conservative scenario to 804,985 under a mid-range scenario. Because SNAP is federally funded, this expansion would bring an estimated $1.6 to $1.8 billion in additional federal nutrition dollars into Texas annually, with minimal state budget exposure beyond administrative costs.

setwd("C:/Users/rayo-garza/Downloads")
# quarto::quarto_render("SNAP_Eligibility_Alignment.qmd")

library(tidycensus)

Warning: package 'tidycensus' was built under R version 4.5.3

library(tidyverse)

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Warning: package 'stringr' was built under R version 4.5.3

Warning: package 'forcats' was built under R version 4.5.3

Warning: package 'lubridate' was built under R version 4.5.3

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.2.0     ✔ readr     2.2.0
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.2     ✔ tibble    3.3.1
✔ lubridate 1.9.5     ✔ tidyr     1.3.2
✔ purrr     1.2.1     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

tx_pums <- get_pums(
  variables = c("POVPIP", "AGEP"),
  state = "TX",
  year = 2023,
  survey = "acs1"
)

Getting data from the 2023 1-year ACS Public Use Microdata Sample

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# Filter to 165–185% FPL
target_pop <- tx_pums %>%
  filter(POVPIP >= 165 & POVPIP <= 185)

library(tidycensus)
library(tidyverse)

#Filter to 165–185% FPL band 
# These are people currently ineligible who would newly qualify
target_pop <- tx_pums %>%
  filter(POVPIP >= 165 & POVPIP <= 185)

# ──--Age group breakdown ─────
age_breakdown <- target_pop %>%
  mutate(age_group = case_when(
    AGEP < 18              ~ "Children (under 18)",
    AGEP >= 18 & AGEP < 60 ~ "Working-Age Adults (18–59)",
    AGEP >= 60             ~ "Seniors (60+)"
  )) %>%
  group_by(age_group) %>%
  summarise(
    population = sum(PWGTP),
    pct = sum(PWGTP) / sum(target_pop$PWGTP) * 100
  ) %>%
  arrange(desc(population))

cat("\n── Newly Eligible Population by Age Group ──\n")

── Newly Eligible Population by Age Group ──

print(age_breakdown)

# A tibble: 3 × 3
  age_group                  population   pct
  <chr>                           <dbl> <dbl>
1 Working-Age Adults (18–59)     536586  50.0
2 Children (under 18)            334808  31.2
3 Seniors (60+)                  201919  18.8

# ── Total newly eligible ───────────────────────────────────────────
# Applying person weights...PWGTP is the Census person weight whereby each record represents N real people
newly_eligible <- target_pop %>%
  summarise(total = sum(PWGTP))

cat("\nTotal newly eligible:", scales::comma(newly_eligible$total), "\n")

Total newly eligible: 1,073,313 

# ── Participation rate scenarios ───────────────────────────────────
# Texas participation rate...need to verify with AO which to use.
# Source: https://sph.uth.edu/research/centers/dell/legislative-initiatives/tx-rpc-project-reports/SNAP%20Gap%20Brief_2023.pdf
# Average monthly SNAP benefit per person in Texas
# Source: https://www.cbpp.org/sites/default/files/atoms/files/snap_factsheet_texas.pdf
avg_monthly_benefit <- 188

scenarios <- tibble(
  scenario = c("Conservative (65%)", "Mid (75%)", "High (82%)"),
  participation_rate = c(0.65, 0.75, 0.82),
  estimated_enrollees = newly_eligible$total * c(0.65, 0.75, 0.82),
  annual_cost = estimated_enrollees * avg_monthly_benefit * 12
) %>%
  mutate(
    estimated_enrollees = round(estimated_enrollees),
    annual_cost_fmt = paste0("$", scales::comma(round(annual_cost)))
  )

cat("\n── Cost Scenarios ──────────────────────────────────────────────\n")

── Cost Scenarios ──────────────────────────────────────────────

scenarios %>%
  select(scenario, estimated_enrollees, annual_cost_fmt) %>%
  print()

# A tibble: 3 × 3
  scenario           estimated_enrollees annual_cost_fmt
  <chr>                            <dbl> <chr>          
1 Conservative (65%)              697653 $1,573,906,183 
2 Mid (75%)                       804985 $1,816,045,596 
3 High (82%)                      880117 $1,985,543,185 

Interesting State Level Analysis

This could be something we use for other SNAP work…Maybe the HARM grant??

library(tidycensus)
library(tidyverse)

# Re-pull PUMS with PUMA variable ────────────────────────
tx_pums_county <- get_pums(
  variables = c("POVPIP", "AGEP", "PUMA"),
  state = "TX",
  year = 2023,
  survey = "acs1"
)

Getting data from the 2023 1-year ACS Public Use Microdata Sample

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# Filter to 165–185% FPL band ───────────────────────────
target_pop_puma <- tx_pums_county %>%
  filter(POVPIP >= 165 & POVPIP <= 185)

# PUMA-to-county crosswalk ────────────────────------------─────
# crosswalk from here: https://mcdc.missouri.edu/geography/sumlevs/geocorr2022_tx_puma_county.csv

crosswalk <- read_csv("C:/Users/rayo-garza/Downloads/geocorr2022_2608606279.csv") %>%
  slice(-1) %>%  #second label row MCDC always includes...taking it out
  mutate(
    afact = as.numeric(afact),
    puma22 = as.character(puma22)
  ) %>%
  filter(!is.na(afact))

Rows: 401 Columns: 8

── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (8): state, puma22, county, stab, CountyName, PUMA22name, pop20, afact

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Join and allocate population to counties ──────────────
county_estimates <- target_pop_puma %>%
  mutate(PUMA = as.character(PUMA)) %>% 
  rename(puma22 = PUMA) %>%
  left_join(crosswalk, by = "puma22", relationship = "many-to-many") %>%
  mutate(allocated_weight = PWGTP * afact) %>%
  group_by(county, CountyName) %>%
  summarise(
    newly_eligible = round(sum(allocated_weight, na.rm = TRUE)),
    .groups = "drop"
  ) %>%
  mutate(
    estimated_enrollees_mid = round(newly_eligible * 0.75),
    annual_cost_mid = paste0("$", scales::comma(round(estimated_enrollees_mid * 188 * 12)))
  ) %>%
  arrange(desc(newly_eligible))

cat("\n── Top 20 Counties by Newly Eligible Population ──\n")

── Top 20 Counties by Newly Eligible Population ──

print(head(county_estimates, 20))

# A tibble: 20 × 5
   county CountyName    newly_eligible estimated_enrollees_mid annual_cost_mid
   <chr>  <chr>                  <dbl>                   <dbl> <chr>          
 1 48201  Harris TX             186165                  139624 $314,991,744   
 2 48113  Dallas TX              92108                   69081 $155,846,736   
 3 48439  Tarrant TX             78496                   58872 $132,815,232   
 4 48029  Bexar TX               74378                   55784 $125,848,704   
 5 48215  Hidalgo TX             43038                   32278 $72,819,168    
 6 48141  El Paso TX             37700                   28275 $63,788,400    
 7 48453  Travis TX              29067                   21800 $49,180,800    
 8 48085  Collin TX              21100                   15825 $35,701,200    
 9 48027  Bell TX                21060                   15795 $35,633,520    
10 48355  Nueces TX              20982                   15736 $35,500,416    
11 48121  Denton TX              20896                   15672 $35,356,032    
12 48339  Montgomery TX          20033                   15025 $33,896,400    
13 48157  Fort Bend TX           19116                   14337 $32,344,272    
14 48061  Cameron TX             17985                   13489 $30,431,184    
15 48479  Webb TX                17286                   12964 $29,246,784    
16 48039  Brazoria TX            16436                   12327 $27,809,712    
17 48167  Galveston TX           16238                   12178 $27,473,568    
18 48491  Williamson TX          15058                   11294 $25,479,264    
19 48139  Ellis TX               11853                    8890 $20,055,840    
20 48303  Lubbock TX             10632                    7974 $17,989,344    

• Harris County alone accounts for 186,165 newly eligible residents…more than 17% of the statewide total. This represents an estimated $315 million in annual federal benefits

• The four largest urban counties (Harris, Dallas, Tarrant, and Bexar) collectively account for 431,147 newly eligible Texans, or roughly 40% of the statewide total.

• Border counties are significantly represented, with Hidalgo, El Paso, Cameron, and Webb counties together accounting for over 115,000 newly eligible residents

• Every major Texas metropolitan area has newly eligible populations in the tens of thousands, indicating this is a statewide issue rather than isolated to any single region…this is important because in many SNAP participation maps out there (including the one we have in our data hub), the border region stands out…this helps counter narrative about that.