SNAP_Amber

Author

CRG

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.

library(tidycensus)
library(tidyverse)

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


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

library(tidycensus)
library(tidyverse)

# ── 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)     508526  50.1
2 Children (under 18)            314262  31.0
3 Seniors (60+)                  191481  18.9

# ── Total newly eligible ────────────────────────────────────────────
newly_eligible <- target_pop %>%
  summarise(total = sum(PWGTP))

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


Total newly eligible: 1,014,269

# ── Scenarios with age breakdown ────────────────────────────────────
avg_monthly_benefit <- 188

participation_rates <- c(
  "Conservative (65%)" = 0.65,
  "Texas-Specific (69%)" = 0.69,
  "Mid (75%)" = 0.75,
  "High (82%)" = 0.82
)

# Build age-specific scenarios
age_scenarios <- age_breakdown %>%
  crossing(
    tibble(
      scenario = names(participation_rates),
      participation_rate = participation_rates
    )
  ) %>%
  mutate(
    estimated_enrollees = round(population * participation_rate),
    annual_cost = estimated_enrollees * avg_monthly_benefit * 12,
    annual_cost_fmt = paste0("$", scales::comma(round(annual_cost)))
  ) %>%
  select(scenario, age_group, population, estimated_enrollees, annual_cost_fmt)

# Overall totals by scenario
scenarios <- tibble(
  scenario = names(participation_rates),
  participation_rate = participation_rates,
  estimated_enrollees = round(newly_eligible$total * participation_rates),
  annual_cost = estimated_enrollees * avg_monthly_benefit * 12
) %>%
  mutate(annual_cost_fmt = paste0("$", scales::comma(round(annual_cost))))

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


── Overall Cost Scenarios ──────────────────────────────────────

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

# A tibble: 4 × 3
  scenario             estimated_enrollees annual_cost_fmt
  <chr>                              <dbl> <chr>          
1 Conservative (65%)                659275 $1,487,324,400 
2 Texas-Specific (69%)              699846 $1,578,852,576 
3 Mid (75%)                         760702 $1,716,143,712 
4 High (82%)                        831701 $1,876,317,456

cat("\n── Enrollment by Age Group and Scenario ───────────────────────\n")


── Enrollment by Age Group and Scenario ───────────────────────

age_scenarios %>%
  select(scenario, age_group, estimated_enrollees, annual_cost_fmt) %>%
  arrange(scenario, age_group) %>%
  print()

# A tibble: 12 × 4
   scenario             age_group            estimated_enrollees annual_cost_fmt
   <chr>                <chr>                              <dbl> <chr>          
 1 Conservative (65%)   Children (under 18)               204270 $460,833,120   
 2 Conservative (65%)   Seniors (60+)                     124463 $280,788,528   
 3 Conservative (65%)   Working-Age Adults …              330542 $745,702,752   
 4 High (82%)           Children (under 18)               257695 $581,359,920   
 5 High (82%)           Seniors (60+)                     157014 $354,223,584   
 6 High (82%)           Working-Age Adults …              416991 $940,731,696   
 7 Mid (75%)            Children (under 18)               235696 $531,730,176   
 8 Mid (75%)            Seniors (60+)                     143611 $323,986,416   
 9 Mid (75%)            Working-Age Adults …              381394 $860,424,864   
10 Texas-Specific (69%) Children (under 18)               216841 $489,193,296   
11 Texas-Specific (69%) Seniors (60+)                     132122 $298,067,232   
12 Texas-Specific (69%) Working-Age Adults …              350883 $791,592,048

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             172971                  129728 $292,666,368   
 2 48113  Dallas TX              86243                   64682 $145,922,592   
 3 48439  Tarrant TX             74531                   55898 $126,105,888   
 4 48029  Bexar TX               72172                   54129 $122,115,024   
 5 48215  Hidalgo TX             42515                   31886 $71,934,816    
 6 48141  El Paso TX             35231                   26423 $59,610,288    
 7 48453  Travis TX              28147                   21110 $47,624,160    
 8 48085  Collin TX              20459                   15344 $34,616,064    
 9 48355  Nueces TX              20050                   15038 $33,925,728    
10 48121  Denton TX              19992                   14994 $33,826,464    
11 48339  Montgomery TX          19607                   14705 $33,174,480    
12 48157  Fort Bend TX           18660                   13995 $31,572,720    
13 48027  Bell TX                18523                   13892 $31,340,352    
14 48479  Webb TX                16669                   12502 $28,204,512    
15 48167  Galveston TX           15955                   11966 $26,995,296    
16 48039  Brazoria TX            15828                   11871 $26,780,976    
17 48061  Cameron TX             15066                   11300 $25,492,800    
18 48491  Williamson TX          14378                   10784 $24,328,704    
19 48139  Ellis TX               11553                    8665 $19,548,240    
20 48423  Smith TX               10421                    7816 $17,632,896

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.

write_csv(age_breakdown, "C:/Users/rayo-garza/Downloads/snap_age_breakdown2.csv")

write_csv(scenarios, "C:/Users/rayo-garza/Downloads/snap_scenarios.csv")

write_csv(county_estimates, "C:/Users/rayo-garza/Downloads/snap_county_estimates.csv")

cat("All tables exported to Downloads folder\n")

All tables exported to Downloads folder