Secondary Analysis

Questions to answer:

NOTE: I used 2024 data to answer these, but can easily look at it for another year or overtime.

Here’s a sample of the data.

Which district types have the biggest discrepancy between cost of living and average salary in 2024?

By TEA district type

Expand this to see code 
above_below_alice |> filter(end_year == 2024) |> 
  group_by (tea_description) |> 
  summarize( avg_difference = mean(difference),
             total_districts_of_this_type = n(),
             total_students_affected = sum(total_enrollment)) |> 
  arrange(avg_difference)

Of the TEA district types, Non-metropolitan Fast Growing districts have the worst discrepancy between the average salary for a district and that district’s county’s ALICE cost of living. There are 34 districts and almost 62,000 students that fall into this category of Non-metropolitan fast-growing.

By bill category (enrollment)

Expand this to see code 
above_below_alice |> filter(end_year == 2024) |> 
  group_by (bill_category) |> 
  summarize( avg_difference = mean(difference),
             total_districts_of_this_type = n(),
             total_students_affected = sum(total_enrollment)) |> 
  arrange(avg_difference)

Of the categories outlined by the bill (enrollment-based), districts that have under 1600 students (but more than 300) have the worst discrepancy between the average salary for a district and that district’s county’s ALICE cost of living. There are 425 districts and about 340,000 students that fall into this category of under 1600 enrollment.

What proportion of districts in each category are above/below alice threshold in 2024?

Based on the TEA categories

Expand this to see code 
above_below_alice |> filter(end_year == 2024) |> 
  group_by(tea_description) |> 
  summarize(under_alice = sum(above_alice == FALSE),
            total_districts_2024 = n()) |> 
  mutate(prop_under_alice = (under_alice / total_districts_2024) |> round_half_up(3)) |> 
  arrange(desc(prop_under_alice))

The above table shows the number of districts in each TEA district type category where the average salary does not meet the ALICE cost of living threshold (under_alice), the total number of districts in 2024 that fell into that district type category (total_districts_2024) and the proportion of districts in that district type category that where the average salary does not meet the ALICE cost of living threshold (prop_under_alice).

Based on the bill categories (enrollment)

Expand this to see code 
above_below_alice |> filter(end_year == 2024) |> 
  group_by(bill_category) |> 
  summarize(under_alice = sum(above_alice == FALSE),
            total_districts_2024 = n()) |> 
  mutate(prop_under_alice = (under_alice / total_districts_2024) |> round_half_up(3)) |> 
  arrange(desc(prop_under_alice))

This table shows the number of districts in each bill-defined enrollment category where the average salary does not meet the ALICE cost of living threshold (under_alice), the total number of districts in 2024 that fell into that bill-defined enrollment category (total_districts_2024) and the proportion of districts in that category that where the average salary does not meet the ALICE cost of living threshold (prop_under_alice).

How many districts of each type are under 5000 enrollment and what proportion of districts in each type are under 5000 enrollment?

This is based on the TEA district types.

Expand this to see code 
above_below_alice |> filter(end_year == 2024 ) |> 
  group_by(tea_description) |> 
  summarize(districts_under_5000 = sum(total_enrollment < 5000),
            total_districts = n()) |> 
  mutate(
    prop_districts_under_5000 = (districts_under_5000 / total_districts) |> round_half_up(3)
  ) |> arrange(desc(prop_districts_under_5000))

In 2024, the “Rural” district type had the most districts with enrollment under 5000 at 461.

How many major urban/suburban districts are under 5000 and how do they pay their teachers?

Based on the table from the last question, there were 0 districts in 2024 that were considered Major Urban and had enrollment under 5000. There were 7 districts in 2025 that were considered Major Suburban and had enrollment under 5000. Let’s look at those 7.

Expand this to see code 
above_below_alice |> filter(tea_description == "Major Suburban" & end_year == 2024 & total_enrollment < 5000) # add select to simplify

Of these 7 districts, 3 had an average salary that did not meet the ALICE cost of living threshold.

What number of districts in Texas pay their teachers under living wage? What’s that in a percentage?

Expand this to see code 
above_below_alice |> ungroup() |> filter(end_year == 2024 & above_alice == F) |> 
  summarize(total_districts_in_Texas_under_alice_threshold = n())

In 2024, 463 districts in Texas on average paid their teachers under a living wage.

Now, the percentage.

Expand this to see code 
above_below_alice |> ungroup() |>  filter(end_year == 2024) |> 
  summarise( under_alice = sum(above_alice == F),
             total_districts = n()) |> 
  mutate(percent_districts_under_alice_threshold = ((under_alice / total_districts)*100) |> round_half_up(1))

In 2024, 45.4% of districts in Texas on average paid their teachers under a living wage.

What percentage of students are served by teachers that are underpaid?

Expand this to see code 
above_below_alice |> filter(end_year == 2024 ) |> ungroup() |> 
  summarise(total_students_served_by_underpaid_teachers = sum(total_enrollment[above_alice == F]),
    total_students = sum(total_enrollment)
  ) |> mutate(
    percent_students_served_by_underpaid_teachers = (total_students_served_by_underpaid_teachers / total_students * 100) |> round_half_up(2)
  ) #|> select(percent_students_served_by_underpaid_teachers)

In 2024, 31.15% of students were served by teachers who were on average paid under a living wage.

Are there more districts being helped in the TEA “Rural” definition or the bill’s enrollment breakdown?

Districts being helped by TEA “Rural” description

Expand this to see code 
above_below_alice |> filter(end_year == 2024 & tea_description == "Rural") |> ungroup() |> 
  count(name = "total_districts_TEA_rural")

464 districts would get money if the bill targeted TEA’s “Rural” districts based on 2022-2023 TEA district types.

Distircts being helped based on bill categories of enrollment.

Expand this to see code 
above_below_alice |> filter(end_year == 2024 & (bill_category == "under 300" | bill_category == "under 1600" | bill_category == "under 5000")) |> 
  group_by(bill_category) |> 
  count(name = "total_districts") |> adorn_totals()

827 total districts would get money with the bill targeting districts under 5000, under 1600 and under 300 enrollment based on 2023-2024 enrollment.