# TIA Structural Inequality Study Design

## Research Question

Does the Teacher Incentive Allotment (TIA) produce higher teacher designation rates and per-pupil allotments at campuses with selective enrollment compared to open-enrollment campuses serving higher-need populations — replicating at state scale the structural inequity Dallas ISD discovered and corrected in its Teacher Excellence Initiative (TEI)?

## Background

Dallas ISD's TEI, launched in 2014 under Superintendent Mike Miles, established a performance-based compensation system that inadvertently channeled higher compensation to teachers at "choice" (magnet/selective) campuses serving lower-need populations. Alliance-AFT's 2019 PIR revealed that the median salary gap between choice and comprehensive campuses widened from $2,394 to $11,500 between 2016 and 2020 (DeRocha analysis). Dallas ISD addressed this inequity in 2020-21 by implementing separate targeted distributions for choice and comprehensive campus types.

The statewide TIA, created by HB 3 in 2019 and modeled on the original (pre-fix) TEI, contains no campus-type adjustment. TIA uses only socioeconomic tier and rural/non-rural status to differentiate allotment levels. This study examines whether this omission systematically disadvantages students at high-need, open-enrollment campuses.

## Data Sources

### Available (Downloaded)

| Dataset | File | Campuses | Key Fields |
|---------|------|----------|------------|
| TIA Funding Map 2024-25 | `tia-allotment-funding-2024-25.xlsx` | 9,187 | Per-teacher allotment by tier, charter flag, rural flag, enrollment, participation status |
| TIA Funding Map 2022-23 | `2022-23-allotment-funding-data.xlsx` | 9,175 | Same structure — enables trend analysis |
| TIA Annual Report 2025 | `2025-TIA-Annual-Report.pdf` | N/A | Aggregate stats: 42,294 designated teachers, $481M total |
| Rural Campus List 2024-25 | `rural-campus-list-2024-25.xlsx` | 2,169 | Rural-designated campuses |
| Dallas ISD TEI PIR Data | `insd-5941/sources/tei-analysis/*.csv` | ~230 | Campus-level TEI salary data 2016-2020 (DeRocha) |
| TEA Data MCP | (live query) | ~9,600 | ARC factors, demographics, FSP data |

### Key Gap: Teacher Designation Counts

The TIA Funding Map shows **per-teacher allotment amounts** (calculated from campus demographics), NOT actual designation counts. To determine whether selective-enrollment campuses have higher designation rates, we need **campus-level counts of designated teachers by tier** (Recognized/Exemplary/Master). This data is not publicly available — requires PIR #A.

## Methodology

### Phase A: Campus Selectivity Classification

Since no statewide "choice vs. comprehensive" dataset exists, construct a three-tier proxy:

| Tier | Criteria | Rationale |
|------|----------|-----------|
| **Tier 1: Selective** | Charter school (OPEN ENROLLMENT CHARTER or CAMPUS CHARTER) OR campus %EcoDis >15pp below district %EcoDis | Charters control admissions via lottery; ISD campuses far below district poverty average likely have selective mechanisms (magnet, transfer, application) |
| **Tier 2: Mixed** | ISD campus with %EcoDis within 15pp of district average | Neighborhood schools with demographics near district average |
| **Tier 3: High-Need/Comprehensive** | ISD campus with %EcoDis at or above district average | Open-enrollment campuses serving highest-need populations |

**Data needed:** Campus %EcoDis from TEA Data MCP (already available for ~9,600 campuses). District %EcoDis for delta calculation.

**Magnet flag:** TAPR campus profiles include magnet program indicators — verify availability via TAPR download. If available, move magnet-flagged campuses to Tier 1 regardless of demographic deviation.

### Phase B: Descriptive Analysis (No PIR Required)

Using the existing TIA Funding Map data and TEA demographics:

1. **Allotment comparison by selectivity tier**: The per-teacher allotment amounts are themselves informative because they're calculated from campus-level socioeconomic data. Compare mean/median allotment levels across Tiers 1-3.
   - Hypothesis: Tier 1 (selective) campuses will have lower allotments because they serve lower-need populations — but if they have more designated teachers, total TIA funding may still flow disproportionately to them.

2. **Participation rate by campus type**: What proportion of charter vs. ISD campuses are at TIA-participating districts?
   - Already available: 1,056 charter campuses, 8,131 ISD campuses; 4,803 at LDS-approved districts

3. **Demographic profiles**: Compare mean %EcoDis, %ELL, %AtRisk across campuses at TIA-participating vs. non-participating districts, charter vs. ISD

### Phase C: Designation Rate Analysis (Requires PIR #A)

With campus-level designation counts:

1. **Designation rate by selectivity tier**: (Designated teachers / Total teachers) across Tiers 1-3
   - This is the core analysis: do selective-enrollment campuses have higher designation rates?

2. **Replicate DeRocha's quartile approach**: Sort campuses statewide by %EcoDis, compare top vs. bottom quartile designation rates within charter and ISD groups

3. **Total TIA funding by selectivity tier**: Multiply per-teacher allotment × designated teacher count per campus, aggregate by tier

4. **Visualization**: Scatterplot of campus %EcoDis vs. (designation rate OR per-pupil TIA funding), color-coded by selectivity tier

### Phase D: Dallas ISD Case Study

The strongest component — we have both TEI and TIA data for the same district.

| Period | Data Source | What It Shows |
|--------|-----------|---------------|
| Pre-fix TEI (2016-2020) | DeRocha PIR data | Choice/comprehensive salary gap: $2,394 → $11,500 |
| Post-fix TEI (2020-2025) | PIR #C | Whether the equity fix worked |
| TIA in Dallas ISD (2020-2025) | PIR #A (Dallas subset) + TIA map | Whether TIA reopens the gap that TEI closed |

Key comparison: Under TEI (post-fix), Dallas ISD applies campus-type distributions. Under TIA, no such adjustment exists. If the same teachers at the same campuses show different patterns under TEI vs. TIA, this isolates the effect of the campus-type provision.

### Methodological Caveats

1. **Labor market sorting vs. campus production**: TIA designations belong to individual teachers who can move between campuses. A high designation rate at a selective campus may reflect experienced/effective teachers choosing to work there, not the campus producing more designations. Either mechanism constitutes a structural equity problem — students at high-need campuses lose access to designated teachers regardless of cause.

2. **Endogeneity**: Districts with existing performance-pay systems (like Dallas ISD) self-selected into early TIA cohorts. Results may not generalize to later adopters.

3. **Allotment formula design**: The TIA allotment formula intentionally pays MORE per designated teacher at high-need campuses. The study tests whether this mechanism is sufficient to overcome the designation rate differential, or whether selective campuses accumulate so many more designated teachers that they receive more total TIA funding despite lower per-teacher rates.

## Scope and Deliverables (Capstone-Appropriate)

- Descriptive statistics (means, medians, quartiles) — NOT causal inference
- Dallas ISD case study with before/after comparison
- 3-5 key visualizations
- Regression analysis noted as potential extension, not a required deliverable
- Discussion connecting findings to constitutional "efficiency" framework from Edgewood/Morath

## Timeline

| Phase | Dependency | Estimated Time |
|-------|-----------|----------------|
| Phase A: Classification | TEA Data MCP (available now) | 1-2 days |
| Phase B: Descriptive | TIA Map data (available now) | 2-3 days |
| Phase C: Designation rates | PIR #A response (~10 business days) | 2-3 days after data |
| Phase D: Dallas case study | PIR #C response + existing data | 3-4 days |
| Write-up | All phases complete | 3-5 da