4.7 KiB
Texas RRC Inspection Expenses Analysis
Empirical analysis of whether organizational capacity (budget and staffing) predicts regulatory outputs at the Texas Railroad Commission (RRC), using a fixed-effects panel model across 13 districts from FY2016–2025.
Research Questions
Does OGI budget and FTE predict more inspections, higher compliance rates, and faster violation resolution? How is that relationship moderated by goal ambiguity, district-level heterogeneity, and geographic context?
Four hypotheses:
- H1 — Capacity → Outputs: Higher OGI budget and FTE predict better regulatory performance
- H2 — Goal Ambiguity: Higher share of non-inspection budget weakens the capacity-output relationship
- H3 — District Heterogeneity: The capacity-output relationship varies across RRC districts
- H4 — Geographic Moderation: Offshore and border-proximate districts moderate the capacity-output relationship
Data
| Source | Description |
|---|---|
| RRC Administrative Database (PostgreSQL) | ~1.9M inspection events, ~193K enforcement actions, FY2016–2025, 13 districts |
RRC Budget Data.xlsx |
Legislative Appropriations Requests, FY2016–2024, disaggregated by goal/strategy |
Unit of analysis: District-year (13 districts × 10 years; regression sample N = 104, FY2016–2023)
Key constraint: Budget varies only at the statewide level; identification relies on year-to-year temporal variation within districts.
Measures
Dependent variables:
- Total inspections per district-year
- Compliance rate (% of inspections with no failure)
- Violation resolution rate (% resulting in operator compliance on re-inspection)
- Average days-to-enforcement
Key independent variables:
- OGI total appropriations ($M) — primary capacity measure
- OGI authorized FTE — auxiliary capacity measure
- Inspection budget share = OGI / (OGI + ERD) — goal ambiguity proxy (range: 0.59–0.67)
- Offshore district (Districts 02, 03, 04) and border district (Districts 01–04) indicators
Methods
Fixed-effects panel regression with district fixed effects and district-clustered standard errors. Four model specifications for H1–H4. Robustness checks include wild cluster bootstrap (B = 999, Rademacher weights) for small-sample inference (G = 13 clusters) and a one-year distributed lag specification.
Key Findings
H1 — Supported with caveats: Each $1M increase in OGI budget is associated with ~666 additional inspections (p < .01), +0.26 pp compliance rate (p = .02), and +1.05 pp resolution rate (p < .01). However, wild cluster bootstrap p-values (≈0.49–0.51) indicate asymptotic inference substantially overstates precision; results are directionally consistent but do not survive bootstrap testing with only 13 clusters.
H2 — Exploratory: Budget × inspection-share interaction is negative and significant for compliance (β = −6.53, p < .01), consistent with diminishing returns as the inspection mandate becomes better-resourced, but cannot rule out year-specific confounders given budget share varies only over time.
H3 — Descriptive: Budget-compliance slopes range from −0.34 pp/$1M (District 03) to +1.36 pp/$1M (District 6E). Near-perfect multicollinearity renders standard errors unreliable; slopes are descriptive only.
H4 — Partial support: Offshore districts show +7.6 pp baseline compliance (p = .02); border districts show +6.0 pp (p = .03). These are level effects—budget sensitivity does not significantly differ by geography. Moran's I = −0.051 (no spatial autocorrelation).
Descriptive trends (2016–2023): OGI budget +86%, mean inspections/district +85%, mean compliance rate +8.5 pp (83.1% → 91.6%), mean resolution rate +32.9 pp (36.8% → 69.7%).
Limitations
- Budget identified from temporal variation only (statewide, not district-level allocation)
- Small panel: N = 104 over 8 years limits statistical power and slope heterogeneity estimation
- Wild cluster bootstrap undercuts asymptotic inference; results should be treated as suggestive
- Parallel pre-trends assumption cannot be directly tested
- District-level budget allocation unobserved; some decentralization unmeasured
- FY2025 enforcement metrics subject to right-censoring
Repository Structure
texas_inspection_expenses.ipynb # Main analysis notebook
RRC Budget Data.xlsx # Budget source data
requirements.txt # Python dependencies
texas_inspection_expenses.html # Rendered notebook export
Setup
pip install -r requirements.txt
Requires a PostgreSQL connection to the texas_data database with RRC inspection/violation tables. Configure credentials via environment variables (see python-dotenv).