From 4f2e724e714b964bc654f4637b512cd8d0bf24d7 Mon Sep 17 00:00:00 2001
From: dadams <david@dadams.io>
Date: Wed, 25 Feb 2026 12:56:33 -0800
Subject: [PATCH] Add hypotheses summary table (Table 6)

Markdown cell summarizing all 9 hypothesis tests with predictions,
key coefficients, and support status including footnotes for H2
diminishing-returns finding, H3 multicollinearity caveat, and H4
level-vs-slope distinction.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 texas_inspection_expenses.ipynb | 29 +++++++++++++++++++++++++++++
 1 file changed, 29 insertions(+)

diff --git a/texas_inspection_expenses.ipynb b/texas_inspection_expenses.ipynb
index 3336798..643a7df 100644
--- a/texas_inspection_expenses.ipynb
+++ b/texas_inspection_expenses.ipynb
@@ -3037,6 +3037,35 @@
     "levels but not budget sensitivity, and spatial autocorrelation diagnostics provide no\n",
     "evidence of unmodeled geographic spillover processes.\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90c60ad1",
+   "metadata": {},
+   "source": [
+    "## Hypotheses Summary\n",
+    "\n",
+    "**Table 6. Summary of Hypotheses, Predictions, Findings, and Empirical Support**\n",
+    "\n",
+    "| # | Hypothesis | Prediction | Key Result | Support |\n",
+    "|:---:|---|---|---|:---:|\n",
+    "| **H1a** | Capacity → Inspection volume | Higher OGI budget predicts more inspections per district | β = 666.3 inspections per $1M (z = 3.13, p < .01); R² = .769 | ✓ |\n",
+    "| **H1b** | Capacity → Compliance | Higher OGI budget predicts higher district compliance rate | β = 0.26 pp per $1M (z = 2.31, p = .02); R² = .538 | ✓ |\n",
+    "| **H1c** | Capacity → Resolution | Higher OGI budget predicts higher violation resolution rate | β = 1.05 pp per $1M (z = 3.28, p < .01); R² = .624 | ✓ |\n",
+    "| **H2a** | Goal ambiguity moderates capacity → compliance | Clearer inspection focus amplifies budget effect | Interaction significant but **negative** (β = −6.53, z = −3.55, p < .01): higher inspection share produces diminishing, not amplified, returns | Partial† |\n",
+    "| **H2b** | Goal ambiguity moderates capacity → resolution | Clearer inspection focus amplifies budget effect | Interaction not significant (p = .24) | ✗ |\n",
+    "| **H3** | District heterogeneity in budget slopes | Budget → compliance slope varies across districts | Point estimates range from −0.34 pp/$1M (D03) to +1.36 pp/$1M (D6E); inference unreliable due to multicollinearity | Descriptive only‡ |\n",
+    "| **H4a** | Offshore jurisdiction moderates budget effect | Offshore districts show different budget → compliance slope | Level effect significant (+7.6 pp, p = .02); slope interaction not significant (β = −0.03, p = .87) | Partial§ |\n",
+    "| **H4b** | Border proximity moderates budget effect | Border districts show different budget → compliance slope | Level effect significant (+6.0 pp, p = .03); slope interaction marginal (β = −0.25, p = .08) | Partial§ |\n",
+    "| **H4c** | Spatial autocorrelation in residuals | Geographic spillovers produce clustered residuals | Moran's I = −0.051; no significant spatial autocorrelation | ✗ |\n",
+    "\n",
+    "*Notes:*\n",
+    "*† H2 moderation operates through a diminishing-returns mechanism rather than amplification. At mean inspection budget share (≈ 0.62), the implied marginal budget effect on compliance is approximately 0.15 pp per $1M.*\n",
+    "*‡ H3 interaction standard errors are unreliable (near-perfect multicollinearity in the saturated model); budget slopes are reported as descriptive point estimates only.*\n",
+    "*§ Geographic classification predicts compliance **levels** but not budget sensitivity. Offshore and border districts exhibit systematically higher compliance regardless of annual budget variation.*\n",
+    "\n",
+    "**Regression sample:** N = 104 (13 districts × 8 years, 2016–2023). All models include district fixed effects; standard errors clustered at the district level.\n"
+   ]
   }
  ],
  "metadata": {