{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ff943903",
   "metadata": {},
   "source": [
    "# Where Your Degree Takes You — 2. Jobs, AI Exposure & the 'Real' Premium\n",
    "\n",
    "Each major maps to a spray of occupations; those occupations have wages and AI exposure. This notebook reproduces the **CIP→SOC weighting**, the **three-way AI-exposure reconciliation**, and the **selection-adjusted earnings premium**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f2924be0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T18:49:46.230862Z",
     "iopub.status.busy": "2026-06-19T18:49:46.230862Z",
     "iopub.status.idle": "2026-06-19T18:49:46.831695Z",
     "shell.execute_reply": "2026-06-19T18:49:46.831695Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading committed data from: C:\\Users\\jelbe\\palavir-estate-audit\\repos\\data-portfolio\\public\\data\\degree\n"
     ]
    }
   ],
   "source": [
    "import json, os\n",
    "import numpy as np, pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"dark_background\")\n",
    "plt.rcParams.update({\"figure.facecolor\": \"#0a0a0a\", \"axes.facecolor\": \"#0a0a0a\",\n",
    "                     \"savefig.facecolor\": \"#0a0a0a\", \"axes.edgecolor\": \"#444\",\n",
    "                     \"font.size\": 11, \"axes.grid\": True, \"grid.alpha\": 0.15})\n",
    "PURPLE, GREEN, ROSE = \"#a855f7\", \"#10b981\", \"#f43f5e\"\n",
    "DATA = os.environ.get(\"DEGREE_DATA\", os.path.join(\"..\", \"public\", \"data\", \"degree\"))\n",
    "load = lambda n: json.load(open(os.path.join(DATA, n), encoding=\"utf-8\"))\n",
    "print(\"Reading committed data from:\", os.path.abspath(DATA))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62a32a87",
   "metadata": {},
   "source": [
    "## CIP → SOC: a major is many jobs\n",
    "\n",
    "The NCES CIP→SOC crosswalk is many-to-many with no native weights, so we weight each occupation by national employment and **disclose the method per edge**. Example: Computer Science (CIP 11.07)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1fc4f1d4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T18:49:46.831695Z",
     "iopub.status.busy": "2026-06-19T18:49:46.831695Z",
     "iopub.status.idle": "2026-06-19T18:49:46.847732Z",
     "shell.execute_reply": "2026-06-19T18:49:46.847732Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>occupation</th>\n",
       "      <th>grad_weight</th>\n",
       "      <th>AI_beta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Software Developers</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.8684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Computer and Information Systems Managers</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.5303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Computer Occupations, All Other</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.7358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Data Scientists</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.6948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Software Quality Assurance Analysts and Testers</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.8772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Information Security Analysts</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.6522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        occupation  grad_weight  AI_beta\n",
       "0                              Software Developers        0.425   0.8684\n",
       "1        Computer and Information Systems Managers        0.167   0.5303\n",
       "2                  Computer Occupations, All Other        0.117   0.7358\n",
       "3                                  Data Scientists        0.059   0.6948\n",
       "4  Software Quality Assurance Analysts and Testers        0.051   0.8772\n",
       "5                    Information Security Analysts        0.045   0.6522"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flows = load('degree_occupation_flows.json')\n",
    "occ = {o['soc6']: o for o in load('occupations.json')}\n",
    "cs = sorted([f for f in flows if f['cip4']=='11.07' and f.get('grad_weight')],\n",
    "            key=lambda f:-f['grad_weight'])\n",
    "pd.DataFrame([{'occupation': occ.get(f['soc6'],{}).get('soc_title'),\n",
    "               'grad_weight': round(f['grad_weight'],3),\n",
    "               'AI_beta': occ.get(f['soc6'],{}).get('ai_beta')} for f in cs[:6]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "077afbd9",
   "metadata": {},
   "source": [
    "## Do three AI-exposure measures agree?\n",
    "\n",
    "We compare two **published** measures (Eloundou *GPTs-are-GPTs* β, and AIOE) with a third we derive independently from the *text* of O\\*NET task statements (sentence embeddings). If they agree, the signal is robust. We recompute the Spearman rank correlations from the published per-occupation table.\n",
    "\n",
    "*AI exposure = GPT-4-era (2023) task overlap, **not** a forecast of job loss.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "12e40748",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T18:49:46.847732Z",
     "iopub.status.busy": "2026-06-19T18:49:46.847732Z",
     "iopub.status.idle": "2026-06-19T18:49:46.860304Z",
     "shell.execute_reply": "2026-06-19T18:49:46.860304Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "backend: sentence-transformers/all-MiniLM-L6-v2\n",
      "     ai_beta vs aioe         rho = +0.866\n",
      "     ai_beta vs embed_score  rho = +0.822\n",
      "        aioe vs embed_score  rho = +0.812\n",
      "stored: {'eloundou_beta_vs_aioe': 0.866, 'eloundou_beta_vs_embedding': 0.822, 'aioe_vs_embedding': 0.812}\n"
     ]
    }
   ],
   "source": [
    "tai = load('task_ai_map.json')\n",
    "o = pd.DataFrame(tai['occupations'])\n",
    "def spearman(a,b):\n",
    "    m = o[[a,b]].dropna()\n",
    "    return np.corrcoef(m[a].rank(), m[b].rank())[0,1]\n",
    "print('backend:', tai['embedding_backend'])\n",
    "for pair in [('ai_beta','aioe'),('ai_beta','embed_score'),('aioe','embed_score')]:\n",
    "    print(f'{pair[0]:>12} vs {pair[1]:<12} rho = {spearman(*pair):+.3f}')\n",
    "print('stored:', tai['correlations'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97725690",
   "metadata": {},
   "source": [
    "All three independently-built signals correlate **0.74–0.87** — the exposure ranking is a real property of the work, not an artifact of one method. The pay-vs-AI map of every major:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7a901df4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T18:49:46.860304Z",
     "iopub.status.busy": "2026-06-19T18:49:46.860304Z",
     "iopub.status.idle": "2026-06-19T18:49:47.012203Z",
     "shell.execute_reply": "2026-06-19T18:49:47.012203Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "L = pd.DataFrame(load('major_landscape.json')['majors'])\n",
    "g = L.dropna(subset=['earn_5yr','ai_beta'])\n",
    "xm = g['earn_5yr'].median()\n",
    "colors = np.where((g['earn_5yr']>=xm)&(g['ai_beta']>=0.5), ROSE,\n",
    "         np.where((g['earn_5yr']>=xm)&(g['ai_beta']<0.5), GREEN, '#64748b'))\n",
    "fig, ax = plt.subplots(figsize=(9,6))\n",
    "ax.scatter(g['earn_5yr'], g['ai_beta']*100, s=np.sqrt(g['n_programs'])*4, c=colors, alpha=0.7)\n",
    "ax.axvline(xm, color='#666', ls='--'); ax.axhline(50, color='#666', ls='--')\n",
    "ax.set_xlabel('Median 5-yr earnings ($)'); ax.set_ylabel('AI task exposure (%)')\n",
    "ax.set_title('Every major: pay vs AI exposure (red = danger zone)')\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4876c89",
   "metadata": {},
   "source": [
    "## The 'real' premium: the major, or who gets in?\n",
    "\n",
    "A major's raw earnings edge mixes the field's value with *who enrolls* (selective schools admit higher earners). We regress 5-yr earnings on institution controls (selectivity, net price, completion, Pell share, size, region) and take the **mean residual per major** as the selection-adjusted premium. It is **observational, not causal**. Where raw and adjusted diverge, the edge was really about admissions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bc416f39",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-19T18:49:47.014208Z",
     "iopub.status.busy": "2026-06-19T18:49:47.014208Z",
     "iopub.status.idle": "2026-06-19T18:49:47.023699Z",
     "shell.execute_reply": "2026-06-19T18:49:47.023699Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model R^2: 0.179\n",
      "\n",
      "Edge SURVIVES adjustment (real signal):\n",
      "                                              cip_title  raw_premium  adjusted_premium\n",
      "                                       Computer Science        48460             45982\n",
      "                                   Computer Engineering        48201             44728\n",
      "  Pharmacy, Pharmaceutical Sciences, and Administration        45149             43458\n",
      "Electrical, Electronics, and Communications Engineering        38769             35722\n",
      "         Construction Engineering Technology/Technician        29594             31114\n",
      "\n",
      "Edge mostly DISAPPEARS (it was selection):\n",
      "                                         cip_title  raw_premium  adjusted_premium\n",
      "                                      Area Studies        -1625            -13059\n",
      "                    Neurobiology and Neurosciences         5511             -5141\n",
      "                            Public Policy Analysis         9327              -817\n",
      "East Asian Languages, Literatures, and Linguistics        -9224            -19199\n",
      "                             Materials Engineering        28480             19228\n"
     ]
    }
   ],
   "source": [
    "prem = pd.DataFrame(load('premium.json')['majors'])\n",
    "prem['shrink'] = prem['raw_premium'] - prem['adjusted_premium']\n",
    "big = prem[prem['n']>=20].copy()\n",
    "print('Model R^2:', load('premium.json')['model']['r2'])\n",
    "print('\\nEdge SURVIVES adjustment (real signal):')\n",
    "print(big.sort_values('adjusted_premium', ascending=False)[['cip_title','raw_premium','adjusted_premium']].head(5).to_string(index=False))\n",
    "print('\\nEdge mostly DISAPPEARS (it was selection):')\n",
    "print(big.sort_values('shrink', ascending=False)[['cip_title','raw_premium','adjusted_premium']].head(5).to_string(index=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3120016e",
   "metadata": {},
   "source": [
    "Computer Science keeps almost all of its edge; selective science majors collapse toward zero. Next: [notebook 3](03_geography_affordability.ipynb) — geography, affordability, and the debt-vs-earnings paradox."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
