{
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   "source": [
    "# Labor Force Participation Rate: Trends, Drivers, and Python Analysis Tutorial\n",
    "\n",
    "## Complete US Analysis Notebook\n",
    "\n",
    "**Article data through July 2026**\n",
    "\n",
    "This notebook is the reproducible companion to the tutorial article. It teaches the full workflow used to analyze the US labor force participation rate.\n",
    "\n",
    "You will learn how to:\n",
    "\n",
    "1. Define the participation rate correctly.\n",
    "2. Pull the main BLS series with Python.\n",
    "3. Clean monthly labor market data.\n",
    "4. Calculate one month, six month, and twelve month changes.\n",
    "5. Compare the total rate with prime age participation.\n",
    "6. Compare participation for men and women.\n",
    "7. Compare age groups.\n",
    "8. Read participation with unemployment and the employment population ratio.\n",
    "9. Review demographic and population control effects.\n",
    "10. Build charts and export clean tables.\n",
    "\n",
    "The core article snapshot is embedded so this notebook runs offline. A live BLS API workflow is also included for future monthly refreshes.\n"
   ]
  },
  {
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   "id": "ab8a703c",
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    "execution": {
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "from IPython.display import display\n",
    "\n",
    "pd.set_option(\"display.max_columns\", None)\n",
    "pd.set_option(\"display.width\", 140)\n",
    "\n",
    "OUTPUT_DIR = Path(\"lfpr_analysis_output\")\n",
    "OUTPUT_DIR.mkdir(exist_ok=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0f583ac",
   "metadata": {},
   "source": [
    "## 1. Understand the measure\n",
    "\n",
    "The labor force participation rate is the share of the civilian noninstitutional population age 16 and older that is either employed or actively looking for work.\n",
    "\n",
    "**Formula**\n",
    "\n",
    "Participation rate = Labor force divided by civilian noninstitutional population, multiplied by 100.\n",
    "\n",
    "This is different from unemployment. A person who is not working and is not actively looking for work is outside the labor force and is not counted as unemployed.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0c64fcbf",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-08-21T09:29:16.992517Z"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61.4%\n"
     ]
    }
   ],
   "source": [
    "def participation_rate(labor_force, civilian_population):\n",
    "    return (labor_force / civilian_population) * 100\n",
    "\n",
    "# Simple illustrative example\n",
    "print(f\"{participation_rate(61.4, 100):.1f}%\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c9ad907",
   "metadata": {},
   "source": [
    "## 2. Core BLS series IDs\n",
    "\n",
    "| Measure | BLS series ID |\n",
    "| --- | --- |\n",
    "| Total participation rate | LNS11300000 |\n",
    "| Prime age participation, age 25 to 54 | LNS11300060 |\n",
    "| Men participation | LNS11300001 |\n",
    "| Women participation | LNS11300002 |\n",
    "| Unemployment rate | LNS14000000 |\n",
    "| Employment population ratio | LNS12300000 |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1c618be2",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-08-21T09:29:16.998323Z"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Total participation rate': 'LNS11300000',\n",
       " 'Prime age participation': 'LNS11300060',\n",
       " 'Men participation': 'LNS11300001',\n",
       " 'Women participation': 'LNS11300002',\n",
       " 'Unemployment rate': 'LNS14000000',\n",
       " 'Employment population ratio': 'LNS12300000'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SERIES = {\n",
    "    \"Total participation rate\": \"LNS11300000\",\n",
    "    \"Prime age participation\": \"LNS11300060\",\n",
    "    \"Men participation\": \"LNS11300001\",\n",
    "    \"Women participation\": \"LNS11300002\",\n",
    "    \"Unemployment rate\": \"LNS14000000\",\n",
    "    \"Employment population ratio\": \"LNS12300000\",\n",
    "}\n",
    "\n",
    "START_YEAR = 1948\n",
    "END_YEAR = 2026\n",
    "SERIES\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ec2fcbc",
   "metadata": {},
   "source": [
    "## 3. Live BLS API workflow\n",
    "\n",
    "This cell defines the download functions. It does not make a live request unless you call `fetch_bls_series`.\n",
    "\n",
    "For an unregistered workflow, the history is split into ten year windows.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3808fd84",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-08-21T09:29:17.005258Z"
    }
   },
   "outputs": [],
   "source": [
    "import time\n",
    "import requests\n",
    "\n",
    "API_URL = \"https://api.bls.gov/publicAPI/v1/timeseries/data/\"\n",
    "\n",
    "def make_year_chunks(start_year, end_year, years_per_request=10):\n",
    "    chunks = []\n",
    "    current = start_year\n",
    "\n",
    "    while current <= end_year:\n",
    "        chunk_end = min(current + years_per_request - 1, end_year)\n",
    "        chunks.append((current, chunk_end))\n",
    "        current = chunk_end + 1\n",
    "\n",
    "    return chunks\n",
    "\n",
    "\n",
    "def fetch_bls_series(series_map, start_year, end_year):\n",
    "    id_to_name = {series_id: name for name, series_id in series_map.items()}\n",
    "    records = []\n",
    "\n",
    "    for chunk_start, chunk_end in make_year_chunks(start_year, end_year):\n",
    "        payload = {\n",
    "            \"seriesid\": list(id_to_name.keys()),\n",
    "            \"startyear\": str(chunk_start),\n",
    "            \"endyear\": str(chunk_end),\n",
    "        }\n",
    "\n",
    "        response = requests.post(API_URL, json=payload, timeout=30)\n",
    "        response.raise_for_status()\n",
    "        data = response.json()\n",
    "\n",
    "        if data.get(\"status\") != \"REQUEST_SUCCEEDED\":\n",
    "            raise RuntimeError(data.get(\"message\", \"BLS request failed\"))\n",
    "\n",
    "        for series in data[\"Results\"][\"series\"]:\n",
    "            series_id = series[\"seriesID\"]\n",
    "            measure = id_to_name[series_id]\n",
    "\n",
    "            for item in series[\"data\"]:\n",
    "                period = item[\"period\"]\n",
    "\n",
    "                if not period.startswith(\"M\") or period == \"M13\":\n",
    "                    continue\n",
    "\n",
    "                records.append(\n",
    "                    {\n",
    "                        \"date\": pd.Timestamp(\n",
    "                            year=int(item[\"year\"]),\n",
    "                            month=int(period[1:]),\n",
    "                            day=1,\n",
    "                        ),\n",
    "                        \"measure\": measure,\n",
    "                        \"series_id\": series_id,\n",
    "                        \"value\": float(item[\"value\"]),\n",
    "                    }\n",
    "                )\n",
    "\n",
    "        time.sleep(0.2)\n",
    "\n",
    "    return (\n",
    "        pd.DataFrame(records)\n",
    "        .sort_values([\"date\", \"measure\"])\n",
    "        .reset_index(drop=True)\n",
    "    )\n",
    "\n",
    "\n",
    "def make_wide_table(long_data):\n",
    "    return (\n",
    "        long_data.pivot_table(\n",
    "            index=\"date\",\n",
    "            columns=\"measure\",\n",
    "            values=\"value\",\n",
    "            aggfunc=\"last\",\n",
    "        )\n",
    "        .sort_index()\n",
    "    )\n",
    "\n",
    "# Future live refresh:\n",
    "# live_long = fetch_bls_series(SERIES, START_YEAR, END_YEAR)\n",
    "# live_wide = make_wide_table(live_long)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c225df64",
   "metadata": {},
   "source": [
    "## 4. Long run total participation trend\n",
    "\n",
    "The article uses selected benchmark observations from the BLS total participation series. These are trend points, not every monthly observation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9408d968",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2026-08-21T09:29:17.016941Z",
     "shell.execute_reply": "2026-08-21T09:29:17.016440Z"
    }
   },
   "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>date</th>\n",
       "      <th>Participation rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>62.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>62.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2025-01-01</td>\n",
       "      <td>62.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2025-07-01</td>\n",
       "      <td>62.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2025-12-01</td>\n",
       "      <td>62.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2026-01-01</td>\n",
       "      <td>62.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2026-02-01</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2026-03-01</td>\n",
       "      <td>61.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>61.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2026-05-01</td>\n",
       "      <td>61.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2026-06-01</td>\n",
       "      <td>61.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2026-07-01</td>\n",
       "      <td>61.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  Participation rate\n",
       "8  2023-01-01                62.4\n",
       "9  2024-01-01                62.5\n",
       "10 2025-01-01                62.6\n",
       "11 2025-07-01                62.2\n",
       "12 2025-12-01                62.4\n",
       "13 2026-01-01                62.1\n",
       "14 2026-02-01                62.0\n",
       "15 2026-03-01                61.9\n",
       "16 2026-04-01                61.8\n",
       "17 2026-05-01                61.8\n",
       "18 2026-06-01                61.5\n",
       "19 2026-07-01                61.4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "long_run_total = pd.DataFrame(\n",
    "    [\n",
    "        (\"1948-01-01\", 58.6),\n",
    "        (\"1990-01-01\", 66.8),\n",
    "        (\"2000-04-01\", 67.3),\n",
    "        (\"2010-01-01\", 64.8),\n",
    "        (\"2019-12-01\", 63.3),\n",
    "        (\"2020-04-01\", 60.1),\n",
    "        (\"2021-01-01\", 61.4),\n",
    "        (\"2022-01-01\", 62.2),\n",
    "        (\"2023-01-01\", 62.4),\n",
    "        (\"2024-01-01\", 62.5),\n",
    "        (\"2025-01-01\", 62.6),\n",
    "        (\"2025-07-01\", 62.2),\n",
    "        (\"2025-12-01\", 62.4),\n",
    "        (\"2026-01-01\", 62.1),\n",
    "        (\"2026-02-01\", 62.0),\n",
    "        (\"2026-03-01\", 61.9),\n",
    "        (\"2026-04-01\", 61.8),\n",
    "        (\"2026-05-01\", 61.8),\n",
    "        (\"2026-06-01\", 61.5),\n",
    "        (\"2026-07-01\", 61.4),\n",
    "    ],\n",
    "    columns=[\"date\", \"Participation rate\"],\n",
    ")\n",
    "long_run_total[\"date\"] = pd.to_datetime(long_run_total[\"date\"])\n",
    "display(long_run_total.tail(12))\n"
   ]
  },
  {
   "cell_type": "code",
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   "id": "1b30dd9e",
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    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1100x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(11, 5.5))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(long_run_total[\"date\"], long_run_total[\"Participation rate\"], marker=\"o\")\n",
    "ax.set_title(\"US Labor Force Participation Rate, Selected Observations\")\n",
    "ax.set_xlabel(\"Date\")\n",
    "ax.set_ylabel(\"Percent\")\n",
    "ax.grid(True, alpha=0.25)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74e4cab8",
   "metadata": {},
   "source": [
    "## 5. Latest labor market snapshot\n",
    "\n",
    "These are the article's documented July 2026, June 2026, and July 2025 values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "55af79bc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-21T09:29:17.172066Z",
     "iopub.status.busy": "2026-08-21T09:29:17.171960Z",
     "iopub.status.idle": "2026-08-21T09:29:17.180821Z",
     "shell.execute_reply": "2026-08-21T09:29:17.180402Z"
    }
   },
   "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>Measure</th>\n",
       "      <th>Jul 2026</th>\n",
       "      <th>Jun 2026</th>\n",
       "      <th>Jul 2025</th>\n",
       "      <th>Unit</th>\n",
       "      <th>1 month change</th>\n",
       "      <th>12 month change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Participation rate</td>\n",
       "      <td>61.400</td>\n",
       "      <td>61.500</td>\n",
       "      <td>62.200</td>\n",
       "      <td>percent</td>\n",
       "      <td>-0.100</td>\n",
       "      <td>-0.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prime age participation, age 25 to 54</td>\n",
       "      <td>83.400</td>\n",
       "      <td>83.300</td>\n",
       "      <td>83.400</td>\n",
       "      <td>percent</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Employment population ratio</td>\n",
       "      <td>58.900</td>\n",
       "      <td>59.000</td>\n",
       "      <td>59.600</td>\n",
       "      <td>percent</td>\n",
       "      <td>-0.100</td>\n",
       "      <td>-0.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Unemployment rate</td>\n",
       "      <td>4.100</td>\n",
       "      <td>4.200</td>\n",
       "      <td>4.300</td>\n",
       "      <td>percent</td>\n",
       "      <td>-0.100</td>\n",
       "      <td>-0.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Civilian labor force</td>\n",
       "      <td>169.094</td>\n",
       "      <td>169.358</td>\n",
       "      <td>170.412</td>\n",
       "      <td>million people</td>\n",
       "      <td>-0.264</td>\n",
       "      <td>-1.318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Not in labor force and want a job</td>\n",
       "      <td>5.920</td>\n",
       "      <td>6.045</td>\n",
       "      <td>6.186</td>\n",
       "      <td>million people</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>-0.266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Measure  Jul 2026  Jun 2026  Jul 2025            Unit  1 month change  12 month change\n",
       "0                     Participation rate    61.400    61.500    62.200         percent          -0.100           -0.800\n",
       "1  Prime age participation, age 25 to 54    83.400    83.300    83.400         percent           0.100            0.000\n",
       "2            Employment population ratio    58.900    59.000    59.600         percent          -0.100           -0.700\n",
       "3                      Unemployment rate     4.100     4.200     4.300         percent          -0.100           -0.200\n",
       "4                   Civilian labor force   169.094   169.358   170.412  million people          -0.264           -1.318\n",
       "5      Not in labor force and want a job     5.920     6.045     6.186  million people          -0.125           -0.266"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "snapshot = pd.DataFrame(\n",
    "    [\n",
    "        [\"Participation rate\", 61.4, 61.5, 62.2, \"percent\"],\n",
    "        [\"Prime age participation, age 25 to 54\", 83.4, 83.3, 83.4, \"percent\"],\n",
    "        [\"Employment population ratio\", 58.9, 59.0, 59.6, \"percent\"],\n",
    "        [\"Unemployment rate\", 4.1, 4.2, 4.3, \"percent\"],\n",
    "        [\"Civilian labor force\", 169.094, 169.358, 170.412, \"million people\"],\n",
    "        [\"Not in labor force and want a job\", 5.920, 6.045, 6.186, \"million people\"],\n",
    "    ],\n",
    "    columns=[\"Measure\", \"Jul 2026\", \"Jun 2026\", \"Jul 2025\", \"Unit\"],\n",
    ")\n",
    "\n",
    "snapshot[\"1 month change\"] = snapshot[\"Jul 2026\"] - snapshot[\"Jun 2026\"]\n",
    "snapshot[\"12 month change\"] = snapshot[\"Jul 2026\"] - snapshot[\"Jul 2025\"]\n",
    "display(snapshot)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e40001b",
   "metadata": {},
   "source": [
    "### What the latest snapshot says\n",
    "\n",
    "The total participation rate fell 0.8 percentage point from July 2025 to July 2026.\n",
    "\n",
    "Prime age participation was 83.4 percent in both July 2025 and July 2026.\n",
    "\n",
    "The employment population ratio was 0.7 point lower over the year, while the unemployment rate was 0.2 point lower.\n",
    "\n",
    "The number of people outside the labor force who said they wanted a job was also lower over the year. This is one reason the participation decline should not automatically be described as hidden unemployment.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adb36b53",
   "metadata": {},
   "source": [
    "## 6. Calculate the six month change in the total rate\n",
    "\n",
    "The article reports a total participation rate of 62.1 percent in January 2026 and 61.4 percent in July 2026.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "223c0330",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-21T09:29:17.182309Z",
     "iopub.status.busy": "2026-08-21T09:29:17.182211Z",
     "iopub.status.idle": "2026-08-21T09:29:17.187092Z",
     "shell.execute_reply": "2026-08-21T09:29:17.186373Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "January 2026: 62.1%\n",
      "July 2026: 61.4%\n",
      "Six month change: -0.7 percentage point\n"
     ]
    }
   ],
   "source": [
    "jan_2026_total = float(\n",
    "    long_run_total.loc[\n",
    "        long_run_total[\"date\"] == pd.Timestamp(\"2026-01-01\"),\n",
    "        \"Participation rate\",\n",
    "    ].iloc[0]\n",
    ")\n",
    "jul_2026_total = float(\n",
    "    long_run_total.loc[\n",
    "        long_run_total[\"date\"] == pd.Timestamp(\"2026-07-01\"),\n",
    "        \"Participation rate\",\n",
    "    ].iloc[0]\n",
    ")\n",
    "\n",
    "six_month_change = jul_2026_total - jan_2026_total\n",
    "print(f\"January 2026: {jan_2026_total:.1f}%\")\n",
    "print(f\"July 2026: {jul_2026_total:.1f}%\")\n",
    "print(f\"Six month change: {six_month_change:.1f} percentage point\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a27b2ee4",
   "metadata": {},
   "source": [
    "## 7. Compare total participation with prime age participation\n",
    "\n",
    "Prime age adults are less affected by school and retirement decisions. A stable prime age rate can change how you interpret a falling headline rate.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d65faf4d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-21T09:29:17.188080Z",
     "iopub.status.busy": "2026-08-21T09:29:17.187986Z",
     "iopub.status.idle": "2026-08-21T09:29:17.193276Z",
     "shell.execute_reply": "2026-08-21T09:29:17.192790Z"
    }
   },
   "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>Period</th>\n",
       "      <th>Total participation</th>\n",
       "      <th>Prime age participation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jul 2025</td>\n",
       "      <td>62.2</td>\n",
       "      <td>83.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jun 2026</td>\n",
       "      <td>61.5</td>\n",
       "      <td>83.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jul 2026</td>\n",
       "      <td>61.4</td>\n",
       "      <td>83.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Period  Total participation  Prime age participation\n",
       "0  Jul 2025                 62.2                     83.4\n",
       "1  Jun 2026                 61.5                     83.3\n",
       "2  Jul 2026                 61.4                     83.4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prime_comparison = pd.DataFrame(\n",
    "    [\n",
    "        [\"Jul 2025\", 62.2, 83.4],\n",
    "        [\"Jun 2026\", 61.5, 83.3],\n",
    "        [\"Jul 2026\", 61.4, 83.4],\n",
    "    ],\n",
    "    columns=[\"Period\", \"Total participation\", \"Prime age participation\"],\n",
    ")\n",
    "display(prime_comparison)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bbda850f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-21T09:29:17.194447Z",
     "iopub.status.busy": "2026-08-21T09:29:17.194349Z",
     "iopub.status.idle": "2026-08-21T09:29:17.267449Z",
     "shell.execute_reply": "2026-08-21T09:29:17.266843Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(9, 5))\n",
    "ax = fig.add_subplot(111)\n",
    "x = np.arange(len(prime_comparison))\n",
    "ax.plot(x, prime_comparison[\"Total participation\"], marker=\"o\", label=\"Total\")\n",
    "ax.plot(x, prime_comparison[\"Prime age participation\"], marker=\"o\", label=\"Prime age\")\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(prime_comparison[\"Period\"])\n",
    "ax.set_title(\"Total and Prime Age Participation\")\n",
    "ax.set_ylabel(\"Percent\")\n",
    "ax.legend()\n",
    "ax.grid(True, alpha=0.25)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59cafd7f",
   "metadata": {},
   "source": [
    "## 8. Compare participation by sex\n",
    "\n",
    "The article documents selected long run benchmarks for men and women.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "41b978a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-21T09:29:17.269105Z",
     "iopub.status.busy": "2026-08-21T09:29:17.268980Z",
     "iopub.status.idle": "2026-08-21T09:29:17.277645Z",
     "shell.execute_reply": "2026-08-21T09:29:17.276944Z"
    }
   },
   "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>date</th>\n",
       "      <th>Men participation</th>\n",
       "      <th>Women participation</th>\n",
       "      <th>Gap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1948-01-01</td>\n",
       "      <td>86.7</td>\n",
       "      <td>32.0</td>\n",
       "      <td>54.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>75.1</td>\n",
       "      <td>60.1</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>71.2</td>\n",
       "      <td>58.8</td>\n",
       "      <td>12.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>69.2</td>\n",
       "      <td>57.8</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2026-07-01</td>\n",
       "      <td>66.8</td>\n",
       "      <td>56.4</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  Men participation  Women participation   Gap\n",
       "0 1948-01-01               86.7                 32.0  54.7\n",
       "1 2000-01-01               75.1                 60.1  15.0\n",
       "2 2010-01-01               71.2                 58.8  12.4\n",
       "3 2020-01-01               69.2                 57.8  11.4\n",
       "4 2026-07-01               66.8                 56.4  10.4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sex_benchmarks = pd.DataFrame(\n",
    "    [\n",
    "        (\"1948-01-01\", 86.7, 32.0),\n",
    "        (\"2000-01-01\", 75.1, 60.1),\n",
    "        (\"2010-01-01\", 71.2, 58.8),\n",
    "        (\"2020-01-01\", 69.2, 57.8),\n",
    "        (\"2026-07-01\", 66.8, 56.4),\n",
    "    ],\n",
    "    columns=[\"date\", \"Men participation\", \"Women participation\"],\n",
    ")\n",
    "sex_benchmarks[\"date\"] = pd.to_datetime(sex_benchmarks[\"date\"])\n",
    "sex_benchmarks[\"Gap\"] = sex_benchmarks[\"Men participation\"] - sex_benchmarks[\"Women participation\"]\n",
    "display(sex_benchmarks)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0fae0936",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-21T09:29:17.278953Z",
     "iopub.status.busy": "2026-08-21T09:29:17.278831Z",
     "iopub.status.idle": "2026-08-21T09:29:17.420719Z",
     "shell.execute_reply": "2026-08-21T09:29:17.420141Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(11, 5.5))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(sex_benchmarks[\"date\"], sex_benchmarks[\"Men participation\"], marker=\"o\", label=\"Men\")\n",
    "ax.plot(sex_benchmarks[\"date\"], sex_benchmarks[\"Women participation\"], marker=\"o\", label=\"Women\")\n",
    "ax.set_title(\"US Labor Force Participation by Sex, Selected Observations\")\n",
    "ax.set_xlabel(\"Date\")\n",
    "ax.set_ylabel(\"Percent\")\n",
    "ax.legend()\n",
    "ax.grid(True, alpha=0.25)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "74f9ea23",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-21T09:29:17.422546Z",
     "iopub.status.busy": "2026-08-21T09:29:17.422407Z",
     "iopub.status.idle": "2026-08-21T09:29:17.426581Z",
     "shell.execute_reply": "2026-08-21T09:29:17.426157Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gap in 1948: 54.7 percentage points\n",
      "Gap in July 2026: 10.4 percentage points\n",
      "Gap narrowed by: 44.3 percentage points\n"
     ]
    }
   ],
   "source": [
    "gap_1948 = sex_benchmarks.loc[0, \"Gap\"]\n",
    "gap_2026 = sex_benchmarks.loc[sex_benchmarks.index[-1], \"Gap\"]\n",
    "\n",
    "print(f\"Gap in 1948: {gap_1948:.1f} percentage points\")\n",
    "print(f\"Gap in July 2026: {gap_2026:.1f} percentage points\")\n",
    "print(f\"Gap narrowed by: {gap_1948 - gap_2026:.1f} percentage points\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "853b1d2a",
   "metadata": {},
   "source": [
    "## 9. Compare participation by age\n",
    "\n",
    "Age structure is one of the biggest reasons the aggregate rate can move even when core working age participation is stable.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "42722570",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-21T09:29:17.427848Z",
     "iopub.status.busy": "2026-08-21T09:29:17.427742Z",
     "iopub.status.idle": "2026-08-21T09:29:17.436730Z",
     "shell.execute_reply": "2026-08-21T09:29:17.435766Z"
    }
   },
   "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>date</th>\n",
       "      <th>Age 16 to 19</th>\n",
       "      <th>Age 25 to 54</th>\n",
       "      <th>Age 55 and older</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1948-01-01</td>\n",
       "      <td>53.2</td>\n",
       "      <td>64.2</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>52.2</td>\n",
       "      <td>84.4</td>\n",
       "      <td>32.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>35.2</td>\n",
       "      <td>82.4</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>36.3</td>\n",
       "      <td>83.1</td>\n",
       "      <td>40.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2026-07-01</td>\n",
       "      <td>34.9</td>\n",
       "      <td>83.4</td>\n",
       "      <td>36.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  Age 16 to 19  Age 25 to 54  Age 55 and older\n",
       "0 1948-01-01          53.2          64.2              43.0\n",
       "1 2000-01-01          52.2          84.4              32.3\n",
       "2 2010-01-01          35.2          82.4              40.0\n",
       "3 2020-01-01          36.3          83.1              40.2\n",
       "4 2026-07-01          34.9          83.4              36.9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_benchmarks = pd.DataFrame(\n",
    "    [\n",
    "        (\"1948-01-01\", 53.2, 64.2, 43.0),\n",
    "        (\"2000-01-01\", 52.2, 84.4, 32.3),\n",
    "        (\"2010-01-01\", 35.2, 82.4, 40.0),\n",
    "        (\"2020-01-01\", 36.3, 83.1, 40.2),\n",
    "        (\"2026-07-01\", 34.9, 83.4, 36.9),\n",
    "    ],\n",
    "    columns=[\"date\", \"Age 16 to 19\", \"Age 25 to 54\", \"Age 55 and older\"],\n",
    ")\n",
    "age_benchmarks[\"date\"] = pd.to_datetime(age_benchmarks[\"date\"])\n",
    "display(age_benchmarks)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ac9b56c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-21T09:29:17.438278Z",
     "iopub.status.busy": "2026-08-21T09:29:17.438158Z",
     "iopub.status.idle": "2026-08-21T09:29:17.588462Z",
     "shell.execute_reply": "2026-08-21T09:29:17.587746Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(11, 5.5))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "for column in [\"Age 16 to 19\", \"Age 25 to 54\", \"Age 55 and older\"]:\n",
    "    ax.plot(age_benchmarks[\"date\"], age_benchmarks[column], marker=\"o\", label=column)\n",
    "\n",
    "ax.set_title(\"Labor Force Participation by Age Group, Selected Observations\")\n",
    "ax.set_xlabel(\"Date\")\n",
    "ax.set_ylabel(\"Percent\")\n",
    "ax.legend()\n",
    "ax.grid(True, alpha=0.25)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d719fa9a",
   "metadata": {},
   "source": [
    "## 10. Review the recent decline decomposition\n",
    "\n",
    "Federal Reserve Bank of St. Louis research estimated the following components of the measured decline from December 2025 through June 2026.\n",
    "\n",
    "The population control revision and population aging together account for 59 percent of the estimated decline.\n",
    "\n",
    "This is a Federal Reserve research decomposition using CPS microdata. It is not an official BLS causal estimate.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3ba17d9e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-21T09:29:17.590150Z",
     "iopub.status.busy": "2026-08-21T09:29:17.590023Z",
     "iopub.status.idle": "2026-08-21T09:29:17.596758Z",
     "shell.execute_reply": "2026-08-21T09:29:17.595855Z"
    }
   },
   "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>Component</th>\n",
       "      <th>Share of measured decline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>January 2026 population control revision</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Population aging</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Within age group participation changes</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Component  Share of measured decline\n",
       "0  January 2026 population control revision                         43\n",
       "1                          Population aging                         16\n",
       "2    Within age group participation changes                         41"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "decomposition = pd.DataFrame(\n",
    "    {\n",
    "        \"Component\": [\n",
    "            \"January 2026 population control revision\",\n",
    "            \"Population aging\",\n",
    "            \"Within age group participation changes\",\n",
    "        ],\n",
    "        \"Share of measured decline\": [43, 16, 41],\n",
    "    }\n",
    ")\n",
    "display(decomposition)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8220e9ba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-21T09:29:17.598153Z",
     "iopub.status.busy": "2026-08-21T09:29:17.598040Z",
     "iopub.status.idle": "2026-08-21T09:29:17.684076Z",
     "shell.execute_reply": "2026-08-21T09:29:17.683517Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 5))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.bar(decomposition[\"Component\"], decomposition[\"Share of measured decline\"])\n",
    "ax.set_title(\"Estimated Components of the Participation Decline Through June 2026\")\n",
    "ax.set_ylabel(\"Percent of measured decline\")\n",
    "ax.tick_params(axis=\"x\", rotation=18)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e29a17b5",
   "metadata": {},
   "source": [
    "## 11. Build a compact article dashboard\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "14572b92",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-21T09:29:17.685209Z",
     "iopub.status.busy": "2026-08-21T09:29:17.685099Z",
     "iopub.status.idle": "2026-08-21T09:29:17.693532Z",
     "shell.execute_reply": "2026-08-21T09:29:17.692938Z"
    }
   },
   "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>Measure</th>\n",
       "      <th>Jul 2026</th>\n",
       "      <th>Jun 2026</th>\n",
       "      <th>Jul 2025</th>\n",
       "      <th>Unit</th>\n",
       "      <th>1 month change</th>\n",
       "      <th>12 month change</th>\n",
       "      <th>Interpretation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Participation rate</td>\n",
       "      <td>61.4</td>\n",
       "      <td>61.5</td>\n",
       "      <td>62.2</td>\n",
       "      <td>percent</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>Headline labor supply measure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prime age participation, age 25 to 54</td>\n",
       "      <td>83.4</td>\n",
       "      <td>83.3</td>\n",
       "      <td>83.4</td>\n",
       "      <td>percent</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Core working age labor supply</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Employment population ratio</td>\n",
       "      <td>58.9</td>\n",
       "      <td>59.0</td>\n",
       "      <td>59.6</td>\n",
       "      <td>percent</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>Share of eligible population that is employed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Unemployment rate</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.3</td>\n",
       "      <td>percent</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>Joblessness among people in the labor force</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Measure  Jul 2026  Jun 2026  Jul 2025     Unit  1 month change  12 month change  \\\n",
       "0                     Participation rate      61.4      61.5      62.2  percent            -0.1             -0.8   \n",
       "1  Prime age participation, age 25 to 54      83.4      83.3      83.4  percent             0.1              0.0   \n",
       "2            Employment population ratio      58.9      59.0      59.6  percent            -0.1             -0.7   \n",
       "3                      Unemployment rate       4.1       4.2       4.3  percent            -0.1             -0.2   \n",
       "\n",
       "                                  Interpretation  \n",
       "0                  Headline labor supply measure  \n",
       "1                  Core working age labor supply  \n",
       "2  Share of eligible population that is employed  \n",
       "3    Joblessness among people in the labor force  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "article_dashboard = snapshot[\n",
    "    snapshot[\"Measure\"].isin(\n",
    "        [\n",
    "            \"Participation rate\",\n",
    "            \"Prime age participation, age 25 to 54\",\n",
    "            \"Employment population ratio\",\n",
    "            \"Unemployment rate\",\n",
    "        ]\n",
    "    )\n",
    "].copy()\n",
    "\n",
    "article_dashboard[\"Interpretation\"] = [\n",
    "    \"Headline labor supply measure\",\n",
    "    \"Core working age labor supply\",\n",
    "    \"Share of eligible population that is employed\",\n",
    "    \"Joblessness among people in the labor force\",\n",
    "]\n",
    "\n",
    "display(article_dashboard)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b55e8ad4",
   "metadata": {},
   "source": [
    "## 12. Use an interpretation rule carefully\n",
    "\n",
    "This simple rule compares the total twelve month change with the prime age twelve month change.\n",
    "\n",
    "It is a screening tool. It is not a substitute for BLS significance testing, population controls, subgroup analysis, or causal research.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f7b56247",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-21T09:29:17.694582Z",
     "iopub.status.busy": "2026-08-21T09:29:17.694479Z",
     "iopub.status.idle": "2026-08-21T09:29:17.698073Z",
     "shell.execute_reply": "2026-08-21T09:29:17.697792Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The headline rate fell while prime age participation stayed broadly stable. Check aging, retirement, population controls, and older age groups before calling this a broad labor market exit.\n"
     ]
    }
   ],
   "source": [
    "total_12m = float(\n",
    "    snapshot.loc[snapshot[\"Measure\"] == \"Participation rate\", \"12 month change\"].iloc[0]\n",
    ")\n",
    "prime_12m = float(\n",
    "    snapshot.loc[\n",
    "        snapshot[\"Measure\"] == \"Prime age participation, age 25 to 54\",\n",
    "        \"12 month change\",\n",
    "    ].iloc[0]\n",
    ")\n",
    "\n",
    "if total_12m < -0.2 and abs(prime_12m) <= 0.2:\n",
    "    message = (\n",
    "        \"The headline rate fell while prime age participation stayed broadly stable. \"\n",
    "        \"Check aging, retirement, population controls, and older age groups before \"\n",
    "        \"calling this a broad labor market exit.\"\n",
    "    )\n",
    "elif total_12m < -0.2 and prime_12m < -0.2:\n",
    "    message = (\n",
    "        \"Both headline and prime age participation fell. Review unemployment, employment, \"\n",
    "        \"subgroup data, and BLS significance tests for evidence of broader weakness.\"\n",
    "    )\n",
    "else:\n",
    "    message = (\n",
    "        \"The pattern is mixed or modest. Review subgroup data and broader labor indicators \"\n",
    "        \"before drawing a strong conclusion.\"\n",
    "    )\n",
    "\n",
    "print(message)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3229d740",
   "metadata": {},
   "source": [
    "## 13. Export the analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e121968d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-21T09:29:17.699398Z",
     "iopub.status.busy": "2026-08-21T09:29:17.699302Z",
     "iopub.status.idle": "2026-08-21T09:29:17.705637Z",
     "shell.execute_reply": "2026-08-21T09:29:17.704853Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved files:\n",
      "lfpr_analysis_output/article_snapshot_recent_monthly.csv\n",
      "lfpr_analysis_output/article_summary_table.csv\n",
      "lfpr_analysis_output/latest_change_table.csv\n",
      "lfpr_analysis_output/latest_labor_market_snapshot.csv\n",
      "lfpr_analysis_output/participation_by_age.csv\n",
      "lfpr_analysis_output/participation_by_sex.csv\n",
      "lfpr_analysis_output/participation_decline_decomposition.csv\n",
      "lfpr_analysis_output/recent_decline_decomposition.csv\n",
      "lfpr_analysis_output/total_participation_benchmarks.csv\n",
      "lfpr_analysis_output/total_vs_prime_age.csv\n"
     ]
    }
   ],
   "source": [
    "long_run_total.to_csv(OUTPUT_DIR / \"total_participation_benchmarks.csv\", index=False)\n",
    "snapshot.to_csv(OUTPUT_DIR / \"latest_labor_market_snapshot.csv\", index=False)\n",
    "prime_comparison.to_csv(OUTPUT_DIR / \"total_vs_prime_age.csv\", index=False)\n",
    "sex_benchmarks.to_csv(OUTPUT_DIR / \"participation_by_sex.csv\", index=False)\n",
    "age_benchmarks.to_csv(OUTPUT_DIR / \"participation_by_age.csv\", index=False)\n",
    "decomposition.to_csv(OUTPUT_DIR / \"recent_decline_decomposition.csv\", index=False)\n",
    "\n",
    "print(\"Saved files:\")\n",
    "for path in sorted(OUTPUT_DIR.glob(\"*.csv\")):\n",
    "    print(path)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8db2e813",
   "metadata": {},
   "source": [
    "## 14. Monthly update checklist\n",
    "\n",
    "After each BLS Employment Situation release:\n",
    "\n",
    "1. Pull the newest observations.\n",
    "2. Confirm the latest month and revision notes.\n",
    "3. Calculate the one month, six month, and twelve month changes.\n",
    "4. Compare the total rate with prime age participation.\n",
    "5. Review men and women.\n",
    "6. Review youth and older workers when the story requires it.\n",
    "7. Compare unemployment and the employment population ratio.\n",
    "8. Read BLS statistical significance guidance.\n",
    "9. Check for population control revisions or survey breaks.\n",
    "10. Update the charts.\n",
    "11. Update the tables.\n",
    "12. Rewrite the conclusion only after reviewing the full pattern.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f35ff4be",
   "metadata": {},
   "source": [
    "## 15. Common mistakes to avoid\n",
    "\n",
    "**Treating a lower participation rate as hidden unemployment.**  \n",
    "Some people are retired, in school, caring for family, or outside the labor force by choice.\n",
    "\n",
    "**Ignoring age composition.**  \n",
    "An aging population can reduce the total rate even when prime age participation remains strong.\n",
    "\n",
    "**Using only one month of data.**  \n",
    "Monthly household survey estimates can be noisy.\n",
    "\n",
    "**Ignoring statistical significance.**  \n",
    "A numerical change may not be large enough to distinguish from sampling variation.\n",
    "\n",
    "**Calling correlation a cause.**  \n",
    "Charts show patterns. They do not prove causation.\n",
    "\n",
    "**Ignoring survey revisions and population controls.**  \n",
    "Population adjustments can change measured levels.\n",
    "\n",
    "**Looking at participation alone.**  \n",
    "Read it with employment, unemployment, wages, hiring, and other labor demand measures.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dc3ef51",
   "metadata": {},
   "source": [
    "## 16. Bottom line\n",
    "\n",
    "The US labor force participation rate was 61.4 percent in July 2026, down from 62.2 percent one year earlier.\n",
    "\n",
    "Prime age participation was 83.4 percent, the same as one year earlier in the article data. This makes the composition of the decline important.\n",
    "\n",
    "Federal Reserve research also estimated that population aging and the January 2026 population control revision explained more than half of the measured decline through June.\n",
    "\n",
    "The stronger conclusion is therefore not that US workers broadly left the labor market. The evidence points to a mix of demographic change, population measurement adjustments, retirement, and changes within age groups.\n",
    "\n",
    "The next monthly analysis should focus on whether prime age participation weakens, whether older worker participation continues to fall, and whether labor demand remains strong enough to draw people into work or active job search.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbbcda17",
   "metadata": {},
   "source": [
    "## 17. Sources\n",
    "\n",
    "US Bureau of Labor Statistics, Employment Situation, July 2026  \n",
    "https://www.bls.gov/news.release/archives/empsit_08072026.htm\n",
    "\n",
    "BLS Public Data API  \n",
    "https://www.bls.gov/developers/\n",
    "\n",
    "BLS Current Population Survey tables  \n",
    "https://www.bls.gov/web/empsit/cpseea08b.htm\n",
    "\n",
    "BLS statistical significance guidance  \n",
    "https://www.bls.gov/web/empsit/cpssigsuma.htm\n",
    "\n",
    "Federal Reserve Bank of St. Louis, recent participation decline analysis  \n",
    "https://www.stlouisfed.org/on-the-economy/2026/aug/what-is-behind-sharp-drop-labor-force-participation\n",
    "\n",
    "Federal Reserve Board, labor force growth and potential GDP  \n",
    "https://www.federalreserve.gov/econres/notes/feds-notes/labor-force-growth-breakeven-employment-and-potential-gdp-growth-20260402.html\n",
    "\n",
    "FRED, Civilian Labor Force Participation Rate  \n",
    "https://fred.stlouisfed.org/series/CIVPART\n",
    "\n",
    "### Reproducibility note\n",
    "\n",
    "The executed notebook uses the article snapshot so it works offline. Run the live BLS API functions when you want to refresh the analysis with a new monthly release.\n"
   ]
  }
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