{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keane and Wolpin (1994)\n",
    "\n",
    "*Note that most of the code cells are hidden from this notebook for a better reading flow. Check out the notebook in the documentation folder in the Github repository for more details.*\n",
    "\n",
    "Keane and Wolpin (1994) and the previously published working paper Keane and Wolpin (1994b) generate three different Monte Carlo samples. This notebook replicates some of their results as well as giving other insights into the model.\n",
    "\n",
    "## Some intuitions\n",
    "\n",
    "We first plot the returns to experience while holding education constant at the initial ten years. Occupation B is more skill intensive in the sense that own experience has higher return than is the case for Occupation A. There is some general skill learned in Occupation A which is transferable to Occupation B. However, work experience is occupation-specific in Occupation B."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "%matplotlib agg\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import respy as rp\n",
    "import warnings\n",
    "\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "plt.style.use(\"../_static/respy.mplstyle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "# Load example model and convert parameter and options to model attributes.\n",
    "params, options = rp.get_example_model(\"kw_94_one\", with_data=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "ret_schooling_occ_a = params.loc[(\"wage_a\", \"exp_edu\"), \"value\"]\n",
    "ret_schooling_occ_b = params.loc[(\"wage_b\", \"exp_edu\"), \"value\"]\n",
    "\n",
    "ret_exp_a_occ_a = params.loc[(\"wage_a\", \"exp_a\"), \"value\"]\n",
    "ret_exp_a_sq_occ_a = params.loc[(\"wage_a\", \"exp_a_square\"), \"value\"]\n",
    "ret_exp_b_occ_a = params.loc[(\"wage_a\", \"exp_b\"), \"value\"]\n",
    "ret_exp_b_sq_occ_a = params.loc[(\"wage_a\", \"exp_b_square\"), \"value\"]\n",
    "log_rental_price_occ_a = params.loc[(\"wage_a\", \"constant\"), \"value\"]\n",
    "\n",
    "ret_exp_a_occ_b = params.loc[(\"wage_b\", \"exp_a\"), \"value\"]\n",
    "ret_exp_a_sq_occ_b = params.loc[(\"wage_b\", \"exp_a_square\"), \"value\"]\n",
    "ret_exp_b_occ_b = params.loc[(\"wage_b\", \"exp_b\"), \"value\"]\n",
    "ret_exp_b_sq_occ_b = params.loc[(\"wage_b\", \"exp_b_square\"), \"value\"]\n",
    "log_rental_price_occ_b = params.loc[(\"wage_b\", \"constant\"), \"value\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "x, y = np.meshgrid(range(0, 20), range(0, 20))\n",
    "\n",
    "z_a = np.exp(\n",
    "    log_rental_price_occ_a + ret_schooling_occ_a * 10\n",
    "    + ret_exp_a_occ_a * x + ret_exp_a_sq_occ_a * x ** 2\n",
    "    + ret_exp_b_occ_a * y + ret_exp_b_sq_occ_a * y ** 2\n",
    ")\n",
    "\n",
    "z_b = np.exp(\n",
    "    log_rental_price_occ_b + ret_schooling_occ_b * 10\n",
    "    + ret_exp_a_occ_b * x + ret_exp_a_sq_occ_b * x ** 2\n",
    "    + ret_exp_b_occ_b * y + ret_exp_b_sq_occ_b * y ** 2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(8, 4))\n",
    "ax1 = fig.add_subplot(1, 2, 1, projection=\"3d\")\n",
    "ax2 = fig.add_subplot(1, 2, 2, projection=\"3d\")\n",
    "\n",
    "ax1.plot_surface(x, y, z_a / 1_000, cmap=\"Greens\")\n",
    "ax2.plot_surface(x, y, z_b / 1_000, cmap=\"Reds\")\n",
    "\n",
    "for ax in [ax1, ax2]:\n",
    "    ax.set_xticks(range(0, 21, 5))\n",
    "    ax.set_yticks(range(0, 21, 5))\n",
    "    ax.set_zticks(range(10, 36, 5))\n",
    "    ax.set_zticklabels(range(10, 36, 5))\n",
    "    ax.set_zlim(10, 35)\n",
    "    ax.invert_xaxis()\n",
    "    ax.set_xlabel(\"Experience A\")\n",
    "    ax.set_ylabel(\"Experience B\")\n",
    "    ax.set_proj_type('ortho')\n",
    "    ax.set_zlabel(\"Wages in (\\$1,000)\", labelpad=-1)\n",
    "    \n",
    "ax1.set_title(\"(a)\", y=-0.15)\n",
    "ax2.set_title(\"(b)\", y=-0.15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next figure shows that the returns to schooling are larger in Occupation B. While its initial wage is lower, it does increase faster with schooling compared to Occupation A. The graphs are generated by holding experience in both sectors constant at five years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "years_schooling = np.arange(10, 21)\n",
    "\n",
    "wage_a = np.exp(\n",
    "    log_rental_price_occ_a + ret_schooling_occ_a * years_schooling\n",
    "    + ret_exp_a_occ_a * 5 + ret_exp_a_sq_occ_a * 5 ** 2\n",
    "    + ret_exp_b_occ_a * 5 + ret_exp_b_sq_occ_a * 5 ** 2\n",
    ")\n",
    "\n",
    "wage_b = np.exp(\n",
    "    log_rental_price_occ_b + ret_schooling_occ_b * years_schooling\n",
    "    + ret_exp_a_occ_b * 5 + ret_exp_a_sq_occ_b * 5 ** 2\n",
    "    + ret_exp_b_occ_b * 5 + ret_exp_b_sq_occ_b * 5 ** 2\n",
    ")\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(years_schooling, wage_a / 1_000, color=\"C2\", label=\"Occupation A\")\n",
    "ax.plot(years_schooling, wage_b / 1_000, color=\"C3\", label=\"Occupation B\")\n",
    "\n",
    "ax.set_xlim(10, 20)\n",
    "ax.set_ylim(14, 30)\n",
    "ax.set_yticks(range(16, 31, 2))\n",
    "\n",
    "ax.set_xlabel(\"Years of Schooling\")\n",
    "ax.set_ylabel(\"Wages (in \\$1,000)\")\n",
    "\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Replication - Effect of college tuition subsidy\n",
    "\n",
    "This section replicates Table 6 in Keane and Wolpin (1994) which studies the effect of different amounts of tuition subsidies. First, we are going to show the effect of the tuition subsidy on each of the parametrizations for a sample of 1,000 simulated individuals. After that, Table 6 is replicated where the policy effect is measured as the average difference in experience for 40 samples with 100 invididuals with and without the tutition subsidy. The authors apply a 500 USD tuition subsidy on the first parametrization, 1,000 USD on the second and 2,000 USD on the third parametrization.\n",
    "\n",
    "The following figures show the impact of the tuition subsidies for each of the parametrizations next to each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "# Build simulate function as only parameters change, it can be reused.\n",
    "params, options, _ = rp.get_example_model(\"kw_94_one\")\n",
    "options[\"simulation_agents\"] = 4_000\n",
    "simulate = rp.get_simulate_func(params, options)\n",
    "\n",
    "models = np.repeat([\"one\", \"two\", \"three\"], 2)\n",
    "tuition_subsidies = [0, 500, 0, 1_000, 0, 2_000]\n",
    "\n",
    "data_frames = []\n",
    "\n",
    "for model, tuition_subsidy in zip(models, tuition_subsidies):\n",
    "    params, _, _ = rp.get_example_model(f\"kw_94_{model}\")\n",
    "    params.loc[(\"nonpec_edu\", \"at_least_twelve_exp_edu\"), \"value\"] += tuition_subsidy\n",
    "    data_frames.append(simulate(params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(3, 2, figsize=(8, 12), sharey=True, sharex=True)\n",
    "\n",
    "axs = axs.flatten()\n",
    "\n",
    "for df, ax, model, tuition_subsidy in zip(data_frames, axs, models, tuition_subsidies):\n",
    "    shares = df.groupby(\"Period\").Choice.value_counts(normalize=True).unstack()[[\"home\", \"edu\", \"a\", \"b\"]]\n",
    "\n",
    "    shares.plot.bar(stacked=True, ax=ax, width=1, legend=True)\n",
    "\n",
    "    ax.set_ylim(0, 1)\n",
    "    ax.set_xticks(range(0, 40, 5))\n",
    "    ax.set_xticklabels(range(0, 40, 5), rotation=\"horizontal\")\n",
    "    ax.set_ylabel(\"Share of population\")\n",
    "    handles, labels = ax.get_legend_handles_labels()\n",
    "    ax.get_legend().remove()\n",
    "    if tuition_subsidy:\n",
    "        label = f\"with a tuition subsidy of {tuition_subsidy:,} USD\"\n",
    "    else:\n",
    "        label = \"without a tuition subsidy\"\n",
    "    ax.set_title(f\"Parameterization {model.title()} \\n {label}\")\n",
    "    \n",
    "fig.legend(\n",
    "    handles,\n",
    "    [\"Home\", \"Education\", \"A\", \"B\"],\n",
    "    loc=\"lower center\",\n",
    "    bbox_to_anchor=(0.5, -0.01),\n",
    "    ncol=4\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x1200 with 6 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "columns = [\"Bootstrap_Sample\", \"Experience_Edu\", \"Experience_A\", \"Experience_B\"]\n",
    "parameterizations = [\"Data Set One\", \"Data Set Two\", \"Data Set Three\"]\n",
    "bootstrapped_statistics = []\n",
    "\n",
    "for i, title in zip(range(0, 6, 2), parameterizations):\n",
    "    # Select sample with and without tuition subsidy.\n",
    "    df_wo_ts = data_frames[i]\n",
    "    df_w_ts = data_frames[i + 1]\n",
    "\n",
    "    # Assign bootstrap sample number.\n",
    "    df_wo_ts[\"Bootstrap_Sample\"] = pd.cut(\n",
    "        df_wo_ts.Identifier, bins=40, labels=np.arange(1, 41)\n",
    "    )\n",
    "    df_w_ts[\"Bootstrap_Sample\"] = pd.cut(\n",
    "        df_w_ts.Identifier, bins=40, labels=np.arange(1, 41)\n",
    "    )\n",
    "\n",
    "    # Calculate mean experiences.\n",
    "    mean_exp_wo_ts = (\n",
    "        df_wo_ts.loc[df_wo_ts.Period.eq(39), columns]\n",
    "        .groupby(\"Bootstrap_Sample\")\n",
    "        .mean()\n",
    "    )\n",
    "    mean_exp_w_ts = (\n",
    "        df_w_ts.loc[df_w_ts.Period.eq(39), columns]\n",
    "        .groupby(\"Bootstrap_Sample\")\n",
    "        .mean()\n",
    "    )\n",
    "    \n",
    "    mean_exp_wo_ts = df_wo_ts.loc[df_wo_ts.Period.eq(39), [\"Bootstrap_Sample\", \"Experience_Edu\", \"Experience_A\", \"Experience_B\"]].groupby(\"Bootstrap_Sample\").mean()\n",
    "    mean_exp_w_ts = df_w_ts.loc[df_w_ts.Period.eq(39), [\"Bootstrap_Sample\", \"Experience_Edu\", \"Experience_A\", \"Experience_B\"]].groupby(\"Bootstrap_Sample\").mean()\n",
    "    \n",
    "    # Calculate bootstrap statistics.\n",
    "    diff = (\n",
    "        mean_exp_w_ts.subtract(mean_exp_wo_ts)\n",
    "        .assign(Data=title)\n",
    "        .reset_index()\n",
    "        .set_index([\"Data\", \"Bootstrap_Sample\"])\n",
    "        .stack()\n",
    "        .unstack([0, 2])\n",
    "    )\n",
    "    bootstrapped_statistics.append(diff)\n",
    "    \n",
    "replication = pd.concat([bs.agg([\"mean\", \"std\"]) for bs in bootstrapped_statistics], axis=1).round(3)\n",
    "replication.columns = [replication.columns.get_level_values(0), [\"edu\", \"a\", \"b\"] * 3]\n",
    "replication.index = [\"Exact Solution - Mean\", \"Exact Solution - Std\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following two tables refer to summary statistics of the tuition subsidy experiment. The first table refers to the replication and the second contains the results from Keane and Wolpin (1994). The tables show the differences in experience between the sample without and with subsidy for all parameterizations. The standard deviations of the mean are computed by simulating 40 bootstrap samples á 100 individuals. All means of the replication lie within one standard deviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Data Set One</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Data Set Two</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Data Set Three</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>edu</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>edu</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>edu</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Exact Solution - Mean</th>\n",
       "      <td>1.449</td>\n",
       "      <td>-3.450</td>\n",
       "      <td>2.213</td>\n",
       "      <td>1.116</td>\n",
       "      <td>-2.685</td>\n",
       "      <td>2.004</td>\n",
       "      <td>1.693</td>\n",
       "      <td>-1.349</td>\n",
       "      <td>-0.212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exact Solution - Std</th>\n",
       "      <td>0.217</td>\n",
       "      <td>0.987</td>\n",
       "      <td>0.887</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.622</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.242</td>\n",
       "      <td>0.111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Data Set One               Data Set Two                \\\n",
       "                               edu      a      b          edu      a      b   \n",
       "Exact Solution - Mean        1.449 -3.450  2.213        1.116 -2.685  2.004   \n",
       "Exact Solution - Std         0.217  0.987  0.887        0.236  0.622  0.516   \n",
       "\n",
       "                      Data Set Three                \n",
       "                                 edu      a      b  \n",
       "Exact Solution - Mean          1.693 -1.349 -0.212  \n",
       "Exact Solution - Std           0.226  0.242  0.111  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "replication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "table_6 = pd.read_csv(rp.config.TEST_RESOURCES_DIR / \"kw_94_table_6.csv\", nrows=2, header=1, index_col=0)\n",
    "table_6.columns = [np.repeat(parameterizations, 3), [\"edu\", \"a\", \"b\"] * 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Data Set One</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Data Set Two</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Data Set Three</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>edu</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>edu</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>edu</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Exact Solution - Mean</th>\n",
       "      <td>1.44</td>\n",
       "      <td>-3.43</td>\n",
       "      <td>2.19</td>\n",
       "      <td>1.12</td>\n",
       "      <td>-2.71</td>\n",
       "      <td>2.08</td>\n",
       "      <td>1.67</td>\n",
       "      <td>-1.27</td>\n",
       "      <td>-0.236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exact Solution - Std</th>\n",
       "      <td>0.18</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Data Set One             Data Set Two              \\\n",
       "                               edu     a     b          edu     a     b   \n",
       "Exact Solution - Mean         1.44 -3.43  2.19         1.12 -2.71  2.08   \n",
       "Exact Solution - Std          0.18  0.94  0.89         0.22  0.53  0.43   \n",
       "\n",
       "                      Data Set Three               \n",
       "                                 edu     a      b  \n",
       "Exact Solution - Mean           1.67 -1.27 -0.236  \n",
       "Exact Solution - Std            0.20  0.18  0.100  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Replication - Choice distributions\n",
    "\n",
    "This section replicates the choice distributions for all three parameterizations in Keane and Wolpin (1994b) Table 2.1-2.3. The tables show the share of individuals choosing each choice for each period. The original results are on the left hand side and the replications on the right hand side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "params, options, df = rp.get_example_model(\"kw_94_one\")\n",
    "\n",
    "table_2_1 = (\n",
    "    pd.read_csv(rp.config.TEST_RESOURCES_DIR / \"kw_94_wp_table_2_1.csv\")\n",
    "    .drop(columns=\"period\")\n",
    ")\n",
    "table_2_1.columns = [[\"Table 2.1\"] * 4, table_2_1.columns]\n",
    "\n",
    "repl = (\n",
    "    df.groupby(\"Period\").Choice.value_counts(normalize=True)\n",
    "    .unstack(\"Choice\")\n",
    "    .fillna(0)\n",
    "    .round(3)\n",
    ")\n",
    "repl.columns = [[\"Replication\"] * 4, repl.columns]\n",
    "\n",
    "table_2_1 = pd.concat([table_2_1, repl], axis=1)\n",
    "table_2_1.index.name = \"Period\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">Table 2.1</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Replication</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>edu</th>\n",
       "      <th>home</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>edu</th>\n",
       "      <th>home</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.386</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.442</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.453</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.427</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.474</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.293</td>\n",
       "      <td>0.049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.444</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.308</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.459</td>\n",
       "      <td>0.263</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.473</td>\n",
       "      <td>0.261</td>\n",
       "      <td>0.227</td>\n",
       "      <td>0.039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.417</td>\n",
       "      <td>0.332</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.473</td>\n",
       "      <td>0.292</td>\n",
       "      <td>0.197</td>\n",
       "      <td>0.038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.427</td>\n",
       "      <td>0.374</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.467</td>\n",
       "      <td>0.337</td>\n",
       "      <td>0.171</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.412</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.457</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.399</td>\n",
       "      <td>0.421</td>\n",
       "      <td>0.155</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.419</td>\n",
       "      <td>0.407</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.372</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.421</td>\n",
       "      <td>0.428</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.355</td>\n",
       "      <td>0.501</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.415</td>\n",
       "      <td>0.456</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.340</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.416</td>\n",
       "      <td>0.492</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.342</td>\n",
       "      <td>0.567</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.379</td>\n",
       "      <td>0.534</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.322</td>\n",
       "      <td>0.585</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.385</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.321</td>\n",
       "      <td>0.612</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.374</td>\n",
       "      <td>0.568</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.303</td>\n",
       "      <td>0.619</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.349</td>\n",
       "      <td>0.586</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.297</td>\n",
       "      <td>0.640</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.358</td>\n",
       "      <td>0.593</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.290</td>\n",
       "      <td>0.664</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.346</td>\n",
       "      <td>0.601</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.304</td>\n",
       "      <td>0.656</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.339</td>\n",
       "      <td>0.623</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.283</td>\n",
       "      <td>0.686</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.324</td>\n",
       "      <td>0.652</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.277</td>\n",
       "      <td>0.695</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.314</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.288</td>\n",
       "      <td>0.691</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.316</td>\n",
       "      <td>0.663</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.266</td>\n",
       "      <td>0.716</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.308</td>\n",
       "      <td>0.667</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.268</td>\n",
       "      <td>0.717</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.318</td>\n",
       "      <td>0.668</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.258</td>\n",
       "      <td>0.731</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.305</td>\n",
       "      <td>0.678</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.265</td>\n",
       "      <td>0.715</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.306</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.270</td>\n",
       "      <td>0.720</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.304</td>\n",
       "      <td>0.677</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.254</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.298</td>\n",
       "      <td>0.691</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.252</td>\n",
       "      <td>0.743</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.303</td>\n",
       "      <td>0.679</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.249</td>\n",
       "      <td>0.736</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.307</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.241</td>\n",
       "      <td>0.742</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.288</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.246</td>\n",
       "      <td>0.743</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.289</td>\n",
       "      <td>0.699</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.243</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.686</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.242</td>\n",
       "      <td>0.748</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.288</td>\n",
       "      <td>0.694</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.243</td>\n",
       "      <td>0.746</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0.706</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.229</td>\n",
       "      <td>0.757</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0.704</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.244</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.279</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.234</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.278</td>\n",
       "      <td>0.702</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.238</td>\n",
       "      <td>0.749</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.278</td>\n",
       "      <td>0.704</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.231</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.278</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.230</td>\n",
       "      <td>0.758</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.274</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Table 2.1                      Replication                     \n",
       "               a      b    edu   home           a      b    edu   home\n",
       "Period                                                                \n",
       "0          0.386  0.116  0.490  0.008       0.442  0.093  0.453  0.012\n",
       "1          0.427  0.175  0.354  0.044       0.474  0.184  0.293  0.049\n",
       "2          0.444  0.220  0.308  0.028       0.490  0.228  0.244  0.038\n",
       "3          0.459  0.263  0.255  0.023       0.473  0.261  0.227  0.039\n",
       "4          0.417  0.332  0.218  0.033       0.473  0.292  0.197  0.038\n",
       "5          0.427  0.374  0.175  0.024       0.467  0.337  0.171  0.025\n",
       "6          0.412  0.387  0.179  0.022       0.457  0.361  0.165  0.017\n",
       "7          0.399  0.421  0.155  0.025       0.419  0.407  0.153  0.021\n",
       "8          0.372  0.475  0.130  0.023       0.421  0.428  0.133  0.018\n",
       "9          0.355  0.501  0.126  0.018       0.415  0.456  0.107  0.022\n",
       "10         0.340  0.537  0.099  0.024       0.416  0.492  0.076  0.016\n",
       "11         0.342  0.567  0.081  0.010       0.379  0.534  0.070  0.017\n",
       "12         0.322  0.585  0.073  0.020       0.385  0.532  0.064  0.019\n",
       "13         0.321  0.612  0.056  0.011       0.374  0.568  0.047  0.011\n",
       "14         0.303  0.619  0.062  0.016       0.349  0.586  0.045  0.020\n",
       "15         0.297  0.640  0.052  0.011       0.358  0.593  0.039  0.010\n",
       "16         0.290  0.664  0.034  0.012       0.346  0.601  0.039  0.014\n",
       "17         0.304  0.656  0.028  0.012       0.339  0.623  0.026  0.012\n",
       "18         0.283  0.686  0.018  0.013       0.324  0.652  0.017  0.007\n",
       "19         0.277  0.695  0.016  0.012       0.314  0.655  0.012  0.019\n",
       "20         0.288  0.691  0.011  0.010       0.316  0.663  0.005  0.016\n",
       "21         0.266  0.716  0.003  0.015       0.308  0.667  0.009  0.016\n",
       "22         0.268  0.717  0.006  0.009       0.318  0.668  0.006  0.008\n",
       "23         0.258  0.731  0.001  0.010       0.305  0.678  0.003  0.014\n",
       "24         0.265  0.715  0.005  0.015       0.306  0.683  0.000  0.011\n",
       "25         0.270  0.720  0.003  0.007       0.304  0.677  0.001  0.018\n",
       "26         0.254  0.730  0.000  0.016       0.298  0.691  0.001  0.010\n",
       "27         0.252  0.743  0.000  0.005       0.303  0.679  0.000  0.018\n",
       "28         0.249  0.736  0.000  0.015       0.307  0.683  0.000  0.010\n",
       "29         0.241  0.742  0.000  0.017       0.288  0.698  0.000  0.014\n",
       "30         0.246  0.743  0.000  0.011       0.289  0.699  0.000  0.012\n",
       "31         0.243  0.750  0.000  0.007       0.302  0.686  0.000  0.012\n",
       "32         0.242  0.748  0.000  0.010       0.288  0.694  0.000  0.018\n",
       "33         0.243  0.746  0.000  0.011       0.282  0.706  0.000  0.012\n",
       "34         0.229  0.757  0.000  0.014       0.282  0.704  0.000  0.014\n",
       "35         0.244  0.750  0.000  0.006       0.279  0.703  0.000  0.018\n",
       "36         0.234  0.755  0.000  0.011       0.278  0.702  0.000  0.020\n",
       "37         0.238  0.749  0.000  0.013       0.278  0.704  0.000  0.018\n",
       "38         0.231  0.753  0.000  0.016       0.278  0.710  0.000  0.012\n",
       "39         0.230  0.758  0.000  0.012       0.274  0.708  0.000  0.018"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_2_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "params, options, df = rp.get_example_model(\"kw_94_two\")\n",
    "\n",
    "table_2_2 = (\n",
    "    pd.read_csv(rp.config.TEST_RESOURCES_DIR / \"kw_94_wp_table_2_2.csv\")\n",
    "    .drop(columns=\"period\")\n",
    ")\n",
    "table_2_2.columns = [[\"Table 2.2\"] * 4, table_2_2.columns]\n",
    "\n",
    "repl = (\n",
    "    df.groupby(\"Period\").Choice.value_counts(normalize=True)\n",
    "    .unstack(\"Choice\")\n",
    "    .fillna(0)\n",
    "    .round(3)\n",
    ")\n",
    "repl.columns = [[\"Replication\"] * 4, repl.columns]\n",
    "\n",
    "table_2_2 = pd.concat([table_2_2, repl], axis=1)\n",
    "table_2_2.index.name = \"Period\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">Table 2.2</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Replication</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>edu</th>\n",
       "      <th>home</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>edu</th>\n",
       "      <th>home</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.344</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.304</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0.047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.481</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.460</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.401</td>\n",
       "      <td>0.072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.606</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.238</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.582</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.232</td>\n",
       "      <td>0.092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.633</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.176</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.658</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.632</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.659</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.084</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.662</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.628</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.642</td>\n",
       "      <td>0.182</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.621</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.657</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.084</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.632</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.657</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.648</td>\n",
       "      <td>0.227</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.642</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.632</td>\n",
       "      <td>0.264</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.641</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.629</td>\n",
       "      <td>0.269</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.643</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.627</td>\n",
       "      <td>0.279</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.633</td>\n",
       "      <td>0.278</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.626</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.625</td>\n",
       "      <td>0.291</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.304</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.623</td>\n",
       "      <td>0.305</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.643</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.628</td>\n",
       "      <td>0.289</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.613</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.599</td>\n",
       "      <td>0.325</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.292</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.597</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.621</td>\n",
       "      <td>0.317</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.594</td>\n",
       "      <td>0.355</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.613</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.562</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.585</td>\n",
       "      <td>0.358</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.577</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.580</td>\n",
       "      <td>0.360</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.390</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.596</td>\n",
       "      <td>0.344</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.573</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.622</td>\n",
       "      <td>0.334</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.583</td>\n",
       "      <td>0.370</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.566</td>\n",
       "      <td>0.376</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.553</td>\n",
       "      <td>0.381</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.567</td>\n",
       "      <td>0.386</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.548</td>\n",
       "      <td>0.394</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.584</td>\n",
       "      <td>0.370</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.560</td>\n",
       "      <td>0.373</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.552</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.562</td>\n",
       "      <td>0.374</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.548</td>\n",
       "      <td>0.398</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.568</td>\n",
       "      <td>0.388</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.544</td>\n",
       "      <td>0.394</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.562</td>\n",
       "      <td>0.374</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.554</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.569</td>\n",
       "      <td>0.367</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.540</td>\n",
       "      <td>0.397</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.578</td>\n",
       "      <td>0.369</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.407</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.557</td>\n",
       "      <td>0.390</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.541</td>\n",
       "      <td>0.395</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.562</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.542</td>\n",
       "      <td>0.397</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.376</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.562</td>\n",
       "      <td>0.385</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.555</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.551</td>\n",
       "      <td>0.390</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.569</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.051</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Table 2.2                      Replication                     \n",
       "               a      b    edu   home           a      b    edu   home\n",
       "Period                                                                \n",
       "0          0.344  0.038  0.575  0.043       0.304  0.033  0.616  0.047\n",
       "1          0.481  0.059  0.375  0.085       0.460  0.067  0.401  0.072\n",
       "2          0.606  0.073  0.238  0.083       0.582  0.094  0.232  0.092\n",
       "3          0.633  0.115  0.176  0.076       0.600  0.125  0.174  0.101\n",
       "4          0.658  0.126  0.143  0.073       0.632  0.117  0.161  0.090\n",
       "5          0.659  0.146  0.111  0.084       0.625  0.151  0.158  0.066\n",
       "6          0.662  0.151  0.096  0.091       0.628  0.174  0.128  0.070\n",
       "7          0.642  0.182  0.097  0.079       0.621  0.174  0.125  0.080\n",
       "8          0.657  0.174  0.084  0.085       0.614  0.212  0.092  0.082\n",
       "9          0.632  0.210  0.082  0.076       0.657  0.214  0.065  0.064\n",
       "10         0.648  0.227  0.056  0.069       0.650  0.236  0.050  0.064\n",
       "11         0.642  0.241  0.046  0.071       0.632  0.264  0.044  0.060\n",
       "12         0.641  0.254  0.044  0.061       0.629  0.269  0.041  0.061\n",
       "13         0.643  0.265  0.036  0.056       0.627  0.279  0.045  0.049\n",
       "14         0.633  0.278  0.029  0.060       0.626  0.272  0.046  0.056\n",
       "15         0.625  0.291  0.023  0.061       0.610  0.304  0.031  0.055\n",
       "16         0.623  0.305  0.020  0.052       0.643  0.282  0.026  0.049\n",
       "17         0.628  0.289  0.028  0.055       0.613  0.300  0.026  0.061\n",
       "18         0.599  0.325  0.014  0.062       0.625  0.292  0.021  0.062\n",
       "19         0.597  0.322  0.020  0.061       0.590  0.330  0.014  0.066\n",
       "20         0.621  0.317  0.017  0.045       0.594  0.355  0.006  0.045\n",
       "21         0.613  0.327  0.010  0.050       0.562  0.372  0.011  0.055\n",
       "22         0.585  0.358  0.006  0.051       0.577  0.359  0.006  0.058\n",
       "23         0.580  0.360  0.005  0.055       0.545  0.390  0.004  0.061\n",
       "24         0.596  0.344  0.000  0.060       0.573  0.375  0.001  0.051\n",
       "25         0.622  0.334  0.003  0.041       0.583  0.370  0.004  0.043\n",
       "26         0.566  0.376  0.002  0.056       0.553  0.381  0.005  0.061\n",
       "27         0.567  0.386  0.001  0.046       0.565  0.365  0.002  0.068\n",
       "28         0.548  0.394  0.000  0.058       0.584  0.370  0.000  0.046\n",
       "29         0.560  0.373  0.002  0.065       0.552  0.387  0.000  0.061\n",
       "30         0.562  0.374  0.000  0.064       0.548  0.398  0.000  0.054\n",
       "31         0.568  0.388  0.000  0.044       0.544  0.394  0.000  0.062\n",
       "32         0.562  0.374  0.000  0.064       0.554  0.387  0.000  0.059\n",
       "33         0.569  0.367  0.000  0.064       0.540  0.397  0.000  0.063\n",
       "34         0.578  0.369  0.000  0.053       0.536  0.407  0.000  0.057\n",
       "35         0.557  0.390  0.000  0.053       0.541  0.395  0.000  0.064\n",
       "36         0.562  0.387  0.000  0.051       0.545  0.400  0.000  0.055\n",
       "37         0.542  0.397  0.000  0.061       0.561  0.376  0.000  0.063\n",
       "38         0.562  0.385  0.000  0.053       0.555  0.387  0.000  0.058\n",
       "39         0.551  0.390  0.000  0.059       0.569  0.380  0.000  0.051"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_2_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "params, options, df = rp.get_example_model(\"kw_94_three\")\n",
    "\n",
    "table_2_3 = (\n",
    "    pd.read_csv(rp.config.TEST_RESOURCES_DIR / \"kw_94_wp_table_2_3.csv\")\n",
    "    .drop(columns=\"period\")\n",
    ")\n",
    "table_2_3.columns = [[\"Table 2.3\"] * 4, table_2_3.columns]\n",
    "\n",
    "repl = (\n",
    "    df.groupby(\"Period\").Choice.value_counts(normalize=True)\n",
    "    .unstack(\"Choice\")\n",
    "    .fillna(0)\n",
    "    .round(3)\n",
    ")\n",
    "repl.columns = [[\"Replication\"] * 4, repl.columns]\n",
    "\n",
    "table_2_3 = pd.concat([table_2_3, repl], axis=1)\n",
    "table_2_3.index.name = \"Period\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">Table 2.3</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Replication</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>edu</th>\n",
       "      <th>home</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>edu</th>\n",
       "      <th>home</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.169</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.752</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.769</td>\n",
       "      <td>0.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.308</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.594</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.315</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.585</td>\n",
       "      <td>0.052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.455</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.430</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.418</td>\n",
       "      <td>0.065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.574</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.326</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.544</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.321</td>\n",
       "      <td>0.068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.628</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.631</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.710</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.189</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.682</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.725</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.746</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.752</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.758</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.762</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.795</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.782</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.797</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.788</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.793</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.799</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.782</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.808</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.788</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.801</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.779</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.787</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.783</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.775</td>\n",
       "      <td>0.182</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.771</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.776</td>\n",
       "      <td>0.192</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.773</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.763</td>\n",
       "      <td>0.208</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.757</td>\n",
       "      <td>0.208</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.757</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.754</td>\n",
       "      <td>0.224</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.740</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.246</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.704</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.709</td>\n",
       "      <td>0.278</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.712</td>\n",
       "      <td>0.274</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.712</td>\n",
       "      <td>0.269</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.698</td>\n",
       "      <td>0.290</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.335</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.657</td>\n",
       "      <td>0.332</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.646</td>\n",
       "      <td>0.342</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.625</td>\n",
       "      <td>0.368</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.630</td>\n",
       "      <td>0.357</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.628</td>\n",
       "      <td>0.369</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.376</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.587</td>\n",
       "      <td>0.396</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.595</td>\n",
       "      <td>0.395</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.557</td>\n",
       "      <td>0.433</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.549</td>\n",
       "      <td>0.430</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.541</td>\n",
       "      <td>0.452</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.525</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.516</td>\n",
       "      <td>0.468</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.512</td>\n",
       "      <td>0.467</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.494</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.456</td>\n",
       "      <td>0.512</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.445</td>\n",
       "      <td>0.518</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.388</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.393</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.370</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.579</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.329</td>\n",
       "      <td>0.584</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.560</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.306</td>\n",
       "      <td>0.595</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.304</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.270</td>\n",
       "      <td>0.604</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.283</td>\n",
       "      <td>0.569</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Table 2.3                      Replication                     \n",
       "               a      b    edu   home           a      b    edu   home\n",
       "Period                                                                \n",
       "0          0.169  0.036  0.752  0.043       0.153  0.028  0.769  0.050\n",
       "1          0.308  0.042  0.594  0.056       0.315  0.048  0.585  0.052\n",
       "2          0.455  0.058  0.430  0.057       0.463  0.054  0.418  0.065\n",
       "3          0.574  0.066  0.326  0.034       0.544  0.067  0.321  0.068\n",
       "4          0.628  0.070  0.255  0.047       0.631  0.064  0.258  0.047\n",
       "5          0.710  0.071  0.189  0.030       0.682  0.076  0.200  0.042\n",
       "6          0.725  0.080  0.166  0.029       0.741  0.076  0.156  0.027\n",
       "7          0.746  0.090  0.139  0.025       0.760  0.077  0.124  0.039\n",
       "8          0.752  0.090  0.132  0.026       0.758  0.103  0.111  0.028\n",
       "9          0.762  0.101  0.123  0.014       0.795  0.103  0.081  0.021\n",
       "10         0.782  0.115  0.083  0.020       0.816  0.107  0.063  0.014\n",
       "11         0.797  0.120  0.071  0.012       0.788  0.129  0.069  0.014\n",
       "12         0.793  0.129  0.070  0.008       0.799  0.127  0.065  0.009\n",
       "13         0.782  0.153  0.059  0.006       0.808  0.123  0.056  0.013\n",
       "14         0.788  0.148  0.055  0.009       0.801  0.144  0.047  0.008\n",
       "15         0.779  0.158  0.054  0.009       0.787  0.162  0.045  0.006\n",
       "16         0.783  0.173  0.042  0.002       0.790  0.167  0.035  0.008\n",
       "17         0.775  0.182  0.035  0.008       0.771  0.183  0.037  0.009\n",
       "18         0.776  0.192  0.029  0.003       0.773  0.183  0.037  0.007\n",
       "19         0.763  0.208  0.028  0.001       0.757  0.208  0.028  0.007\n",
       "20         0.757  0.218  0.022  0.003       0.754  0.224  0.019  0.003\n",
       "21         0.740  0.235  0.020  0.005       0.735  0.246  0.015  0.004\n",
       "22         0.704  0.280  0.014  0.002       0.709  0.278  0.010  0.003\n",
       "23         0.712  0.274  0.012  0.002       0.708  0.280  0.009  0.003\n",
       "24         0.712  0.269  0.013  0.006       0.703  0.282  0.010  0.005\n",
       "25         0.698  0.290  0.008  0.004       0.645  0.335  0.011  0.009\n",
       "26         0.657  0.332  0.004  0.007       0.646  0.342  0.008  0.004\n",
       "27         0.625  0.368  0.003  0.004       0.630  0.357  0.006  0.007\n",
       "28         0.628  0.369  0.001  0.002       0.617  0.376  0.002  0.005\n",
       "29         0.587  0.396  0.004  0.013       0.595  0.395  0.003  0.007\n",
       "30         0.557  0.433  0.001  0.009       0.549  0.430  0.001  0.020\n",
       "31         0.541  0.452  0.000  0.007       0.525  0.454  0.002  0.019\n",
       "32         0.516  0.468  0.000  0.016       0.512  0.467  0.002  0.019\n",
       "33         0.494  0.484  0.001  0.021       0.456  0.512  0.000  0.032\n",
       "34         0.445  0.518  0.000  0.037       0.425  0.533  0.000  0.042\n",
       "35         0.388  0.571  0.000  0.041       0.393  0.557  0.000  0.050\n",
       "36         0.370  0.575  0.001  0.054       0.350  0.579  0.000  0.071\n",
       "37         0.329  0.584  0.000  0.087       0.345  0.560  0.000  0.095\n",
       "38         0.306  0.595  0.000  0.099       0.304  0.592  0.000  0.104\n",
       "39         0.270  0.604  0.000  0.126       0.283  0.569  0.000  0.148"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_2_3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "> Keane, M. P. and  Wolpin, K. I. (1994). [The Solution and Estimation of Discrete Choice Dynamic Programming Models by Simulation and Interpolation: Monte Carlo Evidence](https://doi.org/10.2307/2109768). *The Review of Economics and Statistics*, 76(4): 648-672.\n",
    "\n",
    "> Keane, M. P. and  Wolpin, K. I. (1994b). [The Solution and Estimation of Discrete Choice Dynamic Programming Models by Simulation and Interpolation: Monte Carlo Evidence](https://www.minneapolisfed.org/research/staff-reports/the-solution-and-estimation-of-discrete-choice-dynamic-programming-models-by-simulation-and-interpolation-monte-carlo-evidence). *Federal Reserve Bank of Minneapolis*, No. 181."
   ]
  }
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