lectures/robinson-economy/notebook.ipynb

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   "source": [
    "import yaml\n",
    "import sys\n",
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "import respy as rp\n",
    "\n",
    "sys.path.insert(0, \"python\")\n",
    "from auxiliary import plot_average_wages_over_time  # noqa: E402\n",
    "from auxiliary import plot_choice_probabilities  # noqa: E402\n",
    "from auxiliary import plot_distibution_wages  # noqa: E402"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Robinson Crusoe economy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `respy` package provides several example models that can be used to explore selected issues in the structural microeconometrics. We focus on the `robinson` example, which allows for quick iterations. All example models share the same interface so they can be easily swapped in and out of any analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-24T07:25:44.924245Z",
     "start_time": "2020-04-24T07:25:44.874244Z"
    }
   },
   "outputs": [],
   "source": [
    "options_base = yaml.safe_load(open(os.environ[\"ROBINSON_SPEC\"] + \"/robinson.yaml\", \"r\"))\n",
    "\n",
    "params_base = pd.read_csv(open(os.environ[\"ROBINSON_SPEC\"] + \"/robinson.csv\", \"r\"))\n",
    "params_base.set_index([\"category\", \"name\"], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the parameterization of the model, the specified options and a simulated dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-24T07:25:45.009824Z",
     "start_time": "2020-04-24T07:25:44.926143Z"
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category</th>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>delta</th>\n",
       "      <th>delta</th>\n",
       "      <td>0.950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wage_fishing</th>\n",
       "      <th>exp_fishing</th>\n",
       "      <td>0.070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nonpec_fishing</th>\n",
       "      <th>constant</th>\n",
       "      <td>-0.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nonpec_hammock</th>\n",
       "      <th>constant</th>\n",
       "      <td>1.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">shocks_sdcorr</th>\n",
       "      <th>sd_fishing</th>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sd_hammock</th>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>corr_hammock_fishing</th>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "                                     value\n",
       "category       name                       \n",
       "delta          delta                 0.950\n",
       "wage_fishing   exp_fishing           0.070\n",
       "nonpec_fishing constant             -0.100\n",
       "nonpec_hammock constant              1.046\n",
       "shocks_sdcorr  sd_fishing            0.010\n",
       "               sd_hammock            0.010\n",
       "               corr_hammock_fishing  0.000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_base"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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     "start_time": "2020-04-24T07:25:45.018491Z"
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    {
     "data": {
      "text/plain": [
       "{'estimation_draws': 100,\n",
       " 'estimation_seed': 100,\n",
       " 'estimation_tau': 0.001,\n",
       " 'interpolation_points': -1,\n",
       " 'n_periods': 5,\n",
       " 'simulation_agents': 1000,\n",
       " 'simulation_seed': 132,\n",
       " 'solution_draws': 100,\n",
       " 'solution_seed': 456,\n",
       " 'covariates': {'constant': '1'}}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "options_base"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready to simulate a sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-24T07:26:16.863048Z",
     "start_time": "2020-04-24T07:26:16.394537Z"
    }
   },
   "outputs": [],
   "source": [
    "simulate = rp.get_simulate_func(params_base, options_base)\n",
    "df = simulate(params_base)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first look at the general structure of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-24T07:25:46.410638Z",
     "start_time": "2020-04-24T07:25:46.296911Z"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Experience_Fishing</th>\n",
       "      <th>Shock_Reward_Fishing</th>\n",
       "      <th>Meas_Error_Wage_Fishing</th>\n",
       "      <th>Shock_Reward_Hammock</th>\n",
       "      <th>Meas_Error_Wage_Hammock</th>\n",
       "      <th>Choice</th>\n",
       "      <th>Wage</th>\n",
       "      <th>Discount_Rate</th>\n",
       "      <th>Present_Bias</th>\n",
       "      <th>Nonpecuniary_Reward_Fishing</th>\n",
       "      <th>Wage_Fishing</th>\n",
       "      <th>Flow_Utility_Fishing</th>\n",
       "      <th>Value_Function_Fishing</th>\n",
       "      <th>Continuation_Value_Fishing</th>\n",
       "      <th>Nonpecuniary_Reward_Hammock</th>\n",
       "      <th>Wage_Hammock</th>\n",
       "      <th>Flow_Utility_Hammock</th>\n",
       "      <th>Value_Function_Hammock</th>\n",
       "      <th>Continuation_Value_Hammock</th>\n",
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       "    <tr>\n",
       "      <th>Identifier</th>\n",
       "      <th>Period</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>0</td>\n",
       "      <td>1.007197</td>\n",
       "      <td>1</td>\n",
       "      <td>0.010305</td>\n",
       "      <td>1</td>\n",
       "      <td>fishing</td>\n",
       "      <td>1.007197</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1</td>\n",
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       "      <td>1.007197</td>\n",
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       "      <td>4.042536</td>\n",
       "      <td>1.046</td>\n",
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       "      <td>1.056305</td>\n",
       "      <td>4.742931</td>\n",
       "      <td>3.880659</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.981015</td>\n",
       "      <td>1</td>\n",
       "      <td>0.010596</td>\n",
       "      <td>1</td>\n",
       "      <td>fishing</td>\n",
       "      <td>1.052147</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>1.052147</td>\n",
       "      <td>0.952147</td>\n",
       "      <td>4.022193</td>\n",
       "      <td>3.231627</td>\n",
       "      <td>1.046</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.056596</td>\n",
       "      <td>3.903085</td>\n",
       "      <td>2.996304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.998980</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.003797</td>\n",
       "      <td>1</td>\n",
       "      <td>fishing</td>\n",
       "      <td>1.149101</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>1.149101</td>\n",
       "      <td>1.049101</td>\n",
       "      <td>3.230306</td>\n",
       "      <td>2.296006</td>\n",
       "      <td>1.046</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.042203</td>\n",
       "      <td>3.063422</td>\n",
       "      <td>2.127598</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.989253</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.011702</td>\n",
       "      <td>1</td>\n",
       "      <td>fishing</td>\n",
       "      <td>1.220419</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>1.220419</td>\n",
       "      <td>1.120419</td>\n",
       "      <td>2.282845</td>\n",
       "      <td>1.223607</td>\n",
       "      <td>1.046</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.034298</td>\n",
       "      <td>2.111715</td>\n",
       "      <td>1.134123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.010431</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.002176</td>\n",
       "      <td>1</td>\n",
       "      <td>fishing</td>\n",
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       "      <td>0.95</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>1.336932</td>\n",
       "      <td>1.236932</td>\n",
       "      <td>1.236932</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.046</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.043824</td>\n",
       "      <td>1.043824</td>\n",
       "      <td>0.000000</td>\n",
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      "text/plain": [
       "                   Experience_Fishing  Shock_Reward_Fishing  \\\n",
       "Identifier Period                                             \n",
       "0          0                        0              1.007197   \n",
       "           1                        1              0.981015   \n",
       "           2                        2              0.998980   \n",
       "           3                        3              0.989253   \n",
       "           4                        4              1.010431   \n",
       "\n",
       "                   Meas_Error_Wage_Fishing  Shock_Reward_Hammock  \\\n",
       "Identifier Period                                                  \n",
       "0          0                             1              0.010305   \n",
       "           1                             1              0.010596   \n",
       "           2                             1             -0.003797   \n",
       "           3                             1             -0.011702   \n",
       "           4                             1             -0.002176   \n",
       "\n",
       "                   Meas_Error_Wage_Hammock   Choice      Wage  Discount_Rate  \\\n",
       "Identifier Period                                                              \n",
       "0          0                             1  fishing  1.007197           0.95   \n",
       "           1                             1  fishing  1.052147           0.95   \n",
       "           2                             1  fishing  1.149101           0.95   \n",
       "           3                             1  fishing  1.220419           0.95   \n",
       "           4                             1  fishing  1.336932           0.95   \n",
       "\n",
       "                   Present_Bias  Nonpecuniary_Reward_Fishing  Wage_Fishing  \\\n",
       "Identifier Period                                                            \n",
       "0          0                  1                         -0.1      1.007197   \n",
       "           1                  1                         -0.1      1.052147   \n",
       "           2                  1                         -0.1      1.149101   \n",
       "           3                  1                         -0.1      1.220419   \n",
       "           4                  1                         -0.1      1.336932   \n",
       "\n",
       "                   Flow_Utility_Fishing  Value_Function_Fishing  \\\n",
       "Identifier Period                                                 \n",
       "0          0                   0.907197                4.747606   \n",
       "           1                   0.952147                4.022193   \n",
       "           2                   1.049101                3.230306   \n",
       "           3                   1.120419                2.282845   \n",
       "           4                   1.236932                1.236932   \n",
       "\n",
       "                   Continuation_Value_Fishing  Nonpecuniary_Reward_Hammock  \\\n",
       "Identifier Period                                                            \n",
       "0          0                         4.042536                        1.046   \n",
       "           1                         3.231627                        1.046   \n",
       "           2                         2.296006                        1.046   \n",
       "           3                         1.223607                        1.046   \n",
       "           4                         0.000000                        1.046   \n",
       "\n",
       "                   Wage_Hammock  Flow_Utility_Hammock  Value_Function_Hammock  \\\n",
       "Identifier Period                                                               \n",
       "0          0                NaN              1.056305                4.742931   \n",
       "           1                NaN              1.056596                3.903085   \n",
       "           2                NaN              1.042203                3.063422   \n",
       "           3                NaN              1.034298                2.111715   \n",
       "           4                NaN              1.043824                1.043824   \n",
       "\n",
       "                   Continuation_Value_Hammock  \n",
       "Identifier Period                              \n",
       "0          0                         3.880659  \n",
       "           1                         2.996304  \n",
       "           2                         2.127598  \n",
       "           3                         1.134123  \n",
       "           4                         0.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can study the resulting choice patterns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-24T07:25:46.937652Z",
     "start_time": "2020-04-24T07:25:46.416857Z"
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    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_choice_probabilities(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look at the distirbution of wages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-24T07:26:12.144477Z",
     "start_time": "2020-04-24T07:26:11.215818Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sSdK0G2aSxGOBby56fgvwrKUHJdkKbO2f3pHkS0N85iAOAr4z5s9YaxutTxutP2Cf1ouN1qeJ9idnjuxUl1TVlqWNwwRUlmmr+zVUnQ2cPcTn7JUkc1U1u1aftxY2Wp82Wn/APq0XG61PG60/Sw1zie8W4PBFzw8DvjVcOZIkdYYJqE8DT0zyc0keDLwKuHA0ZUmSpt2qL/FV1d1J3gB8AtgHOKeqbhxZZau3ZpcT19BG69NG6w/Yp/Vio/Vpo/Xnp6Tqfl8bSZI0ca4kIUlqkgElSWrSugmoJOck2Z3khj28/vNJrkpyV5K3LGo/PMmlSW5KcmOSN65d1Q9siD7tn+TaJJ/r+3T62lW9Z6vtz6LX90ny2SQfH3+1gxmmT0l2JPlCkuuTzK1NxSsbsk+PTHJBki/2/6aevTZVP7Ah/i09uf/7LNxuS/Kmtat8eUP+jd7c/3fhhiTnJ9l/baoevXUTUMC5wP1+yLXId4H/ALxzSfvdwClV9RTgaOD1DS3JdC6r69NdwPOr6unAZmBLkqPHUuHeOZfV9WfBG4GbRlzTsM5luD49r6o2N/ZblXNZfZ/eS/ejyp8Hnk47f69zWUWfqupL/d9nM/AM4IfAR8dV5F44l1X0J8lj+/bZqnoa3QS2V42pxrFbNwFVVZfT/VH29Pruqvo08OMl7Tur6jP949vp/kE9dpy1DmqIPlVV3dE/3a+/TXy2y2r7A5DkMOAE4I/GV+HeG6ZPrVptn5I8AngO8P7+uB9V1ffHWeugRvR3Og74SlV9fdT17a0h+7Mv8JAk+wIPZR3/PnXdBNQoJNkEHAFcM9lKhtdfDrse2A1sq6r13qf/Dvwm8JNJFzJCBXwyyXX9kl/r3eOAeeCP+0uxf5TkgEkXNUKvAs6fdBHDqKpb6UZV3wB2Aj+oqk9OtqrVm5qASvIw4MPAm6rqtknXM6yquqe/LHEYcFSSp026ptVKciKwu6qum3QtI3ZsVR1Jt+L/65M8Z9IFDWlf4EjgrKo6Avh/wIbYZqdfbOAlwIcmXcswkhxIt2j3zwE/AxyQ5NcmW9XqTUVAJdmPLpw+UFUfmXQ9o9RfYrmMB75e3bpjgZck2UG3Kv7zk/yvyZY0vKr6Vn+/m+57jaMmW9HQbgFuWTRav4AusDaC44HPVNWuSRcypBcAX6uq+ar6MfAR4JgJ17RqGz6gkoTumvlNVfXuSdczCklmkjyyf/wQuv9RfnGyVa1eVf12VR1WVZvoLrP8dVWt2//XB5DkgCQPX3gMvAhYdkbWelFV3wa+meTJfdNxwN9NsKRRejXr/PJe7xvA0Uke2v+37zjamciy19bNShJJzgeeS7e8/C7gbXSTA6iq/5nkMcAc8Ai67zHuoNtI8ReAvwW+wH3fb7y1qi5ey/qXM0SfNgHn0c3QeRDwwap6+xqXfz+r7c/iS65Jngu8papOXNPi92CIv9FB3DcbbF/gz6rqv6xp8XswzN8pyWa6iSwPBr4K/Juq+t6ad2KJIfv0ULqtgx5XVT+YQPn3M2R/Tgd+hW4G82eB11bVXWveiRFYNwElSZouG/4SnyRpfTKgJElNMqAkSU0yoCRJTTKgJElNMqCkASQ5OUn1awYubj+zb/+1Je0v7NvX7Y8kpUkzoKTBXNnfLw2cY+hWwF6u/S5goy3fJK0ZA0oazBfpVpe+N4j6JbSeAfwJywfU3Hr9gaTUAgNKGkB1v2i/ip8OoiOAAH8I/NNFSxs9CHgWcGWSE5Js6zefuy3J1UletPT8SV6Z5OYkd6bbYPOI/hLhry857rX9ZnR3Jfl6kt8cU5eliTOgpMFdCWzu1z8EeDbdJbwbgO/ThRLAPwH+EfApulWl/wJ4DfCK/hx/meTYhZMmmaVbJPczwMuBC4H/vfTDk/xH4Czg/wAn9o/fkeQNI+2l1Ih9J12AtI5cSbce2jOBy+lGU1dVVSW5un/+V9w3yrqyquYX3tyPrC6lC7CT6QIM4LfoFvR8VT9Su6S/fHjmovc+gm49tt+rqtP75m39OnK/k+SsqrpnHJ2WJsURlDS4a+kW4FwIoGPoLvsBXL2k/eaqmk9yWJLzktzav/fHdCubP2nReZ8J/EX99MKYFy757GcDBwAfSrLvwg34a+AQun3BpA3FEZQ0oKr6Yb+L8TH9dPPDuC+grgJO6UdJxwBX9I8vBB4O/Gfgy3Sb/L0dOHjRqR9Dt1PtYkufH9Tf37iH8g4HJr5VuTRKBpS0d64EfpUuhHZU1c6+/Rq6IPrnwBPoLs89gW4ixfFVdcnCCRZ9h7Xg28DMkralz7/b359It/3CUl/au25I7fMSn7R3PkU3mjmJ+0ZPVNXtdKObt/RNVwILQXTvVPMk/5huB+HFPg38Ur/B3IKXLDnmKuBO4Geqam6Z2+1D9ktqjiMoae8sTGw4HnjjkteuAv4d8D26SQ8Pptsm/V1JfpduhHU6cOuS951JNwL78yR/DDylPw/0m2xW1feTnAa8tw+5y+n+D+aTgOdV1ctH1UGpFY6gpL1QVbfSbasdFo2gelcttFfnLuCX6SZHXAC8A/hvwN8sOecc3Zbjz6CbQv4K4Df6l29bdNzvA1vpwvFjdFuU/yu6HaOlDccddaUG9Wv7/SndNuRfm3Q90iR4iU9qQJKzgG10lwePBH4HuMhw0jQzoKQ2PJpuyaRHA39Pt5KEyxhpqnmJT5LUJCdJSJKaZEBJkppkQEmSmmRASZKaZEBJkpr0/wF0RH//d7iPoAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAATt0lEQVR4nO3dfZBkdX3v8fcnoiUKXlEGQoRkg0+JqSQLd0WFVC5eogHxisQyCYkGc/Fuyqu39MY8EBOjhlRdSEWjVYkkJBC59yJ5AB9IICjZaIjuunEhKEsWxYdVF1d2EBWMhCe/+eOc2R3GmZ2e6Z7p32y/X1Vd3f07p8/59o8ePvs7ffp3UlVIktSa7xp3AZIkzceAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgNLES/LmJDXr9uUkVyZ58oi2/64k20a0rSuSfHgJ6x+X5KEkd45i/9JqOmjcBUiN+AZwav/4WOA8YFOSH6qqfxty2+cBBw+5jSVLEuAPgWn8W9ca5AhK6jxYVR/rb+8Gzga+D3jBcjeY5GCAqvpsVW0fUZ1L8TLgSOCSMexbGpoBJc3vhv5+HUCSRyf5vSRfSnJfkk8keVh4JdmZ5K1J3phkF3B33/4dh/iSrE+yKcm3knwtyWVJjpyzzjFJrklyb7/tVw5afJJDgQuAXwHuX+qbl1rgsF+a37r+/iv9/RXACcCbgM8CPw1clWRDVd0063U/B9wC/E8W+PtKMgV8GNjRr38IcD5wXb+9+/vDc+8HDgfOAf4deAvwBOC2Aer/bWBHVb0vyfpB3rDUGgNK6iWZ+Xs4FngncA/w90lOAU4HTq6qf+zX+WCSpwG/Cbx0zqZeWFX/vp9dvb6//8mqmhllfRrYCrwEuBw4DTgOeHZVbe3XuYEuHPcbUEmeDrwaeNb+37HUNg/xSZ0nAg/0t0/RhdTPVNVu4CfoRlIfTXLQzA3YBGyYs51Ni4QTdCOxD86EE0BV/TOwE/ixWevcMRNO/TpfYN+hx/15B/Cuqrp5gHWlZjmCkjrfoAuiogujL9e+qf4PB76bLrzmemjO8zsG2NdRdIcB57qD7hAe/f72zLPOHuDQhTac5DTgJOA1SR7fNz+6W5THA/dW1X0D1CiNnQEldR6sqoV+q3QXcDvw4gG2M8j1a3YDR8zTfiT7RkhfWWCdI4B797Ptp9N9pzXfYcCvAW8EfneAGqWxM6CkxW2i+97om1V16wi2txV4VZJDq+oegCTPpDsx4yP9Oh8H3pTkWbO+g/pe4Hjgo/vZ9hXATXPaXgGcCZwBfH4E9UurwoCSFncd8AG6s+wuoDs89zhgPfDoqvqNJW7vbcCrgA/025s5i+9m4Mp+nWuATwB/neTX6c7i+x3mP+y3V1XtAnbNbktyMvBAVX14iXVKY+VJEtIi+u+iforuB6+vowurPwGew74Rz1K2Nw08ly50Lgf+CPgn4HlVdf+sfb4I+Nd+v2+nmxViy5BvR1oz4iXfJUktcgQlSWqSASVJapIBJUlqkgElSWrSqp5mfuqpp9a11167mruUJLUv8zWu6gjqzju9qKckaTAe4pMkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJK+pqYqw79+pxl7DXzvNPH3cJUvMcQUmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmrRoQCU5JsmHkuxIckuS1/btb05ye5Kb+tsLVr5cSdKkGORyGw8Cr6+qG5McCtyQ5Lp+2R9U1e+vXHmSpEm1aEBV1W5gd//4niQ7gCetdGGSpMm2pO+gkqwDjgO29k2vSfLJJJckOWyB12xMsi3Jtunp6aGKlSRNjoEDKskhwJXA66rqbuBC4MnAeroR1lvne11VXVRVG6pqw9TU1AhKliRNgoECKskj6cLpsqp6D0BV3VFVD1XVt4E/BU5YuTIlSZNmkLP4AlwM7Kiqt81qP2rWamcC20dfniRpUg1yFt9JwMuBm5Pc1Le9ATgryXqggJ3AL61IhZKkiTTIWXwfATLPomtGX44kSR1nkpAkNcmAkiQ1yYCSJDXJgJIkNWmQs/gkjdi6c68edwkPs/P808ddgvQdHEFJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKa5CXfJTV1CXovP68ZjqAkSU0yoCRJTTKgJElNMqAkSU1aNKCSHJPkQ0l2JLklyWv79ickuS7Jbf39YStfriRpUgwygnoQeH1V/SDwbODVSZ4BnAtsqqqnApv655IkjcSiAVVVu6vqxv7xPcAO4EnAGcCl/WqXAi9eqSIlSZNnSd9BJVkHHAdsBY6sqt3QhRhwxAKv2ZhkW5Jt09PTw1UrSZoYAwdUkkOAK4HXVdXdg76uqi6qqg1VtWFqamo5NUqSJtBAAZXkkXThdFlVvadvviPJUf3yo4A9K1OiJGkSDXIWX4CLgR1V9bZZi64Czu4fnw28f/TlSZIm1SBz8Z0EvBy4OclNfdsbgPOBv0pyDvBF4KUrU6LWqpbmd5O09iwaUFX1ESALLD5ltOVIktRxJglJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMWDagklyTZk2T7rLY3J7k9yU397QUrW6YkadIMMoJ6F3DqPO1/UFXr+9s1oy1LkjTpFg2oqroeuGsVapEkaa9hvoN6TZJP9ocAD1topSQbk2xLsm16enqI3UmSJslyA+pC4MnAemA38NaFVqyqi6pqQ1VtmJqaWubuJEmTZlkBVVV3VNVDVfVt4E+BE0ZbliRp0i0roJIcNevpmcD2hdaVJGk5DlpshSSXAycDhyfZBbwJODnJeqCAncAvrWCNkqQJtGhAVdVZ8zRfvAK1SJK0lzNJSJKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKatGhAJbkkyZ4k22e1PSHJdUlu6+8PW9kyJUmTZpAR1LuAU+e0nQtsqqqnApv655IkjcyiAVVV1wN3zWk+A7i0f3wp8OIR1yVJmnDL/Q7qyKraDdDfH7HQikk2JtmWZNv09PQydydJmjQrfpJEVV1UVRuqasPU1NRK706SdIBYbkDdkeQogP5+z+hKkiRp+QF1FXB2//hs4P2jKUeSpM4gp5lfDmwBnp5kV5JzgPOB5yW5DXhe/1ySpJE5aLEVquqsBRadMuJaJIl151497hIeZuf5p4+7hInlTBKSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYtesFCrS2tXexNkpbLEZQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSUPNxZdkJ3AP8BDwYFVtGEVRkiSNYrLY51bVnSPYjiRJe3mIT5LUpGEDqoAPJrkhycb5VkiyMcm2JNump6eH3J0kaVIMG1AnVdXxwGnAq5P8+NwVquqiqtpQVRumpqaG3J0kaVIMFVBV9eX+fg/wXuCEURQlSdKyAyrJY5McOvMYeD6wfVSFSZIm2zBn8R0JvDfJzHbeXVXXjqQqSdLEW3ZAVdXngB8dYS2SJO3laeaSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJo3iku+SdMBad+7V4y5hr53nnz7uElaVIyhJUpMMKElSkzzEN6SWhv+SdCBxBCVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapJTHUnSGtHa1GorPbu6IyhJUpPW5AiqtX9FSJJGb6gRVJJTk3wqyWeSnDuqoiRJWnZAJXkE8EfAacAzgLOSPGNUhUmSJtswI6gTgM9U1eeq6n7gL4AzRlOWJGnSDfMd1JOAL816vgt41tyVkmwENvZPv5nkU0Psc1CHA3euwn4OZPbh8OzD4dmHo7Ei/ZgLRrapa6vq1LmNwwRU5mmr72iougi4aIj9LFmSbVW1YTX3eaCxD4dnHw7PPhyNtdqPwxzi2wUcM+v50cCXhytHkqTOMAH1ceCpSb4/yaOAnwWuGk1ZkqRJt+xDfFX1YJLXAB8AHgFcUlW3jKyy4azqIcUDlH04PPtwePbhaKzJfkzVd3xtJEnS2DnVkSSpSQaUJKlJayqgklySZE+S7Qss//kkn+xvm5P86Kxl/zvJLUm2J7k8yaNXr/J2DNCHZ/T9d1OSbUl+bNYyp7Zi+X2Y5JgkH0qyo/8svnZ1K2/HMJ/DfvkjkvxLkr9dnYrbNOTf8+OTXJHk1v4z+ZzVq3xAVbVmbsCPA8cD2xdYfiJwWP/4NGBr//hJwOeBg/vnfwW8Ytzvp9E+PIR9303+CHBr//gRwGeBY4FHAZ8AnjHu97PG+vAo4Pj+8aHAp+3DpfXhrOW/DLwb+Ntxv5e12o/ApcAr+8ePAh4/7vcz97amRlBVdT1w136Wb66qr/VPP0b326wZBwEHJzkIeAwT+putAfrwm9V/YoHHsu/H105t1VtuH1bV7qq6sX98D7CD7h9PE2eIzyFJjgZOB/5sRYtcA5bbj0keRxduF/fr3V9VX1/hcpdsTQXUEp0D/B1AVd0O/D7wRWA38I2q+uAYa2takjOT3ApcDfz3vnm+qa0m8n+ug1igD2cvXwccB2xd3crWjv304duBXwO+PZbC1pgF+vFYYBr48/5Q6Z8leezYilzAARlQSZ5LF1C/3j8/jO5f+98PfA/w2CQvG1+Fbauq91bVDwAvBs7rmwea2kqdBfoQgCSHAFcCr6uqu8dR31owXx8meSGwp6puGGtxa8gCn8WD6A4NXlhVxwH/BjT3vfIBF1BJfoRu6H9GVX21b/4J4PNVNV1VDwDvofu+SvvRHz54cpLDcWqrZZnThyR5JF04XVZV7xlrcWvEnD48CXhRkp10h5n/a5L/P8761op5/p53VdXMCP4KusBqygEVUEm+ly58Xl5Vn5616IvAs5M8JkmAU+iO/2uOJE/p+4gkx9N9efpVnNpqYAv1Yd92MbCjqt42zhpbt1AfVtVvVNXRVbWO7jP4D1Xl0ZAF7KcfvwJ8KcnT+1VPAf51TGUuaE1d8j3J5cDJwOFJdgFvAh4JUFV/DPw28ETgnf1/kwerakNVbU1yBXAj8CDwL6zRqT+GNUAfvgT4hSQPAPcCP9N/ydry1Fararl92J/i+3Lg5iQ39Zt7Q1Vds9rvYdyG+BxqliH78X8Bl/X/4Pwc8IurXP6inOpIktSkA+oQnyTpwGFASZKaZEBJkppkQEmSmmRASZKaZEBJA0hyTpLq54Gb3X5B3/6yOe3P69v9Qbi0TAaUNJjN/f3cwDkR+NYC7fcBTskjLZMBJQ3mVrpZo/cGUT9t0X8G/i/zB9S2qrpv1SqUDjAGlDSA/tf3W3h4EB1HN4nuO4EfTnIoQJLvAp4FbE5yepLr+ovK3Z3kY0meP3f7SV6a5LYk9/YXNTyuP0T4ijnrvbK/2OF9Sb6Q5NdW6C1LY2dASYPbDKxPcnD//Dl0h/C2A1+nCyWAHwL+E/BRuhn0/4ZuiqOX9Nv4uyQnzWw0yQa6iU9vBM6km+PwL+fuPMmvAhcC7wNe2D8+r5+CSjrgrKm5+KQx20w3z9kzgevpRlNb+nn2PtY//3v2jbI2V9X0zIv7kdWH6ALsHLoAg+6yMDuAn+1Hatf2hw8vmPXax9HNs/a7VfWWvvm6JI8BfivJhVX10Eq8aWlcHEFJg/tnusmGZwLoRLrDftBdwXl2+21VNZ3k6CSXJrm9f+0DwPOBp83a7jOBv5kzGercmeKfQ3dF1L9OctDMDfgH4EgefvVo6YDgCEoaUFV9q5+F/MT+dPOj2RdQW4DX96OkE4GP9I+vAg6lm2n/M3QXhvsd4IhZm/5uuqubzjb3+eH9/UIzyB8DfGHJb0pqmAElLc1m4OfoQmhnVe3u27fSBdF/AZ5Cd3juKXQnUpxWVdfObGDWd1gzvgJMzWmb+/yu/v6FwB3z1PWppb0NqX0e4pOW5qN0o5mz2Td6oqruoRvd/ErftBmYCaK9p5on+T66q8LO9nHgv81cWK73ojnrbKG7ns/3VNW2eW73DPm+pOY4gpKWZubEhtOA185ZtgX4H8DX6E56eBTdpbXfmuSNdCOstwC3z3ndBXQjsL9I8ufAD/bbAfg2QFV9PcmbgXf0IXc93T8wnwY8t6rOHNUblFrhCEpagqq6Hfgi3e+ftsxZvGWmvTr3AT9Fd3LEFcB5wP8B/nHONrcBZ9H96Pd9dKejv6pffPes9X4P2EgXju8HLgd+Hvin0b1DqR1eUVdqUD+33/8Djq2qz4+7HmkcPMQnNSDJhcB1dIcHjwd+C7jacNIkM6CkNjyRbsqkJwJfpZtJwmmMNNE8xCdJapInSUiSmmRASZKaZEBJkppkQEmSmmRASZKa9B/T2qrCS7djlwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_distibution_wages(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look at average wages over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-24T07:25:48.804051Z",
     "start_time": "2020-04-24T07:25:48.512585Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_average_wages_over_time(df)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}