examples.ipynb

{
 "cells": [
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   "id": "b9e2a07a-2826-4508-b0a3-4d4715a8ba5b",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from multisynth import multisynth"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0bd6835-e810-4dbb-a784-ac24932cabf5",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# 1. Use multisynth to estimate a penalized synthetic control model measuring the effects of the staggered entry of Walmart Supercenters on local labor markets (Wiltshire 2023)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5bff450-3a01-4ab6-952e-37a629a91c21",
   "metadata": {},
   "source": [
    "In this notebook I re-create estimates from Wiltshire (2023), who uses a staggered treatment penalized synthetic control setup to estimate the local labor market impacts of Walmart Supercenter openings. \n",
    "\n",
    "Wiltshire's data panel consists of county-level labor data on counties Walmart opened supercenters in from 1990-2005. In addition, the panel includes data on counties that Wal-Mart tried but failed to open supercenters in. We will use these control counties to construct synthetic versions of each treated county, then stack the estimates and perform inference. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00916f92-d31b-42de-9fe5-71e4d79c2cd5",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "synth_walmart = pd.read_stata(\"data/allsynth_walmart.dta\", convert_dates=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4be6b15d-182d-4afa-91a0-66f0634ad58f",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cty_fips</th>\n",
       "      <th>year</th>\n",
       "      <th>pe_salary_n44</th>\n",
       "      <th>pe_salary_n10</th>\n",
       "      <th>emps_n44</th>\n",
       "      <th>emps_n10</th>\n",
       "      <th>pop_t</th>\n",
       "      <th>super_year</th>\n",
       "      <th>unr</th>\n",
       "      <th>lfp</th>\n",
       "      <th>emr</th>\n",
       "      <th>czone</th>\n",
       "      <th>emps_shr_n44</th>\n",
       "      <th>supercenter</th>\n",
       "      <th>prop_white</th>\n",
       "      <th>pop90</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>1990</td>\n",
       "      <td>21313.800781</td>\n",
       "      <td>30677.488281</td>\n",
       "      <td>1184.333374</td>\n",
       "      <td>7870.833496</td>\n",
       "      <td>34353</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>0.065246</td>\n",
       "      <td>0.649237</td>\n",
       "      <td>0.606877</td>\n",
       "      <td>11101.0</td>\n",
       "      <td>0.150471</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.795185</td>\n",
       "      <td>34353.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>1991</td>\n",
       "      <td>20566.085938</td>\n",
       "      <td>30883.527344</td>\n",
       "      <td>1320.916748</td>\n",
       "      <td>8077.500000</td>\n",
       "      <td>35010</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>0.067638</td>\n",
       "      <td>0.637902</td>\n",
       "      <td>0.594756</td>\n",
       "      <td>11101.0</td>\n",
       "      <td>0.163530</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.798372</td>\n",
       "      <td>34353.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>1992</td>\n",
       "      <td>20853.433594</td>\n",
       "      <td>30596.441406</td>\n",
       "      <td>1405.833252</td>\n",
       "      <td>8397.083008</td>\n",
       "      <td>35985</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>0.065063</td>\n",
       "      <td>0.645066</td>\n",
       "      <td>0.603096</td>\n",
       "      <td>11101.0</td>\n",
       "      <td>0.167419</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.800528</td>\n",
       "      <td>34353.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9677</th>\n",
       "      <td>56037</td>\n",
       "      <td>2003</td>\n",
       "      <td>25858.203125</td>\n",
       "      <td>48054.632812</td>\n",
       "      <td>2260.500000</td>\n",
       "      <td>19453.083984</td>\n",
       "      <td>37450</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.040029</td>\n",
       "      <td>0.712191</td>\n",
       "      <td>0.683683</td>\n",
       "      <td>36404.0</td>\n",
       "      <td>0.116203</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.957196</td>\n",
       "      <td>38792.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9678</th>\n",
       "      <td>56037</td>\n",
       "      <td>2004</td>\n",
       "      <td>26705.759766</td>\n",
       "      <td>48523.820312</td>\n",
       "      <td>2345.500000</td>\n",
       "      <td>20439.250000</td>\n",
       "      <td>38026</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.032738</td>\n",
       "      <td>0.717975</td>\n",
       "      <td>0.694471</td>\n",
       "      <td>36404.0</td>\n",
       "      <td>0.114755</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.956688</td>\n",
       "      <td>38792.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9679</th>\n",
       "      <td>56037</td>\n",
       "      <td>2005</td>\n",
       "      <td>28030.039062</td>\n",
       "      <td>51025.187500</td>\n",
       "      <td>2419.083252</td>\n",
       "      <td>21849.750000</td>\n",
       "      <td>38739</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.028955</td>\n",
       "      <td>0.735648</td>\n",
       "      <td>0.714347</td>\n",
       "      <td>36404.0</td>\n",
       "      <td>0.110714</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.955394</td>\n",
       "      <td>38792.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      cty_fips  year  pe_salary_n44  pe_salary_n10     emps_n44      emps_n10  \\\n",
       "0         1001  1990   21313.800781   30677.488281  1184.333374   7870.833496   \n",
       "1         1001  1991   20566.085938   30883.527344  1320.916748   8077.500000   \n",
       "2         1001  1992   20853.433594   30596.441406  1405.833252   8397.083008   \n",
       "9677     56037  2003   25858.203125   48054.632812  2260.500000  19453.083984   \n",
       "9678     56037  2004   26705.759766   48523.820312  2345.500000  20439.250000   \n",
       "9679     56037  2005   28030.039062   51025.187500  2419.083252  21849.750000   \n",
       "\n",
       "      pop_t  super_year       unr       lfp       emr    czone  emps_shr_n44  \\\n",
       "0     34353      1996.0  0.065246  0.649237  0.606877  11101.0      0.150471   \n",
       "1     35010      1996.0  0.067638  0.637902  0.594756  11101.0      0.163530   \n",
       "2     35985      1996.0  0.065063  0.645066  0.603096  11101.0      0.167419   \n",
       "9677  37450      2000.0  0.040029  0.712191  0.683683  36404.0      0.116203   \n",
       "9678  38026      2000.0  0.032738  0.717975  0.694471  36404.0      0.114755   \n",
       "9679  38739      2000.0  0.028955  0.735648  0.714347  36404.0      0.110714   \n",
       "\n",
       "      supercenter  prop_white    pop90  \n",
       "0             1.0    0.795185  34353.0  \n",
       "1             1.0    0.798372  34353.0  \n",
       "2             1.0    0.800528  34353.0  \n",
       "9677          1.0    0.957196  38792.0  \n",
       "9678          1.0    0.956688  38792.0  \n",
       "9679          1.0    0.955394  38792.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([synth_walmart.head(3), synth_walmart.tail(3)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79236a8f-1441-4147-9764-58254924d92b",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
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    "tags": []
   },
   "source": [
    "## Formatting calls to multisynth.fit\n",
    "Stacked synthetic control models are fit using the method multisynth.fit_multisynth. The method takes the following arguments:\n",
    "1. **data** a pandas dataframe in the form above.\n",
    "2. **variable_specs** A dictionary describing which variables in the panel should be used  for synthetic control matching and how the variables should be mutated (e.g. averaged over a set of years, normalized, including for specific years)  \n",
    "3. **treatment_var** (str) Name of column describing when an entity receives treatment (e.g. *super_year*)\n",
    "4. **unit_var** (str) Name of column containing unit identifiers  (e.g. *cty_fips*)\n",
    "5. **time_var** (str) Name of column containing time of observations in panel (e.g. *year*)\n",
    "6. **outcome_var** (str) Name of column containing outcome variable (e.g. *emps_n10*, *pe_salary_n10*)\n",
    "7. **penalized** (bool) If true, use an estimator that penalizes distances between synthetic covariates and treated covariates (based on Abadie & L'hour 2021; Wiltshire (2023)) \n",
    "8. **normalize_outcomes** (bool) If true, report outcomes in %  change from value in the period before treatment\n",
    "9. **verbose** (bool) If true, for each treated unit, print the following\n",
    "    1. A dataframe comparing treated covariates to synthetic covariates\n",
    "    2. The lambda value that optimizes "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bec59f00-5728-407c-b286-85b613bb40e3",
   "metadata": {},
   "source": [
    "## 1.1 Specify stacked synthetic control setup and build **variable_specs**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc13c46a-cc9b-45ba-b500-0d0001ef93f9",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
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   "source": [
    "Following Wiltshire (2023), we match counties to synthetic donors on the following variables:\n",
    "\n",
    "    1. All industry employment in years 1990, 1991, 1992, and 1993\n",
    "    2. All industry employment averaged over 1990-1994\n",
    "    3. Retail industry employment averaged over 1990-1994\n",
    "    4. All industry average wages averaged over 1990-1994\n",
    "    5. Share of labor in retail industry  averaged over 1990-1994\n",
    "    6. Population  averaged over 1990-1994\n",
    "    7. Unemployment rate\n",
    "    8. Employment rate\n",
    "    9. White share of population\n",
    "\n",
    "We normalize the first six variables for each county to their value in the year before treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "df875ab0-35f8-48b8-8c8c-c70f360c7d2c",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "variable_specs_walmart = [\n",
    "    {\n",
    "        \"var\": \"emps_n10\",\n",
    "        \"specification\": {\n",
    "            \"aggregate_option\": \"individual\",\n",
    "            \"normalize\": True,\n",
    "            \"periods\": [1990, 1991, 1992, 1993]\n",
    "        }\n",
    "    },\n",
    "    {\n",
    "        \"var\": \"emps_n10\",\n",
    "        \"specification\": {\n",
    "            \"aggregate_option\": \"avg\",\n",
    "            \"normalize\": True,\n",
    "            \"periods\": range(1990,1995)\n",
    "        }\n",
    "    },\n",
    "    {\n",
    "        \"var\": \"emps_n44\",\n",
    "        \"specification\": {\n",
    "            \"aggregate_option\": \"avg\",\n",
    "            \"normalize\": True,\n",
    "            \"periods\": range(1990,1995)\n",
    "        }\n",
    "    },\n",
    "    {\n",
    "        \"var\": \"pe_salary_n10\",\n",
    "        \"specification\": {\n",
    "            \"aggregate_option\": \"avg\",\n",
    "            \"normalize\": True,\n",
    "            \"periods\": range(1990,1995)\n",
    "        }\n",
    "    },\n",
    "    {\n",
    "        \"var\": \"pe_salary_n44\",\n",
    "        \"specification\": {\n",
    "            \"aggregate_option\": \"avg\",\n",
    "            \"normalize\": True,\n",
    "            \"periods\": range(1990,1995)\n",
    "        }\n",
    "    },\n",
    "    {\n",
    "        \"var\": \"emps_shr_n44\",\n",
    "        \"specification\": {\n",
    "            \"aggregate_option\": \"avg\",\n",
    "            \"normalize\": True,\n",
    "            \"periods\": range(1990,1995)\n",
    "        }\n",
    "    },\n",
    "    {\n",
    "        \"var\": \"pop_t\",\n",
    "        \"specification\": {\n",
    "            \"aggregate_option\": \"avg\",\n",
    "            \"normalize\": True,\n",
    "            \"periods\": range(1990,1995)\n",
    "        }\n",
    "    },\n",
    "    {\n",
    "        \"var\": \"unr\",\n",
    "        \"specification\": {\n",
    "            \"aggregate_option\": \"avg\",\n",
    "            \"periods\": range(1990,1995)\n",
    "        }\n",
    "    },\n",
    "    {\n",
    "        \"var\": \"emr\",\n",
    "        \"specification\": {\n",
    "            \"aggregate_option\": \"avg\",\n",
    "            \"periods\": range(1990,1995)\n",
    "        }\n",
    "    },\n",
    "    {\n",
    "        \"var\": \"lfp\",\n",
    "        \"specification\": {\n",
    "            \"aggregate_option\": \"avg\",\n",
    "            \"periods\": range(1990,1995)\n",
    "        }\n",
    "    },\n",
    "    {\n",
    "        \"var\": \"prop_white\",\n",
    "        \"specification\": {\n",
    "            \"aggregate_option\": \"avg\",\n",
    "            \"periods\": range(1990,1995)\n",
    "        }\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f61efa7f-2ad7-4ff8-9d6f-b2b2a04d2a40",
   "metadata": {},
   "source": [
    "## 1.2 Fit the synthetic control model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7a70aee-e1bf-49a6-beb8-857a65117d11",
   "metadata": {},
   "source": [
    "Below I estimate the effect of Walmart supercenter entry on total county employment. To speed up estimates, I limit treated counties to the state of Indiana (fips = 18) but use control counties from across the country. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0598494f-08c0-46c9-bc2b-ad6145bf273e",
   "metadata": {
    "collapsed": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimating synthetic control for unit cty_fips=18179 (1/26)\n",
      "Estimating synthetic control for unit cty_fips=18053 (2/26)\n",
      "Estimating synthetic control for unit cty_fips=18181 (3/26)\n",
      "Estimating synthetic control for unit cty_fips=18055 (4/26)\n",
      "Estimating synthetic control for unit cty_fips=18183 (5/26)\n",
      "Estimating synthetic control for unit cty_fips=18057 (6/26)\n",
      "Estimating synthetic control for unit cty_fips=18167 (7/26)\n",
      "Estimating synthetic control for unit cty_fips=18063 (8/26)\n",
      "Estimating synthetic control for unit cty_fips=18067 (9/26)\n",
      "Estimating synthetic control for unit cty_fips=18071 (10/26)\n",
      "Estimating synthetic control for unit cty_fips=18077 (11/26)\n",
      "Estimating synthetic control for unit cty_fips=18081 (12/26)\n",
      "Estimating synthetic control for unit cty_fips=18083 (13/26)\n",
      "Estimating synthetic control for unit cty_fips=18097 (14/26)\n",
      "Estimating synthetic control for unit cty_fips=18117 (15/26)\n",
      "Estimating synthetic control for unit cty_fips=18127 (16/26)\n",
      "Estimating synthetic control for unit cty_fips=18003 (17/26)\n",
      "Estimating synthetic control for unit cty_fips=18141 (18/26)\n",
      "Estimating synthetic control for unit cty_fips=18143 (19/26)\n",
      "Estimating synthetic control for unit cty_fips=18029 (20/26)\n",
      "Estimating synthetic control for unit cty_fips=18157 (21/26)\n",
      "Estimating synthetic control for unit cty_fips=18031 (22/26)\n",
      "Estimating synthetic control for unit cty_fips=18033 (23/26)\n",
      "Estimating synthetic control for unit cty_fips=18163 (24/26)\n",
      "Estimating synthetic control for unit cty_fips=18037 (25/26)\n",
      "Estimating synthetic control for unit cty_fips=18039 (26/26)\n"
     ]
    }
   ],
   "source": [
    "m = multisynth.MultiSynth()\n",
    "\n",
    "synth_walmart = synth_walmart[(synth_walmart[\"supercenter\"] == 0) | (np.floor(synth_walmart[\"cty_fips\"]/1000) == 18)]\n",
    "weights = m.fit_multisynth(data=synth_walmart, \n",
    "                           variable_specs=variable_specs_walmart,\n",
    "                           treatment_var=\"super_year\",\n",
    "                           unit_var=\"cty_fips\",\n",
    "                           time_var=\"year\",\n",
    "                           outcome_var=\"emps_n10\",\n",
    "                           penalized=True,\n",
    "                           normalize_outcomes=True,\n",
    "                           verbose=False,\n",
    "                        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f4b9175-1fb1-498d-b3e3-d9374724cb78",
   "metadata": {},
   "source": [
    "## 1.2.1 Inspect covariate balance between treated and synthetic units"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45ce4b43-54e9-4f3a-b2c9-a4e948ed85dc",
   "metadata": {},
   "source": [
    "Below we preview how well synthetic control units mimic treated units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6e4b10cf-0b0a-4bb3-b7b0-7ab6c370e2fc",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cty_fips</th>\n",
       "      <th>18031</th>\n",
       "      <th>synth_18031</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>emps_n10_1990</th>\n",
       "      <td>0.693214</td>\n",
       "      <td>0.686799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emps_n10_1991</th>\n",
       "      <td>0.701913</td>\n",
       "      <td>0.715298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emps_n10_1992</th>\n",
       "      <td>0.755094</td>\n",
       "      <td>0.761536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emps_n10_1993</th>\n",
       "      <td>0.820709</td>\n",
       "      <td>0.832106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emps_n10_avg_1990_1994</th>\n",
       "      <td>0.771467</td>\n",
       "      <td>0.774290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emps_n44_avg_1990_1994</th>\n",
       "      <td>0.890606</td>\n",
       "      <td>0.892367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emps_shr_n44_avg_1990_1994</th>\n",
       "      <td>1.159004</td>\n",
       "      <td>1.160136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emr_avg_1990_1994</th>\n",
       "      <td>0.689953</td>\n",
       "      <td>0.661017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lfp_avg_1990_1994</th>\n",
       "      <td>0.718353</td>\n",
       "      <td>0.701567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pe_salary_n10_avg_1990_1994</th>\n",
       "      <td>0.917050</td>\n",
       "      <td>0.908435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pe_salary_n44_avg_1990_1994</th>\n",
       "      <td>0.864247</td>\n",
       "      <td>0.916569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pop_t_avg_1990_1994</th>\n",
       "      <td>0.973399</td>\n",
       "      <td>0.936919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prop_white_avg_1990_1994</th>\n",
       "      <td>0.994223</td>\n",
       "      <td>0.986661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unr_avg_1990_1994</th>\n",
       "      <td>0.039704</td>\n",
       "      <td>0.057839</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cty_fips                        18031  synth_18031\n",
       "var                                               \n",
       "emps_n10_1990                0.693214     0.686799\n",
       "emps_n10_1991                0.701913     0.715298\n",
       "emps_n10_1992                0.755094     0.761536\n",
       "emps_n10_1993                0.820709     0.832106\n",
       "emps_n10_avg_1990_1994       0.771467     0.774290\n",
       "emps_n44_avg_1990_1994       0.890606     0.892367\n",
       "emps_shr_n44_avg_1990_1994   1.159004     1.160136\n",
       "emr_avg_1990_1994            0.689953     0.661017\n",
       "lfp_avg_1990_1994            0.718353     0.701567\n",
       "pe_salary_n10_avg_1990_1994  0.917050     0.908435\n",
       "pe_salary_n44_avg_1990_1994  0.864247     0.916569\n",
       "pop_t_avg_1990_1994          0.973399     0.936919\n",
       "prop_white_avg_1990_1994     0.994223     0.986661\n",
       "unr_avg_1990_1994            0.039704     0.057839"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_treated_unit = random.choice(weights.index.unique())\n",
    "m.get_synthetic_covariate_comparisons(random_treated_unit)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebbeb2cc-462b-49d7-b614-511891372ab6",
   "metadata": {},
   "source": [
    "## 1.2.2 View synthetic control results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "98b5088d-2ef5-43ed-acc2-d9a88eb42320",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m.plot_treatment_and_placebos(num_placebos=100, \n",
    "                              pre_treat_periods=5, \n",
    "                              post_treat_periods=5, \n",
    "                              ylim=(-0.2, 0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "28e54718-0bc7-4e61-b68c-a409f1504911",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.06359965368053268"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View treatment effects averaged over 5 periods post-treatment \n",
    "m.get_ate(post_treat_periods = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bb15679e-13a9-4548-a850-33d652ba6f61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.219"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.get_p_value()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f56d74ea-4079-4434-bed9-ce55a1cc18f3",
   "metadata": {},
   "source": [
    "# 2. Estimate a penalized synthetic control model for a classical single treatment case (Abadie, Diamond, and Hainmueller 2010)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "346ec9b5-f630-43c5-a385-377b43862499",
   "metadata": {},
   "source": [
    "Below I re-create estimates from Abadie, Diamond, and Hainmueller 2010), who use a synthetic control setup to estimate the effect of California's Proposition 99, a tobacco control program, on smoking rates in the state. I use a penalized estimator. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a19f6925-839d-4f91-bd67-060e76f09bc9",
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>cigsale</th>\n",
       "      <th>lnincome</th>\n",
       "      <th>beer</th>\n",
       "      <th>age15to24</th>\n",
       "      <th>retprice</th>\n",
       "      <th>treated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>1970</td>\n",
       "      <td>123.900000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.183158</td>\n",
       "      <td>39.299999</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>1970</td>\n",
       "      <td>99.800003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.178044</td>\n",
       "      <td>39.900002</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>1970</td>\n",
       "      <td>134.600010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.176516</td>\n",
       "      <td>30.600000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1206</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>2000</td>\n",
       "      <td>88.900002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>301.100010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1207</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>2000</td>\n",
       "      <td>99.900002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>265.399990</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1208</th>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>2000</td>\n",
       "      <td>83.099998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>322.899990</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             state  year     cigsale  lnincome  beer  age15to24    retprice  \\\n",
       "0     Rhode Island  1970  123.900000       NaN   NaN   0.183158   39.299999   \n",
       "1        Tennessee  1970   99.800003       NaN   NaN   0.178044   39.900002   \n",
       "2          Indiana  1970  134.600010       NaN   NaN   0.176516   30.600000   \n",
       "1206       Vermont  2000   88.900002       NaN   NaN        NaN  301.100010   \n",
       "1207          Ohio  2000   99.900002       NaN   NaN        NaN  265.399990   \n",
       "1208  Rhode Island  2000   83.099998       NaN   NaN        NaN  322.899990   \n",
       "\n",
       "      treated  \n",
       "0         NaN  \n",
       "1         NaN  \n",
       "2         NaN  \n",
       "1206      NaN  \n",
       "1207      NaN  \n",
       "1208      NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synth_smoking = pd.read_csv(\"data/synth_smoking.csv\")\n",
    "synth_smoking[\"treated\"] = synth_smoking.apply(lambda row: 1989 if row[\"state\"]== \"California\" else None, axis=1)\n",
    "pd.concat([synth_smoking.head(3), synth_smoking.tail(3)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc7c1d36-3b9d-4a92-8b0e-0e0384ada355",
   "metadata": {},
   "source": [
    "## 2.1 Specify stacked synthetic control setup and build **variable_specs**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "984141bd-a092-403c-91f3-89accf704a0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "smoking_variable_specs = [\n",
    "    {\n",
    "        \"var\": \"beer\",\n",
    "        \"specification\": {\n",
    "            \"aggregate_option\": \"avg\",\n",
    "            \"periods\": range(1984,1988)\n",
    "        }\n",
    "    },\n",
    "    {\n",
    "        \"var\": \"lnincome\",\n",
    "        \"specification\": {\n",
    "            \"aggregate_option\": \"avg\",\n",
    "        }\n",
    "    },\n",
    "    {\n",
    "        \"var\": \"retprice\",\n",
    "        \"specification\": {\n",
    "            \"aggregate_option\": \"avg\",\n",
    "        }\n",
    "    },\n",
    "    {\n",
    "        \"var\": \"age15to24\",\n",
    "        \"specification\": {\n",
    "            \"aggregate_option\": \"avg\"\n",
    "        }\n",
    "    },\n",
    "    {\n",
    "        \"var\": \"cigsale\",\n",
    "        \"specification\": {\n",
    "            \"aggregate_option\": \"individual\",\n",
    "            \"periods\": [1988, 1980, 1975]\n",
    "        }\n",
    "    }\n",
    "] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3d7dfda-184a-48e0-a266-e89ed2946b7b",
   "metadata": {},
   "source": [
    "## 2.2 Fit the synthetic control model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0f4e3dc2-df91-44eb-8ebd-c35c4458ef31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimating synthetic control for unit state=California (1/1)\n"
     ]
    }
   ],
   "source": [
    "m = multisynth.MultiSynth()\n",
    "weights = m.fit_multisynth(data=synth_smoking, \n",
    "                           variable_specs=smoking_variable_specs,\n",
    "                           treatment_var=\"treated\",\n",
    "                           unit_var=\"state\",\n",
    "                           time_var=\"year\",\n",
    "                           outcome_var=\"cigsale\",\n",
    "                           normalize_outcomes=True,\n",
    "                           penalized=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1701db3d-6098-4bbb-bfec-a504c760f4c6",
   "metadata": {},
   "source": [
    "### 2.2.1 Inspect synthetic control composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "77506ade-2fd6-4333-a827-0f681617b07a",
   "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 th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unit</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>0.051070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>0.573514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Montana</td>\n",
       "      <td>0.252631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>0.122784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           unit    weight\n",
       "6        Nevada  0.051070\n",
       "9         Idaho  0.573514\n",
       "10      Montana  0.252631\n",
       "33  Connecticut  0.122784"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.get_unit_weights(\"California\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e519b9b-50b6-4602-b57e-a94bb4a3fba3",
   "metadata": {},
   "source": [
    "### 2.2.2 Inspect covariate balance between treated and synthetic units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8b7f8937-79b7-4f58-b9b0-40b064a0b33b",
   "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 th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>state</th>\n",
       "      <th>California</th>\n",
       "      <th>synth_California</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age15to24_avg_1970_1988</th>\n",
       "      <td>0.178662</td>\n",
       "      <td>0.165384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beer_avg_1984_1987</th>\n",
       "      <td>24.450000</td>\n",
       "      <td>24.366983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cigsale_1975</th>\n",
       "      <td>127.100000</td>\n",
       "      <td>125.975234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cigsale_1980</th>\n",
       "      <td>120.200000</td>\n",
       "      <td>120.453583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cigsale_1988</th>\n",
       "      <td>90.099998</td>\n",
       "      <td>90.580795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lnincome_avg_1970_1988</th>\n",
       "      <td>10.031759</td>\n",
       "      <td>9.787598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retprice_avg_1970_1988</th>\n",
       "      <td>66.636843</td>\n",
       "      <td>64.770564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state                    California  synth_California\n",
       "var                                                  \n",
       "age15to24_avg_1970_1988    0.178662          0.165384\n",
       "beer_avg_1984_1987        24.450000         24.366983\n",
       "cigsale_1975             127.100000        125.975234\n",
       "cigsale_1980             120.200000        120.453583\n",
       "cigsale_1988              90.099998         90.580795\n",
       "lnincome_avg_1970_1988    10.031759          9.787598\n",
       "retprice_avg_1970_1988    66.636843         64.770564"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.get_synthetic_covariate_comparisons(\"California\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "945caaad-531f-497f-94aa-ae6a1069010a",
   "metadata": {},
   "source": [
    "## 2.3.3 View synthetic control results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f65c8f7f-2935-430f-a11c-132c2f885b5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m.plot_treatment_and_placebos(pre_treat_periods=10, \n",
    "                              post_treat_periods=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8db31f10-e21e-45db-90c7-0f865e21b921",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.24664250191747422"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View treatment effects averaged over 5 periods post-treatment \n",
    "m.get_ate(post_treat_periods = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8de91239-9a15-432c-8701-bdbecdb8e8c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.024"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.get_p_value()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13c5a613-fa25-4553-bd4a-e2c8e91b0568",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py311",
   "language": "python",
   "name": "py311"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}