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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "1afb4980",
+ "metadata": {},
+ "source": [
+ "# Using Inverse Optimal Tax Theory to Reveal Preferences of Political Candidates\n",
+ "\n",
+ "This notebook uses Inverse Optimal Tax Theory to determine the generalized social welfare weights associated with the tax policy proposals of candidates for President of the United States from 2012-2020.\n",
+ "\n",
+ "## Step 1: Import necessary Python modules"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "16ed9b3c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/2m/db9b_l2950xckts1f65_rc2m0000gn/T/ipykernel_85313/831081070.py:5: DeprecationWarning:\n",
+ "\n",
+ "Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ " \n",
+ " "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# imports\n",
+ "from iot.iot_user import iot_comparison\n",
+ "import taxcalc as tc\n",
+ "import copy\n",
+ "from IPython.core.display import display, HTML\n",
+ "from plotly.offline import init_notebook_mode, plot\n",
+ "import plotly.express as px\n",
+ "init_notebook_mode(connected=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "19d2ed28",
+ "metadata": {},
+ "source": [
+ "# Step 2: Give paths/urls to JSON files representing candidates' policy proposals\n",
+ "\n",
+ "We put these paths in a dictionary along with the year that is relevant for the policy proposals and what the current law baseline policy was at the time."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "093d86ac",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "obama2015_path = \"https://raw.githubusercontent.com/jdebacker/examples/pres_proposals/psl_examples/taxcalc/Obama2015.json\"\n",
+ "romney2012_path = \"https://raw.githubusercontent.com/jdebacker/examples/pres_proposals/psl_examples/taxcalc/Romney2012.json\"\n",
+ "clinton2016_path = \"https://raw.githubusercontent.com/jdebacker/examples/pres_proposals/psl_examples/taxcalc/Clinton2016.json\"\n",
+ "trump2016_path = \"https://raw.githubusercontent.com/PSLmodels/examples/main/psl_examples/taxcalc/Trump2016.json\"\n",
+ "biden2020_path = \"https://raw.githubusercontent.com/PSLmodels/examples/main/psl_examples/taxcalc/Biden2020.json\"\n",
+ "trump2020_path = \"https://raw.githubusercontent.com/PSLmodels/examples/main/psl_examples/taxcalc/TCJA.json\"\n",
+ "\n",
+ "pre_2020_baseline = \"https://raw.githubusercontent.com/PSLmodels/examples/main/psl_examples/taxcalc/2017_law.json\"\n",
+ "baseline_2020 = \"https://raw.githubusercontent.com/PSLmodels/examples/main/psl_examples/taxcalc/TCJA.json\"\n",
+ "\n",
+ "\n",
+ "candidate_dict = {\n",
+ " \"Obama 2015\": {\"policy_path\": obama2015_path, \"baseline_path\": pre_2020_baseline, \"start_year\": 2016},\n",
+ " \"Romney 2012\": {\"policy_path\": romney2012_path, \"baseline_path\": pre_2020_baseline, \"start_year\": 2014}, # can't use year before 2014 with CPS file\n",
+ " \"Clinton 2016\": {\"policy_path\": clinton2016_path, \"baseline_path\": pre_2020_baseline, \"start_year\": 2017},\n",
+ " \"Trump 2016\": {\"policy_path\": trump2016_path, \"baseline_path\": pre_2020_baseline, \"start_year\": 2017},\n",
+ " \"Biden 2020\": {\"policy_path\": biden2020_path, \"baseline_path\": baseline_2020, \"start_year\": 2022},\n",
+ " \"Trump 2020\": {\"policy_path\": trump2020_path, \"baseline_path\": baseline_2020, \"start_year\": 2022}}\n",
+ "\n",
+ "# Add to the candidate dictionary the Policy objects representing the baseline for each candidate\n",
+ "base_2017 = tc.Policy()\n",
+ "base_2017_ref = tc.Policy.read_json_reform(pre_2020_baseline)\n",
+ "base_2017.implement_reform(base_2017_ref, print_warnings=False, raise_errors=False)\n",
+ "\n",
+ "base_2020 = copy.deepcopy(base_2017)\n",
+ "tcja = tc.Policy.read_json_reform(baseline_2020)\n",
+ "base_2020.implement_reform(tcja, print_warnings=False, raise_errors=False)\n",
+ "\n",
+ "candidate_dict[\"Obama 2015\"][\"baseline\"] = base_2017\n",
+ "candidate_dict[\"Romney 2012\"][\"baseline\"] = base_2017\n",
+ "candidate_dict[\"Clinton 2016\"][\"baseline\"] = base_2017\n",
+ "candidate_dict[\"Trump 2016\"][\"baseline\"] = base_2017\n",
+ "candidate_dict[\"Biden 2020\"][\"baseline\"] = base_2020\n",
+ "candidate_dict[\"Trump 2020\"][\"baseline\"] = base_2017"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e36798a9",
+ "metadata": {},
+ "source": [
+ "## Step 3: Instantiate an `iot_comparison` class object\n",
+ "\n",
+ "Here, we create an `iot_comparison` class object with lists representing all the candidates included in the dictionary defined above."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "45ea24da",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "income_measure = \"expanded_income\"\n",
+ "iot1 = iot_comparison(\n",
+ " years=[d['start_year'] for d in list(candidate_dict.values())],\n",
+ " baseline_policies=[[d['baseline_path']] for d in list(candidate_dict.values())],\n",
+ " policies=[d['policy_path'] for d in list(candidate_dict.values())],\n",
+ " labels=list(candidate_dict.keys()),\n",
+ " data=\"CPS\",\n",
+ " compare_default=False,\n",
+ " mtr_wrt=\"e00200p\",\n",
+ " income_measure=income_measure,\n",
+ " weight_var=\"s006\",\n",
+ " eti=0.25,\n",
+ " dist_type=\"log_normal\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "3c69a171",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.plotly.v1+json": {
+ "config": {
+ "plotlyServerURL": "https://plot.ly"
+ },
+ "data": [
+ {
+ "hovertemplate": "Expanded Income=%{x:$,.2f}
$z_m / z$=%{y:.3f}