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openfisca-us-demo.ipynb
{
"nbformat": 4,
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"metadata": {
"colab": {
"name": "OpenFisca US demo.ipynb",
"provenance": [],
"collapsed_sections": [],
"authorship_tag": "ABX9TyPUR76aaChhJyDYM+mEkvnl",
"include_colab_link": true
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"kernelspec": {
"name": "python3",
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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/MaxGhenis/6605bcca4db42bf16463459c13476fec/openfisca-us-demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# OpenFisca US tutorial\n",
"\n",
"OpenFisca US is [PolicyEngine](https://policyengine.org)'s open source tax and benefit microsimulation model.\n",
"\n",
"This tutorial will teach you how to construct a household, calculate variables, and simulate a range of scenarios, using California's SNAP program as a case study. \n",
"\n",
"*See https://openfisca.us for full documentation.*\n",
"\n",
"## Installation\n",
"\n",
"OpenFisca US is available on PyPI."
],
"metadata": {
"id": "WSwNepc-Tuui"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_wQ1Ld9hEMvC",
"outputId": "40fd6c43-d691-4de7-e975-7f1d1e525b40"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
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]
}
],
"source": [
"!pip install openfisca-us"
]
},
{
"cell_type": "markdown",
"source": [
"# Constructing households\n",
"\n",
"To calculate taxes and benefits, first define the household via:\n",
"1. The `IndividualSim` constructor.\n",
"2. The `add_person` method.\n",
"\n",
"Let's start with a single person with \\$1,000 monthly employment income (inputs and outputs are all annual)."
],
"metadata": {
"id": "_uvPNJyRPd3q"
}
},
{
"cell_type": "code",
"source": [
"from openfisca_us import IndividualSim\n",
"\n",
"sim = IndividualSim(year=2022)\n",
"sim.add_person(name=\"person\", employment_income=1000 * 12)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "P5NsStTGEQjI",
"outputId": "e7b088bc-d115-4370-9d83-a62cc5b3d754"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/openfisca_core/parameters/config.py:17: LibYAMLWarning: libyaml is not installed in your environment. This can make OpenFisca slower to start. Once you have installed libyaml, run 'pip uninstall pyyaml && pip install pyyaml --no-cache-dir' so that it is used in your Python environment.\n",
"\n",
" warnings.warn(\" \".join(message), LibYAMLWarning)\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Calculating variables\n",
"\n",
"Now we can calculate any variables in the [`openfisca_us/variables` folder](https://github.com/PolicyEngine/openfisca-us/tree/master/openfisca_us/variables) via the `calc` method.\n",
"\n",
"For example, let's recover `employment_income`:"
],
"metadata": {
"id": "1RTLMDrwRC33"
}
},
{
"cell_type": "code",
"source": [
"sim.calc(\"employment_income\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2f04mTDDRlIH",
"outputId": "856f2b8b-0b61-40b3-8372-df33b1d8f0d3"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([12000.], dtype=float32)"
]
},
"metadata": {},
"execution_count": 3
}
]
},
{
"cell_type": "markdown",
"source": [
"**Exercise 1: Calculate SNAP**\n",
"\n",
"Calculate three SNAP-related variables:\n",
"1. `snap_normal_allotment`, which they would receive outside of Covid.\n",
"2. `snap_emergency_allotment`, which bumps up eligible households to the maximum allotment during the pandemic.\n",
"3. `snap`, which sums the two.\n",
"\n",
"Print them as monthly amounts."
],
"metadata": {
"id": "lpDCSx4oR6cR"
}
},
{
"cell_type": "code",
"source": [
"# SOLUTION\n",
"\n",
"print(\n",
" \"SNAP normal allotment: \",\n",
" round(sim.calc(\"snap_normal_allotment\")[0] / 12),\n",
")\n",
"print(\n",
" \"SNAP emergency allotment: \",\n",
" round(sim.calc(\"snap_emergency_allotment\")[0] / 12),\n",
")\n",
"print(\"Total SNAP: \", round(sim.calc(\"snap\")[0] / 12))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qhUZ2jU0RBPl",
"outputId": "1d5043e7-3867-411d-8943-47de6c8cbb37"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"SNAP normal allotment: 60\n",
"SNAP emergency allotment: 190\n",
"Total SNAP: 250\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Households with multiple people\n",
"\n",
"To create households with multiple people, we need to assign them to a household unit.\n",
"US tax and benefit programs group people in different ways, and SNAP uses a *SPM unit*, or a group of people that cohabit and share resources (SPM stands for the Supplemental Poverty Measure).\n",
"\n",
"Let's now model a two-person household, still with \\$1,000 monthly employment income, and group them with the `add_spm_unit` method.\n",
"This also enables us to add SPM-unit-level characteristics, like housing costs (which affect SNAP benefits)."
],
"metadata": {
"id": "9GHwdDfrTd8F"
}
},
{
"cell_type": "code",
"source": [
"sim = IndividualSim(year=2022)\n",
"sim.add_person(name=\"parent\", employment_income=1000 * 12)\n",
"sim.add_person(name=\"child\")\n",
"sim.add_spm_unit(\n",
" name=\"spm_unit\", members=[\"parent\", \"child\"], housing_cost=600 * 12\n",
")"
],
"metadata": {
"id": "6q3mH6bLa8IA"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Now let's recalculate SNAP."
],
"metadata": {
"id": "UytFo1Kabas2"
}
},
{
"cell_type": "code",
"source": [
"print(\n",
" \"SNAP normal allotment: \",\n",
" round(sim.calc(\"snap_normal_allotment\")[0] / 12),\n",
")\n",
"print(\n",
" \"SNAP emergency allotment: \",\n",
" round(sim.calc(\"snap_emergency_allotment\")[0] / 12),\n",
")\n",
"print(\"Total SNAP: \", round(sim.calc(\"snap\")[0] / 12))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "EMcis19qbfyy",
"outputId": "a33ba784-62f1-4466-d8b0-ebaa615ad6f8"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"SNAP normal allotment: 354\n",
"SNAP emergency allotment: 105\n",
"Total SNAP: 459\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"**Exercise 2: Customize a household**\n",
"\n",
"Create a new household with a different number of people, or different income or housing costs.\n",
"Recalculate SNAP."
],
"metadata": {
"id": "ey1n_m0ybjTw"
}
},
{
"cell_type": "code",
"source": [
"### SOLUTION\n",
"\n",
"# Varies."
],
"metadata": {
"id": "lMLmzrqybyYy"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Scenario analysis\n",
"\n",
"Suppose we want to explore a range of inputs, rather than manually specifying one or two.\n",
"We can apply techniques like list comprehension, but openfisca-us makes it easier with the `vary` method.\n",
"\n",
"When we call `sim.vary`, subsequent `calc` calls calculate over the range of inputs specified in `vary`.\n",
"\n",
"Let's vary employment income, going up to \\$3,000 per month in increments of \\$10 per month."
],
"metadata": {
"id": "pkPuMdkJb01b"
}
},
{
"cell_type": "code",
"source": [
"sim.vary(\"employment_income\", max=3000 * 12, step=10 * 12)"
],
"metadata": {
"id": "xXkUlKRZcpCb"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Now when we call `calc`, we get an array rather than a scalar. Let's start by recovering employment income again."
],
"metadata": {
"id": "l5dyC7fAcsQl"
}
},
{
"cell_type": "code",
"source": [
"sim.calc(\"employment_income\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2H9VdE3hfX4t",
"outputId": "d6ad54c4-c52c-4552-995a-91b15d90ed30"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[ 0., 120., 240., 360., 480., 600., 720., 840.,\n",
" 960., 1080., 1200., 1320., 1440., 1560., 1680., 1800.,\n",
" 1920., 2040., 2160., 2280., 2400., 2520., 2640., 2760.,\n",
" 2880., 3000., 3120., 3240., 3360., 3480., 3600., 3720.,\n",
" 3840., 3960., 4080., 4200., 4320., 4440., 4560., 4680.,\n",
" 4800., 4920., 5040., 5160., 5280., 5400., 5520., 5640.,\n",
" 5760., 5880., 6000., 6120., 6240., 6360., 6480., 6600.,\n",
" 6720., 6840., 6960., 7080., 7200., 7320., 7440., 7560.,\n",
" 7680., 7800., 7920., 8040., 8160., 8280., 8400., 8520.,\n",
" 8640., 8760., 8880., 9000., 9120., 9240., 9360., 9480.,\n",
" 9600., 9720., 9840., 9960., 10080., 10200., 10320., 10440.,\n",
" 10560., 10680., 10800., 10920., 11040., 11160., 11280., 11400.,\n",
" 11520., 11640., 11760., 11880., 12000., 12120., 12240., 12360.,\n",
" 12480., 12600., 12720., 12840., 12960., 13080., 13200., 13320.,\n",
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},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "markdown",
"source": [
"Since `employment_income` is person-level, `calc` returns a two-row array. `vary` varies the first person's variable by default, so the child's income stays fixed at zero.\n",
"\n",
"### Calculating SNAP as income varies\n",
"\n",
"Now we can calculate normal SNAP allotments.\n",
"Each value corresponds to the varied employment income above.\n",
"\n",
"For example, at \\$0 employment income, the household will get \\$5,508 in annual benefits.\n",
"At \\$3,000 monthly employment income, they are not eligible for any SNAP benefits.\n",
"Since `snap_normal_allotment` is at the SPM unit level, `calc` now returns a one-row array."
],
"metadata": {
"id": "KRzYPtrhfZRV"
}
},
{
"cell_type": "code",
"source": [
"sim.calc(\"snap_normal_allotment\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fgR_DjJPcwxA",
"outputId": "aea45821-39a0-44a9-b659-6fd434fe6d0e"
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"execution_count": null,
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{
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" 1571.3999, 1528.2 , 1484.9998, 1441.7998, 1398.5996, 1355.3999,\n",
" 1312.1997, 1269. , 1225.7998, 1184.3999, 1155.5996, 1126.7998,\n",
" 1098. , 1069.1997, 1040.3999, 1011.5996, 982.7998, 954. ,\n",
" 925.1997, 896.3999, 867.5996, 838.7998, 810. , 781.1997,\n",
" 752.3999, 723.5996, 694.7998, 666. , 637.1997, 608.3999,\n",
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},
"metadata": {},
"execution_count": 10
}
]
},
{
"cell_type": "markdown",
"source": [
"**Exercise 3: Visualization**\n",
"\n",
"Use your preferred data visualization library to create a plot of employment income on the x axis and normal (non-Covid) SNAP benefits on the y axis.\n",
"\n",
"*Hint: `IndividualSim.calc` returns a list of all households' values. Since we're only working with one household, extract the first with `calc(x)[0]`.*"
],
"metadata": {
"id": "75v5UVbAb7dU"
}
},
{
"cell_type": "code",
"source": [
"### SOLUTION\n",
"\n",
"import pandas as pd\n",
"import plotly.express as px\n",
"\n",
"LABELS = dict(\n",
" employment_income=\"Monthly employment income\",\n",
" snap_normal_allotment=\"Monthly normal SNAP allotment\",\n",
")\n",
"\n",
"df = pd.DataFrame(\n",
" dict(\n",
" employment_income=sim.calc(\"employment_income\")[0],\n",
" snap_normal_allotment=sim.calc(\"snap_normal_allotment\")[0],\n",
" )\n",
")\n",
"\n",
"fig = px.line(\n",
" df,\n",
" \"employment_income\",\n",
" \"snap_normal_allotment\",\n",
" labels=LABELS,\n",
" title=\"Normal SNAP allotment for a two-person household in California with $600 monthly housing costs\",\n",
")\n",
"fig.update_layout(xaxis_tickformat=\"$,\", yaxis_tickformat=\"$,\")\n",
"fig.show()"
],
"metadata": {
"id": "y2mz3743LfNS",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"outputId": "cd5f91e8-af9d-44fc-fea2-2398997c7298"
},
"execution_count": null,
"outputs": [
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"source": [
"## Learn more\n",
"\n",
"Visit https://openfisca.us for examples of other programs, calculating marginal tax rates, and more."
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