{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "zero_ltcg_bracket.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MaxGhenis/taxcalc-notebooks/blob/master/zero_ltcg_bracket.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ILT5RSEet0tR",
        "colab_type": "text"
      },
      "source": [
        "# Zero long-term capital gains bracket\n",
        "\n",
        "Several \n",
        "\n",
        "https://quarryhilladvisors.com/blog/can-capital-gains-push-me-into-a-higher-tax-bracket says:\n",
        ">Starting in 2018 and until (at least) 2025, the long-term capital gains tax is 0% if the seller is roughly in the 12% ordinary income tax bracket (married couples with a combined salary of \\$78,750 or single filers with an income of \\$39,375).\n",
        "\n",
        "This evaluates how tax liabilities change for single tax units with low ordinary income, depending on the amount of long-term capital gains realized.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vFTm_0RqwxW_",
        "colab_type": "text"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d9HoCi9u4L1I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import sys\n",
        "!pip install matplotlib-label-lines\n",
        "if 'microdf' not in sys.modules:\n",
        "    !pip install git+git://github.com/maxghenis/microdf.git\n",
        "if 'taxcalc' not in sys.modules:\n",
        "    !pip install git+git://github.com/PSLmodels/Tax-Calculator.git"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LRSD1C_cwyIQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import microdf as mdf\n",
        "import taxcalc as tc\n",
        "import itertools"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dZQrV2nmdk6u",
        "colab_type": "text"
      },
      "source": [
        "## Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vHLitX0aK6lO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def cartesian_product(d):\n",
        "    index = pd.MultiIndex.from_product(d.values(), names=d.keys())\n",
        "    return pd.DataFrame(index=index).reset_index()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8cLWdrpVEwSW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Wages, salaries, and tips for taxpayer net of pension contributions\n",
        "e00200p = np.arange(0, 50001, 10000)\n",
        "# Net long-term capital gains/losses\n",
        "p23250 = np.arange(0, 100001, 1000)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X0fSlnPUKfdF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "d = {'e00200p': e00200p, \n",
        "     'p23250': p23250}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wTnOdSiHGB76",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = cartesian_product(d)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "il3xyvJZGWyb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data['RECID'] = data.index + 1\n",
        "data['MARS'] = 1  # Single.\n",
        "data['e00200'] = data.e00200p  # Asserts e00200 == e00200p + e00200s."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i6NMMv2KCotd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "recs = tc.Records(data=data, start_year=2019)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ehEf8WRUBMhu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = mdf.calc_df(records=recs, group_vars=['e00200p', 'p23250'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nf1vYS8mDQld",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "48287af9-f8c2-46b1-acca-2090a81a8624"
      },
      "source": [
        "df[df.e00200p == 0][['p23250', 'tax']].set_index('p23250').plot()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8de3f644e0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rSHrkotboSXQRqVe27dxFXn4BW3eU8dTIgVlXPOqSCoiI1Bvuzq0vzGL2Z+sZe1WMozqq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\nRKRa/j/GcAnOKQx0cAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Xkh7NlQHsXC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "d0d07229-a11b-4a44-918e-6ec3f10fbd51"
      },
      "source": [
        "df[df.e00200p == 30000][['p23250', 'tax']].set_index('p23250').plot()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8de2498a20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0nTw6ZxUG/OnOJK7t3PSs64hI2VCwSFg7UVDE8x+u480vtpDQqj6vDelOq4Y1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\n9t6Px9sAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZLsW3WNAHibq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "outputId": "49fd9b00-3acc-4c68-c64b-d4e6aaa2a62d"
      },
      "source": [
        "df[(df.e00200p == 30000) & df.p23250.isin([50e3, 51e3])][['p23250', 'tax']]"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>p23250</th>\n",
              "      <th>tax</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RECID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>354</th>\n",
              "      <td>50000.0</td>\n",
              "      <td>10762.9608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>355</th>\n",
              "      <td>51000.0</td>\n",
              "      <td>10912.9608</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        p23250         tax\n",
              "RECID                     \n",
              "354    50000.0  10762.9608\n",
              "355    51000.0  10912.9608"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dViTsX2kILXR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "outputId": "1cab5d3b-2a4b-44c4-df92-75c1ff520dc9"
      },
      "source": [
        "df[(df.e00200p == 0) & df.p23250.isin([80e3, 81e3])][['p23250', 'tax']]"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>p23250</th>\n",
              "      <th>tax</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RECID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>80000.0</td>\n",
              "      <td>4239.984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>81000.0</td>\n",
              "      <td>4389.984</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        p23250       tax\n",
              "RECID                   \n",
              "81     80000.0  4239.984\n",
              "82     81000.0  4389.984"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HXTHf6POIbd2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 79
        },
        "outputId": "b4a77b46-0eb3-43b8-b675-947a87009c36"
      },
      "source": [
        "df[(df.e00200p == 40000) & df.p23250.isin([290e3, 291e3])][['p23250', 'tax']]"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>p23250</th>\n",
              "      <th>tax</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RECID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [p23250, tax]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    }
  ]
}