{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Matching CPS observations over time\n", "\n", "##### Example with flow from disability to work\n", "\n", "---\n", "\n", "January 14, 2019\n", "\n", "Brian Dew, @bd_econ\n", "\n", "---\n", "\n", "Example uses CPS microdata that has already been cleaned and saved as feather format files, to calculate what percent of people age 25 to 54 who were not working the year before due to disability but are employed in the current month. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2021-12-10T01:59:31.755116Z", "start_time": "2021-12-10T01:59:31.465161Z" } }, "outputs": [], "source": [ "# Import packages and set preliminaries\n", "%matplotlib inline\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import os\n", "from pathlib import Path\n", "\n", "os.chdir('/home/brian/Documents/CPS/')\n", "\n", "cps_dir = Path('/home/brian/Documents/CPS/data/clean/')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2021-12-10T01:59:31.759200Z", "start_time": "2021-12-10T01:59:31.756214Z" } }, "outputs": [], "source": [ "def cps_1mo(cps_dir, cps_dt, cols):\n", " '''\n", " Return 1 month of bd_CPS variables cols ending cps_dt\n", " '''\n", "\n", " if 'MONTH' not in cols:\n", " cols = cols + ['MONTH']\n", " if 'YEAR' not in cols:\n", " cols = cols + ['YEAR']\n", "\n", " cps_year = cps_dt.year\n", " cps_month = cps_dt.month\n", " df = (pd.read_feather(cps_dir / f'cps{cps_year}.ft', columns=cols)\n", " .query('MONTH == @cps_month'))\n", " \n", " return df" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2021-12-10T02:00:11.725698Z", "start_time": "2021-12-10T01:59:31.760435Z" } }, "outputs": [], "source": [ "cols = ['CPSID', 'AGE', 'FEMALE', 'WBHAO', 'NILFREASON', \n", " 'LFS', 'MIS', 'BASICWGT', 'LINENO', 'RETIRED']\n", "\n", "emp = lambda x: np.where(x.LFS_y == 'Employed', 1, 0)\n", "\n", "dates = pd.date_range('1997-01-01', '2021-11-01', freq='MS')\n", "\n", "d = {}\n", "\n", "for dt in dates:\n", " df1 = (cps_1mo(cps_dir, dt - pd.DateOffset(years=1), cols)\n", " .query('RE'))\n", " df2 = cps_1mo(cps_dir, dt, cols)\n", " data = (pd.merge(df1, df2, on=['CPSID', 'LINENO', 'FEMALE', 'WBHAO'])\n", " .query('25 <= AGE_y <= 54'))\n", " data = (data.loc[(data.AGE_x <= data.AGE_y) & \n", " (data.AGE_y - 2 <= data.AGE_x)]).assign(EMP = emp)\n", "\n", " d[dt] = np.average(data.EMP, weights=data.BASICWGT_y) * 100\n", " \n", "df = pd.Series(d).rolling(12).mean().dropna().rename('Share')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2021-12-10T02:00:11.849709Z", "start_time": "2021-12-10T02:00:11.726756Z" } }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df.plot(color='blue', legend=None, title='Flow, Disability to Work');" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 2 }