From dfa0556165a22dda6602fa7e186697e9b646017a Mon Sep 17 00:00:00 2001 From: Nikhil Woodruff Date: Tue, 12 Nov 2024 14:23:55 +0000 Subject: [PATCH 1/5] Add dataset comparisons to docs --- changelog_entry.yaml | 4 + docs/_toc.yml | 1 + docs/results.ipynb | 7298 ++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 7303 insertions(+) create mode 100644 docs/results.ipynb diff --git a/changelog_entry.yaml b/changelog_entry.yaml index e69de29..20e4b5f 100644 --- a/changelog_entry.yaml +++ b/changelog_entry.yaml @@ -0,0 +1,4 @@ +- bump: patch + changes: + added: + - Metric comparisons by dataset to the documentation. diff --git a/docs/_toc.yml b/docs/_toc.yml index 5dd18b6..0c3ef62 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -2,3 +2,4 @@ format: jb-book root: intro chapters: - file: validation.ipynb +- file: results.ipynb diff --git a/docs/results.ipynb b/docs/results.ipynb new file mode 100644 index 0000000..acb152e --- /dev/null +++ b/docs/results.ipynb @@ -0,0 +1,7298 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Comparison\n", + "\n", + "The table below shows various calculated inequality metrics:\n", + "\n", + "| Metric | Description | Formula | Notes |\n", + "| --- | --- | --- | --- |\n", + "| Gini | Gini coefficient | $1 - 2 \\sum_{i=1}^n \\frac{w_i}{W} \\left( \\frac{W - w_i}{W} \\right) \\frac{r_i}{R}$ | $w_i$ is the weight of the $i$-th household, $W$ is the sum of all weights, $r_i$ is the income of the $i$-th individual, $R$ is the sum of all incomes |\n", + "| Top 10% share | Share of total income received by the top 10% | $\\sum_{i=1}^{n_{10}} w_i r_i / R$ | $n_{10}$ is the number of households in the top 10% |\n", + "| Top 1% share | Share of total income received by the top 1% | $\\sum_{i=1}^{n_{1}} w_i r_i / R$ | $n_{1}$ is the number of households in the top 1% |\n", + "| SPM poverty rate | Share of individuals with income below the SPM poverty threshold | $n_{\\text{poor}} / n$ | $n_{\\text{poor}}$ is the number of individuals with income below the SPM poverty threshold, $n$ is the total number of individuals |\n", + "| Mean weight | Mean of the weights | $\\sum_{i=1}^n w_i / n$ | $n$ is the total number of households |\n", + "| Median weight | Median of the weights | | |\n", + "| Weight standard deviation | Standard deviation of the weights | | |\n", + "| Nonzero weight share | Share of households with nonzero weight | $n_{\\text{nonzero}} / n$ | $n_{\\text{nonzero}}$ is the number of households with nonzero weight, $n$ is the total number of households |\n", + "\n", + "The first table below shows these calculated metrics for the CPS and ECPS." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from policyengine_us import Microsimulation\n", + "from policyengine_us_data import PUF_2024\n", + "import pandas as pd\n", + "\n", + "cps = Microsimulation()\n", + "ecps = Microsimulation(dataset=\"enhanced_cps_2024\")\n", + "puf = Microsimulation(dataset=PUF_2024)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DatasetCPSEnhanced CPS
Metric
Gini0.4490.556
Mean weight2379.0741290.472
Median weight2260.3201.037
Nonzero weight share1.0000.538
SPM poverty rate0.1270.249
Top 1% share0.0730.154
Top 10% share0.3230.416
Weight standard deviation1422.47911868.958
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" + ], + "text/plain": [ + "Dataset CPS Enhanced CPS\n", + "Metric \n", + "Gini 0.449 0.556\n", + "Mean weight 2379.074 1290.472\n", + "Median weight 2260.320 1.037\n", + "Nonzero weight share 1.000 0.538\n", + "SPM poverty rate 0.127 0.249\n", + "Top 1% share 0.073 0.154\n", + "Top 10% share 0.323 0.416\n", + "Weight standard deviation 1422.479 11868.958" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gini = lambda sim: sim.calculate(\"household_net_income\", 2024).gini().round(3)\n", + "top_10_pct = lambda sim: (sim.calculate(\"household_net_income\", 2024).top_10_pct_share().round(3))\n", + "top_1_pct = lambda sim: (sim.calculate(\"household_net_income\", 2024).top_1_pct_share().round(3))\n", + "spm_poverty = lambda sim: sim.calculate(\"in_poverty\", 2024, map_to=\"person\").mean().round(3)\n", + "weight_mean = lambda sim: pd.Series(sim.calculate(\"household_weight\", 2024).values).mean()\n", + "weight_median = lambda sim: pd.Series(sim.calculate(\"household_weight\", 2024).values).median()\n", + "weight_sd = lambda sim: pd.Series(sim.calculate(\"household_weight\", 2024).values).std()\n", + "weights_nonzero_share = lambda sim: (pd.Series(sim.calculate(\"household_weight\", 2024).values) > 0.1).mean()\n", + "\n", + "metric_names = [\"Gini\", \"Top 10% share\", \"Top 1% share\", \"SPM poverty rate\", \"Mean weight\", \"Median weight\", \"Weight standard deviation\", \"Nonzero weight share\"]\n", + "metric_funcs = [gini, top_10_pct, top_1_pct, spm_poverty, weight_mean, weight_median, weight_sd, weights_nonzero_share]\n", + "\n", + "datasets = []\n", + "metrics = []\n", + "values = []\n", + "\n", + "for dataset, sim in zip([\"CPS\", \"PUF\", \"Enhanced CPS\"], [cps, puf, ecps]):\n", + " for metric, func in zip(metric_names, metric_funcs):\n", + " datasets.append(dataset)\n", + " metrics.append(metric)\n", + " values.append(func(sim))\n", + "\n", + "df = pd.DataFrame({\"Dataset\": datasets, \"Metric\": metrics, \"Value\": values})\n", + "df = df[df.Dataset != \"PUF\"].pivot(index=\"Dataset\", columns=\"Metric\", values=\"Value\")\n", + "df.round(3).T" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The below table shows the same metrics, but computed over tax units and including the IRS SOI PUF." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DatasetCPSEnhanced CPSPUF
Metric
Gini0.4950.5720.570
Mean weight1728.428937.545776.056
Median weight1388.4680.641353.541
Nonzero weight share1.0000.5341.000
Top 1% share0.0850.1540.150
Top 10% share0.3610.4250.410
Weight standard deviation1347.7069124.232720.281
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" + ], + "text/plain": [ + "Dataset CPS Enhanced CPS PUF\n", + "Metric \n", + "Gini 0.495 0.572 0.570\n", + "Mean weight 1728.428 937.545 776.056\n", + "Median weight 1388.468 0.641 353.541\n", + "Nonzero weight share 1.000 0.534 1.000\n", + "Top 1% share 0.085 0.154 0.150\n", + "Top 10% share 0.361 0.425 0.410\n", + "Weight standard deviation 1347.706 9124.232 720.281" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gini = lambda sim: sim.calculate(\"household_net_income\", 2024, map_to=\"tax_unit\").gini().round(3)\n", + "top_10_pct = lambda sim: (sim.calculate(\"household_net_income\", 2024, map_to=\"tax_unit\").top_10_pct_share().round(3))\n", + "top_1_pct = lambda sim: (sim.calculate(\"household_net_income\", 2024, map_to=\"tax_unit\").top_1_pct_share().round(3))\n", + "spm_poverty = lambda sim: sim.calculate(\"in_poverty\", 2024, map_to=\"person\").mean().round(3)\n", + "weight_mean = lambda sim: pd.Series(sim.calculate(\"household_weight\", 2024, map_to=\"tax_unit\").values).mean()\n", + "weight_median = lambda sim: pd.Series(sim.calculate(\"household_weight\", 2024, map_to=\"tax_unit\").values).median()\n", + "weight_sd = lambda sim: pd.Series(sim.calculate(\"household_weight\", 2024, map_to=\"tax_unit\").values).std()\n", + "weights_nonzero_share = lambda sim: (pd.Series(sim.calculate(\"household_weight\", 2024, map_to=\"tax_unit\").values) > 0.1).mean()\n", + "\n", + "metric_names = [\"Gini\", \"Top 10% share\", \"Top 1% share\", \"SPM poverty rate\", \"Mean weight\", \"Median weight\", \"Weight standard deviation\", \"Nonzero weight share\"]\n", + "metric_funcs = [gini, top_10_pct, top_1_pct, spm_poverty, weight_mean, weight_median, weight_sd, weights_nonzero_share]\n", + "\n", + "datasets = []\n", + "metrics = []\n", + "values = []\n", + "\n", + "for dataset, sim in zip([\"CPS\", \"PUF\", \"Enhanced CPS\"], [cps, puf, ecps]):\n", + " for metric, func in zip(metric_names, metric_funcs):\n", + " datasets.append(dataset)\n", + " metrics.append(metric)\n", + " values.append(func(sim))\n", + "\n", + "df = pd.DataFrame({\"Dataset\": datasets, \"Metric\": metrics, \"Value\": values})\n", + "df = df[df.Metric != \"SPM poverty rate\"].pivot(index=\"Dataset\", columns=\"Metric\", values=\"Value\")\n", + "df.round(3).T" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Weight distributions\n", + "\n", + "Below, we compare the weight distributions of the three datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Dataset=CPS
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"white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Household weight distribution" + }, + "width": 800, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "tickformat": ".0%", + "title": { + "text": "Quantile" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Weight" + }, + "type": "log" + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import plotly.express as px\n", + "from policyengine_core.charts import *\n", + "\n", + "cps_dist = pd.Series(cps.calculate(\"household_weight\").values).quantile(np.linspace(0, 1, 1001))\n", + "ecps_dist = pd.Series(ecps.calculate(\"household_weight\").values).quantile(np.linspace(0, 1, 1001))\n", + "puf_dist = pd.Series(puf.calculate(\"household_weight\").values).quantile(np.linspace(0, 1, 1001))\n", + "\n", + "df = pd.DataFrame({\"CPS\": cps_dist, \"Enhanced CPS\": ecps_dist, \"PUF\": puf_dist, \"Quantile\": np.linspace(0, 1, 1001)})\n", + "\n", + "fig = px.line(df, x=\"Quantile\", y=[\"CPS\", \"Enhanced CPS\", \"PUF\"], title=\"Household weight distribution\", labels={\"value\": \"Weight\", \"variable\": \"Dataset\"}, log_y=True, color_discrete_sequence=px.colors.qualitative.T10)\n", + "fig.update_layout(\n", + " width=800,\n", + " height=600,\n", + " xaxis=dict(\n", + " title=\"Quantile\",\n", + " tickformat=\".0%\",\n", + " ),\n", + " yaxis=dict(\n", + " title=\"Weight\",\n", + " type=\"log\",\n", + " ),\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From bfe274691f60df96fbf1f6945e7cbac286948752 Mon Sep 17 00:00:00 2001 From: Nikhil Woodruff Date: Tue, 19 Nov 2024 11:01:51 +0000 Subject: [PATCH 2/5] Add inequality to book --- Makefile | 2 ++ docs/add_plotly_to_book.py | 27 +++++++++++++++++++++++++++ docs/results.ipynb | 5 +++-- 3 files changed, 32 insertions(+), 2 deletions(-) create mode 100644 docs/add_plotly_to_book.py diff --git a/Makefile b/Makefile index 9bf8088..da9c5a7 100644 --- a/Makefile +++ b/Makefile @@ -31,6 +31,8 @@ docker: documentation: jb clean docs && jb build docs + python docs/add_plotly_to_book.py docs + data: python policyengine_us_data/datasets/acs/acs.py diff --git a/docs/add_plotly_to_book.py b/docs/add_plotly_to_book.py new file mode 100644 index 0000000..822e77a --- /dev/null +++ b/docs/add_plotly_to_book.py @@ -0,0 +1,27 @@ +import argparse +from pathlib import Path + +# This command-line tools enables Plotly charts to show in the HTML files for the Jupyter Book documentation. + +parser = argparse.ArgumentParser() +parser.add_argument("book_path", help="Path to the Jupyter Book.") + +args = parser.parse_args() + +# Find every HTML file in the Jupyter Book. Then, add a script tag to the start of the tag in each file, with the contents: +# + +book_folder = Path(args.book_path) + +for html_file in book_folder.glob("**/*.html"): + with open(html_file, "r") as f: + html = f.read() + + # Add the script tag to the start of the tag. + html = html.replace( + "", + '', + ) + + with open(html_file, "w") as f: + f.write(html) diff --git a/docs/results.ipynb b/docs/results.ipynb index acb152e..f65669e 100644 --- a/docs/results.ipynb +++ b/docs/results.ipynb @@ -310,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 94, "metadata": {}, "outputs": [ { @@ -7270,7 +7270,8 @@ " title=\"Weight\",\n", " type=\"log\",\n", " ),\n", - ")" + ")\n", + "fig" ] } ], From 384768635917f42424cc747b66c4eca65cb35c6f Mon Sep 17 00:00:00 2001 From: Nikhil Woodruff Date: Tue, 19 Nov 2024 11:08:33 +0000 Subject: [PATCH 3/5] Add population by state to calibration --- .gitignore | 1 + .../storage/population_by_state.csv | 53 +++++++++++++++++++ policyengine_us_data/utils/loss.py | 18 +++++++ 3 files changed, 72 insertions(+) create mode 100644 policyengine_us_data/storage/population_by_state.csv diff --git a/.gitignore b/.gitignore index ebbab06..c2b68b7 100644 --- a/.gitignore +++ b/.gitignore @@ -9,3 +9,4 @@ !eitc.csv !spm_threshold_agi.csv **/_build +!population_by_state.csv diff --git a/policyengine_us_data/storage/population_by_state.csv b/policyengine_us_data/storage/population_by_state.csv new file mode 100644 index 0000000..0931747 --- /dev/null +++ b/policyengine_us_data/storage/population_by_state.csv @@ -0,0 +1,53 @@ +state,population,population_under_5 +CA,38965193.00,2104120.00 +TX,30503301.00,1921708.00 +FL,22610726.00,1130536.00 +NY,19571216.00,1037274.00 +PA,12961683.00,661046.00 +IL,12549689.00,665134.00 +OH,11785935.00,660012.00 +GA,11029227.00,639695.00 +NC,10835491.00,606787.00 +MI,10037261.00,531975.00 +NJ,9290841.00,520287.00 +VA,8715698.00,488079.00 +WA,7812880.00,421896.00 +AZ,7431344.00,393861.00 +TN,7126489.00,413336.00 +MA,7001399.00,343069.00 +IN,6862199.00,404870.00 +MO,6196156.00,353181.00 +MD,6180253.00,352274.00 +WI,5910955.00,313281.00 +CO,5877610.00,311513.00 +MN,5737915.00,327061.00 +SC,5373555.00,290172.00 +AL,5108468.00,291183.00 +LA,4573749.00,278999.00 +KY,4526154.00,262517.00 +OR,4233358.00,203201.00 +OK,4053824.00,243229.00 +CT,3617176.00,180859.00 +UT,3417734.00,232406.00 +IA,3207004.00,186006.00 +PR,3205691.00,96171.00 +NV,3194176.00,172486.00 +AR,3067732.00,180996.00 +KS,2940546.00,176433.00 +MS,2939690.00,173442.00 +NM,2114371.00,107833.00 +NE,1978379.00,124638.00 +ID,1964726.00,113954.00 +WV,1770071.00,86733.00 +HI,1435138.00,77497.00 +NH,1402054.00,63092.00 +ME,1395722.00,61412.00 +MT,1132812.00,57773.00 +RI,1095962.00,52606.00 +DE,1031890.00,54690.00 +SD,919318.00,57917.00 +ND,783926.00,49387.00 +AK,733406.00,46205.00 +DC,678972.00,38701.00 +VT,647464.00,27193.00 +WY,584057.00,30955.00 \ No newline at end of file diff --git a/policyengine_us_data/utils/loss.py b/policyengine_us_data/utils/loss.py index 4669f89..a01b16a 100644 --- a/policyengine_us_data/utils/loss.py +++ b/policyengine_us_data/utils/loss.py @@ -322,6 +322,24 @@ def build_loss_matrix(dataset: type, time_period): ) targets_array.append(row["count"]) + # Population by state and population under 5 by state + + state_population = pd.read_csv(STORAGE_FOLDER / "population_by_state.csv") + + for _, row in state_population.iterrows(): + in_state = sim.calculate("state_code", map_to="person") == row["state"] + label = f"census/population_by_state/{row['state']}" + loss_matrix[label] = sim.map_result(in_state, "person", "household") + targets_array.append(row["population"]) + + under_5 = sim.calculate("age").values < 5 + in_state_under_5 = in_state * under_5 + label = f"census/population_under_5_by_state/{row['state']}" + loss_matrix[label] = sim.map_result( + in_state_under_5, "person", "household" + ) + targets_array.append(row["population_under_5"]) + if any(loss_matrix.isna().sum() > 0): raise ValueError("Some targets are missing from the loss matrix") From b16f46f4c283cdff316e8ef912651c54000422be Mon Sep 17 00:00:00 2001 From: Nikhil Woodruff Date: Tue, 19 Nov 2024 12:16:45 +0000 Subject: [PATCH 4/5] Update uprating factors --- policyengine_us_data/storage/uprating_factors.csv | 1 + policyengine_us_data/storage/uprating_growth_factors.csv | 1 + 2 files changed, 2 insertions(+) diff --git a/policyengine_us_data/storage/uprating_factors.csv b/policyengine_us_data/storage/uprating_factors.csv index 3269bdc..897e3b1 100644 --- a/policyengine_us_data/storage/uprating_factors.csv +++ b/policyengine_us_data/storage/uprating_factors.csv @@ -3,6 +3,7 @@ alimony_expense,1.0,1.166,1.148,1.215,1.28,1.318,1.35,1.389,1.428,1.467,1.513,1. alimony_income,1.0,1.255,1.322,1.357,1.446,1.504,1.535,1.567,1.576,1.595,1.622,1.655,1.689,1.723,1.779 american_opportunity_credit,1.0,1.166,1.148,1.215,1.28,1.318,1.35,1.389,1.428,1.467,1.513,1.561,1.611,1.663,1.718 amt_foreign_tax_credit,1.0,1.166,1.148,1.215,1.28,1.318,1.35,1.389,1.428,1.467,1.513,1.561,1.611,1.663,1.718 +capital_gains_before_response,1.0,1.824,1.11,1.195,1.244,1.195,1.14,1.122,1.126,1.145,1.173,1.206,1.243,1.283,1.326 casualty_loss,1.0,1.166,1.148,1.215,1.28,1.318,1.35,1.389,1.428,1.467,1.513,1.561,1.611,1.663,1.718 cdcc_relevant_expenses,1.0,1.166,1.148,1.215,1.28,1.318,1.35,1.389,1.428,1.467,1.513,1.561,1.611,1.663,1.718 charitable_cash_donations,1.0,1.166,1.148,1.215,1.28,1.318,1.35,1.389,1.428,1.467,1.513,1.561,1.611,1.663,1.718 diff --git a/policyengine_us_data/storage/uprating_growth_factors.csv b/policyengine_us_data/storage/uprating_growth_factors.csv index d020204..380f22d 100644 --- a/policyengine_us_data/storage/uprating_growth_factors.csv +++ b/policyengine_us_data/storage/uprating_growth_factors.csv @@ -3,6 +3,7 @@ alimony_expense,0,0.166,-0.015,0.058,0.053,0.03,0.024,0.029,0.028,0.027,0.031,0. alimony_income,0,0.255,0.053,0.026,0.066,0.04,0.021,0.021,0.006,0.012,0.017,0.02,0.021,0.02,0.033 american_opportunity_credit,0,0.166,-0.015,0.058,0.053,0.03,0.024,0.029,0.028,0.027,0.031,0.032,0.032,0.032,0.033 amt_foreign_tax_credit,0,0.166,-0.015,0.058,0.053,0.03,0.024,0.029,0.028,0.027,0.031,0.032,0.032,0.032,0.033 +capital_gains_before_response,0,0.824,-0.391,0.077,0.041,-0.039,-0.046,-0.016,0.004,0.017,0.024,0.028,0.031,0.032,0.034 casualty_loss,0,0.166,-0.015,0.058,0.053,0.03,0.024,0.029,0.028,0.027,0.031,0.032,0.032,0.032,0.033 cdcc_relevant_expenses,0,0.166,-0.015,0.058,0.053,0.03,0.024,0.029,0.028,0.027,0.031,0.032,0.032,0.032,0.033 charitable_cash_donations,0,0.166,-0.015,0.058,0.053,0.03,0.024,0.029,0.028,0.027,0.031,0.032,0.032,0.032,0.033 From 555c6c6a11372faa523925e5ee059819792be030 Mon Sep 17 00:00:00 2001 From: Nikhil Woodruff Date: Tue, 19 Nov 2024 12:28:25 +0000 Subject: [PATCH 5/5] Update versioning and dataset URLs --- changelog_entry.yaml | 3 ++- policyengine_us_data/datasets/acs/acs.py | 2 +- policyengine_us_data/datasets/cps/cps.py | 4 ++-- policyengine_us_data/datasets/cps/enhanced_cps.py | 2 +- 4 files changed, 6 insertions(+), 5 deletions(-) diff --git a/changelog_entry.yaml b/changelog_entry.yaml index 20e4b5f..11e0c33 100644 --- a/changelog_entry.yaml +++ b/changelog_entry.yaml @@ -1,4 +1,5 @@ -- bump: patch +- bump: minor changes: added: - Metric comparisons by dataset to the documentation. + - Calibration of state populations. diff --git a/policyengine_us_data/datasets/acs/acs.py b/policyengine_us_data/datasets/acs/acs.py index 43f0c4d..0ecd3ee 100644 --- a/policyengine_us_data/datasets/acs/acs.py +++ b/policyengine_us_data/datasets/acs/acs.py @@ -111,7 +111,7 @@ class ACS_2022(ACS): time_period = 2022 file_path = STORAGE_FOLDER / "acs_2022.h5" census_acs = CensusACS_2022 - url = "release://PolicyEngine/policyengine-us-data/1.11.0/acs_2022.h5" + url = "release://PolicyEngine/policyengine-us-data/1.13.0/acs_2022.h5" if __name__ == "__main__": diff --git a/policyengine_us_data/datasets/cps/cps.py b/policyengine_us_data/datasets/cps/cps.py index cbcd756..ce51aef 100644 --- a/policyengine_us_data/datasets/cps/cps.py +++ b/policyengine_us_data/datasets/cps/cps.py @@ -648,7 +648,7 @@ class CPS_2024(CPS): label = "CPS 2024 (2022-based)" file_path = STORAGE_FOLDER / "cps_2024.h5" time_period = 2024 - url = "release://policyengine/policyengine-us-data/1.11.0/cps_2024.h5" + url = "release://policyengine/policyengine-us-data/1.13.0/cps_2024.h5" class PooledCPS(Dataset): @@ -707,7 +707,7 @@ class Pooled_3_Year_CPS_2023(PooledCPS): CPS_2023, ] time_period = 2023 - url = "release://PolicyEngine/policyengine-us-data/1.11.0/pooled_3_year_cps_2023.h5" + url = "release://PolicyEngine/policyengine-us-data/1.13.0/pooled_3_year_cps_2023.h5" if __name__ == "__main__": diff --git a/policyengine_us_data/datasets/cps/enhanced_cps.py b/policyengine_us_data/datasets/cps/enhanced_cps.py index da28b34..b9171c3 100644 --- a/policyengine_us_data/datasets/cps/enhanced_cps.py +++ b/policyengine_us_data/datasets/cps/enhanced_cps.py @@ -189,7 +189,7 @@ class EnhancedCPS_2024(EnhancedCPS): name = "enhanced_cps_2024" label = "Enhanced CPS 2024" file_path = STORAGE_FOLDER / "enhanced_cps_2024.h5" - url = "release://policyengine/policyengine-us-data/1.11.0/enhanced_cps_2024.h5" + url = "release://policyengine/policyengine-us-data/1.13.0/enhanced_cps_2024.h5" if __name__ == "__main__":