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format and add plots
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@jdebacker
jdebacker committed Feb 14, 2025
1 parent 57f417f commit 4a10721
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Showing 3 changed files with 157 additions and 50 deletions.
143 changes: 124 additions & 19 deletions examples/Simulate_all_policies.ipynb
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Expand Up @@ -40,7 +40,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -60,13 +60,16 @@
"\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}, #wanted to do 13, but taxcalc with CPS only goes to 13\n",
" \"Romney 2012\": {\"policy_path\": romney2012_path, \"baseline_path\": [pre_2020_baseline], \"start_year\": 2014}, #wanted to do 13, but taxcalc with CPS only goes to 14\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\": [pre_2020_baseline, baseline_2020], \"start_year\": 2021},\n",
" \"Trump 2020\": {\"policy_path\": trump2020_path, \"baseline_path\": [pre_2020_baseline], \"start_year\": 2021},\n",
" \"Harris 2024\": {\"policy_path\": trump2020_path, \"baseline_path\": [pre_2020_baseline], \"start_year\": 2025},\n",
" \"Trump 2024\": {\"policy_path\": trump2020_path, \"baseline_path\": [pre_2020_baseline], \"start_year\": 2025}}"
" # \"Harris 2024\": {\"policy_path\": harris2024_path, \"baseline_path\": [pre_2020_baseline, baseline_2020], \"start_year\": 2025},\n",
" # \"Trump 2024\": {\"policy_path\": trump2024_path, \"baseline_path\": [pre_2020_baseline, baseline_2020], \"start_year\": 2025}\n",
" \"Harris 2024\": {\"policy_path\": harris2024_path, \"baseline_path\": None, \"start_year\": 2026}, # Use 2026 for these candidates since largest diff is how treat TCJA expirations\n",
" \"Trump 2024\": {\"policy_path\": trump2024_path, \"baseline_path\": None, \"start_year\": 2026}\n",
" }"
]
},
{
Expand Down Expand Up @@ -111,7 +114,34 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"iot_all.iot[-1].mtr"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"iot_all.iot[-2].mtr"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"len(iot_all.iot)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -127,7 +157,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -144,7 +174,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -193,7 +223,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -244,7 +274,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -290,12 +320,24 @@
"gz_plot.write_image(\n",
" os.path.join(path, \"gz_numerical_all.png\"),\n",
" scale=4\n",
" )\n",
"\n",
"# Make the same plot but stop at 200_000\n",
"gz_plot.update_xaxes(range=[0, 200_000])\n",
"gz_plot.write_image(\n",
" os.path.join(path, \"gz_numerical_all_200k.png\"),\n",
" scale=4\n",
" )\n",
"gz_plot.update_xaxes(range=[0, 100_000])\n",
"gz_plot.write_image(\n",
" os.path.join(path, \"gz_numerical_all_100k.png\"),\n",
" scale=4\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -342,12 +384,22 @@
"gz_plot.write_image(\n",
" os.path.join(path, \"gz_numerical_democrats.png\"),\n",
" scale=4\n",
" )\n",
"gz_plot.update_xaxes(range=[0, 200_000])\n",
"gz_plot.write_image(\n",
" os.path.join(path, \"gz_numerical_democrats_200k.png\"),\n",
" scale=4\n",
" )\n",
"gz_plot.update_xaxes(range=[0, 100_000])\n",
"gz_plot.write_image(\n",
" os.path.join(path, \"gz_numerical_democrats_100k.png\"),\n",
" scale=4\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -394,12 +446,22 @@
"gz_plot.write_image(\n",
" os.path.join(path, \"gz_numerical_republicans.png\"),\n",
" scale=4\n",
" )\n",
"gz_plot.update_xaxes(range=[0, 200_000])\n",
"gz_plot.write_image(\n",
" os.path.join(path, \"gz_numerical_republicans_200k.png\"),\n",
" scale=4\n",
" )\n",
"gz_plot.update_xaxes(range=[0, 100_000])\n",
"gz_plot.write_image(\n",
" os.path.join(path, \"gz_numerical_republicans_100k.png\"),\n",
" scale=4\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -419,7 +481,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -445,7 +507,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -518,7 +580,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -583,7 +645,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -617,7 +679,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -638,6 +700,17 @@
" title=\"Candidate:\",\n",
" ),\n",
")\n",
"candidate_name = [\"Obama 2012\", \"Romney 2012\"]\n",
"label_dict = {}\n",
"for i, v in enumerate(candidate_name):\n",
" label_dict[\"wide_variable_\" + str(i)] = str(candidate_name[i])\n",
"fig.for_each_trace(lambda t: t.update(name = label_dict[t.name], legendgroup = label_dict[t.name],\n",
" hovertemplate = t.hovertemplate.replace(t.name, label_dict[t.name])))\n",
"\n",
"fig.write_image(\n",
" os.path.join(path, \"romney_obama_g_z_numerical.png\"),\n",
" scale=4\n",
" )\n",
"candidate_name = [\"Clinton 2016\", \"Trump 2016\"]\n",
"label_dict = {}\n",
"for i, v in enumerate(candidate_name):\n",
Expand All @@ -649,9 +722,32 @@
" os.path.join(path, \"trump_clinton_g_z_numerical.png\"),\n",
" scale=4\n",
" )\n",
"candidate_name = [\"Biden 2020\", \"Trump 2020\"]\n",
"label_dict = {}\n",
"for i, v in enumerate(candidate_name):\n",
" label_dict[\"wide_variable_\" + str(i)] = str(candidate_name[i])\n",
"fig.for_each_trace(lambda t: t.update(name = label_dict[t.name], legendgroup = label_dict[t.name],\n",
" hovertemplate = t.hovertemplate.replace(t.name, label_dict[t.name])))\n",
"\n",
"fig.write_image(\n",
" os.path.join(path, \"trump_biden_g_z_numerical.png\"),\n",
" scale=4\n",
" )\n",
"candidate_name = [\"Harris 2024\", \"Trump 2024\"]\n",
"label_dict = {}\n",
"for i, v in enumerate(candidate_name):\n",
" label_dict[\"wide_variable_\" + str(i)] = str(candidate_name[i])\n",
"fig.for_each_trace(lambda t: t.update(name = label_dict[t.name], legendgroup = label_dict[t.name],\n",
" hovertemplate = t.hovertemplate.replace(t.name, label_dict[t.name])))\n",
"\n",
"fig.write_image(\n",
" os.path.join(path, \"trump_harris_g_z_numerical.png\"),\n",
" scale=4\n",
" )\n",
"# Now find the epsilon(z) that would give Trump's policies the same g(z) as Clinton\n",
"eti_beliefs_lw, eti_beliefs_jjz = iot.inverse_optimal_tax.find_eti(iot_all.iot[2], iot_all.iot[3], g_z_type=\"g_z\")\n",
"idx = np.where(np.absolute(eti_beliefs_jjz[1:]) < 10)[0]\n",
"# idx = (np.where(np.absolute(eti_beliefs_jjz[1:]) < 1))[0]\n",
"idx = (np.where(((iot_all.iot[3].df().g_z.values) > 1.05) | ((iot_all.iot[3].df().g_z.values) < 0.95)))[0]\n",
"fig2 = px.line(\n",
" x=iot_all.iot[2].df().z[idx],\n",
" y=eti_beliefs_jjz[idx],\n",
Expand All @@ -668,7 +764,16 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"(np.where(((iot_all.iot[2].df().z.values) > 1.01) | ((iot_all.iot[2].df().z.values) < 0.99)))[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand Down
15 changes: 13 additions & 2 deletions iot/inverse_optimal_tax.py
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Original file line number Diff line number Diff line change
Expand Up @@ -76,7 +76,12 @@ def __init__(
self.eti = eti_spl(self.z)
# compute marginal tax rate schedule
self.mtr, self.mtr_prime = self.compute_mtr_dist(
data, weight_var, income_measure, mtr_smoother, mtr_smooth_param, kreg_bw
data,
weight_var,
income_measure,
mtr_smoother,
mtr_smooth_param,
kreg_bw,
)
# compute theta_z, the elasticity of the tax base
self.theta_z = 1 + ((self.z * self.f_prime) / self.f)
Expand Down Expand Up @@ -109,7 +114,13 @@ def df(self):
return df

def compute_mtr_dist(
self, data, weight_var, income_measure, mtr_smoother, mtr_smooth_param, kreg_bw
self,
data,
weight_var,
income_measure,
mtr_smoother,
mtr_smooth_param,
kreg_bw,
):
"""
Compute marginal tax rates over the income distribution and
Expand Down
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