import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
from ogusa.utils import MVKDE
from ogusa.constants import CODE_PATH
[docs]
def get_bequest_matrix(
J=7,
lambdas=np.array([0.25, 0.25, 0.2, 0.1, 0.1, 0.09, 0.01]),
data_path=None,
output_path=None,
):
"""
Returns S x J matrix representing the fraction of aggregate
bequests that go to each household by age and lifetime income group.
Args:
J (int): number of lifetime income groups
lambdas (Numpy array): length J array of lifetime income group
proportions
data_path (str): path to PSID data
output_path (str): path to save output plots and data
Returns:
kde_matrix (Numpy array): SxJ shaped array that represents the
smoothed distribution of proportions going to each (s,j)
"""
# Read in PSID data
if data_path is None:
# Read data file shipped with OG-USA package
df = pd.read_csv(
os.path.join(CODE_PATH, "psid_lifetime_income.csv.gz")
)
else:
# This is the case when running this from a branch of the OG-USA repo
df = pd.read_csv(data_path)
# Do some tabs with data file...
# 'net_wealth', 'inheritance', 'value_inheritance_1st',
# 'value_inheritance_2nd', 'value_inheritance_3rd's
# inheritance available from 1988 onwards...
# Note that the resulting distribution is very different from what
# Rick has found with the SCF
df["sum_inherit"] = (
df["value_inheritance_1st"]
+ df["value_inheritance_2nd"]
+ df["value_inheritance_3rd"]
)
# print(df[['sum_inherit', 'inheritance']].describe())
if output_path is not None:
# Create plot path directory if it doesn't already exist
if not os.path.exists(output_path):
os.makedirs(output_path)
# Total inheritances by year
df.groupby("year_data").mean(numeric_only=True).plot(y="inheritance")
plt.savefig(os.path.join(output_path, "inheritance_year.png"))
df.groupby("year_data").mean(numeric_only=True).plot(y="sum_inherit")
plt.savefig(os.path.join(output_path, "sum_inherit_year.png"))
# not that summing up inheritances gives a much larger value than
# taking the inheritance variable
# Fraction of inheritances in a year by age
# line plot
df[df["year_data"] >= 1988].groupby("age").mean(
numeric_only=True
).plot(y="net_wealth")
plt.savefig(os.path.join(output_path, "net_wealth_age.png"))
df[df["year_data"] >= 1988].groupby("age").mean(
numeric_only=True
).plot(y="inheritance")
plt.savefig(os.path.join(output_path, "inheritance_age.png"))
# Inheritances by lifetime income group
# bar plot
df[df["year_data"] >= 1988].groupby("li_group").mean(
numeric_only=True
).plot.bar(y="net_wealth")
plt.savefig(os.path.join(output_path, "net_wealth_li.png"))
df[df["year_data"] >= 1988].groupby("li_group").mean(
numeric_only=True
).plot.bar(y="inheritance")
plt.savefig(os.path.join(output_path, "inheritance_li.png"))
# lifecycle plots with line for each ability type
pd.pivot_table(
df[df["year_data"] >= 1988],
values="net_wealth",
index="age",
columns="li_group",
aggfunc="mean",
).plot(legend=True)
plt.savefig(os.path.join(output_path, "net_wealth_age_li.png"))
pd.pivot_table(
df[df["year_data"] >= 1988],
values="inheritance",
index="age",
columns="li_group",
aggfunc="mean",
).plot(legend=True)
plt.savefig(os.path.join(output_path, "inheritance_age_li.png"))
# Matrix Fraction of inheritances in a year by age and lifetime_inc
inheritance_matrix = pd.pivot_table(
df[df["year_data"] >= 1988],
values="inheritance",
index="age",
columns="li_group",
aggfunc="sum",
)
# replace NaN with zero
inheritance_matrix.fillna(value=0, inplace=True)
inheritance_matrix = inheritance_matrix / inheritance_matrix.sum().sum()
# estimate kernel density of bequests
if output_path is not None:
filename = os.path.join(output_path, "inheritance_kde.png")
else:
filename = None
kde_matrix = MVKDE(
80,
7,
inheritance_matrix.to_numpy(),
filename=filename,
plot=(output_path is not None),
bandwidth=0.5,
)
if (J == 10) and np.array_equal(
np.squeeze(lambdas[:6]), np.array([0.25, 0.25, 0.2, 0.1, 0.1, 0.09])
):
kde_matrix_new = np.zeros((80, J))
kde_matrix_new[:, :6] = kde_matrix[:, :6]
kde_matrix_new[:, 6:] = (
kde_matrix[:, 6:].sum(axis=1).reshape(80, 1)
* np.tile(np.reshape(lambdas[6:], (1, 4)), (80, 1))
/ lambdas[6:].sum()
)
kde_matrix = kde_matrix_new
if output_path is not None:
np.savetxt(
os.path.join(output_path, "bequest_matrix_kde.csv"),
kde_matrix,
delimiter=",",
)
return kde_matrix
