import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
from linearmodels import PanelOLS
import ogcore # import just for MPL style file
# Create directory if output directory does not already exist
cur_path = os.path.split(os.path.abspath(__file__))[0]
output_fldr = "csv_output_files"
output_dir = os.path.join(cur_path, "..", "data", output_fldr)
if not os.access(output_dir, os.F_OK):
os.makedirs(output_dir)
[docs]
def estimate_profiles(graphs=False):
"""
Function to estimate deterministic lifecycle profiles of hourly
earnings. Follows methodology of Fullerton and Rogers (1993).
Args:
graphs (bool): whether to create graphs of profiles
Returns:
reg_results (Pandas DataFrame): regression model coefficients
for lifetime earnings profiles
"""
# Read in dataframe of PSID data
df = ogcore.utils.safe_read_pickle(
os.path.join(
cur_path, "..", "data", "PSID", "psid_lifetime_income.pkl"
)
)
model_results = {
"Names": [
"Constant",
"",
"Head Age",
"",
"Head Age^2",
"",
"Head Age^3",
"",
"R-Squared",
"Observations",
]
}
cats_pct = ["0-25", "26-50", "51-70", "71-80", "81-90", "91-99", "100"]
long_model_results = {
"Lifetime Income Group": [],
"Constant": [],
"Age": [],
"Age^2": [],
"Age^3": [],
"Observations": [],
}
for i, group in enumerate(cats_pct):
data = df[df[group] == 1].copy()
data["ones"] = np.ones(len(data.index))
mod = PanelOLS(
data.ln_earn_rate, data[["ones", "age", "age2", "age3"]]
)
res = mod.fit(cov_type="clustered", cluster_entity=True)
# print('Summary for lifetime income group ', group)
# print(res.summary)
# Save model results to dictionary
model_results[group] = [
res.params["ones"],
res.std_errors["ones"],
res.params["age"],
res.std_errors["age"],
res.params["age2"],
res.std_errors["age2"],
res.params["age3"],
res.std_errors["age3"],
res.rsquared,
res.nobs,
]
long_model_results["Lifetime Income Group"].extend([cats_pct[i], ""])
long_model_results["Constant"].extend(
[res.params["ones"], res.std_errors["ones"]]
)
long_model_results["Age"].extend(
[res.params["age"], res.std_errors["age"]]
)
long_model_results["Age^2"].extend(
[res.params["age2"], res.std_errors["age2"]]
)
long_model_results["Age^3"].extend(
[res.params["age3"], res.std_errors["age3"]]
)
long_model_results["Observations"].extend([res.nobs, ""])
reg_results = pd.DataFrame.from_dict(model_results)
reg_results.to_csv(
os.path.join(output_dir, "DeterministicProfileRegResults.csv")
)
long_reg_results = pd.DataFrame.from_dict(model_results)
long_reg_results.to_csv(
os.path.join(output_dir, "DeterministicProfileRegResults_long.csv")
)
if graphs:
# Plot lifecycles of hourly earnings from processes estimated above
age_vec = np.arange(20, 81, step=1)
for i, group in enumerate(cats_pct):
earn_profile = (
model_results[group][0]
+ model_results[group][2] * age_vec
+ model_results[group][4] * age_vec**2
+ model_results[group][6] * age_vec**3
)
plt.plot(age_vec, earn_profile, label=group)
plt.title(
"Estimated Lifecycle Earnings Profiles by Lifetime Income Group"
)
plt.legend()
plt.savefig(
os.path.join(output_dir, "lifecycle_earnings_profiles.png")
)
# Plot of lifecycles of hourly earnings from processes from data
pd.pivot_table(
df,
values="ln_earn_rate",
index="age",
columns="li_group",
aggfunc="mean",
).plot(legend=True)
plt.title(
"Empirical Lifecycle Earnings Profiles by Lifetime Income Group"
)
plt.savefig(
os.path.join(output_dir, "lifecycle_earnings_profiles_data.png")
)
# Plot of lifecycle profiles of hours by lifetime income group
# create variable from fraction of time endowment work
df["labor_supply"] = df["earnhours_hh"] / (
24 * 5 * (df["married"] + 1) * 50
)
pd.pivot_table(
df,
values="labor_supply",
index="age",
columns="li_group",
aggfunc="mean",
).plot(legend=True)
plt.title("Lifecycle Profiles of Hours by Lifetime Income Group")
plt.savefig(os.path.join(output_dir, "lifecycle_laborsupply.png"))
return reg_results
