🧠💸 Does Money Predict Parenthood?
Just ran a Pearson correlation between total personal income (S1Q10A) and number of children under 18 in the household (CHLD0_17) using the NESARC dataset. The result?
💥 Yes, it's statistically significant.
😅 But practically? Not so much.
This teeny-tiny negative correlation means higher income is slightly linked to fewer kids—but the effect is so small it’s basically a shrug from the universe. 🤷♂️📉
In short:
Money ≠ more (or fewer) children — at least not in any meaningful way.
in case you want to try out the code:
import pandas as pd
import numpy as np
import seaborn as sns
import scipy.stats
import matplotlib.pyplot as plt
data = pd.read_csv('nesarc.csv', low_memory=False)
Convert to numeric properly
data['CHLD0_17'] = pd.to_numeric(data['CHLD0_17'], errors='coerce')
data['S1Q10A'] = pd.to_numeric(data['S1Q10A'], errors='coerce')
Drop rows with NA in either of the two columns
data_clean = data[['S1Q10A', 'CHLD0_17']].dropna()
sns.regplot(x="S1Q10A", y="CHLD0_17", fit_reg=True, data=data_clean)
plt.xlabel('Total Personal Income (USD)')
plt.ylabel('Number of Children Under 18 in Household')
plt.title('Scatterplot: Income vs. Number of Children')
plt.show()
print('Association between S1Q10A (income) and CHLD0_17 (number of children):')
r, p = scipy.stats.pearsonr(data_clean['S1Q10A'], data_clean['CHLD0_17'])
print(f"Pearson r = {r:.4f}, p-value = {p:.4e}")