Datasets:
File size: 4,854 Bytes
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Poverty Headcount Africa Dataset Generator
Parameter Evidence Table:
| Parameter | Source | Value | Notes |
|-----------|--------|-------|-------|
| $2.15/day poverty line | World Bank | $2.15 | Extreme poverty threshold |
| $3.00/day | World Bank 2024 | 46% of SSA | Current poverty rate |
| $3.65/day | World Bank | - | Working poverty threshold |
| $6.85/day | World Bank | - | Upper middle-income threshold |
Countries: 15 African nations
Years: 2018-2025
"""
import numpy as np
import pandas as pd
from pathlib import Path
COUNTRIES = [
"Nigeria", "Ethiopia", "Tanzania", "Kenya", "Uganda",
"Mozambique", "Ghana", "Cote d'Ivoire", "Cameroon", "Senegal",
"Zambia", "Malawi", "Rwanda", "Burkina Faso", "Mali"
]
YEARS = list(range(2018, 2026))
BASE_RATES = {
"below_2.15": 0.28,
"below_3.00": 0.46,
"below_3.65": 0.58,
"below_6.85": 0.82
}
COUNTRY_ADJUSTMENTS = {
"Nigeria": {"below_2.15": 1.3, "below_3.00": 1.2, "below_3.65": 1.15, "below_6.85": 1.1},
"Ethiopia": {"below_2.15": 0.9, "below_3.00": 0.85, "below_3.65": 0.9, "below_6.85": 0.95},
"Tanzania": {"below_2.15": 0.85, "below_3.00": 0.9, "below_3.65": 0.9, "below_6.85": 0.9},
"Kenya": {"below_2.15": 0.6, "below_3.00": 0.55, "below_3.65": 0.5, "below_6.85": 0.45},
"Uganda": {"below_2.15": 0.75, "below_3.00": 0.8, "below_3.65": 0.85, "below_6.85": 0.9},
"Mozambique": {"below_2.15": 1.1, "below_3.00": 1.05, "below_3.65": 1.0, "below_6.85": 1.0},
"Ghana": {"below_2.15": 0.5, "below_3.00": 0.45, "below_3.65": 0.4, "below_6.85": 0.35},
"Cote d'Ivoire": {"below_2.15": 0.7, "below_3.00": 0.65, "below_3.65": 0.6, "below_6.85": 0.55},
"Cameroon": {"below_2.15": 0.8, "below_3.00": 0.75, "below_3.65": 0.7, "below_6.85": 0.65},
"Senegal": {"below_2.15": 0.65, "below_3.00": 0.6, "below_3.65": 0.55, "below_6.85": 0.5},
"Zambia": {"below_2.15": 0.9, "below_3.00": 0.85, "below_3.65": 0.8, "below_6.85": 0.75},
"Malawi": {"below_2.15": 1.0, "below_3.00": 0.95, "below_3.65": 0.9, "below_6.85": 0.85},
"Rwanda": {"below_2.15": 0.7, "below_3.00": 0.65, "below_3.65": 0.6, "below_6.85": 0.55},
"Burkina Faso": {"below_2.15": 0.85, "below_3.00": 0.8, "below_3.65": 0.75, "below_6.85": 0.7},
"Mali": {"below_2.15": 0.95, "below_3.00": 0.9, "below_3.65": 0.85, "below_6.85": 0.8}
}
def dag_sample(probabilities, n_samples, rng):
"""DAG-based sampling with probability weighting."""
indices = rng.choice(len(probabilities), size=n_samples, p=probabilities, replace=True)
unique, counts = np.unique(indices, return_counts=True)
return dict(zip(unique, counts))
def generate_dataset(scenario: str, seed: int) -> pd.DataFrame:
"""Generate poverty headcount dataset for Africa."""
np.random.seed(seed)
rng = np.random.default_rng(seed)
scenario_config = {
"low_burden": {"n": 4000, "variance_scale": 0.8},
"moderate": {"n": 5000, "variance_scale": 1.0},
"high_burden": {"n": 6000, "variance_scale": 1.2}
}
config = scenario_config[scenario]
n = config["n"]
scale = config["variance_scale"]
records = []
for year in YEARS:
year_factor = 1.0 - (year - 2018) * 0.015
for country in COUNTRIES:
adj = COUNTRY_ADJUSTMENTS[country]
for threshold in ["below_2.15", "below_3.00", "below_3.65", "below_6.85"]:
base_rate = BASE_RATES[threshold]
adjusted_rate = min(0.99, base_rate * adj[threshold] * year_factor * scale)
adjusted_rate = max(0.01, adjusted_rate)
n_country_year = n // (len(YEARS) * len(COUNTRIES))
poverty_count = int(n_country_year * adjusted_rate)
for _ in range(n_country_year):
records.append({
"country": country,
"year": year,
"poverty_line": threshold,
"headcount_ratio": adjusted_rate + rng.normal(0, 0.02 * scale),
"population_below": poverty_count * 1000,
"total_population": n_country_year * 1000
})
df = pd.DataFrame(records)
df["headcount_ratio"] = df["headcount_ratio"].clip(0, 1)
return df
def main():
output_dir = Path(__file__).parent
output_dir.mkdir(parents=True, exist_ok=True)
scenarios = {
"low_burden": 42,
"moderate": 43,
"high_burden": 44
}
for scenario, seed in scenarios.items():
df = generate_dataset(scenario, seed)
df.to_csv(output_dir / f"poverty_headcount_{scenario}.csv", index=False)
print(f"Generated {scenario}: {len(df)} rows, seed={seed}")
if __name__ == "__main__":
main()
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