| """ |
| 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() |
|
|
