""" African Public Debt Management Synthetic Dataset Generator Generates realistic synthetic data for 12 Sub-Saharan African countries across 3 policy scenarios, parameterized from IMF, World Bank, and AfDB reports. Sources informing parameters: - IMF Regional Economic Outlook: Sub-Saharan Africa (April 2025) - World Bank International Debt Report 2025 - AfDB "State of Play of Debt Burden in Africa 2024" - UNCTAD external debt sustainability data - ONE Data "African Debt" (Feb 2025) """ import argparse import json import os from pathlib import Path import numpy as np import pandas as pd from scipy import stats SEED = 42 N_RECORDS_PER_SCENARIO = 10_000 # ── Country parameters (literature-informed) ────────────────────────────── # Each dict: debt_to_gdp (mean, std), external_pct (mean, std), # debt_service_ratio (mean, std), concessional_pct (mean, std), # avg_maturity_years (mean, std), avg_interest_rate (mean, std), # primary_balance_pct (mean, std), gdp_growth_pct (mean, std), # inflation_pct (mean, std), reserves_months_imports (mean, std), # revenue_gdp_pct (mean, std), expenditure_gdp_pct (mean, std) # # References: # Nigeria ~39-55% debt/GDP, external share rising, high service/revenue # Kenya ~67% debt/GDP, Eurobond-heavy, refinancing pressure # Ghana ~44% post-restructuring (was 80%+), IMF program, debt distress # South Africa ~78-79%, predominantly domestic, SOE contingent liabilities # DRC ~26%, heavily concessional, IDA-eligible # Ethiopia ~28%, restructuring under Common Framework # Tanzania ~46%, moderate debt, strong fiscal discipline # Rwanda ~85%, rising rapidly, concessional-heavy # Mozambique ~97%, debt distress, hidden debt scandal legacy # Zambia ~restructured under Common Framework, ~80% pre-restructuring # Senegal ~55-100% (oil/gas transition), rising # Côte d'Ivoire ~55%, infrastructure-driven borrowing COUNTRY_PARAMS = { "Nigeria": { "debt_to_gdp": (48.0, 6.0), "external_pct": (42.0, 8.0), "debt_service_ratio": (28.0, 5.0), "concessional_pct": (30.0, 8.0), "avg_maturity_years": (8.5, 2.0), "avg_interest_rate": (7.5, 1.5), "primary_balance_pct": (-2.5, 1.5), "gdp_growth_pct": (3.0, 1.2), "inflation_pct": (18.0, 4.0), "reserves_months_imports": (7.0, 1.5), "revenue_gdp_pct": (7.5, 1.5), "expenditure_gdp_pct": (12.0, 2.0), }, "Kenya": { "debt_to_gdp": (68.0, 7.0), "external_pct": (52.0, 8.0), "debt_service_ratio": (32.0, 6.0), "concessional_pct": (35.0, 8.0), "avg_maturity_years": (7.0, 2.0), "avg_interest_rate": (8.0, 1.8), "primary_balance_pct": (-3.5, 1.5), "gdp_growth_pct": (5.0, 1.0), "inflation_pct": (7.0, 2.5), "reserves_months_imports": (4.5, 1.0), "revenue_gdp_pct": (16.0, 2.0), "expenditure_gdp_pct": (22.0, 2.5), }, "Ghana": { "debt_to_gdp": (55.0, 10.0), "external_pct": (48.0, 10.0), "debt_service_ratio": (40.0, 8.0), "concessional_pct": (28.0, 10.0), "avg_maturity_years": (6.5, 2.0), "avg_interest_rate": (9.0, 2.0), "primary_balance_pct": (-4.0, 2.0), "gdp_growth_pct": (4.5, 1.5), "inflation_pct": (22.0, 6.0), "reserves_months_imports": (3.0, 1.0), "revenue_gdp_pct": (13.0, 2.0), "expenditure_gdp_pct": (20.0, 3.0), }, "South Africa": { "debt_to_gdp": (77.0, 5.0), "external_pct": (12.0, 4.0), "debt_service_ratio": (18.0, 3.0), "concessional_pct": (8.0, 4.0), "avg_maturity_years": (14.0, 3.0), "avg_interest_rate": (7.0, 1.0), "primary_balance_pct": (-4.5, 1.0), "gdp_growth_pct": (1.5, 1.0), "inflation_pct": (5.5, 1.5), "reserves_months_imports": (5.0, 1.0), "revenue_gdp_pct": (27.0, 2.0), "expenditure_gdp_pct": (33.0, 2.5), }, "DRC": { "debt_to_gdp": (24.0, 5.0), "external_pct": (75.0, 10.0), "debt_service_ratio": (8.0, 3.0), "concessional_pct": (80.0, 8.0), "avg_maturity_years": (20.0, 4.0), "avg_interest_rate": (2.5, 1.0), "primary_balance_pct": (1.0, 2.0), "gdp_growth_pct": (6.5, 2.0), "inflation_pct": (12.0, 5.0), "reserves_months_imports": (1.5, 0.8), "revenue_gdp_pct": (12.0, 2.5), "expenditure_gdp_pct": (14.0, 3.0), }, "Ethiopia": { "debt_to_gdp": (30.0, 5.0), "external_pct": (58.0, 10.0), "debt_service_ratio": (12.0, 4.0), "concessional_pct": (55.0, 10.0), "avg_maturity_years": (15.0, 4.0), "avg_interest_rate": (3.5, 1.5), "primary_balance_pct": (-1.5, 2.0), "gdp_growth_pct": (6.0, 2.0), "inflation_pct": (20.0, 8.0), "reserves_months_imports": (2.0, 0.8), "revenue_gdp_pct": (10.0, 2.0), "expenditure_gdp_pct": (14.0, 2.5), }, "Tanzania": { "debt_to_gdp": (42.0, 5.0), "external_pct": (48.0, 8.0), "debt_service_ratio": (15.0, 4.0), "concessional_pct": (55.0, 10.0), "avg_maturity_years": (14.0, 3.0), "avg_interest_rate": (3.5, 1.2), "primary_balance_pct": (-1.0, 1.5), "gdp_growth_pct": (5.5, 1.0), "inflation_pct": (4.5, 2.0), "reserves_months_imports": (5.0, 1.0), "revenue_gdp_pct": (15.0, 2.0), "expenditure_gdp_pct": (18.0, 2.0), }, "Rwanda": { "debt_to_gdp": (72.0, 8.0), "external_pct": (62.0, 8.0), "debt_service_ratio": (18.0, 5.0), "concessional_pct": (60.0, 10.0), "avg_maturity_years": (16.0, 3.0), "avg_interest_rate": (3.0, 1.0), "primary_balance_pct": (-5.0, 2.0), "gdp_growth_pct": (7.5, 1.5), "inflation_pct": (7.0, 3.0), "reserves_months_imports": (4.0, 1.0), "revenue_gdp_pct": (16.0, 2.0), "expenditure_gdp_pct": (24.0, 3.0), }, "Mozambique": { "debt_to_gdp": (95.0, 8.0), "external_pct": (65.0, 10.0), "debt_service_ratio": (22.0, 5.0), "concessional_pct": (40.0, 12.0), "avg_maturity_years": (10.0, 3.0), "avg_interest_rate": (5.0, 1.5), "primary_balance_pct": (-3.0, 2.0), "gdp_growth_pct": (4.0, 2.0), "inflation_pct": (10.0, 4.0), "reserves_months_imports": (3.5, 1.0), "revenue_gdp_pct": (25.0, 3.0), "expenditure_gdp_pct": (30.0, 3.0), }, "Zambia": { "debt_to_gdp": (70.0, 10.0), "external_pct": (58.0, 10.0), "debt_service_ratio": (25.0, 6.0), "concessional_pct": (30.0, 10.0), "avg_maturity_years": (8.0, 2.5), "avg_interest_rate": (7.0, 2.0), "primary_balance_pct": (-3.5, 2.0), "gdp_growth_pct": (4.0, 1.5), "inflation_pct": (12.0, 4.0), "reserves_months_imports": (3.0, 1.0), "revenue_gdp_pct": (18.0, 2.5), "expenditure_gdp_pct": (24.0, 3.0), }, "Senegal": { "debt_to_gdp": (65.0, 8.0), "external_pct": (55.0, 8.0), "debt_service_ratio": (16.0, 4.0), "concessional_pct": (45.0, 10.0), "avg_maturity_years": (12.0, 3.0), "avg_interest_rate": (4.5, 1.5), "primary_balance_pct": (-4.5, 2.0), "gdp_growth_pct": (7.0, 2.0), "inflation_pct": (3.0, 1.5), "reserves_months_imports": (5.5, 1.5), "revenue_gdp_pct": (22.0, 2.5), "expenditure_gdp_pct": (28.0, 3.0), }, "Cote d'Ivoire": { "debt_to_gdp": (56.0, 6.0), "external_pct": (48.0, 8.0), "debt_service_ratio": (14.0, 4.0), "concessional_pct": (42.0, 10.0), "avg_maturity_years": (11.0, 3.0), "avg_interest_rate": (5.0, 1.5), "primary_balance_pct": (-2.5, 1.5), "gdp_growth_pct": (6.5, 1.5), "inflation_pct": (4.0, 2.0), "reserves_months_imports": (4.5, 1.0), "revenue_gdp_pct": (16.0, 2.0), "expenditure_gdp_pct": (21.0, 2.5), }, } # ── Scenario multipliers ─────────────────────────────────────────────────── SCENARIO_SHIFTS = { "baseline": { "debt_to_gdp": 1.0, "external_pct": 1.0, "debt_service_ratio": 1.0, "concessional_pct": 1.0, "avg_maturity_years": 1.0, "avg_interest_rate": 1.0, "primary_balance_pct": 1.0, "gdp_growth_pct": 1.0, "inflation_pct": 1.0, "reserves_months_imports": 1.0, }, "debt_consolidation": { "debt_to_gdp": 0.85, "external_pct": 0.90, "debt_service_ratio": 0.75, "concessional_pct": 1.15, "avg_maturity_years": 1.15, "avg_interest_rate": 0.85, "primary_balance_pct": -0.5, "gdp_growth_pct": 1.20, "inflation_pct": 0.80, "reserves_months_imports": 1.20, }, "debt_distress": { "debt_to_gdp": 1.25, "external_pct": 1.15, "debt_service_ratio": 1.50, "concessional_pct": 0.70, "avg_maturity_years": 0.75, "avg_interest_rate": 1.30, "primary_balance_pct": -2.0, "gdp_growth_pct": 0.60, "inflation_pct": 1.50, "reserves_months_imports": 0.70, }, } def compute_derived_features(df: pd.DataFrame) -> pd.DataFrame: """Compute derived/aggregate features from base variables.""" # Domestic debt percentage (complement of external) df["domestic_debt_pct"] = 100.0 - df["external_debt_pct"] # Debt sustainability rating (DSR) from debt-to-GDP and service ratio dsr_score = ( 0.4 * df["debt_to_gdp_pct"] / 100 + 0.35 * df["debt_service_ratio_pct"] / 50 + 0.15 * (1 - df["concessional_debt_pct"] / 100) + 0.10 * df["average_interest_rate_pct"] / 15 ) # Map to 1-5 rating (1=low risk, 5=debt distress) df["debt_sustainability_rating"] = np.clip( np.round(1 + 4 * dsr_score / dsr_score.quantile(0.95)).values, 1, 5 ).astype(int) # Refinancing risk score (0-100) df["refinancing_risk_score"] = np.clip( 20 + 0.3 * df["debt_to_gdp_pct"] + 0.5 * (100 / df["average_maturity_years"].clip(lower=1)) - 0.3 * df["concessional_debt_pct"] + 0.2 * df["average_interest_rate_pct"] + np.random.normal(0, 5, len(df)), 0, 100, ).round(1) # Fiscal space score (0-100, higher = more room) df["fiscal_space_score"] = np.clip( 50 + 1.5 * df["primary_balance_pct"] - 0.4 * df["debt_to_gdp_pct"] + 0.8 * df["revenue_to_gdp_pct"] - 0.3 * df["debt_service_ratio_pct"] + np.random.normal(0, 6, len(df)), 0, 100, ).round(1) return df def assign_year(scenario: str, rng: np.random.Generator) -> np.ndarray: """Assign observation years with scenario-dependent distributions.""" if scenario == "baseline": return rng.choice(range(2018, 2026), size=1, p=[0.06, 0.07, 0.08, 0.10, 0.12, 0.14, 0.18, 0.25])[0] elif scenario == "debt_consolidation": return rng.choice(range(2022, 2031), size=1, p=[0.05, 0.06, 0.08, 0.10, 0.12, 0.14, 0.14, 0.16, 0.15])[0] else: return rng.choice(range(2020, 2029), size=1, p=[0.06, 0.08, 0.10, 0.12, 0.13, 0.14, 0.14, 0.13, 0.10])[0] def generate_scenario( scenario: str, n_records: int, rng: np.random.Generator ) -> pd.DataFrame: """Generate n_records for a given scenario across all countries.""" rows = [] records_per_country = n_records // len(COUNTRY_PARAMS) shifts = SCENARIO_SHIFTS[scenario] for country, params in COUNTRY_PARAMS.items(): for _ in range(records_per_country): year = assign_year(scenario, rng) # Apply scenario shifts with random noise dtg = max(5, rng.normal( params["debt_to_gdp"][0] * shifts["debt_to_gdp"], params["debt_to_gdp"][1], )) ext_pct = np.clip( rng.normal( params["external_pct"][0] * shifts["external_pct"], params["external_pct"][1], ), 5, 95, ) dsr = max(1, rng.normal( params["debt_service_ratio"][0] * shifts["debt_service_ratio"], params["debt_service_ratio"][1], )) con_pct = np.clip( rng.normal( params["concessional_pct"][0] * shifts["concessional_pct"], params["concessional_pct"][1], ), 0, 95, ) maturity = np.clip(rng.normal( params["avg_maturity_years"][0] * shifts["avg_maturity_years"], params["avg_maturity_years"][1], ), 1, 40) interest = max(0.1, rng.normal( params["avg_interest_rate"][0] * shifts["avg_interest_rate"], params["avg_interest_rate"][1], )) pb = rng.normal( params["primary_balance_pct"][0] + shifts["primary_balance_pct"] - 1.0, params["primary_balance_pct"][1], ) gdp_g = max(-10, rng.normal( params["gdp_growth_pct"][0] * shifts["gdp_growth_pct"], params["gdp_growth_pct"][1], )) inflation = max(0, rng.normal( params["inflation_pct"][0] * shifts["inflation_pct"], params["inflation_pct"][1], )) reserves = max(0.1, rng.normal( params["reserves_months_imports"][0] * shifts["reserves_months_imports"], params["reserves_months_imports"][1], )) rev_gdp = max(1, rng.normal( params["revenue_gdp_pct"][0], params["revenue_gdp_pct"][1], )) exp_gdp = max(rev_gdp, rng.normal( params["expenditure_gdp_pct"][0], params["expenditure_gdp_pct"][1], )) rows.append({ "country": country, "year": year, "scenario": scenario, "debt_to_gdp_pct": round(dtg, 2), "external_debt_pct": round(ext_pct, 2), "debt_service_ratio_pct": round(dsr, 2), "concessional_debt_pct": round(con_pct, 2), "average_maturity_years": round(maturity, 2), "average_interest_rate_pct": round(interest, 2), "primary_balance_pct": round(pb, 2), "gdp_growth_pct": round(gdp_g, 2), "inflation_pct": round(inflation, 2), "reserves_months_imports": round(reserves, 2), "revenue_to_gdp_pct": round(rev_gdp, 2), "expenditure_to_gdp_pct": round(exp_gdp, 2), }) df = pd.DataFrame(rows) df = compute_derived_features(df) # Reorder columns for clarity col_order = [ "country", "year", "scenario", "debt_to_gdp_pct", "external_debt_pct", "domestic_debt_pct", "debt_service_ratio_pct", "concessional_debt_pct", "average_maturity_years", "average_interest_rate_pct", "debt_sustainability_rating", "refinancing_risk_score", "primary_balance_pct", "fiscal_space_score", "gdp_growth_pct", "inflation_pct", "reserves_months_imports", "revenue_to_gdp_pct", "expenditure_to_gdp_pct", ] return df[col_order] def main(): parser = argparse.ArgumentParser(description="Generate African Public Debt Management dataset") parser.add_argument("--output-dir", default="data", help="Output directory") parser.add_argument("--records-per-scenario", type=int, default=N_RECORDS_PER_SCENARIO) parser.add_argument("--seed", type=int, default=SEED) args = parser.parse_args() os.makedirs(args.output_dir, exist_ok=True) rng = np.random.default_rng(args.seed) for scenario in ["baseline", "debt_consolidation", "debt_distress"]: print(f"Generating {scenario} scenario ({args.records_per_scenario} records)...") df = generate_scenario(scenario, args.records_per_scenario, rng) out_path = os.path.join(args.output_dir, f"{scenario}.csv") df.to_csv(out_path, index=False) print(f" Saved to {out_path} — {len(df)} rows, {len(df.columns)} columns") # Save combined dataset dfs = [] for scenario in ["baseline", "debt_consolidation", "debt_distress"]: dfs.append(pd.read_csv(os.path.join(args.output_dir, f"{scenario}.csv"))) combined = pd.concat(dfs, ignore_index=True) combined_path = os.path.join(args.output_dir, "all_scenarios.csv") combined.to_csv(combined_path, index=False) print(f"Combined dataset saved to {combined_path} — {len(combined)} rows") # Save dataset card metadata meta = { "name": "African Public Debt Management", "description": "Synthetic dataset of public debt indicators for 12 Sub-Saharan African countries", "n_countries": len(COUNTRY_PARAMS), "countries": sorted(COUNTRY_PARAMS.keys()), "scenarios": ["baseline", "debt_consolidation", "debt_distress"], "n_records_per_scenario": args.records_per_scenario, "n_total_records": len(combined), "n_variables": len(combined.columns), "columns": list(combined.columns), "seed": args.seed, } with open(os.path.join(args.output_dir, "metadata.json"), "w") as f: json.dump(meta, f, indent=2) print("Metadata saved to data/metadata.json") if __name__ == "__main__": main()