#!/usr/bin/env python3 """ African Anti-Corruption Enforcement Dataset Generator Generates synthetic records for 12 Sub-Saharan African countries across three enforcement scenarios. Parameters are informed by: - Transparency International CPI 2025 (SSA avg=32, range 9-68) - EFCC Nigeria 2024 statistics (12,928 investigated, 5,083 prosecuted, 4,111 convictions, ~$214M recovered) - South Africa SIU 2023/24 (1,919 investigations, 583 referrals, R8B saved, ~80% conviction rate in commercial cases) - Kenya EACC 2023/24 (5,171 reports, 534 under probe, 26.7% conviction rate, Ksh2.9B recovered) - AU Common African Position on Asset Recovery (asset recovery <10%) - G20 2025 Accountability Report on Whistleblower Protection """ import os import argparse import numpy as np import pandas as pd from scipy import stats RNG_SEED = 42 # ── Country parameters ────────────────────────────────────────────── # Based on TI CPI 2025, population, anti-corruption agency capacity, # whistleblower protection maturity, and typical caseload volumes. COUNTRIES = { "Nigeria": { "cpi_mean": 32, "cpi_std": 2.0, "pop_millions": 220, "agency_independence_mean": 42, "agency_independence_std": 8, "whistleblower_protection_mean": 33, "whistleblower_protection_std": 7, "cases_investigated_range": (10000, 18000), # EFCC 12,928 in 2024 "prosecution_rate_base": 0.35, # EFCC: 5,083/12,928 ≈ 39% "conviction_rate_base": 0.72, # EFCC high conviction capacity "asset_recovery_rate_base": 0.08, "assets_usd_millions_range": (80, 250), # EFCC $214M in 2024 }, "South Africa": { "cpi_mean": 43, "cpi_std": 2.5, "pop_millions": 60, "agency_independence_mean": 55, "agency_independence_std": 10, "whistleblower_protection_mean": 48, "whistleblower_protection_std": 9, "cases_investigated_range": (1800, 2800), # SIU 1,919 closed in 2023/24 "prosecution_rate_base": 0.28, # SIU 583 referrals from 1,919 "conviction_rate_base": 0.75, # NPA ~80% in commercial cases "asset_recovery_rate_base": 0.12, "assets_usd_millions_range": (100, 350), # SIU R8B ≈ $430M }, "Kenya": { "cpi_mean": 30, "cpi_std": 2.0, "pop_millions": 55, "agency_independence_mean": 38, "agency_independence_std": 7, "whistleblower_protection_mean": 30, "whistleblower_protection_std": 6, "cases_investigated_range": (4000, 6000), # EACC 5,171 reports "prosecution_rate_base": 0.22, # EACC forwards to ODPP "conviction_rate_base": 0.45, # EACC 26.7% (2023/24), improving "asset_recovery_rate_base": 0.06, "assets_usd_millions_range": (15, 40), }, "Ghana": { "cpi_mean": 43, "cpi_std": 2.5, "pop_millions": 34, "agency_independence_mean": 50, "agency_independence_std": 8, "whistleblower_protection_mean": 40, "whistleblower_protection_std": 7, "cases_investigated_range": (800, 1500), "prosecution_rate_base": 0.30, "conviction_rate_base": 0.55, "asset_recovery_rate_base": 0.07, "assets_usd_millions_range": (8, 30), }, "Tanzania": { "cpi_mean": 38, "cpi_std": 2.0, "pop_millions": 65, "agency_independence_mean": 35, "agency_independence_std": 7, "whistleblower_protection_mean": 25, "whistleblower_protection_std": 6, "cases_investigated_range": (2000, 4000), "prosecution_rate_base": 0.25, "conviction_rate_base": 0.60, "asset_recovery_rate_base": 0.05, "assets_usd_millions_range": (10, 35), }, "Uganda": { "cpi_mean": 26, "cpi_std": 2.0, "pop_millions": 48, "agency_independence_mean": 30, "agency_independence_std": 7, "whistleblower_protection_mean": 22, "whistleblower_protection_std": 5, "cases_investigated_range": (1500, 3000), "prosecution_rate_base": 0.18, "conviction_rate_base": 0.40, "asset_recovery_rate_base": 0.04, "assets_usd_millions_range": (5, 20), }, "Ethiopia": { "cpi_mean": 30, "cpi_std": 2.5, "pop_millions": 120, "agency_independence_mean": 28, "agency_independence_std": 8, "whistleblower_protection_mean": 18, "whistleblower_protection_std": 5, "cases_investigated_range": (1000, 2500), "prosecution_rate_base": 0.15, "conviction_rate_base": 0.50, "asset_recovery_rate_base": 0.04, "assets_usd_millions_range": (5, 25), }, "Senegal": { "cpi_mean": 46, "cpi_std": 2.0, "pop_millions": 18, "agency_independence_mean": 55, "agency_independence_std": 8, "whistleblower_protection_mean": 42, "whistleblower_protection_std": 7, "cases_investigated_range": (600, 1200), "prosecution_rate_base": 0.32, "conviction_rate_base": 0.55, "asset_recovery_rate_base": 0.08, "assets_usd_millions_range": (5, 20), }, "Mozambique": { "cpi_mean": 21, "cpi_std": 2.0, "pop_millions": 33, "agency_independence_mean": 22, "agency_independence_std": 6, "whistleblower_protection_mean": 15, "whistleblower_protection_std": 4, "cases_investigated_range": (300, 800), "prosecution_rate_base": 0.12, "conviction_rate_base": 0.35, "asset_recovery_rate_base": 0.03, "assets_usd_millions_range": (2, 10), }, "Zambia": { "cpi_mean": 33, "cpi_std": 2.0, "pop_millions": 20, "agency_independence_mean": 40, "agency_independence_std": 7, "whistleblower_protection_mean": 28, "whistleblower_protection_std": 6, "cases_investigated_range": (500, 1200), "prosecution_rate_base": 0.20, "conviction_rate_base": 0.45, "asset_recovery_rate_base": 0.05, "assets_usd_millions_range": (3, 15), }, "Rwanda": { "cpi_mean": 58, "cpi_std": 2.5, "pop_millions": 14, "agency_independence_mean": 65, "agency_independence_std": 8, "whistleblower_protection_mean": 55, "whistleblower_protection_std": 8, "cases_investigated_range": (400, 900), "prosecution_rate_base": 0.45, "conviction_rate_base": 0.70, "asset_recovery_rate_base": 0.15, "assets_usd_millions_range": (3, 12), }, "Botswana": { "cpi_mean": 58, "cpi_std": 2.5, "pop_millions": 2.6, "agency_independence_mean": 62, "agency_independence_std": 8, "whistleblower_protection_mean": 50, "whistleblower_protection_std": 8, "cases_investigated_range": (200, 500), "prosecution_rate_base": 0.40, "conviction_rate_base": 0.65, "asset_recovery_rate_base": 0.12, "assets_usd_millions_range": (2, 8), }, } SCENARIOS = { "baseline": { "cpi_shift": 0, "agency_shift": 0, "whistleblower_shift": 0, "prosecution_multiplier": 1.0, "conviction_multiplier": 1.0, "asset_recovery_multiplier": 1.0, "investigated_multiplier": 1.0, }, "strengthened_enforcement": { "cpi_shift": 8, # +8 points on CPI "agency_shift": 18, # +18 on independence score "whistleblower_shift": 20, # +20 on protection score "prosecution_multiplier": 1.45, "conviction_multiplier": 1.15, "asset_recovery_multiplier": 2.0, "investigated_multiplier": 1.25, }, "weakened_accountability": { "cpi_shift": -10, "agency_shift": -15, "whistleblower_shift": -15, "prosecution_multiplier": 0.60, "conviction_multiplier": 0.80, "asset_recovery_multiplier": 0.40, "investigated_multiplier": 0.70, }, } def clamp(x, lo, hi): return np.clip(x, lo, hi) def generate_country_scenario(country_name, params, scenario_name, scenario_params, n_records, rng): """Generate n_records for one country under one scenario.""" # ── Scores derived from scenario ── cpi = rng.normal( params["cpi_mean"] + scenario_params["cpi_shift"], params["cpi_std"], n_records ) cpi = clamp(cpi, 0, 100) agency_independence = rng.normal( params["agency_independence_mean"] + scenario_params["agency_shift"], params["agency_independence_std"], n_records ) agency_independence = clamp(agency_independence, 0, 100) whistleblower_protection = rng.normal( params["whistleblower_protection_mean"] + scenario_params["whistleblower_shift"], params["whistleblower_protection_std"], n_records ) whistleblower_protection = clamp(whistleblower_protection, 0, 100) # ── Cases investigated (population-scaled) ── pop_scale = params["pop_millions"] / 60 # normalise to SA baseline inv_lo, inv_hi = params["cases_investigated_range"] inv_base = rng.uniform(inv_lo * pop_scale, inv_hi * pop_scale, n_records) cases_investigated = np.round( inv_base * scenario_params["investigated_multiplier"] ).astype(int) cases_investigated = np.maximum(cases_investigated, 10) # ── Prosecution rate (correlated with agency independence) ── prot_base = params["prosecution_rate_base"] * scenario_params["prosecution_multiplier"] # Agency independence boosts prosecution rate agency_effect = (agency_independence - 50) / 500 # ±0.1 shift prosecution_rate = prot_base + agency_effect + rng.normal(0, 0.04, n_records) prosecution_rate = clamp(prosecution_rate, 0.02, 0.85) cases_prosecuted = np.round(cases_investigated * prosecution_rate).astype(int) cases_prosecuted = np.minimum(cases_prosecuted, cases_investigated) cases_prosecuted = np.maximum(cases_prosecuted, 0) # ── Conviction rate ── conv_base = params["conviction_rate_base"] * scenario_params["conviction_multiplier"] conv_rate = conv_base + rng.normal(0, 0.06, n_records) conv_rate = clamp(conv_rate, 0.05, 0.95) convictions = np.round(cases_prosecuted * conv_rate).astype(int) convictions = np.minimum(convictions, cases_prosecuted) convictions = np.maximum(convictions, 0) # ── Asset confiscation ── asset_lo, asset_hi = params["assets_usd_millions_range"] assets_confiscated = rng.uniform(asset_lo, asset_hi, n_records) * scenario_params["asset_recovery_multiplier"] assets_confiscated = np.maximum(assets_confiscated, 0.1) asset_recovery_rate = params["asset_recovery_rate_base"] * scenario_params["asset_recovery_multiplier"] asset_recovery_rate = clamp( asset_recovery_rate + rng.normal(0, 0.02, n_records), 0.01, 0.50 ) # ── Whistleblower reports ── wb_base = cases_investigated * rng.uniform(0.3, 0.8, n_records) wb_effect = 1 + (whistleblower_protection - 30) / 100 whistleblower_reports = np.round(wb_base * wb_effect).astype(int) whistleblower_reports = np.maximum(whistleblower_reports, 0) # ── Enforcement effectiveness composite ── enforcement_effectiveness = ( 0.25 * (prosecution_rate / 0.5) + 0.25 * (conv_rate / 0.8) + 0.20 * (asset_recovery_rate / 0.15) + 0.15 * (agency_independence / 100) + 0.15 * (whistleblower_protection / 100) ) * 100 enforcement_effectiveness = clamp(enforcement_effectiveness, 0, 100) # ── Enforcement class ── enforcement_class = np.where( enforcement_effectiveness >= 60, "strong", np.where(enforcement_effectiveness >= 35, "moderate", "weak") ) # ── Build DataFrame ── df = pd.DataFrame({ "country": country_name, "scenario": scenario_name, "corruption_perception_index": np.round(cpi, 1), "cases_investigated": cases_investigated, "cases_prosecuted": cases_prosecuted, "prosecution_rate": np.round(prosecution_rate, 4), "convictions": convictions, "conviction_rate": np.round(conv_rate, 4), "assets_confiscated_usd_millions": np.round(assets_confiscated, 2), "asset_recovery_rate": np.round(asset_recovery_rate, 4), "whistleblower_reports": whistleblower_reports, "whistleblower_protection_score": np.round(whistleblower_protection, 1), "agency_independence_score": np.round(agency_independence, 1), "enforcement_effectiveness_score": np.round(enforcement_effectiveness, 1), "enforcement_class": enforcement_class, }) return df def generate_dataset(scenario, n_per_country=834, seed=RNG_SEED): """Generate full dataset for one scenario across all 12 countries.""" rng = np.random.default_rng(seed) scenario_params = SCENARIOS[scenario] frames = [] for country_name, params in COUNTRIES.items(): df = generate_country_scenario( country_name, params, scenario, scenario_params, n_per_country, rng ) frames.append(df) full = pd.concat(frames, ignore_index=True) # Shuffle full = full.sample(frac=1, random_state=seed).reset_index(drop=True) return full def main(): parser = argparse.ArgumentParser(description="Generate anti-corruption enforcement dataset") parser.add_argument("--scenario", type=str, default="all", choices=list(SCENARIOS.keys()) + ["all"]) parser.add_argument("--n-records", type=int, default=10000, help="Total records per scenario (split across 12 countries)") parser.add_argument("--output-dir", type=str, default="data") args = parser.parse_args() os.makedirs(args.output_dir, exist_ok=True) scenarios = list(SCENARIOS.keys()) if args.scenario == "all" else [args.scenario] for scen in scenarios: n_per_country = args.n_records // len(COUNTRIES) remainder = args.n_records - n_per_country * len(COUNTRIES) df = generate_dataset(scen, n_per_country=n_per_country) # Add extra records to first country to hit exact count if remainder > 0: rng = np.random.default_rng(RNG_SEED + 99) extra = generate_country_scenario( list(COUNTRIES.keys())[0], COUNTRIES[list(COUNTRIES.keys())[0]], scen, SCENARIOS[scen], remainder, rng ) df = pd.concat([df, extra], ignore_index=True) df = df.sample(frac=1, random_state=RNG_SEED).reset_index(drop=True) out_path = os.path.join(args.output_dir, f"{scen}.csv") df.to_csv(out_path, index=False) print(f"Generated {len(df)} records → {out_path}") if __name__ == "__main__": main()