| |
| """ |
| African Civil Service Capacity — Synthetic Dataset Generator |
| ============================================================ |
| Electric Sheep Africa | electricsheepafrica on HuggingFace |
| |
| References |
| ---------- |
| [1] SA Public Service Commission, Public Service Reforms Report, 2024 |
| [2] SA National Treasury, Compensation and Employment Data MTBPS, 2024 |
| [3] Stats SA, Annual Report 2023/24 |
| [4] Uganda Ministry of Public Service, State of HR 2023 |
| [5] OECD/AUC, Africa's Development Dynamics 2024 |
| [6] Africa Careers Network, Employability in Africa 2023 |
| """ |
|
|
| import argparse, os, sys |
| import numpy as np |
| import pandas as pd |
| from scipy.stats import truncnorm |
|
|
| COUNTRIES = { |
| "South Africa": {"population_2024": 62.0, "tier": "advanced", "civil_servants_per_10k": 530, "vacancy_baseline": 0.19, "degree_rate": 0.45, "training_hours": 40, "growth_rate": 0.008}, |
| "Kenya": {"population_2024": 55.0, "tier": "advanced", "civil_servants_per_10k": 350, "vacancy_baseline": 0.15, "degree_rate": 0.40, "training_hours": 35, "growth_rate": 0.021}, |
| "Nigeria": {"population_2024": 230.0, "tier": "developing", "civil_servants_per_10k": 200, "vacancy_baseline": 0.25, "degree_rate": 0.30, "training_hours": 20, "growth_rate": 0.025}, |
| "Ghana": {"population_2024": 34.0, "tier": "developing", "civil_servants_per_10k": 250, "vacancy_baseline": 0.20, "degree_rate": 0.35, "training_hours": 25, "growth_rate": 0.021}, |
| "Tanzania": {"population_2024": 67.0, "tier": "developing", "civil_servants_per_10k": 180, "vacancy_baseline": 0.22, "degree_rate": 0.28, "training_hours": 18, "growth_rate": 0.030}, |
| "Uganda": {"population_2024": 49.0, "tier": "developing", "civil_servants_per_10k": 80, "vacancy_baseline": 0.30, "degree_rate": 0.25, "training_hours": 15, "growth_rate": 0.032}, |
| "Rwanda": {"population_2024": 14.0, "tier": "advanced", "civil_servants_per_10k": 300, "vacancy_baseline": 0.12, "degree_rate": 0.42, "training_hours": 50, "growth_rate": 0.024}, |
| "Ethiopia": {"population_2024": 126.0, "tier": "early", "civil_servants_per_10k": 120, "vacancy_baseline": 0.35, "degree_rate": 0.20, "training_hours": 10, "growth_rate": 0.025}, |
| "Senegal": {"population_2024": 18.0, "tier": "developing", "civil_servants_per_10k": 220, "vacancy_baseline": 0.18, "degree_rate": 0.32, "training_hours": 22, "growth_rate": 0.027}, |
| "DRC": {"population_2024": 105.0, "tier": "early", "civil_servants_per_10k": 60, "vacancy_baseline": 0.40, "degree_rate": 0.15, "training_hours": 8, "growth_rate": 0.032}, |
| "Mozambique": {"population_2024": 33.0, "tier": "early", "civil_servants_per_10k": 100, "vacancy_baseline": 0.32, "degree_rate": 0.18, "training_hours": 12, "growth_rate": 0.027}, |
| "Botswana": {"population_2024": 2.6, "tier": "advanced", "civil_servants_per_10k": 450, "vacancy_baseline": 0.14, "degree_rate": 0.48, "training_hours": 45, "growth_rate": 0.015}, |
| } |
|
|
| SECTORS = ["Health", "Education", "Finance", "Infrastructure", "Agriculture", "Security", "Justice", "Social_Services", "Administration", "ICT"] |
| SECTOR_WEIGHTS = [0.18, 0.22, 0.08, 0.08, 0.06, 0.12, 0.05, 0.08, 0.08, 0.05] |
|
|
| GRADE_LEVELS = ["Junior", "Mid_Level", "Senior", "Director", "Executive"] |
| GRADE_WEIGHTS = [0.45, 0.30, 0.15, 0.07, 0.03] |
|
|
| SCENARIOS = { |
| "baseline": {"description": "Current SSA civil service landscape", "vacancy_mult": 1.0, "qualification_mult": 1.0, "training_mult": 1.0}, |
| "reform_modernized": {"description": "Civil service reform with meritocratic recruitment", "vacancy_mult": 0.7, "qualification_mult": 1.3, "training_mult": 1.5}, |
| "underresourced": {"description": "Austerity and brain drain", "vacancy_mult": 1.5, "qualification_mult": 0.7, "training_mult": 0.5}, |
| } |
|
|
| REGION_TYPES = ["capital", "urban", "rural", "remote"] |
| REGION_WEIGHTS = {"advanced": [0.30, 0.35, 0.25, 0.10], "developing": [0.25, 0.30, 0.30, 0.15], "early": [0.20, 0.25, 0.35, 0.20]} |
| REGION_VACANCY_ADJUST = {"capital": 0.8, "urban": 0.9, "rural": 1.2, "remote": 1.5} |
| REGION_QUALIFICATION_ADJUST = {"capital": 1.2, "urban": 1.1, "rural": 0.8, "remote": 0.6} |
|
|
| def generate_dataset(n=10000, scenario="baseline", seed=42): |
| rng = np.random.default_rng(seed) |
| sp = SCENARIOS[scenario] |
| records = [] |
| for i in range(n): |
| country = rng.choice(list(COUNTRIES.keys())) |
| cd = COUNTRIES[country] |
| year = rng.integers(2018, 2026) |
| region = rng.choice(REGION_TYPES, p=REGION_WEIGHTS[cd["tier"]]) |
| sector = rng.choice(SECTORS, p=SECTOR_WEIGHTS) |
| grade = rng.choice(GRADE_LEVELS, p=GRADE_WEIGHTS) |
| pop = cd["population_2024"] * (1 + cd["growth_rate"]) ** (year - 2024) |
| pop = max(0.5, pop) |
| staff_per_10k = cd["civil_servants_per_10k"] * rng.uniform(0.85, 1.15) |
| total_staff = int(pop * 1_000_000 * staff_per_10k / 10000) |
| vacancy_rate = cd["vacancy_baseline"] * REGION_VACANCY_ADJUST[region] * sp["vacancy_mult"] * rng.uniform(0.8, 1.2) |
| vacancy_rate = np.clip(vacancy_rate, 0.05, 0.60) |
| filled_posts = int(total_staff * (1 - vacancy_rate)) |
| degree_rate = cd["degree_rate"] * REGION_QUALIFICATION_ADJUST[region] * sp["qualification_mult"] * rng.uniform(0.85, 1.15) |
| degree_rate = np.clip(degree_rate, 0.10, 0.80) |
| degree_holders = int(filled_posts * degree_rate) |
| training_hours = cd["training_hours"] * sp["training_mult"] * rng.uniform(0.7, 1.3) |
| training_hours = max(0, int(training_hours)) |
| training_participation = 0.5 + (training_hours / 100) * 0.4 |
| training_participation = np.clip(training_participation * rng.uniform(0.85, 1.15), 0.2, 0.95) |
| retention_rate = 0.85 if cd["tier"] == "advanced" else (0.75 if cd["tier"] == "developing" else 0.65) |
| retention_rate *= rng.uniform(0.9, 1.1) |
| retention_rate = np.clip(retention_rate, 0.50, 0.98) |
| avg_salary_usd = (500 if cd["tier"] == "advanced" else (250 if cd["tier"] == "developing" else 120)) * rng.uniform(0.7, 1.3) |
| if grade == "Senior": avg_salary_usd *= 1.5 |
| elif grade == "Director": avg_salary_usd *= 2.5 |
| elif grade == "Executive": avg_salary_usd *= 4.0 |
| wage_bill_pct_gdp = (0.08 if cd["tier"] == "advanced" else 0.06) * rng.uniform(0.8, 1.2) |
| performance_eval_rate = 0.70 if cd["tier"] == "advanced" else (0.50 if cd["tier"] == "developing" else 0.30) |
| performance_eval_rate *= sp["qualification_mult"] * rng.uniform(0.85, 1.15) |
| performance_eval_rate = np.clip(performance_eval_rate, 0.15, 0.95) |
| digital_literacy = 0.65 if cd["tier"] == "advanced" else (0.40 if cd["tier"] == "developing" else 0.20) |
| digital_literacy *= rng.uniform(0.8, 1.2) |
| digital_literacy = np.clip(digital_literacy, 0.10, 0.90) |
| capacity_score = (0.25 * (1 - vacancy_rate) + 0.25 * degree_rate + 0.15 * (training_hours / 60) + 0.15 * retention_rate + 0.10 * performance_eval_rate + 0.10 * digital_literacy) |
| capacity_score = np.clip(capacity_score, 0, 1) |
| if capacity_score >= 0.65: capacity_class = "high" |
| elif capacity_score >= 0.50: capacity_class = "moderate" |
| elif capacity_score >= 0.35: capacity_class = "low" |
| else: capacity_class = "critical" |
| record = { |
| "record_id": i + 1, "country": country, "year": year, "region_type": region, |
| "sector": sector, "grade_level": grade, "population_millions": round(pop, 2), |
| "total_posts": total_staff, "vacancy_rate": round(vacancy_rate, 4), |
| "filled_posts": filled_posts, "degree_rate": round(degree_rate, 4), |
| "degree_holders": degree_holders, "training_hours_annual": training_hours, |
| "training_participation_rate": round(training_participation, 4), |
| "retention_rate": round(retention_rate, 4), "avg_salary_usd": round(avg_salary_usd, 2), |
| "wage_bill_pct_gdp": round(wage_bill_pct_gdp, 4), |
| "performance_eval_rate": round(performance_eval_rate, 4), |
| "digital_literacy_rate": round(digital_literacy, 4), |
| "capacity_score": round(capacity_score, 4), "capacity_class": capacity_class, |
| } |
| records.append(record) |
| df = pd.DataFrame(records) |
| print(f"\n=== Generated {scenario} scenario ({n} records) ===") |
| print(f"Mean vacancy rate: {df['vacancy_rate'].mean():.3f}") |
| print(f"Mean degree rate: {df['degree_rate'].mean():.3f}") |
| print(f"Mean training hours: {df['training_hours_annual'].mean():.1f}") |
| print(f"Mean capacity score: {df['capacity_score'].mean():.3f}") |
| print(f"Capacity class distribution:"); print(df['capacity_class'].value_counts().sort_index()) |
| return df |
|
|
| if __name__ == "__main__": |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--scenario", type=str, default="baseline", choices=list(SCENARIOS.keys())) |
| parser.add_argument("--n", type=int, default=10000) |
| parser.add_argument("--seed", type=int, default=42) |
| parser.add_argument("--output", type=str, default=None) |
| args = parser.parse_args() |
| df = generate_dataset(n=args.n, scenario=args.scenario, seed=args.seed) |
| if args.output is None: |
| os.makedirs("data", exist_ok=True) |
| args.output = f"data/{args.scenario}.csv" |
| df.to_csv(args.output, index=False) |
| print(f"\nSaved to {args.output}") |
