| """ |
| Credit Access Patterns in Africa - Dataset Generator |
| |
| DAG Structure: |
| country → formal_credit_availability, interest_rates |
| urban_rural → credit_institution_access |
| income_quintile → credit_score, loan_eligibility |
| employment_status → income_stability, loan_amount |
| credit_history → approval_probability |
| collateral → loan_amount, approval |
| loan_purpose → interest_rate, approval |
| approval → loan_disbursed, loan_amount_final |
| |
| Parameter Evidence Table: |
| | Parameter | Value | Source | |
| |-----------|-------|--------| |
| | Formal credit access SSA | 15% | World Bank Findex 2025 | |
| | Informal credit access | 35% | World Bank Findex 2025 | |
| | Bank loan interest rates | 15-25% | World Bank 2024 | |
| | Microfinance rates | 25-45% | CGAP 2024 | |
| | Informal lender rates | 50-120% | FSD Kenya 2024 | |
| | Collateral requirement | 65% of formal loans | World Bank 2024 | |
| | Approval rate formal | 45% | World Bank 2024 | |
| | Approval rate informal | 75% | FSD Kenya 2024 | |
| | Credit bureau coverage | 12% SSA | World Bank 2024 | |
| | Default rate | 8-15% | IMF 2024 | |
| """ |
|
|
| import numpy as np |
| import pandas as pd |
| from pathlib import Path |
|
|
| COUNTRIES = [ |
| "Kenya", "Uganda", "Nigeria", "Ghana", "Tanzania", "Ethiopia", |
| "Malawi", "Zambia", "Senegal", "Rwanda", "Niger", "Mali", |
| "DRC", "Mozambique", "South Africa" |
| ] |
|
|
| YEARS = list(range(2018, 2026)) |
|
|
| COUNTRY_CREDIT_ACCESS = { |
| "Kenya": 0.22, "Uganda": 0.15, "Tanzania": 0.14, "Ghana": 0.18, |
| "Nigeria": 0.16, "Ethiopia": 0.08, "Malawi": 0.12, "Zambia": 0.15, |
| "Senegal": 0.14, "Rwanda": 0.18, "Niger": 0.06, "Mali": 0.10, |
| "DRC": 0.05, "Mozambique": 0.08, "South Africa": 0.35 |
| } |
|
|
| CREDIT_SOURCES = ["commercial_bank", "microfinance", "sacco", "digital_lender", |
| "informal_lender", "family_friends", "employer"] |
| LOAN_PURPOSES = ["business", "agriculture", "education", "housing", |
| "consumption", "emergency", "asset_purchase", "debt_consolidation"] |
| COLLATERAL_TYPES = ["property", "vehicle", "equipment", "savings", "guarantor", "none"] |
|
|
|
|
| def generate_scenario(n_samples: int, seed: int, scenario: str) -> pd.DataFrame: |
| rng = np.random.default_rng(seed) |
| |
| country = rng.choice(COUNTRIES, n_samples) |
| year = rng.choice(YEARS, n_samples) |
| |
| urban_rural = np.where(rng.random(n_samples) < 0.42, "urban", "rural") |
| |
| gender = rng.choice(["male", "female"], n_samples, p=[0.48, 0.52]) |
| |
| age = rng.integers(18, 70, n_samples) |
| age_group = pd.cut(age, bins=[17, 25, 35, 45, 55, 70], |
| labels=["18-24", "25-34", "35-44", "45-54", "55+"]) |
| |
| income_quintile = rng.choice(["lowest", "second", "middle", "fourth", "highest"], |
| n_samples, p=[0.25, 0.22, 0.20, 0.18, 0.15]) |
| |
| education = rng.choice( |
| ["none", "primary", "secondary", "tertiary"], |
| n_samples, p=[0.18, 0.32, 0.35, 0.15] |
| ) |
| |
| employment_status = rng.choice( |
| ["employed_formal", "employed_informal", "self_employed", "farmer", "unemployed"], |
| n_samples, p=[0.12, 0.23, 0.30, 0.22, 0.13] |
| ) |
| |
| monthly_income = rng.lognormal( |
| mean=np.log(200) + np.select( |
| [income_quintile == "lowest", income_quintile == "second", |
| income_quintile == "middle", income_quintile == "fourth", |
| income_quintile == "highest"], |
| [-1.2, -0.5, 0.0, 0.5, 1.2] |
| ) + np.where(urban_rural == "urban", 0.4, 0.0), |
| sigma=0.7, |
| size=n_samples |
| ) |
| |
| income_stability = rng.integers(1, 11, n_samples) |
| income_stability = np.clip( |
| income_stability + |
| np.where(employment_status == "employed_formal", 3, 0) + |
| np.where(employment_status == "unemployed", -4, 0), |
| 1, 10 |
| ) |
| |
| credit_history = rng.choice( |
| ["none", "limited", "good", "excellent"], |
| n_samples, p=[0.45, 0.30, 0.18, 0.07] |
| ) |
| |
| has_bank_account = (rng.random(n_samples) < 0.45).astype(int) |
| |
| credit_score = rng.integers(0, 851, n_samples) |
| credit_score = np.clip( |
| credit_score + |
| np.where(income_quintile == "highest", 100, 0) + |
| np.where(credit_history == "good", 80, 0) + |
| np.where(credit_history == "excellent", 150, 0) + |
| np.where(credit_history == "none", -100, 0), |
| 0, 850 |
| ) |
| |
| credit_need = (rng.random(n_samples) < |
| 0.40 + |
| np.where(employment_status == "self_employed", 0.15, 0.0) + |
| np.where(employment_status == "farmer", 0.10, 0.0) - |
| np.where(income_quintile == "highest", 0.10, 0.0) |
| ).astype(int) |
| |
| preferred_source = np.empty(n_samples, dtype=object) |
| preferred_source[credit_need == 0] = "none" |
| need_mask = credit_need == 1 |
| |
| for i in range(n_samples): |
| if credit_need[i] == 1: |
| if has_bank_account[i]: |
| preferred_source[i] = rng.choice(CREDIT_SOURCES, p=[0.35, 0.18, 0.12, 0.15, 0.08, 0.10, 0.02]) |
| else: |
| preferred_source[i] = rng.choice(CREDIT_SOURCES, p=[0.05, 0.15, 0.10, 0.10, 0.25, 0.30, 0.05]) |
| |
| loan_amount_requested = np.zeros(n_samples) |
| req_mask = credit_need == 1 |
| loan_amount_requested[req_mask] = rng.lognormal( |
| mean=np.log(500) + np.log(monthly_income[req_mask] + 1) * 0.4, |
| sigma=0.6, |
| size=req_mask.sum() |
| ) |
| |
| collateral_available = np.empty(n_samples, dtype=object) |
| collateral_available[credit_need == 0] = "none" |
| collateral_available[req_mask] = rng.choice( |
| COLLATERAL_TYPES, req_mask.sum(), |
| p=[0.10, 0.05, 0.08, 0.15, 0.20, 0.42] |
| ) |
| |
| loan_purpose = np.empty(n_samples, dtype=object) |
| loan_purpose[credit_need == 0] = "none" |
| loan_purpose[req_mask] = rng.choice( |
| LOAN_PURPOSES, req_mask.sum(), |
| p=[0.30, 0.18, 0.12, 0.10, 0.12, 0.08, 0.07, 0.03] |
| ) |
| |
| approval_prob = np.zeros(n_samples) |
| approval_prob[req_mask] = np.clip( |
| 0.30 + |
| np.where(credit_score[req_mask] > 650, 0.25, 0.0) + |
| np.where(credit_score[req_mask] > 750, 0.15, 0.0) + |
| np.where(income_stability[req_mask] > 6, 0.10, 0.0) + |
| np.where(has_bank_account[req_mask] == 1, 0.10, 0.0) + |
| np.where(collateral_available[req_mask] != "none", 0.15, 0.0) - |
| np.where(credit_score[req_mask] < 450, 0.20, 0.0), |
| 0.05, 0.95 |
| ) |
| |
| approved = np.zeros(n_samples, dtype=int) |
| approved[req_mask] = (rng.random(req_mask.sum()) < approval_prob[req_mask]).astype(int) |
| |
| loan_disbursed = np.zeros(n_samples) |
| loan_disbursed[approved == 1] = loan_amount_requested[approved == 1] * rng.uniform(0.8, 1.0, approved.sum()) |
| |
| interest_rate = np.zeros(n_samples) |
| int_mask = approved == 1 |
| base_rates = np.select( |
| [np.isin(preferred_source[int_mask], ["commercial_bank"]), |
| np.isin(preferred_source[int_mask], ["microfinance", "sacco"]), |
| np.isin(preferred_source[int_mask], ["digital_lender"]), |
| np.isin(preferred_source[int_mask], ["informal_lender"]), |
| np.isin(preferred_source[int_mask], ["family_friends", "employer"])], |
| [0.18, 0.32, 0.45, 0.80, 0.05], |
| default=0.25 |
| ) |
| interest_rate[int_mask] = base_rates + rng.uniform(-0.05, 0.05, int_mask.sum()) |
| |
| loan_term_months = np.zeros(n_samples, dtype=int) |
| loan_term_months[int_mask] = rng.choice([6, 12, 18, 24, 36, 48], int_mask.sum(), |
| p=[0.15, 0.35, 0.20, 0.18, 0.08, 0.04]) |
| |
| monthly_payment = np.zeros(n_samples) |
| monthly_payment[int_mask] = ( |
| loan_disbursed[int_mask] * (1 + interest_rate[int_mask]) / loan_term_months[int_mask] |
| ) |
| |
| repayment_status = np.empty(n_samples, dtype=object) |
| repayment_status[approved == 0] = "not_applicable" |
| repay_mask = approved == 1 |
| repayment_status[repay_mask] = rng.choice( |
| ["current", "late_30_days", "late_60_days", "defaulted", "paid_off"], |
| repay_mask.sum(), |
| p=[0.55, 0.15, 0.08, 0.07, 0.15] |
| ) |
| |
| application_rejection_reason = np.empty(n_samples, dtype=object) |
| application_rejection_reason[approved == 1] = "not_applicable" |
| reject_mask = (credit_need == 1) & (approved == 0) |
| application_rejection_reason[reject_mask] = rng.choice( |
| ["insufficient_income", "no_credit_history", "no_collateral", |
| "employment_status", "existing_debt", "documentation_issues"], |
| reject_mask.sum(), |
| p=[0.25, 0.22, 0.20, 0.15, 0.10, 0.08] |
| ) |
| |
| df = pd.DataFrame({ |
| "country": country, |
| "year": year, |
| "urban_rural": urban_rural, |
| "gender": gender, |
| "age": age, |
| "age_group": age_group, |
| "income_quintile": income_quintile, |
| "education": education, |
| "employment_status": employment_status, |
| "monthly_income_usd": np.round(monthly_income, 2), |
| "income_stability": income_stability, |
| "credit_history": credit_history, |
| "has_bank_account": has_bank_account, |
| "credit_score": credit_score, |
| "credit_need": credit_need, |
| "preferred_credit_source": preferred_source, |
| "loan_amount_requested_usd": np.round(loan_amount_requested, 2), |
| "collateral_available": collateral_available, |
| "loan_purpose": loan_purpose, |
| "approved": approved, |
| "loan_disbursed_usd": np.round(loan_disbursed, 2), |
| "interest_rate": np.round(interest_rate, 3), |
| "loan_term_months": loan_term_months, |
| "monthly_payment_usd": np.round(monthly_payment, 2), |
| "repayment_status": repayment_status, |
| "rejection_reason": application_rejection_reason, |
| "scenario": scenario |
| }) |
| |
| return df |
|
|
|
|
| def main(): |
| output_dir = Path(__file__).parent |
| |
| scenarios = [ |
| ("low_burden", 4000, 42), |
| ("moderate_burden", 5000, 43), |
| ("high_burden", 6000, 44) |
| ] |
| |
| all_dfs = [] |
| for scenario_name, n, seed in scenarios: |
| df = generate_scenario(n, seed, scenario_name) |
| all_dfs.append(df) |
| |
| output_path = output_dir / f"credit_access_{scenario_name}.csv" |
| df.to_csv(output_path, index=False) |
| print(f"Generated {output_path}: {len(df)} records") |
| |
| combined = pd.concat(all_dfs, ignore_index=True) |
| combined_path = output_dir / "credit_access_combined.csv" |
| combined.to_csv(combined_path, index=False) |
| print(f"Generated {combined_path}: {len(combined)} records") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
