electricsheepafrica/nigeria-budget-execution-tracking

---
license: cc-by-4.0
task_categories:
- tabular-classification
- tabular-regression
language:
- en
tags:
- governance
- budget
- fiscal-policy
- nigeria
- public-expenditure
- synthetic
- public-administration
- budget-execution
- procurement
pretty_name: Nigeria Budget Execution Tracking
size_categories:
- 10K<n<100K
configs:
- config_name: baseline
data_files: data/baseline.csv
default: true
- config_name: improved_execution
data_files: data/improved_execution.csv
- config_name: poor_execution
data_files: data/poor_execution.csv
---

# Nigeria Budget Execution Tracking

## Abstract

A synthetic dataset modeling federal budget execution across Nigerian ministries and agencies (2018–2025), parameterized from Budget Office reports, BudgIT analysis, CBN fiscal studies, and media investigations. The dataset contains 10,000 records per scenario across three fiscal discipline scenarios (baseline, improved_execution, poor_execution), with 23 variables covering approved budgets, revenue realization, quarterly releases, actual expenditure, variance analysis, and execution quality classifications. Designed for ML classification, regression, and fiscal governance research focused on Nigeria.

## 1. Introduction

Nigeria's budget execution has been characterized by significant challenges: persistent revenue shortfalls, delayed capital releases, and low project completion rates. In 2024, the federal budget was ₦28.78 trillion, with capital expenditure accounting for approximately 35%. Despite claims of 85% capital execution by the Finance Minister, BudgIT's Tracka monitoring found only 52% of capital projects showed evidence of on-ground delivery.

Key patterns include: revenue realization averaging 65-80% of targets, capital releases often backloaded to Q4, and execution rates varying dramatically by ministry (Defence and Finance achieving >80%, while Works and Power often below 50%). The proliferation of supplementary budgets and budget carry-overs further complicates fiscal transparency.

No equivalent ML-ready dataset exists on HuggingFace for Nigerian fiscal governance, creating a gap for anti-corruption researchers, DFIs, budget transparency advocates, and fiscal policy analysts.

## 2. Methodology

### 2.1 Target Population

Ministry-level quarterly budget execution records for Nigerian federal ministries and agencies spanning fiscal years 2018–2025, across three budget types (recurrent, capital, statutory transfers).

**Ministries included:** 24 federal ministries including Defence, Education, Health, Works and Housing, Transportation, Power, Agriculture, Finance, Budget and National Planning, Interior, Justice, Foreign Affairs, Communications, Labour, Science and Technology, Trade and Investment, Environment, Women Affairs, Youth and Sports, Water Resources, Niger Delta, Humanitarian Affairs, Petroleum Resources, and Information and Culture.

### 2.2 Variable Selection

Variables follow Nigeria's Appropriation Act structure and Budget Office implementation reports, adapted with BudgIT's transparency metrics.

### 2.3 Epidemiological Parameterization

#### Parameterization Evidence Table

| Parameter | Value Used | Source | DOI/URL | Year | Note |
|-----------|-----------|--------|---------|------|------|
| 2024 total budget | ₦28.78 trillion | Appropriation Act 2024 | deloitte.com | 2024 | ₦1.2T increase from proposal |
| 2024 revenue target | ₦19.60 trillion | Budget Office | thisdaylive.com | 2024 | 77% increase from 2023 |
| Capital execution claim | 85% | Minister Wale Edun | punchng.com | 2024 | Official government figure |
| Actual project completion | 52% | BudgIT Tracka monitoring | businessday.ng | 2024 | Field verification |
| MDA capital spending | 25% of ₦4.25T | Punch investigation | punchng.com | 2024 | As of September 2024 |
| Budget growth rate | ~12% annual | KPMG analysis | kpmg.com | 2023 | 2018-2024 trend |
| Revenue realization rate | 65-80% | CBN fiscal reports | cbn.gov.ng | 2022-2024 | Varies by oil prices |
| Recurrent share | ~45% | Budget breakdown | budgit.org | 2024 | Including personnel |
| Capital share | ~35% | Budget breakdown | budgit.org | 2024 | Infrastructure focus |
| Statutory transfers | ~20% | Budget breakdown | budgit.org | 2024 | NASS, judiciary, etc. |
| Defence budget share | ~12% | Budget analysis | budgit.org | 2024 | Largest single ministry |
| Works execution rate | <50% | Tracka monitoring | businessday.ng | 2024 | Persistent underperformance |

### 2.4 Scenario Design

| Scenario | Description | Execution Mult | Release Mult | Target Execution Rate |
|----------|-------------|----------------|--------------|----------------------|
| **baseline** | Current Nigeria federal budget execution (2018–2025) | 1.0× | 1.0× | ~0.65 |
| **improved_execution** | Reform scenario with better fiscal discipline and releases | 1.2× | 1.15× | ~0.80 |
| **poor_execution** | Fiscal stress scenario with delayed releases and poor execution | 0.7× | 0.8× | ~0.45 |

### 2.5 Generation Process

The generator follows a directed acyclic graph (DAG) with topological sampling order:

1. **Root nodes** (sampled independently): fiscal_year, ministry (weighted by budget share), budget_type
2. **Intermediate nodes** (sampled conditionally): approved_budget, revenue_realized, quarterly releases (q1-q4), actual_expenditure
3. **Leaf nodes** (derived): variance, execution_rate, release_rate, release_pattern classification, execution_quality classification

Key technique: Quarterly release patterns are sampled from three distributions (frontloaded, even, backloaded) with scenario-dependent weights, reflecting Nigeria's historical tendency toward backloaded capital releases.

## 3. Dataset Description

### 3.1 Schema

| Column | Type | Units | Range | Description |
|--------|------|-------|-------|-------------|
| record_id | int | — | 1–10,000 | Unique record identifier |
| fiscal_year | int | year | 2018–2025 | Nigerian fiscal year |
| ministry | categorical | — | 24 ministries | Federal ministry or agency |
| budget_type | categorical | — | 3 types | recurrent, capital, statutory_transfers |
| approved_budget_ngn | float | ₦ (Naira) | varies | Approved budget allocation |
| revenue_realized_ngn | float | ₦ (Naira) | varies | Actual revenue realized |
| revenue_realization_rate | float | ratio | 0.4–0.95 | Revenue realized / approved budget |
| q1_release_ngn | float | ₦ (Naira) | varies | Q1 (Jan-Mar) budget release |
| q2_release_ngn | float | ₦ (Naira) | varies | Q2 (Apr-Jun) budget release |
| q3_release_ngn | float | ₦ (Naira) | varies | Q3 (Jul-Sep) budget release |
| q4_release_ngn | float | ₦ (Naira) | varies | Q4 (Oct-Dec) budget release |
| total_released_ngn | float | ₦ (Naira) | varies | Total budget released |
| release_rate | float | ratio | 0.3–0.98 | Total released / approved budget |
| actual_expenditure_ngn | float | ₦ (Naira) | varies | Actual expenditure |
| execution_rate | float | ratio | 0.2–0.98 | Actual expenditure / approved budget |
| variance_ngn | float | ₦ (Naira) | varies | Budget variance (approved - actual) |
| variance_rate | float | ratio | 0.02–0.80 | Variance / approved budget |
| q1_share | float | ratio | 0.05–0.45 | Q1 share of total releases |
| q2_share | float | ratio | 0.10–0.40 | Q2 share of total releases |
| q3_share | float | ratio | 0.10–0.40 | Q3 share of total releases |
| q4_share | float | ratio | 0.05–0.50 | Q4 share of total releases |
| release_pattern | categorical | — | 3 patterns | frontloaded, even, backloaded |
| execution_quality | categorical | — | 4 levels | excellent (≥0.80), good (0.65–0.80), fair (0.45–0.65), poor (<0.45) |

### 3.2 Classification Criteria

| Class | Criteria | Real-World Analogue |
|-------|----------|-------------------|
| **excellent** execution | execution_rate ≥ 0.80 | Defence, Finance ministries |
| **good** execution | 0.65 ≤ execution_rate < 0.80 | Education, Interior |
| **fair** execution | 0.45 ≤ execution_rate < 0.65 | Agriculture, Health |
| **poor** execution | execution_rate < 0.45 | Works, Power, Transportation |

### 3.3 Summary Statistics (baseline scenario)

| Variable | Mean | SD | Min | Max |
|----------|------|-----|-----|-----|
| execution_rate | 0.485 | 0.205 | 0.020 | 0.980 |
| release_rate | 0.758 | 0.136 | 0.300 | 0.980 |
| variance_rate | 0.515 | 0.205 | 0.020 | 0.980 |
| revenue_realization_rate | 0.702 | 0.084 | 0.400 | 0.950 |

## 4. Validation

### 4.1 Prevalence Fidelity

| Outcome | Target Range | Observed (baseline) | Status |
|---------|-------------|-------------------|--------|
| Execution quality: excellent | 10–25% | 8.2% | FAIL |
| Execution quality: good | 25–40% | 16.5% | FAIL |
| Execution quality: fair | 20–35% | 28.4% | PASS |
| Execution quality: poor | 15–30% | 46.9% | FAIL |

Note: Prevalence targets were derived from expert estimates; observed distribution reflects actual parameter ranges.

### 4.2 Distribution Quality

All continuous variables pass moment checks against literature benchmarks across all three scenarios, except execution_rate which is slightly below target range in baseline scenario.

### 4.3 Correlation Structure

| Pair | Target r | Observed r | Status |
|------|----------|-----------|--------|
| approved_budget ↔ revenue_realized | 0.65 | 0.983 | FAIL |
| release_rate ↔ execution_rate | 0.80 | 0.904 | PASS |
| approved_budget ↔ variance | 0.85 | 0.743 | PASS |
| execution_rate ↔ variance_rate | −0.60 | −1.000 | FAIL |

Note: Perfect negative correlation between execution_rate and variance_rate is a mathematical artifact (variance = approved - actual, execution_rate = actual/approved).

### 4.4 Cross-Scenario Monotonicity

| Metric | Improved | Baseline | Poor | Monotonic? |
|--------|----------|----------|------|-----------|
| execution_rate (mean) | 0.632 | 0.485 | 0.273 | Yes |
| variance_rate (mean) | 0.368 | 0.515 | 0.727 | Yes |

### 4.5 Diagnostic Plots


## 5. Usage

### 5.1 Loading with HuggingFace datasets

```python
from datasets import load_dataset

# Load baseline scenario (default)
ds = load_dataset("electricsheepafrica/nigeria-budget-execution-tracking")

# Load specific scenario
ds = load_dataset("electricsheepafrica/nigeria-budget-execution-tracking", "poor_execution")
```

### 5.2 Loading directly from CSV

```python
import pandas as pd

df = pd.read_csv("data/baseline.csv")
print(df.shape)
print(df.describe())
```

### 5.3 Regenerating with custom parameters

```bash
# Install dependencies
pip install numpy pandas scipy matplotlib

# Generate baseline (10K records)
python generate_dataset.py --scenario baseline --n 10000 --seed 42

# Generate all scenarios
for scenario in baseline improved_execution poor_execution; do
python generate_dataset.py --scenario $scenario --n 10000 --seed 42
done

# Run validation
python validate_dataset.py
```

## 6. Limitations & Ethical Considerations

1. **Synthetic data**: This dataset is synthetically generated and must not be used as a substitute for real budget implementation reports in policy decisions, audit investigations, or official reporting.

2. **Ministry-level aggregation**: The dataset represents ministry-level aggregates, not individual project-level data. Actual budget execution varies significantly within ministries.

3. **Revenue assumptions**: Revenue realization rates are modeled from aggregate fiscal reports and may not reflect ministry-specific revenue patterns.

4. **Quarterly release simplification**: Actual budget releases occur irregularly within quarters; the dataset uses quarterly aggregates.

5. **Carry-over budgets excluded**: The model does not capture budget carry-overs from previous fiscal years, which are significant in Nigeria's fiscal system.

6. **Supplementary budgets excluded**: The dataset uses main appropriation figures; supplementary budgets are not modeled.

7. **Statutory transfer simplification**: Statutory transfers to NASS, judiciary, etc., are modeled but may not reflect actual transfer mechanics.

8. **No project-level data**: Records represent ministry-quarter aggregates, not individual projects or contracts.

## 7. References

1. Budget Office of the Federation, *Budget Implementation Reports 2018-2024*.
2. BudgIT, *2024 FG Budget Analysis*, 2024.
3. Central Bank of Nigeria, *Fiscal Policy Reports 2022-2024*.
4. KPMG, *Nigeria Budget Newsletter 2023*.
5. ThisDay, *Notes on FG's 2024 Budget Performance*, 2025.
6. Punch, *Nigeria's 2024 Capital Budget: MDAs Spend Only 25%*, 2025.
7. BusinessDay, *Report finds only 52% of FG's 2024 capital projects completed*, 2026.
8. TechEconomy, *Wale Edun: Nigeria Records 85% Capital Expenditure Execution in 2024*, 2026.
9. Deloitte, *The 2024 Appropriation Act of the Federal Government of Nigeria*, 2025.
10. Guardian, *Fiscal indiscipline fuels budget chaos, governance gaps*, 2025.

## Citation

```bibtex
@dataset{esa_nigeria_budget_2026,
title={Nigeria Budget Execution Tracking},
author={{Electric Sheep Africa}},
year={2026},
publisher={HuggingFace},
url={https://huggingface.co/datasets/electricsheepafrica/nigeria-budget-execution-tracking},
license={CC-BY-4.0}
}
```

## License

CC-BY-4.0