electricsheepafrica/nigeria-tax-revenue-administration

---
license: cc-by-4.0
task_categories:
- tabular-classification
- tabular-regression
language:
- en
tags:
- governance
- taxation
- fiscal-policy
- nigeria
- tax-revenue
- synthetic
- public-administration
- tax-compliance
- firs
pretty_name: Nigeria Tax Revenue Administration
size_categories:
- 10K<n<100K
configs:
- config_name: baseline
data_files: data/baseline.csv
default: true
- config_name: reform_accelerated
data_files: data/reform_accelerated.csv
- config_name: fiscal_stress
data_files: data/fiscal_stress.csv
---

# Nigeria Tax Revenue Administration

## Abstract

A synthetic dataset modeling tax revenue administration across Nigeria's 36 states and FCT (2018–2025), parameterized from FIRS collection reports, OECD Revenue Statistics, and fiscal policy studies. Contains 10,000 records per scenario across three fiscal scenarios (baseline, reform_accelerated, fiscal_stress), with 19 variables covering tax collection by type and sector, compliance rates, audit assessments, tax gap estimates, digital filing rates, and administration effectiveness scores. Designed for ML classification, regression, and fiscal governance research focused on Nigeria's tax system.

## 1. Introduction

Nigeria's tax-to-GDP ratio has historically been among the lowest globally. The OECD reports Nigeria's ratio at 7.9% in 2022 (revised computation), compared to the Africa average of 16.0% and the OECD average of 34.1%. The Federal Inland Revenue Service (FIRS) has made significant strides: collections grew from ₦5.26 trillion in 2020 to ₦21.6 trillion in 2024 (112% of target). The current administration has set an ambitious target of 18% tax-to-GDP by 2026-2027.

Key challenges persist: a fragmented system with 37 state tax authorities, a huge untaxed informal economy, narrow tax base, and over-reliance on corporate income tax (36% of revenue). The FIRS launched the TaxPro Max platform in 2021, improving digital filing to approximately 65% by 2024. The Nigeria Tax Bill proposes consolidating the FIRS into a Nigerian Revenue Service and reducing CIT from 30% to 25% by 2026.

## 2. Methodology

### 2.1 Parameterization Evidence Table

| Parameter | Value Used | Source | Year | Note |
|-----------|-----------|--------|------|------|
| Tax-to-GDP ratio | 13.5% (2025) | FIRS Chairman | 2025 | Up from 10% at administration start |
| FIRS 2024 collection | ₦21.6 trillion | FIRS reports | 2025 | 112% of ₦19.4T target |
| FIRS 2023 collection | ₦12.3 trillion | Mondaq analysis | 2025 | 76% increase to 2024 |
| CIT share | 36% of revenue | Mondaq | 2024 | Highest contributor |
| VAT share | 28% of revenue | Mondaq | 2024 | Growing share |
| PPT share | 26% of revenue | Mondaq | 2024 | Oil-dependent |
| Corporate tax rate | 30% (reducing to 25%) | Nigeria Tax Bill | 2025 | One of highest globally |
| Africa average tax/GDP | 15.6% | OECD | 2024 | Nigeria below average |
| OECD average tax/GDP | 34.1% | OECD | 2024 | Benchmark |
| Taxpayer registration | 55-62 million | Estimated | 2024-25 | Growing with TIN enforcement |

### 2.2 Scenario Design

| Scenario | Description | Collection Mult | Compliance Mult | Efficiency Mult |
|----------|-------------|-----------------|-----------------|-----------------|
| **baseline** | Current Nigeria tax administration (2018–2025) | 1.0× | 1.0× | 1.0× |
| **reform_accelerated** | Tax reform with improved compliance and digitization | 1.3× | 1.4× | 1.3× |
| **fiscal_stress** | Economic downturn with declining compliance | 0.7× | 0.7× | 0.8× |

## 3. Dataset Description

### 3.1 Schema

| Column | Type | Units | Range | Description |
|--------|------|-------|-------|-------------|
| record_id | int | — | 1–10,000 | Unique record identifier |
| year | int | year | 2018–2025 | Tax year |
| state | categorical | — | 36 states + FCT | Nigerian state |
| tax_type | categorical | — | 7 types | CIT, VAT, PPT, PIT, WHT, Stamp_Duties, Other |
| sector | categorical | — | 10 sectors | Economic sector |
| collection_ngn_billions | float | ₦ billions | varies | Tax collected |
| target_ngn_billions | float | ₦ billions | varies | Revenue target |
| compliance_rate | float | ratio | 0.15–0.95 | Compliance level |
| audit_rate | float | ratio | 0.02–0.20 | Audit coverage |
| audit_assessment_ngn_billions | float | ₦ billions | varies | Additional assessments |
| yield_rate | float | ratio | 0.30–0.90 | Assessment to collection conversion |
| yield_ngn_billions | float | ₦ billions | varies | Revenue from audits |
| tax_gap_pct | float | ratio | 0.10–0.80 | Estimated uncollected revenue share |
| tax_gap_ngn_billions | float | ₦ billions | varies | Estimated revenue leakage |
| processing_days | int | days | 7–90 | Processing time |
| digital_filing_rate | float | ratio | 0.20–0.95 | Digital filing adoption |
| taxpayer_density_per_1000 | float | ratio | varies | Taxpayers per 1000 population |
| efficiency_score | float | score | 0.0–1.0 | Composite efficiency |
| admin_effectiveness | categorical | — | 4 levels | effective, moderate, limited, ineffective |

### 3.2 Summary Statistics (baseline)

| Variable | Mean | SD | Min | Max |
|----------|------|-----|-----|-----|
| collection_ngn_billions | 180.20 | 350.50 | 0.01 | 5000 |
| compliance_rate | 0.530 | 0.120 | 0.15 | 0.95 |
| tax_gap_pct | 0.471 | 0.120 | 0.10 | 0.80 |
| digital_filing_rate | 0.620 | 0.130 | 0.20 | 0.95 |
| efficiency_score | 0.553 | 0.090 | 0.00 | 1.00 |

## 4. Usage

### 4.1 Loading with HuggingFace datasets

```python
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/nigeria-tax-revenue-administration")
ds_reform = load_dataset("electricsheepafrica/nigeria-tax-revenue-administration", "reform_accelerated")
```

### 4.2 Regenerating

```bash
pip install numpy pandas scipy matplotlib
python generate_dataset.py --scenario baseline --n 10000 --seed 42
python validate_dataset.py
```

## 5. Limitations

1. **Synthetic data**: Not suitable for audit investigations or official reporting.
2. **State-level aggregation**: Does not capture LGA-level variations.
3. **Informal sector underrepresentation**: Informal economy tax gap is modeled but actual data is scarce.
4. **No individual taxpayer data**: All records represent sector-state aggregates.

## 6. References

1. FIRS, *Tax-to-GDP ratio 13.5%, targeting 18% by 2027*, 2025.
2. FIRS, *₦21.6 trillion collected in 2024*, 2025.
3. FIRS, *₦22.59 trillion YTD October 2025*, 2025.
4. OECD, *Revenue Statistics in Africa 2024 - Nigeria*, 2024.
5. Mondaq, *Navigating Nigeria's Tax Landscape 2024/2025*, 2025.
6. Mondaq, *Achieving Tax Yield Growth in Turbulent Economy*, 2024.
7. Mondaq, *Tax Revenue Generation Beyond Corporate Taxes*, 2024.
8. Mondaq, *Navigating Fiscal Challenges Through Tax Reform*, 2025.
9. NBS, *Tax-to-GDP Ratio Revised Computation*, 2023.
10. Oxford Business Group, *Nigeria aims to diversify revenue*, 2024.

## Citation

```bibtex
@dataset{esa_nigeria_tax_2026,
title={Nigeria Tax Revenue Administration},
author={{Electric Sheep Africa}},
year={2026},
publisher={HuggingFace},
url={https://huggingface.co/datasets/electricsheepafrica/nigeria-tax-revenue-administration},
license={CC-BY-4.0}
}
```

## License

CC-BY-4.0