Enhancing taxpayer registration with inter-institutional data sharing—evidence from Uganda
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In many African countries, limited population data pose a challenge for tax administrations struggling with informal economies. This study examines Uganda’s integration of national ID data into tax registration through “Instant TIN,” an interface linking the Uganda Revenue Authority (URA) with the National Identification and Registration Agency (NIRA) and the Uganda Registration Service Bureau (URSB). This reform aims to streamline taxpayer registration and improve data quality. Using a mixed-methods approach—combining interviews with government officials and administrative data analysis—we identify three key findings. First, Instant TIN registrants differ significantly from those using the conventional process. They are more likely to be individuals, female, younger, and previously informal, highlighting the reform’s role in bringing in marginalised taxpayers. Second, Instant TIN improves data quality. It reduces TIN duplications for individuals and enhances contact accuracy, decreasing invalid or missing email addresses by eight percentage points and invalid phone numbers by six. However, it worsens sector data quality, increasing missing or incorrect sector information by 12 percentage points. Third, while Instant TIN reduces duplication, manual data entry, and administrative burdens, challenges remain. Infrequent updates in external datasets and a lack of validation within the interface increase administrative costs and complicate taxpayer engagement. Additionally, mandatory in-person updates and invalid contact details add to compliance burdens. Overall, Uganda’s experience highlights both the potential and limitations of integrating national ID data for tax administration, offering insights for other countries considering similar reforms.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it