Mobile Money Taxation and Informal Workers: Evidence from Ghana’s E-Levy
Bibliographic record
Abstract
The use of digital financial services, including money transfers and mobile money, have expanded widely in lower-income countries in the past decade; 47 per cent of the population of sub-Saharan Africa (548 million) had a registered mobile money account in 2020, with 29 per cent of those accounts representing active users (Andersson-Manjang and Naghavi 2021: 8). Among lower-income countries for which data is available, the average number of mobile money accounts is more than double the number of commercial bank accounts. In many lower-middle-income countries, mobile money usage is the same or more than commercial bank usage (Bazarbash et al. 2020). Alongside this growth, governments have increasingly sought to tax DFS, rooted in deeper discussions about the role that technology can play in increasing tax revenue and strengthening overall state capacity (Fan et al. 2020; Okunogbe and Santoro 2021). While capturing revenue from DFS can come from many sources, mobile money taxes in particular have often been introduced due to the untapped revenue potential and the relatively convenient and easy nature of the tax handle (Lees and Akol 2021a) – particularly in relation to, say, corporate income taxes on financial service providers. As noted above, the search for revenue is often closely linked to a desire to capture revenue from workers in the informal economy, who are often framed as tax evaders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".