Mobile money taxation and informal workers: Evidence from Ghana's E‐levy
Why this work is in the frame
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Bibliographic record
Abstract
Summary Motivation In recent years, governments in low‐income countries have increasingly introduced taxes on mobile money transfers. These are often explicitly promoted as a way of taxing informal economic activity, but critics have noted their potential negative impact on lower‐income groups and specifically those in the informal sector. Yet there is virtually no evidence base on the effects of mobile money taxes on informal workers. Purpose This article assesses how informal workers in Accra, Ghana, use mobile money and how they perceive Ghana's Electronic Transfer Levy (E‐levy), introduced in May 2022. This provides a particularly interesting case study to explore the equity implications of the tax, as the policy was explicitly justified as a way of taxing the informal economy but also includes measures to limit the tax burden on lower‐income groups. Methods and approach The article uses data from a survey of 2,700 self‐employed informal workers in the Accra Metropolitan Assembly to capture citizen perceptions of the policy and to examine the likely impact of the E‐levy on informal workers with reference to equity. Findings Overall, our results suggest that the E‐levy is highly regressive. Further, we show that most informal workers disapprove of the E‐levy, reflecting not just concerns about its equity impacts, but also disappointment with the government's performance. Policy implications Our findings suggest that taxes on digital financial services should be reconsidered from an equity perspective. While some policy measures, including those undertaken in Ghana, can protect low‐income earners, they are often insufficient to counteract overall regressive impacts. Where they are implemented, social spending from the revenue from these taxes should target low‐income populations in the informal economy, while governments should focus on building trust among informal workers with regard to revenue raising and spending.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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