Taxing the Informal Economy: Challenges, Possibilities and Remaining Questions*
Bibliographic record
Abstract
Summary Recent years have witnessed significantly increased attention to the challenge of taxing small businesses in the informal sector. However, much of this recent attention has remained focused on comparatively technical issues of revenue maximisation and policy design. This paper argues that this debate should focus increasingly on the wider development implications of informal sector taxation, as well as the political and institutional barriers to improved performance. When considering the merits of committing scarce resources to taxing small informal sector firms, debate has frequently focused on limited revenue potential, high costs of collection and potentially perverse impacts on small firms. By contrast, recent arguments have increasingly emphasised more indirect benefits of informal taxation in relation to economic growth, tax compliance and governance. These potentially broader benefits are increasingly finding support in recent research, but they are contingent on government support and consequently demand further attention. When we turn our attention away from whether tax authorities should tax small informal businesses towards the challenge of how to do so more effectively, we again argue that a broader frame of analysis is needed. Most existing research has focused on developing less distortionary tax regimes and on tax simplification in order to reduce the costs of compliance. However, while important, there strategies remain too narrow. Encouraging tax compliance demands not only lowering costs but also strengthening the potential benefits of formalisation, from increased security to new economic opportunities. As importantly, successful reform needs political support from political leaders, tax administrators and taxpayers alike. This demands greater attention to strengthening political incentives for reform, through strategic policy, administrative and institutional reform. With this in mind, the paper highlights a number of recent experiences that have sought to address these challenges, but which need further study.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".