Furthering a feminist fiscal agenda: Engendering tax and development
Bibliographic record
Abstract
Abstract Motivation Taxation has received increasing attention from researchers and in the context of development policy, though less attention has been paid to the gendered impacts of taxation, particularly in lower‐income countries. Purpose We seek to understand how taxation affects men and women differently and relates to gender equity in lower‐income countries. In turn, we aim to identify how researchers and policy‐makers can further a feminist fiscal agenda to strengthen gender equity. Approach and methods We review the growing evidence on how tax policy and administration affect the lives and livelihoods of women in lower‐income countries. Through this review of research and development practice, we identify impact gaps and channels through which taxation can lead to gendered outcomes. Findings Three findings emerge. First, in considering the impact of tax policy on gender outcomes, there is a need to focus on those issues that most affect women in lower‐income countries. In part, this means focusing on the ways in which the informal sector is taxed, as well as how subnational and informal taxes and fees affect men and women differently. Second, while research has focused on the impacts of tax policy on gender outcomes, greater attention needs to be paid to the gendered impacts of tax administration. Third, bringing a gender lens to tax and development requires considering revenue and expenditure together to ensure that the effects of progressive tax policies are not undermined by gender‐insensitive budgets. Policy implications An evidence review points to various ways that policy‐makers can try to ensure that taxation does not negatively affect gender equity, including rethinking how the informal sector is taxed, supporting women within tax administrations, undertaking progressive tax policy, and linking tax policies to gender‐sensitive budgeting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".