Two decades of tax-benefit reforms in Ecuador: How much have they contributed to poverty and inequality reduction?
Bibliographic record
Abstract
• Understanding the role of tax-benefit reforms in reducing income inequality and poverty over time is crucial to assess the effectiveness of government intervention. • We quantify the contribution of policy reforms to changes in poverty and inequality using decomposition methods based on counterfactual distributions. • Tax-benefit reforms introduced in four subperiods between 2003 and 2020 always contributed to the reduction of poverty and inequality in Ecuador. • The effect of the reforms on poverty was significant but limited, whereas the effect on inequality was significant only between 2003 and 2008. • The post-pandemic economic recovery was broadly due to an improvement of market income. The aim of this paper is to analyze the contribution of tax-benefit reforms to changes in income poverty and inequality in Ecuador from 2003 to 2022. For this, we use decomposition methods based on counterfactual distributions obtained using tax-benefit microsimulations which allow quantifying the relative contribution of policy reforms to changes in income poverty and inequality, compared to other contributors, including demographic characteristics and changes in the market income distribution. The focus is on changes over five subperiods, namely 2003–08, 2008–14, 2014–2019, 2019–20 and 2020–22. Our results show that tax-benefit reforms introduced between 2003 and 2020 contributed to the reduction of poverty and inequality in Ecuador, reinforcing the positive contribution of changes in market income and other population factors in all subperiods between 2003 and 2014, and mitigating the negative contribution of such factors between 2014 and 2020. Over the last period of analysis (2020–22), the post-pandemic economic recovery was broadly due to an improvement of market income with an almost nil contribution of tax-benefit reforms.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".