Two decades of tax-benefit reforms in Ecuador: How much have they contributed to poverty and inequality reduction?
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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.
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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 | 0.002 | 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 | 0.000 |
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 it