Poverty, inequality, and redistribution: An analysis of the equalizing effects of social investment policy
Bibliographic record
Abstract
Social investment (SI) policies have been implemented by governments of affluent countries in hopes of safeguarding against new social risks and mitigating social exclusion by encouraging employment and making it easier for parents to balance work and family. Governments hope that human capital investment (education and job training) will better prepare workers for jobs, promote their employment and social inclusion, and reduce poverty. This article investigates whether SI policies contribute to lower poverty and inequality by analyzing data from 18 Organization for Economic Cooperation and Development countries between 1980 and 2013. The analysis finds, first, that SI policies (education and active labor market policy (ALMP)) alone may be less effective in generating lower poverty and inequality without redistribution, but when accompanied and supported by redistribution, SI policies are more effective in creating lower poverty and inequality. I propose the explanation that SI policies create lower-income poverty and inequality by creating individuals and households that can be salvaged and lifted out of poverty with redistribution, because SI policies help improve their skills and knowledge and employability, although they may be not quite able to escape poverty or low income without redistribution. As partial evidence, I present the result that education is associated with a lower poverty gap in market income. The analysis also finds that education and ALMP produce lower poverty and/or inequality in interaction with social market economies that redistribute more, and that augments the equalizing effects of education and ALMP. The results, thus, suggest the complementary roles of SI policies and redistribution.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".