Targeted transfers, a left-wing policy? The impact of left-wing governments and corporatism on transfers to low-income families (1982–2019)
Bibliographic record
Abstract
In the last decades, several countries introduced new income-tested child benefits and targeted in-work tax credits to boost the income of low-income families. Inspired by the power resource theory, I postulate that left-wing governments tend to increase benefits to low-income families because their ideology favours redistribution and to consolidate the vote of low-income families, but that both right- and left-wing governments increase benefits for middle-income families. The impact of left-wing governments should be stronger in countries with a weak bargaining system as social partners are unable to reduce inequalities between families. To demonstrate this argument, I use statistical analyses based on OECD data to measure the effect of government ideology and corporatism on the level of benefits received by low- and middle-income families in OECD countries from 1982 to 2019. The results indicate that left-wing parties have a significant impact on benefits received by low-income families, but not on benefits received by middle-income families. Also, even though corporatism is associated with different types of child benefits, it does not influence the relationship between left-wing governments and benefits received by low-income families.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".