The Effect of Structural Reforms: Do They Differ between GDP and Adjusted Household Disposable Income?
Bibliographic record
Abstract
The paper considers whether structural reforms have a different impact on adjusted household disposable income (AHDI) compared to GDP, particularly given that while the latter is currently used as the basis for the OECD Economics Department’s framework for evaluating the effect of structural policy reforms, the former is arguably a better measure of welfare. The main findings are that there are indeed a number of structural policies where the long-run effects on GDP and AHDI are proportionately different, so that percentage changes in the two aggregates are significantly different following a policy reform. One group of structural policies, typically those where the transmission mechanism depends mainly on productivity and capital intensity (including cuts in corporate income tax and policies to simulate business R&D) or which can weaken the bargaining power of labour (for example a loosening of EPL), have weaker long-run positive effects on AHDI than GDP. Other structural reform policies (including in-kind family benefits, family cash benefits and cuts in the income tax wedge) have a magnified effect on AHDI, so that following a policy reform, long-run percentage changes in AHDI are larger than for GDP. Cross-referencing the analysis in the paper with structural reform priorities previously identified in the OECD’s regular Going for Growth surveillance exercise, suggests that increased spending on childcare and early childhood education might usefully be part of any policy package to address the ‘cost of living crisis’ currently being faced by many OECD households.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".