Is aggregate demand wage-led or profit-led? \nNational and global effects
Bibliographic record
Abstract
This paper estimates the effects of a change in the wage share on growth in the G20 countries using a post-Keynesian/post-Kaleckian model, analyses the interactions among different economies, and calculates the global multiplier effects of a simultaneous decline in the wage share. At the national level, a decrease in the wage share leads to lower growth in the euro area, Germany, France, Italy, UK, US, Japan, Turkey, and Korea, i.e. these economies are wage-led, whereas it stimulates growth in Canada, Australia, Argentina, Mexico, China, India, and South Africa; thus the latter group of countries are profit-led. However, a simultaneous decline in the wage share in all these countries leads to a decline in global growth. Furthermore, Canada, Argentina, Mexico, and India also contract when they decrease their wage-share along with their trading partners. Thus the global economy in aggregate is wage-led. The policy conclusions of the paper shed light on the limits of strategies of international competitiveness based on wage competition in a highly integrated global economy, and point at the possibilities to correct global imbalances via coordinated macroeconomic and wage policy, where domestic demand plays an important role. There is room for a wage-led recovery in the global economy based on a simultaneous increase in the wage shares, where global GDP as well as all individual countries can grow.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".