The G20’s Promise to Create More and Better Jobs: Missed Opportunities and a Way Forward
Bibliographic record
Abstract
The Group of 20 (G20) was launched as a leaders' forum in the midst of the 2008 financial crisis and quickly agreed to undertake coordinated economic stimulus efforts. While those early measures helped stabilize the global economy, the negative impacts of the crisis on employment continued to mount through 2009. The leaders turned their attention to labour market issues; labour and employment ministers met in 2010 and thereafter. However, the G20 and a number of other countries erroneously reversed the stimulus approach beginning in Toronto in 2010, leading to weak recovery, entrenchment of unemployment and stagnation of wages. Labour ministers increasingly advocated more robust labour market policies, but were resisted by finance ministers. The leaders themselves agreed to increasingly strong statements on wages, inequality and social issues but most G20 countries did not implement them. When the political backlash against globalization emerged in 2016 the G20 was seen by many as part of the out-of-touch elite that failed to address the difficulties and economic anxiety suffered by many G20 member households. The G20 should adjust course by implementing, in a coordinated manner, policies that can increase employment and incomes and reverse growing inequality. This paper lays out two practical examples of such policies. The first is a coordinated increase in minimum wages across the G20 to provide direct support to low-wage workers, restart overall wage growth and increase demand. If implemented by the entire G20 this would provide a serious stimulus to global demand, which still remains weak, and avoid competitive undercutting among G20 members. The second is a coordinated increase in financing for programmes to help those who have lost as a result of globalization. Losers often suffer very harsh economic effects and few G20 countries compensate them adequately. A well-advertised, coordinated effort including policies such as these could demonstrate the relevance of the G20 to populations that have benefited little from the group's efforts to date.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.036 | 0.010 |
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".