Club convergence in regional labor productivity: how do Australian states and territories compare to the US, UK, and Canadian subnational regions?
Bibliographic record
Abstract
Abstract Developing strategies to enhance productivity growth requires identifying leading and lagging regions, industries, and growth drivers. However, there are limited cross-country studies using subnational data. Our study goes beyond the traditional country convergence approach and estimates labor productivity convergence using Philips and Sul’s club convergence approach and subnational data from 2004 to 2020. We aim to determine whether labor productivity growth rates in Australian states and territories are equal, converging, or divergent as compared to United States, United Kingdom, and Canadian subnational regions. The results show that five Australian jurisdictions, including Western Australia and New South Wales, are in the high labor productivity group (Club 1), while the remaining three i.e., Australian Capital Territory, Victoria, and Queensland, are in the moderate growth group (Club 2). We also used fixed effects models with least squares dummy variable estimators to identify the club’s characteristics. The results show that regions with a high proportion of employees in information and communications technology, energy, mining, and resources experienced higher labor productivity growth than those with a manufacturing sector. Human capital, innovation, and household disposable income were associated with high labor productivity, whereas high old-age dependency and children-to-women ratios were associated with lower growth. These results are consistent with the macroeconomic phenomenon that economic development leads to inequality and polarization in certain regions of a country. Nonetheless, the findings are useful for decision-makers and researchers to use in benchmarking and improving regional strategies by identifying regional peers and factors that influence convergence or divergence that can be improved. The results provide insightful findings for consideration by policymakers seeking to boost labor productivity or to bridge regional gaps in productivity.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".