The Relationship between Population Growth and Economic Growth Over 1870-2013: Evidence from a Bootstrapped Panel-Granger Causality Test
Bibliographic record
Abstract
This study applies the bootstrap panel causality test proposed by Kónya (2006), which accounts for both dependency and heterogeneity across countries, to test the causal link between population growth and economic growth in 21 countries over the period of 1870-2013. With regards to the direction of population growth-economic growth nexus, we found one-way Granger causality running from population growth to economic growth for Finland, France, Portugal, and Sweden, one-way Granger causality running from economic growth to population growth for Canada, Germany, Japan, Norway and Switzerland, and no causal relationship between population growth and economic growth is found in Belgium, Brazil, Denmark, Netherlands, New Zealand, Spain, Sri Lanka, the UK, the USA and Uruguay. Furthermore, we found feedback between population growth and economic growth for Austria and Italy. Dividing the sample into two subsamples due to a structural break yielded different results in that for the first period of 1871-1951 we found that population growth Granger cause economic growth only for Finland and France, economic growth Granger cause population growth for Denmark, Japan, and Norway and that there is bidirectional causality between population growth and economic growth for both Austria and Italy. For the period of 1952-2013 we found that population growth Granger cause economic growth only for Sri Lanka, economic growth Granger cause population growth for Belgium, Denmark, France, Germany, New Zealand, Spain, Switzerland, and Uruguay and that found bidirectional causality between population growth and economic growth only for Japan. Our empirical results have important policy implications for these 21 countries under study as the directions of causality tend to differ across countries and depending on the time period under question.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".