Export-led growth: a survey of the empirical literature and some non-causality results. Part 1
Bibliographic record
Abstract
The economic development and growth literature contains extensive discussions on relationships between exports and economic growth. One debate centres on whether countries should promote the export sector to obtain economic growth. An abundant empirical literature on this export-led growth (ELG) hypothesis has followed. We aim to contribute to this literature in two ways. In this paper, part 1, we provide a comprehensive survey of more than 150 export-growth applied papers. We describe the changes that have occurred, over the last two decades, in the methodologies used empirically to examine for relationships between exports and economic growth, and we provide information on the current findings.The last decade has seen an abundance of time series studies that focus on examining for causality via exclusions restrictions tests, impulse response function analysis and forecast error variance decompositions. Our second contribution is to examine some of these time series methods. We show, in part 2, that ELG results based on standard causality techniques are not typically robust to specification or method. We do this by reconsidering two export-led growth applications – Oxley’s (1993) study for Portugal, and Henriques and Sadorsky’s (1996) analysis for Canada. Our results suggest that extreme care should be exercised when interpreting much of the applied research on the ELG hypothesis.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.036 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".