Export-led growth: a survey of the empirical literature and some non-causality results. Part 1
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The economic development and growth literature contains extensive discussions on relationships between exports and economic growth. One debate centers 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 contribute to this literature in two ways. In this paper, part 1, we provide a comprehensive survey of more than one hundred and fifty export-growth applied papers. We describe the changes that have occurred, over the last two decades, in the methodologies used to empirically 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it