The Marshall-Lerner Condition and the J-Curve Effect: Balance of Payments Adjustment in the Caribbean
Bibliographic record
Abstract
This paper examines how the balance of payments responds to domestic and foreign real incomes and the real exchange rate for 10 Caribbean economies, using a variety of econometric approaches. In particular we are concerned to measure whether real devaluations lead to an improvement in the balance of payments and whether there is a Jcurve effect by which the balance of payments deteriorates before improving after a devaluation. This is pertinent to these small economies both in the light of evidence for the usual prescription of IMF intervention and the effectiveness of adjustment instruments given specific structural economic characteristics. If the structure of these economies is such that Jcurve effects are not likely then it may be that the cost of adjustment in the light of fiscal dominance is greater than it may first appear real devaluations have insignificant trade effects so that the adjustment has to take place mostly through a reduction in output. This appears to be the case in some countries with historically high inflation where adjustment to trended low inflation growth appears difficult to attain. The ten countries we examine are split into three that have floating exchange rates, Jamaica, Guyana and Trinidad and Tobago and seven that have fixed exchange rates: five members of the East Caribbean Central Bank (Antigua and Barbuda (AB), Dominica (Dom), Grenada (Gre), St. Kitts and Nevis (SKN) and St. Vincent and the Grenadines (SVG) and Barbados (Bar) and Belize (Bel). For those with fixed exchange rates all the real exchange rate adjustment takes place through prices. In a related paper, Boyd and Smith (2003) we examined the interaction of money, income and inflation in these economies plus St Lucia and Bahamas. Our investigation here follows a procedure set out in detail in Boyd, Caporale and Smith (2001) where we examined real exchange effects on the balance of trade of eight industrial countries. Firstly, we set out the simple model of the balance of payments that we will use and the econometric approach that we shall adopt to organise the econometrics. Secondly, we describe the data and the balance of payments position for these countries. Thirdly, we describe the results and fourthly, we offer some concluding comments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".