Remittances, electricity consumption and electric power losses in Jamaica
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the impact of remittance inflows (remittances) on electricity consumption and electric power losses in Jamaica. Design/methodology/approach The authors use annual data from 1976 to 2014 and apply vector error correction modelling, Granger causality testing and impulse response analysis. Findings First, the authors find that there is co-integration between remittances and the energy variables, namely electricity consumption and electric power losses. Second, short-run Granger causality exists between the energy variables and remittances. This causality is bidirectional between the energy variables and positive changes in remittances, but it is unidirectional running from the energy variables to negative movements in remittances. Third, the authors find that in the long-run remittances have a negative relationship with electric power losses and a positive relationship with the consumption of electricity. Practical implications Findings from this paper will help to elucidate the relationship between electricity consumption, and electric power losses, and remittances. Social implications The problem of electric power losses is acute in Jamaica and it is mostly due to theft. At the same time, Jamaica receives significant remittances. Social policy could have a role to encourage the use of remittances to help stem the theft of electricity. Originality/value This is the first study that examines the relationships between remittances, electricity consumption and electric power losses.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".