Economic Prospects and International Labor Migration
Bibliographic record
Abstract
The expectation was a crucial element in the decision-making process about migration, so this study analyzed the impact of economic prospects on international labor migration on Java Island. The data sourced from the Indonesian Statistics includes international labor migration, economic prospects, unemployment, wage difference, and poverty in six provinces on Java Island from 2011 to 2020. This study utilized regression analysis based on the Panel Fully Modified Ordinary Least Square (FMOLS) and the Panel Dynamic Ordinary Least Square (DOLS). This method was complemented by expert judgments summarized in the Delphi analysis to identify the causes of international labor migration. The result shows that improving economic prospects negatively affects international labor migration. The better economic prospects would be followed by lower international labor migration. Meanwhile, increased unemployment and poverty rates lead to a rise in international labor migration. However, the wage difference has no impact on international labor migration. The Delphi analysis revealed that international labor migration is associated with some causes, such as vacancies in the home country, poverty, economic prospects in the home province, language similarity with the home country, distance to the destination country, and living costs in the destination country. Nevertheless, the wage difference between the wage rate in home country and the destination countries unrelated to international labor migration.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".