Measuring the Impact of Economic Stability and Remittances of Overseas Workers on Bank Deposits: the Case of Jordan
Bibliographic record
Abstract
This study aims at measuring the impact of economic stability represented by (Inflation rate, Economic growth) and remittances of overseas workers on bank deposits in Jordan during the period (2000-2015).The study used the multiple linear regression method by the (E-views) program to study the impact of the independent variables on the dependent variables. Statistical analysis showed the presence of a statistically significant, positive correlation for both the rate of inflation and workers' remittances on current deposits, saving deposits and time deposits. The statistical analysis also showed that there is a statistically significant, negative correlation between economic growth rate on current deposits, saving deposits and time deposits.The study reached a number of recommendations, most important is: the need for local banks administration to take into consideration the economic factors and variables, including inflation, economic growth and remittances of overseas workers, which directly affect the bank deposits in Jordan, study their evolution and forecast their future value to take various measures that will be positively reflected on the growth of bank deposits.Another recommendation is the need for local banks administration to attract more remittances of overseas workers, through genuine and honest programs that provide benefits for overseas workers and the bank alike.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".