Exploring Economy Dependence in the Middle East Using Governmental Accounting Indicators: The Case of Palestine, Jordan & Israel
Bibliographic record
Abstract
This paper aims at examining the causality between Palestine, Jordan, and Israel economics using three macroeconomic (governmental accounting) measurement indices: Gross Domestic Product [GDP], Inflation Rate [IR] and Unemployment Rate [UR]. In order to achieve this purpose, this manuscript employs a macroeconomic time series analysis on data gathered Palestine, Jordan, and Israel from 1997-2014. The paper employs a variety of econometric statistical methods (e.g. descriptive statistics, correlation tests, ordinary least squares, and Granger causality test). The findings of this paper statistically support the notion that both GDP in Israel and GDP in Jordan effects the Palestinian GDP. These findings put an emphasis on the dependency of the Palestinian economy on both the Jordanian and Israeli economies. Furthermore, in lieu of the findings, this study recommends that fiscal policy makers in Palestine exert serious efforts to attract additional foreign and expatriate investments, attempt to create a stable and attractive entrepreneurial and investment climate, and build national support for local products and services to minimize the interdependence. These recommendation could inspire greater confidence in the Palestinian economy and help create a better investment climate.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".