An Empirical Assessment of the Effect of Taxes and Interest Rate on Economic Growth in Jordan: An Application of Dynamic Autoregressive-Distributed Lag
Bibliographic record
Abstract
This study aims to examine the effect of taxes and interest rate on economic growth in Jordan by employing the time series data from 1970-2019. Furthermore, this study applies the Augmented Dickey-Fuller, Phillips-Perron, Saikonen and Lütkepohl and Zivot-Andrews test of unit root. Moreover, the study uses cointegration test developed by Gregory and Hansen to investigate the long-run relationship and the dynamic autoregressive distributive lags were used for the estimation result. The long run and short-run estimates reveal the positive and negative effects of taxes and the interest rate on economic growth respectively. While the 1997 Asian financial crisis and 2015 food crisis show a negative effect on economic growth. Based on the findings, the study recommends that the government authorities in Jordan should lower the interest rate that will increase the investment in order to have faster economic growth. The government should urgently plan to broaden the tax base to stimulate economic growth in Jordan. Regulators should encourage banks to start raising capital immediately to strengthen capital ratios well above prudential norms, and prepare schemes for public recapitalization and, where appropriate, public purchases of non-performing assets. The next policy fulfils the government's need to enhance agricultural productivity through better technology to ensure long-term food security and reduce poverty, as well as help to boost economic growth.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".