Effect of Internal and External Investment on Economic Growth and Unemployment of Pakistan
Bibliographic record
Abstract
This study focus on the impact of internal and external investments on unemployment rate and economic growth in Pakistan. The study uses data consist of the time span of 1984-2019.For the empirical estimation of the results, the Autoregressive Distributed Lags (ARDL) Model used for short-run and for long-run ARDL-Bound test approach use. The study’s empirical results suggest that internal investment increase the short-run as well as long-run GDP growth in Pakistan. Similarly, internal investment also decrees the long-run as well as short-run unemployment in Pakistan. On the other hand, the foreign investment have only short-run favorable impact on both Pakistan’s unemployment and economic growth. The short-run effect of foreign investment did not transmit into long-run because empirical evidence depicts the foreign investment’s insignificant impact on the unemployment rate and economic growth of Pakistan in long-run. The findings of the study ended with the policy measure and suggestions to channelize the effect of foreign and internal investment.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".