INVESTIGATION OF THE IMPACT OF FOREIGN REMITTANCE ON AGRICULTURAL DEVELOPMENT IN PAKISTAN: A Time Series Analysis
Bibliographic record
Abstract
The present study investigates the impact of foreign remittance on agricultural development in Pakistan, from different regions of the world. Segregated time series data of remittance, agricultural GDP, primary school enrollment and gross fixed capital formation in agriculture sector were taken for the period 1972 to 2012. Co-integration technique was employed to analyze the longrun impact of these variables on agricultural GDP. The coefficients of remittance from Kingdom of Saudi Arabia, UAE, United Kingdom and other Gulf and European countries were found to be significant and positive in the long-run, but it was non-significant in the short-run. The effect of remittance from advanced countries as USA, Canada and Australia showed a negative and significant effect on agricultural GDP in the long-run but it was non-significant in the short-run. The variables of primary school enrollment and gross fixed capital formation were also significantly and positively associated with agricultural GDP growth in the long-run. The findings reveal that remittance play a vital role to meet needs of the agricultural sector. This study suggests that government should devise a policy to encourage migrant’s households in rural Pakistan and use remittance in productive activities. The results also suggest that policies should also be devised to promote primary education and increase the fixed-capital formation in agriculture sector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".