Empirical Evidence of Okun's Law in the Philippine Economy: A Cointegration Analysis
Bibliographic record
Abstract
This study investigates the influence of relevant macroeconomic variables on unemployment in the Philippines. Specifically, it aims to: find a coefficient that would characterize the relationship of the between unemployment and GDP using a standard cointegration approach; investigate relevant macroeconomic factors that may affect unemployment; and draw economic and policy implications that will guide policymakers in crafting public policies in improving employment generation. It used quarterly time series data from the first quarter of 1989 to fourth quarter of 2004. Cointegration test was used to determine the long-run relationship while an error correction was employed to determine the short-run behavior of the data. Results of the study reveals that in the long-run a 1% increase in GDP is associated with a reduction in unemployment by 0.7%. While in the short-run, GDP has a larger effect on cutting unemployment; an increase in GDP by 1% results to a decrease in unemployment by 0.95%. Thus there is evidence to indicate that Okun’s law is relevant in the Philippine economy. The sectoral analysis shows that the economy’s industrial and agriculture sectors are found to have a negative effect on the general unemployment level. However, the service sector on the other has a positive effect, implying that as its output grows, unemployment tends to rise as well. This is probably due to job and skills mismatch in the service sector which leads to structural unemployment. It is recommended that the industry sector be prioritized especially the manufacturing and construction subsectors, in the development planning process.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; both teacher heads agree on what is shown here.
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".