Scaling Up Infrastructure Spending in the Philippines: A CGE Top-Down Bottom-Up Microsimulation Approach
Bibliographic record
Abstract
In this paper we use a top-down bottom-up CGE microsimulation model with endogenous labour supply and unemployment to explore the impact of scaling up infrastructure spending in the Philippines. In the current debate on the importance of scaling up infrastructure to stimulate growth, some analysts raise concerns about potential negative macroeconomic impacts (Dutch disease). This study aims to provide some insight into this debate by extending the analysis to include distributional analysis. We draw from the infrastructure productivity literature to postulate positive productive externalities of new infrastructure and from Fay and Yepes (2003) to include operating and maintenance costs associated with new infrastructure. We investigate two fiscal tools and foreign aid as mechanisms to fund the new infrastructure and associated costs. The distributional analysis is performed with FGT indices and growth incidence curves. Our results reveal that infrastructure spending reduces poverty. Foreign aid is shown to be the most equitable funding mechanism, whereas a value added tax provides the strongest poverty reduction.
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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 | 0.000 | 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".