A Segmented Labour Supply Model Estimation for the Construction of a CGE Microsimulation Model: An Application to the Philippines
Bibliographic record
Abstract
Labour market analysis is an important element to understand the inequality and poverty within a given population. The literature reveals that the informal sector is characterised by a great deal of flexibility and is exempt from formal market rigidities on the one hand, but on the other hand, this sector can constitute a trap from which it is difficult to exit for workers active in the sector with low wages. In this article, we aim to identify the main characteristics differentiating the labour supply of workers on the informal and formal market in the Philippines while estimating these two labour supplies, capturing discrete choice or changes in employment status. We use these estimates to construct a labour supply model that can serve as an input for a broader macro–microsimulation model applied to the Philippines. The results of the estimation provide relatively intuitive findings, highlighting some differences between the two markets. We also shed some light on this macro–microsimulation modelling framework that is generally opaque, in describing how to construct a microsimulation model with endogenous discrete choice model linked to a computable general equilibrium (CGE) model. JEL Classification: C35, O53, J24, C81, O17
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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 | 0.004 | 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".