Philippine Household Income Mobility Measurement and its Decomposition using a Pseudo-Longitudinal Panel Data
Bibliographic record
Abstract
When economic growth does not translate into poverty reduction and it remains a challenge, it is crucial to examine income mobility since income is a measure of individual economic status or poverty status.To understand the role of economic growth on welfare when there is income mobility, this study measured the Philippine households' income mobility utilizing pseudo-longitudinal panel data from the Family Income and Expenditures Survey (FIES) of 2003 to 2012.Using various income mobility indices such as chi-square, average jump index and Shorrocks mobility index, the results revealed that the households' income movement was more mobile than expected.This means that the households' income status improved through time, low-income rank moved to higher-income rank in a given income distribution.In addition, short-run income inequality was reduced by 87.30 percent (87.30%) when there was income mobility.The presence of income mobility in the country was mainly due to the transfer effect which indicates that households did not take the economic opportunities of economic growth to increase their economic status.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".