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Record W3034633082 · doi:10.6000/1929-7092.2020.09.24

Philippine Household Income Mobility Measurement and its Decomposition using a Pseudo-Longitudinal Panel Data

2020· article· en· W3034633082 on OpenAlexvenueno aff
Melcah Pascua Monsura

Bibliographic record

VenueJournal of Reviews on Global Economics · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsIncome distributionTotal personal incomePovertyNet national incomeIndex (typography)Economic inequalityEconomic mobilityDemographic economicsWelfareDistribution (mathematics)Income in kindPanel dataComprehensive incomeHousehold incomeLabour economicsSocial mobilityInequalityGross incomeEconometricsEconomic growthGeographyPublic economics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.374
GPT teacher head0.390
Teacher spread0.016 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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Same venueJournal of Reviews on Global EconomicsSame topicIncome, Poverty, and InequalityFrench-language works237,207