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Record W3036296976 · doi:10.5430/rwe.v11n3p124

Income Diversification Among Rural Households in the Mekong River Delta, Vietnam: A Look Back at the Economic Transition Period

2020· article· en· W3036296976 on OpenAlexvenueno aff
Long Hau Le, Tan Nghiem Le

Bibliographic record

VenueResearch in World Economy · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Innovations and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsDiversification (marketing strategy)WageAgricultureEconomicsSocioeconomicsDemographic economicsHousehold incomeMekong deltaGeographyLabour economicsAgricultural economicsBusiness

Abstract

fetched live from OpenAlex

Based on five Living Standard Measurement Surveys (LSMSs) conducted over a thirteen year period (1993 to 2006), this paper examined patterns of income diversification in rural areas of the Mekong River Delta (MRD). In terms of quintile specific patterns, over the period 1993-2006, across all quintiles there is a sharp reduction in the time spent on farm self-employment (9.4 to 20.7 percentage points) and an increase in the share of time spent on non-farm wage employment (11.3 to 14.3 percentage points). While there are differences across quintiles, the patterns are broadly similar across expenditure groups and it does not seem that the increase in non-farm wage employment is restricted to particular groups of households. As may be expected given the changes in the activity-allocation pattern, over time, there is an increase in reliance on non-farm wage income by about 6.4 to 11.4 percentage points across quintiles. The interesting aspect is that while households in the poorest income quintiles still continue to rely heavily on agriculture related income (61.2 versus 39.9 percent for the richest quintile) they experience similar patterns of change in terms of a movement from relying on farm income to non-farm sources of income.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.093
GPT teacher head0.294
Teacher spread0.201 · 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.

Study designObservational
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

Citations3
Published2020
Admission routes1
Has abstractyes

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