Better Opportunities for All : Vietnam Poverty and Shared Prosperity Update
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A massive reorganization of the rural \n labor market is underway, with workers leaving agriculture \n in large numbers. The agriculture sector has been \n consistently losing an average of 4 percent of its workforce \n annually since 2013. Most of those leaving agriculture have \n remained in rural areas and been absorbed into \n non-agriculture sectors, which have been creating rural \n nonagricultural jobs at a rapid pace. Nearly 4 million \n off-farm jobs have been created in rural areas since 2013, \n mostly in the industry sector, led by manufacturing. There \n are now almost as many non-agricultural jobs as agricultural \n jobs in rural areas. The share of people in wage employment \n in rural areas has risen dramatically, reaching 38 percent \n in the first quarter of 2018, compared with just 28 percent \n in 2013. Unlike densely populated areas, growth of \n non-agriculture sectors in more distant, low-density areas \n is normally based on absolute advantage, driven by external \n demand, and delivered mostly by small and medium enterprises \n (SMEs) due to the limited scope for achieving scale. \n Strategies to expand economic opportunities in these areas \n should aim to: (i) create a secondary economy supporting \n industries based on the regional absolute advantages; (ii) \n integrate these areas into the network economy to expand \n their market potential; and (iii) reduce the cost of \n migration to increase long-distance migration domestically. \n This report is therefore focused on identifying the \n challenges preventing, and ways to enhance, the poor’s \n participation in more productive income-generating \n opportunities. The analysis focuses exclusively on rural \n areas, where 95 percent of the current poor reside. It is \n presented in four sections. The first section presents the \n evolution of rural incomes in Vietnam since 2010, showing \n how non-agricultural incomes have grown in importance and \n broadly transformed rural livelihoods. The second section \n then explores the role of household and farm-specific \n attributes, alongside local economies, in facilitating \n non-agricultural employment, to identify the most critical \n factors holding back the poor from being integrated into \n off-farm activities. The third section turns to \n opportunities in agriculture. This focuses on identifying \n challenges and policy remedies for optimizing crop and \n land-use choices among lagging groups to maximize their \n agricultural incomes. The report concludes with a section on \n policy implications, building on the presented analysis to \n suggest policy options that provide a pathway for the \n economic integration of the poor.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it