Job satisfaction in developing countries
Bibliographic record
Abstract
Purpose The purpose of this paper is to contribute to this literature on developing countries by investigating the determinants of job satisfaction in Vietnam where the economics literature on this issue is virtually non-existent. The authors also contribute to the literature on income comparison by extending beyond the within-firm co-worker income comparison. Design/methodology/approach The authors estimate a generalized order logit model for job satisfaction as statistical tests suggest that the parallel-lines assumption, which is often invoked in previous studies using the standard logit model, does not hold. Findings For Vietnam, the authors find that absolute and relative incomes as well as human resource practices such as efficiency wage and training policy have an impact on workers’ satisfaction. Workers in the foreign direct investment (FDI) sectors behave a bit differently from their peers in the domestic sector. Originality/value Taking advantage of a unique matched employer–employee data set collected in 2008 by the North-South Institute (Canada) and the Vietnam Academy of Social Sciences, the authors are able to investigate the impact of a number of important job characteristics on job satisfaction such as absolute and reference incomes, wage policy, training plan for workers, union membership and job position, and, at the same time, to disentangle the possible differences in job satisfaction of workers in domestic vs FDI firms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".