Reservation Wage for High Skilled Graduates in Bangladesh: Preliminary Factors
Bibliographic record
Abstract
This paper preliminarily discusses about the determinants of reservation wage for graduates in Bangladesh. The researchers discussed several models in order to identify the common factors which determine the reservation wage of the tertiary graduates in Bangladesh. All these models are multiple linear regression models. Data was collected on both employed and unemployed individuals across the country who graduated from different private and public universities. Initially the researchers investigated the following factors: socio-economic status (Parental income), selection of occupation, duration of unemployment, difference between actual and reservation wage, managerial level of job, sources of different media of job applications (both formal and informal) and in the revised phase of the model the researchers discovered that that inclusion of ‘previous working experience’ made a significant difference in the relationship among dependent and independent variables. One noticeable difference in the findings of the current researchers compared to those who worked on the same topic earlier was: positive relationship between duration of unemployment and reservation price of the graduates. The researchers further explored reasons of this above result theoretically. Finally, the researchers accepted the following variables (rejected as null hypothesis) as significant factors (Socio-economic background, difference between actual salary and reservation price, managerial level of job, media of job applications in terms of formal or informal, previous working experience) to influence the graduate reservation wage in Bangladesh (in the alternatives). Additionally, the researchers also explained why’ duration of unemployment’ was not selected as a crucial determinant in the current paper. Finally the researchers prescribed some positive solutions on how firms in Bangladesh could deal with the increasing demand of wage premiums from high skilled graduates and what the possible consequences are if they failed to do so.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.013 | 0.001 |
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".