The decision to migrate: A simultaneous decision making approach
Bibliographic record
Abstract
Discrete choice models are used to investigate the individual’s choice among a discrete number of alternatives. The characteristics of each alternative, by means of multinomial and nested multinomial models, have been taken into account. Specifically, this study analyses the impact of choice-specific characteristics (economic and socio-political attributes) in a model of choice between different country locations. Individual IT-graduates are assumed to choose a single type of move, stay-home or go-abroad, while simultaneously choosing a country of their choice. We demonstrate that a nested logit model is appropriate on both theoretical and empirical grounds. The sample consists of 1,500 IT-graduates from India. The results show on the one hand a high migration propensity for foreign destinations and on the other hand a quite large number of IT-Graduates who want to stay at home. By comparing the direct elasticities (at branch level) of home with those of foreign destination types we observe that both the economic as well as socio-political factors tend to have a greater impact for the foreign destinations. Based on the cross elasticities values, a location comparison between the destinations Germany and the USA/Canada shows that the magnitude of the values of elasticities are found to be higher for North American countries than for Germany. This suggests that IT-Graduates evaluate the economic as well as the socio-political factors as more important and significantly higher for North American destinations than for Germany. In addition we find strong evidence for a competition between countries with high potentials, with India emerging as an attractive location.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".