Modeling of Job Mobility and Location Choice Decisions
Bibliographic record
Abstract
This paper presents a comprehensive framework for modeling continuous decisions about changing jobs that individuals make over the course of their lives. The key objective of the research was to develop disaggregate econometric models for decision making about job mobility and location choice to implement longitudinal job mobility behavior of people within a dynamic microsimulation-based integrated urban modeling system. The paper includes two behavioral model components: (a) a job mobility model and (b) a job location choice model. The models were implemented empirically with a retrospective survey of the greater Toronto and Hamilton area in Ontario, Canada. The first component investigated the timing of job mobility with the use of a competing risk duration modeling approach for four event types: a job switch, a return to school, short-term unemployment, and withdrawal from the labor force. The second component, job location choice, was empirically estimated by applying the discrete choice methodology. One of the key features of the model was that it examined the influence of current employment in making decisions about the next job location and specified a gain–loss utility structure by the prospect–theoretic, reference-dependent choice modeling approach. A mixed logit model was developed to account for unobserved heterogeneity in the location preferences. These models were expected to be implemented in the Integrated Land Use, Transportation, Environment (ILUTE) modeling system, which had recently been updated with comparable disaggregate behavioral residential location models.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".