Multinomial Choice with Social Interactions: Occupations in Victorian London
Bibliographic record
Abstract
This paper presents a multinomial choice model with social interactions in an incomplete network. Individuals form heterogenous rational expectations about the behavior of peers by taking into account their characteristics and the strength of their ties. We show the network conditions under which the endogenous and exogenous effects can be separately identified even in the presence of correlated effects at the group level. Conditions for unique equilibrium are established. We apply our empirical model to occupational choice in nineteenth century London. Thanks to a newly constructed dataset, we use ecclesiastical parish boundaries as proxies for social groups and geographic distances between individuals as measures of the strength of their ties. Our results show that endogenous network effects were important above and beyond correlated and exogenous effects. We uncover distinct impact by occupation type: peers in professional and industrial occupations have a positive impact on the likelihood of following a similar occupation while commercial have a negative one.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".