Quantifying the Self-Selection Effects in Residential Location Choice with a Structural Equation Model
Bibliographic record
Abstract
Land use policy is viewed as a way to mitigate congestion and alleviate greenhouse gas emissions. Many studies have confirmed the reduction effect for vehicle miles traveled (VMT) of compact and mixed land use development. However, debates on self-selection effects have arisen in recent years. Researchers argue that the correlation between land use pattern and VMT may be caused by self-selection of residential location based on a person's attitude toward traveling. This paper develops an urban form indicator for describing local land use patterns and then establishes a structural equation model (SEM) with VMT, vehicle ownership, and the urban form of residential location estimated simultaneously. Residential self-selection was controlled in two ways in the model: implicitly through the correlated error terms in multiple equations and explicitly through the incorporation of expected VMT in the equation of residential location choice. The model was estimated with household travel survey data collected in 2007 in the Washington, D.C., area. The results showed that land use itself could influence travel behavior after self-selection effects were removed. A comparison of the results of the full SEM results with that of a reference SEM confirmed the existence of self-selection effects. The comparison verified that the VMT reduction effect of land use would be exaggerated without consideration of self-selection, and self-selection effect accounted for a larger part of the total effect in more compact and better mixed development areas. However, the self-selection effect was small compared with the effect of land use itself in the analyzed case.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".