An Analysis of Habitual Mode Use in the Years of Rising Oil Prices
Bibliographic record
Abstract
The existence of state dependence derived from panel data has played a very important role in studying employment and labor policies. This study is about state dependence of the transportation sector using retrospective panel survey data. The Ministry of Land, Infrastructure and Transport of Korea has conducted the survey to monitor changes in vehicle ownership and usage nationwide and to prepare measures when oil prices tend to rise sharply. From this data, we identify the existence of state dependence on passenger cars, public transportation, and nonmotorized modes. To do this, we estimate and analyze the dynamic random effects probit model that explains the selection of each transportation mode after controlling for the unobserved individual heterogeneity. Our results indicate that despite the rise of oil prices, behavior of habitual use (i.e., state dependence) of transportation modes is found in all three modes. The amount of state dependence of nonmotorized modes was the largest, followed by passenger cars and public transportation. From the estimated models, important policy implications can be drawn from the fact that the presence of state dependence and the importance of early habit formation are important not only in nonmotorized modes but also in public transportation. In other words, if policy makers want to encourage people to use public transportation in a new city, it suggests that a sufficient and convenient public transportation network should be built before people move to the city. Once cities are built without sufficient public transportation networks and people have become accustomed to using private cars, then it will be more difficult to change their transportation modes, requiring much more social efforts and costs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".