Understanding the Impacts of Flooding on Social Vulnerability and Analyzing the Effect of Coverage Maximums on Flood Insurance Demand
Bibliographic record
Abstract
This research explores geospatial patterns in social vulnerability to flooding and experimentally examines the effect of coverage maximums on flood insurance demand. In the first chapter, I analyze census data for the City of Calgary from 1991-2016 to identify trends in social vulnerability based on flood hazard level. Using a quasi-experimental design, I estimate the short-term changes in social vulnerability attributable to the 2013 Calgary flood. The results show that the Calgary flood was associated with a 2.6% increase in postsecondary education, a 1.4% decrease in the immigrant population, a 1.7% decrease in the visible minority population, a $7,100 increase in median family income, 2.8% decrease in home ownership, 3.7% increase in housing construction and 2.2% increase in recent movers. Together, these findings suggest that the highest flood hazard areas in Calgary are generally comprised of lower vulnerability populations; absolute loss potential from floods is getting higher over time due to higher property wealth in high flood hazard areas; and flooding events are associated with a decline in social vulnerability over the short-term. In the second chapter, I examine flood insurance coverage preferences through the use of a hypothetical choice experiment. The experiment was designed to examine the effect of dwelling value and coverage limit on the probability of flood insurance purchase, while holding the probability of flooding and insurance price constant. Controlling for income, the results indicate that amount of coverage is negatively related to flood insurance demand, however, for people in high-value dwellings the opposite is observed. This may suggest an approach to flood insurance as an investment into high-value properties as a financial asset, but the trade-off in higher yearly premiums may not seem worth the investment for lower-valued dwellings. This research shows an inconsistent demand for flood insurance, dependent on dwelling value and independent of income.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".