Whether to insure against the weather: demand for extreme weather insurance in developing and developed country contexts
Bibliographic record
Abstract
Many households in developing and developed countries will face increased extreme weather events due to climate change. Insurance could be a key coping strategy against the associated impacts of extreme weather. There is value in better understanding the characteristics that make insurance an appropriate means of coping for some sub-groups over others. The framework for household decisions to insure used in this research focuses on four factors: 1. economic, 2. social and cultural, 3. structural, and 4. personal and demographic. This thesis considers two case studies: agricultural index-based microinsurance in rural Uganda and home flood insurance in the USA It seeks to understand intended demand and the related drivers for insurance in these settings through the use of large-N surveys, field games, and on-line simulations. The rural Ugandan survey tool was implemented using innovative smart-phone technology and yielded 3000+ observations of expressed willingness-to-join (WTJ) and willingness-to-pay (WTP) for agricultural microinsurance. This tool also obtained information concerning propensity to engage with alternative coping strategies, both formal and informal. It also obtained household indicators of the factor classesnoted above. A separate field game in Uganda investigated attitudes towards basis risk arising from index insurance using a novel, iterative game involving farmers allocating their wealth between insurance and crop production. The game is played in partner sets to gauge the relative influence of others’ decisions and outcomes on one’s choice to insure. The USA study compares propensity to purchase flood insurance between those affected and unaffected by Hurricane Sandy in the same geographic areas. We obtained 800 observations from an online survey tool, combining survey questions and a flood insurance purchase simulation. In the simulation we include as a treatment a more extensive (graphical) presentation of expected losses to assess the effect oninsurance uptake rates. In the Ugandan case, WTJ is over 95% and the average WTP is moderate relative to household wealth. For our sample there is evidence that microinsurance and loans are substitutes and the most frequently chosen traditional coping strategy is selling cattle. In the American study, respondents insure in just over 50% of the presented simulations and over 60% have a positive stated WTJ. Notably, there is little insurance demand difference between cohorts affected and unaffected by Hurricane Sandy. In both studies, a significant proportion of respondents with disparate personal characteristics chose to always or never insure, regardless of the details of the simulation scenarios, though WTJ varies positively with expected losses; this behaviour may be related to affect from the feeling of insurance. In the Ugandan study, occurrence of basis risk reduces WTJ in the following period and respondents clearly are affected by the choices made by their partners. In the American study, insurance adoption is greater for the cohort exposed to the more extensive (graphical) presentation of expected losses. In both cases we find that of the four factor classes social and cultural as well as structural factors are frequently significant in regression models for intended insurance demand. As weather-related covariate risks increase in the future, households need coping mechanisms that are culturally viable and conform to individuals’ preferences. This thesis demonstrates methods by which to determine intended demand for extreme weather insurance in the developing and developed country contexts. Such information can inform the development of insurance tools consistent with consumer preferences and help identify households that may be the best candidates for use of insurance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".