How do different compensation schemes and loss experience affect insurance decisions? Experimental evidence from two independent and heterogeneous samples

Osberghaus, Daniel; Reif, Christiane

doi:10.1016/j.ecolecon.2021.107087

Cited by 9 publications

(1 citation statement)

References 65 publications

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“…Only a few studies in the existing literature conduct insurance demand experiments with both in-person and online samples. Osberghaus and Reif (2021) explore the role of governmental relief schemes and loss experience in the decision to insure against natural disasters. They use a university sample and also recruit online subjects from a previous survey of households in flood-prone areas; their main results are similar across the groups and so they report detailed results for the student sample only.…”

Section: Related Literaturementioning

confidence: 99%

Insurance Demand Experiments: Comparing Crowdworking to the Lab

2021

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Section: Related Literaturementioning

confidence: 99%

Insurance Demand Experiments: Comparing Crowdworking to the Lab

2021

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The presence of moral hazard regarding flood insurance and German private businesses

Hudson

Thieken

2022

Nat Hazards

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There is a movement towards the concepts of integrated flood risk management and governance. In these concepts, each stakeholder prone to flooding is tasked with actively limiting flood impacts. Currently, relatively more research has focused upon the adaptation of private households and not on private businesses operating in flood-prone areas. This paper offers an extension of this literature on business-level flood adaptation by exploring the potential presence of moral hazard. The analyses are based on survey data collected in the aftermath of six floods across Germany between 2002 and 2013 to provide a first indication of the presence of moral hazard in private businesses. Moral hazard is where increased insurance coverage results in policyholders preparing less, increasing the risk they face, a counterproductive outcome. We present an initial study of moral hazard occurring through three channels: the performance of emergency measures during a flood, changes in precautionary behavior employed before a given flood occurred, and changes in the intention to employ additional precautionary measures after a flood. We find, much like for private households, no strong indication that moral hazard is present regarding past adaptation. However, there is a potential avenue after 2005 for insurance coverage to lower businesses’ intentions to employ more adaptation measures after a flood. This has significant policy relevance such as opportunities for strengthening the link between insurance and risk reduction measures and boosting insurance coverage against flooding in general.

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What Influences the Demand for a Potential Flood Insurance Product in an Area with Low Previous Exposure to Insurance? – A Case Study in the West African Lower Mono River Basin (LMRB)

Wagner,

Thiam,

Dossoumou

et al. 2023

EconDisCliCha

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Floods portray a severe problem in the riverine areas of West Africa while more frequent and intense heavy precipitation events are projected under climatic change scenarios. Already, floods cause manifold impacts, leaving the population to cope with the financial impacts of floods through their own means. As formal risk transfer mechanisms (e.g., insurance) are not yet widely available to the population, efforts to increase their accessibility are being intensified. However, studies assessing flood insurance demand currently mostly focus on regions with more established markets. Also, they are majorly applying conventional statistical modeling approaches that consider only a small number of parameters. Contrarily, this study aims to provide an approach for assessing flood insurance in a context of low previous exposure to such products, to allow for a better consideration of the research context. Therefore, a parameter selection framework is provided and machine learning and deep learning models are applied to selected parameters from an existing household survey data set. In addition, the deep learning sequential neural networks outperformed all machine learning models achieving an accuracy between 93.5—100% depending on the loss function and optimizer used. The risk to be covered, insurance perception, no access to any source, access to support from community solidarity funds, access to governmental support, or drawing upon own resources for financial coping, financial recovery time, lack of means and prioritizing more essential needs emerged as important model parameters in researching insurance demand. Future roll-out campaigns could consider the parameters pointed out by this study.

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How do different compensation schemes and loss experience affect insurance decisions? Experimental evidence from two independent and heterogeneous samples

Abstract: How do different compensation schemes and loss experience affect insurance decisions?Experimental evidence from two independent and heterogeneous samples

Cited by 9 publications

References 65 publications

Insurance Demand Experiments: Comparing Crowdworking to the Lab

Insurance Demand Experiments: Comparing Crowdworking to the Lab

The presence of moral hazard regarding flood insurance and German private businesses

What Influences the Demand for a Potential Flood Insurance Product in an Area with Low Previous Exposure to Insurance? – A Case Study in the West African Lower Mono River Basin (LMRB)

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