Earthquake insurance can be a useful tool to build more sustainable societies and disaster- resilient communities. However, the coverage is not common in many countries. This article aims to contribute to the literature through an empirical analysis of the online interest in earthquake insurance through Google Trends. The proposed methodology implies to move from a top-down conceptual approach to a bottom-up/data-enabled one. It allows us to explore potential triggers and dynamic patterns of online interest in earthquake insurance at daily time-scale through the lens of Big Data. In order to validate the meth- odology, the article considers Italy as a test area. For this country, where the coverage rate is low, we fuse multiple databases to create 16-year daily time series of public search activities about the insurance in Italy and analyse it with other data sources. As a result, the peak analysis shows a connection with the occurrences of large domestic earthquakes, overseas earthquakes, and policy decisions, which create time windows of opportunities for insurers and policymakers to boost the public’s motivation towards the coverages. The research outcomes suggest that the data-enabled approach can additionally be applied in other countries where the coverage rate is low and stakeholders are facing the challenge to strive against earthquake under-insurance.
Time windows of opportunities to fight earthquake under-insurance: evidence from Google Trends
Porrini, Donatella
2020-01-01
Abstract
Earthquake insurance can be a useful tool to build more sustainable societies and disaster- resilient communities. However, the coverage is not common in many countries. This article aims to contribute to the literature through an empirical analysis of the online interest in earthquake insurance through Google Trends. The proposed methodology implies to move from a top-down conceptual approach to a bottom-up/data-enabled one. It allows us to explore potential triggers and dynamic patterns of online interest in earthquake insurance at daily time-scale through the lens of Big Data. In order to validate the meth- odology, the article considers Italy as a test area. For this country, where the coverage rate is low, we fuse multiple databases to create 16-year daily time series of public search activities about the insurance in Italy and analyse it with other data sources. As a result, the peak analysis shows a connection with the occurrences of large domestic earthquakes, overseas earthquakes, and policy decisions, which create time windows of opportunities for insurers and policymakers to boost the public’s motivation towards the coverages. The research outcomes suggest that the data-enabled approach can additionally be applied in other countries where the coverage rate is low and stakeholders are facing the challenge to strive against earthquake under-insurance.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
