Impact of natural disasters on consumer behavior: case of the 2017 El Nino phenomenon in Peru

TL;DR

This study analyzes how the 2017 El Nino event in Peru affected consumer behavior using multi-scale bank transaction data, revealing macro-level impacts and individual purchasing pattern changes.

Contribution

It introduces a new multi-scale analysis method to characterize the effects of extreme weather events on consumer behavior at different granularity levels.

Findings

El Nino's presence and recovery time are detectable at coarse granularity.

Purchasing patterns and merchant relevance change during the event.

Society's economic resilience helped withstand the disaster.

Abstract

El Nino is an extreme weather event featuring unusual warming of surface waters in the eastern equatorial Pacific Ocean. This phenomenon is characterized by heavy rains and floods that negatively affect the economic activities of the impacted areas. Understanding how this phenomenon influences consumption behavior at different granularity levels is essential for recommending strategies to normalize the situation. With this aim, we performed a multi-scale analysis of data associated with bank transactions involving credit and debit cards. Our findings can be summarized into two main results: Coarse-grained analysis reveals the presence of the El Ni\~no phenomenon and the recovery time in a given territory, while fine-grained analysis demonstrates a change in individuals' purchasing patterns and in merchant relevance as a consequence of the climatic event. The results also indicate that society successfully withstood the natural disaster owing to the economic structure built over time. In this study, we present a new method that may be useful for better characterizing future extreme events.