Who is more ready to get back in shape?

TL;DR

This empirical study analyzes resilience to flooding in Cambodia using microfinance data and unsupervised learning to identify factors influencing adaptive capacity and resilience development.

Contribution

It introduces an unsupervised learning approach to assess resilience using large-scale microfinance data, highlighting regional and individual factors affecting adaptability.

Findings

Certain areas show higher resilience based on customer characteristics

Individual choices significantly influence adaptive capacity

The approach has limitations in capturing all resilience factors

Abstract

This empirical study estimates resilience (adaptive capacity) around the periods of the 2013 heavy flood in Cambodia. We use nearly 1.2 million microfinance institution (MFI) customer data and implement the unsupervised learning method. Our results highlight the opportunity to develop resilience by having a better understanding of which areas are likely to be more or less resilient based on the characteristics of the MFI customers, and the individual choices or situations that support stronger adaptiveness. We also discuss the limitation of this approach.