Abstract

The concept of resilience has been applied in several fields of academic research and has also grown in popularity among economists. The main contribution of this article is the systematic analysis and interpretation of the existing large body of resilience literature in economics by using topic modeling, a modern machine-learning research method. The advantage of this method is that it offers a more in-depth understanding of the themes in the resilience literature as opposed to the terminological classifications typically used in bibliometric studies. The results show that the identified topics are spread widely across different subareas of economics and deal with diverse themes, such as adaptation to climate change, stability of the financial system, and various types of shocks in regional economies. The findings reveal that the literature can be divided into two domains: one that deal with incremental changes occurring over a long period of time and the other dealing with unexpected, transient, and sudden changes. Furthermore, according to the results, well-known, highly cited research papers combine knowledge from different fields. Policymakers seeking to support cutting-edge research projects may benefit from this finding, as it emphasizes the need for policy measures to enhance cross-fertilized research.