Learning from the past: a machine-learning approach for predicting the resilience of locked-in regions after a natural shock

ABSTRACT

Italy has been affected by many different shocks in recent years, from the Great Recession to many natural hazards. While many studies have analysed the effects of natural and socio-economic shocks on urbanized and developed areas, very few have focused on locked-in and less developed regions. In this study we focus on the pernicious effects of three earthquakes that have affected the labour markets of rural and inner municipalities of Central Italy during the last 20 years. We adopt a machine-learning technique that allows us to provide a scenario five to seven years after the earthquake for 133 municipalities affected by the Central Italy earthquake in 2016.

KEYWORDS:

• economic resilience
• locked-in regions
• socio-natural disasters
• machine learning

JEL:

• Q52
• Q54
• R11
• R12

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Notes

1. Generally, inner areas are economically vulnerable municipalities situated far from cities and large urban agglomerates.

2. We exclude municipalities affected by major earthquakes before the 1990s (e.g., the Friuli earthquake in 1976 and the Irpinia earthquake in 1980) due to changes in the structure and content of the Italian Census over the years.

3. The start of the ‘five-year period after the earthquake’ is lagged two years after the event.

4. The nominal number of features in the model is actually 31. This is due to the feature ‘Population trend’, recording the population growth trend in the period before the earthquake, which is split into five features recording yearly variations rates.

5. The different logical and mathematical functioning of the algorithms is not discussed here, but see Aggarwal (2014).

6. Indeed, there are different methods to solve, for example, a logistic regression, therefore different specific algorithms can be employed. These ‘specific’ algorithms are called solvers.

7. We provide results for the RFE only; details on the F-test are available from the authors upon request.

8. Liblinear is an open-source library for large-scale linear classification. The solver is a linear classificator that supports logistic regression and linear support vector machines. It is recommended when one has a high-dimension dataset.