Developing a Synthetic Socio-Economic Index through Autoencoders: Evidence from Florence's Suburban Areas

TL;DR

This paper introduces AutoSynth, a neural network-based method using autoencoders to create synthetic socio-economic indices, demonstrated through applications in Florence's suburbs and U.S. counties.

Contribution

The paper presents a novel autoencoder-based methodology for generating composite indices from complex datasets, applicable across multiple sectors.

Findings

AutoSynth effectively summarizes multidimensional data into a single index.

Application to Florence's suburbs reveals socio-economic vulnerabilities.

Validation with U.S. county data confirms the method's robustness.

Abstract

The interest in summarizing complex and multidimensional phenomena often related to one or more specific sectors (social, economic, environmental, political, etc.) to make them easily understandable even to non-experts is far from waning. A widely adopted approach for this purpose is the use of composite indices, statistical measures that aggregate multiple indicators into a single comprehensive measure. In this paper, we present a novel methodology called AutoSynth, designed to condense potentially extensive datasets into a single synthetic index or a hierarchy of such indices. AutoSynth leverages an Autoencoder, a neural network technique, to represent a matrix of features in a lower-dimensional space. Although this approach is not limited to the creation of a particular composite index and can be applied broadly across various sectors, the motivation behind this work arises from a real-world need. Specifically, we aim to assess the vulnerability of the Italian city of Florence at the suburban level across three dimensions: economic, demographic, and social. To demonstrate the methodology's effectiveness, it is also applied to estimate a vulnerability index using a rich, publicly available dataset on U.S. counties and validated through a simulation study.