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

Giulio Grossi, Emilia Rocco

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.

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

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.

Paper Structure

This paper contains 16 sections, 8 equations, 11 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. A brief overview of the key steps in constructing a composite index
  3. Mazziotta-Pareto Index
  4. Constructing synthetic indicators using autoencoders
  5. Input weights
  6. Post-estimation indicators relevance
  7. Sequential autoencoders
  8. Polarity
  9. Measuring Autosynth performances
  10. Assessing vulnerability in Florence
  11. Three pillars for vulnerability
  12. The data
  13. Results
  14. AutoSynth Index in a different Context: Assessing community vulnerability in the U.S.
  15. Simulations
  16. ...and 1 more sections

Figures (11)

  • Figure 1: Basic scheme of autoencoders. In this application, inputs will be indicators of socio-economic development, while the code will be the synthetic indicator
  • Figure 2: AMPI, PCA and AutoSynth vulnerability Index for Florence, normed data
  • Figure 4: Stress values for autosynth index, compared to the other methods - normed data
  • Figure 5: Stress values for autosynth index, compared to the other methods - normed data
  • Figure 6: AMPI, PCA and AutoSynth vulnerability Index for US counties, normed data
  • ...and 6 more figures