The use of ICTs and income distribution in Brazil: A machine learning explanation using SHAP values

Loading...
Thumbnail Image
File version

Version of Record (VoR)

Author(s)
Herrera, GP
Constantino, M
Su, JJ
Naranpanawa, A
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Date
2023
Size
File type(s)
Location
Abstract

This study explores the complex relationship between information and communication technologies (ICTs) and socioeconomic characteristics. We employ a cutting-edge explainable machine learning approach, known as SHAP values, to interpret an XGBoost and neural network model, as well as benchmark traditional econometric methods. The application of machine learning algorithms combined with the SHAP methodology reveals complex nonlinear relationships in the data and important insights to guide tailored policy-making. Our results suggest that there is an interaction between education and ICTs that contributes to income prediction. Furthermore, level of education and age are found to be positively associated with income, while gender presents a negative relationship; that is, women earn less than men on average. This study highlights the need for more efficient public policies to fight gender inequality in Brazil. It is also important to introduce policies that promote quality education and the teaching of skills related to technology and digitalization to prepare individuals for changes in the job market and avoid the digital divide and increasing social inequality.

Journal Title

Telecommunications Policy

Conference Title
Book Title
Edition
Volume

47

Issue

8

Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement

© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Item Access Status
Note
Access the data
Related item(s)
Subject

Business information systems

Data management and data science

Machine learning

Political economy and social change

Persistent link to this record
Citation

Herrera, GP; Constantino, M; Su, JJ; Naranpanawa, A, The use of ICTs and income distribution in Brazil: A machine learning explanation using SHAP values, Telecommunications Policy, 2023, 47 (8), pp. 102598

Collections