Income inequality analysis through complex network and nonlinear time series approaches: an Econophysics perspective

The present work delineates the potential of nonlinear time series, phase portraits, and complex network analyses in investigating and forecasting income inequality, taking India, the world’s most populous country, as an example. The multidimensional Econophysics analysis unravels the correlation of income inequality with gross domestic product (GDP), Gini coefficient, population, and income shares. The Lyapunov exponent ( L ) and sample entropy ( S ) analyses indicate the rising income inequality. By segmenting the data into three regions, the L analysis suggests a converging phase portrait and reducing income inequality. Self-similarity and Spearman correlation-based complex network analyses show strong data correlation confirming the results of L and S . The GDP, population, and income shares in region 3 (R3) exhibit a third-order polynomial relation with a high R 2 value substantiating the forecast. The increasing GDP, decreasing income inequality, and retarded population growth in R3 suggest a bright future for the Indian economy. The study, thus, highlights the application of time series and complex network analyses in the emerging interdisciplinary field of Econophysics, representing a new frontier for enhancing our understanding of complex economic systems and guiding evidence-based policy interventions, as illustrated by the analysis of income inequality data. Graphical abstract