How does digitally enabled micro-finance promote income equality for the vulnerable in the expanded BRICS block during the pandemic?

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Frontiers in Big Data Pub Date : 2024-12-02 eCollection Date: 2024-01-01 DOI:10.3389/fdata.2024.1417752
Manoj Kumar M V, Nasser Almuraqab, Immanuel Azaad Moonesar, Udo Christian Braendle, Ananth Rao
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Abstract

Introduction: Tech-enabled alternative micro-finance promotes income equality in growing BRICS and Austria across financial crises and pandemics. Are financial access and digital skills equally economically valuable? Our study uses inputs: Human Capital, Alternative Micro-finance, Digitization, Governance, and Entrepreneurship, GDP, inflation, population growth, pandemics, and economic crises using the global 2000-2022 to explain income equality using SWIID Gini disposable and market income index as outputs.

Methods: The study uses Principal component analysis for reducing data dimensionality and collinearity. The study uses OLS, Dynamic Mixed Model, and random forest tree, a machine learning technique, as models to model digitally enable micro-finance.

Results: RFT model diagnostics consistently were better than OLS and GMM. Reduced income inequalities resulted from public and private infrastructure investments, government policy interventions to fight pandemics, economic crises, and conflicts, as well as from expansion in GDP.

Discussion: The study concludes that digitally enabled micro-finance plays a crucial role in reducing income inequalities, particularly during times of crisis. Key policy implications include the need for government support in digital infrastructure to enhance financial inclusion. By pooling their resources, the BRICS block can empower micro-finance organizations to ameliorate disruptions from COVID-19 and economic crises.

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导言:在不断发展的金砖国家和奥地利,技术驱动的替代性小额信贷促进了收入平等,并跨越了金融危机和流行病。金融服务和数字技能是否具有同等的经济价值?我们的研究利用了这些投入:人力资本、替代性小额信贷、数字化、治理和创业精神、国内生产总值、通货膨胀、人口增长、大流行病和 2000-2022 年全球经济危机,使用 SWIID 基尼可支配指数和市场收入指数作为输出来解释收入平等:研究采用主成分分析法降低数据维度和共线性。研究采用 OLS、动态混合模型和随机森林树(一种机器学习技术)作为模型,对数字化小额信贷进行建模:结果:RFT 模型的诊断结果始终优于 OLS 和 GMM。公共和私人基础设施投资、政府为抗击流行病、经济危机和冲突而采取的政策干预措施以及国内生产总值的增长都导致收入不平等现象的减少:本研究的结论是,数字化的小额信贷在减少收入不平等方面发挥着至关重要的作用,尤其是在危机时期。主要的政策影响包括政府需要支持数字基础设施建设,以提高金融包容性。通过汇集资源,金砖国家集团可以增强小额信贷组织的能力,以减轻 COVID-19 和经济危机造成的破坏。
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来源期刊
CiteScore
5.20
自引率
3.20%
发文量
122
审稿时长
13 weeks
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