分析社会经济指标对斯里兰卡性别不平等的影响:基于机器学习的方法
Sherin Kularathne1, Amanda Perera2, Namal Rathnayake3
1Faculty of Graduate Studies and Research, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka.
PloS one
|December 26, 2024
概括
经济增长和政府收入减少了斯里兰卡的性别不平等,而失业加剧了这种不平等. 政策制定者可以利用这些见解进行有针对性的干预,以缩小性别差距.
科学领域:
- 社会经济学 社会经济学
- 性别研究是性别研究.
- 数据科学数据科学数据科学
背景情况:
- 性别不平等仍然是斯里兰卡的一个重大挑战.
- 了解性别差异的社会经济驱动因素对于有效的政策至关重要.
- 性别不平等指数 (GII) 提供了一个综合性衡量性别差异的指标.
研究的目的:
- 分析1990年至2022年斯里兰卡社会经济因素与性别不平等指数 (GII) 之间的关系.
- 确定影响性别不平等的关键经济指标.
- 为决策者提供基于证据的建议.
主要方法:
- 机器学习技术的应用,包括决策树和集体方法.
- 分析社会经济数据,包括人均GDP,政府支出,政府收入和失业率.
- 利用可解释的人工智能 (SHAP) 来解释模型预测和识别影响因素.
主要成果:
- 人均GDP和政府收入的增加与性别不平等的减少有很大关系.
- 失业率的上升与性别差距的加剧有关.
- 可解释性人工智能 (SHAP) 强调了政府政策和经济发展在缓解性别不平等方面的重要性.
结论:
- 经济增长和强的政府收入对于减少斯里兰卡的性别不平等至关重要.
- 解决失业问题对于防止性别差距扩大至关重要.
- 建议以经济发展和包容性公共支出为重点的有针对性的政策干预措施来加强性别平等.
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