创业
中国
弹性网正则化
Lasso(编程语言)
山脊
特征(语言学)
古典经济学
经济地理学
营销
经济
计量经济学
业务
人工智能
计算机科学
政治学
地理
特征选择
财务
哲学
万维网
地图学
语言学
法学
作者
Feng Li,Xiao Long,Lin Dong,Mingjie Fang
标识
DOI:10.1016/j.chieco.2023.102029
摘要
Entrepreneurs and entrepreneurship are essential for new wealth creation and economic growth, particularly in developing countries. The objective of this study is to develop a model to identify the determinants of entrepreneurship, and reveal how it can be predicted based on individual characteristics, family environment, and social environment. Employing 16 sets of machine learning algorithms on data collected from the Chinese General Social Survey in 2017, we find the best-performing algorithms (i.e., lasso, ridge, and elastic net regression) and examine the effects of the feature variables on entrepreneurship. Overall, this study provides significant theoretical underpinnings for entrepreneurship research, and offers insights for individuals and policymakers by revealing various drivers of entrepreneurship.
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