代理(统计)
估计员
计量经济学
因子分析
收益
估计
构造(python库)
回归
统计
经济
计算机科学
数学
会计
管理
程序设计语言
作者
Mohitosh Kejriwal,Xiaoxiao Li,Linh Nguyen,Evan Totty
摘要
Summary A common approach to addressing ability bias is to augment the earnings‐schooling regression with proxies for cognitive and non‐cognitive skills. We evaluate this approach using a factor model framework, which allows consistent estimation of the returns to schooling without relying on proxies. The factor model estimators may be viewed as implicitly estimating proxy measurement error and/or accounting for omitted dimensions of ability. A bias decomposition quantifies the contribution of the proxies while the estimated latent skills are used to construct direct tests for their viability. Both sets of results confirm the inadequacy of the proxies in capturing the latent skills.
科研通智能强力驱动
Strongly Powered by AbleSci AI