A Machine Learning Approach to Improving Occupational Income Scores

These files are the replication files for "A Machine Learning Approach to Improving Occupational Income Scores" by Martin Saavedra and Tate Twinam.Abstract: Historical studies of labor markets frequently lack data on individual income. The occupational income score (OCCSCORE) is often used as an alternative measure of labor market outcomes. We consider the consequences of using OCCSCORE when researchers are interested in earnings regressions. We estimate race and gender earnings gaps in modern decennial Censuses as well as the 1915 Iowa State Census. Using OCCSCORE biases results towards zero and can result in gaps of the wrong sign. We use a machine learning approach to construct a new adjusted score based on industry, occupation, and demographics. The new income score provides estimates closer to earnings regressions. Lastly, we consider the consequences for estimates of intergenerational mobility elasticities.

本数据集为 Martin Saavedra 与 Tate Twinam 所著《利用机器学习方法提高职业收入评分》一文的复制文件。摘要：劳动市场的历史研究往往缺乏关于个人收入的数据。职业收入评分（OCCSCORE）常被用作衡量劳动力市场结果的替代指标。本研究探讨了当研究者对收入回归感兴趣时，使用 OCCSCORE 所带来的影响。我们估算现代十年一次人口普查以及 1915 年爱荷华州人口普查中的种族和性别收入差距。使用 OCCSCORE 会导致结果偏向于零，并可能产生错误符号的差距。我们采用机器学习方法，基于行业、职业和人口统计学构建了一个新的调整评分。新的收入评分提供了更接近收入回归的估算。最后，我们考虑了这些估算对代际流动性弹性的影响。