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 estimated 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.

对劳动力市场的历史研究经常缺乏有关个人收入的数据。职业收入分数（OCCSCORE）通常用作劳动力市场成果的替代度量。当研究人员对收益回归感兴趣时，我们考虑使用OCCSCORE的后果。我们估算了现代十年人口普查以及1915年爱荷华州人口普查中种族和性别收入的差距。使用OCCSCORE偏差会使结果趋于零，并可能导致错误符号的估计间隙。我们使用机器学习方法，根据行业，职业和人口统计数据构建新的调整后分数。新的收入得分提供了更接近收益回归的估​​计值。最后，我们考虑了代际流动弹性估计的结果。