许多研究将受教育的收入进行回归，并将估计系数报告为“明瑟回报率”。最近的文献使用工具变量。本章考虑这些分析的经济学解释，以及重复截面和面板数据的可用性如何提高分析师估计收益率的能力。我们考虑明瑟模型在什么条件下估计事后收益率。我们在美国人口普查数据的六个横截面上对模型进行了检验和拒绝。我们提出了一种估计边际内部收益率的一般非参数方法，该方法考虑了学费、所得税和各种形式的不确定性。我们还使用合成队列方法，对基于单个数据横截面的估计与基于实际队列后重复横截面的估计进行对比。根据重复截面数据拟合的基于队列的模型提供了事后收益的更可靠估计。考虑不确定性影响对收益率的估计。对连续披露的信息进行会计处理，对内部收益率作为政策分析工具的有效性提出了质疑。 An alternative approach to computing economic rates of return that accounts for sequential revelation of information is proposed and the evidence is summarized. We distinguish ex ante from ex post returns. New panel data methods for estimating the uncertainty and psychic costs facing agents are reviewed. We report recent evidence that demonstrates that there are large psychic costs of schooling. This helps to explain why persons do not attend school even though the financial rewards for doing so are high. We present methods for computing distributions of returns ex ante and ex post. We review the literature on IV estimation. The link of the estimates to the economics is not strong. The traditional instruments are weak, and this literature has not produced decisive empirical estimates. We exposit new methods that interpret the economic content of different instruments within a unified framework.