Evaluation of Policy Impacts

该资助涵盖的第一个项目是劳动力市场一般均衡模型估计工作的延续（由NSF-SBR 9730657支持）。众所周知，应用微观经济学家很少提到，人们无法在“部分均衡环境”中正确评估国家政策。对于处理技能获取的政策尤其如此。在使用数据进行政策分析时，劳动经济学家通常只关注技能的供给，而忽略了需求。问题在于，随着拥有一项特定技能的人数增加，该技能在劳动力市场上的价值就会下降。这个效果阻碍了技能的形成。我们之前的研究表明，在进行政策评估时考虑一般均衡效应可以将结果改变十倍之多。迄今为止，我们工作的主要弱点是实证方法。我们使用标准方法估计了个体工人的参数，该方法隐含地假设劳动力市场不随时间变化。这种“稳定状态”的假设显然是有问题的，因为已经有大量文献证明了工资结构的变化。最大的问题是，我们估计的模型与我们模拟时使用的一般均衡模型不一致。这项工作的主要目标是估计过去三十年美国劳动力市场的动态一般均衡模型。这包括同时估计和模拟美国劳动力市场变化的模型。这一过程保证了模拟模型与美国经济的相似性。我们正在使用估计模型来研究几种不同的政策，这些政策旨在解决劳动力中日益严重的不平等问题。这些项目的一个主要目标是降低收入不平等。这个版本的模型将对经济中的收入分配提供一致的估计，并使我们能够比以前的工作更好地估计政策对收入不平等的影响。我们还扩展了该模型，以考虑针对低工资工人的福利政策和工资补贴的影响，以及一些旨在解决收入不平等问题的其他政策。第二个项目的目标是解决一个特定的方面，这个方面在大多数差中差估计器的实现中可能非常重要。当一个单位“改变”某些特定的策略时，关键参数的识别经常出现。研究人员在模型中进行推理时，通常假设观察次数和时间周期的乘积很大。然而，即使单位或时间段的数量很大，在数据中观察到的实际策略变化的数量通常也很小。在这种情况下，研究人员在这些模型中用于推理的这些标准方法是不合适的，可能会产生很大的误导。我们正在开发两种不同的方法，使研究人员能够在这些模型中进行推理。对于第一种方法，我们假设数据中有有限数量的策略变化，但使用渐近逼近，因为观察数量和时间周期的乘积变得很大。在第二种情况下，我们通过使用精确检验来避免大样本近似。

The first project covered in this grant is a continuation of work with on the estimation of general equilibrium models of the labor market (supported by NSF-SBR 9730657). It is well known but rarely addressed by applied microeconomists that one can not properly evaluate national policies in a "partial equilibrium environment." This is particularly true for policies that address skill acquisition. When using data to perform policy analysis, labor economists typically only concern themselves with the supply of skill and ignore demand. The problem is that as the number of individuals possessing a particular skill increases, the value the skill in the labor market falls. This effect discourages skill formation. Our previous research shows that accounting for general equilibrium effects when performing policy evaluations can alter the results by as much as a factor of ten. The major weakness of our work to date is the empirical approach. We have estimated the parameters of the individual worker using standard methods which implicitly assume that the labor market is not changing over time. This "steady state" assumption is clearly questionable as there has been a large literature demonstrating the changes in the wage structure. The biggest problem is that the model we estimated is not consistent with the general equilibrium model that we used when we simulated it. The main goal of this work is to estimate a dynamic general equilibrium model of the U.S. labor market over the last thirty years. This involves simultaneously estimating and simulating the model accounting for the changing U.S. labor market. This procedure guarantees similarity between the simulated model and the U.S. economy. We are using the estimated model to examine several different policies aimed to address the increasing inequality in the workforce. A major goal of these programs is to lower earnings inequality. This version of the model will provide a consistent estimate of the distribution of earnings in the economy and will allow us to do a much better job of estimating the effects of the policies on earnings inequality than our previous work. We are also extending the model to consider the effects of welfare policies and wage subsidies aimed a low wage workers as well as a number of other policies aimed at addressing earnings inequality.The goal of the second project is to address one particular aspect that is likely to be very important in most implementations of difference-in-differences estimators. Identification of the key parameter often arises when a unit "changes" some particular policy. Researchers typically assume that the product of the number of observations and time period is large when they perform inference in their models. However, even when the number of units or time periods is large, the number of actual policy changes observed in the data is typically small. In this case these standard methods that researchers use for inference in these models are not appropriate and may be wildly misleading. We are developing two different approaches which allow researchers to perform inference in these models. For the first approach we assume that there are a finite number of policy changes in the data, but use asymptotic approximations as the product of the number of observations and time period gets large. In the second, we avoid large sample approximations altogether by using exact tests.