Evaluation of Policy Impacts

Northwestern University, Evanston IL

Investigators

Abstract

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.