Estimation of a Heterogeneous Demand Function with Berkson Errors

Berkson errors are commonplace in empirical microeconomics. In consumer demand this form of measurement error occurs when the price an individual pays is measured by the (weighted) average price paid by individuals in a group (e.g., a county), rather than the true transaction price. We show the importance of Berkson errors for demand estimation with nonseparable unobserved heterogeneity. We develop a consistent estimator using external information on the true price distribution. Examining gasoline demand in the U.S., we document substantial within-market price variability. Accounting for Berkson errors is quantitatively important. Imposing the Slutsky shape constraint reduces sensitivity to Berkson errors.

AN INTERVIEW WITH JAMES J. HECKMAN

James Heckman is one of the most important and influential scholars to have graced the economics profession. His work is deeply rooted at the intersection of economic theory and empirical microeconomics, and he has made significant contributions to the study of labor economics, microeconometrics, and the use of micro data in macroeconomic analysis. Heckman's work is motivated by the scientific method, in which theory is held up to the scrutiny of the data and empirical analysis is informed by economic theory. During the course of his work, he has made lasting contributions to the study of sample selection bias, duration analysis, heterogeneity, and treatment effects in microeconometrics. In labor economics, he has applied these econometric methods to the study of labor supply and life-cycle dynamic models of unemployment, wage growth, and skill formation. In addition, he is the leading scholar on the evaluation of active labor market programs. As an applied microeconomist, one cannot do research on labor supply, sample selection, duration models, or life-cycle dynamics without encountering Jim Heckman's work.

The Credibility Revolution in Empirical Economics: How Better Research Design is Taking the Con out of Econometrics

Since Edward Leamer's memorable 1983 paper, “Let's Take the Con out of Econometrics,” empirical microeconomics has experienced a credibility revolution. While Leamer's suggested remedy, sensitivity analysis, has played a role in this, we argue that the primary engine driving improvement has been a focus on the quality of empirical research designs. The advantages of a good research design are perhaps most easily apparent in research using random assignment. We begin with an overview of Leamer's 1983 critique and his proposed remedies. We then turn to the key factors we see contributing to improved empirical work, including the availability of more and better data, along with advances in theoretical econometric understanding, but especially the fact that research design has moved front and center in much of empirical micro. We offer a brief digression into macroeconomics and industrial organization, where progress—by our lights—is less dramatic, although there is work in both fields that we find encouraging. Finally, we discuss the view that the design pendulum has swung too far. Critics of design-driven studies argue that in pursuit of clean and credible research designs, researchers seek good answers instead of good questions. We briefly respond to this concern, which worries us little.

Recent Developments in the Econometrics of Program Evaluation

Many empirical questions in economics and other social sciences depend on causal effects of programs or policies. In the last two decades, much research has been done on the econometric and statistical analysis of such causal effects. This recent theoretical literature has built on, and combined features of, earlier work in both the statistics and econometrics literatures. It has by now reached a level of maturity that makes it an important tool in many areas of empirical research in economics, including labor economics, public finance, development economics, industrial organization, and other areas of empirical microeconomics. In this review, we discuss some of the recent developments. We focus primarily on practical issues for empirical researchers, as well as provide a historical overview of the area and give references to more technical research.