Essays On Productivity And Distortions

My dissertation uses theoretical tools and empirical methods to address substantive questions in the intersection of Macroeconomics and Industrial Organization. In particular, my research focuses on understanding the underlying forces that determine efficiency and distortions. In the first chapter, Privatization, Distortions, and Productivity, I ask that to what extent and through what channels privatization has contributed to the rapid growth of GDP and TFP in China's manufacturing sector. Privatization, like many other industrial policies, affects firms' output and productivity through a direct effect on productivity and an indirect effect on resource allocation. Understanding the consequences of privatization on both productivity and allocative efficiency is crucial for policy evaluation and subsequent reforms. To do so, I first develop a method to separately identify productivity and factor misallocations. This links to two distinct literatures. One is the macro literature on misallocation that use wedges or distortions to measure the deviation from firm's optimal scale. The other is the empirical IO literature to estimate production function using input and output, together with firm's optimal choices. I augment the empirical production function estimation framework by introducing wedges between marginal revenues and marginal costs as an additional unobserved variable that distorts the firms output away from their optimal input choices. And I show that how to identify and estimate productivity, distortions and parameters in production function. I then estimate the productivity and distortion effects of privatization. Finally I propose a decomposition method for aggregate productivity growth to explicitly account for the direct effect of privatization. Second, I discussed market frictions, specifically search and informational frictions that prevents efficient allocations in the labor markets. In Understanding Transitions using Directed Search with Mike Peters (UBC) and Steven Xu (HKU), we develop a directed search model to study worker transitions between jobs in the French labor market. In particular, the model we consider allows workers to have incomplete information about each other's' marketable characteristics (types) at the point where they make their search decisions. The theory provides a series of testable predictions about the relationship between the wage a worker receives at the job that he or she leaves, and the wage they get at their next job. The paper then uses data from the French DADS data set to study transitions in a variety of different labor markets in France in 2007, and compares these transitions to what is predicted by the theory. We find that the labor markets for skilled workers and industrial type of workers support more of our model. Third, I explore the return of productivity and distortions in scientific world. In Is Citation Behavior Biased? The Influence of Journal Editors with Bruno Biais (TSE) and Augustin Landier (TSE), we test the null hypothesis that the number of citations provides an unbiased measure of an article's scientific value. To do so, we exploit a shock that is exogenous to the scientific value of a paper, namely the ending time of editorial appointments of colleagues of a paper's author(s). We show that during the course of an editor's appointment, the citation rates of her papers in the journal she edits go up by 23 percentage points. During the same period, these articles don't have any significant citation premium at other journals. The citation premium fades away when the editor steps down. The same phenomenon applies to colleagues of the editor. This provides a counterexample to the null hypothesis that is both large in size and in the population of papers that is affected.