TL;DR: In this paper, the authors use an equilibrium model to suggest an estimation algorithm that takes into account the relationship between productivity on the one hand and both input demand and survival on the other.
Abstract: Technological change and deregulation have caused a major restructuring of the telecommunications equipment industry over the last two decades. We estimate the parameters of a production function for the equipment industry and then use those estimates to analyze the evolution of plant level productivity over this period. The restructuring involved significant entry and exit and large changes in the sizes of incumbents. Since firms' choices on whether to liquidate and on the quantities of inputs demanded should they continue depend on their productivity, we use an equilibrium model to suggest an estimation algorithm that takes into account the relationship between productivity on the one hand. and both input demand and survival on the other. A fully parametric version of the estimation algorithm would be both computationally burdensome and require a host of auxiliary assumptions. So we develop a semi parametric technique which is both consistent with a quite general version of the theoretical framework and easy to use. The algorithm produces markedly different estimates of both production function parameters and of productivity movements than traditional estimation procedures. We find an increase in the rate of industry productivity growth after deregulation. This in spite of the fact there was no increase in the average of the plants' rates of productivity growth, and there was actually a fall in our index of the efficiency of the allocation of variable factors conditional on the existing distribution of fixed factors. Deregulation was, however, followed by a reallocation of capital towards more productive establishments (by a down sizing, often shutdown. of unproductive plants and by disproportionate growth of productive establishments) which more than offset the other factors' negative impacts on aggregate productivity.
TL;DR: In this article, a nonparametric programming method (activity analysis) is used to compute the Malmquist productivity indexes, which are decomposed into two component measures, namely, technical change and efficiency change.
Abstract: This paper analyzes productivity growth in 17 OECD countries over the period 1979-1988. A nonparametric programming method (activity analysis) is used to compute Malmquist productivity indexes. These are decomposed into two component measures, namely, technical change and efficiency change. We find that U.S. productivity growth is slightly higher than average, all of which is due to technical change. Japan's productivity growth is the highest in the sample, with almost half due to efficiency change. (JEL C43, D24) In this paper we apply recently developed
TL;DR: The authors presented the first comprehensive set of firm-level total factor productivity (TFP) estimates for China's manufacturing sector that spans China's entry into the WTO and found that net entry accounts for over two thirds of total TFP growth.
TL;DR: In this article, the authors investigate the nature of selection and productivity growth using data from industries where they observe producer-level quantities and prices separately, and show that there are important differences between revenue and physical productivity.
Abstract: There is considerable evidence that producer-level churning contributes substantially to aggregate (industry) productivity growth, as more productive businesses displace less productive ones. However, this research has been limited by the fact that producer-level prices are typically unobserved; thus within-industry price differences are embodied in productivity measures. If prices reflect idiosyncratic demand or market power shifts, high "productivity" businesses may not be particularly efficient, and the literature's findings might be better interpreted as evidence of entering businesses displacing less profitable, but not necessarily less productive, exiting businesses. In this paper, we investigate the nature of selection and productivity growth using data from industries where we observe producer-level quantities and prices separately. We show there are important differences between revenue and physical productivity. A key dissimilarity is that physical productivity is inversely correlated with plant-level prices while revenue productivity is positively correlated with prices. This implies that previous work linking (revenue-based) productivity to survival has confounded the separate and opposing effects of technical efficiency and demand on survival, understating the true impacts of both. We further show that young producers charge lower prices than incumbents, and as such the literature understates the productivity advantage of new producers and the contribution of entry to aggregate productivity growth.