Linking investment spikes and productivity growth

We investigate the relationship between productivity growth and investment spikes using Census Bureau’s plant-level dataset for the U.S. food manufacturing industry. There are differences in productivity growth and investment spike patterns across different sub-industries and food manufacturing industry in general. Our study finds empirical support for the learning-by-doing hypothesis by identifying some cases where the impact of investment spikes on TFP growth presents a U-shaped investment age–productivity growth pattern. However, efficiency and the learning period associated with investment spikes differ among plants across industries. The most pronounced impact of investment age on productivity growth (5.3 % for meat products, 4% for dairy products, and 2.8 % in all food manufacturing plants) occurs during the fifth year of post-investment spike. Thus, in general, the productivity gains tend to be fully realized with a 5-year technology learning period for this industry.     

Environmental Policy with Endogenous Technology from a Game Theoretic Perspective: The Case of the US Pulp and Paper Industry

The merits of different types of regulatory tools in eliminating pollution and at the same time inducing innovation have long been an interest of researchers in both environmental economics and industrial organization. Although there is a substantial theoretical literature investigating the potential for various environmental policies to attain these dual goals, this is a challenging empirical problem because every industry has its own inherent characteristics that play an important role in determining the performance of different regulatory tools. The majority of the work to date focuses on pollution abatement while leaving pollution prevention understudied. In most of the literature firms are also assumed to be symmetric. Asymmetries among firms add another degree and level of complexity to their strategic interactions, and affect the performance of different regulatory tools. This paper investigates the performance of two alternative regulatory tools, an emissions performance standard and an emissions tax, in reducing pollution and inducing pollution prevention and abatement R&D in the US pulp and paper industry. We construct a model representing the industry in an asymmetric Cournot duopoly framework, calibrate the model to disaggregated industry data, and run scenarios to replicate the behavior of the firms in an imperfectly competitive output market. Our results suggest that pollution prevention R&D can respond quite differently than abatement R&D to different policy instruments. The results indicate that R&D spillovers among firms play crucial role in technology development and strategies of the firms. Our results also suggest that strategic interactions between firms in an imperfectly competitive industry can have significant impacts of the levels of both types of R&D.      

Productivity growth patterns in US dairy products manufacturing plants

We analyse the productivity growth patterns in the US dairy products industry using the Census Bureau's plant-level data set. We decompose Total Factor Productivity (TFP) growth into the scale and technical change components and analyse variability of plants’ productivity by constructing transition matrices. We observe a cross-sectional dispersion in plant-level productivity growth in the industry. Even though the industry aggregate shows a small TFP growth rate ?0.3%, quartile rank analysis shows that while the lowest productivity quartile plants average 1.9% loss in productivity, the highest productivity quartile plants average 1.1% growth annually. Our results show considerable movements of plants in their productivity rank categories overall and across age groups, and we find that the scale effect contribution to TFP growth accounts for about 90% of TFP growth on average in the industry. These plants extract scale efficiencies over technological progress to fuel TFP growth. The youngest plants start with the lowest productivity growth at the initial time period, but they catch up older plants productivity, which present the highest average growth rate through years. This may indicate a ‘learning-by-doing’ process for the industry.     

Measuring the Impact of Price-Induced Innovation on Technological Progress: Application to the U.S. Food Processing and Distribution Sector

A model of price-induced innovation is presented incorporating long-run prices as arguments in the production function serving the role of stimulating firms to seek innovations. The empirical application examines the production structure and technological progress of the U.S. food processing and distribution sector for the period 1948–1991. The empirical model separates scarcity, innovation, and exogenous technical change responses in analyzing the Morishima elasticities for input combinations and multifactor input biases. The results suggest significant structural changes occurring in the food processing and distribution sector since 1980. Focusing on multifactor input bias, results suggest that there are no wide changes in technical change patterns over the last forty years. However, Morishima elasticity results suggest a more varied pattern of technical change between inputs. The price-induced technical progress has a dominant contribution on input decisions compared with the exogenous technical change.     

Corporate Social and Financial Performance Re-Examined: Industry Effects in a Linear Mixed Model Analysis

In this research, we shed new light on the empirical link between corporate social performance (CSP) and corporate financial performance (CFP) via the application of empirical models and methods new to the CSP?CCFP literature. Applying advanced financial models to a uniquely constructed panel dataset, we demonstrate that a significant overall CSP?CCFP relationship exists and that this relationship is, in part, conditioned on firms?? industry-specific context. To accommodate the estimation of time-invariant industry and industry-interaction effects, we estimate linear mixed models in our test of the CSP?CCFP relationship. Our results show both a significant overall CSP effect as well as significant industry effects between CSP and CFP. In conflict with expectations, the unweighted average effect of CSP on CFP is negative. Our industry analysis, however, shows that in over 17% of the industries in our sample, the effect of CSP on CFP for socially responsible firms is positive. We also examine the multidimensional nature of the CSP construct in an industry context by exploring the CSP dimension?Cindustry nexus and identify dimensions of social performance that are associated with either better or worse financial performance. Our results confirm the existence of disparate CSP dimension?Cindustry effects on CFP, thus our results provide important and actionable information to decision makers considering whether and how to commit corporate resources to social performance.