Nonparametric Dynamic Production Analysis and the Theory of Cost

While the dynamic theory of production provides little insight towards identifying a specific functional form for the firm's technology, dynamic production analysis has been explored traditionally in a parametric framework. A nonparametric dynamic dual cost approach to production analysis is developed in this article. Recovering technological information from intertemporal cost minimizing behavior is possible without imposing a parametric functional form on the firm's technology. Nonparametric tests to analyze the structure of a dynamic technology are presented from a dynamic cost minimizing perspective. The empirical implementation of these tests is illustrated for a balanced panel data set of Pennsylvania dairy operators during the time period 1986–1992.     

Learning-by-doing and the sources of productivity growth: A dynamic model with application to U.S. agriculture

The significance of learning to productivity growth is formulated within a dynamic adjustment-cost framework. Explicitly treating the acquisition of knowledge as a firm-specific capital good entering the production function along with other conventional inputs, the dynamic optimization model integrates the learning-by-doing hypothesis with technical change, scale, and disequilibrium input use effects in the aggregate productivity analysis. The theoretical framework is applied to examining the dynamic components accounting for the growth of U.S. production agriculture over the 1950–82 period. The results imply a less important role for technical change and assign a substantial role to the previously unmeasured contribution of learning-by-doing to the growth of aggregate agriculture industry.The editor for this paper was Melvyn Fuss.     

Assessing the Impact of Changing Economic Environment on Productivity Growth: The Case of the Spanish Dairy Processing Industry

This article analyzes the productivity growth of the Spanish dairy processing industry from 1996 till 2011, which concerns the period of increased EU regulation regarding food safety as well as economic crisis. Data envelopment analysis is used to compute the Malmquist index and its components. The results suggest that productivity on average declined during the period under investigation. The decomposition of the Malmquist index finds that technical regress contributed to productivity decline despite improvements in technical and scale efficiencies. Our results also suggest that food safety regulations did not have a negative impact on productivity growth. Technical change and scale efficiency change made significant negative contributions to productivity growth in the period under crisis, whereas firms significantly improved their technical efficiency in that period.     

ECONOMIES OF SIZE REVISITED

Empirical and theoretical investigations as to how firms evolve in size have been of interest to economists, agricultural economists and policymakers for decades. While returns to size have been frequently estimated, measurement has been carried out using a wide range of methodological approaches. This paper offers a critique and discussion of the conceptual and methodological issues surrounding the measurement of size economies.     

Dynamic Efficiency Measurement: Theory and Application

Nonparametric dynamic measures of production efficiency are developed in the context of an adjustment-cost technology and intertemporal cost minimization. Bounds on each efficiency measure are derived for each firm using a nonparametric revealed preference approach. Long-run efficiency measures indicate the relative efficiency of both variable and dynamic factors while short-run measures of efficiency indicate whether variable inputs are employed efficiently in the production process. The efficiency measures are temporal in nature by describing the degree of efficiency of the firm at a particular point along its adjustment path. The empirical implementation is illustrated for a balanced panel of Pennsylvania dairy operators during 1986–1992.     

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.