Technical Change as a Stochastic Trend in a Fisheries Model

Abstract

Technical change is generally seen as a major source of growth, but usually cannot be observed directly and measurement can be difficult. With only aggregate data, measurement puts further demands on the empirical strategy. Structural time series models and the state-space form are well suited for unobserved phenomena, such as technical change. In fisheries, technical advance often contributes to increased fishing pressure, and improved productivity measures are important for managers concerned with efficiency or conservation. I apply a structural time series model with a stochastic trend to measure technical change in a Cobb-Douglas production function, considering both single equation and multivariate models. Results from the Norwegian Lofoten cod fishery show that the approach has both methodological and empirical advantages when compared with results from the general index approach, which has been applied in the literature.