Assessing the Dynamic Productivity of Ghana’s Fishery Industry

This paper is to statistically and empirically evaluate the dynamic productivity of fishery firms in Ghana, explore the drivers of this productivity change and investigate the contextual variables that impact dynamic productivity. A quantitative research methodology that includes extensive structural equation modeling to explore causal relationships and determine the relative strengths of various variables was used. This paper through a two-stage non-parametric DEA Malmquist analysis investigates the dynamic productivity of fishery firms using 20 registered Ghanaian fishing vessels from 2008 to 2021. In the first stage, the bootstrapped dynamic productivity of the fishery firms are ascertained. The second stage explores the impact of some external factors on the first stage estimations through advanced robust econometric techniques. The result of the first stage showed that the firms’ dynamic productivity regressed by about 1%, and this was mainly driven by technological change. The Malmquist Productivity Change was further decomposed into 4 factors, and it was revealed that there exist statistical differences in the drivers of the MPI. The results of the random effect robust regression from the second stage revealed that all variables (AGE, EXC, FIN, LOA, INF, GDP and Covid) are statistically significant, with EXC exhibiting a negative relationship. Again, although LOA is positive, it showed no influence on the MPI.