Right, I have a couple of things forthcoming. One is, as the post-title suggests, on technical change in fisheries, where I, in my first sole-authored paper in five years, suggest a state space approach to measure technical change in fisheries. The approach is applied to data from the Norwegian Lofoten cod fishery, a data set that previously has been analyzed with other, more typical methods (linear regressions).
The paper has a long history. It started in 2008, when I was a visiting grad student at the economics department of the University of California, San Diego (UCSD). There, Dale Squires, who I am proud to call my friend, presented an analysis of the Lofoten data. During my visit to UCSD, I had spent considerable time studying state space models and the Kalman filter, and during Dale’s talk I wondered whether a state space model would do a better job in estimating technical change. Dale’s analysis was published in 2010, at a time when I already had acquired the data and had started to develop a model and an algorithm. In 2011, during a train trip, I started to get promising results. Progress was doomed to be slow, however, because the entire project was a side project that I only worked on in short stints every now and then. At some point in 2012, I nevertheless had a manuscript ready for submission. I sent it to the same journal where Dale’s analysis was published. After an interesting and instructive review-process, the manuscript was rejected. In the years that followed, the manuscript was sent to a handful of journals (the manuscript took various forms over the years; condensed into the letter-format at one point), but the verdict was always the same: rejection. Over these years, Dale, who I kept in touch with, was always optimistic and encouraging, suggesting alternative journals. Early in 2015, the manuscript was finally sent to Marine Resource Economics, where it was accepted after no less than three rounds of revisions. In the last round, I had to pull out my initial version, written more than three years earlier, and add discussion that was revised out at some point along the road but which obviously had its place. The manuscript was formally accepted early this year (2016), eight years after I had the initial idea.
Late in 2014, more than six years into the process, I had another idea for how to carry out the analysis. I decided to pursue this new idea in another side project. This spin-off project had much faster progress, and less than six months later, a letter-form manuscript was already rejected. After some further work, expanding the manuscript to the more typical article form, the manuscript was submitted again, and I am now awaiting its review. This much faster progress on the second side project is partly taken, by me, as evidence that I have become better at what I do. The lower degree of complexity is, of course, also an important factor in the progress.
‘Technical Change as a Stochastic Trend in a Fisheries Model’ will appear in Marine Resource Economics during the fall. The abstract reads as follows:
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 advances often contribute 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.
UPDATE: The article is now available here: