Minimum information management and price‐abundance relationships in a fishery

Abstract

We explore the information content of dockside prices and fishing costs in the estimation of stock abundance. Our approach is two-pronged: we first examine whether the implied biomass, that is, the biomass that is consistent with a simple microeconomic model calibrated with observed prices and costs, offers an approximation of actual stock assessments-both agree over the first 20 years of observation, but diverge over the last five. In a second approach, we use annual data in Vector Autoregressive (VAR), Bayesian VAR (B-VAR), and Vector Error Correction (VEC) frameworks and add monthly data in a mixed-frequency data analysis including Mixed-Frequency Bayesian VAR (MF-BVAR) and Mixed-Data Sampling (MIDAS) frameworks for log-differenced time series. Parameter uncertainties are addressed through Bayesian regression and forecasting methods. We find a statistically significant correlation between biometric estimates and changes in a price-based indicator that is robust to the inclusion of confounding factors. We conclude that the combination of price data and per-trip landings, when interpreted with care, can serve as a complementary, but comparatively affordable and timely, source of information for stock assessments.

Using the best of two worlds: A bio‐economic stock assessment (BESA) method using catch and price data

“…In this paper, we present a new bio‐economic stock assessment (BESA) approach that utilizes an alternative source of time‐series data—namely prices—in addition to catch data. While economists have long studied the relationship between price and resource abundance (Hotelling, 1931), and while the value of price data for inference on stock status has been recognized before (Marvasti & Dakhlia, 2021; Pinnegar et al, 2006; Smith et al, 2017), to the best of our knowledge, no price‐reliant stock assessment method is currently used. The paper closest to ours is that of Marvasti and Dakhlia (2021); it shows by use of multivariate time‐series analyses that price data, combined with per‐trip‐landings, is useful to assess biomass development level.…”

“…While economists have long studied the relationship between price and resource abundance (Hotelling, 1931), and while the value of price data for inference on stock status has been recognized before (Marvasti & Dakhlia, 2021; Pinnegar et al, 2006; Smith et al, 2017), to the best of our knowledge, no price‐reliant stock assessment method is currently used. The paper closest to ours is that of Marvasti and Dakhlia (2021); it shows by use of multivariate time‐series analyses that price data, combined with per‐trip‐landings, is useful to assess biomass development level. Being able to use the additional information inherent in prices may provide an improvement comparable to including standardized indices in an assessment model, producing more reliable stock assessment estimates solely on readily available catch and price data.…”