Search

Discrete choice models of demand are growing in popularity for understanding markets for seafood, but have thus far been limited to applications using individual-level choice data. The random coefficients logit model is a discrete choice demand model designed for aggregate sales data and imparts a number of theoretical and empirical advantages. Instrumental variables account for price endogeneity, which can arise when there are unobserved product characteristics. Furthermore, correlated preferences can be accommodated in the random coefficients as well as through demographic interactions, which is especially important for seafood where product characteristics are primarily qualitative. We estimate this model for salmon fillets using four years of county-level seafood sales in California, and demonstrate the insights that can be drawn regarding consumer preferences and substitution patterns. Although the model is computationally burdensome, it offers considerable potential for further seafood demand analysis.

Hedonic price functions are frequently used to identify the marginal implicit prices for characteristics of differentiated products, including seafood products. These marginal implicit prices are determined by both consumer preferences and producer costs. When preferences or costs for a single characteristic differ systematically across omitted submarkets, the results are an average of the heterogeneous effects. An empirical test for the joint significance of interaction terms is sufficient to identify the presence of submarkets. We motivate the selection of submarkets on the basis of production differences across salmon species, then use county-level aggregate retailer scanner data for the US state of California from 2013 to 2016 to estimate hedonic price functions. The results indicate that species and production method submarkets exist. Letting differences in production motivate the dimensions for submarkets results in a more accurate picture of the market.