DETERMINANTS OF RETAIL PRICE AND SALES VOLUME OF CATFISH PRODUCTS IN THE UNITED STATES: AN APPLICATION OF RETAIL SCANNER DATA
Notice bibliographique
Résumé
Abstract Catfish market research is important in terms of the viability and sustainability of the catfish aquaculture industry in the U.S. Analysis of market trends, retail level price and sales volume have particular importance in the context of the industry's efforts to promote and market their products. Using retail level scanner data, this study examined the market trends of different catfish product forms. The study also investigated the factors affecting prices and sales for frozen catfish products. The present study is a pioneer in using market/city level retail scanner data to study marketing behavior of catfish products under different product categories (i.e., breaded, unbreaded and entrees). The results of the study highlight a need for a non-price competition strategy for catfish retail market. This includes several activities that attempt to provide added value or incentives to consumers, wholesalers, retailers, or other organizational customers to stimulate immediate sales. Keywords: catfishmarket trendsretail scanner datasales and price response model ACKNOWLEDGMENTS The authors gratefully acknowledge the able research support provided by Mr. Prasanna Surathkal, including his critical help in organizing the data. The findings, opinions, and recommendations expressed are those of the authors and not necessarily those of the Southern Regional Aquaculture Center, the Mississippi Agricultural and Forestry Experiment Station, or the U.S. Department of Agriculture. Notes Note: Figures in parenthesis show % change in prices over 2005–2006. X = represents interaction. ***Significant at 1%, **significant at 5%, *significant at 10%. X = represents interaction. ***Significant at 1%, **significant at 5%, *significant at 10%. ***Significant at 1%, **significant at 5%, *significant at 10%. Note: Elasticities were calculated from significant coefficients of prices of different fish and interaction of prices and region for unbreaded catfish sales model. The term catfish used in this article refers to Ictalurus species, which includes channel catfish and its hybrids. A.C. Nielsen Inc. collects weekly store-level scanner data at the market level (52 markets in the U.S.) and national level. However, national-level data is not an aggregate of the data from the 52 individual markets and includes few more markets than those for which individual market-level data are provided. We used market level data for econometric analysis. But, for brevity in presentation and for space consideration, we have reported the trends at national level only. Wal-Mart has recently started sharing scanner data with A.C. Nielsen Inc in a limited scale for 10 U.S. markets. The sampling framework of the 10 markets where World-Mart participates and that of the 52 markets data we used in this paper are different and are not compatible. A similar approach has also been used at other levels in the seafood supply chain. This includes studies at the harvest level (Carroll et al., Citation2001; McConnell & Strand, Citation2000) and the wholesale level (Asche & Guillen, Citation2012). These studies have found that the attributes and their values differ at different levels in the chain. If the series are integrated of same order, they can be co-integrated. Since dependent variables are price and sales volumes, therefore, we did not test the order of integration and hence co-integration with other variables. The retail price model used in this study shows retailers' pricing behavior and its does not include quantities of seafood sales as explanatory variables. We, therefore, cannot estimate own- and cross-price elasticties directly from the retail price model. In the scanner data a product type is denoted as 'regular' if specific 'type' information is not mentioned in the product description. For example, "CTL BR CTF FLT FZ 12 OZ" is a store brand 12 oz regular catfish frozen fillet; "CTL BR CTF FLT BL SL FZ 32 OZ" is a store brand 32 oz boneless-skinless catfish frozen fillet. Singh et al. (2014) used the geographical disaggregation at the U.S. census division (9 in number) level; whereas this paper uses disaggregation at the U.S. census regions level (4 in number). In the scanner data a product type is denoted as 'regular' if specific 'type' information is not mentioned in the product description. For example, "CTL BR CTF NGT BR 80 OZ" is a store brand 80 oz catfish frozen nugget with regular breading; "CTL BR CTF STR NGT BR 13 OZ" is a store brand 13 oz catfish frozen nugget with cornmeal breading.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».