A vast body of literature exists on estimating hedonic price functions, which relate the price of wine to its attributes. Some existing literature has employed producer-specific variables such as quantity sold and producer reputation in hedonic functions to potentially capture supply influences on prices. This practice is inconsistent with the original Rosen (1974) hedonic theoretic foundation. To overcome this deficiency, we extend the literature by using the Rosen two-stage approach, employing data from multi-markets for similar wines to estimate inverse supply functions. The application to Australian produced wines sold in different countries demonstrates the importance of a wine's quality and age as attributes in inverse supply functions. Results imply the additional costs of producing better quality and older wines are increased as both quality and age are increased. Estimates also suggest that lower marginal costs for attributes are associated with a smaller producer size and older more established producers. (JEL Classifications: C21, Q11)

Wines are usually evaluated by wine experts and enthusiasts who give numeric ratings as well as text reviews. While most wine classification studies have been based on conventional statistical models using numeric variables, there has been very limited work on implementing neural network models using wine reviews. In this paper, we apply neural network models (CNN, BiLSTM, and BERT) to extract useful information from wine reviews and classify wines according to different rating classes. Using a large collection of wine reviews from Wine Spectator, the study shows that BERT, a neural network framework recently developed by Google, has the best performance. In the two-class classification (90–100 and 80–89), BERT achieves an accuracy of 89.12%, followed by BiLSTM (88.69%) and CNN (88.02%). In the four-class classification (95–100, 90–94, 85–89, and 80–84), BERT yields an 81.57% accuracy, while the other two produce an 80% accuracy. The neural network models in the paper are independent of domain knowledge and thus can be easily extended to other kinds of text analysis. Expanding the limited work on wine text review classification studies, these models are up-to-date and provide valuable additions to wine data analysis. (JEL Classifications: C45, C88, D83)

This paper explores growers’ supply response to the 2005 “Sideways effect” demand shock (Cuellar, Karnowsky, and Acosta, 2009) triggered by the 2004 release of the movie Sideways. We use a modified difference-in-difference approach to evaluate the supply response in California and regional supply response differences within California. We use U.S. Department of Agriculture data for the period 1999–2012 and find evidence of a supply response in the post-release period that is consistent with the “Sideways effect” on wine demand. The positive supply response for Pinot Noir is stronger than the negative response for Merlot and concentrated in lower value Central Valley vineyards. (JEL Classifications: D25, Q12)

There is an ongoing debate on whether wine reviews provide meaningful information on wine properties and quality. However, few studies have been conducted aiming directly at comparing the utility of wine reviews and numeric measurements in wine data analysis. Based on data from close to 300,000 wines reviewed by Wine Spectator, we use logistic regression models to investigate whether wine reviews are useful in predicting a wine's quality classification. We group our sample into one of two binary quality brackets, wines with a critical rating of 90 or above and the other group with ratings of 89 or below. This binary outcome constitutes our dependent variable. The explanatory variables include different combinations of numerical covariates such as the price and age of wines and numerical representations of text reviews. By comparing the explanatory accuracy of the models, our results suggest that wine review descriptors are more accurate in predicting binary wine quality classifications than are various numerical covariates—including the wine's price. In the study, we include three different feature extraction methods in text analysis: latent Dirichlet allocation, term frequency-inverse document frequency, and Doc2Vec text embedding. We find that Doc2Vec is the best performing feature extraction method that produces the highest classification accuracy due to its capability of using contextual information from text documents. (JEL Classifications: C45, C88, D83)