Much research shows that the ratings that critics, judges, and consumers assign to wines are heteroscedastic. A rating observed is one draw from a latent distribution that is wine- and judge-specific. Estimating the shape of a rating’s distribution by minimizing a sum of cross entropies has been proposed and tested. This article proposes a method of improving the accuracy of that estimate by using information about the context of a wine competition or cross-section ratings data. Tests using the distributions implied by 90 blind triplicate ratings show that the sum of squared errors for the solution using context or cross-section information is 50% more accurate than not using such information and over 99% more accurate than ignoring the uncertainty about a rating.

Understanding consumer choices and their drivers of willingness to pay (WTP) for a bottle of wine has been a research challenge in wine economics, particularly in niche markets such as sparkling wine. This study investigates the determinants of WTP for sparkling wine based on data from Portuguese consumers. The results provided by two alternative methodologies are compared: a traditional econometric model, based on the estimation of an ordered probit model; and a modelling approach based on data-driven and using machine learning algorithms. Both approaches present similar results, highlighting the relevance of some determinants including income, Champagne brand, not being a protected designation of origin and being a red wine consumer as main predictors of WTP for sparkling wine in Portugal.

Currently, scholars hold that the government’s principal contribution to the California wine industry’s recovery from Prohibition in the 1930s was to get out of the way, freeing entrepreneurs to conduct business properly; according to this interpretation, the United States only taxed the product and impeded progress. But this article argues that in the areas of regulation, promotion, and protection of the wine industry, the federal government provided a framework for California winemakers to succeed and that, moreover, it often did so at their request and in cooperation with them. Though New Deal laws and regulations did not benefit all stakeholders equally, they did work to bring economic recovery to an industry that suffered from both Prohibition and the Depression.

The adoption of fungus-resistant grapevines may be a key strategy for substantially reducing fungicide use in pesticide-intensive viticulture. In a representative survey conducted among 436 grapevine growers in Switzerland, we elicited growers’ expected share of land devoted to fungus-resistant varieties in ten years. More specifically, using regression analyses, we explore the main predictors behind the stated adoption intentions. We find that one-third of new plantings in the next decade will be fungus-resistant varieties. As a result, the expected share of land devoted to fungus-resistant varieties in ten years is 27.4% (compared to 10.2% in 2022), thus increasing by 169%. Farmer- and farm characteristics explain most of the adoption dynamics, especially growers’ beneficial health perceptions about fungus-resistant varieties, which correlate positively with their expected land share devoted to these varieties. Moreover, non-organic grapevine growers are particularly likely to increase their land devoted to these varieties. These findings have important implications for agricultural policy and industry in Europe and elsewhere, facilitating the expected plantation increase using a policy mix tailored to farmer- and farm-level characteristics.

Consumers often struggle to make their choice in the highly diversified wine market. With wine being an experience good, consumers must rely on extrinsic characteristics, e.g., information on the label. Thus, easily available quality signals like consumer ratings have become an increasingly useful and widespread tool. Vivino is one of the largest online wine communities with over 60 million users, which have more than doubled since 2018. Hence, users have easy access to peer ratings, while established wine expert ratings are being challenged. This study analyzes data from Vivino to explore factors affecting consumer ratings at different price points, considering several wine attributes like geographical indications, brand, and the so-called “community effect.” We show that there is a small but significant community effect on wine's perceived quality related to its popularity among users of the Vivino community, as well as effects from specific wine attributes. Moreover, we estimate a hedonic quantile model on similar price ranges to compare the effect of the same regressors on wine prices. Results contribute to a better understanding of how different factors affect consumers’ wine evaluations, allowing to compare their effect on the “pure” consumer preference (i.e., consumer ratings) and market value.

The wine industry, considered to be male-dominated, has seen a growing share of women winemakers. Using a randomized online experiment, we investigate how the producer’s gender influences consumers’ willingness to pay for the wine. Gender can be identified either from the first name of the producer or from a gendered group of wine producers. Using a Tobit and a double-hurdle model, our results suggest that consumers’ willingness to pay is lower for wine produced by female winemaker groups. This reduction appears to be particularly pronounced when the consumer is male.

In 1790, the Revolutionary government expropriated most property owned by the Church and its entities, and sold it by auction. This effectively ended the centuries-old participation of the Church in wine production in France. Focusing on Burgundy, this article sketches the contours of the sale of vineyards and other wine-related property owned by the Church. It shows that auctions fetched prices well above the assessed value of the properties that were sold and speculates on the reasons.

This paper explores the processes of specialized viticulture in the province of Gallia Narbonensis over the first three centuries CE and brings this evidence to bear on broader economic questions, particularly as they relate to the effects of connectivity and globalization on Roman economic development. Evidence from small farms to sprawling villas suggests that specialized production stretched across multiple strata of society in Narbonensis, from so-called peasants to the wealthiest elites. The existence of specialized agricultural production at the scale documented in Narbonensis required significant demand, well-connected and integrated markets, sustained trade, and an awareness of these economic factors by the residents of the province. The evidence presented here demonstrates that the residents of Narbonensis recognized that they were part of an economic environment in which high levels of connectivity and integrated markets allowed them to pursue more profitable production strategies and that they pursued these opportunities.

The COVID-19 pandemic itself constitutes an environment for people to experience the potential loss of control and freedom due to social distancing measures and other government orders. Variety-seeking has been treated as a mechanism to regain a sense of self-control. Using Machine Learning model and household-level data with a focus on the wine market in the United States, this study showcases the changing variety-seeking behavior over the pandemic year of 2020, in which people’s perception of the status of restriction measures influences the degree of their use of variety-seeking behavior as a coping strategy. It is the shopping pattern and store environments that drive the behavioral responses in wine purchases to freedom-limited circumstances. Coupon use is associated with a lower variety-seeking tendency at the beginning of the stay-at-home order, but the variety level resumes when more time has passed in the restriction periods. Variety-seeking tendency increases with shopping frequency at the beginning of the social distancing measure but decreases to a level lower than all the non-restriction periods.

The changing legislative landscape of the U.S. wine market provides a scenario to examine the effect of regulation on the size distribution of firms. Using the variation across states and time in the sum of in-state and out-of-state adult populations between 2002–2017, and a difference in difference-style empirical model, I examine how restrictions on Direct to Consumer (DTC) sales impact the number of establishments and the employment at wineries. I find that the expansion of the potential wine market by 10 M adults caused about a 3.5% increase in the number of wineries. While reduced DTC restrictions explain growth in the number of wineries, I find no effect of lessened restrictions on the number of winery employees, though there is evidence of a lagged effect. Additionally, I find that the growth of smaller wineries substantially outpaces that of larger wineries when regulations are lessened. These results suggest that regulatory barriers in particular industries may allow states to maintain an artificial size distribution.

Analyses and aggregations of the ratings that wine critics and judges assign to wines are made difficult by stochastic error and biases that remain even when wines are assessed blind to price, label, capsule, and closure. Stochastic error is due to the partially random nature of ratings. Cognitive and omitted-variable biases are due to anchoring, expectation, serial position, commercial, and other factors. Differences in decanting, filtering, aeration, and temperature can also affect ratings.

Much research shows that the ratings that judges assign to the same wine are uncertain. And while the ratings may be independent, research also shows that they are not identically distributed. Thus, an acute difficulty in ratings-related research and in calculating consensus among judges is that each rating is one observation drawn from a latent distribution that is wine- and judge-specific. What can be deduced about the shape of a latent distribution from one observation? A simple maximum entropy estimator is proposed to describe the distribution of a rating observed. The estimator can express the implications of zero, one, a few blind replicates, and many observations. Several tests of the estimator show that results are consistent with the results of experiments with blind replicates and that results are more accurate than results based on observed ratings alone.