This issue of the Journal of Wine Economics opens with “Convergence in National Alcohol Consumption Patterns: New Global Indicators” by Alexander Holmes and Kym Anderson (Holmes and Anderson, Reference Holmes and Anderson2017). The authors draw on data for all countries of the world since 1961 and introduce two new summary indicators to capture additional dimensions of the convergence in total alcohol consumption and its mix of beverages. They also consider unrecorded alcohol consumption and whether alcohol consumption focuses on wine, beer or spirits. Some selected results are: Alcohol consumption, volume as well as expenditure, first grows with per capita income to a certain point and falls thereafter. In volume terms, the inverse U-shaped link to incomes applies to all three beverages. In value terms, only spirits follow this pattern. “When countries are grouped by geographic region, all three beverage consumption volume intensity indexes converged toward unity for North America and Eastern Europe, but they diverged for Western Europe and Africa/Middle East. The consumption mix similarity indexes moved closer to one during 2001 to 2015 for most regional country groups, and also for beer-focused and spirits-focused countries – but not for wine-focused countries and only barely for Western Europe.”

Olena Sambucci and Julian Alston estimate the value of California wine grapes (Sambucci and Alston, Reference Sambucci and Alston2017). They show that the total grape crush value reported in USDA/NASS California Grape Crush Reports, the authoritative source of information on production and returns, may underestimate the value of the total crush by as much as 14–20%, depending on the year. Since the crush prices are directly observed only for those wine grapes that are sold, not for those used in winemaking by the grower, the Grape Crush Reports partially rely on estimated prices. Sambucci and Alston show that these estimates are downward biased and suggest a small change in procedure that would provide more accurate estimates.

In a paper entitled “Wine, Women, Men, and Type-II Error,” Jeffrey Bodington examines whether there is a statistically meaningful difference between women and men in wine tasting (Bodington, Reference Bodington2017). Since state fairs and other wine competitions typically pool the scores of female and male judges, the existence of idiosyncratic differences would yield a bias in the overall score. Drawing on data from 23 wine tastings Bodington finds little to no statistical difference in gender-specific scores. “The potential for accept-a-false-null-hypothesis Type II error when pooling female and male judges’ wine-related opinions appears to be small.”

Joseph Breeden and Sisi Liang analyze “Auction price dynamics for fine wines from age-period-cohort models” (Breeden and Liang, Reference Breeden and Liang2017). In order to identify the main determinants of fine wine prices Breeden and Liang apply an age-period-cohort algorithm (APC) to a database of 1.5 million wine auction results. APC algorithms are designed to separate price appreciation with the age of the wine from overall wine market conditions as well as adjusting for the unique value of specific vintages. Here, the APC modeling resembles the hedonic method with specific controls regarding specification errors. Breeden and Liang examine the “Lafite Bubble,” nonlinear relationships between wine ratings and prices, and show the wide price dispersion among auction houses.

This issue of the Journal of Wine Economics closes with a comparison of three different ranking methods in wine tasting by Jing Cao and Lynne Stokes (Cao and Stokes, Reference Cao and Stokes2017). Based on the simulated data, the authors compare three ranking methods: (1) score average, (2) rank average (Borda count), and (3) Shapley ranking where a judge only needs to say whether or not he/she likes the wine (Ginsburgh and Zang, Reference Ginsburgh and Zang2012). The comparison is based on two criteria. The first one is the squared-error loss, which calculates the sum of squared differences between the estimated ranks and the true ranks. The other is called the percentile loss, which only considers whether the wines are correctly put into a certain subset. The authors summarize their findings as follows, “ranking based on score average in general is more accurate than the one based on rank average. Shapley ranking, if taken into consideration that it puts less burden on judges in wine tasting, may outperform the other methods in certain conditions.”