A dynamic analysis of wine pricing in Argentina

Abstract

Purpose The purpose of the study is to investigate micro determinants for dynamic wine pricing in Argentina. We test whether attributes and time affect the price rate of change. The rate of change is selected given the inflationary context of the country. The analysis provides valuable information for wine marketing decisions. Design/methodology/approach The modeling approach relies on panel data analysis for exploiting the data cross-section and time dimension. The contribution explores a massive price dataset at a weekly frequency. The dependent variable is the weekly price variation rate for product/wine and covariates are attributes, time and nominal variables. Given that endogeneity issues arose, the estimations rely on a two-stage least squares and instrumental variables with cluster-robust errors. Findings Estimations show that attributes, time and cost variables are statistically significant, with clear seasonal patterns and quality segmentation affecting pricing: wines made out of specific grapes such as Chenin, Merlot and Seedling or composing a broad category such as red wine, exhibit price undershooting (price rate of change below average). On the other hand, wines out of grapes such as Bonarda, Margaux, Mistela, Moscatel, Oporto, Tannat and Sauvignon Blanc show price overshooting (rate of change above average). In summary, wine made from determined grapes and specific wineries show divergent pricing. Research limitations/implications Covariates such as alcohol content, label descriptor information, winery history, substitute competition and vintage, among others, have not been considered given that the research analyzes more than 750 wine products. Another limitation is that the work does not explore many time-series covariates, such as promotions and idiosyncratic shocks. Practical implications The contribution presents new information on wine pricing patterns affected by weeks, months and years, including the effect of the prolonged 2020 Argentine lockdown. It also analyzes estimations on pricing at the level of grape/blend and wineries previously unknown in this market. The information can influence inventory decisions on the side of the sellers and purchase decisions on the side of consumers. Social implications The analysis includes fine but also low-cost wines that form part of the diet of low-income families in the country. The work detects a divergent pattern in pricing divided by the quality/price of the wine. It also presents information on price timing that may help consumers in the best moment to buy. Originality/value The contribution analyzes unprecedented information on weekly wine prices and presents evidence of pricing tactics from a point-of-sale perspective: It identifies different adjustment speeds related to product features and time effects.

Could Collected Chemical Parameters Be Utilized to Build Soft Sensors Capable of Predicting the Provenance, Vintages, and Price Points of New Zealand Pinot Noir Wines Simultaneously?

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Simulation of the impact of a wine supply management tool: a potential implementation of a climatic reserve in Switzerland

“…We will use a similar methodology to predict the variation in price with monthly series due to a release of stocks from the CR. Concerning price simulations of wines, Larrosa et al (2023) find that attributes, time and cost variables have an important share in explaining the dynamic of wine pricing. Better information on pricing patterns and price timing can have an influence on wine sellers for stock decisions and on consumers for purchase decisions like the best moment to buy a wine.…”

“…Masset et al (2022), who study fine wine pricing in Switzerland using the hedonic regression model, use similarly inflation-adjusted prices through the consumer price index (CPI). Larrosa et al (2023) also take into account the inflationary context of Argentina when analyzing dynamic analysis of wine pricing. We, therefore, use a similar methodology to simulate prices from 2000 to 2011 according to the annual CPI, published by the Federal Office for Statistics (based on December 2015 ¼ 100) because the sales prices of the Nielsen panel are only available since 2012.…”