The Environmental Benefits of Organic Wine: Exploring Consumer Willingness-to-Pay Premiums?

Consumers and the producers have shown interest in organics, but there exist gaps in their common interest. Producers require premiums for their products based on the peculiarity of their production circumstances and the perceived benefits inherent in their products. Predicting consumer willingness to pay a premium for the environmental benefits of organics (WTPe) is a challenge and is of interest in this study. The contingent valuation method was used to gather information on WTPe from a sample that yielded 2,099 surveys. The econometric test ordered probit regression and other analyses were carried out. On average, respondents indicated WTP 23% premium. The proposed hypotheses were tested, and all were accepted except social demographic variables that presented a mixed outcome. The results have implications for consumer sensitization and enlightenment programs of the wine industry, wine market segmentation, and government’s climate change policy. This study is exploratory and has presented a snapshot scenario. Confirmatory and longitudinal studies are recommended to validate this study’s outcomes.     

Differencing of random walks and near random walks

The traditional rationale for differencing time series data is to attain stationarity. For a nearly non-stationary first-order autoregressive process—AR (1) with positive slope parameter near unity—we were led to a complementary rationale. If one suspects near non-stationarity of the AR (1) process, if the sample size is ‘small’ or ‘moderate’, and if good one-step-ahead prediction performance is the goal, then it is wise to difference the data and treat the differences as observations on a stationary AR (1) process. Estimation by Ordinary Least Squares then appears to be at least as satisfactory as nonlinear least squares. Use of differencing for an already stationary process can be motivated by Bayesian concepts: differencing can be viewed as an easy way to incorporate non-diffuse prior judgement—that the process is nearly non-stationary—into one's analysis. Random walks and near random walks are often encountered in economics. Unless one's sample size is large, the same statistical analyses apply to either.     

Exploring the concept of asymmetry: A typology for analysing customer-supplier relationships

In seeking to understand relationships between smaller suppliers and larger customers, there is a growing interest in examining the characteristics of asymmetry in relationships. However, there is a paucity of research that looks at the consequences of size asymmetry for smaller suppliers. Building on IMP (Industrial Marketing and Purchasing Group) research, this paper presents a typology for analysing the consequences of size asymmetry in customer-supplier relationships from the smaller supplier's perspective. The paper reports on the findings from a study involving a total of 48 interviews and eight in-depth case studies of suppliers in the UK textile industry involved in relationships with larger customers. The findings from the study show that the consequences of size asymmetry may vary widely across different relationship characteristics, with both positive and negative outcomes for suppliers. The implications of these findings are that suppliers may take advantage of the positive and constructive consequences of size asymmetry to capitalise on developing their current relationships with customers. In addition, by focusing on the positive consequences of size asymmetry, suppliers may develop the confidence and assurance to develop constructive and more balanced new customer relationships. The paper concludes by identifying the managerial implications for the development of opportunities and customer relationship options for suppliers in asymmetric relationships and proposes that it is important for suppliers to have an assessment instrument to identify the extent of asymmetry or symmetry across their customer relationships.     

Errors, robustness, and the fourth quadrant

The paper presents evidence that econometric techniques based on variance–L² norm–are flawed and do not replicate. The result is un-computability of the role of tail events. The paper proposes a methodology to calibrate decisions to the degree (and computability) of forecast error. It classifies decision payoffs in two types: simple (true/false or binary) and complex (higher moments); and randomness into type-1 (thin tails) and type-2 (true fat tails), and shows the errors for the estimation of small probability payoffs for type 2 randomness. The fourth quadrant is where payoffs are complex with type-2 randomness. We propose solutions to mitigate the effect of the fourth quadrant, based on the nature of complex systems.     

Learning and stability of the Bayesian–Walrasian equilibrium

Under the Bayesian–Walrasian Equilibrium (BWE) (see Balder and Yannelis, 2009), agents form price estimates based on their own private information, and in terms of those prices they can formulate estimated budget sets. Then, based on his/her own private information, each agent maximizes interim expected utility subject to his/her own estimated budget set. From the imprecision due to the price estimation it follows that the resulting equilibrium allocation may not clear the markets for every state of nature, i.e., exact feasibility of allocations may not occur. This paper shows that if the economy is repeated from period to period and agents refine their private information by observing the past BWE, then in the limit all agents will obtain the same information and market clearing will be reached. The converse is also true. The analysis provides a new way of looking at the asymmetric equilibrium which has a statistical foundation.