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Given the fact that the income disparity has become extremely severe in rural China, the purpose of this paper is to examine heterogeneity in food consumption among various income classes and to investigate the impact of changes in income distribution patterns on food demand in rural China.

In this study, the authors partition the households into five income classes according to the distribution of household per capita net income. Using household data drawn from the China Health and Nutrition Survey in 2011, a two-stage demand model is applied to estimate a food demand system for each of the income classes. After obtaining the estimated income elasticities of eight studied food groups for each income class, the authors then examine the responsiveness of food demand to the changes in income distribution by means of four scenarios with varying income distribution.

The empirical results indicate that substantial differences in food consumption exist across various income classes. Specifically, the lowest-income households are more sensitive to price and income changes for most studied food groups than the highest-income households are. In general, income responsiveness is higher for meats, aquatic products and dairy products. Based on estimated income elasticities, the projected food consumption under different income distribution patterns shows that changes in income distribution have significant influences on food consumption. In addition, the authors conclude that a more equal distribution of income would be associated with a higher demand for food in rural China.

This paper employs a two-stage demand model to estimate food demand in rural China by income classes. The results imply substantial differences in food demand for various income classes. Therefore, income distribution should be taken into account instead of an average estimation for the population as a whole when investigating food demand in rural China. Given the significant changes in income distribution in rural China, this study provides several important policy implications to alleviate income inequality and poverty, as well as to improve nutrition for lower-income classes.

The purpose of this paper is to detect the existence of price bubbles and examine the possible contributing factors that associate with price bubble occurrences in China agricultural commodity markets.

Using recently developed rolling window right-side augmented Dickey–Fuller test, we first detect the dates of price bubbles in China's two important agricultural commodity markets, namely corn and soybeans. Then, we use a penalized maximum likelihood estimation of a multinomial logistic model to estimate the contributing factors of price bubbles in both markets, respectively.

Results from the bubble detection indicate that price bubbles account for 5.48% (3.91%) of the studied periods for corn (soybeans). More importantly, we find that market liquidity and speculation have opposite effects on the occurrences of bubbles in the corn and soybeans market. World stocks-to-use and exchange rates affect the occurrences of bubbles in a different way for each commodity, as well. Price bubbles are more likely associated with strong economic activity, high interest rates and low inflation levels.

This is the first study considering commodity-specific features into the formation of price bubbles. Through accurately identifying the bubble dates and fixing the estimation bias of rare events models, this study enables us to obtain robust results for each commodity. The results imply that China's corn and soybeans market respond differently to the speculative activity and external shocks from international markets. Therefore, future policy regulations on commodity markets should focus on more commodity-specific factors when aiming at avoiding bubble occurrences.

The purpose of this paper is to evaluate the possible advantages of applying prediction markets to supply network management. Are the same encouraging results possible as in the election application of prediction markets?

This is a paper focused on the requirements and the possible results of the application based on the literature for supply network management and prediction markets. It discusses the potential of prediction markets to improve information management in supply networks.

The paper finds that prediction markets are a new instrument to collect the diverse information among the supply chain members, and to publish this information to the other members.

Prediction markets are able to improve the information basis for decision making in supply chains.

This paper shows the application of prediction markets in a supply network management case and the possibilities and limitations of prediction markets to collect, and publish information within the supply chain.

This article aims to estimate the relationship between brand loyalty and price promotions on the German yoghurt market. It considers consumer loyalty to various corporate brands and their respective sub-brands to analyze promotional strategies between and within certain corporate brands with a larger loyal consumer segment and a moderate strength of consumer loyalty are well suited for effective price promotions following the idea of loss leader by Lal and Matutes (1994).

The paper’s approach follows Allender and Richards’ (2012) and extends to explicitly considering the product line management of every manufacturer in the market. In the first step, a random coefficient logit specification is estimated to compute measures of brand loyalty for each brand. In the second step, the relationship between brand loyalty measures and the frequency and depth of price promotions is analysed.

The results suggest that weaker corporate brands are promoted more aggressively supporting the model hypotheses by Koças and Bohlmann (2008). Within the manufacturer’s product line, sub-brands with a larger loyal consumer segment and a moderate strength of consumer loyalty are more often used for effective price promotions which reflects the idea of loss leading first introduced by Lal and Matutes (1994).

The results are limited to a static relationship between brand loyalty and price promotions. Analyzing the dynamics of the relationship between brand loyalty and price promotions should prove fruitful in enhancing the understanding of retailer strategies and provides additional implications for managerial decisions in retailing.

Managers need to be more aware of the linkages between product line management and promotional strategies. Changes in the product line management may require a redirection of the promotional measures and strategies.

Consumer behavior with respect to brand loyalty to some extent determines price promotional strategies of retailers. The promotional strategies provide opportunities to save expenditures, especially for non-loyal and low income households.

Matching and analyzing two detailed (consumer, retail) scanner data sets to investigate the relationship between the measures of brand loyalty and the retailers’ price promotional strategies. Novel is the modeling of two different dimensions of brand loyalty (size and strength) and the consideration of sub-brands. The results clearly show that promotional strategies vary not only between corporate brands but also between sub-brands of the same corporate brand.