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This article determines the potential effects of policies to address concerns about lower producer prices due to increased use of marketing agreements. Policies considered are banning alternative marketing agreements, compensating producers who sell on the cash market, and restricting the quantity of marketing agreements. We use an agent-based model in a common-value auction framework to analyze these policies. The common-value auction framework is used because it closely resembles how livestock are actually purchased. The agent-based model is used to find the common-value auction equilibrium. A ban on marketing agreements reduces social welfare and the other policy interventions have little effect on prices. Past theoretical studies predict marketing agreements will cause large reductions in prices paid to producers. Conversely, empirical studies show slight effects. This article offers an alternative theory that more closely matches livestock markets and our results reduce the gap between theoretical and empirical research. The common-value auction model predicts negative effects on producer prices close to those found in past empirical research.

In this paper, we use a unique data set of the prices paid to farmers in Argentina for grapes to examine the prices paid by non-varietal wine processing cooperatives and investor-oriented firms (IOFs). Motivated by contrasting theoretical predictions of cooperative price effects generated by the yardstick of competition and property rights theories, we apply a multilevel regression model to identify price differences at the transaction level and the departmental level. On average, farmers selling to cooperatives receive a 3.4 % lower price than farmers selling to IOFs. However, we find cooperatives pay approximately 2.4 % more in departments where cooperatives have larger market shares. We suggest that the inability of cooperatives to pay a price equal to or greater than the one paid by IOFs can be explained by the market structure for non-varietal wine in Argentina. Specifically, there is evidence that cooperative members differ from other farmers in terms of size, assets and the cost of accessing the market. We conclude that the analysis of cooperative pricing cannot solely focus on the price differential between cooperatives and IOFs, but instead must consider other factors that are important to the members.

This study derives a general characterization of the misalignment between socially and privately optimal spatial pricing in an uncontested procurement market. In doing so it clarifies the link between firm-gate price markdown (pricing the input below its marginal value product at the firm’s location) and spatial price discrimination (varying markdown by distance). We subsequently examine the implications of our results for regulatory prescriptions. Our analysis reveals that, in the absence of regulation, increased firm-gate price markdown is necessarily accompanied by intensified spatial price discrimination, and that discrimination is always conducted against nearby producers. We find that if regulation targets price markdown at the firm gate, then spatial price discrimination is also inhibited resulting in welfare gains. In contrast, directly targeting spatial price discrimination (as generally prescribed by enacted and proposed legislation) cannot attain a first best, and may in fact result in efficiency losses relative to the unregulated equilibrium.

We pair Nielsen TDLinx data, 2004–2014, with Consumer Price Index data to investigate how changes in food retail market structure drive food price inflation. We find, in corroboration with much of the evidence to date, that market concentration is positively and significantly associated with higher food prices. We find the same to be true for store format concentration, or the homogeneity of food markets. As the market shares, or penetration, of supercenters, warehouse stores, limited assortment stores, and superettes increase at expense of traditional supermarkets, food price inflation decreases.

Production location matters to many consumers and regulators and policymakers are pressed to statue on labels about country of origin, local foods and geographical indications (GIs). This paper investigates the incidence of the EU policy on GIs on bilateral trade flows. We develop ttheoretical arguments and provide empirical evidence to analyze heterogeneity in consumer preferences regarding country of origin ( domestic versus foreign ) and the implicit quality signals from GI logos. The objective of the paper is to investigate whether producing GIs really boots bilateral trade, assuming heterogeneity in consumers’ preference. We first develop an analytical framework of a simple partial equilibrium two-country model through a Cobb-Douglas utility structure to assess the impact of GIs on trade. In addition, we empirically corroborate the analytical findings with a unique data on protected GIs by product and European country. Our main findings indicate that GI-products have ambiguous effect on international trade. Indeed, their trade-impact depends on the importance of product for consumers (i. e., the intensity and the reputation of GI-product considered as deterministic weight in consumers’ preference). As expected, a heterogeneity in consumers’ preference – due to home bias about local or foreign varieties – can increase or decrease trade, despite the presence of GI-products.

We examine the short-run forecasting problem in a data set of daily prices from 134 corn buying locations from seven states – Iowa, Illinois, Indiana, Ohio, Minnesota, Nebraska, and Kansas. We ask the question: is there useful forecasting information in the cash bids from nearby markets? We use several criteria, including a Granger causality criterion, to specify forecast models that rely on the recent history of a market, the recent histories of nearby markets, and the recent histories of futures prices. For about 65% of the markets studied, the model consisting of futures prices, a market’s own history, and the history of nearby markets forecasts better than a model only incorporating futures prices and the market’s own history. That is, nearby markets have predictive content. But the magnitude varies with the forecast horizon. For short-run forecasts, the forecast accuracy improvement from including nearby markets is modest. As the forecast horizon increases, however, including nearby prices tends to significantly improve forecasts. We also examine the role played by physical market density in determining the value of incorporating nearby prices into a forecast model.