We measure corn and total agricultural area response to the biofuels boom in the United States from 2006 to 2010. Specifically, we use newly available micro‐scale grid cell data to test whether a location's corn and total agricultural cultivation rose in response to the capacity of ethanol refineries in their vicinity. Based on these data, acreage in corn and overall agriculture not only grew in already‐cultivated areas but also expanded into previously uncultivated areas. Acreage in corn and total agriculture also correlated with proximity to ethanol plants, though the relationship dampened over the time period. A formal estimation of the link between acreage and ethanol refineries, however, must account for the endogenous location decisions of ethanol plants and areas of corn supply. We present historical evidence to support the use of the US railroad network as a valid instrument for ethanol plant locations. Our estimates show that a location's neighborhood refining capacity exerts strong and significant effects on acreage planted in corn and total agricultural acreage. The largest impacts of ethanol plants were felt in locations where cultivation area was relatively low. This high‐resolution evidence of ethanol impacts on local agricultural outcomes can inform researchers and policy‐makers concerned with crop diversity, environmental sustainability, and rural economic development.

We estimate source-differentiated wine demand in China using the absolute price version of the Rotterdam demand system. Within the last decade, China has gone from obscurity to an important participant in global wine trade. The continual growth of Chinese wine imports suggests that a one-time structural shift approach may not fully capture how consumption patterns or demand preferences have changed over time. Thus, a rolling or moving regression procedure is used to account for continual adjustments in import demand patterns and to evaluate overall parameter instability. Our results confirm that Chinese consumers hold French wine in high regard and that French wine demand has consistently increased over the last decade, more than any other exporting source. Consumers in China have gone from allocating about 1/3 to over 1/2 of every dollar to French wine and the expenditure elasticity for French wine mostly increased while the market was expanding. Although Australian wine has a very solid standing in the Chinese market, results suggest that its market share will likely remain unchanged. Marginal budget share and expenditure elasticity estimates indicate that Australia will continue to account for about 20 per cent of the foreign wine market in China.

Despite a growing literature on the integration of energy and agriculture, few have rigorously examined structural changes in the evolution of related energy and agricultural prices. Motivated by strong comovement and increasing volatility of energy and agricultural prices, we examine dynamic evolutions of ethanol, gasoline, and corn prices over the period of March 2005-March 2011. A structural change is found around March 2008 in the pairwise dynamic correlations between the prices in a multivariate GARCH model. A structural VAR (SVAR) model is then estimated on two subsamples, one before and one after the identified change point. Using the novel method of "identification through heteroscedasticity", we exploit the time-varying price volatilities to fully identify the SVAR model. In the more recent period, ethanol, gasoline, and corn prices are found to be more closely linked. Specifically, ethanol (corn) shocks have the largest impact on corn (ethanol) price. The strengthened corn-ethanol relation can be largely explained by the new developments of the biofuel industry and related policy instruments. Variance decomposition shows that for each market a significant and relatively large share of the price variation could be explained by the price changes in the other two markets. For example, corn price changes explain 27% of the variation of ethanol prices, and shocks to ethanol price account for 23% of the variance of corn prices. The results are robust to the inclusion of seasonal dummies, and various representative macroeconomic and financial indicators.

Despite extensive literature on contributing factors to the high commodity prices and volatility in the recent years, few have examined these causal factors together in one analysis. We quantify empirically the relative importance of three factors: global demand, speculation, and energy prices/policy in explaining corn price volatility. A structural vector auto-regression model is developed and variance decomposition is applied to measure the contribution of each factor in explaining corn price variation. We find that speculation is important, but only in the short run. However, in the long run, energy is the most important followed by global demand.

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