When grocery stores began using electronic scanners to capture prices paid for meat, it was assumed that the livestock industry could capitalize on having these point-of-sale data available as a measure of the value of its products. This report compares scanner price data (SPD) with publicly available data collected. Of the two data types, SPD provide more info. about retail meat markets, including a wider variety of meat-cut prices, multiple measures of an average price, the volume of sales, and the relative importance of discounted prices. The SPD sample, however, is not statistically drawn, and complicated processing requirements delay its release, which makes SPD less useful than other data for analyzing current market conditions. Illus.

This paper studies the impact of the level and volatility of the commodity terms of trade on economic growth, as well as on the three main growth channels: total factor productivity, physical capital accumulation, and human capital acquisition. We use the standard system GMM approach as well as a cross-sectionally augmented version of the pooled mean group (CPMG) methodology of Pesaran et al. (1999) for estimation. The latter takes account of cross-country heterogeneity and cross-sectional dependence, while the former controls for biases associated with simultaneity and unobserved country-specific effects. Using both annual data for 1970-2007 and five-year non-overlapping observations, we find that while commodity terms of trade growth enhances real output per capita, volatility exerts a negative impact on economic growth operating mainly through lower accumulation of physical capital. Our results indicate that the negative growth effects of commodity terms of trade volatility offset the positive impact of commodity booms; and export diversification of primary commodity abundant countries contribute to faster growth. Therefore, we argue that volatility, rather than abundance per se, drives the "resource curse" paradox.

Building on recent work on the role of speculation and inventories in oil markets, we embed a competitive oil storage model within a DSGE model of the U.S. economy. This enables us to formally analyze the impact of a (speculative) storage demand shock and to assess how the effects of various demand and supply shocks change in the presence of oil storage facility. We find that business-cycle driven oil demand shocks are the most important drivers of U.S. oil price fluctuations during 1982-2007. Disregarding the storage facility in the model causes a considerable upward bias in the estimated role of oil supply shocks in driving oil price fluctuations. Our results also confirm that a change in the composition of shocks helps explain the resilience of the macroeconomic environment to the oil price surge after 2003. Finally, speculative storage is shown to have a mitigating or amplifying role depending on the nature of the shock.