commodity prices has been well established by numerous authors. See, for example, Lamm (1980), Gardner (1981), and Baffes and Dennis (2015) for agriculture and Gilbert (1989), Baffes (1997), and Akram (2009) for metals.

Among the sectoral fundamentals, the effect of a rise in the _stock‐to‐use ratio_ is, as expected, negative and highly significant, with a panel estimate of ‐0.37. The estimates are remarkably similar to findings reported elsewhere. For example, Bobenrieth, Wright, and Zeng (2012) estimated correlation coefficients between stock‐to‐use ratios and real de‐ trended prices for wheat, maize, and rice of ‐0.40, ‐0.50, and ‐0.17, respectively (compared to ‐0.46, ‐0.48, and ‐0.49 in the present study). Similarly, FAO (2008, p. 6) reported corre‐ lation coefficients between the cereals price index and various measures of stock‐to‐use ratios, ranging from ‐0.47 and ‐0.65.

Likewise, the effect of a rise in the _real oil price_ is found to be significantly different from zero for all six commodities. The panel estimate at 0.15 implies that a 10 percent in‐ crease in oil prices is associated with a 1.5 percent increase in agricultural prices. The strong relationship between energy and non‐energy commodity prices was established in the lit‐ erature long before the post‐2004 price boom. Gilbert (1989), for example, using quarterly data from 1965‐86, estimated the transmission elasticity from energy to non‐energy com‐ modities at 0.12 and that from energy to food commodities at 0.25. Hanson and others (1993), based on a general equilibrium model, found a significant effect of oil price changes on agricultural producer prices in the United States. Borensztein and Reinhart (1994), using quarterly data from 1970 to 1992, estimated a transmission elasticity to non‐energy com‐ modities of 0.11, and Chaudhuri (2001), too, found a strong relationship between energy and non‐energy prices. Baffes (2007), using annual data from 1960 to 2005, estimated elasticities for non‐energy and food commodities of 0.16 and 0.18, respectively. Moss and others (2010) found that the demand of U.S. agriculture for energy responds strongly to changes in the energy prices.<sup>**12**</sup>

# **_3.3 What matters most?_**

During the commodity price boom, most of the macroeconomic and sectoral fundamentals

> **12** Yet not all studies concur with a strong oil‐non‐oil price relationship. Saghaian (2010) established a strong correlation among oil and other commodity prices (including food prices) but the evidence for a causal link was mixed. Gilbert (2010) found a correlation between oil and food prices, but noted that this could reflect common causation rather than a causal link. Zhang and others (2010) found no direct long‐run relationship between fuel and agricultural commodity prices, and only a limited short‐run relationship. Reboredo (2012) concluded that grain prices are not driven by oil price fluctuations. The mixed evidence on the energy‐non‐ energy price link could reflect the frequency of the data series used in the analysis or the presence of biofuels (Baffes 2013). Zilberman and others (2013) noted that higher frequency (and hence “noisier”) data are typically associated with weaker correlations. On the other hand, an exogenous shock pushing crude oil prices down under a mandated ethanol‐gasoline mixture would increase fuel consumption and push ethanol and maize prices up, and thus lead to a negative relationship between food and oil prices, other things being equal (De Gorter and Just 2008).

_9_