The model was estimated within an OLS (ordinary least‐squares) and a panel frame‐ work. This choice was motivated by the desire to estimate the effects of the fundamentals on the prices of individual commodities (OLS estimates, reported in the first six columns of Table 2) and also to have a sense of the average effects across all commodities (panel estimates, reported in the last column of Table 2). Based on a Hausman test, the fixed effect model was rejected in favor of a random effect model (the chi‐square statistic was 0.40 with a p‐value of 0.995).

Both the conventional and co‐integration statistics and the expected signs of the parameter estimates indicate a satisfactory performance of the OLS models.<sup>**7**</sup> For example, 23 out of the 30 parameter estimates of all six regressions (excluding the constant terms) are significantly different from zero at the 5 percent level, and almost 60 percent of the price variation is accounted for by the five fundamentals. The _R_<sup>_2_</sup> ranges from 0.50 for soy‐ beans and wheat to 0.70 for rice.

In all six equations, the parameter estimate of _income_ is negative and highly signif‐ icant, with values ranging within a remarkably tight band. The panel estimate of ‐0.48 in‐ dicates that a 10 percent increase in the income of low‐ and middle‐income countries re‐ duces the real price of agricultural commodities by about 5 percent. Counterintuitive as it may seem, this result is consistent with the Prebisch‐Singer hypothesis—which states that as income grows the price ratio of primary commodities over manufactured goods de‐ clines—as well as with Engel’s Law of less‐than‐unitary income elasticity for food commod‐ ities.<sup>**8**</sup> Thus, the negative sign of the income elasticity (‐0.5 in the case of the panel model) should be interpreted as the difference between the effect of income on nominal food prices and the effect of income on the deflator. The former is generally lower than one, the latter greater than one.

Though it has often been argued, especially in the context of the 2008 food price spike, that the changing consumption patterns of emerging economies, especially China and India, were key drivers of rising prices,<sup>**9**</sup> the evidence points to a lower impact than

> **7** Based on the same tests, all the statistics but one confirm co‐integration at the 1 percent level of significance (co‐integration statistics are not reported here). Detailed stationarity tests of all variables can be found in Baffes and Etienne (2016), who used the same data within a panel autoregressive distributed lag model to estimate the long‐run impact of income on commodity prices.

> **8** The declining terms of trade of primary commodities versus manufactured goods were first observed in the mid‐19th century, when the German statistician Ernst Engel observed that poor families spent a greater pro‐ portion of their total expenditure on food compared to their wealthier counterparts, leading to Engel’s Law of less than unitary income elasticity of food commodities (Engel 1875). Almost a century later, Kindleberger (1943, p. 349) argued that “[t]he terms of trade move against agricultural and raw‐material countries as the world’s standard of living increases ... and as Engel’s Law of consumption operates.” Kindleberger’s thesis was empirically verified by Prebisch (1950) and by Kindleberger (1958) himself; it was also emphasized by Singer (1950). The decline of the terms of trade was later dubbed the Prebisch‐Singer hypothesis.

> **9** See, for example, Krugman (2008), Wolf (2008), and Bourne (2009). On the empirical side, Gilbert (2010) concluded that demand‐side variables, including GDP growth, are important in explaining aggregate food

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