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From boom to bust: a typology of real commodity prices in the long run

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Abstract

This paper considers the evidence on real commodity prices from 1900 to 2015 for 40 commodities, representing 8.72 trillion US dollars of production in 2011. In doing so, it suggests and documents a comprehensive typology of real commodity prices, comprising long-run trends, medium-run cycles, and short-run boom/bust episodes. The main findings can be summarized as follows: (1) real commodity prices have been on the rise—albeit modestly—from 1950; (2) there is a pattern—in both past and present—of commodity price cycles, entailing large and long-lived deviations from underlying trends; (3) these commodity price cycles are themselves punctuated by boom/bust episodes which are historically pervasive.

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Fig. 1
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Fig. 6

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Notes

  1. Here, it is very important to emphasize that the notion of cycles is not meant to evoke a sense of regularity—much less, predictability—in commodity price dynamics but instead provides us with a convenient means of statistically characterizing deviations from long-run trends.

  2. In related work, Jacks and Stuermer (2018) consider the dynamic effects of commodity demand shocks, commodity supply shocks, and storage demand or other commodity-specific demand shocks on real commodity prices in the long run. There, commodity demand shocks strongly dominate commodity supply shocks in driving prices and are growing in importance over time.

  3. Neglecting energy products, these production values are still in excess of 4.54 trillion USD. Furthermore, there is likely very little room for sample selection issues in driving the results presented below. In particular, there may be concerns about the potential influence of once-important, but now-irrelevant commodities or once-irrelevant, but now-important commodities which would be ruled out on the basis of the criteria laid out here. For example, uranium had no wide commercial application until the atomic age and, thus, remains outside of the sample. At the same time, production of uranium in 2011 was valued at 6.65 billion USD—that is, a mere 0.08% of the current sample’s cumulative value of production in the same year.

  4. Naturally, to the extent that the quality of commodities has remain unchanged over time, any upward bias in the US CPI induced by insufficient correction for changes in the quality of other goods over time will lead to a downward bias in the calculation of increases in real commodity price documented below.

  5. The accompanying chartbook (available at http://www.sfu.ca/~djacks) documents the evolution of real prices on a commodity-by-commodity basis from 1850 to 2015. Visual inspection of these series reveals the well-known “big variability” of real commodity prices (Cashin and McDermott 2002). With respect to long-run trends in the real commodity price data, there are a few clear patterns across product categories. Notwithstanding some common global shocks like the peaks in real prices surrounding World War I, the 1970s, and the 2000s as well as the troughs in the 1930s and 1990s, there is a divergence in between those commodities exhibiting a secular downward trend—notably, grains and soft commodities—and those exhibiting a secular upward trend—notably, energy and precious metals.

  6. To implement the band pass filter, Christiano and Fitzgerald assume that the underlying data-generating process is integrated of order one (that is, it is a random walk). Even though the simulations in their paper strongly suggest that the filter remains unaffected by potential misspecification of the data-generating process, it is very easy to check this assumption. Testing for a unit root in the differenced commodity price index series yields the following set of results under the following set of unit root tests:

    1. 1.

      Levin–Lin–Chu adjusted t = − 3.3139 [p value = 0.0050]

    2. 2.

      Breitung lambda = − 6.8269 [p value = 0.0000]

    3. 3.

      Im–Pesaran–Shin Z − t-tilde-bar = − 6.9422 [p value = 0.0000]

    4. 4.

      Fisher–Philips–Perron inverse Chi squared = 72.0873 [p value = 0.0000]

    Even though all of these tests embed different assumptions and entail different strengths and weaknesses, all of them entail the use of the null hypothesis that the differenced commodity price index series contains a unit root. Critically, this hypothesis is decisively rejected across all tests, and so it seems justified to invoke the assumption that the series is indeed integrated of order one.

  7. In what follows, there is also little material difference in estimated trends or cycles when alternate asymmetric band pass filters are used. For example, using the Butterworth band pass filter, the results remain broadly unaffected in that: (1) the Christiano–Fitzgerald filter estimates an index value of 160.99 in 2015 versus the Butterworth filter which estimates an index value of 154.59 in the same year; and (2) the Christiano–Fitzgerald filter estimates complete cycles for the years from 1903 to 1932 and from 1965 to 1996 versus the Butterworth filter which estimates complete cycles for the years from 1900 to 1932 and from 1966 to 1997. In this instance, the use of the Hodrick–Prescott filter has been avoided as: (1) it is well known that it is slow in establishing turning points in long-run trends and, thus, the HP filter estimates that real commodity prices have continued to rise, even in the face of the significant reversal in real commodity prices dating from 2011; (2) recent work by Hamilton (2017) strongly advises against the use of the HP filter in that it “produces series with spurious dynamic relations that have no basis in the underlying data-generating process” (p. 2).

  8. Thus, “commodities to be grown” would include all animal products, grains, and soft commodities and “commodities in the ground” would include all energy products, metals, minerals, and precious metals.

  9. The accompanying chartbook also provides a complete set of figures for real commodity price cycles and boom/bust episodes on a commodity-by-commodity basis.

  10. These commodities are composed of chromium, cocoa, copper, corn, cottonseed, gold, iron ore, lead, nickel, petroleum, phosphate, platinum, potash, rice, rubber, rye, silver, steel, tin, and wool.

  11. Given the dramatic decline in real commodity prices starting in 2014, it may also be instructive to have a sense of how sensitive the estimation of long-run trends and medium-run cycles is to innovations at the end of the sample. To that end, we can estimate two sets of long-run trends/medium-run cycles. The first set is the long-run trend and medium-run cycle estimated from the full sample of data from 1900 to 2015 as depicted in Fig. 2a, b. There, the estimated (logged) value of the long-run trend in 2010 is 4.8636 while the last trough and peak in real commodity prices are estimated to have occurred in 1996 and 2010, respectively. The second set is the long-run trend and medium-run cycle estimated from a restricted sample of data from 1900 to 2010 only. In this case, the estimated (logged) value of the long-run trend in 2010 is 5.0145 while the last trough in real commodity prices is estimated to have occurred in 1996 but with an indeterminate peak. Thus, there is a perhaps unsurprising dependence in between the estimated long-run trend and the terminal sample values of real commodity prices. At the same time, there is a perhaps surprising independence in between the estimated medium-run cycle and the terminal sample values of real commodity prices.

  12. This standardization was motivated by two elements: (1) for expositional purposes, it makes it much easier to speak of thresholds as defined by the number of (unitary) standard deviations since the values of the standard deviations will vary by commodity; and (2) while the SRC terms effectively act as white-noise residual terms, they generally have near, but not exactly zero means. For instance, the SRC term for the real commodity price index depicted in Fig. 6c has a mean of 0.0101. Furthermore, using the raw series on SRC terms, we fail to reject the null hypothesis that the sample came from a normally distributed population when using the standard tests of normality like Jarque–Bera and Kolmogorov–Smirnov.

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Acknowledgements

This paper was prepared for the ANU Centre for Economic History/Centre for Applied Macroeconomic Analysis conference on “Commodity Price Volatility, Past and Present” held in Canberra. I thank the conference organizers for their hospitality and providing the impetus for this paper. I also thank the University of New South Wales for their hospitality while this paper was completed, Stephan Pfaffenzeller and Nigel Stapledon for help with the data, and the editor and two referees for their comments. I also appreciate comments received from seminars at Adelaide, the Federal Reserve Bank of Dallas, Hong Kong University of Science and Technology, New South Wales, Oxford, Peking University Guanghua School of Management and School of Economics, Shanghai University of Finance and Economics, Shanghai University of International Business and Economics, UIBE, and Wake Forest as well as from the EH-Clio Lab Annual Meeting and the Muenster Workshop on the Determinants and Impact of Commodity Price Dynamics. Finally, I gratefully acknowledge the Social Science and Humanities Research Council of Canada for research support.

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Authors and Affiliations

  1. Simon Fraser University, Burnaby, Canada

    David S. Jacks

  2. National Bureau of Economic Research, Cambridge, USA

    David S. Jacks

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Correspondence to David S. Jacks.

Appendix

Appendix

This appendix details the sources of the real commodity prices used throughout this paper. As such, there are a few key sources of data: the annual Sauerbeck/Statist (SS) series dating from 1850 to 1950; the annual Grilli and Yang (GY) series dating from 1900 to 1986; the annual unit values of mineral production provided by the United States Geographical Survey (USGS) dating from 1900; the annual Pfaffenzeller, Newbold, and Rayner (PNR) update to Grilli and Yang’s series dating from 1987 to 2010; and the monthly International Monetary Fund (IMF), United Nations Conference on Trade and Development (UNCTAD), and World Bank (WB) series dating variously from 1960 and 1980. The relevant references are:

  • Grilli, E.R. and M.C. Yang (1988), “Primary Commodity Prices, Manufactured Goods Prices, and the Terms of Trade of Developing Countries: What the Long Run Shows.” World Bank Economic Review 2(1): 1–47.

  • Pfaffenzeller, S., P. Newbold, and A. Rayner (2007), “A Short Note on Updating the Grilli and Yang Commodity Price Index.” World Bank Economic Review 21(1): 151–163.

  • Sauerbeck, A. (1886), “Prices of Commodities and the Precious Metals.” Journal of the Statistical Society of London 49(3): 581–648.

  • Sauerbeck, A. (1893), “Prices of Commodities During the Last Seven Years.” Journal of the Royal Statistical Society 56(2): 215–254.

  • Sauerbeck, A. (1908), “Prices of Commodities in 1908.” Journal of the Royal Statistical Society 72(1): 68–80.

  • Sauerbeck, A. (1917), “Wholesale Prices of Commodities in 1916.” Journal of the Royal Statistical Society 80(2): 289–309.

  • The Statist (1930), “Wholesale Prices of Commodities in 1929.” Journal of the Royal Statistical Society 93(2): 271–87.

  • “Wholesale Prices in 1950.” Journal of the Royal Statistical Society 114(3): 408–422.

A more detailed enumeration of the sources for each individual series is as follows.

  • Aluminum: 1900–2010, GY and PNR; 2011–2015, UNCTAD

  • Barley: 1850–1869, SS; 1870–1959, Manthy, R.S. (1974), Natural Resource Commodities—A Century of Statistics. Baltimore and London: Johns Hopkins Press; 1960–2015, WB

  • Bauxite: 1900–2015, USGS

  • Beef: 1850–1899, SS; 1900–1959, GY; 1960–2015, WB

  • Chromium: 1900–2015, USGS

  • Coal: 1850–1851, Cole, A.H. (1938), Wholesale Commodity Prices in the United States, 1700–1861: Statistical Supplement. Cambridge: Harvard University Press; 1852–1859, Bezanson, A. (1954), Wholesale Prices in Philadelphia 1852–1896. Philadelphia: University of Pennsylvania Press; 1880–1948, Carter, S. et al. (2006), Historical Statistics of the United States, Millennial Edition. Cambridge: Cambridge University Press; 1949–2010, United States Energy Information Administration; 2011–2015, BP Statistical Review of World Energy 2015

  • Cocoa: 1850–1899, Global Financial Data; 1900–1959, GY; 1960–2015, WB

  • Coffee: 1850–1959, Global Financial Data; 1960–2015, WB

  • Copper: 1850–1899, SS; 1900–2010, GY and PNR; 2011–2015, UNCTAD

  • Corn: 1850–1851, Cole, A.H. (1938), Wholesale Commodity Prices in the United States, 1700–1861: Statistical Supplement. Cambridge: Harvard University Press; 1852–1859; Bezanson, A. (1954), Wholesale Prices in Philadelphia 1852–1896. Philadelphia: University of Pennsylvania Press; 1860–1999, Global Financial Data; 2000–2015, United States Department of Agriculture National Agricultural Statistics Service

  • Cotton: 1850–1899, SS; 1900–1959, GY; 1960–2015, WB

  • Cottonseed: 1874–1972, Manthy, R.S. (1974), Natural Resource Commodities—A Century of Statistics. Baltimore and London: Johns Hopkins Press; 1973–2015, National Agricultural Statistics Service

  • Gold: 1850–1999, Global Financial Data; 2000–2015, Kitco

  • Hides: 1850–1899, SS; 1900–1959, GY; 1960–2015, UNCTAD

  • Iron ore: 1900–2015, USGS

  • Lamb: 1850–1914, SS; 1915–1970, GY; 1971–2015, WB

  • Lead: 1850–1899, SS; 1900–2010, GY and PNR; 2011–2015, UNCTAD

  • Manganese: 1900–2015, USGS

  • Natural gas: 1900–1921, Carter, S. et al. (2006), Historical Statistics of the United States, Millennial Edition. Cambridge: Cambridge University Press; 1922–2015, United States Energy Information Administration

  • Nickel: 1850–2010, USGS; 2011–2015, IMF

  • Palm oil: 1850–1899, SS; 1900–1959, GY; 1960–2015, WB

  • Peanuts: 1870–1972, Manthy, R.S. (1974), Natural Resource Commodities—A Century of Statistics. Baltimore and London: Johns Hopkins Press; 1973–1979, National Agricultural Statistics Service; 1980–2015, WB

  • Petroleum: 1860–2000, Global Financial Data; 2001–2015, IMF

  • Phosphate: 1880–1959, Manthy, R.S. (1974), Natural Resource Commodities—A Century of Statistics. Baltimore and London: Johns Hopkins Press; 1960–2015, WB

  • Platinum: 1900–1909, USGS; 1910–1997, Global Financial Data; 1998–2015, Kitco

  • Pork: 1850–1851, Cole, A.H. (1938), Wholesale Commodity Prices in the United States, 1700–1861: Statistical Supplement. Cambridge: Harvard University Press; 1852–1857, Bezanson, A. (1954), Wholesale Prices in Philadelphia 1852–1896. Philadelphia: University of Pennsylvania Press; 1858–1979, Global Financial Data; 1980–2015, IMF

  • Potash: 1900–2015, USGS

  • Rice: 1850–1899, SS; 1900–1956, GY; 1957–1979, Global Financial Data; 1980–2015, IMF

  • Rubber: 1890–1899, Global Financial Data; 1900–1959, GY; 1960–2015, WB

  • Rye: 1850–1851, Cole, A.H. (1938), Wholesale Commodity Prices in the United States, 1700–1861: Statistical Supplement. Cambridge: Harvard University Press; 1852–1869, Bezanson, A. (1954), Wholesale Prices in Philadelphia 1852–1896. Philadelphia: University of Pennsylvania Press; 1870–1970, Manthy, R.S. (1974), Natural Resource Commodities—A Century of Statistics. Baltimore and London: Johns Hopkins Press; 1971–2015, National Agricultural Statistics Service

  • Silver: 1850–2015, Kitco

  • Steel: 1850–1998, USGS; 1999–2015, WB

  • Sugar: 1850–1899, SS; 1900–1959, GY; 1960–2015, WB

  • Sulfur: 1870–1899, Manthy, R.S. (1974), Natural Resource Commodities—A Century of Statistics. Baltimore and London: Johns Hopkins Press; 1900–2010, USGS

  • Tea: 1850–1899, SS; 1900–1959, GY; 1960–2015, WB

  • Tin: 1850–1899, SS; 1900–2010, GY and PNR; 2011–2015, UNCTAD

  • Tobacco: 1850–1865, Clark, G. (2005), “The Condition of the Working Class in England, 1209–2004.” Journal of Political Economy 113(6): 1307–1340; 1866–1899, Carter, S. et al. (2006), Historical Statistics of the United States, Millennial Edition. Cambridge: Cambridge University Press; 1900–1959, GY; 1960–2015, WB

  • Wheat: 1850–1999, Global Financial Data; 2000–2015, United States Department of Agriculture National Agricultural Statistics Service

  • Wool: 1850–1899, SS; 1900–1979, GY; 1980–2015, IMF

  • Zinc: 1850–2000, Global Financial Data; 2001–2015, IMF

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Jacks, D.S. From boom to bust: a typology of real commodity prices in the long run. Cliometrica 13, 201–220 (2019). https://doi.org/10.1007/s11698-018-0173-5

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