“… how long most people would look at the best book before they would give the price of a large turbot for it!”
Appendix I: Derivation of the Data
The data have been derived from a number of sources. The starting point for the two price indexes was a pair of data series developed and maintained by the Research Department at the IMF: (a) prices of 34 non-fuel primary commodities that are important in world trade (available monthly from January 1957), and (b) unit values of manufactured goods exported by industrial countries (available monthly from January 1948). Both series are expressed in U.S. dollars and indexed to 1980=100; the data used here are annual averages of monthly data. For a detailed description of the commodity price data, see IMF (1986), Appendix I. The prices of manufactured goods cover SITC categories 5 through 8 and are averaged from OECD country-specific data using 1985 exports of manufactured goods as weights.
Both price series have been extended back to 1900 by splicing the IMF data to unit value indexes from United Nations (1969). The U.N. data for the post- World War II period are derived from comprehensive world trade data, but the coverage becomes progressively narrower as the data reach farther back in time; it is estimated (p. 366 of United Nations (1968)) that the primary product coverage is about 50-55 percent for the period 1900-13. (Further details on the U.N. indexes are provided in United Nations (1968)) Primary commodities (“other goods”) comprise SITC sections 0-4 and 9. These data thus include petroleum trade in primary commodities, which reduces the correspondence in coverage with the IMF data. The importance of this difference, however, is much smaller for the period in question than it would be for the more recent period. Schlote (1952, p. 58) notes, for example, that “mineral oils” accounted for just 3.1 percent of British imports of raw materials in the period 1909-13, and 10.8 percent for 1927-29.
The U.N. data omit the years 1914-20, 1939-47, and 1949. Earlier studies using these data have left these gaps in place, but for this study the timing of changes during the missing years has been estimated on the basis of partial data from national sources. To interpolate the World War I period, I have used British trade data from Schlote (1952, Table 26). Schlote’s data cover three categories: (a) foodstuffs and livestock, (b) raw materials and semi-manufactured goods, and (c) manufactured goods. Schlote presents a price index covering total trade (including re-exports) in manufactures; to get an index for primary commodities, I have aggregated the indexes for imports of (a) and (b) (since exports of primary commodities were a very small component of British overseas trade in that period). Lewis (1952, p. 136) estimates that for the period 1913-29, the ratio of raw materials to food in world trade was 9:6; I therefore have assigned a weight of 60 percent to raw materials for this aggregation. These indexes have been converted from pounds sterling to U.S. dollars using annual average exchange rates, from Board of Governors of the Federal Reserve System (1943). Next, I have divided the total change in the U.N. indexes for world trade for 1913-21 by the total change in the British indexes, multiplied the year-to-year percentage changes in the British indexes by that factor, and used the resulting percentage changes to interpolate the U.N. data.
The World War II period and its aftermath (1939-49) have been interpolated using annual data from the United States as well as the United Kingdom. The U.S. data, from the Department of Commerce (1947, 1951), include price indexes for imports and exports of (a) crude materials, (b) crude foodstuffs, (c) manufactured foodstuffs, (d) semimanufactures, and (e) finished manufactures; and percentage distributions in trade, which I used to derive weights. The first two categories (a and b) have been aggregated to get primary commodities, and the remaining three to get manufactured goods.
The United Kingdom data for the 1939-49 period come from the Central Statistical Office (1952), with a few missing observations filled in from the Records and Statistics supplement to the Economist for September 6, 1947. The categories in this case are (a) food, drink, and tobacco, (b) raw materials and articles mainly unmanufactured, and (c) articles wholly or mainly manufactured. Import prices for (a) and (b) have been averaged to derive a price series for primary commodities, and total import and export prices for (c) have been averaged to derive a series for manufactures. In each case, average relative value figures for the period 1938-50 (from the same source) have been used as weights, and the indexes have been converted to U.S. dollars at annual average exchange rates.
The next step in deriving data for this period is to average the price movements for the United States and the United Kingdom. In order to obtain comparable data, and to avoid distortions arising from the disruption of trade during the war, I have used relative trade data from the League of Nations (1945, pp. 157, 158, and 166) covering the period 1930-38. Those data show that the United States accounted for 49 percent of the two countries’ total trade in primary commodities, and 44 percent of total trade in manufactures. The two countries together accounted for around 25 percent of total world trade in both commodities and manufactures. Percentage changes in the weighted average price series have been used to interpolate the world data, as in the World War I period discussed above.
Data for 1854-99 have been derived from Schlote (1952), generally following the choices selected by Lewis (1952). These data cover British traded goods and are expressed in U.S. gold dollars at the 1913 parity. Raw materials prices (“raw materials and semi-manufactured goods”) are an average of British import and export prices (excluding re-exports), weighted by trade shares. Food prices (”foodstuffs and livestock”) are just for imports; Schlote notes that British food exports in that period consisted primarily of processed goods. The two are weighted by trade shares to obtain a price index for primary commodities. Prices of manufactured goods are for total trade (again excluding re-exports); Lewis used an unweighted average so as not to give too much emphasis to exports, but the weighted average seems more appropriate for the present study. All of these data have been derived through 1913; the period of overlap with the United Nations data (1900-13) has been used to adjust percentage changes so that the two series could be smoothly spliced.
The remaining data—real output and the nominal interest rate—relate only to the United States. Data are available for the United Kingdom as well, but aggregation across the two countries over such a long time period would raise difficult measurement issues. Real output is defined as real net national product. Data from 1939 on are from the Council of Economic Advisors (1991). That series has been extended back to 1869 using annual percentage changes in data from Table 4.8 in Friedman and Schwartz (1982). The interest rate is the yield on Aaa corporate bonds, with data for 1919-41 from Board of Governors (1943) and data for 1942-90 from Council of Economic Advisors (1991). That series has been extended back to 1867 using data from column 8 in Friedman and Schwartz’ Table 4.8. There was a fairly stable difference in the two series during the period 1919-39, which averaged 36 basis points; that amount was added to each observation in the Friedman-Schwartz data. The coefficients reported in this paper are semi-elasticities; that is, they are scaled for the interest rate measured as a decimal rather than in percent.
Appendix II: Derivation of the Reduced Form
The relevant portion of the flexprice model developed in Boughton and Branson (1991) comprises the following three equations, characterizing a two-sector economy producing and consuming both a primary commodity and a manufactured good, with the commodity price determined in a flexible market with forward-looking expectations:
arbitrage in the commodity market:
demand for the manufactured good:
and gradual adjustment of excess demand for the manufactured good:
where the notation is as in the text, with the addition of ym (output of the manufactured good); y ≡ αym + (1- α)yc, where yc is final output of primary commodities. If commodities are used only as inputs, α = 1. Equation (1) in the text is derived by combining equations (A1) through (A3) and solving for the rate of change in the relative price (pr ≡ pc - pm):
where η = π/(1/-πσ) > 0 by assumption (if η ≤ 0, a positive shock to the excess demand for the manufactured good would not raise the price of the good).
Ardeni, Pier Giorgio and Brian Wright, “The Long-Term Behavior of Commodity Prices,” Policy, Research, and External Affairs Working Paper WPS 358, International Economics Department, The World Bank, March 1990.
Boughton, J., and W.H. Branson, “The Use of Commodity Prices to Forecast Inflation,” Staff Studies for the World Economic Outlook (Washington, D.C.: International Monetary Fund, September 1990).
Boughton, J., and W.H. Branson, “Commodity Prices as a Leading Indicator of Inflation,” Chapter 17, eds. Kajal Lahiri and Geoffrey H. Moore, in Leading Economic Indicators: New Approaches and Forecasting Records. (Cambridge University Press: 1991).
Boughton, J., and A. Muttardy, “Commodity Prices and Inflation: Evidence from Seven Large Industrial Countries,” NBER. Working Paper No. 3158 (1989).
Brayton, F., W. Kan, P.A. Tinsley, and P. von zur Muehlen, “Here’s Looking At You: Modelling and Policy Use of Auction Price Expectations,” Federal Reserve Board. Finance and Economics Discussion Series, No. 126 (May 1990).
Engle, R.F., and C.W.J. Granger, “Cointegration and Error Correction: Representation, Estimation, and Testing,” Econometrica, Vol. 55 (No. 2, March 1987), pp. 251–76.
Friedman, M., and A.J. Schwartz, Monetary Trends in the United States and the United Kingdom (University of Chicago Press, 1982).
Grilli, Enzo R. and Maw Cheng Yang, “Primary Commodities, Manufactured Goods Prices, and she Terms of Trade of Developing Countries: What the Long Run Shows,” The World Bank Economic Review. Vol. 2 (January 1988), pp. 1–47.
Helg, Rodolfo, “A Note on the Stationarity of the Primary Commodities Relative Price Index,” Centro Studi Luca d’Agliano - Queen Elizabeth House, Development Studies Working Paper No. 31, University of Oxford, September 1990.
Hook, A., and D. Walton, “Commodity Price Indicators: A Case for Discretion Rather than Rules,” Blueprints for Exchange Rate Management, eds., Marcus Miller, Barry Eichengreen, and Richard Portes (Academic Press: 1989).
Johansen, S., “Statistical Analysis of Cointegration Vectors,” Journal of Economic Dynamics and Control. Vol. 12 (June-September 1988), pp. 231–54.
Johansen, S., and K. Juselius, “Maximum Likelihood Estimation and Inference on Cointegration, with Applications to the Demand for Money,” Oxford Bulletin of Economics and Statistics. Vol. 52 (May 1990), pp. 169–210.
League of Nations, Industrialization and Foreign Trade (1945).
Lewis, W.A., “World Production, Prices, and Trade (1870–1960,” The Manchester School of Economic and Social Studies, No. 20 (May 1952), pp. 105–38.
Perron, P., “Trends and Random Walks in Macroeconomic Time Series, Further Evidence from a New Approach,” Journal of Economic Dynamics and Control, Vol. 12 (1988), pp. 297–332.
Rappaport, P., and L. Reichlin, “Segmented Trends and Non-Stationary Time Series,” The Economic Journal, Vol. 99 (Conference 1989), pp. 168–77.
Ridler, D., and C. Yandle, “A Simplified Method for Analyzing the Effects of Exchange Rate Changes on Exports of a Primary Commodity,” Vol. 19 (1972), pp. 559–575.
Romer, Christina, “Spurious Volatility in Historical Unemployment Data,” Journal of Political Economy. 94 (February 1986), pp. 1–37.
Sapsford, D. (1985a), “Real Primary Commodity Prices: An Analysis of Long-Run Movements,” International Monetary Fund DM/85/31 (May 17, 1985).
Sapsford, D. (1985b), “The Statistical Debate on the Net Barter Terms of Trade between Primary Commodities and Manufactures: A Comment and Some Additional Evidence,” The Economic Journal. 95 (1985), pp. 781–88, with reply by John Spraos, p. 789.
Sapsford, D. “A Simple Model of Primary Commodity Price Determination: 1900–80,” The Journal of Development Studies. Vol. 23 (January 1987), pp. 265–74.
Schlote, W., British Overseas Trade from 1700 to the 1930s (translated by W.O. Henderson and W.H. Chaloner ) (Oxford: Basil Blackwell, 1952).
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)| false “ Singer, Hans, The Distribution of Gains between Investing and Borrowing Countries,” American Economic Review. No. 11 ( May 1950). Reprinted in. ( The Strategy of International Development: Essays in the Economics of Backwardness White Plains, New York: International Arts and Sciences Press, 1975), pp. 43– 57.
Spraos, J., “The Statistical Debate on the Net Barter Terms of Trade between Primary Commodities and Manufactures,” The Economic Journal. No. 90 (March 1980), pp. 107–28.
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I would like to thank Bill Branson, Andrea Chisholm, Bob Flood, Carmen Reinhart, Blair Rourke, Peter Wickham, and participants at an IMF seminar for helpful suggestions, without implicating them in the analysis or the conclusions.
For an even longer picture of primary commodity prices, see Commodity Research Bureau (1939, pp. 28-29), which shows a chart of an annual U.S. wholesale commodity price index covering the period 1720-1940. To my knowledge, there are, however, no estimates of prices of manufactured goods prior to 1854.
The data are constructed as follows [where for a number such as x = 9/2, the notation is that integer(x) = 4 and remainder(x) = 1]:
it = integer (t/10)+1, t = 1,…., 1200;
j1 = 1, and jt = remainder(it-1/2), t = 2,…, 1200;
kt = 5 if jt = 0, and otherwise kt = 1;
pct = kt + 0.1t;
and pmt = pct - 4((-1)jt) ((t-1)/10 + 1 - it-1); pm1 = pc1.
The data are deterministic, so the test statistics are illustrative. A more substantive analysis would require a monte carlo study incorporating less regular disturbances.
The Durbin-Watson statistic is misleading with the long sample. Because it is based on one-period changes, it shows just as much nonstationarity in the long sample as in the short one. If the frequency of the data were lengthened as well, then the Durbin-Watson statistic would rise rapidly toward 2.
Lewis’ conclusion is suspect, however, because there was a very strong positive trend component in his manufacturing output data, which was the other argument in the equation determining the level of the price of raw materials. It is thus difficult to disentangle the trend from the influence of output.
The regression is specified as pr = α + /βt/100 + μ. With OLS estimation,
The trend line was estimated over the period 1870-1990 by the equation
where Rt is the ratio of the two 15-year moving standard deviations.
It is possible that the change in the variance arises from changes in the way the data are constructed over time; that problem was identified by Romer (1986) in studying U.S. unemployment data from 1900 through 1980. It may be noted, however, that the data on U.K. traded goods prices developed by Schlote (1952)—which are measured consistently from 1854 through 1933—show a clear jump in relative volatility after the onset of World War I.
Most of the empirical tests have been carried out in PC-GIVE; see Hendry (1989). The Phillips-Perron statistics were computed by Mark Taylor, using RATS.
Though not shown here, the test statistics discussed below all indicate that neither index is I(0).
The full regression, estimated with a first-order autoregressive residual, is
where trend = the year (1855, …, 1990) /100; dum50 = 1 from 1950 onward; dt50 = dum50*trend; and ρ is the autocorrelation coefficient (estimated by Gauss-Newton iteration). For a description and rationale for this test, and for the critical values, see Rappaport and Reichlin (1989).
The basic cointegrating equation, using a second-order autoregressive distribution lag, is
In this form, μt has a Durbin-Watson statistic of 0.23 (insignificantly different from zero at the 95 percent level by the Sargan-Bhargava test), and the regression Δμt = - βμt-l yields tβ = -2.87, which is insignificantly different from zero by the Dickey-Fuller test. The solution of the expanded model is
The Durbin-Watson statistic now is 0.51, and tβ = - 4.27, both of which imply rejection of the unit root hypothesis. The residuals of both equations, however, display severe kurtosis and first- and second-order autocorrelation.
See Appendix I for a description of the data. As with the two price indexes, tests were also conducted to see if each series was I(0); all of those tests failed to reject the existence of a unit root in the levels of the data.
For example, a 4th-order ADL yielded this long-run result:
with standard errors in parentheses; the sample period was 1854-1990. The residuals from this long-run equation have a Durbin-Watson statistic of 0.39, which is high enough to reject the unit root hypothesis at the 95 percent level. The ADL displays significant skewness and excess kurtosis, but no significant autocorrelation or heteroscedasticity. Note that the coefficient on p m is insignificantly different from unity. The negative sign on the coefficient on the interest rate is inconsistent with the model.
With no trend in the system and five lags on each variable, the trace statistic (see Table Al in Johansen and Juselius (1990)) was 56.67, compared with a 99 percent confidence level of 53.79 for rejecting the null hypothesis of no cointegrating vectors; the maximal eigenvalue statistic was 31.72, compared with a 95 percent level of 27.17 and a 99 percent level of 31.94. With a trend included, the corresponding statistics were 66.93 and 41.02, both of which exceed the 99 percent threshold. In each case, the hypothesis of no more than a single cointegrating vector was not rejected at the 95 percent level.
For historical discussions of developments affecting commodity markets, see Commodity Research Bureau (1939 et seq.).
The cumulative effects take account of the lagged effects working through the error-correction term. Ignoring all other variables, the model is estimated in the form Δp = -(1-α)p-1 + β1 D + β2 trend, where D is the market-conditions dummy. If D = 1 in periods 1,…,N and 0 thereafter, then its cumulative impact at time T (T ≥ N) is
Standard errors, in parentheses, are corrected for heteroscedasticity. The residuals from equation (3) display some significant negative first-order serial correlation, but they are otherwise normal and homoscedastic. Estimation by recursive least squares reveals no significant instabilities as measured by N-step Chow tests. The serial correlation is induced by the construction of the combined dummy variable; if that term is somewhat simplified, the serial correlation is eliminated but the residuals become heteroscedastic and the recursive Chow tests indicate instabilities.