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A Short Note on Updating the Grilli and Yang Commodity Price Index

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The Grilli and Yang commodity price index is one of the most widely used commodity price series in the applied economics literature. This note provides some practical advice on updating this data series by listing the base period index values, identifying relevant data sources, and describing a method for computing subindex weights.
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A Short Note on Updating the Grilli and Yang
Commodity Price Index
Stephan Pfaffenzeller, Paul Newbold, and Anthony Rayner
Abstract
The Grilli and Yang commodity price index is one of the most widely used commodity
price series in the applied economics literature. This note provides some practical advice
on updating this data series by listing the base period index values, identifying relevant
data sources, and describing a method for computing subindex weights.
Keywords: primary commodities, Grilli Yang index, Prebisch Singer Hypothesis
JEL classification: O13, F1
Stephan Pfaffenzeller is a lecturer in economics at the University of Liverpool; his email
address is s.pfaffenzeller@liverpool.ac.uk. Paul Newbold is a professor of econometrics
at the University of Nottingham; his email address is paul.newbold@nottingham.ac.uk.
Anthony Rayner is emeritus professor of economics at the University of Nottingham; his
email address is anthony.rayner@nottingham.ac.uk. The authors thank the late Enzo
Grilli for providing background information on data sources and Betty Dow for providing
data from the primary commodity price database. They are also indebted to Prof. David
Sapsford and Dr. Paul Cashin as well as three anonymous referees for helpful comments.
Stephan Pfaffenzeller gratefully acknowledges the financial support provided by a UK
Ministry of Agriculture, Fisheries, and Food (MAFF) studentship. Supplemental
appendixes to this article are available at http://wber.oxfordjournals.org/.
2
In 1988 Enzo Grilli and Maw Cheng Yang published their seminal article on the
long-run development of an index of 24 primary commodity prices (GYCPI) deflated by
an index of manufactured goods’ unit values. The sample of average annual primary
commodity prices covers about 54 percent of the primary commodity trade in the index
reference period, 1977–79 (Grilli and Yang 1988 p. 3, n. 2). The deflators considered for
manufacturing prices were the U.S. manufacturing price index (USMPI) and the
manufacturing unit value index (MUV).
1
Widely used and discussed, the Grilli and Yang dataset has been extended by a
number of researchers (for example, Cashin and McDermott 2002; Lutz 1999; León and
Soto 1997). The data have been employed in a variety of contexts in later studies (for
example, Bleaney and Greenaway 2001 and Kim and others 2003). Thus the GYCPI data
continue to enjoy wide popularity in their own right and as a benchmark for new
approaches to empirical studies. However, because the data sources consulted for updates
have differed, it may not always be clear when differences in results arise from
differences in the data used and when they arise from differences in the econometric
methodology employed.
The obvious need for occasional updates of the series is in marked contrast with
the absence of an accessible central reference for suitable data sources and the
appropriate weights to be applied to individual commodity prices over the various
subindices. This note aims to identify suitable data sources and composite index and
subindex weights. These have not all been directly and publicly available from an
1
The MUV had to be interpolated for 1914–20 and 1939–47.
3
accessible source.
2
The data sources identified are a compromise between continuity with
the original Grilli and Yang data and accessibility. The intention is to allow individual
researchers a realistic opportunity to obtain identical updates of the Grilli and Yang index
from clearly identified sources.
I.
DATA SOURCES FOR UPDATES
Data for updating the commodity price data come directly from the World Bank
Development Prospects Group's primary commodity price database, the International
Monetary Fund (IMF) commodity price tables, and the Organisation for Economic Co-
operation and Development (OECD) international trade by commodities statistics. Most
of the World Bank’s primary commodity prices and the IMF’s commodity price tables
are available online. Online access to the OECD trade statistics requires a subscription.
MUV updates were obtained from the Global Economic Prospects team of the World
Bank’s Development Prospects Group.
3
A possible cause of confusion is the frequent revisions of the reported data and
the occasional lack of continuity in the available data series. Often, more than one series
2
At least some of this information was reportedly published in a working paper
preceding Grilli and Yang (1988). However, we have been unable to obtain a copy
despite repeated efforts.
3
The data are quoted online in the commodity price appendix to World Development
Indicators. The 2005 edition is available at
http://devdata.worldbank.org/wdi2005/Table6_4.htm .
4
is available for one commodity, and the researcher will have to use discretion in selecting
the most appropriate one. Obviously, a close correspondence to the historic series is
desirable, but a perfect match may not always be possible.
The following list of the 24 commodities in the Grilli and Yang dataset identifies
the data series used to update them from 1987 onwards. The IMF commodity price table
data are identified by their series descriptors or series code. OECD trade data are
identified by their four-digit Standard International Trade Classification, Revision 2
(SITC Rev. 2) code. Data obtained directly from the primary commodity price database
of the World Bank's Development Prospects Group are also identified.
4
Any decision to
deviate from the data series used in the original GYCPI data set is identified in the
commodity description.
5
Aluminium: London Metal Exchange (LME), unalloyed primary ingots, high grade,
minimum 99.7% purity, from the primary commodity price database.
Bananas: Central and South American, U.S. import price, free on truck (f.o.t.) gulf ports,
from the primary commodity price database.
Beef: IMF commodity price tables series PBEEF, beef, Australian and New Zealand 85%
lean fores. These data deviate from the Argentinean export unit values used in the
original study, which were obtained from Argentinean national statistics ("Comercio
4
Updates are available online from the World Bank's “Pink Sheets”
(www.worldbank.org/prospects).
5
Information on the original data sources was obtained from a list kindly provided by
Enzo Grilli detailing data sources and definitions for Grilli and Yang's work.
5
Exterior" Argentina, Instituto Nacional de Estadística y Censos). The national statistics
are not readily available online or in print at the required level of detail.
Cocoa:
International Cocoa Organization daily price, average of the first three positions
on the terminal markets of New York and London, nearest three future trading months,
from the primary commodity price database.
Coffee: International Coffee Organization, other mild Arabica, from the primary
commodity price database.
Copper: LME grade A minimum 99.9935% purity, cathodes and wire bar shapes,
settlement price, from the primary commodity price database.
Cotton: Cotton Outlook A Index, middling 1 3/32 inch staple, Europe cost, insurance,
and freight (c.i.f.), from the primary commodity price database.
Hides: IMF commodity price tables series PHIDE, hides, heavy native steers, over 53
pounds.
Jute: Raw white D, free on board (f.o.b.) Chittagong. This series, obtained directly from
the World Bank and quoted on the Pink Sheets, was discontinued after 2004. More recent
jute prices are quoted by the Food and Agriculture Organization (FAO).
6
Lamb: New Zealand, frozen whole carcasses, wholesale price, London, from the primary
commodity price database.
6
These are available online from the Food and Agriculture Organization Commodities
and Trade home page (www.fao.org/es/esc/en/index.html). At the time of writing, prices
could be obtained from an interactive databank by following the “Prices” link under
“Publications.”
6
Lead: LME refined, 99.97% purity, settlement price, from the primary commodity price
database.
Maize: U.S. No.2 yellow, f.o.b. gulf port, from the primary commodity price database.
Palm oil: 5% bulk, Malaysian, c.i.f. NW Europe, from the primary commodity price
database.
Rice: Thai 5%, milled, indicative price based on weekly surveys of export transactions,
government standard, f.o.b. Bangkok, from the primary commodity price database. The
original Grilli and Yang dataset used the Board of Trade–posted price series, which was
phased out after 1991. In the interest of continuity, the series from the primary
commodity price database is used for the updated series from 1987 onwards.
Rubber: RSS no.1 Rubber Traders Association spot New York, from the primary
commodity price database.
Silver: Handy & Harman 99.9% New York, from the primary commodity price database.
Sugar: International Sugar Agreement daily price, raw, f.o.b. and stowed at greater
Caribbean ports, from the primary commodity price database.
Tea: Three-auction average (Kolkata, Colombo, Mombasa), from the primary
commodity price database. The original Grilli and Yang dataset used the London auctions
series, which was phased out in 1998. In the interest of continuity, the three-auction
average listed in the World Bank’s Pink Sheets is used in the updated series from 1987
onwards.
Timber: OECD international trade by commodities statistics, through ESDS
International, UK import unit values, SITC Rev.2 series 2482 (sawn wood, coniferous
species).
7
Tin: LME 99.85% purity, settlement price, from the primary commodity price database.
Tobacco: U.S. import unit values, unmanufactured leaves. Data were obtained directly
from the primary commodity price database of the World Bank's Development Prospects
Group.
7
Wheat: No.1 Canadian western red spring, in store, St. Lawrence, export price, from the
primary commodity price database.
Wool: IMF commodity price tables series PWOOLC, wool, coarse, 23 micron, Australian
Wool Exchange spot quote.
Zinc: LME, special high grade, minimum 99.995% purity, weekly average bid/asked
price, official morning session; prior to April 1990, high grade, minimum 99.95% purity,
settlement price, from the primary commodity price database.
II.
FURTHER DETAILS ON COMMODITY PRICES
All commodity price series have been indexed to their 1977–79 average in
constructing the GYCPI and its subindices. Both the new data and the original Grilli and
Yang component series were first indexed to their 1980 values, and the updated
component of this series was subsequently indexed to the 1977–79 average of the
combined index series. Table 1 shows the 1977–79 index values for each commodity, as
well as the 1990 and 2000 index values, to facilitate future extensions of the series.
7
The data series is not listed in the Pink Sheets but is available online from the
commodity price data appendix of World Development Indicators. The 2005 edition is
available at http://devdata.worldbank.org/wdi2005/Table6_4.htm .
8
{Table 1 about here}
The individual commodity price series can be used to update the GYCPI and its
various subindices.
III.
RECONSTRUCTING THE GRILLI AND YANG COMMODITY PRICE INDEX
This section provides further information on how to reconstruct the GYCPI
commodity price index and the various subindices mentioned in Grilli and Yang (1988)
from individual price series.
The Index and Subindices
The basic GYCPI is a trade-weighted average of all 24 of the commodity price
series shown in table 1. In addition, Grilli and Yang (1988) construct subindices for
agricultural food commodities (GYCPIF), nonfood agricultural commodities
(GYCPINF), and metals (GYCPIM). The weights are based on each commodity’s
average export share during the 1977–79 base period and are quoted for the GYCPI as
percentage weights in Cuddington (1992) and in table 1.
The composite index is then computed simply as a weighted average of the
commodity prices in question as:
9
(1)
=
=
n
i
tiit
PaCPI
1
,
where
CPI is the commodity price index in question, n = 24 for the overall GYCPI, a
i
is
the appropriate commodity weight, and
P
i,t
is commodity i's price in period t indexed to
its 1977–79 average.
The GYCPI relative to the MUV index and the update undertaken here are shown in
figure 1. Weights for the subindices are easily reconstructed from the percentage shares
for the overall index.
8
{Figure 1 about here}
The arithmetically weighted index described in equation 1 has been used most
frequently in the literature. However, Cuddington and Wei (1992) argue that a geometric
aggregation is more appropriate. Such a geometric index would be computed as:
(2)
=
=
n
i
a
tit
i
PGPI
1
,
8
With one commodity (commodity 1) used as the numeraire, the i
th
commodity's weight
in any subindex is given by
()
(
)
1
1
1
1
/
=
=
n
i
i
i
i
ss
s
s
a , where the i subscript refers to the i
th
commodity in the relevant subindex and s
i
is the i
th
commodity's share in the overall
GYCPI.
10
The properties of this alternative index are discussed in depth by Cuddington and
Wei (1992). This note reports geometric index alternatives alongside the conventional
arithmetic aggregations in the appendix and in figure 2.
{Figure 2 about here}
The GYCPIF, GYCPINF, and GYCPIM subindices are listed together with the
GYCPI and the MUV in Grilli and Yang (1988) for the period 1900–86. A comparison of
the indices reconstructed on the basis of the weights shown in table 1 with those in Grilli
and Yang (1988) shows a close overall correspondence for the 1900–86 period.
Table 1 lists the percentage shares for each commodity in the overall GYCPI index
and the weights for the food, nonfood, and metals indices (last column).
The Manufacturing Unit Value index
The deflator used alongside the GYCPI index is the manufacturing unit value
index, currently implemented as the MUV-G5 index.
9
It is a trade-weighted index of the
five major developed countries’ (France, Germany, Japan, United Kingdom, and United
States) exports of manufactured commodities to developing countries. The most
frequently used deflator in the literature, the MUV is also used by the World Bank. As a
measure of developing country imports, it is far from perfect. Its use in the present
context is based on the rather strong assumption that G-5 manufacturing exports are
generally representative of developing country imports. However, the MUV is the only
9
This index is referred to as either the MUV or the MUV-G5. The MUV-G5 is more
specific, since it takes the current definition of the MUV index as an explicit point of
reference. Grilli and Yang (1988) refer to the index as the MUVUN.
11
readily available trade-based manufacturing price measure available over a suitably long
time horizon, which explains its continued use.
Updates of the MUV index were obtained from the World Bank Development
Prospects Group, Global Economic Prospects team.
10
At the time of writing, the MUV is
typically indexed to a 1990 base, whereas Grilli and Yang consistently use the 1977–79
average as their base period. The 1977–79 average for the MUV with a base year of 1990
is 60.008. This figure can be used to reindex the series.
IV.
CONCLUSION
This note has explained how to update the Grilli and Yang index and how to
obtain index weights for the various subindices of the GYCPI. This method can also be
used to compile new subindices from subsets of the individual data series. In the future it
would seem highly desirable for the World Bank or the IMF to publish updates to the
GYCPI and its component indices. Meanwhile, this note should enable interested
researchers to extend the Grilli and Yang index series further in the absence of a
published updated version.
10
The MUV series from Cashin and McDermott (2002), kindly supplied by Dr. Cashin,
was used for the 1987–98 period.
12
A
PPENDIX. COMMODITY PRICE INDICES
This appendix lists the various commodity price indices
11
and the MUV as well as
an update of the data published in Grilli and Yang (1988). All series are indexed to their
1977–79 averages.
The price indices listed are:
GYCPI: The Grilli and Yang commodity price index.
MUV: The manufacturing unit value index.
GYCPIM: The metals index (copper, aluminium, tin, silver, lead, and zinc).
GYCPINF: The index of agricultural nonfood commodities (cotton, jute, wool, hides,
tobacco, rubber, and timber).
GYCPIF: The index of agricultural food commodities (coffee, cocoa, tea, rice, wheat,
maize, sugar, lamb, beef, bananas, and palm oil).
Alternative geometric aggregations of the composite indices are identified by a CW
suffix in the table below.
11
A spreadsheet with the individual commodity price indices and the composite index
series is available in appendix S.1. at http://wber.oxfordjournals.org/.
13
Year GYCPI MUV GYCPIM GYCPINF GYCPIF GYCPI-CW GYCPIM-CW GYCPINF-CW GYCPIF-CW
1900 19.309 14.607 27.778 21.310 15.587 12.866 20.064 11.049 12.014
1901 18.236 13.858 27.522 19.292 14.716 12.008 18.485 10.366 11.232
1902 18.145 13.483 25.518 19.268 15.209 11.878 17.268 10.433 11.220
1903 19.006 13.483 26.668 22.860 14.634 12.173 18.672 11.424 10.938
1904 20.586 13.858 27.526 24.450 16.444 13.070 18.835 11.337 12.459
1905 21.621 13.858 29.150 26.226 16.924 13.624 20.898 11.693 12.793
1906 21.610 14.607 31.726 27.547 15.422 13.759 23.904 12.519 12.057
1907 22.757 15.356 36.699 25.967 16.672 14.089 25.435 12.420 12.386
1908 20.427 14.232 24.245 22.291 18.276 13.526 17.797 11.501 13.410
1909 21.554 14.232 20.822 28.973 18.143 13.787 16.623 12.837 13.443
1910 22.630 14.232 21.026 32.924 18.088 14.224 16.781 13.504 13.834
1911 21.909 14.232 19.923 28.122 19.498 14.773 16.731 12.863 15.195
1912 22.640 14.607 23.176 28.166 19.739 15.611 19.731 13.338 15.642
1913 20.461 14.607 23.134 25.440 17.149 14.592 19.151 13.488 13.893
1914 20.210 13.858 19.291 22.239 19.509 14.642 16.272 13.231 14.878
1915 24.468 14.232 31.321 24.388 22.292 17.723 23.260 15.839 17.158
1916 31.933 17.603 50.327 30.897 26.497 22.237 34.085 21.671 19.614
1917 39.396 20.974 45.271 40.257 37.074 27.255 34.109 30.256 24.070
1918 42.028 25.468 35.121 42.841 43.861 30.731 30.419 35.787 28.595
1919 39.208 26.966 30.853 43.292 39.902 31.150 25.861 34.477 31.464
1920 41.951 28.839 29.684 39.641 47.052 29.631 24.672 32.652 29.968
1921 21.356 24.345 20.219 21.605 21.602 16.904 16.433 18.896 16.145
1922 21.910 21.723 19.919 24.771 21.147 17.176 17.097 19.401 16.195
1923 26.407 21.723 24.587 29.989 25.234 19.615 20.316 22.481 18.128
1924 26.521 21.723 25.066 28.365 26.086 20.319 20.534 20.843 19.996
1925 29.381 22.097 26.315 36.778 26.637 22.112 22.075 24.005 21.243
1926 25.758 20.974 25.962 28.691 24.250 20.117 21.822 20.045 19.628
1927 25.143 19.850 24.028 26.823 24.677 19.759 20.124 19.904 19.570
1928 24.423 19.850 23.585 25.393 24.217 19.970 20.015 19.890 19.995
1929 23.266 19.101 25.210 22.332 23.098 19.114 21.143 18.161 18.975
1930 18.277 18.727 21.655 16.949 17.838 15.085 16.727 15.769 14.272
1931 13.610 15.356 18.479 12.308 12.675 11.002 12.894 10.130 10.888
1932 10.797 12.734 16.883 8.958 9.734 8.787 10.734 7.722 8.779
1933 12.591 14.232 18.388 12.357 10.833 10.243 13.292 10.291 9.393
14
Year GYCPI MUV GYCPIM GYCPINF GYCPIF GYCPI-CW GYCPIM-CW GYCPINF-CW GYCPIF-CW
1934 15.763 16.854 18.591 16.427 14.522 12.825 14.813 13.623 11.880
1935 17.294 16.479 18.383 16.229 17.465 13.654 15.178 13.270 13.382
1936 18.418 16.479 18.677 18.348 18.369 14.543 15.299 14.632 14.263
1937 21.361 16.854 20.931 20.366 21.988 17.127 18.064 16.840 16.976
1938 16.552 17.603 18.474 16.198 16.105 13.481 15.153 14.173 12.663
1939 16.019 16.105 19.188 17.499 14.267 13.062 16.027 14.749 11.513
1940 17.237 17.603 18.932 20.547 15.063 14.122 16.023 17.897 12.058
1941 20.093 18.727 18.452 24.844 18.288 17.032 16.131 22.107 15.237
1942 23.073 21.723 18.039 27.716 22.419 19.593 16.107 24.761 18.594
1943 24.283 24.345 18.132 29.094 23.905 20.605 16.350 26.568 19.583
1944 25.243 27.715 18.132 30.786 24.816 21.278 16.350 28.627 20.010
1945 25.832 28.464 18.232 30.112 26.186 21.504 16.584 27.149 20.843
1946 31.232 28.839 19.485 32.688 34.314 25.501 18.361 29.720 26.293
1947 40.389 34.831 24.709 37.349 46.952 33.426 23.146 33.635 37.532
1948 38.722 35.581 27.980 40.934 41.107 33.168 26.122 37.349 33.790
1949 35.845 33.333 26.479 35.727 38.930 30.331 24.901 30.596 32.192
1950 39.263 30.337 27.767 45.060 40.130 32.653 26.036 36.674 33.175
1951 48.093 35.955 32.466 58.702 47.929 39.647 30.557 48.526 39.031
1952 40.508 36.704 31.825 45.983 40.623 35.287 29.973 41.726 34.241
1953 37.897 35.206 32.214 40.839 38.289 33.477 29.985 36.948 33.041
1954 38.565 34.457 33.066 39.797 39.738 34.216 30.543 35.714 34.752
1955 38.233 34.831 38.267 42.537 36.107 34.213 34.695 38.528 32.116
1956 39.895 36.330 40.977 41.517 38.747 36.664 36.931 38.421 35.741
1957 40.108 36.704 35.376 42.372 40.525 36.585 32.541 39.059 36.790
1958 36.231 36.330 32.546 38.647 36.235 33.525 30.069 36.057 33.498
1959 37.113 36.330 35.379 40.667 35.926 34.707 32.675 37.072 34.256
1960 37.327 37.079 36.781 41.799 35.305 35.045 33.763 39.222 33.548
1961 36.466 37.453 35.242 40.424 34.917 34.053 32.738 38.153 32.604
1962 36.486 37.453 34.734 39.893 35.377 33.719 32.650 37.110 32.495
1963 41.419 37.453 34.747 39.084 44.723 36.693 33.158 36.075 38.236
1964 41.046 38.202 37.620 39.782 42.774 38.251 36.349 37.174 39.441
1965 38.119 38.951 40.499 39.990 36.429 35.758 39.095 38.326 33.569
1966 37.935 39.700 40.568 37.445 37.325 35.297 38.953 35.370 34.154
1967 36.846 39.700 41.509 33.813 36.830 34.425 39.874 32.214 33.921
15
Year GYCPI MUV GYCPIM GYCPINF GYCPIF GYCPI-CW GYCPIM-CW GYCPINF-CW GYCPIF-CW
1968 37.431 39.326 43.914 34.620 36.718 35.211 42.213 33.233 34.167
1969 39.761 40.449 47.712 37.459 38.322 37.805 45.324 36.109 36.468
1970 42.201 42.697 53.5 36.438 41.381 40.194 49.763 35.596 39.833
1971 42.324 45.318 50.293 37.638 42.051 40.034 46.982 37.038 39.504
1972 46.625 48.689 49.613 43.823 47.037 43.607 46.895 42.538 43.119
1973 69.472 58.801 55.72 69.054 74.123 63.951 53.118 66.967 66.380
1974 102.41 71.161 79.813 74.718 123.330 84.803 77.415 73.730 93.600
1975 85.156 79.026 76.09 65.807 97.598 73.494 74.452 65.020 77.757
1976 83.11 78.652 81.408 78.946 85.707 80.105 79.964 77.760 81.336
1977 93.125 86.517 87.752 90.681 96.064 92.193 87.316 90.251 94.823
1978 93.627 98.876 91.149 94.173 94.159 93.426 90.943 94.141 93.890
1979 113.25 114.610 121.1 115.150 109.780 112.388 120.204 114.786 108.827
1980 138.83 125.470 144.72 126.490 142.990 128.818 138.845 125.138 127.547
1981 117.94 119.100 124.21 108.870 120.380 113.227 123.156 108.409 112.583
1982 96.784 115.730 110.54 96.727 92.364 94.597 108.241 95.845 89.976
1983 102.78 110.490 118.37 103.150 97.566 100.094 114.464 102.299 94.814
1984 103.54 108.610 112.81 105.290 99.686 100.297 108.995 104.256 95.783
1985 91.268 109.590 105.59 90.490 87.022 88.034 100.879 89.379 83.608
1986 88.358 130.300 105.34 86.026 84.013 84.122 97.014 84.284 80.253
1987 95.215 142.900 108.047 118.203 79.694 90.567 103.931 117.070 76.295
1988 116.574 153.300 155.777 124.230 100.101 109.537 142.006 121.927 95.511
1989 118.705 152.925 151.529 129.335 102.826 108.972 140.342 127.158 93.027
1990 113.918 166.647 135.879 139.309 94.255 102.306 124.454 135.078 83.691
1991 103.689 165.558 111.752 130.249 87.945 94.312 102.724 125.237 79.734
1992 101.897 171.841 111.151 122.767 88.580 91.564 102.345 117.187 78.179
1993 99.068 170.123 95.373 115.683 92.048 89.730 88.804 111.072 81.015
1994 114.839 170.123 115.390 132.639 105.858 109.502 107.210 130.351 101.148
1995 128.768 171.841 138.508 154.315 112.983 121.763 126.433 151.673 107.908
1996 123.471 168.075 118.752 146.121 113.797 115.837 112.093 142.603 105.637
1997 120.882 168.634 122.247 142.559 109.720 115.790 112.656 138.579 106.891
1998 106.333 167.617 99.364 125.907 98.909 101.750 94.276 119.959 96.139
1999 93.311 165.445 97.679 115.649 80.850 87.322 92.105 108.425 77.115
2000 92.753 161.945 107.338 112.766 78.136 84.939 99.488 107.261 71.907
2001 88.680 157.179 95.077 106.410 77.842 79.425 88.170 100.669 68.292
16
Year GYCPI MUV GYCPIM GYCPINF GYCPIF GYCPI-CW GYCPIM-CW GYCPINF-CW GYCPIF-CW
2002 92.114 155.212 90.655 112.585 82.463 83.803 84.233 107.987 73.805
2003 98.879 166.853 99.672 127.848 84.297 90.456 93.482 125.158 76.220
17
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Price Trends: Aggregation, Model Selection, and Implications.” Estudios
Económicos 7 (2): 159–179.
Grilli, Enzo, and Maw Cheng Yang. 1988. “Primary Commodity Prices, Manufactured
Goods Prices, and the Terms of Trade of Developing Countries: What the Long
Run Shows.” The World Bank Economic Review 2 (1): 1–47
Kim, Thae-Hwan, Stephan Pfaffenzeller, Anthony Rayner, and Paul Newbold. 2003.
“Testing for Linear Trend with Application to Relative Primary Commodity
Prices.” Journal of Time Series Analysis 24 (5): 539–51.
León, Javier, and Raimundo Soto. 1997. “Structural Breaks and Long-Run Trends in
Commodity Prices.” Journal of International Development 9 (3): 347–66.
18
Lutz, Matthias. 1999. “A General Test of the Prebisch-Singer Hypothesis.Review of
Development Economics 3 (1): 44–57.
Online Sources
IMF Primary Commodity Price Tables. www.imf.org/external/np/res/commod/index.asp.
World Bank Commodity Price Data (Pink Sheet). www.worldbank.org/prospects.
World Bank World Development Indicators. www.worldbank.org/data/.
Economic and Social Data Service (ESDS) International (subscription only).
www.esds.ac.uk/international/.
19
Figures
Figure 1: GYCPI/MUV Index and Update
Figure 2: GYCPI/MUV Arithmetic and Geometric Indices
20
Table 1: Commodity Price Index Data for Selected Years and Index Weights
(1980 = 100)
Weights
(% share)
Commodity 1977 1978 1979 1990 2000 GYCPI Subindices
Food
Bananas
72.479 75.803 85.909 142.718 111.873
0.9 1.64
Beef
47.098 50.394 84.283 92.872 70.125
5.1 9.27
Cocoa
145.689 130.935 126.619 48.654 34.791
2.7 4.91
Coffee
154.493 106.410 110.897 56.901 55.386
10.3 18.73
Lamb
57.191 69.049 87.550 92.044 90.722
0.9 1.64
Maize
76.060 80.370 92.185 87.231 70.658
6.8 12.36
Palm oil
89.146 102.340 111.137 49.666 53.171
8.3 15.09
Rice
62.732 84.693 76.354 65.942 49.275
3.0 5.45
Sugar
28.322 27.206 33.693 43.805 28.544
7.3 13.27
Tea
120.454 98.122 96.640 124.000 113.076
1.6 2.91
Wheat
60.691 70.649 90.356 81.855 77.113
8.1 14.73
Nonfood primary
commodities
Cotton
85.951 77.863 88.414 88.862 63.613
4.3 15.81
Hides
80.559 102.785 159.132 200.862 174.718
2.3 8.46
Jute
91.034 106.476 107.462 132.565 90.077
0.2 0.74
Rubber
56.410 68.171 87.568 62.836 51.217
2.8 10.29
Timber
66.483 65.982 82.204 114.992 94.658
12.0 44.12
Tobacco
80.101 93.367 97.632 149.051 130.773
2.9 10.66
Wool
80.750 83.702 96.032 79.887 52.185
2.7 9.93
Metals
Aluminium
73.917 76.511 85.587 112.569 106.397
5.1 28.65
Copper
64.282 63.988 90.114 121.975 83.110
5.9 33.15
Lead
72.358 79.311 124.092 89.514 50.104
1.3 7.30
Silver
22.406 26.172 53.749 23.688 24.226
1.7 9.55
Tin
63.191 74.420 84.186 36.277 32.404
2.2 12.36
Zinc
91.958 82.810 99.707 198.817 148.244
1.6 8.99
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The purpose of this paper is twofold: first, it tests the Prebisch-Singer hypothesis of a secular deteriorating trend, and, second, presents a time-series analysis of the dynamics of commodity prices. Using annual data for the 1900-92 period and employing recently developed econometric techniques, we show that 17 of the 24 commodity prices studied present negative long-run trends, three are trendless and four have positive trends. Contrary to previous findings, this evidence suggests that although the Prebisch-Singer hypothesis is not a universal phenomenon, it is the case of most commodities. Moreover, the estimated long-run persistence of commodity price shocks challenges the conventional policy recommendations to overcome the negative effects of price instability on economic performance. In several cases, the estimated persistence is much lower than previous empirical results, suggesting that commodity stabilization funds can be successful in smoothing export revenues. © 1997 John Wiley & Sons, Ltd.
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The purpose of this paper is twofold: first, it tests the Prebisch-Singer hypothesis of a secular deteriorating trend, and, second, presents a time-series analysis of the dynamics of commodity prices. Using annual data for the 1900-92 period and employing recently developed econometric techniques, we show that 17 of the 24 commodity prices studied present negative long-run trends, three are trendless and four have positive trends. Contrary to previous findings, this evidence suggests that although the Prebisch-Singer hypothesis is not a universal phenomenon, it is the case of most commodities. Moreover, the estimated long-run persistence of commodity price shocks challenges the conventional policy recommendations to overcome the negative effects of price instability on economic performance. In several cases, the estimated persistence is much lower than previous empirical results, suggesting that commodity stabilization funds can be successful in smoothing export revenues.
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This paper re-examines the empirical validity of the Prebish-Singer hypothesis of a secular decline in the relative price of primary commodities in terms of manufacturing goods. The empirical findings based on arithmetic and geometric commodity price indices are compared. When Grilli-Yang's arithmetic mean index is employed, the findings regarding commodity price trends are inconclusive. A new geometric mean index, in contrast, yields results that are robust.
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Much research has been devoted to assessing the evidence for linear trend in a time series. We discuss the statistical implications of some recent developments, with specific application to 24 time series of relative primary commodities prices. Copyright 2003 Blackwell Publishing Ltd.
Structural Breaks and Long-Run Trends in Commodity PricesA General Test of the Prebisch-Singer Hypothesis
  • León
  • Javier
  • Soto
León, Javier, and Raimundo Soto. 1997. “Structural Breaks and Long-Run Trends in Commodity Prices.” Journal of International Development 9 (3): 347–66. r18 Lutz, Matthias. 1999. “A General Test of the Prebisch-Singer Hypothesis.” Review of Development Economics 3 (1): 44–57