Issues In World Commodity Markets

ISSUES IN WORLD COMMODITY MARKETS Presented by Hartwig de Haen Assistant Director-General Economics and Social Department Welcome to this special consultation on commodity prices which has been organized by the Commodities and Trade Division of FAO. The Consultation was prompted by the interest expressed in different FAO fora, notably the intergovernmental groups, and elsewhere in the various international organisations involved with agricultural commodity production and trade, to take a closer look at the current depressed levels of most agricultural commodity prices. The annual average prices of all basic foodstuffs except dairy products have declined steadily from peaks reached in the mid 1990s to levels not seen for nearly two decades. More dramatic has been the decline in prices for tropical products: coffee and cocoa prices had fallen by 2000 to their lowest levels for more than thirty years, although tea prices had held up until 2001. Among raw materials, cotton prices are at their lowest level since 1985. The coincidence of low prices across many commodities has perhaps attracted more attention than would a depressed market for just one commodity, reviving interest in 'commodity problems'. However, this coincidence may mask a range of different causal factors affecting individual commodities, making it difficult to define clear-cut and overarching solutions to the problems. For a number of years, discussion of world agricultural commodity prices has been dominated by the issue of trade liberalisation and the negotiations on improvement of market access and limitation of export subsidies. But, there remains a 'commodity problem' over and above what trade liberalisation can address, and which has its origins in market fundamentals rather than policy-induced market distortions alone. Those developing countries exporting agricultural commodities, especially beverages and raw materials, have obviously been particularly concerned by recent low world price levels and, given the inelastic nature of demand for their commodities, the consequent decline in their export earnings. The value of agricultural export earnings of developing countries declined since the mid-1990s to current stable low levels. Developing country export earnings from beverage crops fell by 18 percent between 1999 and 2000. Overall, their export earnings actually increased by one percent, but their food import bills increased by 10 percent. The problem is obviously most severe where countries are dependent for a significant share of their export earnings on one or a few agricultural exports, notably in the case of developing country exporters of coffee, cocoa, sugar, bananas and cotton. Forty-three countries, concentrated in Sub-Saharan Africa, Latin America and the Caribbean, earn more than twenty percent of their total merchandise export revenue and more than fifty percent of their total agricultural export revenue from just one agricultural commodity. Thirty-two of these countries are least-developed countries or small island developing states. In countries with high dependence, there can be a clear and direct link between

commodity trade performance and economic growth and food security. This has rekindled interest in the possibilities for some form of international action to curtail export volumes and at least slow the rate of price decline. On the other hand, lower international prices for basic foodstuffs such as cereals and oilseeds should slow the growth in the food import bills of the importing developing countries which include many of the poorest countries in the world. Clearly, the implications of current low agricultural commodity prices are different depending upon whether the perspective is that of an commodity exporter or a food importer. I would like to group my comments in relation to the three basic issues around which the consultation is structured. The first is the nature of the current depressed state of commodity prices, and whether this indicates a departure from past patterns of market behaviour. The second is the implications of lower commodity prices and their impact upon commodity exporting and food importing developing countries - essentially the question of whether lower commodity prices should be grounds for concern. The answer to this question leads to the third issue which is whether actions to redress lower commodity prices should be considered and if so, what those actions might be. Are current commodity prices too low? It is important to be clear as to what perceived commodity price problem we are talking about, and to distinguish clearly between three features of commodity prices - secular decline, volatility and the current trough. Discussion of any of these aspects requires a long-term perspective. The Commodities and Trade Division has put together information and analysis on commodity price levels and variability over the past thirty years as background. It is also necessary to distinguish clearly between short-term volatility and longer- term trends in prices. There is no doubt that commodity prices are volatile, as the course of cereal prices or coffee prices over the past decade indicates. However, there seems to be little evidence to support the view that price volatility has increased in general. Indeed, it appears from the analyses undertaken by the Commodities and Trade Division that for many commodities prices were more stable in the 1990s than in the 1980s. The particular current concern is with the levels of prices rather than volatility. In general, world commodity prices are at historically low levels, and also in general the short term prospects do not indicate a return to higher average levels. Perhaps the most widely-publicized cases have been those of coffee and cocoa whose prices have trended downwards from their peaks in the mid 1970s to levels lower even in nominal terms than those of thirty or more years ago. The course of most raw material prices has been less dramatic, and there has been greater diversity between individual commodities, but a broadly similar pattern emerges. Cereals and oilseeds prices have been rather more variable but they too had in general reached historically low levels by the end of the 1990s. Depressed price levels are, of course, not a new phenomenon, and neither are the questions they provoke about underlying causes, whether they mark a departure from previous market behaviour, and whether there is a case for remedial action. Secular relative decline in agricultural commodity prices is expected as technological progress

reduces costs and induces supply expansion at a faster rate than population and income growth expand demand. These forces and the tendency for resources to shift only slowly away from agriculture because of social, economic and institutional factors are the background to long-term relative decline in agricultural commodity prices and the familiar agricultural adjustment problem. The apparent co-movement of prices within different commodity groups over the longer term, and the coincidence of almost all commodity prices falling together since the late 1990s prompts the question as to whether there are common explanations for the recent price falls. Over the years there have been a number of exogenous shocks - the 1970s oil price hikes, el Niño, the Asian crisis, for example - which have impacted on all commodity markets and prices. However, there are no obvious similar exogenous factors at work more recently. At a more general level, all commodity prices are obviously affected by the same basic factors, namely the market fundamentals of demand and supply. However, the nature, strength and driving forces of these demand and supply factors varies from one commodity to another. In the case of natural fibres, for example, competition from synthetics is an important demand shifter while for livestock products income growth is important. Major impacts on demand and supply on world markets for particular commodities are also affected by the entry and exit of countries as importers and exporters. A move towards self-sufficiency decreases import demand, and there are a number of instances of major importers becoming major exporters - China and cereals or India and sugar, for example. The emergence of new low-cost suppliers can also disturb market balance - Viet Nam and coffee, for example. Explanation of the reasons for commodity price decline, and by implication any actions proposed to ameliorate it therefore need to be commodity-specific. The basic facts concerning production and consumption of the various commodities are summarised in the commodity profiles put together as background information. While market fundamentals continue to explain commodity price movements these can change through time as a result of changes in technology, consumer preferences, market structures, policies or institutions. There are a number of current trends which should be highlighted in this context. With respect to technological change, wider adoption of biotechnology may boost the rate of increase of yields and production of some crops leading to increased downward pressure on prices impacting differently on countries depending upon the rate of uptake of the new technologies. Technical change can also lead to pressure on prices from the demand side: improved efficiency of raw material use in processing can mean a declining share of the raw commodity in the finished product. With respect to consumer preferences, there is increasing evidence of globalisation in food consumption one facet of which is increasing livestock products consumption in developing countries in particular, though this might be offset to some extent by lower consumption in the developed countries. With respect to changes in market structures, increasing concentration and specialization of production and vertical integration change supply-price relationships, while increasing concentration in processing, marketing and distribution modify commodity demand-price relationships. In relation to the latter, the increasing market power of the transnational trading and processing companies and of multiple retailers in consuming markets have changed the market relationships with developing country suppliers. With respect to policy changes, the Agreement on Agriculture marked the beginning of an ongoing process of

agricultural trade liberalisation and policy reform which will change the policy and institutional context of the operation of agricultural commodity markets. The import restrictions and export subsidies used by some OECD countries, notably the EU and the US, have been blamed for low international prices of, for example, cereals, sugar and livestock products, and reform of those polices is expected to raise world prices. I will return to the effects of trade liberalisation in my comments on possible actions to improve commodity prices. However, the limited extent of reform to date has arguably had little significant impact on falling prices. It has been suggested that the recent commodity price decline may reflect changes in market relationships between demand, supply and price as a result of the kind of factors just mentioned. However, econometric analysis for several commodities by the Commodities and Trade Division indicates no significant changes thus far in the relationship between prices and consumption and stocks. That analysis therefore seems to support the view that market fundamentals have continued to be the dominant influence on agricultural commodity prices and that the nature of that influence has not changed. In that sense we are still facing the same type of 'commodity problems' as in the past. So in answer to the question as to whether commodity prices are too low we can certainly say that prices for most commodities are low by historic standards, but it is not so certain that they can be said to be too low given current market conditions of demand and supply. Should we be concerned about low agricultural commodity prices? If we accept that commodity prices are unusually low by historic standards, then the next question to ask is whether this should be a matter for concern and hence whether some kind of remedial action is warranted. Unfortunately, the answers to these questions are not straightforward since there are both winners and losers from low prices. For food importing developing countries, low world food prices mean lower food import bills, and provided that low world prices are transmitted through to consumer prices then consumers gain. If, as suggested by the forthcoming FAO study Agriculture: Towards 2015/30, the agricultural trade deficit of developing countries will increase significantly over the next thirty years, then the benefits of lower food prices become more pronounced. The agricultural imports of the least developed countries are already twice as much as their agricultural exports, and their trade deficit is expected to grow in real terms by four times, over the next thirty years. Higher food prices on world markets can only serve to worsen this situation. However, there may be some qualifications to that view, notably concerning the impact of lower prices on the domestic agricultural sector and food production. Even in the case of food importing countries, sustained low prices may not be desirable if they prejudice future food production through failing to provide adequate incentives for the maintenance of production levels. Seventy percent of the world's poor live in rural areas, and most depend directly or indirectly on local agriculture not only for their incomes, but also their food supplies. A prosperous agriculture therefore provides the basis for overall rural growth and is a necessary foundation for overall poverty reduction. This is not to say that agriculture in the net food importing countries can only generate

sufficient income to the often poor farmers through higher prices. It is important that investments are made in, for example, infrastructure, more efficient technologies, and product quality improvements. This could also be achieved under low world market prices if the gains from them were appropriately channelled into pro-poor investments in rural areas. In the case of developing countries which are agricultural exporters, especially of tropical products and raw materials, the benefits of higher commodity prices are clear. Remunerative prices are needed if production and exports are to be sustained and developed, and again provide a platform for broader development. In cases where exporting countries are highly dependent on commodity exports but import food, significant losses of export earnings due to lower world prices threaten food security and compromise the pursuit of development goals. So, the question of whether low commodity prices are a good or bad thing is more difficult to answer unequivocally than might be expected. Low prices are presumably immediately beneficial to consumers in importing countries, particularly low income food deficit developing countries, but they are presumably not to the advantage of commodity producers/exporters. There is therefore a dilemma in how we should regard low commodity prices. This dilemma is further complicated by the fact that in practice who wins and who loses is not a simple matter of food importers versus commodity exporters. Other factors such as national strategic goals, the competitiveness of the importers and exporters concerned, and the time period under consideration also need to be taken into account. What could or should be done? Over the years many different actions have been proposed or tried to cope with lower prices in world agricultural commodity markets and enhance food security in exporting countries or to limit or compensate for price variability. These actions have included measures to reduce price variability and to increase mean price levels through specific interventions to control the supply or expand the demand for particular commodities as well as more general progress towards trade liberalisation for all commodities. As an alternative to more direct market interventions, actions have also been proposed to compensate for price depression and/or variability or to encourage withdrawal from production of particular commodities and diversify into more potentially profitable lines of production. Again, it is important to be clear just what problem we are trying to address - secular decline in commodity prices, volatility, or the current trough (if that is seen as a something other than a manifestation of volatility). Different actions are appropriate to each of these perceived problems. It has to be said that the record is not generally one of success, and why that is so is one of the issues this consultation needs to address. Discussions of commodity price problems usually recall the international commodity agreements (ICAs) with economic clauses which were widely seen as a solution in the late 1970s. These involved market interventions to influence prices with the objective of price stabilisation either through export quota arrangements or stock management. The record of the ICAs' success in these areas was not altogether encouraging, and today existing ICAs focus on measures to improve the functioning of markets. Nevertheless, there has been a recent revival of interest in supply management through export

retention schemes, or diversion of low grades into alternative uses, although again, the experience has not been encouraging. It seems difficult to maintain the continuing commitment of the parties to the discipline of the agreement, while free rider problems persist with those suppliers outside. The difficulties of sustaining co-operative market interventions as a response to volatility and short-term adverse price movements has led to interest in price, revenue or income compensation schemes to compensate for export earnings shortfalls or import costs surges. The idea of a revolving fund to assist food importing countries meet increases in food import bills is being considered in the context of the continued reform of agricultural trade policy under the WTO. Interest in risk management and market based tools to reduce the adverse impacts of variable prices has been kindled by the World Bank, but the feasibility of the widespread applicability of such techniques needs to be established. Market interventions, compensation schemes and risk management might be used to address the problems of price volatility and short-term price troughs. However they cannot be used in a one-sided way to counter the tendency for relative commodity prices to decline in the long-run. This can only be achieved by an improved balance between supply and demand. Permanent reductions in supply require the exit of resources from production of the commodity concerned. Diversification has frequently been discussed as an escape from low prices for specific commodities, especially where dependency rates are high and where exports are not competitive - due to a loss of trade preferences, for example. But again the record is not good: countries uncompetitive in one commodity are often also not competitive in the agricultural alternatives; and niche products such as organics which have often been suggested as diversification opportunities cannot compensate in terms of income and employment for the loss of bulk commodity trade. The result under these circumstances may be a further acceleration of rural-urban migration. Co-operative international actions to stimulate demand - generic promotion, consumer education programmes, for example - have also been used to counter secular price decline. FAO's Tea Mark is an interesting example. The difficulty, as with supply-side co-operative actions, is to find an institutional manager for such programmes and a means to finance them which minimises free-rider problems. For some commodities the scope for expansion of demand through product and market development is potentially huge: hard fibres and jute have significant unexploited potential in geo-textile and automotive uses, for example. The Common Fund for Commodities (CFC) has an important role in product and market development, but its procedures constrain it from being more pro-active in identifying projects which meet international commodity market priorities. Discussions of international agricultural commodity markets have been dominated for some years by the issue of trade liberalisation. Trade liberalisation is not specifically geared towards raising commodity prices but that is supposed to be a result. The agricultural policies of certain countries, notably of some OECD countries, have supported domestic prices at higher than world price levels through market price support bolstered by high import tariffs and or quotas while the excess production thus engendered was exported on to world markets with the aid of export subsidies. The

result was to reduce world prices, and arguably to increase price variability. The process of liberalisation is therefore generally seen as one which will lead at least to higher commodity prices in the short-run. However, there are a number of qualifications which need to be stressed. The first, as I said before, is that while for food exporters higher world prices are beneficial, this is not the case for food importing countries. In practice, the effects of liberalisation on commodity prices so far following the Uruguay Round is apparently small. The second point to note is that liberalisation also erodes trade preferences enjoyed by certain developing country exporters of certain products - sugar and bananas, for example. Finally, while liberalisation in the sense of improving market access is important for food products, tariff levels for tropical products and raw materials at least in their less-processed forms are in any case typically not high. Market access is not a major issue in most cases. A more significant issue is that of tariff escalation which can discourage developing country exporters from capturing valueadded and hence greater export earnings. This problem has received relatively little attention and needs to be addressed more seriously in trade negotiations. Objectives of the Consultation I have tried to identify some of the issues which we expect this consultation to address. There are three basic questions in relation to the current situation of many commodity markets. Are prices unusually depressed? If so, is this grounds for concern? And if so, what could or should be done? In terms of specific outcomes of this consultation, we would like to see: •

•

• •

a definitive review of the recent history of commodity price movements and the evidence concerning the factors affecting them in an attempt to dispel some of the uncertainty and misconceptions concerning the current situation and remedial actions a detailed assessment of the implications of depressed commodity prices and a balanced view of the interests of food importing developing countries and commodity exporting developing countries a reasoned consideration of the case, if any, for international action to reverse short term declines in prices and to slow the rate of secular decline specific recommendations, based on a review of past experiences and current proposals for effective forms of international action compatible with the current international trade regulatory framework and including workable and sustainable funding and coordination mechanisms.

We have gathered a wide range of interests and expertise to participate in this consultation and are grateful for your interest and participation. Although we do have distinguished speakers presenting their views on different topics related to commodity prices, the meeting should emphasise discussion rather than formal presentations. We would like to see a widening of the discussion of agricultural prices and commodity markets away from a focus on trade liberalisation alone to address commodity problems based in market fundamentals. We hope that this consultation will be a first step in this process.

COMMODITY PRICES, EXCHANGE RATES AND THE INTERNATIONAL MONETARY SYSTEM [2]

Presented by Dr Robert Mundell University Professor of Economics Columbia University (1999 Nobel Prize in Economic Science) In discussing commodity prices, one is dealing with commodity prices in currencies; so it may be expected that monetary variables are among the explanations of real change. International commodity prices are mostly expressed in dollars, for example, in the IMF International Financial Statistics, or in terms of indices based on dollar prices. Such commodity prices are obviously affected by inflation as well as real developments, and also by the value of the dollar exchange rate. There is a definite link between monetary policies, exchange rates and commodity prices, and this is the subject which I wish to discuss today. Pricing in gold and dollars under fixed rates Prices are relationships between two quantities, a quantity of the object for sale, and a quantity of a quid pro quo-usually money--offered for it. It may therefore be expected that changes in prices could reflect not only market-specific trends but also monetary development. In a world of inflation, for example, commodity prices would be rising, and in a world of deflation they would be falling. Both would be clear manifestations of monetary rather than real disturbances. There would not be a problem of "commodity prices," there would be a problem of monetary stability. To analyze significant trends in "commodity prices," therefore it is important first to isolate the monetary disturbances (if they are present) from the real disturbances. Superimposed on general movements of world-wide inflation or deflation are influences of exchange rates. In our world of multiple currencies and flexible exchange rates, commodity prices might rise in one currency but fall in another. The statement of commodity prices in dollars could reveal either a problem concerning commodity prices or a problem of the dollar. This bring sup the question: in what currency or currencies should commodity prices be quoted? In the post-war world, the dollar was by far the most important currency in the world and had been since World War I. It was natural to use it as the basic unit of account and the convertible dollar --the "1944 gold dollar"-- was the anchor for exchange rates. Parities for currencies were expressed in weights of gold (the dollar was 1/35 of an ounce or .888671 grams of gold), but currency units and exchange rates were more normally expressed in terms of the more familiar gold dollar. As long as the dollar was exchangeable into gold at $35 an ounce, the dollar had the legal role and legitimate status as the international unit of account. It was natural also to use dollar quotations as the basis for the index of commodity prices. All that changed with the international monetary system broke down in the early 1970s. The dollar was no longer convertible into gold, and foreign currencies were no longer

convertible into the dollar. The dollar lost its judicial status as both monetary anchor and unit of account. Exchange rates became flexible. The IMF Board of Governors then officially scrapped the IMF constitution based on fixed exchange rates and officially accepted the a new regime of market-based "managed" flexible exchange rates. The idea was to let markets determine exchange rates. At the same time it was decided to rid gold of its "mystique," and to auction off at least part of IMF gold stocks as well as US Treasury holdings, and to introduce in its place as a numeraire the index of the value of a basket of a few major currencies that the Special Drawing Rights (SDR) had become. What numeraire under flexible exchange rates? Unfortunately, there was not at the time much understanding of how the new regime would work or of what would fulfil the functions formerly filled by gold and the dollar. Unlike the previous system, which had been built upon the experience of hundreds of years of monetary history, there was no precedent for the new regime of paper currencies connected by fluctuating exchange rates. In addition there had been little theoretical analysis of the problems likely to be encountered. One of the problems related to the use of a unit of account. With all currencies on the same footing, international payments would be in chaos. At the most rudimentary level, how would exchange rates be quoted? With n currencies in the world there are ½ n (n-1) exchange rates. If n = 200 there are 9900 exchange rates! Flexible exchange rates in the absence of a numeraire in which to express currency prices would involve enormous confusion. Fortunately, the market found the solution. Under flexible exchange rates the dollar was more rather than less important than before. Exchange rates were quoted mainly in dollars, the currency most frequently used in exchange markets and the main reserve asset (apart from gold) of central banks. If all currencies were quoted in dollars and perfect arbitrage could be relied on there would be "only" 199 independently flexible exchange rates. The same solution was adopted in the statistical monthly, IMFIFS. There was no longer any legal basis for using the dollar as the numeraire for expressing exchange rates but it was the expedient solution. Dollar exchange rates gave some coherence to international monetary transactions. But it was far from a solution. The usefulness of a currency as numeraire depends partly on its stability. But was the dollar stable? Impact of the dollar cycle There would have been no problem if the dollar had been stable vis-à-vis other currencies. But in fact that has not been the case. Since the 1970s, the two most important currencies, besides the dollar, have been the German DM and the Japanese yen. The dollar has gyrated against other major currencies. Against the DM, for example, the dollar was DM 3.5 in 1975 and fell in half to DM 1.7 five years later, in 1980. Then the dollar doubled to DM 3.4 by early 1985, and then fell below DM 1.35 in August 1992, at the peak of the ERM crisis in Europe. Since that time the dollar has risen far above DM 2.0 This instability of the dollar/DM rate means that commodity prices in dollars and DM would be for much of the period moving in opposite directions.

In a period when the commodities prices were rising in dollars, they might have been falling in DMs, and vice versa. Yen-dollar fluctuations have been just as extreme. In the hey-day of Bretton Woods, the yen-dollar rate was fixed at 360 yen to the dollar. After the 1970s this rate became flexible. By 1985 the dollar was 250 yen. Ten year later, by April 1995, the dollar had fallen to 78 yen. In other words the yen had tripled in value against the dollar. This was the period in which the balance sheets of Japanese companies were undermined, and Japanese banks ended up with the non-performing loans that persist in trillions of dollars to this day. But from April 1995 to June 1998, the dollar soared from 78 yen to 148 yen, creating the setting for the Asian crisis. After 1948 the dollar fell to 105 yen but then rose again. During these fluctuations, dollar and yen prices frequently moved in opposite directions. The instability of dollar-mark and dollar-yen exchange rates does not prove by itself that the dollar is unstable. The instability could equally be due to events in Germany or Japan or elsewhere. Yet there has been a distinct cycle of the dollar measured against other major currencies, and this means that quotations of commodity prices in dollars may give rise to grave misinterpretation. The movement of the dollar with respect to the SDR was one way of measuring the stability of the dollar. Initially, the SDR was equivalent to 1/35 of an ounce of gold, i.e., a 1944 gold dollar, but its gold basis was stripped away in the later 1970s and it eventually became a basket of five major currencies including, besides the dollar, the yen, the mark, the pound and the franc. The weights were changed as seemed appropriate with changing circumstances. The value of this SDR basket in terms of the dollar was of course unity in 1970, when the first issue of SDRs were made. Then it rose to $1.32 in 1979 with the dollar's depreciation, fell to $0.98 in 1984 as the dollar soared in the early 1980s, then rose to $1.49 in 1995 with the weakening of the dollar and then fell to $1.24 at the end of 2001 as the dollar strengthened in the late 1990s. These fluctuations, it should be noted, have been extremely large, especially in proportion to differences in price levels and inflation rates. Commodity price cycles The question needs to be asked whether the cycle of the dollar against major currencies is related to the cycle of the dollar commodity prices. A casual reading of the statistics suggests that this relationship is quite close. Thus the index of non-oil dollar commodities tripled in the 1970s when the dollar was depreciating sharply relative to the SDR; it then fell by more than 20 per cent from 1980 to 1986 when the dollar was soaring; then it rose by 50 per cent from 1986 to 1995 when the dollar was again depreciating; and it has fallen by 30 per cent since 1995 when the dollar has been appreciating. There is therefore a very pronounced association of the cycle of the dollar against other major currencies (as measured by the SDR) with the cycle of dollar commodity prices. This is of course not unexpected. It is natural that there would be a correlation of the prices of commodities in dollars with the price of currencies in dollar. Whenever US monetary policy is easy, as it was in the late 1970s and the late 1990s and early 1990s, the dollar depreciates against foreign currencies and commodities; and when it is tight,

as in the early 1980s and the late 1990s, the dollar appreciates and dollar commodities prices decline. It makes a great difference if commodity prices are quoted in dollars, euros or SDRs. The IMF world index which covers about 50-70 non-fuel commodities quoted every month in dollar terms, indicates that prices have fallen in the last 3 years. Starting in 1970, the index, with 1995 as one hundred, was 32.8 in 1970, and 57.0 in 1975, and 90.7 in 1980. From 1980 to 1986 it dropped from 90.7 to 67.8, and then rose to the peak of one hundred in 1995. Subsequently it fell to 70.2 at the end of 2001, a very precipitous 30 percent drop. Broken down into major commodity groups, the index in 2001 for food was 76.5, beverages 47.2, agricultural raw materials 68.7, and metals 71.2, while that for fertilizers was quite different at 102.2. Another index, that of the World Bank for lower middle income countries' commodity exports stood at 62.4, with 1995 equal 100. All these numbers show that except for fertilizers which can be considered more of a manufactured product, all other commodity prices showed a very steep decline during the 1995-2001 period. Let us look again at the dollar-SDR rate. From a parity of 100 in 1970 in the dollar-SDR rate, by 1975 the SDR price of the Dollar had fallen to 82.4 cents, and by 1980 had dropped further to 77 cents. Then it soared to 98 cents in 1986. Subsequently it fell to 66 cents and then it rose again, to 89 cents. With one exception in the 1970s, that cycle mirrors that of commodity prices fixed in dollars. When the dollar is weak then commodity prices rise. From 1967 to 1981 commodity prices in dollar terms tripled, while the value of the dollar fell to one-third over that inflationary period. These were the years of the very strong oil prices that brought euro-dollars into the system, a big expansion of the eurodollar market and inflationary prices in the United States, with two-digit inflation rates during 1979 to 1981. Then from 1980 to 1985 a big fall occurred in commodity prices, with the index dropping from 90.7 to 67.8. That coincided almost exactly with the soaring value of the Dollar. The reversal of the policy mix under Ronald Regan included sweeping tax cuts. Marginal tax rates were cut from 70 percent at the federal level to 28 percent for the highest income tax brackets. Corporate taxes were cut from 48 percent to 34 percent and capital gains taxes were also reduced over that period. Big increases in government spending were combined with a tightening of Federal Reserve monetary policy. There was a sharp fall of the price in gold, which dropped from 850 Dollars/ounce in February 1980 to 300 Dollars/ounce within something like two years. This was a period of big deflation or disinflation. The inflation rate in the United States fell from 13 percent in 1980 to 4 percent in 1986. And that period of disinflation was a period of very sharply falling commodity prices. After 1985, there was another shift in United States policy, aimed at depreciating the US Dollar. The Dollar started to go down slowly against the Yen first, and subsequently the Yen soared following the drop in oil prices in 1985/86. Over that period, Dollar/SDR rate fell from 98 cents in 1986 to 66 cents in 1995, and commodity prices soared from an index value of 67.8 in 1986 to 100 in 1995. However, from 1995 onwards index of

commodity prices fell to 70, while during the same period, the Dollar soared against the SDR, rising from 66 to 89. Future of the euro and the dollar What are the indications for the future? Many believe that the Dollar has reached its peak and that the future will see a much weaker Dollar and a stronger Euro as a result of many positive developments in the European economy. However one long-run force which might contribute to a weakness of the Euro would be the accession of countries, such as Poland, Romania and other countries of Central and Eastern Europe, which could raise the level of debt in the Euro zone. On the other hand, the factors which might contribute to a weaker Dollar include a 400 billion Dollar current account deficit, or four percent of the country's 10 trillion Dollar GDP. And with the United States recovery, the trade deficit could rise to 600 billion Dollars, or 6 percent of GDP. Of course, there were deficits of 3.5 percent in the 1980s, but this was a time when the United States was still a net creditor. Now the United States has become the biggest debtor in the world to the extent of 25 percent of GDP; in other words, its international liabilities exceed by 2.5 trillion Dollars its international assets, and that amount is rising by 4-6 percent of GDP every year. At that high rate, the ratio will increase to 29 percent at the end of this year and 35 percent in the year following. Such a rate of increase in indebtedness cannot be sustained for long without giving rise to strong pessimistic views about the future of the Dollar. The only way in which this situation can be corrected is to reduce the United States current account deficit, and to do so would require the depreciation of the Dollar. A halting of lending to the United States would correct a good part of the deficit but probably not all of it, but when market sentiment starts to turn, there could be pressure for a very rapid downward trend in the value of the Dollar. The resultant portfolio shifts could be expected to cause a correction of commodity prices. Productivity, the dollar cycle and commodity prices The link between the commodity price cycle and the dollar cycle is apparent, but the underlying causes are not clear. Obviously, arbitrary exchange rate changes can lead to commodity price changes, and as I have said before dollar prices may not reflect truly trends in real commodity prices. Prices in SDR terms would be better, as would in some cases an index of gold prices. Using some other types of measures, the swings in commodity prices are much attenuated. It should be pointed out that despite the weight of the United States economy in the world economy, there is not necessarily a direct causal relationship between the strength of the dollar in currency markets and commodity prices. It could be that the same factors that cause the dollar cycle also cause the commodity cycle. One of the factors, for example, which has caused the dollar cycle has been the IT revolution and the resultant very rapid increase in productivity in tradable goods, which meant that the real Dollar had to appreciate. With the United States inflation rate around 2.5-3 percent throughout the period, the appreciation of the real Dollar needed because of the "internet economy" was something like 5 percent, which was accompanied by deflation in all the countries that kept their currencies fixed to the Dollar such as Argentina, China, Hong Kong SAR,

the Gulf States and Panama. Every one of those countries had deflation in this period, prime evidence that productivity effects were behind this strong Dollar. The United States economy accounts for about 25 percent of the world economy measured at current exchange rates. So anything that affects the Dollar, the currency of that big economy, is certainly going to affect real events; and those factors that led to the Dollar weakening or strengthening can also lead to fluctuations of commodity prices. Thus, very strong productivity growth and the change in the real exchange rate, coupled with tightness on the part of the Federal Reserve to keep the consumer price index below 3 percent contributed to the slowdown in the United States and the global economy, and that would certainly be an important factor in depressing commodity prices. However, looking for a single cause, is simplistic. For example, there are two kinds of mistakes that one can make in relating exchange rates to basic real commodity prices. One is to say that exchange rates do not matter, while the other is to consider exchange rates as responsible for a whole series of different problems. In fact, in the short run they matter, while in the long run they do not matter very much. Therefore, it would be a good idea to have a reform of the international monetary system in order to avoid any possible link between exchange rates and commodity prices. Moreover, a restoration of a fixed exchange rate system would provide countries with a new rudder for monetary policy and would be a great step in the improvement of economic policy. I want to conclude by emphasizing that the current international monetary arrangements are far from optimal. They do not constitute a system. If the Balkanized world were suddenly transformed into a centralized empire, its first act would be to create a common currency that would be acceptable everywhere, with a great improvement in potential welfare. In the absence of a hegemonic empire, monetary efficiency depends on cooperation which in turn requires a world at peace that can be enforced. The end of the Cold War opened up a new era of globalization and the emergence of a global economy. As Paul Volcker has said, a global economy needs a global currency.

TECHNOLOGY AND PRICES IN AGRICULTURE Presented by Prof. Robert Evenson Professor of Economics Yale University The "farm problem" in most developed countries is usually stated as a price/cost problem. That is, farm interest groups have argued for decades that prices are often too low to cover average costs, where average costs are perceived to include a reasonable return to the labour contributed to the enterprise by farmers and unpaid family workers. Farm programs to "support" higher farm prices have been implemented in virtually all developed countries over the post-WW II decades. Technology is generally recognized as a contributing factor to farm problem prices (Cochrane). It is also regarded to be a contributing factor to changes in the "structure" and organization of agriculture in developed countries. In addition, "technological competition" between producers of the same commodity located in different regions

(e.g., States in the U.S.) has been recognized as an important factor in determining regional farm income levels. As commodity markets have become increasingly integrated and globalized, farm problem prices have also become globalized. World prices for grains reflect world supply and demand, and technological changes have both global and competitive (local) effects. One of the dominant features of international grain markets in recent decades has been the supply provided in developing countries (i.e., the Green Revolution). In the 1950s, the nature and magnitude of the "population boom" in developing countries was becoming apparent. With major improvements in health outcomes (particularly in public health programs), decreases in infant and child mortality rates were triggering "demographic transitions" in virtually all developing countries. The magnitude of the population increases was enormous and historically unprecedented. Global population increased from 2.52 billion in 1950 to 6 billion in 2000. Most of this expansion took place in developing countries. Those demographic transitions were large and rapid, and birth rates have now declined in virtually all developing countries. The food production response to the increase in demand from population expansion has in many ways been as extraordinary as the population boom itself. In the aggregate, food production per capita in developing countries increased by roughly 15 to 20 percent over the past 50 years. But the local country-by-country production response has varied considerably with many countries achieving little or no increase in per capita food production. Technology, of course, had a great deal to do with this food production response. The past 50 years have thus been years of extraordinary RCR performance in agriculture in all developed and most developing countries. RCR rates for agriculture have been approximately double those for the rest of the economy in all countries except for the rapidly-growing Newly Industrialized Countries (NICs). This has placed an adjustment burden on the sector that has been of major magnitude. It has also been a blessing of major magnitude for the present economies of the world. This paper is organized in four parts. In Part I, a review of the basic economics of farm problem prices and the attendant economic adjustments is presented. This part shows that farm problem prices and economic adjustment are virtually unavoidable in the process of economic development. Part II reports estimates of "Real Cost Reduction (RCR)" for developing country regions for four decades (1960s-1990s). Part III summarizes evidence for crop genetic improvement-based RCRs (the Green Revolution). Part IV then reports "counterfactual" simulations of the price effects of these RCR achievements in both developed and developing countries utilizing the multimarket model of the International Food Policy Research Institute (IFPRI). Part V discusses prospects for future price effects. Analytics - Prices and Productivity Technology, when adopted and used by farmers, typically changes the cost curves of farmers and hence, of their supply to markets. Cost changes, however, may be due to efficiency improvements and factor price changes as well as to the adoption of new technology. Section II of this paper discusses measures of "Real Cost Reduction"

(RCR), and Section III discusses Crop Genetic Improvement (CGI) technology. In this section, two types of cost reduction are considered, scale neutral and scale biased. Consider first the simple analytics of scale neutral cost reduction in a closed agricultural economy. Figure 1 presents the essentials. Panel A depicts cost curves for a single farm. Scale neutral cost reduction shifts average (AC) and marginal costs (MC) downward as depicted. Figure 1. Scale Neural Cost Reduction

Panel B depicts the market equilibrium in the short run (i.e., with no entry or exit into the production of this commodity). The supply curve is the horizontal sum of the marginal cost curves for each farm, S0. With cost reduction, the supply curve shifts downward to S1. Equilibrium price declines from P0 to P1. We can first notice that with a scale neutral cost shift, the payments to fixed factors (PFF) on farms actually producing this commodity actually increase (from the area P0A0C0 to the area P1A1C1) as long as the demand curve has some price elasticity. However, the payments to variable factors (PVF) (the area under the supply curve), change from OC0A0Q0 to OC1A1Q1. With inelastic demand (where the elasticity of demand h is between 0 and -1). These payments will decrease (actual total PPF + PVF will decrease). More importantly, it is possible that prices fall by more than average costs even in the short run, creating "farm problem" conditions and short run adjustments. The condition for short run farm problem conditions is expressed by the ratio of average cost changes (

) to price changes (

):

where es is the short run elasticity of supply and ôhdô is the absolute value of the elasticity of demand.

Is the ratio of the change in marginal cost to the change in average costs. In the short run this ratio is less than one. Thus, even in the short run, farm problems arise for variable factors as long as demand is inelastic and for all factors, if supply is more inelastic than demand. With the being less than one, this is further exacerbated. In the long run farms will begin producing this commodity if cost reduction (

term

) is

greater than price reduction ( ) and will exit production if cost reduction is less than price reduction. This is depicted as the horizontal supply curve in Panel C. Note here that price reduction will equal cost reduction in the long run, i.e.,

.

Figure 2 depicts the case for scale biased cost reduction. For individual farms (Panel A) cost reductions are depicted as scale biased, i.e., the minimum point on the AC curve moves to a larger scale of production. This produces a "fanning out" of the MC and supply curves. For this case, PFF will decrease, causing real stresses on farm organization. Essentially, farms will be presumed to prevent loss of income only by becoming larger even in the short run. Figure 2. Scale Biased Cost Reduction

Figure 3 depicts the case of unequal access to cost reduction technology. This is a realistic case both within a country and between countries. Consider the case of Crop Genetic Improvement (CGI) technology. This technology is highly location-specific, i.e., sensitive to soil, climate and related plant disease and pest conditions. Farmers do not have access to this technology unless CGI programs are in place in a given location (agro-ecosystem) "tailoring" CGI to the location. Furthermore, as Section III will show,

these CGI programs sometimes require many years of sustained effect in a location before farmers have access to CGI-based cost reductions. Figure 3. Partial Access to Cost Reduction

Figure 3 depicts the essentials of unequal access. Suppose there are two groups of farmers, Group A and Group B. Since supply curves can be summed horizontally (i.e., for each price there is a corresponding quantity whose marginal cost equals price (the profit maximizing condition). The supply curve for Group A can be depicted as SA. Total supply is SA + SB, and in period, equilibrium price will be P0. Now suppose that Group A farmers do not have access to cost reducing technology, but Group B farmers do. The supply curve of Group A farmers will not shift, but total supply will increase as Group B farmers adopt the cost reducing technology. This will result in increased total supply and a decrease in price to P1. Note now that Group A farmers are harmed by technology made available to Group B farmers, but not to Group A farmers. Their quantity supplied will decline (to QA1) and payments to both fixed and variable factors for Group A farmers will decline. They will now have a serious farm problem. Group B farmers, on the other hand may enjoy increased income (PFF) because of this cost reduction technology even though they do experience price reductions. Unequal access to RCR is a problem both within and between economies. Within countries, it creates regional income problems. For countries with decentralized government structures, unequal access can be reflected in competitive public goods systems. For example, in the case of Group A farmers and Group B farmers, if Group A farmers can develop support for a publicly funded CGI program tailoring CGI technology for them, they will do so, and by doing so, they will be in a competitive position with Group B farmers. As they succeed in gaining access to CGI technology,

this will inflict damage on Group B farmers. This in turn will stimulate Group B farmers to do more, thus setting up competitive production of public goods. This competition model will also affect policies toward private sector firms supplying RCR. Internationally, with integrated global markets this unequal access model is very relevant because global prices are determined in international markets and reflect technology-related shifts in supply curves in many countries. Farmers in different countries do compete in global markets and RCRs realized in one country affect global prices, hence farm problems in other countries. Farmers without access to technology and without competitive RCR delivery systems are penalized in global markets. Consumers, on the other hand, benefit from RCRs pretty much independently of their origin. RCR Evidence for Developing Countries Two concepts of productivity change have been used to characterize agricultural production. These are "partial" productivity measures such as production per worker or production per hectare of land, and "Total Factor Productivity" or TFP measures. When properly calculated, TFP measures are also measures of Real Cost Reductions (RCR). TFP measures can be directly derived from cost function methods and directly measure RCRs (the term "measure of our ignorance" is often applied to TFP measures, but this term refers to the "sources" of TFP or RCR gains, not to the measure itself). The actual measure of RCR (or TFP) is easily derived from the minimized cost function:

where

are cost minimizing input quantities and Ri are factor prices.

More generally, this can be written as:

where R is a vector of factor prices and t is a period indicator. Then

Transforming to rates of change and using the property that marginal costs = prices, we obtain:

The accounting approach to measurement for TFP provides a more general definition

Thus, RCR = TFP and both rates of change measure real cost reduction (i.e., real average cost reductions. Surveys of TFP-RCR measure for developing countries are not comprehensive, but crude measures of TFP are possible from FAO data. Table 1 reports such crude measures for four decades - the 1960s, 1970s, 1980s and 1990s - aggregated from country data for eight major developing country regions. These measures are crude. No attempts to adjust for quality change in factors (particularly in labour) are made, but subject to the crudity, these are measures of real cost reduction (RCRs). (See Section III for details regarding TFP calculations). Production Impacts of CGI This section provides estimates of the magnitude of CGI impacts on production in developing countries. Two estimates are provided. Both are expressed in terms of annual contributions to productivity growth by decade and region. A range of estimates (high, low) is provided to reflect the uncertainty in the estimates. The first estimate provided is for all CGI improvements since 1965 in developing countries. The second estimate provided is for the IARC CGI contributions. These estimates are utilized in Part III, where the economic consequences of CGI on prices, production, trade and welfare are analyzed. In order to measure CGI contributions to TFP-RCR gains, we first require data on actual adoption of modern varieties (varieties produced after 1965). These estimates are summarized in Table 2. The second step in computing the CGI contribution is to estimate the productivity gains associated with the conversion of land area from traditional varieties (TVs; i.e., pre-1965 varieties) to MVs. Estimates of these gains are reported in Evenson and Gollin, 2002, and summarized in Table 3. TFP calculations for the three major crops in developing countries, rice, wheat and maize, are also reported and related to MV adoption (Tables 4 and 5). Table 2 summarizes estimates of MV diffusion by region and crop for 1970, 1980, 1990 and 1998. These estimates are not of equal reliability, being most reliable for wheat and rice, but on the whole they offer a reasonably accurate picture of modern variety diffusion. That picture is one of unevenness by region and crop. This is particularly apparent for the Middle East and North Africa (MENA) and Sub-Saharan African regions where MV adoption rates were low for all crops in 1970 and were still low for most crops in 1980. By contrast, Latin America and Asia have significant MV adoption by 1980. As of 1998, MV adoption was still low for cassava, beans and lentils in all regions and for sorghum, millets and maize in Sub-Saharan Africa. Table 2 also clearly shows that MV diffusion for aggregate crops differs greatly by region. Sub-Saharan Africa had less than one-third the level of MV adoption attained in

Asian economies in 1998. In the 1960s and 1970s Sub-Saharan Africa had a little over 10 percent of the MV adoption levels of Asia. Three sets of evidence are used to evaluate the productivity impacts of MV/TV conversion (and in some cases of MV/MV conversion as well). The first set of evidence is reported in Crop Study chapters in Evenson and Gollin (2002). The second set of evidence is reported in three country study chapters in Evenson and Gollin (2002) (see Table 3). The third set of evidence is based on crude crop TFP calculations based on FAO country data. These calculated TFP growth rates are statistically related to MV/TV conversion data for rice, wheat and maize, where data are available (Table 4). Each set of evidence is subject to limitations and each taken separately may not be regarded to be "consensus" estimates of MV/TV or MV/MV turnover impacts on crop productivity. But taken together, all three sets of evidence are in substantial agreement and this agreement supports the consensus concept. Crops study evidence is of two types. The first type is experimental evidence, where MV/TV yield comparisons (and MV/MV comparisons as well) are made under conditions where experimental controls are utilized. These experiments may be on field station locations or they may be on farm sites with some degree of farm management. In the absence of a statistical design to farm site experiments, however, this evidence is subject to the criticism that real farm experience is not being replicated. The second type of crop study evidence is based on secondary data (e.g., at the province or district level) on production, area and yield. In some cases data on other inputs, fertilizer, labour, machines are available to enable crop TFP calculations. Productivity impacts, whether based on MV/TV conversions or MV/MV turnover, are not necessarily constant as MV/TV ratios change. For rice and to some degree for other crops as well, MV "generations" have been defined. The first generation MVs are based on quantitative-high yielding plant type traits. This generation, once established may have high MV/TV impacts but these are often transitory because of susceptibility to plant diseases and insect pests. The second generation of MVs is based on direct responses to these susceptibilities. Host plant resistance to diseases and pests is sought through qualitative trait breeding. As these varieties are adopted, they replace first generation MVs and expand MV areas to new regions where first generation susceptibility limited first generation MV adoption. Third generation MVs in rice have incorporated host plant tolerance to abiotic stresses (drought, salinity, submergence, etc.). These traits have also enabled expansion of MV area as well as MV/MV turnover. Byerlee and Traxler (1995) have argued that first generation impacts are larger than second and third generation impacts in wheat. For rice, however, the evidence is less clear. A study of the productivity impact of rice varieties by Gollin and Evenson (1998) estimated that improved rice varieties had contributed 13.4 percent to production by 1984 when 41 percent of rice area was planted to modern varieties. A second study for rice (Evenson, 1998) utilizing district data for the 1956-87 period, estimated modern

variety impacts in a multi-equation model where the adoption of MVs was treated as an endogenous variable. Determinants of MV adoption included the availability (in MVs suited to the district) of HPR traits for disease and insects and HPT traits for drought and salinity. The study concluded that the incorporation of these traits into MVs increased the MV coverage from under 40 percent to over 60 percent by 1987. The yield effect was unrelated to the MV coverage variable, indicating that the new area covered achieved yield gains that were roughly of the same order of magnitude as those achieved in the earliest adopting regions. The estimated yield effect was one tonne per hectare (i.e., rice yields would have risen from 1.5 tonnes to 2.5 tonnes with 100 percent MV adoption). Table 3 reports a summary of estimates of yield impacts of MV adoption and of MV turnover on productivity from both crop studies and country studies. Most of the estimates are of MV adoption effects, i.e., the replacement of traditional varieties by MVs. The "percent" estimates are estimates of full (i.e., 100 percent) replacement of TVs by MVs. Some studies are based on statistical studies of micro farm level data and some are based on aggregate panel data of the type utilized in the India chapter. Several of the statistical studies treated the area planted to modern varieties as an endogenous variable to be predicted as a function of variables such as extension service, farmer schooling and of agricultural research services suited to the area. In the Evenson 1998 study, variables measuring the availability of AST traits for drought and submergence tolerance and the number of landraces in the suitable released varieties were also included in the MV adoption specifications. These studies did not fully resolve the comparison between MV/TV versus MV/MV effects, because the HPR and AST traits were incorporated into the second and third generation MVs that were replacing first generation MVs as well as in the MVs replacing TVs. However, the country studies summarized in Table 3 do provide some evidence on the matter of MV/TV versus MV/MV conversion because most of the turnover in Brazil and China was MV/MV conversion, i.e., of new MVs replacing older MVs. These turnover estimates (for 100 percent replacement) are roughly one-third of the gains associated with replacement of TVs. Table 3 also reports mean "consensus" estimates of full MV-TV replacement by crop. These are relatively conservative estimates based on the available evidence. The strategy in the CGI contribution to productivity reported in Table 6 is to apply 2/3 of the consensus estimate to the increments in MV acreage by decade. The remaining one-third is applied to cumulated MV acreage from past and current decades, so that the total effect at the end of each decade is the present MV at that time multiplied by the consensus factor. Evidence for MV/TV conversion impacts directly on TFP growth is presented in Table 4 and 5 for the three major crop commodities in developing countries. This evidence is an important addition to the crop and country study estimates in two respects. First, it is based on TFP calculations rather than yield. Second, it is based on international comparisons as well as comparisons over time thus adding an international dimension to the micro-crop studies and the regional country studies. The TFP growth relationship can be expressed as:

GTFP = GP - SAGA - SWGW - SFGF - SAPGAP- SMGM where GP is the growth rate in production of the crop GA is the Growth rate in land (and water) GW is the growth rate in work hum power use GF is the growth rate in fertilizer use GAP is the growth rate in animal power use GM is the growth rate in mechanical power use. The shares SA, SW, SF, SAP, and SM are cost shares and reflect the marginal products of each factor of production. Under conditions of scale neutrality, cost shares, i.e., the share of the factor in total cost, are the correct shares for this calculation. These shares can be changed from one period to the next if appropriated data are available. FAO maintains a data base for countries from 1961 to date, enabling the following calculations: GP and GA for rice, wheat and maize GW, GF, GAP and GM for all crops. SF, SAP, and SM for all crops. There are two issues then associated with calculating GTFP. First, is it reasonable to use GW, GF, GAP and GM measured for all crops as proxies for crop-specific measures? Second, can one obtain measures of the missing shares, SA and SW ? There is no question that errors of approximation are made when GW, GF, GAP and GM are treated as crop specific. But this error is lower for major crops than for minor crops. Rice, wheat and maize are the three major crops in most developing countries. In aggregate those three crops are planted on roughly two thirds of cropped land in developing countries. The second question is also important because land rent data are not available to compute SA, and wage data are also not effectively available to compute SW. In view of the importance of the crops and the potential value of corroborating evidence from MV/TV impacts, a decision was made to calculate GTFP measures for rice, wheat and maize in countries producing more than one million hectares of the crop. These calculations were made for three periods - 1965-75, 1976-85 and 1986-95. Three year averages were used for the growth measures. Shares were calculated by period for SF, SAP and S M using international (dollar) prices for fertilizer, animal services, tractors and harvester-threshers, and the estimates of the crop agricultural value (in dollars). The shares of land and labour were arbitrarily set to equal half the residual (1- SF - SAP - SM). (This allocation is generally consistent with farm management cost studies.) The reader should, of course, be aware that there are errors of attribution in these measures (note, however, that GP and GA are crop-specific measures).

Tables 4 reports simple analyses of the GTFP measures computed for 54 countries for rice, 32 countries for wheat, and 64 countries for maize. Table 5 reports estimates of MV/TV impact on GTFP for the subset of countries for which MV/TV data are available. Table 4 reports estimates of GTFP measures by decade. These estimates are based on area weighted OLS regressions of GTFP measure on time period (specification 1) and geographic region dummy variables (specification 2). The explanatory power of this regression estimate is low (although all meet the basic F test requirement). This reflects the fundamental nature of international agricultural production data. These data show that rice TFP growth was modest in the first two periods, then declined in the third period. For wheat the picture is one of very high TFP growth in the first period, high growth in the second period and modest growth in the third period. For maize TFP growth has been high in all three periods. These calculations then show high TFP growth rates for both wheat and maize of over 2 percent per year for 30 years and more modest TFP gains for rice (approximately 1.2 percent per year over the 30 year period). Growth in the first (original green revolution) period was highest and has slowed in the past two decades. Table 5 reflects the major objective of this exercise. It relates cumulated TFP growth to cumulated MV percent measures for 17 rice producing countries, 20 wheat producing countries and 19 maize producing countries where MV adoption data are available. These OLS estimates (weighted by area harvested) should be interpreted in the context of a dependent variable with attribution errors as well as weather errors and other measurement errors. The estimates do show that MV/TV conversion produces TFP growth. Note that time dummy variables also show that other factors are producing cumulated TFP growth over time as well. Some variation in the coefficients is apparent. Consider the pooled regression,[3] however. For these three crops in the countries in the sample, MV adoption had reached roughly 65 percent of harvested area for the countries concerned. The MV/TV coefficient of .534 then indicates a CGI contribution to TFP growth of .534 x .65 = .35 percent. This CGI contribution is approximately 55 to 65 percent of realized TFP growth and 44 to 52 percent of realized yield growth for these crops. These estimates while subject to error (note the statistical procedure recognizes these errors in dependent variables) do corroborate the consensus estimate reported in Table 3 from the crop and country studies. Table 6 reports a summary of annual CGI contributions to yield growth by crop by decade. The estimates are produced from the MV adoption data in Table 2 and the consensus MV/TV TFP estimates reported in Table 3 (and supported by Tables 4 and 5) (note the 1960-2000 estimates include projection for 1999 and 2000). (These growth components are reported by crop and region in Table 8.) Since these estimates are based on MV adoption levels and on the consensus productivity estimates, it is not surprising that they are largely determined by MV adoption patterns. The highest growth

contributions over the 40-year period are realized in the "green revolution" crops, wheat and rice. Interestingly, contributions in potatoes are also high. Maize contributions have been important as well. Growth contributions in lentils, beans and cassava have been low, although they are rising rapidly for beans and lentils. Table 6 also enables a comparison of the IARC content of adopted varieties with the IARC content of released varieties over the entire period. For all crops, IARC crosses accounted for 36 percent of releases and 35 percent of area under MVs. It should be noted that IARC crosses have higher levels of multiple releases than NARS crosses (see Chapter 2, Evenson and Gollin), and when this is considered, IARC crosses have a higher proportion in adoption than in releases. This is particularly pronounced in crops other than wheat, maize and potatoes. It can also be noted that both proportions are very high in barley, lentils, beans and cassava, where IARC programs effectively initiated CGI work on the crop in most regions. Table 7 reports the CGI growth estimates for aggregated crops by region and period. The growth picture that emerges here is quite impressive in terms of regional differences and their timing. Many observers have noted that the agricultural productivity performance of Sub-Saharan Africa, and to some extent of the Middle East North Africa region, has been disappointing when compared with expectations and when compared with Asian and Latin American performance. While the CGI component is not the only component contributing to productivity growth, it is the major component in most developing countries (see for estimates that CGI represents as much as one-half or more of the full TFP component. One need look no further than Table 7 for an explanation of regional differences in growth performance. Research systems were simply not delivering MVs that merited adoption to Sub-Saharan and MENA farmers in the 1960s and 1970s. (Note that they were producing MVs but their MVs did not merit adoption.) It was not until the 1980s that MENA farmers realized high growth from CGI programs and not until the 1990s that Sub-Saharan African farmers realized modest growth from CGI programs. Over the 40-year period, Sub-Saharan African farmers received only 30 percent of the CGI growth delivered to Asian farmers. They received only 10 percent of the CGI growth delivered to Asian farmers in the 1960s and 1970s. Table 7 also provides on IARC content indicators for adopted and released varieties. IARC crosses make up higher proportions of both releases and adoption in the MENA and Sub-Saharan Africa regions than in Asia and Latin America. This attests to the relative strengths of NARS programs. The delivery of CGI growth to Asia and Latin America reflects stronger, i.e., better organized and managed, NARS. It also reflects differences in institutional settings, as well as in basic biological factors underlying the production of CGI growth itself. There is little question that CGI growth has been more difficult to obtain in cassava, lentils and beans than in rice and wheat. Much of this is related to the fact that temperate zone developed country CGI systems had achieved gains before 1950 in rice and wheat that were brought to the tropical and sub-tropical regions by IARC programs. (It should also be noted that there are differences in CGI growth achievement between countries in regions and within countries in each region.) The estimation of IARC CGI contributions is complex, but it can reasonably be related to the data on both IARC crosses and NARS crosses, and IARC ancestors. Estimations made in Evenson and Gollin (2002) reported that IARC programs have a germplasmic contribution to NARS CGI programs that in the aggregate was roughly equivalent to the

NARS cross - IARC ancestor proportion in varietal releases. IARC programs were estimated to make NARS programs 30 percent more productive over the period studied. It was also estimated that NARS CGI investment responded positively to the availability of CGI germplasm. This effect was approximately 13 percent and would have led to 7 to 8 percent more NARS varieties. The complexity in calculating the IARC effect is that in the absence of IARC programs, stronger regional and other coordinating programs would have provided some IARC services. In addition, there is a competition effect (noted in Chapter 21) between IARC crosses and NARS crosses. In the absence of IARC crosses, more NAR crosses would have been released and adopted. These crosses, however, would have been affected by the loss of IARC germplasm. Table 8 presents calculations of two alternative IARC CGI growth contributions by crop and region. The IARC CGI calculations are made as follows: 1/4 Substitution = (.75 IX = IA (1 - .75 IX)) x 1960-99 Total CGI Contribution when IX is the proportion of IARC crosses in adopted varieties and IA is the proportion of NARS crosses with IARC ancestry in adopted varieties and 1/2 Substitution = (.5 IX + IA (1 - .5 IX)) x 1960-99 Total CGI Contribution The 1/4 substitution computation postulates that in the absence of IARC programs, NARS programs would have produced 25 percent more varieties that would be adopted by farmers with the same yield impact as the IARC crosses would have had. It also presumes that the germplasm loss (proxied by IA) applies to the 25 percent expansion. The 1/2 substitution computation postulates a 50 percent substitution of NARS varietal production for the IARC crossed varieties. Again, it is presumed that the loss of the IARC germplasmic effect (IA) applies to this substitution proportion. As a result the differences between the two substitutions cases are muted (for all crops all regions the 1/2 substitution calculation is 89 percent of the 1/4 substitution case). The Economic Consequences of CGI Programs In this section, the economic consequences of CGI programs are assessed. The methodology for this assessment requires a multi-market, multi-country model where crop supply and crop demand factors determine market-clearing prices, quantities produced and consumed, and international trade volumes. For this purpose, the IMPACT model of the International Food Policy Research is utilized to created the "counterfactual" or "what if" simulations. The two counterfactual simulations ask the following questions: •

how would food prices, food production, food consumption and international food trade have differed in the year 2000 if the developing countries of the world were constrained to have had no CGI after 1965, while developed countries realized the CGI that they historically achieved? (This is the 1965 CGI counterfactual in this chapter.);

•

how would food prices, food production, food consumption and international food trade have differed in the year 2000 if the IARC system had not been built (and thus the IARC CGI contributions had not been realized), but NARS CGI gains in both developed and developing countries would have been realized? (NARS include both public and private research programs.) (This is the no IARC CGI counterfactual.). The CGI contributions calculated in Table 8 were utilized in the IFPRI-IMPACT simulations.

The economic consequences of CGI are realized through markets and changes in market equilibria. CGI effects are both direct and indirect. The direct effects are the RCR effects, where farmers realize cost reductions from yield improvements. These direct effects, as noted in the previous chapter, vary by crop, region and period. The indirect effects are CGI-induced price effects. These effects tend to be crop specific to some degree (although with substitutability in demand, CGI-induced price effects for one crop are transferred to other crops) but they are global in today's globalized economy. Comparison of economic equilibria is a meaningful way to evaluate economic consequences. It is important to distinguish between people as demanders of food and people as suppliers of food. CGI effects lower costs of production and increase the incentives for producers to supply more food. For given demand conditions this will mean a lower price in the new equilibrium. In a dynamic version of a market model a "base case" rate of growth in demand and in supply is posited. Then a decrease in the CGI contribution will result in less supply and higher prices than in the base case scenario. The extent of the price change will depend on the localization or globalization of the market. If the market is a local autarchic market with little trade between regions and countries, the price response associated with CGI improvements can be quite severe. This is because, in a local market, food demand elasticities can be quite inelastic. Suppose, for example, that an RCR of ½ percent is produced by CGI programs. This would induce farmers to produce ½ percent more under "neutral technical change" conditions. With a demand elasticity of minus one, prices will fall by ½ percent. But if demand is inelastic, this will result in a price decline of more than ½ percent. If this happens, the production economy must make long-term structural adjustments, which in this case means that some producers will exit from production. Thus in this local market situation, consumers will gain (including farmers who are also consumers), but producers will actually lose and may be forced into costly adjustment. Now suppose that producers have differential access to CGI within this localized region. For example, suppose only half of the farmers in the region have the natural resource conditions to benefit from the CGI. Then the supply increase will be half as much as in the case where CGI is available to all. The price effect will be half as large, so consumers will gain half as much. But now the consequences for producers become very different for those with access to CGI and those without access. Those with access will realize RCR gains of ½ percent so their costs may fall by as much or more than prices fall. This may produce a net gain in income for them. The producers without access to CGI will unequivocally lose. Their costs will not fall, but prices will. Thus, their incomes will fall.

This phenomenon of differential delivery of CGI then has important welfare implications. A study of differential CGI delivery by David and Otsuka (1995) for rice farmers noted that agricultural workers can escape the burden of unfavourable delivery if they are mobile. But to the extent that they are mobile they shift more of the burden on the owners of non-mobile assets (family labour and land). This localized economy is increasingly less relevant in a globalized economy. We observe that most countries today have integrated national markets in grains and agricultural products and increasingly, international or global markets are emerging for most commodities. When a local economy opens itself up to trade, there are two consequences. The first is that it can enjoy higher demand elasticities. This means that price effects (both for increases and decreases) will be smaller, easing the burden on producers. In fact for a small open trading economy CGI or RCR gains may have little or no price effects, enabling producer incomes to increase with access and for producer incomes to remain unchanged for those without access. The second consequence of opening to trade is that the local economy is now "exposed" to competition from abroad. If farmers in other countries realized CGI gains that are not delivered to the local economy, the local economy will be in the same position as local producers without access were. Thus, if China is realizing CGI in rice, this will have a negative effect on the incomes of rice farmers in Indonesia and vice-versa. However, consumers in both China and Indonesia will benefit from CGI in China. In a globalized economy, the issue of delivery of CGI is not only an issue within countries, but between countries as well. There are gains from CGI, but the distribution of these gains depends on the nature of CGI delivery. In the previous section, it was noted that CGI delivery has been very uneven regionally, with farmers in Sub-Saharan Africa realizing only 10 percent or so of the gains (per hectare) that farmers in Asia were realizing in the 1960s and 1970s. This had serious negative consequences for the region. Fortunately the situation is more balanced in the 1990s. Another phenomenon is likely to exist in global markets where developing countries realize high rates of CGI gains. Most developing countries are experiencing high rates of population and labour force growth. Only a few are realizing rapid industrial growth. Under these conditions agricultural wage rates will tend to be low and to rise slowly. When these countries realize CGI gains, their supply response is large because wages will rise slowly and because wages are an important part of costs (in developed countries wages are likely to rise faster). Over the past four or five decades, CGI gains in developing countries have been rapid as noted in previous chapters. The supply response to these gains has been large contributing to extraordinary declines in the real prices of crops. The International Model for Policy Analysis of Agricultural Commodities (IMPACT) developed at the International Food Policy Research Institute (IFPRI) is a partial equilibrium model covering 17 commodities and 35 country/regions. It computes global equilibria in real prices. It is synthetic in that it uses price elasticities and non-price parameters from other studies. The model incorporated non-agricultural sector linkages

but does not compute equilibria for markets other than for the 17 agricultural commodities. Each country/region sub-model has a set of equations for supply, demand and prices for each commodity and for intersectoral linkages with the non-agricultural sector. Crop production is determined by area and yield response functions. Area functions include price responses (own and cross-price terms) and a non-price trend reflecting remaining land availability and technology. Yield is a function of the price of commodity and prices of inputs and a non-price total factor productivity (TFP or RCR) term. (This term is discussed further below.) Livestock commodities are similarly modelled. Domestic demand is the sum of food, feed and industrial use demand. Food demand is a function of prices (of all commodities), per capita income and population. Income growth is partially endogenous to the model and agriculture-non-agriculture links are specified. Feed and industrial use demands are derived from final demands. Prices, production and trade volumes are endogenously determined in the model. Domestic prices are linked to global equilibrium prices via exchange rates, and producer-consumer subsidies and trade restrictions are allowed. Other policy instruments (acreage restrictions) are considered. Trade is determined by net supplydemand equilibrium conditions and global market conditions. National Population Growth is Exogenously Based on UN Projections (World Population Prospects UN). The non-price terms in the area and yield functions were developed for each commodity and country/region as follows: First, an accounting structure based on experience in India and Brazil (Rosegrant et al., 1998; Avila and Evenson, 1999) was developed. The accounting components were: 1. Public (IARC-NARS) Research Contributions a. Management Research (non-CGI) Contributions b. Conventional Plan Breeding (CGI) Contributions c. Wide-Crossing-Marker-Aided breeding (CGI) Contributions 2. Private Sector Agriculturally-Related R&D Spill-in Contributions 3. Agricultural Extension Contributions 4. Markets Development Contributions 5. Infrastructure Contributions 6. Irrigation (interacting with technology) Contributions

The yield growth contribution of modern inputs such as fertilizers is accounted for in price effects in the yield response function. The growth accounting contributions of both the public and private agricultural research components include both CGI and non-CGI contributions. CGI contributions affect the value of non-CGI contributions and vice-versa. The CGI calculations reported in the previous section, however, do not include the complementarity between CGI and nonCGI components. These computations, reported more fully in Rosegrant et. al. (2000) were used to simulate a "base case." This base case was actually a forward projection. For our purposes we are using this forward projection to compute a "backcast" or counterfactual simulation. To do this we need first, to check the base case for consistency with the CGI calculations. Then we can "subtract" CGI contributions from the base case and compare the equilibrium calculations with the base case to create the "counterfactual" simulation. The consistency between the CGI reductions requires that the CGI components represent roughly the proportion of RCR growth that were indicated in Table 5. In addition, the population and related demand growth conditions should be similar between the backcast period and the projection period. The first counterfactual is the 1965 CGI counterfactual. This counterfactual is intended to simulate conditions where developing countries are constrained to 1965 CG. For the lower end of the range of this counterfactual, we subtract the CGI components averaged for the 1965-2000 period reported by crop and regions in Table 8. These are our best estimates of the CGI components ignoring CGI-non-CGI complementarity. For the upper end of the 1965 CGI counterfactual we subtract 1.3 times the CGI components in the lower end of the range to reflect CGI-non-CGI complementarity. This estimate is consistent with the IARC-NARS germplasm complementarity estimates and roughly consistent with growth accounting studies evidence. The second counterfactual is the NO IARC CGI counterfactual. For this counterfactual, we subtract the IARC CGI contributions calculated in Table 8. We use the 1/2 substitution case as the lower end of this range and the 1/4 substitution case as the upper end of this range. We also subtract 1/4 of the 1/2 substitution case for wheat and rice in developed countries to reflect the IARC contribution to developed country production (See Alston and Pardey, 1999). Note that, in the 1965 CGI counterfactual, developed countries realize their actual CGI gains. In the NO IARC CGI case, we subtract a small component for wheat and rice from developed country CGI gains. Table 9 reports global aggregate simulations for the two counterfactual scenarios. The simulation results are the percentage differences between the base case, i.e., the simulation representing actual changes, and the counterfactual case. Thus for equilibrium prices (which are global equilibrium prices in U.S. dollars per tonne with allowances for country price differentials because of tariffs) the 1965 CGI counterfactual indicates that equilibrium wheat prices would have been from 29-61 percent higher than they actually were in 2000. For rice, the price increases are from 80-

124 percent higher (note the range). Price increases from CGI reductions in developing countries depend both on actual CGI gains which varied by crops and on the proportion of the crop produced in developing countries. Price increases for rice, which is produced mostly in developing countries, thus exceed those for wheat, half of which is produced in developed countries. For all crops (weighted by production) prices in the 1965 CGI counterfactual would have been from 35 to 66 percent higher. Since prices actually fell by 35 percent or so from 1965 to 2000, this would have more than offset the price fall. Some readers may be surprised that these price differentials were not larger. It should be noted, however, that the counterfactual does not posit lost CGI in developed countries and, with a supply response to price increases, production increases in developed countries partly offset production decreases in developing countries (see below). For the more realistic NO IARC CGI counterfactual, the price effects are smaller, but they are significant. For all food crops, prices without IARC CGI contributions would have been 18 to 21 percent higher. This suggests that, even in the absence of IARC programs, world prices of food crops would have fallen in real terms from 1965 to 2000. This, again, may appear inelastic to many observers who credit the IARCs with creating the "Green Revolution." But, as noted in this volume, the green revolution is largely a joint product of NARS, IARCs and, in some countries, the private seed companies. But much of the reason for the food price decline in the absence of developing country IARC contributions is that developed countries were realizing high rates of CGI gains. Global production decreases under the 1965 CGI counterfactual are also more modest than many would expect. For all food crops, production would have been 8 to 12 percent lower. But this is misleading because it would have increased for developed countries (because of higher prices, see below). Production decreases under the NO IARC CGI counterfactual would have been between 4 to 5 percent of production. This is roughly 45 percent of the decrease under the 1965 CGI counterfactual. The decline in production in the counterfactual is moderated by the strong rise in cereal prices. These price increases induce farmers in both developing and developed countries to expand area and increase the use of other inputs, partially compensating for the loss of crop yield growth. Area effects under the counterfactuals would have been substantial. This is because, if yields are lower and prices higher, farmers would have planted more area to crops with attendant environmental consequences. These area effects are particularly large for rice. For all food crops, area under crops would have expanded by 2.8 to 4.6 percent in the 1965 CGI counterfactual. For the No IARC CGI counterfactual area under crops would have expanded by 1.5 to 2.7 percent. As developing country regions lose competitiveness, they import more of their food crops from developed countries, which have gained competitiveness. For all food crops, developed country exports to developing countries would have risen by 27 to 30 percent under the 1965 CGI counterfactual. Note that this would have been in addition to the expansion in this trade that actually took place over the 1965-2000 period.

To provide further insight into the processes underlying the aggregate data, Area, Yield and Production Effects are reported for Developed Countries (including the transition economies) and Developing Countries (including China) in Table 10. Consider the Yield effects. These include the direct losses of CGI and the indirect CGIinduced price effects. For developed countries, the 1965 CGI counterfactual is entirely the indirect price effect. This effect is substantial for wheat and maize, but not for other crops that are produced predominantly in developing countries. The NO IARC CGI case includes both direct and indirect effects. For developing countries, crop yields would have been significantly lower in 2000 in spite of the positive indirect price effects. The NO IARC CGI effects on yields are also substantial. Area effects, interestingly, are approximately the same for developed and developing countries. This is because they depend on the indirect price effects and these occur globally. The NO IARC CGI area effects are a substantial part of the 1965 CGI effects in developing countries (especially in rice). Production effects then show that, under the 1965 CGI counterfactual, developed countries would have produced approximately 5 to 7 percent more food crops and developing countries would have produced from 16 to 19 percent less. The NO IARC CGI case would also have resulted in 1 to 2 percent more production in developed countries and 7 to 8 percent less production in developing countries. Tables 11 and 12 provide further detail for area and production effects for developing country regions. Table 11 shows that area effects differ by crop and region. The relatively small area effects in Sub-Saharan Africa, for example, are due to the fact that this region had relatively low CGI gains, less than one-third those of other regions. Accordingly, the lost CGI counterfactuals are lower. Had Sub-Saharan African CGI gains been as large as those in Asia, area increases under both counterfactuals would have been more than double those in Asia. It is important to note, however, that the implications of area effects in the 3 to 4 percent range are significant from an environmental perspective. This increased cropland amounts to 9 to 12 million hectares in developed countries and 15 to 20 million hectares in developing countries under the 1956 CGI case (5 to 6 million hectares in developed countries, and 11 to 13 million hectares in developing countries for the NO IARC CGI case). This would constitute an expansion of croplands on marginal areas with higher environmental sensitivity (erodability, etc.) than cropland currently under production. Table 12 shows production effects. Again we note that these are lower in Sub-Saharan Africa because that region had the lower CGI gains over the period. Thus the counterfactuals based on taking these gains away have lowest effects in this region. The IFPRI-IMPACT model can also be used for projections of equilibrium prices. Evenson (1999) reports each projection for a base case to 2020 and for five policy modifications to the base case. The policy modifications shown were: Trade Liberalization - This modification eliminates all barriers to trade.

Delayed Industrialization - The modification here is to delay industrial reforms by a decade. This is important to agriculture because of industrial technological spillovers. IARC-NARS Phaseout - The modification here is similar to the IARC counterfactual case where IARC programs are ended over the next decade Biotechnology Capacity Delay - Developing countries lag developed countries in introduction biotechnology capacity. The modification is for a ten-year delay over the delay built into the base case. Climate Change - This modification is based on studies of climate change (1 degree C rise in global temperature, 3.5 percent increase in rainfall). The salient points regarding Table 13 are: 1. All prices are projected to decline in real terms by 2020. This reflects the technology momentum built into the base case and this in turn is based on productivity gains realized in the Green Revolution and continued in the Gene Revolution. Farm problem prices will continue into the foreseeable future. 2. Slower population growth (the demographic shift) will produce even lower prices. 3. Delayed industrial reform will reduce spillover to agriculture and lead to higher prices. 4. Reduction in IARC support will lead to higher prices. 5. Climate change will have little impact on prices. Concluding observations Technology contributes to real cost reduction (RCR). RCR shifts supply curves and lowers average costs. Low demand elasticities at the global level produce "farm problem" prices where price declines tend to exceed average cost declines. This, in turn, calls for economic adjustment. This phenomenon holds even when significant numbers of farmers are excluded from RCR gains. Over the past half century, RCR gains in developed countries have averaged roughly one percent per year greater in the agricultural sector than in the rest of the economy. Thus, even in the absence of significant RCR gains in developing countries (the Green Revolution) world prices would have declined for most agricultural commodities. World trade volume would have been higher, but without RCR gains in agriculture, developing country impacts would have been income constrained. For developed countries, the pace of "industrialization of agriculture" would have been little affected because it was driven mostly by factor price changes (the price of labour relative to the price of machines; Huffman and Evenson). But the world did witness a Green Revolution in developing countries. RCR gains were high in many, but not all countries. The CGI component of these RCR gains was high. Market model counterfactual simulations showed that these CGI gains did cause lower

world prices than would have been the case with lower RCR gains in developing countries. These lower prices were a boon to consumers in almost all economies. They contributed to lower infant and child mortality and to improved health and nutrition. They accelerated the demographic transitions in developing countries. For farmers, the comparison between decreases in average costs and in prices is what matters. For farmers with little access to CGI gains, world prices fell faster than average costs, and many of the poorest farmers thus experienced severe farm problem prices. But even for farmers with good access to RCR technology, the low elasticities of demand for commodities at the farm level, led to farm problem prices and, hence, to economic adjustments. This was exacerbated in developed countries when scale biased RCR was the norm. Much of the scale bias was due to general price relationships in a growing economy (region wages, falling machinery prices). Thus, agriculture the world over has been subject to economic adjustment pressures in recent decades, and this will continue. We are in the age of biological invention. The Green Revolution will be followed by the Gene Revolution. Farmers will not have the luxury of being able to avoid competition and adjustment. Most farm program solutions in recent decades have not provided this luxury to farmers (In fact, most programs have exacerbated the problem at high cost to tax payers). For developing economies there are two aspects of low prices (relative to costs) that bear further analysis. The first is that many of the poorest families in the world are "technologically trapped." They have little access to RCR technology gains and global prices have declined. These are the "dollar per day" populations. These farmers have few non-farm employment options, and in spite of the anti-technology mood that pervades many of the growing political movements (anti-biotech, anti-globalization). These farmers have few options other than RCR technology to raise their incomes. The second concern is that low prices in many developing countries reduce incentives to invest in capital in agriculture. This capital includes pubic investments in irrigation systems, markets and CGI programs as well as private investments in machinery, and for the world's poorer farmers, capital investment combined with RCR technology represents their only avenues to move out of the dollar per day income category.

Tables 1-7

Table 1: Decadal RCR Estimates by Developing Country Region Region

Latin America & Caribbean Asia (incl. China) Middle East North Africa

1960s

1970s

1980s

1990s

.111 .078 .179

.198 .075 .138

.175 .229 .214

.284 .204 .214

1996-2000 (CGI) Contribution .192 (.066) .147 (.089) .186 (.069)

Sub-Saharan Africa

.154

-.067

.274

.217

.144 (.028)

Table 2: Modern Variety Diffusion 1970, 1980, 1990, 1998. Percent area planted to modern varieties Latin America Asia (including Middle East - North Sub-Saharan Africa China Africa 1970 1980 1990 1998 1970 1980 1990 1998 1970 1980 1990 1998 1970 1980 1990 1998 Wheat 11 46 82 90 19 49 74 86 5 18 38 66 5 22 32 52 Rice 2 22 52 65 10 35 55 65 0 2 15 40 Maize 10 20 30 46 10 25 45 70 1 4 15 17 Sorghum 4 20 54 70 0 8 15 26 Millets 5 30 50 78 0 0 5 14 Barley 2 7 17 49 Lentils Beans 1 Groundnut Cassava 0 Potatoes 25

1 54

2 69

All crops

23

39

8

0 2

15

0

5

23

20 7 84

0 0 30

15 0 50

20 2 70

50 12 90

52

13

43

63

82

4

13

29

58

0 0 0 0

0 0 0 25

2 20 2 50

1

4

13

15 40 18 78 27

Table 3: Synthesis: Estimates of MV/TV and MV/MV Impacts on Yield Crop Studies and Country Studies MV/TV Estimates (Full Replacement) MB/MV (Turnover) Estimates (Full Turnover) Crop Country Tonnes/ha Percent Source Country Tonnes/ha Percent Source Consensus increase increase Wheat India .98 46 Evenson, China .74 24 Chapt. 45% '98 18 45 Chapt. Latin .2 10 Chapt. India .98 19 America 4 Rice India .50 33 Gollin & China 1.6 29 Chapt. 47% Evenson, 12 '99 India .98 65 Evenson, Brazil .5 20 Chapt. '98 20 India .67 43 Chapt. 19 Sub24 Chapt. 6 Saharan Africa (upland) Maize India .98 65 Chapt. Brazil .41 20 Chapt. 50%

SubSaharan Africa Sorghum India India SubSaharan Africa Pearl India Millet India SubSaharan Africa Barley Middle EastNorth Africa Lentils Middle EastNorth Africa Beans Latin America SubSaharan Africa Cassava SubSaharan Africa Latin America Potatoes Global

.60

1.38

.48

45

19 Chapt. 8 Latin America

5-15

20 Chapt. 7

80 Chapt. 9 37-40 Chapt. 9 7-63 Chapt. 9

45 45 38

45%

Chapt. India 19 Chapt. 10 Chapter 10

40-45 Chapt. 10

45%

25

Chapt. 11

41%

41

Chapt. 13

41%

.21

35

25%

.4

55

Chapt. 12 Chapt. 12

3.74

49

Chapt. 16

48%

3.29

29

2.5

35

Chapt. 16 Chapt. 15

35%

Table 4: TFP Growth Estimates, Rice, Wheat and Maize. Dependent Variable TFP Growth by Decade Rice 1965-76 1975-86 1986-95

(1) .147** .159 .059

(2) .083** .094 .015**

Wheat (1) (2) .582** .431** .384** .236** .096** .048**

Maize (1) .282** .186** .308

(2) .254** .150** .279**

Pooled (1) (2) .231** .253** .148** .160** .096** .122**

Wheat Rice East-SE Asia South Asia Middle East North Africa Sub-Saharan Africa # Obser. R2

162 .084

.089** .043 .104

.309** .193** -.046

.143** -.217** -.128

.143** -.131** .134** .045* -.113*

.102

.192

.018

.170**

162 .119

96 .304

96 .436

192 .034

192 .157

450 .034

450 .288

Table 5: TFP-MVA Relationships. Dependent Variable: Cumulated TFP Growth Independent variables Cumulated MV Adoption (CMVA) d 1976-85 d 1985-95 Constant

Rice

Wheat

Maize

Pooled (1)

.720 (3.82)

.470 (1.18)

.644 (3.24)

.534 (2.83)

.059 (.66) -.048 (.42) -.079 (.89)

.302 (1.23) .355 (1.17) .467 (2.43)

NR 2.476 (5.43) -.019 (.15)

51 .339

60 .152

38 .497

D Wheat D rice # Obser R2

Pooled (2) Sq (CMVA) SQRT (CMVA) HA x CMVA

.253 (.73) .156 (.82) 4.88 (1.00)

.179 (1.45) .188 (1.32) .231 (.95) -.302 (2.04) -.306 (2.05) 149 .337

(1.31) .201 (1.57) 1.203 (.73) .421 (2.93) -.218 (1.13) 149 .390

Table 6: CGI Contributions to Yield Growth by Crop

Crop Wheat Rice Maize Sorghum

1960s .514 .342 .311 .055

1970s .981 .940 .481 .391

1980s 1.125 .959 .733 .716

1990s .975 .747 .906 .676

1960-98 .960 .794 .665 .504

IX .32 .29 .23 .22

Contribution Shares Adoption Varieties (1998) (1965-2000) IA IX IA .32 .49 .37 .29 .20 .32 .32 .28 .19 .16 .16 .11

Millets Barley Lentils Beans Cassava Potatoes All Crops

.228 .073 0.0 .022 0.0 .708 .321

.428 .199 0.0 .027 .006 .711 .676

.537 .424 .193 .367 .087 .749 .832

.854 1.01 .750 .331 .636 .846 .823

.565 .490 .283 .208 .222 .739 .718

.27 .50 .70 .80 .74 .08 .35

.38 .30 .20 .20 .19 .09 .30

.15 .49 .54 .72 .53 .17 .36

.50 .20 .65 .05 .16 .08 .22

IX: Varietal cross made in IARC program IA: Varietal Cross in NARS program with IARC ancestor Table 7: CGI Contributions to Yield Growth by Regions

Region Latin America Asia (including China) Middle East- North Africa Sub-Saharan Africa All Regions

1960s .312 .452 .141 .017 .321

1970s .600 .932 .270 .142 .676

1980s .781 1.030 .681 .358 .832

Contribution Shares Adoption Varieties (1998) (1965-2000) 1990s 1960-98 IX IA IX IA .751 .658 .28 .30 .39 .18 .890 .884 .30 .37 .18 .39 1.228 .688 .51 .31 .62 .28 .497 .280 .44 .27 .45 .28 .823 .718 .35 .34 .36 .19

IX: Varietal cross made in IARC program IA: Varietal cross in NARS program with IARC assistance

Tables 8-13

Table 8: CGI and IARC Contributions to Yield Growth Annual Yield Growth Contribution for CGI Crop/Region 1960s 1970s 1980s 1990s 196098 Wheat Latin 0.394 1.320 1.563 0.768 1.059 America Asia 0.678 1.118 1.168 0.846 1.006 M.E.N.A. 0.189 0.531 0.861 1.388 0.829 S.S. Africa 0.183 0.838 1.093 0.855 0.531 All Regions 0.514 0.981 1.125 0.975 0.960 Rice

Adoption shares IX IA

IARC Growth Contribution ¼ ½ Substitution Substitution

0.54

0.30

0.620

0.518

0.23 0.50 0.37 0.32

0.35 0.32 0.26 0.32

0.465 0.477 0.285 0.464

0.427 0.406 0.254 0.412

Latin America Asia S.S. Africa All Regions Maize Latin America Asia S.S. Africa All Regions Sorghum Asia S.S. Africa All Regions Millets Asia S.S. Africa All Regions Barley M.E.N.A. Lentils M.E.N.A. Beans Latin America S.S. Africa All Regions Cassava Latin America Asia S.S. Africa All Regions Potatoes Latin America Asia S.S. Africa All Regions All Crops

0.077 0.787 1.315 0.876 0.818

0.30

0.30

0.374

0.331

0.375 0.998 0.966 0.713 0.868 0.000 0.085 0.572 1.219 0.545 0.342 0.940 0.959 0.747 0.794

0.30 0.20 0.29

0.30 0.20 0.29

0.370 0.174 0.352

0.327 0.153 0.312

0.402 0.474 0.547 0.862 0.625

0.10

0.27

0.203

0.192

0.407 0.694 1.016 1.377 0.959 0.041 0.131 0.481 0.197 0.224 0.311 0.481 0.733 0.906 0.665

0.30 0.20 0.23

0.32 0.50 0.32

0.454 0.129 0.291

0.405 0.123 0.265

0.148 0.622 1.403 0.976 0.847 0.000 0.257 0.316 0.514 0.304 0.055 0.091 0.716 0.683 0.504

0.05 0.50 0.22

0.20 0.10 0.16

0.195 0.133 0.151

0.186 0.122 0.127

0.515 0.963 0.954 1.392 1.043 0.000 0.000 0.205 0.425 0.184 0.228 0.428 0.537 0.854 0.565

0.27 0.26 0.27

0.41 0.26 0.58

0.552 0.075 0.286

0.510 0.066 0.262

0.073 0.199 0.424 1.010 0.490

0.50

0.30

0.278

0.235

0.000 .000 0.193 0.750 0.283

0.70

0.20

0.144

0.112

0.034 0.041 0.463 0.281 0.222

0.70

0.10

0.127

0.092

0.000 0.000 0.188 0.426 0.180 0.022 0.027 0.367 0.331 0.208

0.80 0.75

0.20 0.15

0.122 0.131

0.094 0.098

0.000 0.043 0.055 0.238 0.100

0.05

0.01

0.005

0.003

0.000 0.000 0.091 0.485 0.174 0.000 0.000 0.093 0.771 0.249 0.000 0.000 0.087 0.636 0.222

0.80 0.80 0.74

0.20 0.20 0.19

0.118 0.169 0.142

0.091 0.129 0.109

0.672 0.885 0.631 0.694 0.752

0.07

0.09

0.104

0.092

0.811 0.672 0.759 0.846 0.825 0.000 0.716 0.864 1.099 0.739 0.708 0.711 0.749 0.846 0.807

0.05 0.55 0.08

0.07 0.17 0.09

0.086 0.379 0.117

0.077 0.294 0.102

Latin America Asia M.E.N.A. S.S. Africa All Regions

0.312 0.600 0.781 0.751 0.658

0.28

0.27

0.279 (.42)

0.245 (.37)

0.452 0.141 0.017 0.321

0.26 0.50 0.38 0.30

0.31 0.31 0.24 0.30

0.393 (.44) 0.391 (.57) 0.128 (.46) 0.328 (.46)

0.353 (.40) 0.332 (.48) 0.108 (.33) 0.291 (.41)

0.932 0.270 0.142 0.676

1.030 0.681 0.358 0.832

0.890 1.228 0.497 0.823

0.884 0.688 0.280 0.718

Table 9: Price Production Area and Trade Effects. Alternative Counterfactual Scenarios Wheat Rice Maize

Price Effects (Positive) 1965 CGI NO IARC CGI Production Effects (Negative) 1965 CGI NO IARC CGI Area Effects (Positive) 1965 CGI No IARC CGI Trade Effects (Positive) 1965 CGI NO IARC CGI

Other Grains

Potatoes

Other Root Crops

All Food Crops

80- 23-45 124 19-22 30-35 13-15

21-50

13-31

28-52

35-66

14-16

2-3

15.32

18-21

9-14 11-14 9-12 5-6 4-5 4-5

5-9 3-4

12-18 3-4

2-3 1-2

8-12 4-5

29-61

3.2-2.1 7.59.4 2.1-2.1 2.93.3

1.11.9 .5-.6

.4-2.2

0.0-0.0

2.2-3.2

2.8-4.6

.5-.6

0.0-0.0

1.4-3.2

1.5-2.7

31-19 7-6

45-46 16-18

25-19 1-2

190-192 16-33

21-65 11-12

27-30 6-9

0-2 0-2

Table 10: Yield, Area and Production Effects - Developed and Developing Countries: Counterfactual Scenarios Wheat

Rice

Maize

Other Grains

Potatoes

Other Root Crops

All Crop

4.4-7.5 2.7-5.1

0.0-6.7 0.0-1.0

1.4-3.1 .5-2.5

0.0-1.8 0.0-1.8

1.5-2.0 0.5-1.0

nc nc

2.32-4.7 1.35-2.4

A. YIELD EFFECTS Developed Countries (Positive) 1965 CGI NO IARC CGI Developing Countries

(Negative) 1965 CGI

26.231.3 11.612.9

18.322.9 7.8-8.7

21.525.9 8.7-9.5

15.0-17.1

Developed Countries (Positive) 1965 CGI

4.5-7.5

NO IARC CGI

2.7-3.1

11.815.8 4.8-5.5

Developing Countries (Positive) 1965 CGI 1.7-3.6 NO IARC CGI 1.4-1.5

NO IARC CGI

5.6-5.8

23.528.3 3.4-3.9

4.3-4.4 2.5-4.0

19.4523.50 8.07-8.9

2.2-3.4

.4-1.8

0.0-.1

nc

2.82-4.9

.9-1.1

.3-.4

0.0-.1

nc

1.59-1.8

7.3-9.3 6.1-6.5

.6-1.2 .3-.4

.5-.6 .4-.5

0.0-.1 0.0-.1

´2.2-3.3 1.4-3.3

2.82-4.9 1.59-1.8

15.719.3 3.1-5.5

2.0-5.3

1.8-2.3

1.2-4.9

nc

4.43-6.9

1.6-1.3

1.3-1.4

1.2-1.6

nc

.96-1.68

12.115.2 5.1-5.7

21.024.9 8.5-9.3

14.0-14.6

24.529.1 4.9-5.4

2.0-2.5

15.8518.63 6.48-7.3

B. AREA EFFECTS

C. PRODUCTION EFFECTS Developed Countries (Positive) 1965 CGI 8.3-11.0 NO IARC CGI Developing Countries (Negative) 1965 CGI NO IARC CGI

1.6-2.1

25.028.6 10.411.6

4.9-5.2

1.1-2.1

Table 11. Area Effects (Positive Except where Noted) by Region and Crop: Counterfactual Scenarios Wheat

Latin America 1965 CGI NO IARC CGI Sub-Saharan Africa 1965 CGI NO IARC CGI

Rice

Maize

Other Grains

Potatoes

Other Root Crops

All Food Crops

5.1-9.6 3.1-3.6

9.1-11.7 3.5-4.0

2.1-3.6 1.0-1.2

.4-.6 .4-.6

-1.2-.0 -.1-.0

.3-.5 .3-.3

3.10-5.12 1.54-3.08

2.5-4.4 1.7-2.0

6.7-7.4 2.3-2.6

.8-1.5 .4-.5

2.1-4.8 .2-.3

-.2-2.0 0.1-0

2.5-3.6 1.6-3.6

2.19-4.00 .63-1.01

Middle East-North Africa 1965 CGI 4.0-7.1 NO IARC CGI 2.5-2.9

12.5-14.3 4.3-4.8

1.2-1.3 .2-.3

.9-3.1 .6-.7

-.0-.0 -.1-.0

Asia (including China) 1965 CGI 1.4-1.8 NO IARC CGI .6-.7

7.2-9.2 2.8-3.2

-.5-.0 -0.0-0.0

-.4-.7 .5-.6

-.1-.0 -.1-.0

3.20-5.78 1.84-2.14 2.6-3.5 1.0-1.5

3.52-4.74 1.47-1.71

Table 12: Production Effects by Region and Crop. Alternative Counterfactual Scenarios Wheat

Rice

Latin America(negative) 1965 CGI 25.6-29.6 9.6-12.0 NO IARC CGI 12.3-14.6 3.8-4.3 Sub-Saharan Africa (negative) 1965 CGI 9.3-10.1 1.6-2.0 NO IARC CGI 3.6-3.8 1.6-2.0 Middle East-North Africa (negative) 1965 CGI 27.1-31.5 3.0-3.5 NO IARC CGI 10.9-11.6 3.0-3.5

Maize

Other Grains

Potatoes

Other Root Crops

15.8-18. 26.8-31.1 23.8-28.2 1.5-4.0 3 4.0-4.1 9.8-10.3 4.6-5.0 1.5-4.0 2.0-3.4 1.6-1.9

2.0-5.0 22.5-26.3 1.8-2.5 1.1-1.9 10.8-14.0 .9-1.5

3.3-3.9 2.4-2.5

3.5-5.1 22.5-26.9 1.6-1.7 10.6-15.0

Asia (including China) (negative) 1965 CGI 26.7-30.8 12.9-16.3 27.5-33.3 27.5-32.1 24.1-29.8 NO IARC CGI 10.7-11.4 5.3-5.9 12.0-13.3 10.0-10.6 3.9-4.1 Developed Countries (Positive) 1965 CGI 8.3-11.0 15.7-19.2 2.0-5.3 1.8-2.3 1.2-4.9 NO IARC CGI .6-2.1 3.2-5.5 1.0-1.3 1.3-1.4 1.2-1.6

All Food Crops

15.41-18.32 5.41-5.62 2.04-3.32 1.15-1.73 17.56-20.66 7.36-7.87

.6-1.6 .6-1.6

Developing Countries (Negative) 1965 CGI 25.0-28.6 12.1-15.2 21.0-24.9 14.0-14.6 24.5-29.1 2.0-2.5 NO IARC CGI 10.4-11.6 5.1-5.7 8.5-9.3 4.9-5.2 4.9-5.4 1.1-2.1

20.12-22. 8.30-9.1275 4.43-6.93 .96-1.68 15.85-18.63 6.48-7.30

Table 13: Price Projections 2020/1997: IFPRI IMPACT Model Commodity

Wheat Rice Maize Other Cereals Beef

Base Case 2020/1997 Ratio .93 .88 .99 .89 .96

Trade Liberalization

Delayed Industrialization

8.1 14.0 8.8 8.1 17.5

5.9 7.5 5.2 8.0 7.4

Delayed (LDC) Biotech Access 10.6 20.0 16.8 9.3 1.1

Global Warming 1.2 1.0 1.0 0.1 1.1

Pork Sheep-Goats Poultry

.97 .97 .96

10.9 18.9 11.7

15.5 3.1 2.2

1.1 1.0 1.1

WTO NEGOTIATIONS AND COMMODITY MARKET DEVELOPMENTS Presented by Tim Josling Professor and Senior Fellow Institute for International Studies Stanford University

1.0 1.0 0.1

REFORMS IN GLOBAL COMMODITY MARKETS: A PERSPECTIVE Presented by John Wainio USDA/ERS

A DECLINE IN COMMODITY PRICES: CHALLENGES AND POSSIBLE SOLUTIONS Presented by Don Mitchell Lead Economist, Development Prospects Group and Panos Varangis Senior Economist, Rural Development World Bank

COMMODITY PRICE DEVELOPMENTS SINCE THE 1970S Presented by: Ali Arslan Gürkan Chief, Basic Foodstuffs Service Commodities and Trade Division Food and Agriculture Organization Introduction Since the pioneering work of Prebisch and Singer (1950), which drew international attention to the declining trends in primary commodity prices, economists and policy makers have been intrigued by the causes and consequences of commodity price developments. A rich body of literature already exist on this issue and international actions to cope with such developments have been in place over the last three decades[13]. The available empirical evidence has produced mixed results regarding what the actual tendencies are: with some showing positive, negative or no decline in commodity price trends. In general, most agree that commodity prices are non-stationary, i.e. they do not revert back to their old levels after receiving a shock, but views differ regarding the nature of nonstationarity, i.e. whether the trend is stochastic or deterministic or whether there are structural breaks - Spraos (1980), Thirwall and Bergevin (1985), Grilli and Yang (1988), Diakasavvas and Scandizzo (1991), Cuddington and Urzúa (1989), Reinhart and Wickham (1994). Nonstationarity in commodity prices has found support

recently in empirical evidence provided by Cashing, et. al. (1999), indicating that shocks to primary commodity prices are long lasting with wide variability in persistence levels. Sarris (1998) also found that the underlying trend in cereal prices were deterministic with some tendency of increased volatility during the 1995/96 period. Maizel, Becon and Mavrotas (1997) concluded that commodity prices exhibit a long run decline rather than volatility. Variability in commodity prices arises as a direct consequence of shocks in underlying demand and supply conditions, and is affected significantly by policy measures implemented at the national level. For storable commodities, Deaton and Laroque (1992) characterize the nature of price shocks depending on prevailing market conditions. In tight markets, a sudden increase to consumption induces a sharp rise in prices which are temporal in nature, while in slack markets, the impact of a shock induces the release of stocks and other policy measures that dampen price increases but render them persistent. They conclude that this behaviour of prices results in price cycles often observed with flat tails and sharp spikes. During the 1996 FAO meeting of experts on agricultural price instability[14], it was generally agreed that compared to the past, world commodity markets in the future were likely to be characterized by lower levels of overall stocks, although, at the same time, being less prone to instability because of faster and broad-based adjustments to production/demand shocks. However, the path to the new market environment was seen as uncertain and it was generally felt that price instability would be greater during the transitional period. The primary aim of this document is to summarise the behaviour of agricultural commodity prices over the last three decades to form the back-drop of discussions in the 2002 Commodity Consultation. The first part of this note presents a summary view of monthly developments of representative international prices of 18 important agricultural commodities over the period covering 1970-2000. This is done by splitting the period into three decades in an arbitrary manner and presenting the results of statistical analyses conducted to test for differences in levels and variances of the nominal and real prices. The second part of the note reports on the results of more sophisticated analysis of trends and volatility of the same set of prices. Have there been changes in the levels and variability of international commodity prices over the past three decades? Appendix I presents a graphical display of both nominal and real prices for all commodities considered in this study. In general, over the last three decades, agricultural commodity prices could be characterized as exhibiting a large shock during the early 70s followed by a series of smaller shocks and relatively flat surfaces. One simple way of looking at different aspects of price developments is to divide the various series into a number of periods and compare the changes that have occurred in order to discover consistencies across time and across commodities. In this case the series have been divided into three equal samples (arbitrarily), each representing a different decade (i.e. 1970-80, 1980-90 and 1990-2000). Two aspects of the series, both

in nominal and real terms[15], levels and variances, were then subjected to statistical tests[16]. Table 1 presents the results of the analyses and indicates that the mean real prices of all commodities over the three decades, with the exception of banana during the earlier part of the period under study, appear to have been on the decline. Table 1: Changes in mean levels (real) over time

Commodities Banana Cocoa Coffee Sugar Tea Jute Sisal Rubber Cotton

Change from decade 1 (1970-80) Decade 2 (1980-90) Increase Decrease X X X X X X X X X

Maize Wheat Soybeans Soymeal Sunflower meal Rapeseeds Rape oil Rice Palm oil

Change from decade 2 (1980-90) Decade 3 (1990-00) Increase

X X X X X X X X X

All results are significant at the 10% level Somewhat different results are obtained when nominal prices are analyzed in the same manner. Table 2 indicates that the mean prices of 12 commodities have increased in a statistically significant manner over the first two decades, with 3 experiencing a significant decline and a further three no change. The comparisons over the last two decades, on the other hand, indicate a complete reversal: with the mean prices of 12 commodities decreasing significantly, while 3 increasing and a further 3 exhibiting no change. Table 2: Changes in mean levels (nominal) over time

Decrease X X X X X X X X X X X X X X X X X X

Commodities Banana Cocoa Coffee Sugar Tea Jute Sisal Rubber Cotton Maize Wheat Soybeans Soymeal Sunflower meal Rapeseeds Rape oil Rice Palm Oil

Change from decade 1 (1970-80) Decade 2 (1980-90) Increase Decrease X X X X X X X X X

Change from decade 2 (1980-90) Decade 3 (1990-00) Increase X

X X X X X(a) X X X

X X X(B) X X X X X

(e)

X(e)

Decrease

X(e)

(a) - at the 10% level of significance, the mean in decade 2 = mean in decade 3. (b) - at the 10% level of significance, the mean in decade 1 = mean in decade 2. (e) - at the 10% level of significance, all means are equal. All the other results are significant at the 10%. It is obvious that during the 1990s most of the commodities analyzed have experienced depressed global markets, regardless of the manner in which the prices are measured. More analysis is, however, needed to determine the underlying causes for such a state of affairs. Discussion to be held during the Consultation will hopefully provide guidance for such an undertaking. Tables 3 and 4 present a summary of the results of the analyses related to changes in variability in real and nominal prices over time as revealed by the estimates of interquartile range[17] for the three decades. From the latter table, it is apparent that 9 out of 18 commodities (i.e. in nominal terms) showed a lower level of variability in decade 3, relative to decade 2. However, in real terms, 16 out of 18 commodities showed a decrease in variability for the same period. Where there were ambiguities in interpreting changes in variability, a comparative test was conducted using a two sample test for variances at the 5% level. For both real and nominal commodity prices, the following

X X X X X X X X X

commodities exhibited a decline in variability during decade 3 relative to decade 2: sunflower meal, maize, palm oil, soybeans, soybean meal, rice, cocoa, sugar, and rubber. Is it possible to look at price variability in other ways? Another way of looking at the variability of prices is by removing any consistent component of the price series and analyzing the resulting variability of the residuals. The analysis was carried out by estimating a stochastic trend unobserved component model (for each of the 18 commodities in both real and nominal terms) using the State Space Kalman filter procedure (see Appendix VI for details) and then testing to see whether the resulting distribution of residuals is significantly different from a normal one. In this context, the departure from normality is viewed as being a measurement of volatility caused by factors that could not be represented by the consistent components of the processes generating the time series. The tests tend to support the analyses conducted using more traditional measures of variability/volatility, in that, in real terms, 11 out of 18 commodities (cocoa, maize, palm oil, rapeseed, rubber, soybean, soy meal, sugar, sunflower, rubber, and tea) show less volatility over the last two decades of the period under study: Though not to the same extent[18]. Table 3: Changes in real price variability over time

Commodities Banana Cocoa Coffee Sugar Tea Jute Sisal Rubber Cotton Maize Wheat Soybeans Soymeal Sunflower meal Rapeseeds Rape oil Rice Palm Oil

Change from decade 1 (1970-80) Decade 2 (1980-90) Increase Decrease X X X X X X X X X X X X X X X X X X X

Change from decade 2 (1980-90) Decade 3 (1990-00) Increase Decrease X X X X X X X X X X X X X X X X X X

Other tests were also conducted to test for the stability of a different kind: the stability of the reduced form representation of a number of commodities where time series data are available for all the main variables relevant for the markets, i.e. consumption, production and ending stocks. This was done by testing for the stability of the coefficients of a reduced form price equation. The coefficients were estimated recursively and their behaviour examined. For the subset of commodities, it was observed that the structural parameters were stable throughout the last decade of 1990s, indicating no significant behavioural changes affecting the response coefficients. Is there empirical evidence to suggest structural breaks in the price series over the past three decades? Very little attention has been given to the issue of structural changes in agricultural commodity prices. As the agricultural sector is intertwined with other sectors and constitutes a major contribution to economic activity in many importing and exporting countries, economy-wide changes in the levels of economic activity would have a direct impact on the agricultural sector. Besides, changes in the economic structure, agriculture is perhaps more prone to shocks caused by weather and other natural disasters, which can have sustained and lasting effects. In addition, technological changes can alter productivity levels and can shift the way resources are allocated, thus, leaving permanent effects on the agricultural sector. In addition, major policy reforms both at the national and international levels can induce structural changes in prices. Table 4: Changes in nominal price variability over time

Commodities Banana Cocoa Coffee Sugar Tea Jute Sisal

Change from decade 1 (1970-80) Decade 2 (1980-90) Increase Decrease X X X X X X X

Rubber Cotton Maize Wheat Soybeans Soymeal Sunflower meal Rapeseeds

Change from decade 2 (1980-90) Decade 3 (1990-00) Increase X

Decrease X

X X X X X

X X X X X X X

X

X

X

X X

X X X X X X

X

Rape oil Rice Palm Oil

X X X

X X X

Using the full series available, and assuming that break points are not known a priori, the outlier procedure was used (see Yin and Maddala, 1997 for full details) to identify the year or years in which breaks could have occurred. The rationale of the procedure is that outliers are aberrant observations that are away from the rest of the data, caused by errors in measurements or unusual events such as changes in economic policies, wars, disasters, and so on (Perron, 1989). Structural change outliers are classified as (i) additive outliers (one-time changes) - these occur when there is a spurious change in the data, but the data returns back to its normal pattern in subsequent periods; (ii) level shifts - occur when the effect of a large innovation persists over time. As can be seen from Table 5, for the important basic food commodities 1988 appears to be a year in which persistent level changes have occurred, though not all seem to have had a significant break during the food crisis of early 1970s[19]. For tea and cocoa, early 1980's appears to be a period when level breaks occurred. Table 5: Probable structural changes for selected (real) commodity prices Commodity Tea Jute Cocoa Sugar Banana

Dates Level changes 1983 1974 1981

Additive changes

1974

Maize Wheat

1988 1973, 1988

Rice Soybean Rapeseed

1996 1973, 1988 1988

Break dates were significant at the 1% level. Additive changes refer to one time break and level changes refer to changes that persist over time. References Cashing, P. and Pattilo, C (2000). "Terms of Trade Shocks in Africa: Are they ShortLived or Long-Lived?", IMF Working Paper, WP/00/72.

1981

1988

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For a description of the various international actions, See, "Dealing with Commodity Price Volatility in Developing Countries: A Proposal for Market Based Approach". Discussion Paper for the Round Table on Commodity Risk Management in Developing Countries, International Task Force on Commodity Risk Management in Developing Countries, World Bank, Washington, D. C., September 24th 1999. [14] Report of a Meeting of Experts on Agricultural Price Instability, Commodities and Trade Division, FAO, Rome 10-11 June 1996. [15] Real prices have been obtained by deflating nominal representative prices by the World Bank's index of manufacturing unit values. [16] Non-parametric Wilcoxon signed-rank test, which does not assume normality and equality of variances, was used to test for differences in means across decades. [17] The complete results and detailed description of the statistics used are given in Appendix IV. [18] This may be as expected since tests conducted for the same periods indicate that none of the series are stationary, i.e. the effects of underlying shocks persist in influencing prices over many months (see Appendix VII for the results). [19] This may indeed also be due to the fact no data prior to 1970 were used in the analyses. [13]