EPTD DISCUSSION PAPER NO. 57 COULD FUTURES MARKETS HELP GROWERS BETTER MANAGE COFFEE PRICE RISKS IN COSTA RICA? Peter Hazell

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EPTD DISCUSSION PAPER NO. 57 COULD FUTURES MARKETS HELP GROWERS BETTER MANAGE COFFEE PRICE RISKS IN COSTA RICA? Peter Hazell Environment Production and Technology Division International Food Policy Research
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EPTD DISCUSSION PAPER NO. 57 COULD FUTURES MARKETS HELP GROWERS BETTER MANAGE COFFEE PRICE RISKS IN COSTA RICA? Peter Hazell Environment Production and Technology Division International Food Policy Research Institute 2033 K Street, NW Washington, D.C U.S.A. January 2000 EPTD Discussion Papers contain preliminary material and research results, and are circulated prior to a full peer review in order to stimulate discussion and critical comment. It is expected that most Discussion Papers will eventually be published in some other form, and that their content may also be revised. ABSTRACT Costa Rican coffee farmers are almost fully exposed to world price variability. Yet, despite small farm sizes, specialization in coffee, and a marketing system that prolongs uncertainty and aggravates cash flow problems, this study finds that most farmers still manage their price risks surprisingly well. Farmers are able to forecast prices with comparable accuracy to the New York futures market. They have a favorable seasonal cash flow, ready access to credit, and are willing and able to bear risk. Within this context, the potential gains from using the New York futures market to provide forward price contracts at harvest are found to be modest. CONTENTS 1. Introduction...1 Background Price Forecasting Errors...6 Risk Aversion...11 Cash Flow Potential Role Of Futures Trading...17 The Farm Model...18 The Marketing Options...22 Model Results Conclusions...30 References...32 i COULD FUTURES MARKETS HELP GROWERS BETTER MANAGE COFFEE PRICE RISKS IN COSTA RICA? Peter Hazell 1. INTRODUCTION Many developing countries depend heavily on agricultural exports yet face high levels of instability in their export prices. Price variability can be detrimental to the stability of export earnings, and hence to the ability of developing countries to grow and to service debt. Moreover, if export price risks are transmitted to producers, they may act as an impediment to the expansion of agricultural exports. To buffer producers from the full force of world price movements, many governments have intervened with buffer stock or variable export levy schemes. The experience with these schemes has not been particularly encouraging, not least because they can easily be politicized by producer or urban consumer interests to distort the average prices that farmers receive (Knudsen and Nash, 1990). Moreover, the implementation of these schemes requires a public agency or marketing board with sufficient power to prevent or neutralize cross-border trade at unauthorized prices. This is Peter Hazell is Director of the Environment and Production Technology Division at the International Food Policy Research Institute. Much of the research reported in this paper was undertaken while the author was with the Agriculture and Rural Development Department of the World Bank. This study would not have been possible without the contributions of Mario Vedova, who directed the farm survey; of Rigoberto Stewart and Mauricio Jaramillo, who helped design the survey and contributed to the analysis; and of Apparao Katikeni, who undertook most of the computer work. 2 unlikely to be consistent with the full privatization of market functions, and may be used to rationalize the existence of monopolistic marketing boards that is otherwise undesirable. A better approach is to seek ways of strengthening the ability of the private sector to offer more risk management options to farmers. A promising prospect is the development of appropriate institutional mechanisms whereby a broader range of private agents in developing countries can gain access to international futures and options markets for agricultural commodities. At present developing countries are minor participants in these markets, even though they are sometimes the major suppliers of the underlying commodities (for example, coffee, cocoa, and tea). Several studies have shown how international futures and options markets can be used by government agencies in developing countries to manage the effects of world price risks on a country's trade balance, on government revenue, or on the earnings of public marketing agencies (for example, Gemmill (1985), Glaessens and Varangis (1991), Overdahl (1986), and Meyers (1991)). But very little work has been undertaken on how international futures and options markets might be harnessed through appropriate trade and marketing institutions for the benefit of developing country farmers. This paper analyzes the feasibility and potential economic benefits of using the New York futures market to hedge forward price contracts for smallholder coffee growers in Costa Rica. 3 BACKGROUND Costa Rica is typical of many small developing countries in that the economy depends heavily on a handful of agricultural export commodities. The most important export is coffee, which, despite a declining trend in relative economic importance, accounts for some 30 percent of total export earnings, 25 percent of agricultural GDP, and 5 percent of national GDP. Yet the world coffee price is notoriously unstable; during , the coefficient of variation (CV) around trend of Costa Rica's export price was 38 percent. There is no program to stabilize coffee prices in Costa Rica, hence nearly all the variation in the export price is transmitted to producers; the CV of the average producer price exceeded 30 percent in recent decades and was almost perfectly correlated with the world price (Hazell, Jaramillo, and Williamson, 1990). As Table 1 shows, most of the coffee farmers are small-scale farmers. The average grower has 6.3 ha of coffee, and 90 percent of the growers have less than 10 ha of coffee. Most producers are also specialized, obtaining at lest two thirds of their total household income from coffee. These characteristics suggest that many growers may be vulnerable to price risks, possibly facing severe financial difficulties when coffee prices are low. The present marketing arrangements aggravate the consequences of price risks for Costa Rica's coffee growers. Coffee is harvested and delivered to the mills between November and January each year. At the time of delivery, the farmer receives an initial advance payment from the mill (usually between percent of the expected market 4 Table 1: Characteristics of Costa Rican coffee farms by coffee-farm size group, 1989 Coffee (ha) 25.00 All % Farms in sample Coffee as % of total household income % Farms selling mainly to cooperatives Average coffee area (ha) Source: Survey of 295 randomly selected coffee farms (from ICAFE's national registar) undertaken by PRODESARROLLO and the World Bank in June-August, 1989 5 price), and this is followed by additional advances of percent at two to three month intervals. But the final price is not known or fully paid until each mill has calculated a liquidation price for the entire season s sales. This liquidation price is traditionally announced in November, nearly twelve months after the coffee has been harvested, and some 20 months after the beginning of the growing season which begins in March. The combination of price uncertainty and delayed payments could adversely affect producers' welfare in three ways. First, since decisions about input use, pruning and cash flow management must be made each growing season, price forecasting errors may lead to inappropriate decisions. Second, price risks lead to income variability which, if farmers are risk averse and do not have access to adequate off-farm risk management aids, may lead to under investment in coffee bushes and a reduced willingness to replant with modern varieties or to use recommended levels of fertilizers and other inputs. This in turn would lead to reduced coffee output and income. Third, delayed payments for harvested coffee may increase the need to borrow credit to purchase inputs for the next growing season. This would lead to higher interest payments and hence lower farm incomes on average. In the next three sections, farm survey data are used to examine the empirical significance of each of these potential welfare losses. Subsequently, we suggest how futures trading might be used to reform the current marketing system, and then evaluate the potential gains to farmers with the aid of a mathematical programming model. The survey data used here were collected jointly by the World Bank and the Center for the Promotion of Sciences and Socioeconomic Development (PRODESARROLLO) in San Jose. The survey covered a nationally representative 6 sample of about 300 coffee farms, drawn on a regionally stratified basis from a national registrar of coffee farms maintained by the Costa Rican Coffee Institute (ICAFE), a governmental regulatory agency. The main fieldwork was carried out in June/July 1989, but a follow-up survey of farmers' price expectations was conducted in October PRICE FORECASTING ERRORS Investment decisions about the number and varieties of coffee bushes to grow depend on long-term expectations about the profitability of coffee farming. Since coffee bushes take at least four years to mature, these longer-term price risks cannot be hedged in the futures market, and are not analyzed in this paper. Of greater relevance are short-term decisions about the use of fertilizer, weeding, the timing of pruning and stumping decisions, and the management of seasonal cash flow. These decisions affect coffee production and income within single growing seasons, and may be related to farmer's short-term price expectations. Fertilizer use and weeding can have direct effects on yields within a season. Pruning and stumping (an extreme form of pruning in which the bush is virtually cut off at the ground) must be undertaken regularly to maintain the vigor and longer-term productivity of the bushes, but there is some flexibility in advancing or delaying these decisions to influence short-term yields. The management of seasonal cash flow, which involves decisions about when and how much to borrow or to spend on farm costs and household consumption, is complicated by uncertainty in coffee revenue, yet helps determine borrowing costs, income and family welfare. 7 If a farmer can accurately predict harvest prices each year, then he/she could optimally adjust growing and financial practices within seasons. More accurate price forecasts should therefore lead to higher profits and welfare on average, as well as help reduce the size of income losses in low price years. As part of the June/July 1989 survey, the sampled farms were questioned about their expectations for the 11/89 and 11/90 liquidation prices. In addition to forecasting 'most likely' (or modal) prices, they were also asked to state the lowest and highest prices that could conceivably occur. The same questions were asked again three months later when a sub-sample of 80 farmers was resurveyed. Subsequently, the actual liquidation prices paid for 11/89 and 11/90 were collected from the mills, and this information was used to calculate forecast errors. The timing of the initial survey coincided with the breakdown of the International Coffee Agreement (ICA), and farmers faced considerable uncertainty about future price movements. Since they were being asked to predict a domestic price in their national currency (colones), their forecasts necessarily embodied perceptions about changes in the currency exchange rate as well as movements in world coffee prices. As a simple measure of forecast error, the absolute value of the price error is expressed as a percent of the actual price. That is, e t = p t p * t /p t x 100%, where p t is the actual price and p * t is the forecasted price. Pertinent results are to be found in Table 2 for both the June/July and October 1989 surveys. The June/July forecasts of the 11/89 price had an average error of 8 percent, with 71 percent of the farmers having errors of less than 10 percent, and nearly half having errors of less than 5 percent of their actual prices. By way of comparison, the average 8 Table 2: Farmer's Price Forecasting Performance, 11/89 and 11/90 Liquidation Prices June/July 1989 Survey October 1989 Survey 11/89 Price 11/90 Price 11/89 Price 11/90 Price Forecast error (%) a 8 (8) b 22 (26) b 4 13 Minimum error (%) 0 (0) 0 (1) 0 0 Maximum error (%) 43 (23) 69 (69) Percent farmers with forecast errors less: 1% (5) % 46 (45) 20 (20) % 71 (70) 30 (25) Forecasted range 16 (14) 25 (23) 7 23 (as % of modal price) Forecasted skewness c 1 (-1) -1 (-9) 0-4 (as % of modal price) Sample size 265 (41) 176 (41) a Calculated as *p t p * t */p t where p t is actual price and p * t is the forecasted most likely price. b Figures in parentheses are the June/July means for the subsample of farms re-interviewed in October. c Calculated as [(max mode) + (min -mode)] / mode x 100%, (see Heady and Kaldor, 1954). The index is zero when the distribution is symmetric and negative (positive) when the distribution is skewed to the left (right). 9 price for 12/89 contracts in the New York futures market at the time of the June/July survey turned out to overestimate the realized 12/89 spot price by 40 percent. This forecast error was almost as large as the maximum forecast error observed in the June/July farm survey. The average error for the 11/89 forecast was only half as large in the October survey (4 percent) and nearly all the farmers came within 10 percent of their actual prices. This increased accuracy is to be expected given that the forecast period was reduced from 4-5 months to a single month. The New York futures market also improved its predictive performance during this period. The average price of 12/89 contracts quoted during October came to within 0.1 percent of the average 12/89 spot price. Not surprisingly, farmers did less well in forecasting the 11/90 liquidation price. The average error for the June/July survey (a month forecast) was 22 percent, and only 30 percent of the farmers came within 10 percent of their realized prices. Because the 11/90 liquidation price would be an average of the seasonal prices prevailing between the 1989 harvest and the November liquidation, the appropriate New York comparator is the average error in the futures market between the prices quoted in June/July 1989 for 12/89, 3/90, 5/90, 7/90, and 9/90 contracts, and the spot prices that were realized in each of those months. On this basis, the comparable error in the NY futures market for the 11/90 liquidation was 15.4 percent. Farmers greatly improved their information about the 11/90 price between the June/July and October surveys, even though the forecast period was still about one year. The average error fell from 22 to 13 percent, with 37 percent of the farmers having errors 10 of less than 5 percent. There was a little change in the forecast errors of the New York futures market over the same period; the error between the October 1989 quotes for 12/89, 3/90, 5/90, 7/90 and 9/90 contracts and the realized spot prices was 12.5 percent, down from a 15.4 percent error in the July 1989 quotes. Farmers were reasonably confident of their price forecasts. During the June/July survey, they gave price ranges of 16 and 25 percent, respectively, of their most likely prices for the 11/89 and 11/90 liquidations. The range for the 11/89 price was smaller in the October survey, but it remained unchanged for the 11/90 price, indicating continuing levels of uncertainty. The 11/90 forecasts were also more negatively skewed, indicating higher levels of pessimism than for the 11/89 price. These results show that Costa Rican coffee farmers are generally well informed about the international coffee market, and that their price forecasts were about as accurate as the New York futures market. However, the size of the errors, especially for month price forecasts --almost the length of time between allocating inputs at the beginning of the growing season and the realization of the final liquidation price for the resultant harvest --leaves considerable scope for error in making short-term resource allocation and financial management decisions. Given the sample variation in forecasting errors, it is possible to identify factors that distinguish the more successful farmers. Regressions of the forecast errors against an array of household characteristic variables showed few to be statistically significant. Separate regressions were estimated for the 11/89 and 11/90 forecast errors, as well as for a pooled regression that controlled for unobserved household effects through a random 11 components specification. Three variables proved to be consistently significant. Farmers belonging to a milling cooperative made smaller forecast errors on average than nonmembers. Similarly, farmers who had completed secondary education were also more accurate on average than those with a lesser education. However, the size of the initial advances paid by the mills at harvest apparently misled farmers. The larger the advance, the greater the tendency to over-estimate the eventual liquidation price. RISK AVERSION The volatility in coffee prices inevitably translates into significant risk in coffee farm incomes. If farmers are risk averse and do not have access to adequate on- or off-farm risk management aids, then they may under invest in coffee bushes and be reluctant to replant with modern varieties or to adopt recommended levels of fertilizers and other inputs. As a direct measure of the degree of risk aversion, several 'typical' farmers were selected from the June/July 1981 survey and asked to participate in a carefully designed lottery game. The basis for the game is described in Anderson and Dillon (1992). It involves establishing the certainty equivalents of a series of two-outcome gambles, and then combining this information with an assumed constant absolute risk-aversion utility function to estimate the degree of risk aversion. In all cases, the results showed that the farmers were either risk-neutral or even mildly risk-loving in their behavior. These results are less surprising when placed in the context of the relatively high wealth levels of most coffee farmers. At 1989 prices, a typical 3-hectare coffee farm in the Central Valley was worth about US $60,000. 12 In an attempt to assess farmer's attitudes to coffee price risks, questions were included in the June/July 1989 survey about the value of forward price contracts. As discussed in the previous section, farmers were asked to forecast most likely (modal) prices for the 11/89 and 11/90 liquidations. They were also asked the minimum cash prices they would accept at the time of the survey so as not to have to wait for the unknown liquidation prices. For the 11/89 liquidation, the cash price was stated as payable immediately (since the coffee for that harvest had already been delivered to the mills), whereas the cash price for the 11/90 liquidation was to be agreed at the time of the survey but only paid in full when the coffee was delivered to the mill at harvest later in the year. In establishing these minimum forward prices, the enumerators were encouraged to negotiate with the farmers as if they were representing a miller who was actually prepared to enter into forward contracts. But since the contracts remained hypothetical, we cannot be sure that the farmers reacted with full sincerity. In Table 3, the minimum cash, or forward, prices are expressed as ratios of the most likely liquidation prices forecasted by the farmers. These ratios summarize the price discounts that farmers were willing to accept to lock in cash prices at the time of the June/July survey. They represent the combined benefits from removing further price risk and the time value of money from receiving earlier payments. For the 11/89 price, the forward contract would have advanced all remaining payments by 4-5 months. For the 11/90 price, the forward contract would have paid in full at harvest, thereby accelerating the entire liquidation process
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