extraneous matter are presently recognized by the marketing system (USDA, 1993). The .... procedures given to the market reporters generates the best possible ...
Price Information in Cotton Markets: An Alternative, Objective Approach for Deriving Market Price Differentials for Quality
Darren Hudson
The author is Assistant Professor, Department of Agricultural Economics, Mississippi State University. The comments of O.A. Cleveland, Don Ethridge, and Sukant Misra are greatly appreciated.
Introduction There has been recent discussion about price information and how it affects the cotton market (Ethridge and Hudson), as well as related topics of how the official price information provided by the Agricultural Marketing Service (AMS) affects the Commodity Credit Corporation’s (CCC) loan schedule. These discussions have centered around (1) how information about differentials in price due to quality (premiums and discounts) are derived from cotton prices and (2) how that information is used in determining premiums and discounts for the CCC loan schedule. Before the second issue can be addressed, a full understanding of the first must be achieved. The objectives of this paper are to (1) describe how price differential information is currently derived by AMS, (2) describe an alternative approach to deriving premium and discount information that may be able to improve the accuracy of price information, (3) discuss the advantages and disadvantages of each approach, and (4) discuss the importance of accurate price information for the cotton industry. Background Cotton classification (grading) has changed dramatically in a short period of time. In the early part of this century, cotton classers were concerned mainly with grade and staple. Grade was a conglomeration of all the factors, besides length, that were believed to affect the ability to process cotton efficiently. Technological advancements in textile processing increased the demand for more specific quality information on cotton. New measurement technologies were developed to measure quality attributes as they began to 1
be recognized. This process of increased precision in quality measurement continued, and made a major step forward with the full implementation of the High Volume Instrument (HVI) test lines in 1991. In 1993, the old composite grade code was broken down into a leaf grade to represent leaf content and a color grade, and several other indicators such as grass and bark content, preparation, etc. Information on color grade, leaf grade, fiber length, fiber strength, micronaire, and extraneous matter are presently recognized by the marketing system (USDA, 1993). The information on quality is available to buyers and sellers of cotton when making their sale/purchase decisions. With the current levels of each quality attribute recognized by the grading system, there are over 800,000 possible quality combinations that can exist. In reality, the actual combinations that are available in the market is much smaller, but this provides some perspective on the complex nature of quality in cotton. The sophistication in the grading system introduces complexity into the pricing of cotton. Each different combination of quality has a potentially different market price. That is, the market price for color grade 41, leaf grade 4, staple 34, strength 24.5 grams/tex, 3.5-4.9 micronaire cotton is likely different from a color grade 51 cotton with all other attributes equal. This difference in price is due in part to differences in textile processing costs (demand factors) as well as the relative availability of each type of cotton (supply factors). Thus, as one moves across color grades, there will be differences in price. There will also be differences in price across the other attributes. The complex
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interaction of supply and demand factors for each level of each quality attribute generates a complex set of prices. This set of prices is known as the “price structure.” That is, there is a price for “base” quality (color grade 41, leaf grade 4, staple length 34, 3.5-4.9 micronaire, and 24&25 grams/tex strength). Differences between this price and the prices for the other quality combinations are known as premiums and discounts. The complicating factor is that these premiums and discounts are not observable in the market. That is, the market does not reveal what the price difference is between levels of strength, for example. It simply reveals the price for a mixed lot of cotton. However, knowledge of these premiums and discounts is necessary for correct production and marketing decisions (Ethridge and Hudson). Deriving premiums and discounts from observations of mixed lot sales has been a recognized problem for some time (Cole, 1995), and any method of deriving these premiums and discounts produces only estimates. Current Price Information The U.S. Department of Agriculture, Agricultural Marketing Service, is responsible for producing estimated market prices and quality premiums and discounts for cotton in the United States. The enacting legislation was the Futures Trading Act of 1914, while later legislation has further defined the responsibilities of AMS in estimating and reporting these prices, premiums and discounts. At present, AMS reports prices, premiums and discounts for seven designated marketing regions (Southeast, North Delta, South Delta, East Texas and Oklahoma, West Texas, Desert Southwest, and the San 3
Joaquin Valley) on a daily basis through the Daily Spot Cotton Quotations (DSCQ) (USDA, Daily Issues). DSCQ Estimation Procedures An important element of understanding the strengths and weaknesses of the current system of price reporting is understanding how those price reports are generated. AMS uses four market reporters positioned to address the seven marketing regions. These market reporters collect information from cooperating parties (marketing firms, merchants, shippers, etc.) by calling a selected number of cooperators and soliciting information about recent purchases/sales. This information generally consists of sales recap sheets which provide information on quality and price of mixed lot purchases/sales. The manner in which the data are analyzed by AMS is not clear; the procedures themselves are not documented. In general, data collected by the market reporters are entered into a computer software program, which compares the levels of prices for cotton in general quality groupings to the prices of those groupings the previous day (Cole, 1998). This process helps the market reporter develop a perspective on what happened to the general level of prices during the past 1-2 days, and perhaps whether there have been any shifts in premiums and discounts. However, the decisions on premiums and discounts that are reported is determined largely on the basis of the opinions of a sample of traders who are contacted by telephone. When completed, the market reporter forwards his/her estimates of the premiums and discounts to AMS Market News in Memphis, TN for a final decision and publication. An important point about these procedures is that while 4
data and computers are used, evaluation of the premium/discount structure remains subjective. The DSCQ are designed to represent the “commercial values” of various qualities of cotton within each marketing region (Cole, 1998). That is, the prices, premiums, and discounts reported within the DSCQ are intended to represent the average values within that market on a particular trading day. They are not intended to represent the values at any particular level of the market (e.g., producer price, mill price, etc.). Thus, what is reported in the DSCQ is intended be representative of the average market values for cotton on the day and region for which it is reporting. Strengths and Weaknesses The current system has a couple of major strengths that should be noted. First, the current method of estimating market premiums and discounts has a long history with AMS. All personnel are well acquainted with the methods of arriving at the estimated values and the necessary technical factors such as communication methods and technologies are in place and functioning. Second, data gathering from cooperating individuals and firms is voluntary and does not impose a major burden on cooperators. These elements have made the current system attractive for some time. There are, however, some weaknesses in the current system. First, the sample of data gathered by the market reporter is ad hoc. That is, because the sample characteristics are not known, one cannot verify whether the sample drawn adequately represents the cotton sales occurring within that region on that day. This creates the possibility of 5
unknown bias in the resulting premium and discount estimates. Bias, in this context, means that the estimates that are produced are not representative of the premiums and discounts that exist in the market. A second issue is that the procedures given to market reporters to follow have not been documented and scientifically verified. That is, no assurance can be given that the procedures given to the market reporters generates the best possible estimates of premiums and discounts. Because there is no possible way to evaluate the accuracy of results, the validity of the procedures used to generate the premiums and discounts is the most important factor determining the reliability of the estimates. The procedures used by AMS cannot be scientifically validated, so the estimates generated by AMS cannot be assured to be the most accurate available. An Alternative, Objective Approach An alternative approach for estimating market prices, premiums, and discounts has been put forward by Brown et al. This approach, based on actual market transactions and reliable statistical procedures, is designed to produce the best estimates of premiums and discounts. It is also fully documented (see Hoelscher and Hudson for the current operational procedures and Brown et al. for statistical methods employed). Development of this approach as an experimental technique began two decades ago (Ethridge and Davis) and has been under constant evaluation, retesting, and verification since that time. The Daily Price Estimation System (DPES) began estimating daily cotton market prices, premiums and discounts in 1989, and has operated on a daily basis since. Estimation 6
procedures have been closely examined for scientific validity (Brown et al.) and procedures have been developed to insure accuracy of the results (Brown and Ethridge). DPES Estimation Procedures The DPES utilizes a large sample of actual daily market spot transactions in the Texas and Oklahoma producer market to estimate a base price and quality premiums and discounts for each trading day in those marketing regions. The estimation procedure is based on a concept called hedonic price analysis. On the surface, this might appear to be a complex statistical procedure that is understandable to only a few people. The specific details of the procedure can appear quite complex, but the general concept is not. The following discussion is not designed to be a complete description of the procedure but to convey the central concept underlying the procedure.
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Staple Length (32nds of an inch)
Figure 1. Hypothetical Relationship Between Staple Length and Cotton Price 7
Figure 1 shows a set of hypothetical sale prices of cotton with a given staple length. The horizontal axis shows the staple length of the cotton, while the vertical axis shows the price of cotton. Each point on the graph shows a sale of cotton that has a given staple length at a given price. Figure 1 shows that, in general, the price of cotton increases as the staple length increases. In fact, one can draw a line through the middle of the points which expresses this relationship (e.g., the line drawn in Figure 1). In mathematical terms, the slope of a line shows how much the price of cotton increases as the staple length increases. The amount that the price changes in response to a change in the staple length can be used to calculate the premium or discount for staple. For example, the base level for staple length is 34. Moving to the left (shorter staple) yields a lower average price (a discount). Conversely, moving to the right (longer staple) yields a higher average price (a premium). This is the central concept of hedonics–to determine the relationship between price and the quality attributes. That is, the hedonic approach is simply estimating the slope of the line in Figure 1. Using established statistical techniques, market transactions data are used to find a line that best represents the relationship between the quality attribute and price. That is, the results of the procedure outlined above provide the best possible estimates of the premiums and discounts for quality. No other known procedure will produce more accurate estimates. The concept is not as complicated as it might first appear, but it should be noted that the values of these attributes are not determined in isolation from one another in the 8
market. The value of staple, as determined by the hedonic approach, is not estimated independently from strength, micronaire, etc. The approach used by the DPES recognizes the fact that these market values are determined simultaneously in the market and allows for interactions between qualities. In fact, this technique is best suited for the situation where there are many quality attributes which affect price because it allows the person examining price to perform a similar analysis as that presented in Figure 1 for all quality attributes at the same time and be able to account for all of the interactions between qualities as well. This flexibility is desirable in current cotton price reporting given the complexity of the quality information in the cotton market. It should be noted that sorting out this set of relationships between quality attributes and price is virtually impossible without an established statistical procedure, particularly when there is more than one or two quality attributes. Strengths and Weaknesses There are several major strengths of the approach used by the DPES. First, the technique employed to estimate the premiums and discounts is scientifically verified, objective, and reproducible. That is, the results are based only on market transactions data, not on impressions or opinions about market activity. All the results, given the same set of data, are 100% reproducible. Second, the estimation of premiums and discounts is rapid. In most cases, premiums and discounts are produced and disseminated within one hour of receiving data, and the results are ready before the next business day (Brown et al.). Finally, the increasing complexity of the grading system has no bearing on the 9
accuracy of this estimation approach. That is, this approach is not limited by the number of quality attributes that are priced in the market (for example, the breaking up of the old composite grade code that occurred in 1993 was handled with no loss in the validity and accuracy of the results). The DPES is currently producing price estimates only in the Southwest (West Texas and East Texas-Oklahoma) regions. The primary reason is lack of data availability in the other regions. However, the process of expanding the estimation of prices in the Mid-South and Southeast is being arranged, with development planned to occur during the marketing of the 1998 crop. The DPES also produces price estimates for only the producer market. Attempts are also being made to include merchant-to-merchant sales, but, if successful, will be separate from the producer market. Research on tracking the premiums and discounts in the shipper-to-merchant market is also underway. The DPES requires larger volumes of data than the approach followed by AMS. The reason is that there is a minimum number of sales (about 40 per day) required to assure statistical validity of the estimates; the AMS procedures do not consider validation. Integration of the DPES procedures into the AMS structure would require cooperators to report each mixed lot purchase on a daily basis, rather than selected purchases on a sporadic basis. That is, AMS does not currently sample the same cooperators every day. When contacted, the cooperators are asked to provide information on representative purchases/sales. They do not necessarily provide all of their information. Additionally, cooperators would need to transfer the information electronically and in a standardized 10
format. There would likely be resistance on the part of some firms to cooperate, but the other problems of implementation on a large scale are primarily logistical and the technology to facilitate such data transfer currently exists in most merchant offices. Changing Market Price Reporting? A strong case can be made for changing the procedures currently used by AMS to produce cotton market price reports. There is an available alternative procedure that meets strict scientific criteria for validity in contrast to the current approach. The alternative approach has been developed, tested, evaluated, and refined over a long period of time. The alternative approach can also provide information in a more timely manner than the current system. The alternative approach used by the DPES directly addresses many of the shortcomings of the current approach followed by AMS. However, the DPES does require more data for it to operate efficiently, which will require broad-based support and cooperation from the cotton industry to facilitate its use. If, however, that support is present, it is clear that the alternative system has advantages over the current AMS approach. Telecommunications and computer technology are already present to support the use of a system like the DPES. Consideration should also be given to the advantages of changing market price reporting to the cotton industry. First, improving the accuracy of information to the marketplace will enhance the overall efficiency of the cotton market by better transmitting 11
price signals from one end of the market channel to the other (Ethridge and Hudson). Second, because the official price information produced by AMS is used in the derivation of the premiums and discounts for the CCC loan schedule, improving that information will make the CCC loan premiums and discounts more closely resemble market premiums and discounts.
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References Brown, J. and D. Ethridge. “Functional Form Model Specification: An Application to Hedonic Pricing.” Agricultural and Resource Economics Review, 24 (2, 1995): 166-173. Brown, J., D. Ethridge, D. Hudson, and C. Engels. “An Automated, Econometric Approach for Estimating and Reporting Daily Cotton Market Prices.” Journal of Agricultural and Applied Economics, 27 (2, 1995): 409-422. Cole, R. “Cotton Market News-A Changing Environment.” 1995 Beltwide Cotton Conferences, Proceedings, Cotton Economics and Marketing Conference, National Cotton Council, Memphis, TN, pp. 357-358. Cole, R. “Cotton Market News.” Presentation made to the National Cotton Council Loan Premium and Discount Task Force, National Cotton Council, Memphis, TN, March 18, 1998. Ethridge, D. and B. Davis. “Hedonic Price Estimation for Commodities: An Application to Cotton.” Western Journal of Agricultural Economics, 7 (1982): 156-163. Ethridge, D. and D. Hudson. “Cotton Market Price Information: Its Relevance to the Industry.” The Journal of Cotton Science, 1(2, 1998): http://www.cotton.org/ (in press). Hoelscher, K. and D. Hudson. “Daily Price Estimation System Operations Manual Version 2.” Department of Agricultural and Applied Economics, Texas Tech University, Cotton Economics Research Report CER-97-12, June, 1997. USDA. “The Classification of Cotton.” U.S. Dept. of Ag., Agricultural Marketing Service, Agricultural Handbook 566, Washington, DC, April, 1993. USDA. “Daily Spot Cotton Quotations.” U.S. Dept. of Ag., Agricultural Marketing Service, Memphis, TN, Daily Issues.
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