THE EFFECT OF QUALITY ON CORN EXPORT PRICE DETERMINATION Stephanie Mercier, Conrad Lyford, and Valencia Oliveira

Introduction

Students of world grain market behavior have long expressed interest in how prices for various grains are determined. That market is characterized by heterogeneous goods - grains differing in quality, genotype, and specific physical attributes. There are a number of approaches that can be used to objectively measure the market valuation of various grain characteristics. One such approach, the hedonic price approach, developed in the 1970's, incorporates differences in product quality. Hedonic theory, first developed by Lancaster, Griliches, and Rosen, attempts to disaggregate the price paid for a good into implicit prices paid for a product's various attributes, particularly its quality characteristics. Economic theory suggests that, all else being equal, buyers will pay more for grain with high-valued quality attributes than low-valued attributes, depending upon the character of demand for the final product. Stephanie Mercier is a Senior Economist with the Commodity Economics Division of the Economic Research Service; Conrad Lyford is a Doctoral Student in the Agricultural Economics Department at Michigan State University, who served as an Intern at the Economic Research Service in the summer of 1992; and Valencia Oliveira is a Computer Specialist with the Economic Research Service.

Grain quality and the role it plays in the America's competitive position in the world grain market have been subjects of considerable public debate in the last decade or so. Federal legislation was enacted in both 1986 (the Grain Quality Improvement Act) and 1990 (Title XX of the Food, Agriculture, Conservation and Trade Act) requiring various government agencies to study these issues. In response to these Congressional mandates, the Congressional Office of Technology Assessment and the Economic Research Service (ERS) of the United States Department of Agriculture conducted extensive studies examining the role of quality in the world grain market (United States Congress; United States Department of Agriculture 1986 to 1991, 1988). The OTA study focused on how the production and policy environment in major grain-exporting countries affects grain quality (the supply side), and the ERS study focused on how grain-importing countries place quality in a hierarchy of factors determining the sources of their imported grain (the demand side), and what benefits American producers could reap from providing higher quality grain to the world market. The present study is an extension of the ERS work (Mercier 1993, 1994), examining the role of quality in the price determination process for U.S. corn exports. The majority of previous studies on the relationship between prices and grain quality have concentrated on wheat trade, since that market illustrates both strong competition among rival exporters and diverse end-uses for the commodity (Wilson and Preszler; Veeman; Larue and Lapan). In addition, the broad dividing of wheat into wheat classes for different end uses permits analysts to focus on a subset of wheat price-quality relationships. The major shortcoming of most previous studies is inadequate data: either the data are highly aggregated, or they cover a relatively narrow geographical

Review of Agricultural Economics 16(1994):239-247

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This article examines export price formation for U.S. corn exports, adopting the hedonic approach of treating a commodity as a bundle of identifiable quality attributes for which separate implicit prices can be estimated. The study makes use of a rich data set which includes transaction-level price and quality data for U.S. corn exports for a period of two years. Significant implicit valuations for key quality factors between corn importers for feed use and corn importers for food and industrial uses are found, suggesting that the different end-uses substantially affect how users contract for corn imports.

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where xyi equals quantity of market input i (i= I, ... , m) used in the production of output y; P, equals the given price of output y; and Pix equals the given price of input Xi' The first-order condition with respect to the use of input i is: (3)

where i equals I, ... , m. Solving for the price of the input Xi yields the following demand equation:

Economics of Grain Characteristics

(4)

The economic effects of grain characteristics as inputs in a production process will be illustrated using a variation on the hedonic price model developed by Ladd and Martin for analyzing intrinsic characteristics of production inputs. Other researchers have used this same approach to wheat (Hyslop; Mercier and Young). This approach assumes that profitmaximizing com millers or feed processors demand inputs for their production processes that yield specific end-use results, and thus seek inputs with characteristics that create products with those desired results. For example, drymillers who produce brewer's grits and other intermediate products seek com with low breakage susceptibility that will ultimately produce large particles from milling, while feed processors are chiefly concerned with the com's nutritive value and feeding efficiency. These characteristics are input arguments in a production function. These firms are assumed to operate in a perfectly competitive market setting. For a single firm, this function is:

O, = f(ZYl ... Zyn)

(I)

where Qy equals quantity of output y (y=l, 2, ... , Y); and Zyj equals the quantity of input characteristic j (j=l, ... , n). The firm's objective is to maximize profit, as represented by:

The partial terms in equation (4) represent the marginal physical product of characteristics in the production of y and the marginal yield of characteristic j from the i" input. For simplicity, call Bj the marginal product of characteristic j times the price of y and call the marginal yield Zjj' Rewriting equation (4) with those changes yields the hedonic price function which shall be modelled in this analysis: n

Pix =

EBh

(5)

J

In an actual market setting, the demand for quality characteristics interacts with the available supply of grain with the desired quality attributes. Buyers and sellers meet to negotiate a contract describing the intersection of expectations of the quality of the grain to be shipped. Thus, the model reflects the equilibrium of both the demand for and the supply of the various quality characteristics.

Quality Factors and Their Impact on End-Use Performance Any shipment of grain can be described by its quality characteristics. Measures of such characteristics are often divided into three separate categories. The first category includes

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region and/or chronological period. The project examines aspects of the world com market, which is also comprised of multiple exporters and end uses, though exhibiting less diversity than the wheat market. The most important advance of this study is its use of a rich data set containing a cross-section of individual transaction prices and corresponding quality factors to examine the United States' role in the world com market. The data cover 50 countries over the period between January 1990 and November 1991. This study examines the relationship between price and quality attributes U.S. com importers consider in their sourcing decision, and other factors influencing how com price differs among countries.

TIlE EFFECT OF QUALITY ON CORN EXPORT PRICE

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Wet- and dry-millers, who process com into products for the food and industrial sectors, are more concerned with intrinsic properties of the grain, such as protein and starch content, and kernel hardness and uniformity, as they affect the yield of their final products (Mercier 1994). These intrinsic factors are rarely directly measured prior to shipment because such tests are quite expensive on a large scale. Thus, processors must rely on past performance and knowledge of com genotypes in gauging willingness to pay. BCFM is also a concern to industrial and food processors primarily because its presence increases operating costs. Thus, BCFM must be largely cleaned out before the com enters the processing sequence.' This differentiation into how various importers value quality characteristics justifies examining these submarkets separately.

I Aflatoxin is a toxic substance produced by the soil fungus Aspergillus flavus, which can infect corn and other crops (such as peanuts) when they are stressed during maturation. The infection can also spread under improper storage conditions. When ingested by humans in concentrations of more than 20 parts per billion, aflatoxin is believed by scientists to increase the chance of developing liver cancer.

2Com is the raw ingredient in starch manufacture (called wet-mill processing), an essential intermediate product for many final products, in both the food and the industrial sectors. The wet-milling process involves steeping com in water (and small amounts of sulfur dioxide) to soften the kernels. Small portions of broken kernels (called fines) tend to clog the screens in the steeping tanks, so processors clean often to avoid this problem.

Data and Variables The shipment-level data used for estimating these hedonic price models are a subset of all U.S. com exports shipped between January I, 1990 and November 28,1991 (United States Department of Commerce; United States Department of Agriculture, 1986 to 1991; 1988). Although not strictly a random sample, the data set represents 47 percent of all U.S. com shipments (by volume) inspected and recorded during that period, or nearly 800 total observations, to 50 different countries (see Table 2). The shipment-specific quality and price portion of the data set was constructed by matching FGIS export inspection data with export data on quantity and value collected by the Bureau of Census of the Department of Commerce. The data set was created by matching individual shipment observations based on import destination, United States port of exit, month and year of shipment, and shipment size.

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characteristics describing the grain's physical properties of soundness, such as the level of broken kernels and foreign material (for com, BCFM), and moisture content. The second includes the purity of the grain, such as insect damage and aflatoxin contamination.' The third category includes intrinsic characteristics, such as protein and starch content. This last category is related to chemical properties of the grain, and these factors have a direct impact on the ultimate quality of the final product (United States Congress; Hill et a1.; Watson). The Federal Grain Inspection Service (FGIS) of the United States Department of Agriculture: (I) provides an official set of grades and standards for raw grain and oilseed export commodities; (2) inspects and grades nearly every vessel that moves abroad; and (3) provides an official certificate to the buyers. The quality factors whose measures determine the grade assigned to a given shipment fall largely into the category of physical properties, because they are visible to the eye or otherwise easily tested. Grade-determining factors in the grades for U.S. com are shown in Table I. Measured levels of these factors may fall below the grade limit maximums (or above the minimum for test weight), but may not exceed the limits as an average for the entire shipment. Moisture content is also measured, although it has not been a grade-determining factor since 1985 (Hill). Importers of U.S. com specify a U.S. grade (U.S. No. 3 or better in about 70 percent of exports), and usually also specify a maximum moisture level (14 to 15.5 percent). Many importers also specify tight limits on aflatoxin (0 to 50 parts per billion). Feed processors, who take most of the world's com trade, are chiefly interested in factors that affect grain storability, such as moisture content, aflatoxin, and BCFM, and less with the intrinsic characteristics of the grain (Mercier 1994).

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Table 1. U.S. Corn Grades and Grade Requirements Minimum

Grade

Test Weight Ib/bu

Table 2.

Maximums Heat Damaged Kernels

Countries and Observations in the Data Set

Country

Broken Total Com and Damaged Foreign Kernels Material

- - - - - - Percent - - - - - -

U.S. No. I

56.0

0.1

3.0

2.0

U.S. No.2

54.0

0.2

5.0

3.0

U.S. NO.3

52.0

0.5

7.0

4.0

U.S. No.4

49.0

1.0

10.0

5.0

U.S. No.5

46.0

3.0

15.0

7.0

'Heating means that a portion of a com shipment is overmoist, and begins deteriorating or fermenting in transit. Source: United States Department of Agriculture, Federal Grain Inspection Service, 1988.

The price was derived by dividing shipment value by shipment size, and represents a transaction price for each particular shipment. These transaction prices reflect the price as recorded at export, so they are effectively f.o.b. prices. The quality factors used as independent variables in this analysis were BCFM, total damaged kernels, moisture content (all measured in percentage content), and test weight (measured in pounds per bushel). The data set reports actual levels of these factors measured in the shipments, serving as proxies for the contractual limits that reflect the buyer's expectations. The quality information is derived from the FGlS export data set. Although the data series was not long enough to provide convincing evidence of periodicity, data plots suggested that somewhat higher prices tend to prevail in late spring and

Russia" Saudi Arabia Senegal Spain Syria Taiwan Trinidad Tunisia Turkey United Kingdom Uruguay Venezuela Yemen Yugoslavia

34 8

7 I 3 18 I I 6 3 20 24

2 2 7 9

2 2 18 186 2 28

1 1 4 75 3 10

7 1 3

2 5 1 10 5 160 7 I 13 2 46

5 5 2 1 1 15

2 5

'Shipments were to East Germany in the 1989 to 1990 crop year, prior to reunification. bThe data set did not differentiate between Russia and the rest of the former Soviet Union. Note: Includes only observations used in analysis.

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U.S. Sample Grade: (a) Does not meet the requirements for the grades U.S. No. 1,2,3,4, or 5; (b) Contains more than eight stones that have an aggregate weight in excess of 0.20 percent of the sample weight, two or more pieces of glass, three or more crotalaria seeds (Crotalaria spp.), two or more castor beans (Ricinus communis L.), four or more particles of an unknown foreign substance(s) or a commonly recognized harmful or toxic substance(s), eight or more cockleburs (Xanthus spp.), or similar seeds singly or in combination, or animal filth in excess of 0.2 percent in 1,000 grams; (c) Has a musty, sour, or commercially-objectionable foreign odor; or (d) Is heating, or otherwise of distinctly low quality.'

Algeria Belgium Brazil Bulgaria Cameroon Canada Chile China Costa Rica Cyprus Dominican Republic Egypt EI Salvador Germany" Guatemala Honduras Hungary Italy Jamaica Japan Jordan Korea Malawi Malaysia Malta Mexico Morocco Mozambique Netherlands New Zealand Nicaragua Panama Peru Poland Portugal Romania

Shipments

THE EFFECT OF QUALITY ON CORN EXPORT PRICE

Empirical Results

All equations were estimated with weighted least squares. The shipment size was used as the weighting variable. The weighted least square technique was used to minimize the chance of a small, tightly-specified com shipment skewing the results. A loglinear specification was estimated, but discarded because of poor statistical fit. The linear specification, used in most previous analyses of this type and in keeping with the theoretical model, was deemed to be appropriate. Four separate equations were estimated in this study. The first and second equations split the data set along lines approximating the feed versus the food and industrial uses submarkets (such information was not provided in the data set). The rules adopted to assign com exports to food or industrial uses were as follows: (1) all U.S. No. 1 shipments; (2) U.S. No. 2 com imported by countries with a feed-to-total-use

243

ratio of less than 70 percent; and (3) com shipments with other unusual quality specifications, such as low BCFM or low moisture content. Summary statistics for these two data sets are reported in Table 3. White com exports were included in the food equation, because white com is generally reserved for that purpose. All other corn shipments were assumed to be for feed use. These are somewhat arbitrary decision rules, based to some extent on knowledge of corn-importing countries. The dependent variable in all four equations was the shipment-level price, measured in dollars per ton. Results are reported in Table 4. With only a few exceptions, most of the variables in these equations behaved as expected. Grade did not appear to be a useful predictor of the shipment price, because the signs are contrary to expectation, and the coefficients are not statistically significant, likely due to multicollinearity between grade and the gradedetermining factors. In fact, under no combination of variables in the regression equations (even alone with the intercept) is the coefficient of numerical grade statistically significant. The incidence of quality characteristics is broad enough within a single grade that a shipment's grade designation does not serve well as an indicator of price. The presence ofBCFM is generally viewed as being detrimental to the value of a com shipment, so the expected sign for its coefficient is negative. Com importers ostensibly regard BCFM as an important quality characteristic, especially importers for wet-milling purposes, because BCFM tends to slow down the processing cycle. The signs of the BCFM coefficients in these equations are consistent with the relative importance importers attached to its presence, with a larger coefficient, and thus implicit value, found in the food/industrial use equation. The failure of the coefficients to attain statistical significance suggests some information failure, particularly in light of the importance importers apparently attach to BCFM. The coefficient for damaged kernels shows the expected sign in the food and industrial use equation, and is statistically significant. The implicit price for damaged kernels is unexpectedly positive in the feed-use equation, but

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early summer (May and June), so dummy variables representing those months were included. A categorical variable for crop year (parts of three crop years were contained in the data set) was also included. The expected signs for the coefficients of the quality variables can be drawn from knowledge of whether higher values of a variable detract from or enhance the quality of the final product. Higher measured test weight is viewed as enhancing the value of the final product, so the sign of its coefficient is hypothesized to be positive. Higher values of BCFM, damaged kernels, and moisture content reduce both the millable yield of the grain and the quantity of the final product, so their coefficients are expected to be negative. The expected sign of the coefficient for numerical grade would be negative, as poorer grade com (with higher numbers) would be expected to receive lower prices. The coefficients for the month dummy variables are anticipated to be positive, since the price tends to rise during these months, because of shifting export supply patterns. The crop-year variable is used to check for trends in underlying quality conditions. No particular sign is anticipated for its coefficient.

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REVIEW OF AGRICULTURAL ECONOMICS, Vol. J 6, No.2, May 1994

Table 3.

Summary Statistics for the Data Set

Variable

Food Exports

Corn Export Price Equations, Export Submarkets

Feed Exports

117.58 13.84 83.00

113.21 7.89 58.81

BCFM (%) Mean Standard Deviation Range

2.69 0.42 230

335 0.50 2.60

56.88 0.75 435

56.79 0.73 4.87

Mean Standard Deviation Range

14.08 0.51 2.40

14.24 0.42 2.10

Damaged Kernels (%) Mean Standard Deviation Range

2.71 0.84 4.50

3.01 1.08 6.10

Variable

80.640* (3.07)

289.120* (3.13)

Grade

0.532 (0.67)

7.265 (1.01 )

Broken Com and Foreign Material

-1.249 (-1.75)

-6.180 (-1.53)

Moisture Content

-3.937* (-4.83)

-11.190* (-6.85)

Test Weight

1.630* (3.99)

0.002 (0.01)

Damaged Kernels

0.206 (0.71)

(-I. 98)

May Dummy

4325* (4.27)

June Dummy

2.617* (2.58)

Crop Year

the coefficient is not statistically significant. In interviews conducted in the ERS study, importers in general did not attach much significance to this factor, nor did measured levels often approach grade limits (Mercier 1994). Moisture content, although not a gradedetermining factor in U.S. grades and standards, is nonetheless considered an important characteristic by most corn importers. Moisture's greatest impact is its effect on grain storability, which is of considerable importance to corn users. High moisture content in stored grain increases its susceptibility to mold or mycotoxin damage, softens kernels for insect infestation, leads to overheating and fermentation, and generally lowers the value of the corn. Both feed and food manufacturers are quite concerned about grain storability and preservation of enduse value, so this characteristic is important. Test weight is a grade-determining factor in the U.S. corn grades and standards, and high test weight is regarded as being positively correlated with yield of dry products for the milling industry. Test weight is influenced by other factors (environmental and handling) that

Food and Industrial Exports

Intercept

Moisture (%)

Source: United States Department of Agriculture, Federal Grain Inspection Service, 1986 to 1991.

Feed Exports

-1.494* (-3.02)

-2.549*

-4.751 (-2.75) 30.074* (6.49)

White Com R'

0.19

0.615

Number

659

120

*Indicates coefficient with 5 percent significance level. Note: t-statistics in parentheses.

can cause it to become a poor predictor of milling yield (Hill et al.). Low test weight can be an indicator of corn that experienced hightemperature drying. However, few importers regard test weight as a key determinant in their import decision-making process (Mercier 1994). In addition to the lack of importance attached by many importers to test weight, there is also considerable uniformity in the test weight of U.S. corn exports. The standard deviation of test weight for the entire sample analyzed here was less than 0.75 lbs/bu. Only one shipment out of 779 observations had a test weight below the grade limit for U.S. NO.2 (54 lbs/bu). The test weight coefficient is positive and statistically significant in the feed corn equation, but is not statistically significant in the food/industrial corn equation. The reason for the lack of significance in the latter equation is not clear, except that the

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Price ($/ton) Mean Standard Deviation Range

Test Weight (lbs per bushel) Mean Standard Deviation Range

Table 4.

THE EFFECT OF QUALITY ON CORN EXPORT PRICE

245

about the year the crop was grown are not available. Attempts to test for stability of premiums or discounts for com quality factors across marketing years suggested that implicit prices vary among years, though the explanatory power of the equations was limited. With respect to a comparison of implicit prices across two different crop years (1989 to 1990 and 1991 to 1992), the signs of the coefficients were consistent between the two periods for only BCFM, and the size of the coefficients differed (Table 5). The signs of the coefficients of the other quality characteristics were different across the two periods. The two equations actually represent two different portions of the crop years in question, January to August in the 1989 to 1990 crop year, and October to November of the 1991 to 1992 crop year. To avoid the problem of grain grown in one crop year actually being exported in the following crop year, an equation for the 1990 to 1991 crop year was omitted. Whether or not implicit prices differ between importing countries is an issue for further research. Concluding Remarks This analysis examined hedonic price models for the two major submarkets for com exports: feed use and food and industrial use. Although the data were split somewhat arbitrarily, the two submarket models suggest that importers of U.S. com for different end uses place different values on the three key quality factors for which they possess information. The analysis suggests that prices reflect the quality information provided to importers in the FGIS certificate, but these quality factors in and of themselves do not fully predict price paid for a given com shipment. Evidence also suggests that implicit prices for quality characteristics vary over time. Quality information in terms of BCFM, damaged kernels, moisture content, and test weight is apparently used by U.S. com importers and exporters to set prices, so the U.S. grading system does function in part at least as intended. One drawback in the system is that BCFM is measured prior to loading in the United States,

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coefficient of variation in those shipments with respect to test weight is considerably lower than the variation seen in the other measured factors. In addition, importers had no reason to feel anxiety about test weight in U.S. com in these crop years. Com import demand for food and industrial use exhibited no seasonality, so the monthly dummy variables were not included in this equation. Although the data series was not long enough to give convincing evidence of periodicity in com prices, an examination of data plots suggests a difference in prices paid during the months of May and June. This is the time when the Argentine com harvest begins, and the price relationship between American and Argentine com often switches. The early spring months are also the period when com importers in the Northern Hemisphere with domestic coarse grain production begin to exhaust their domestic stocks and have to go out to com exporters to meet excess demand. Average prices for all shipments made in those months were higher than prices paid in other months, and thus as expected, the sign of both coefficients is positive. The data set represents shipments made during portions of three different crop years, so a dummy variable for the crop year was also included. This variable proxies changes in underlying quality conditions for the entire U.S. com crop. The major effect that this variable accounted for was a year-to-year shift in average crop quality, and the sign and significance of the coefficients suggest that average quality declined during the data period. Examining the actual data partially confirms this: average quality as represented by these observations declined between the 1989 to 1990 and 1990 to 1991 crop years (BCFM and moisture content higher; test weight lower), and quality rebounded in 1991 to 1992. However, as the data set ended on November 28,1991, the 1991 to 1992 crop year portion contains only 13 percent of total observations and does not reflect quality and price relationships over the entire marketing year. In addition, the fact that a shipment occurs in a given marketing year does not necessarily mean it was grown during that marketing year (because of stockholding practices), but data

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REVIEW OF AGRICULTURAL ECONOMICS, Vol. 16, No.2, May 1994

Table 5,

Corn Export Price Equations, Temporal Stability of Implicit Prices

References Griliches, Z., ed. Prices Indexes and Quality Change. Cambridge: Harvard University Press, 1971. Hill, L.D. Grain Grades and Standards: Historical Issues Shaping the Future. Urbana, [L: University oflllinois Press, 1990.

Variable

1989/90

1991/92

Intercept

226.080 (5.77)

10.16 (0.12)

-2.394 ( 1.40)

(-1.01)

Broken Com and Foreign Material

-0.432 (-0.41)

-6.487" (-2.70)

Moisture Content

-6.575' (-6.64)

1.709 (0.63)

Test Weight

-0.387' (-0.57)

1.797 ( 1.23)

Damaged Kernels

0.292 (0.49)

1.499' (2.54)

May Dummy

4.230' (3.58)

Lancaster, KJ. "A New Approach to Consumer Theory." Journal a/Political Economy 74(April 1966):132-57.

June Dummy

2.944* (2.55)

Larue, B. and H, Lapan. "Market Structure, Quality, and World Wheat Market." Canadian Journal of Agricultural Economics 40( 1992):311-28.

Grade 1.465

0.21

0.16

Number

367

94

'Indicates coefficient with 5 percent significance level. Note: t-statistics in parentheses.

so the information provided on breakage is only partial, since it excludes subsequent damage during loading at the export facility and unloading at destination. Inclusion of additional quality information in the official certificate, such as com breakage susceptibility, could enhance the grading system's efficiency, by providing clues as to the likelihood of breakage after BCFM is officially recorded. This suggests that development of a rapid and low-cost test for com breakage susceptibility could provide the U.S. com sector and its customers with better information on how to market and value U.S. com. Past analyses have clearly established a relationship between breakage susceptibility and end-use value, but this knowledge has not been fully integrated into the marketing system. [Received August 1993. Final version received January 1994.]

Hyslop, J.D. "An Economic Analysis of Price-Quality Relationships in Spring Wheat." Ph.D. Dissertation, University of Minnesota, 1967. Ladd, G.W. and M.B. Martin. "Prices and Demands for Input Characteristics." American Journal of Agricultural Economics 58( 1976):21-30.

Mercier, S, The Role of Quality in Corn Import Decisionmaking. Washington, DC: United States Department of Agriculture, Economic Research Service, forthcoming AER, 1994.

_ _ _ _.. The Role of Quality in Wheat Import Decisionmaking. Washington, DC: United States Department of Agriculture, Economic Research Service, AER670, 1993. Mercier, S. and C.E. Young. "Quality as a Wheat Price Determinant." Unpublished paper, United States Department of Agriculture, Economic Research Service, 1993. Rosen, S. "Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition." Journal of Political Economy 82(February 1974):34-55. United States Congress, Office of Technology Assessment. Enhancing the Quality of u.s. Grainfor International Trade. Washington, DC, February 1989. United States Department of Agriculture, Federal Grain Inspection Service. "Export Grain Inspection System Data." Computer database, 1986 to 1991.

_ _ _ _. Official United States Standards for Corn. Washington, DC: Government Printing Office, 1988. United Stales Department of Commerce, Bureau of Census. "U.S. Agricultural Exports." Computer database, October 1992.

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R2

Hill, L.D., M.R. Paulsen, K. Bender, A. Bouzaher, M. Patterson, and A. Kirleis. Economic Evaluation of Quality Characteristics o{ the Dry Milling o{ Corn. Agricultural Experiment Station Bulletin No. 804, University of Illinois Urbana-Champaign, November 1991.

THE EFFECT OF QUALITY ON CORN EXPORT PRICE Veeman, M. "Hedonic Price Functions for Wheat in the World Market: Implications for Canadian Wheat Export Strategy." Canadian Journal of Agricultural Economics 35( 1987):535-52.

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Wilson, W.W. and T. Preszler. "End-Use Performance Uncertainty and Competition in International Wheat Markets." American Journal of Agricultural Economics 74(August 1992):556-63.

Watson, S.A. "Industrial Use of Corn." Corn and Corn Improvement, ed. G.F. Sprague. Madison, WI: American Society of Agronomy, 1977.

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