CROSS-COMMODITY PERSPECTIVE ON CONTRACTING: EVIDENCE FROM MISSISSIPPI



Cross-Commodity Perspective on Contracting: Evidenc e from Mississippi

and catfish producers. Finally, the “Unclassified” represents the respondents who did not
provide their primary product name, and the “Other” group includes all those respondents
who reported “Other” as their primary product.

Price risk for traditional row crops is perceived to be generally lower than for the
FNVO group.3  This likely stems from the fact that the markets for FNVO products are

smaller and are subject to much larger potential changes in price over a growing season.
It should be that these are
perceived price risk measures, and are not derived from actual
transactions price data. Purely from a risk perspective, then, we would expect to see higher
levels of contracting in FNVO crops than in row crop agriculture. However, examination of
the measures of contracting (CONTRACT1 and CONTRACT2) shows that contracting in
row crops is much higher than contracting in FNVO or Livestock categories. Thus, while
risk may be important, it certainly does not appear to be the most important factor. This
result supports previous empirical findings by Allen and Lueck that suggest that risk is not
an important factor in choosing to contract.

A second important factor is transactions cost, which is represented by HOURS.
Here, it can be clearly seen that row crop producers spend more time gathering market
information than either FNVO or Livestock producers.   Higher transactions cost should

lead to a greater use of contracting, which appears to be supported by the data.  While the

numerical values for the number of hours per week appear small, the data suggest that cotton
3 In what follows, the Unclassified and Other categories will not be discussed. The remaining discussion
will be focused on drawing distinctions between the different crops grown by the producers. However, the
regression analysis does contain the observations on these other groups because crop was not a variable in
the model.

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