Agriculture Reference
In-Depth Information
17. We assume, for simplicity, that the inputs
are observable. This leads to a strong prediction about input shares
over time. This prediction, however, is not found elsewhere in the ratchet literature, and does not hold if inputs are
assumed unobservable. Our purpose here is to focus on output shares.
18. The British Columbia-Louisiana data do not have the required information to test these predictions.
19. As we noted in chapter 2, cropshare contracts usually do not contain a side payment. We can still use these
data to test the ratchet effect model, however, because inputs are shared and adjustments can be made to input
shares that are equivalent to adjusting fixed payments. The analysis of case II demonstrated how easily the model
can be adjusted along these lines.
20. In chapter 5 we found a strong positive relationship between the number of inputs shared and the output share,
as well as a strong positive relationship between the size of the input cost share and the output share.
21. We use the log of the odds ratio to create a nonlimited dependent variable, since the share is naturally bounded
from0to1.
22. This estimated equation has an unusually high number of correct predictions. This occurs simply because there
are very few positive responses. For most contracts, the terms of trade remain relatively stable over time.
23. In both chapters 4 and 5, the ROW CROP dummy was an important variable explaining the choice of contract.
24. For example, if we could identify situations where there was a new technology shock or a new crop produced
on the land, we could better isolate the effect of landowner information on the land.
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Chapter 8: Ownership versus Contracting for the Control of Assets
1. Irwin and Smith (1972) note that relatively little equipment is leased in agriculture compared to nonagricultural
industries where leasing of automobiles, trucks, and large-scale equipment is routine. In recent years, however,
year-long leasing of tractors has become more common.
2. Indeed, Hansmann (1996) devoted his topic to analyzing when capitalists, workers, or consumers own the
firm and did not explicitly consider the variation of control within firms. Other theories of ownership have been
developed by Barzel (1997), Grossman and Hart (1986), Henderson and Ioannides (1983), Hart and Moore (1990),
and Wiggins (1990).
3. Joskow (1987) provides evidence for the importance of specific assets in coal production. Nickerson and
Silverman (2002) examine the choice between owner-operator trucking firms and hired truckers. See Shelanski
and Klein (1995) for a survey of this empirical literature.
4. This point is also noted by Hansmann (1996), who argues that specific assets are overrated as a foundation for
explaining ownership. Holmstrom and Roberts (1998) are also skeptical of the general applicability. As we noted
in chapter 3, Coase and others have challenged the classic specific assets story of General Motors and Fisher Body
(see Coase 2000).
5. See Becker and Murphy (1992) for a general analysis of specialization and Allen and Lueck (1998) for an
application to agriculture. Barzel (1997) explicitly introduces the lack of specialization as an important cost of
sole ownership and also discusses the trade-off between this and moral hazard.
6. Reality, of course, is more complicated. Landowners, for instance, may provide other farming inputs, and often
share input costs. Landowners tend not to closely monitor the farmer and often live a long distance from their land.
Similarly, equipment lessors often take care of repairs and large-scale maintenance. In our framework, “control”
is an economic, not a legal, term.
7. The term “custom,” used by farmers and contractors, refers to the case where the owner of the equipment also
supplies the labor. For example, “Custom work entails the hiring of person and machines to perform specified
tasks” (Montana Cooperative Extension Service 1990, 1). It does not mean that the work is somehow customized
to the specific farm.
8. Edwards and Boehle (1980) define timeliness costs as “the indirect cost of lower crop yields that occur because
planting and harvesting are not completed during the optimal time periods” (810). See also Short and Gitu (1991).
