Agriculture Reference
In-Depth Information
type of contract, which we examine in the context of the model. In the process we identify
variables that proxy the key factors in our model: moral hazard losses, specialization gains,
timeliness costs, and capital constraints. The list and definitions of variables used in this
chapter are provided in table 8.3.
Our empirical analysis has two parts. First, using logit regressions, we use the British
Columbia-Louisiana data to estimate the factors that influence the choice between asset
ownership and asset contracting. 18 Second, we examine custom combining on the Great
Plains using historical and contemporary case study information. Custom combining is the
most prevalent type of custom contract in modern agriculture, so its study is important.
With our contract data from British Columbia and Louisiana, we use the following
empirical specification, where for any asset i the complete model is
A i = X i β i + i i =
A i >
A i =
A i
A i
is an unobserved farmland ownership response variable;
A i
is the observed
dichotomous choice of asset ownership for plot
, which is equal to 1 when an asset (land,
equipment, or building) is leased and equal to 0 when the asset is owned;
X i
is a row vector of
β i
exogenous variables including the constant;
is a column vector of unknown coefficients;
is a plot-specific error term. We use a logit model to generate maximum likelihood
estimates of the model given by equations 8.7 and 8.8 for various contract samples.
Land: Ownership versus Contracting
As we have noted many times, the choice of land control is between ownership and a simple
long-term contract. Not all of the incentives identified in table 8.2 are relevant to the decision
to own or lease land. In particular, effort moral hazard, asset specialization, and timeliness
costs are not likely to be important since there is no specialized operator for land as there is
for equipment, and since land is leased for a minimum of a year. Capital constraints, effort
specialization, and asset moral hazard are the most important incentives that determine land
Increases in capital constraints are predicted to increase the probability of leasing. In
our model, capital constraints arise when the farmer does not have the wealth to guarantee
the purchase of the asset. In our econometric estimates we measure wealth in two ways.
WEALTH is equal to the combined value of land, buildings, and equipment belonging to the
farmer. NET WEALTH is the value of buildings and equipment. 19 Effort specialization can
influence the ownership versus lease decision for land. The lower the amount of specialized
human capital (farming skills), the more likely a landowner will lease out his land. We use
a dummy variable, LANDOWNER HUMAN CAPITAL, that identifies landowners who
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