Agriculture Reference
In-Depth Information
EQUIPMENT identifies these types of equipment and is predicted to be positively corre-
lated with the probability of leasing. A farmer may need only two tractors in an average
season, but every now and then a bumper crop or severe weather requires additional ma-
chines. Equipment that is specialized to a crop (for example, a cherry picker) or to a specific
stage (for example, planting, harvest) is more likely to have high timeliness costs with
contracting. A dummy variable SPECIALIZED EQUIPMENT identifies planting and har-
vesting equipment, as well as other equipment that is crop specific, and is used to measure
timeliness costs.
Table 8.5 presents the logit coefficient estimates from two separate equations. Each
of the 4,961 observations is a single piece of farm equipment (for example, combines,
cultivators, sprayers, tractors, trucks), either leased or owned by the farmer. Like the land
equations, the dependent variable equals one if the equipment is rented and zero if owned.
The independent variables are organized into those measuring capital constraints, asset
moral hazard, timeliness costs, and other controls. The coefficient estimates for WEALTH
and NET WEALTH are both negative and statistically significant. All of these estimates are
consistent with our prediction that equipment leasing is more likely to occur when capital
constraints are pressing.
In table 8.5 the estimated coefficients for all measures of timeliness costs support our
predictions. The estimated coefficient for MULTIPLE EQUIPMENT is positive and sta-
tistically significant, indicating that farmers with additional pieces of a machine are more
likely to lease. Likewise, the coefficient for SHARED EQUIPMENT is also positive and
statistically significant. The estimated coefficient for SPECIALIZED EQUIPMENT is neg-
ative and statistically significant, indicating that specialized machinery is less likely to be
leased. 23 More generally, because timeliness costs are not important for land we predict that
land leasing will be more prevalent than equipment leasing (even for assets with approxi-
mately the same value). Indeed, in our data we find 48 percent of the land plots were leased
but only 2 percent of the equipment is leased.
Table 8.5 also shows the coefficient estimates for several control variables. FARM SALES
and ACRES control for the size and value of the farm. The estimated coefficients indicate
that larger farms are more likely to lease equipment. We also use the value of the equipment
in question (EQUIPMENT VALUE) to control for the value of the asset, and find the
coefficient estimates are positive and statistically significant. The estimated coefficients for
EDUCATION are significantly different from zero, indicating that farmer education has a
positive effect on leasing equipment. The estimated coefficients for WORKERS show that
farms with more workers are more likely to lease equipment. Unfortunately we have no
clear empirical measures of asset moral hazard (for example, exploitation of the machinery
by the farmer because of improper use or inattentive maintenance).
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