An Economic Assessment of Banana Genetic Improvement and ...

BANANA PRODUCTIVITY AND TECHNICAL EFFICIENCY IN UGANDA 119
Table 8.3 Cobb-Douglas production estimates for the overall sample
(location dummies excluded)
Variable Coefficient t-value
Production function estimates
Constant 5.773*** 18.55
ln(A) 0.34*** 8.17
ln(L) 0.422*** 9.66
M 0.00003* 1.65
G 0.00002 0.88
C 0.00002 1.28
farm size 0.006 1.32
Ext 0.017 0.74
plotage 0.024*** 4.62
plotage 2 –0.0001** –2.10
Sigatoka –0.304** –2.36
weevils –0.167* –1.65
Log likelihood –639.1
Wald X 2 726.7
TE 0.449
Technical inefficiency estimates
Constant 0.511 0.71
Age 0.024 0.82
Age 2 –0.0002 –0.76
Hplot –0.533*** –3.13
Edhh 0.018 1.02
Hhsz –0.053* –1.83
depr 0.368 1.26
Kk –0.001 –1.00
D –0.005 –0.84
σ V (standard error) 0.38 0.048
Notes: ***, **, and * indicate statistical significance at the 1, 5, and 10 percent levels, respectively. See Table
8.1 for definitions of the variables.
children grow up. The children also contribute
labor, enabling the farmers to implement
timely management decisions.
The education variable gives mixed results,
as expected. In the low-elevation region,
the impact of education on TE is negative.
This finding is consistent with our
hypothesis that educated households are
less efficient if education increases the opportunity
cost of labor, so that farmers reallocate
resources from farm to nonfarm activities.
The impact of education on TE in
the high-elevation region is positive. In this
region, education appears to increase farmers’
management capabilities and their ability
to utilize technologies.
The results for the relationship between
the distance variable and TE show a negative
relationship but is only significant for
high elevations. Distance to paved roads is