4 - Central Institute of Brackishwater Aquaculture

Natlonal Workshop-cum-Training on Bloinformaticr and Information Management in Aquaculture
where Zs are 25 farm-specific variables (table 1 & 2) and 6s are unknown
parameters to be estimated. Since the dependent variable in (2) is defined in
terms of technical inefficiency, a farm-specific variable associated with a negative
coefficient will have a positive impact on technical efficiency (Sharma and Leung,
2000a)
Socio economic and demographic variables (table 1) are expected to have some
impact on technical efficiency. Similarly farm specific variables (table 1) are also
expected to have an impact over technical efficiency affecting carp yield. Farm
specific technical efficiency of the i-th sample farm (TE,) is obtained using the
relationship.
Antilog and expected value TE, = exp (-U,) and E(-U,) ................................... (3)
The prediction of technical efficiencies is based on the conditional expectation of
expression in (3) given the model which specifications (Battese and Coelli,
1988). The parameters for the stochastic production frontier model in (1) and
those for the technical inefficiency model in (2) were estimated simultaneously
with maximum-likelihood estimation (MLE) using an econometric computer
program, FRONTIER 4.1 (Coelli, 1994), that estimates the variance of the
likelihood function as o2 = oU2+av2 and r = oU2/o2
o2 and r are not respondent estimates r=1-nu2/o2
Hypothesis test:
The following hypotheses were tested with a generalized likelihood ratio test to
ensure that inefficiency effects were absent from the model.
Ho: p = 0, the null hypothesis specifies that a simpler half normal
distribution is an adequate distribution of data.
Ho: :/ = 60 = 61 = ...... = 62, = 0, technical inefficiency does not exist.
Ho: y = 0, technical inefficiency is deterministic. Hence explanatory
variables in (2) must be included in along with input and other relevant
variables.
Ho: 61 = S2 = ...... = =0, technical inefficiency effects follows a standard
truncated-normal distribution with no technical inefficiency effects.
The null hypothesis can be tested using the generalized likelihood -ratio
statistic, h, given by:
I = -2 [Ln {L (Ho)) - Ln {L (H,))] ................... .( 4)
where L (Ho) and L (HI) denote the values of likelihood function under the null
(Ho) and alternative (HI) hypotheses respectively. If the given null hypothesis is
true, I has approximately X2 distribution or mixed x2 distribution under y = 0
(Coelli, 1995).
4. Results
4.1. Maximum livelihood estimates and test of hypotheses
The hypothesis that technical inefficiency effects have a half-normal distribution
with mean zero was rejected. Likewise the hypothesis that there is no technical
inefficiency was also rejected. The hypothesis that technical inefficiency is
deterministic is rejected. The null hypothesis that all the parameters in the
technical inefficiency model except the constant term are zero i.e., the technical