Journal of Econometrics 19 (1982) 233-238. North-Holland Publishing Company
ON THE ESTIMATION OF TECHNICAL INEFFICIENCY
IN THE STOCHASTIC FRONTIER PRODUCTION
FUNCTION MODEL*
James JONDROW
Center for Naval Analyses, USA
C.A. KNOX LOVELL
University of North Carolina, Chapel Hill, NC 27514, USA
Ivan S. MATEROV
CEMI, Academy of Sciences, Moscow V-71, USSR
Peter SCHMIDT
Michigan State University, East Lansing, MI 48824, USA
Received March 1981, final version received November 1981
The error term in the stochastic frontier model is of the form (C-U), where u is a normal error
term representing pure randomness, and u is a non-negative error term representing technical
inefficiency. The entire (U-U) is easily estimated for each observation, but a previously unsolved
problem is how to separate it into its two components, tl and U. This paper suggests a solution
to this problem, by considering the expected value of IL, conditional on (U-U). An explicit
formula is given for the half-normal and exponential cases.
1. Introduction
Consider a production function yi =g(xi, fl) + ,Q (i = 1,2,. ., N), where ,yi =
output for observation i, .q=vector of inputs for observation i, /I= vector of
parameters, ~=error term for observation i. The ‘stochastic frontier’ (also
called ‘composed error’) model, introduced by Aigner, Love11 and Schmidt
(1977) and Meeusen and van den Broeck (1977), postulates that the error
term .zi is made up of two independent components,
&i=vi-ll. L2 (1)
*This research has been supported by National Science Foundation grants MCS77-16819 and
SES79-26717. We are indebted to V.K. Smith and two referees for their comments on an earlier
draft of this paper.
0304-4076/82/000&0000/$02.75 0 1982 North-Holland
234 J. Jondrow et al., Estimation of technical inefficiency
where t+ -N(O, c,“) is a two-sided error term representing the usual statistical
noise found in any relationship, and ui >=O is a one-sided error term
representing technical inefficiency. Note that ui measures technical
inefficiency in the sense that it measures the shortfall of output (y,) from its
maximal possible value given by the stochastic frontier [g(x,, 8) + vi].
When a model of this form is estimated, one readily obtains residuals
6;=y-g(xi, fl), which can be regarded as estimates of the error terms si.
However the problem of decomposing these estimates into separate estimates
of the components vi and ui has remained unsolved for some time. Of course,
the auerage technical inefficiency - the mean of the distribution of the ui -
is easily calculated. For example, in the half-normal case [ui distributed as
the absolute value of a N(O,CJ~) variable], the mean technical inefficiency is
0,,/(2/z), and th is can be evaluated given one’s estimate of cur as in Aigner,
Love11 and Schmidt (1977) or Schmidt and Love11 (1979). Or average
technical inefficiency can be estimated by the average of the & But it is also
clearly desirable to be able to estimate the technical inefficiency ui for each
observation. Indeed this was Farrell’s (1957) original motivation for
introducing production frontiers, and the ability to compare levels of
efficiency across observations remains the most compelling reason for
estimating frontiers.
Intuitively, this should be possible because ci= ui-ui can be estimated and
it obviously contains information on ui. In this paper, we proceed by
considering the conditional distribution of ui given si. This distribution
contains whatever information si yields about up Either the mean or the
mode of this distribution can be used as a point estimate of ui. For the
commonly assumed cases of half-normal and exponential ui, these
expressions are easily evaluated.
2. The half-normal case
We consider the two-part disturbance given in (1) above, with vi- N(0, of)
and ui-lN(O, a:)). For notational simplicity, we drop the observation
subscript (i) in this and the following section. We define
u2=u2+u2 ” “2 U, = -U,” E/U2, u; = CT,’ C&T’.
Then our main result (proved in the appendix) is the following:
Theorem I. The conditional distribution of u given E is that of a iV&..o~)
variable truncated at zero.
We can use this distribution to draw inferences about U. For example,
confidence intervals for u are easily constructed. As a point estimate of u, we
J. Jondrow et al., Estimation of technical ineffiency 235
can use either the mean or the mode of its conditional distribution. The
mean is
where f and F represent the standard normal density and cdf, respectively.
We can also note that -&a, = EA/~, where 2 = IJ,,/G”; this is the same point
at which f and F are evaluated in calculating the likelihood function. Thus
we obtain
The second point estimator for u, the mode of the conditional distribution, is
the minimum of ,LL* and zero, which we can write as
M(u 1 E) = - &(fJ,‘/cJ’) if E 5 0,
=o if E>O. (4)
The mode M(u 1 E) can be given an appealing interpretation as a maximum
likelihood estimator; it can be derived by maximizing the joint density of u
and u with respect to u and c, subject to the constraint that v--u=&, as in
Materov (198 1).
Incidentally, it is easily verified that the expressions in (3) and (4) are non-
negative, and monotonic in E. Also, the more general truncated normal case
of Stevenson (1980) yields similar results, with minor algebraic complications.
Of course, ,u* and o.+ are unknown, and thus in using any of the above
results we will have to replace /_L* and o* by their estimates, say fi, and 8,.
[For example, in place of E(u 1 E) we must use _!?(u 1 E), the difference being
evaluation at &, r?* in place of p*, o*; and so forth.] In principle, the
variability due to this sampling error should be taken into account. However.
this would be very difficult to do. Furthermore, it is clear that the sampling
error disappears asymptotically, and thus can be ignored for large enough
samples. This is in contrast to the variability intrinsic to the conditional
distribution of u given E, which is independent of sample size, being just a
reflection of the obvious fact that E contains only imperfect information
about U.
3. The exponential case
This case is identical to the half-normal case, except that now the technical
236 .I. Jondrow et al., Estimation of technical inefficiency
inefficiency error term u is assumed to follow the one-parameter exponential
distribution with density f(u) = exp( -U/C,)/ rrU. Our results are similar to those
for the half-normal case. Define A =E/(T,+cJ,/cJ,. Then we have the following
result:
Theorem 2. The conditional distribution of u given E is that of a N( - IT”A, a:)
variable truncated at zero.
The mean and mode of this distribution are
wI&)=Q” & [
M(uI&)= -&-u;/u”
=o
-A > I (5)
(6)
4. An example
Schmidt and Love11 (1980) estimated a system consisting of a stochastic
frontier production function and first-order conditions for cost minimization,
based on a sample of 111 steam-electric generating plants. The estimates on
which our calculations are based are those reported in the first column
of table 1 of Schmidt and Lovell. In particular, note that c?,” =0.01445,
6: =0.00326, and that the estimated average technical inefficiency (mean of u)
is 0.0959, indicating about 9.6 percent technical inefficiency.
We have calculated (our estimate of) the conditional distribution of u given
E, for each observation, based on the results of section 2 since estimation
assumed half-normal u. We do not present results for all 111 observations,
but rather point out some interesting aspects of these results.
(1) The mean of @u 1 E) is 0.0939, which is in the same ballpark as the 0.0959
reported above, and as the mean of 0.0943 of the -t?. The mean of
fi(u 1 E) is 0.0687.
(2) The most positive E^ (most technically efficient observation) in the sample
is 0.1589 (a modest outlier, about 2.75 standard deviations from the
mean of the E^). This yields ,ii* = -0.1296, 8, =0.0516, so that the
conditional distribution of u given E is the extreme right tail of a normal
- only about 0.006 of the area of N( - 0.1296, 0.051 52) lies to the right
of zero. We have ii?(u ( E) =O, J!$U ( ~)=0.0166; a 95% confidence interval
for u is [0.00046, 0.05681.
(3)
(4)
(5)
J. Jondrow et ul., Estimation of’trchnical inefficiency 237
Twenty observations (including the one just cited) have I\/j(u ) E) =O; each
of these also has a fairly small value of fi(u 1 E). The most technically
efficient observations can be characterized as having relatively high
outputs, low capital stocks, and high levels of fuel consumption and
labor usage. They also represent plants of fairly recent vintage, the mean
year of plant installation being 1959. Their level of allocative inefficiency
[see Schmidt and Love11 (1979)] is below average, though not strongly
SO.
The most technically inefficient observation in the sample had E^=
-0.4554 (a large outlier, about 4 standard deviations from the mean).
This yields ,&=0.3716, 6,=0.0516, so that the conditional distribution
of u given E is basically an untruncated normal distribution. We have
A?(u ( E) = i?(u ( E) =0.3716, with a 95% conlidence interval for u being
CO.2705 0.47271.
Five observations (including the one just cited) have estimated rates of
technical inefficiency in excess of 20%. These five most technically
inefficient observations are more difficult to characterize than are the 20
most technically efficient observations. They have rather average outputs
and (naturally) above average input usage, and they also have slightly
above average levels of allocative inefficiency. They represent plants of
relatively early vintage, their mean year of plant installation being 1951.
5. Conclusions
In this paper, we have proposed a method of separating the error term of
the stochastic frontier model into its two components for each observation.
This enables one to estimate the level of technically inefficiency for each
observation in the sample, and largely removes what had been viewed as a
considerable disadvantage of the stochastic frontier model relative to other
models (so-called deterministic frontiers) for which technical inefficiency is
readily measured for each observation.
Appendix
In the half-normal case, u-N(0, oi), u is distributed as the absolute value
of N(0, oi), v and u are independent, and E = v- u. We wish to find the
distribution of u conditional on E.
The joint density of u and u is the product of their individual densities;
since they are independent,
(A.11
238 J. Jondrow et al., Estimation of technical inefjciency
Making the transformation E = v - u, the joint density of u and E is
1
f(u, 8) = __ *fJIP, exp [ -&2-&(U2+CZ+2UE) . ” ” 1
The density of E is given by eq. (8) of Aigner, Love11
A
and Schmidt (1977),
f(e)= 2 ---(l-F)exp 1
&a [ 1 --Ed , 202
where a* = a,f + ai, A= au/a,,, and F is the standard
#a. Therefore, the conditional density of u given
(A.3), which we can write as
64.2)
(A.3)
normal cdf, evaluated at
E is the ratio of (A.2) to
~~ f(uIE)=ia* llFexp ~u~--$u,-$, , 1 ~10, (A.4)
where af = a,’ az/a2. With a little algebra, this simplifies to
f(u)&)=11
1 -F &a, exp
-l(u + at E/a2)2
2a: 1 , u 2 0. (A.51
Except for the term involving 1 -F, this looks like the density of N(p*,az),
with ,u* = -a,‘&/a*. Finally, note that F is evluated at d/a= -&/a,, and
thus (1 -F) is just the probability that a IV@*, a:) variable be positive. Thus,
(A.5) is indeed the density of a AJ(p*,a:) variable truncated at zero.
References
Aigner, D.J., C.A.K. Love11 and P. Schmidt, 1977, Formulation and estimation of stochastic
frontier production function models, Journal of Econometrics 6, 21-37.
Farrell, M.J., 1957, The measurement of productive efficiency, Journal of the Royal Statistical
Society A 120, 253-281.
Johnson, N.L. and S. Kotz, 1970, Continuous univariate distributions - I (Houghton Mifin
Company, Boston. MA).
Materov, IS., 1981, On full identification of the stochastic production frontier model,
Ekonomika i Matematicheskie Metody 17, 784-788 (in Russian).
Meeusen, W. and J. van den Broeck, 1977, Efficiency estimation from Cobb-Douglas production
functions with composed error, Internation Economic Review 18, 435-444.
Schmidt, P. and C.A.K. Lovell, 1979, Estimating technical and allocative ineficiency relative to
stochastic production and cost frontiers, Journal of Econometrics 9, 343-366.
Schmidt, P. and C.A.K. Lovell, 1980, Estimating stochastic production and cost frontiers when
technical and allocative inefficiency are correlated, Journal of Econometrics 13, X3-100.
Stevenson, R.E., 1980, Likelihood functions for generalized stochastic frontier estimation, Journal
of Econometrics 13, 57-66.
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