VOL. 11, NO. 8, AUGUST 2016
ISSN 1990-6145
ARPN Journal of Agricultural and Biological Science
©2006-2016 Asian Research Publishing Network (ARPN). All rights reserved.
www.arpnjournals.com
TECHNICAL, ALLOCATIVE, COST, PROFIT AND SCALE EFFICIENCIES
IN KEDAH, MALAYSIA RICE PRODUCTION: A DATA
ENVELOPMENT ANALYSIS
Sahubar Ali Bin Mohamed Nadhar Khan1, Md. Azizul Baten1, 2 and Razamin Ramli1
1
Department of Decision Science, School of Quantitative Sciences, Universiti Utara Malaysia, Sintok, Kedah, Malaysia
Department of Statistics, School of Physical Sciences, Shahjalal University of Science and Technology, Sylhet, Bangladesh
Email: [email protected]
2
ABSTRACT
This study estimates profit and scale efficiency using profit DEA model and technical, allocative, and cost
efficiency using cost DEA model under both constant returns to scale (CRS) and variable returns to scale (VRS)
respectively and subsequently the determinants factors were investigated based on the estimated efficiency using survey
data of 70 rice farmers, Kedah, Malaysia. In case of profit efficiency, majority of the rice farmers were operating with
increasing returns to scale 54.29%, 34.29% decreasing returns to scale and only 11.43% with constant returns to scale. In
case of cost efficiency only 4.29% of the farmers were 100% technically efficient under CRS while it is increased into
16.90% under VRS. The average technical, allocative and cost efficiencies were estimated at 0.28, 0.878 and0.255
respectively under CRS while they were increased into 0.61, 0.883 and 0.533 respectively under VRS. In case of profit
efficiency the performance measured in CRS and VRS are showing same while they are performing below average in both
CRS and VRS in case of cost efficiency. The efficiency scores are regressed against the wage, disease control, production,
income, farming system, cost, used in farming operations and discussed.
Keywords: cost DEA model, efficiency, profit DEA model, tobit regression, rice production, Malaysia.
INTRODUCTION
Paddy especially is given a greater emphasis as it
is a staple food for Malaysian. The government is
committed in developing this sector to ensure that rice
production can meet the demand. Various subsidies are
provided to assist farmers in increasing production, where
in the Tenth Malaysia Plan the government set a target of
70% self-sufficiency level. To raise farmer’s efficiency
and productivity, it then becomes imperative to
quantitatively measure the current level of technical
efficiency and policy options available for raising the
present level of efficiency, given the fact that efficiency of
production is directly related to the overall productivity of
the agricultural sector. So, the measurement of productive
efficiency of agricultural production is an important issue
in Malaysia, because it gives pertinent information for
making a sound management decision in resource
allocation and for formulating agricultural policies and
institutional improvement. Like all others parts of
Malaysia, Kedah is an agro-based state of Malaysia and it
produces large quantity of rice.
Data envelopment analysis (DEA) is a nonparametric mathematical programming technique in which
multiple inputs and outputs are used to measure the
relative efficiencies of DMUs (Cooper et al., 2007;
Duzakin and Duzakin, 2007). The original DEA model
was proposed by Charnes et al., (1978). A number of
studies examined the technical efficiency of rice growing
farmers in developing countries using DEA include
Battese and Broca, (1997), Shafiq and Rehman (2000) in
Pakistan farms, Dhungana et al (2004) in Nepal rice farms,
Krasachat (2004) in Thailand rice farms, Chavas et al
(2005) in Gambia farms, Battese and Coelli, (1992),
Battese and Coelli, (1995), in India, Kiatpathomchai,
(2008), in Thailand, Souires and Tabor, (1991), Brazdik,
(2006), in Indonesia,Shafiq and Rehman, (2000), Javed et
al., (2008), in Pakistan, Rahman, (2003), Rahman, (2011),
in Bangladesh, Villano and Fleming, (2004), in the
Philippines, and Tran et al., (1993), Huy, (2009), Khai and
Yabe, (2011), in Vietnam. All of these studies pointed out
substantial inefficiency and the possible potentials to
improve the agricultural productivity. There are also
similar studies that use both DEA and SFA such as
Sharma et al (1999), Wadud (2003) Wadud and White
(2000) and Linh (2012). However, there has been limited
empirical attention on identifying the factors affecting
improvement of rice production efficiency of Malaysia.
A few studies focused on the productivity
analysis in agricultural sector in Malaysia (Akinbile et al
2011; Mailena et al 2014; Pio Lopez, 2007; Singh et al.,
1996; Jayawardane, 1996). A significant body of literature
exist dealing with the technical and allocative efficiency in
different crops and in different regions (Good et al 1993;
Ahmad and Bravo-Ureta 1996; Wilson et al 1998; Wadud
1999; Wang and Schmidt 2002; Larson and Plessman
2002; Villano 2005; Abedullah et al 2007; Laha and Kuri
2011; Asogwa et al 2011; Henderson 2015). According to
our knowledge no study dealt with the profit and cost
efficiency using profit-DEA and cost-DEA among rice
farmers in Malaysia. The present study hopefully would
fill this gap.
In DEA, effects of specific factors of the firm on
technical efficiency cannot be estimated simultaneously
and one stage further after estimation of technical
efficiency use DEA is required. Because of technical
efficiency score is limited in the interval 0 and 1, then the
Tobit regression is most used in the second stage DEA.
This study used the DEA approach because it does not
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VOL. 11, NO. 8, AUGUST 2016
ISSN 1990-6145
ARPN Journal of Agricultural and Biological Science
©2006-2016 Asian Research Publishing Network (ARPN). All rights reserved.
www.arpnjournals.com
impose parametric restrictions on the underlying
technology (Chavas and Aliber, 1993; Featherstone et al.,
1997; Fletschner and Zepeda, 2002; Wu and Prato, 2006;
Watkins et al., 2014).
The present study attempts to measure profit and
scale efficiency using profit DEA model and technical,
allocative, and cost efficiency using cost DEAmodel of
rice growing farmers in rural area of Kedah in Malaysia.
After estimating the efficiency, this paper subsequently
investigates the determinants factors affecting the
efficiency scores of Malaysian rice farmers using Tobit
regression model. The next section focuses on the data and
methodologies. Section three presents the estimation
followed by the empirical results. The last section
concludes the study with the implications of the findings.
METHODOLOGY
Survey area
This study will be conducted in Muda
Agricultural Development Authority (MADA) region in
Kedah, Malaysia to provide a precise picture of rice
growing farmer’s technical efficiency and their
determinants. The area under MADA is the location of the
Muda Irrigation Scheme that covers about 126,155 ha of
which 105, 851 ha are in the north-western part of Kedah
State and 20,304 ha are in the southern part of Perlis State.
About 76 per cent of the land is under rice cultivation
(96,558 ha) and approximately 48,500 farm families reside
there. As the country's main rice producer, Kedah
contributed some 33 per cent of the 2,599,000 metric tons
total produced in 2012. Kedah is the 8th largest state by
land area and 8th most populated state in Malaysia, with a
total land area of 9,500 km2 (3,700 sq mi), and a
population of 1, 890, 098. In Kedah, equatorial climate
with monsoon influence provides a wet season in which
rice grows and a dry season in which it ripens and can be
harvested. Dry months of January and February allow rice
fields to dry out and rice to ripen. Irrigation from Muda
Irrigation Project allows rice fields to be flooded during
dry season and allows two rice crops per year to be grown
under more intensive modern cultivation.
data were collected through interviews with heads of farm
households who all worked as rice farmers in the village.
The sample size needed was calculated using the
following formula:
n = z2 [P(1-P)/d2]*Deff
where, n = sample size, z = two-sided normal variate at
95% confidence level (1.96), P = indicator percentage, d =
precision, Deff = design effect.
To obtain data on indicators at a 10% precision
and 95% confidence interval, assuming a design effect of
0.7288 and the most conservative estimate of indicator
percentage (50%), the minimum sample size required is
70. Therefore, at least 70 firm households will be required
to measure efficiency of rice growing farmers. It is a
statistically representative sample.
The number of firm households needed per
village is 70 and it is expected that 70 households in a
village is sufficient to study any sort of indicators, because
such number of households in a village is widely used by
UNICEF for conducting Multiple Indicator Cluster
Surveys (UNICEF, 1999).
Respondents
The respondents are the rice growing farmers of
the household.
Data collection procedures
The data collection has been done with a primary
objective targeted to meet the objectives of this study
using a structured questionnaire. The questionnaire include
sections on (i) background characteristics of the firm
households e.g. household size, gender, ethnic group,
religion, education level, etc. (ii) knowledge on rice
production or output, yield of rice, human labor used, land
area, amount of seed, amount of manure (ii) firm-specific
information about firm households.
Analytical techniques
Efficiency can be measured in two ways:
parametric econometric method and non-parametric Data
Envelopment Analysis method. The linear programming
technique of data envelopment analysis (DEA) in
measuring technical efficiency does not impose any
assumptions about production functional form and does
not take into account random error hence the efficiency
estimates may be bias if the production process is largely
characterized by stochastic elements. However, the
advantage of DEA is to accommodate a multiplicity of
inputs and outputs. It is also useful because it takes into
consideration returns to scale in calculating efficiency,
allowing for the concept of increasing or decreasing
efficiency based on size and output levels. One of the
limitations of the DEA is that efficiency is measured
relative to this frontier, where all deviations from the
frontier are assumed to be inefficient (Johanson, 2005).
Coelli (1996) reported that where all Decision Making
Units (DMUs) are not operating at optimal scale, due to a
number of constraints limiting their ability to do so, the
use of variable returns to scale (VRS) to characterize the
production process is ideal. The use of VRS specifications
permits the calculation of technical efficiency devoid of
scale efficiency effects.
Sample size and sampling design
Under MADA there are 27 farmer organizations
known as Pertubuhan Peladang Kawasan (PPK) with
around 48500 farmers. Out of 27 PPK, 1 PPK named
MADA Wilayah 2Jitra: Kepala Batas has been selected in
this study. From this PPK, 70 famers were selected. The
Data envelopment analysis
Data envelopment analysis (DEA) is a
nonparametric method in operations research and
economics for the estimation of production frontiers. It is
used to empirically measure productive efficiency of
decision making units. The framework has been adapted
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VOL. 11, NO. 8, AUGUST 2016
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ARPN Journal of Agricultural and Biological Science
©2006-2016 Asian Research Publishing Network (ARPN). All rights reserved.
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from multi-input, multi-output production functions. DEA
develops a function whose form is determined by the most
efficient producers. In most of them, it is very difficult to
obtain the input price due to unavailability of data
information (price data is necessary in order to perform
econometric approach). Essentiality for that reason, this
study uses the non-parametric approach (DEA) to
investigate the efficiency of rice farming households.
CRS Profit DEA
Let us consider n DMUs (decision making unit)
or banks, each one producing different output (y) and
using different inputs (x). The profit efficiency of the rice
farming households assuming constant return scale (CRS)
is measured as follows:
Maxu ,v (u yi / vxi ),
Subject to
u y j  vx j  0,
j  1,2,..., N
u, v  0.
where, x is a vector of rice farming household inputs, y is
a vector of rice farming household output given the inputs,
u is the weighted relative vector associated to output, v is
the weighted relative vector associated to input.
VRS Cost DEA
Imperfect competition, constrain in finance, etc.
may cause a rice farming households to be not operating at
optimal scale, in this case the CRS assumption is not
appropriate because it assumes that rice farming
households are operating at optimal scale. If the CRS
model is used when not all rice farming households are
operating at optimal level, the technical efficiency is
confound with scale efficiency. Banker, Charnes and
Cooper (1984) suggested an extension of the above model
to take into account the variable return to scale (VRS). The
dual form of the above problem as:
Min , ,
 yi  Y  0,
xi  X  0,
St
j 1
TECRS = TEVRS * SE
Where TECRS is the technical efficiency, TEVRS is the pure
technical efficiency, and SE is the Scale efficiency. The
technical efficiency obtained by CRS DEA model can be
decomposed in two parts, one due to scale efficiency, and
one due to pure technical efficiency. Pure technical
efficiency refers to the rice farming households’ ability to
avoid waste by producing as much output as input usage
allows, or by using as little input as output production
allows.
Tobit regression model of technical efficiency
determinants
The Tobit regression model is an econometric
model that is employed when the dependent variable is
limited or censored at both sides. The present study uses a
censored regression to analyze the role of socio-economic,
demography and institutional attributes in explaining
technical, allocative, scale and cost efficiency in rice
production. In order to examine the influence factors that
can hinder the rice production efficiency, Tobit regression
analysis is used as a second stage of the relationship
between the efficiency measure and other relevant
variables. Tobit analysis, a model proposed by Tobin
(1958), assumed that the dependent variable has a number
of its values clustered at a limiting value, usually zero. If
the data to be analyzed contain the values of the dependent
variable that is truncated or censored, the ordinary least
square (OLS) is no longer applicable to the concept of the
estimated regression coefficients. If OLS is directly used it
will lead to biased and inconsistent parameter estimation
whereby the Tobit model, that follows the concept of
maximum likelihood, becomes a better choice to estimate
a regression coefficient (Chu et al., 2010).Because the
ordinary least square (OLS) regression is not appropriate
for this regression analysis that the efficiency score is
limited between 0 and 1, the dependent variable does not
have normal distribution. Tobit regression is more
convenient to have data censored at zero that at 1 (Sharma
et al., 1999). This study employs the following Tobit
regression model and expresses as follow:
K
n

Scale efficiency refers to the rice farming
households’ ability to work at its optimal scale. It can be
proved that:
j
 1 and   0.
where X is m n input matrix, Y is s  n output
matrix,  is an n  1 vector of constant and  is a
scalar. The value of  obtained will be the efficiency
score for i-th rice farming households. It will satisfy   1
, with a value of 1 indicating a point on the frontier and
hence a technical efficiency rice farming households.
U i*   0    j Z ij   i
j 1
Denoting U i as the observed dependent variables,
U i  1 if
U i*  1;
U i  U i* ; if
.U i  0 if
0  U i*  1;
U i*  0.
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VOL. 11, NO. 8, AUGUST 2016
ISSN 1990-6145
ARPN Journal of Agricultural and Biological Science
©2006-2016 Asian Research Publishing Network (ARPN). All rights reserved.
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where U i is an efficiency measures representing technical
efficiency with both CRS and VRS; allocative efficiency,
cost efficiency and scale efficiency of the i-th rice growing
farmers based on DEA estimation;
U i* is the latent inefficiency variable;
Table-1. Background Statistics of the selected farmers of
KepalaBatas, Kedah, Malaysia.
Farming
system
Individual
50
Valid percent
(%)
71.4
Frequency
Z i is a vector of explanatory variables representing of
Estate
19
27.1
farm characteristics;
Total
70
100.0
 j are unknown parameters to be estimated;
Age
 i is the random error term that is independently and
40-49
2
2.9
normally distributed with mean zero and common variance
More than 59
68
97.1
Total
70
100.0
70
100.0
70
100.0
70
100.0

2
, i.e.,
i ~ NID0,  .
2
Since the individual DEA efficiency score varies
between at 0.00 and 1.00, this means the efficiency scores
are double-truncated or censored at 0 and 1. Tobit
regression model which can apply for this type of
dependent variable is two-limit Tobit model (Maddala,
1999) where 0 is lower limit and 1 is upper limit.
The empirical Tobit model specification is
written as follows:
Ui  0  1Z1  2 Z2  3Z3  4 Z4  5Z5  6 Z6 i
where, � represents the wage of the Kepala Batas PPK;
� represents the disease control of theKepala Batas PPK;
� represents the production of theKepala Batas PPK;
� represents the income of theKepala Batas PPK;
� represents the farming system of theKepala Batas PPK;
� represents the cost of the Kepala Batas PPK;
RESULTS AND DISCUSSIONS
DEA Efficiency analysis
A survey was conducted on 70 farmers of the
KepalaBatas PPK from Kedah, Malaysia. Though the
sample was selected randomly, all the selected farmers are
male have education up to UPSR and from Malay race and
about 97% of them are more than 59 years old (Table-1).
More than 71% of the farming systems are individual and
27% farmers are from estate system.
Race
Malay
Education
UPSR
Soil Condition
Moderate
Table-2 presents profit efficiency of selected farmers of
PPK KepalaBatas, Kedah, Malaysia. Minimum efficiency
from constant return to scale and variable return to scale is
almost same which is 0.134 and 0.136 by the same
farmers. Among the selected 70 farmers, seven of them are
showing perfect relative performance in constant return to
scale. However, eleven of them are showing perfect
relative performance in variable return to scale. In scale
efficiency, 26 farmers are showing decreasing return to
scale and 37 farmers are showing increasing return to
scale. The resource - use efficiency, measured in terms of
returns to scale, classified into increasing, decreasing and
constant returns to scale (IRS, DRS, CRS) is shown in
Table-2 and Figure-1. In case of profit efficiency
measurement majority of the rice farmers were operating
with increasing returns to scale 54.29%, 34.29%
decreasing returns to scale and only 11.43% with constant
returns to scale. This shows that majority of the rice
farmers are operating with IRS. The result implies that
only 11.43% of the rice farmers were operating at their
optimal scale, while 34.29% were operating above their
optimal resource utilization. The result is consistent with
Orefi, (2011). Only 10% farmers were found to be full
profit efficient in case of CRS assumption and it is
increased into 15.71% while assumes VRS and 11.43%
farmers were observed for scale efficiency.
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VOL. 11, NO. 8, AUGUST 2016
ISSN 1990-6145
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Table-2. Profit efficiency of selected farmers of Kepala Batas, Kedah, Malaysia.
Farm
Constant
Variable
return to
return to
scale
scale
Profit efficiency
Scale
efficiency
Indicator
Farm
Constant Variable
return to return to
scale
scale
Profit efficiency
Scale
efficiency
Indicator
1
0.692
0.737
0.938
drs
36
0.557
0.565
0.985
irs
2
0.681
0.766
0.889
drs
37
0.665
0.798
0.833
irs
3
0.337
0.373
0.903
irs
38
0.668
0.678
0.985
irs
4
1
1
1
-
39
0.792
0.817
0.97
irs
5
0.726
0.882
0.824
irs
40
0.668
1
0.668
irs
6
0.557
0.568
0.98
irs
41
0.666
0.67
0.994
irs
7
0.546
0.787
0.693
irs
42
0.134
0.136
0.986
drs
8
0.778
0.888
0.877
drs
43
0.607
0.636
0.955
drs
9
0.788
0.855
0.921
drs
44
0.742
0.743
0.999
irs
10
0.742
0.919
0.808
irs
45
0.664
0.75
0.886
irs
11
0.828
0.846
0.978
irs
46
0.657
0.712
0.922
irs
12
0.65
0.673
0.966
irs
47
0.667
1
0.667
irs
13
1
1
1
-
48
0.665
0.697
0.955
drs
14
0.515
0.542
0.95
irs
49
0.668
0.743
0.899
irs
15
0.666
0.69
0.966
irs
50
0.668
0.923
0.724
irs
16
0.742
0.743
0.998
drs
51
0.658
0.662
0.994
irs
17
0.749
0.784
0.955
drs
52
0.923
0.963
0.958
irs
18
0.827
0.859
0.964
drs
53
0.577
0.587
0.982
drs
19
0.659
0.664
0.993
drs
54
0.762
0.861
0.885
drs
20
0.851
0.929
0.916
irs
55
0.618
1
0.618
irs
21
0.643
0.698
0.921
drs
56
0.879
0.913
0.963
irs
22
0.663
0.695
0.953
drs
57
1
1
1
-
23
0.607
0.613
0.99
irs
58
0.501
0.721
0.695
irs
24
0.581
0.665
0.873
drs
59
0.778
0.782
0.995
drs
25
0.733
0.783
0.936
drs
60
0.706
0.714
0.989
drs
26
0.802
0.81
0.991
drs
61
0.666
0.69
0.966
irs
27
1
1
1
-
62
0.322
0.327
0.985
irs
28
0.719
0.746
0.963
drs
63
0.66
0.733
0.901
irs
29
0.723
0.723
1
-
64
0.825
0.844
0.978
drs
30
1
1
1
-
65
0.656
0.692
0.948
irs
31
0.886
1
0.886
irs
66
0.691
0.693
0.997
irs
32
0.658
0.681
0.966
irs
67
0.569
0.622
0.915
drs
33
0.666
0.746
0.893
drs
68
1
1
1
-
34
1
1
1
-
69
0.769
0.975
0.789
irs
35
0.668
0.678
0.985
irs
70
0.769
0.975
0.789
irs
*drs and irs indicates decreasing return to scale and increasing return to scale
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Figure-1 is showing profit efficiency assuming
CRS and VRS of the selected farmers. Performance
measured in CRS and VRS are showing same for most of
the farmers. About 10 farmers are performing below 0.6
and rest others are performing higher relative
performance. This conforms to the results of Balcombe et
al. (2008) and Rahman (2003).
1
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69
CRS
VRS
Figure-1. Profit efficiency of the selected farmers of Kepala Batas, Kedah.
Figure-2 presents the scale efficiency of the
selected farmers of Kepala Batas, Kedah, Malaysia. Scale
efficiency is obtained by dividing the aggregate efficiency
by the technical efficiency. A decision making unit is said
to be scale efficient when its size of operations is optimal.
From the analysis, it is found that 8 out of 70 farmers are
scale efficint, that is, these farmers are using their size
perfectly. The rest other farmers have opportunity to
optimize their size. It is also found that the average scale
efficiency is 0.93. This results conforms with the finding
by Coelli et al (2002) and Linh (2012) where they find the
average scale efficiency values of 0.933 and 0.915
respectively.
1
0,9
0,8
Scale Efficiency
0,7
0,6
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769
Figure-2. Scale efficiency of the selected farmers of Kepala Batas, Kedah.
Table-3 and Table-4 are showing Cost efficiency
assuming CRS and VRS respectivelyof the selected
farmers of PPK Kepala Batas from Kedah, Malaysia. Only
three and six farmers are showing relatively perfect
performance (cost efficient) in CRS and VRS respectively.
Technical Efficiency (TE) which is just the
proportional reduction in inputs possible for a given level
of output in order to obtain the efficient input use, and
Allocative Efficiency (AE) which reflects the ability of the
farm to use the inputs in optimal proportions, given their
respective prices. Except efficient farmers in the all three
aspect (TE, AE and CE), there is four farmers which are
technically efficient. Due to low technical efficiency, cost
efficiency are influenced in both CRS and VRS.The
distributions of technical efficiency under CRS and VRS
are presented in Table-3, Table-4 and Figure-2. In case of
cost efficiency measurement only 4.29% of the farmers
were 100% technically efficient in resource – utilization
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under CRS while 16.90% of the farmers were found 100%
technically efficient in resource – utilization under VRS. It
revealed that about 17% of the farmers were 100%
technically efficient in resource utilization under VRS,
while only 4.29% of the rice farmers were 100%
technically efficient in resource utilization under CRS.
The average technical, allocative and cost efficiencies
of farmers were calculated as 0.28, 0.878 and 0.255
respectively operating by CRS while they were increased
into 0.61, 0.883 and 0.533 respectively by operating VRS.
Total optimum cost requirement was found to be about
53.3%; showing that 46.7% of input costs could be saved
if the farmers follow the results recommended by this
study. This results conforms with the finding by Coelli et
al (2002) where they reported similar technical, allocative
and cost efficiency values.
Table-3. Cost efficiency assuming CRS of the selected farmers of Kepala Batas, Kedah.
1
2
3
4
Technical
efficiency
0.865
0.531
0.231
0.006
Allocative
efficiency
0.951
0.991
0.901
0.859
Cost
efficiency
0.823
0.526
0.208
0.005
36
37
38
39
Technical
efficiency
0.268
0.235
0.209
0.104
Allocative
efficiency
0.824
0.919
0.853
0.899
Cost
efficiency
0.221
0.215
0.179
0.094
5
6
7
8
9
10
1
0.017
0.722
1
0.31
0.022
1
0.807
0.947
1
0.998
0.821
1
0.013
0.684
1
0.309
0.018
40
41
42
43
44
45
0.026
0.384
0.359
0.215
0.167
0.742
0.982
0.858
0.812
0.82
0.98
0.922
0.026
0.33
0.292
0.177
0.164
0.684
11
12
13
14
15
16
0.57
0.338
1
0.375
0.355
0.121
0.692
0.981
1
0.91
0.911
0.733
0.394
0.331
1
0.341
0.324
0.089
46
47
48
49
50
51
0.197
0.315
0.231
0.013
0.776
0.357
0.985
0.981
0.961
0.954
0.996
0.956
0.194
0.309
0.222
0.012
0.772
0.341
17
18
19
20
21
22
0.195
0.092
0.203
0.249
0.596
0.033
0.845
0.741
0.714
0.81
0.973
0.931
0.165
0.068
0.145
0.202
0.58
0.031
52
53
54
55
56
57
0.102
0.143
0.154
0.028
0.246
0.026
0.974
0.977
0.736
0.857
0.68
0.799
0.1
0.139
0.113
0.024
0.167
0.021
23
24
25
26
27
28
29
0.014
0.053
0.125
0.01
0.059
0.219
0.185
0.841
0.722
0.717
0.794
0.635
0.76
0.789
0.011
0.038
0.09
0.008
0.037
0.166
0.146
58
59
60
61
62
63
64
0.514
0.529
0.01
0.476
0.32
0.024
0.108
0.963
0.983
0.769
0.999
0.922
0.872
0.851
0.495
0.52
0.008
0.476
0.295
0.021
0.092
30
31
32
33
34
35
0.348
0.748
0.329
0.225
0.009
0.336
0.974
0.72
0.868
0.996
0.607
0.987
0.339
0.538
0.285
0.224
0.005
0.331
65
66
67
68
69
70
0.224
0.328
0.237
0.009
0.015
0.015
0.974
0.776
0.735
0.992
0.988
0.986
0.218
0.255
0.174
0.009
0.015
0.015
Mean
0.28
0.878
0.255
Farm
Farm
328
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©2006-2016 Asian Research Publishing Network (ARPN). All rights reserved.
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Table-4. Cost efficiency assuming VRS of the selected farmers of Kepala Batas, Kedah.
1
Technical
efficiency
0.815
Allocative
efficiency
0.922
Cost
efficiency
0.751
36
Technical
efficiency
0.521
Allocative
efficiency
0.881
Cost
efficiency
0.459
2
0.485
0.753
0.365
37
0.748
0.779
0.583
3
0.828
0.85
0.704
38
0.423
0.806
0.341
4
1
1
1
39
0.595
0.957
0.569
5
0.781
0.803
0.627
40
1
0.561
0.561
6
0.564
0.957
0.54
41
0.419
0.999
0.418
7
1
0.674
0.674
42
0.531
0.917
0.487
8
1
1
1
43
0.331
0.867
0.287
9
0.401
0.636
0.255
44
0.656
0.979
0.643
10
0.704
0.868
0.611
45
0.691
0.837
0.578
11
1
0.775
0.775
46
0.564
0.76
0.428
12
0.539
0.944
0.508
47
1
1
1
13
1
1
1
48
0.617
0.929
0.573
14
0.662
0.829
0.549
49
0.564
0.724
0.408
15
0.476
0.899
0.428
50
1
0.659
0.659
16
0.529
0.92
0.486
51
0.718
0.92
0.66
17
0.5
0.982
0.492
52
0.791
0.969
0.767
18
0.381
0.965
0.367
53
0.49
0.99
0.485
19
0.526
0.957
0.503
54
0.322
0.917
0.296
20
1
1
1
55
1
0.557
0.557
21
0.493
0.764
0.376
56
0.708
0.884
0.626
22
0.345
0.874
0.302
57
1
1
1
23
0.385
0.931
0.359
58
1
0.555
0.555
24
0.275
0.971
0.267
59
0.433
0.949
0.411
25
0.392
0.947
0.372
60
0.408
0.977
0.398
26
0.349
0.952
0.332
61
0.452
0.936
0.423
27
0.289
0.933
0.27
62
0.656
0.699
0.459
28
0.532
0.994
0.529
63
0.564
0.822
0.463
29
0.39
0.853
0.332
64
0.46
0.99
0.456
30
0.435
0.989
0.431
65
0.567
0.789
0.447
31
0.828
0.962
0.796
66
0.572
0.983
0.562
32
0.433
0.786
0.341
67
0.436
0.976
0.426
33
0.337
0.814
0.274
68
0.475
0.951
0.452
34
0.525
0.928
0.487
69
0.726
0.968
0.702
35
0.374
0.91
0.34
70
0.726
0.969
0.703
Mean
0.61
0.883
0.533
Farm
Technical efficiency assuming CRS and VRS of
the selected farmers are shown in Figure-3. For few
farmers the technical efficiency is same for both
assumptions however, in most of the case the efficiency
Farm
from VRS is greater than that of CRS. In CRS, almost half
of the farmers have technical efficiency very close to zero
and only three farmers have perfect efficiency.
329
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1
0,8
0,6
0,4
0,2
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69
CRS
VRS
Figure-3. Technical efficiency of the selected farmers.
Allocative efficiency is found almost same for the
most of the farmers revealed in Figure-4. Few farmers
have less than 0.6 allocative efficiency assuming VRS
however, there is no farmers have efficiency less than 0.6
assuming CRS.
1
0,9
0,8
0,7
0,6
0,5
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69
CRS
VRS
Figure-4. Allocative efficiency of the selected farmers.
Cost efficiency of the selected farms is presented
in Figure-5. About 20 farmers have relative performance
0.1 or less than that in CRS. Most of the farmers are
performing below average in both CRS and VRS. Though
the petern of relative efficiency in both scale is same,
performance in CRS is showing better compare to
performance in VRS. In CRS, most of the farmers have
relative performance below 0.6 except 5 farmers.
330
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1
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0
1
4
7
10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70
CRS
VRS
Figure-5. Cost efficiency of the selected farmers.
The Tobit model results on the determinants of
resource use inefficiency
In order to identify key determinants of resource
use inefficiency under CRS, VRS and scale, allocative,
technical, and cost inefficiency scores are separately
regressed on selected demographic, socio economic and
institutional variables. The impacts of the factors which
could have influenced the rice efficiency are analyzed by
using the Tobit regression model. This model was applied
to CRS, VRS and Scale efficiency as a dependent variable
and some key socio-economic independent variables
related to inefficiency. Table 5 displays the results of
Tobit regression function for the CRS, VRS and Scale
efficiency. The efficiency scores are regressed against the
wage, disease control, production, income, farming
system, cost, used in farming operations. The disease
control of the sample farmers’ has a negative effect under
CRS and VRS, but a positive effect on efficiency indexes
on scale even though it is not significant related to the
efficiency indexes. Again income of the sample farmers’
has a positive effect under CRS, VRS and scale on
efficiency indexes. Farming System has significant impact
on the efficiency assuming VRS and CRS as well as on
Scale efficiency. It means one more unit of farming
system used in rice production; the efficiency score will
increase and the utilization of farming system displays the
increase in efficiency in this sample area. Wage has
positive significant impact on Scale efficiency. On
efficiency assuming VRS, cost has negative impact which
is not significant; however, it has positive significant
impact on efficiency assuming CRS and on scale
efficiency. Production has positive significant impact on
efficiency assuming CRS whereas it has negative
insignificant impact on efficiency assuming VRS and on
scale efficiency. Estimated standard deviations of the
residuals are -1.713, -3.789 and -4.85 for the model VRS,
CRS and Scale.
Table-5. Results of Tobit regression analysis under VRS, CRS and scale efficiency.
VRS
CRS
Scale
Estimate
Std. error
Estimate
Std. error
Estimate
Std. error
Intercept
0.6735***
0.09077
0.6092***
0.0167
0.8524***
0.003479
Wage
0.000021
0.0000448
-0.0214
0.0000000001
0.0000182***
0.000001
Disease control
-0.000029
0.000128
-0.000008856.
0.00000365
0.000000197
0.000005
Production
-0.02903
0.02013
0.00001168*
0.000007
-0.00279
0.001471
Income
0.00002
0.0000139
0.0000162
0.0000000001
0.00000148
0.000001
Farming System
0.05721*
0.02476
-0.00002531***
0.0000039
0.01431***
0.001045
cost
-0.00002
0.0000276
0.05303***
0.00223
0.0000241***
0.000001
Log(Sigma)
-1.713***
0.09557
-3.789***
0.0133
-4.85***
0.007583
***, ** and * indicates 1%, 5% and 10% level of significance respectively
331
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In Tobit regression, when scale efficiency and
efficiency assuming VRS and CRS are considered as
dependent variable 8, 11 and 7 observations respectively
are right censored (Table-6). That is, estimated values of
these observations were more than one whereas efficiency
cannot exceed one. To keep the estimated values within
limit these observations were right censored however,
there are no left censored observations in the analysis.
Table-6. Censored observation to Tobit regression efficiency
under VRS, CRS and scale.
Observations
Total
Left-censored
Uncensored
Right-censored
VRS
70
0
59
11
CRS
70
0
63
7
Scale
70
0
62
8
they are not significant. Farming System has positive
insignificant impact on the allocative, technical and cost
efficiency. It means one more unit of farming system used
in rice production; the efficiency score will increase once
the farming system utilizes properly in this sample area.
Income and disease control cost has negative impact on
technical and cost efficiency whereas impact on allocative
efficiency is positive; however impacts are not significant.
Estimated standard deviations of the residuals are -2.112, 1.352 and -1.575 for allocative efficiency, technical
efficiency and cost efficiency.
Table-7 presents the estimated parameter to
regress Allocative Efficiency, Technical Efficiency and
Cost Efficiency using Tobit regression. Production has
negative insignificant impact on allocative efficiency
however the impact on technical efficiency and cost
efficiency is positive and significant only on technical
efficiency. Wage has negative insignificant impact on all
these three type of efficiency - allocative efficiency,
technical efficiency and cost efficiency. The income and
disease control of the sample farmers’ has a positive
impact with allocative efficiency while they have positive
impact with technical and cost efficiency indexes though
Table-7. Results of Tobit regression analysis.
Allocative efficiency
Technical efficiency
Cost efficiency
Estimate
Std. error
Estimate
Std. error
Estimate
Std. error
Intercept
0.8863***
0.05651
0.6783***
0.1214
0.5567***
0.09603
Production
-0.007515
0.01232
0.01128***
0.02635
0.008196
0.02105
Wage
-0.00001807
0.00002914
-0.00004349
0.00006249
-0.00004621
0.00004957
Income
0.000004773
0.00000831
-0.000005942
0.00001778
-0.000004362
0.0000142
Disease Control
0.00005754
0.00008508
-0.0001515
0.000182
-0.00009411
0.0001455
Farming System
0.01617
0.01599
0.01682
0.03416
0.03459
0.02719
Log(Sigma)
-2.112***
0.08996
-1.352***
0.09834
-1.575***
0.09124
***, ** and * indicates 1%, 5% and 10% level of significance respectively
Number of censored observations from the Tobit
regression is shown in Table-8. There are no left censored
observations in the estimation of allocative efficiency,
technical efficiency and cost efficiency. However, there
are 6, 12 and 6 right censored observations to predict
allocative efficiency, technical efficiency and cost
efficiency respectively.
Table-8. Censored observation to Tobit regression with allocative, technical and cost efficiency.
Observation
Total
Left-censored
Uncensored
Right-censored
Allocative Efficiency
70
0
64
6
Technical Efficiency
70
0
58
12
Cost Efficiency
70
0
64
6
332
VOL. 11, NO. 8, AUGUST 2016
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©2006-2016 Asian Research Publishing Network (ARPN). All rights reserved.
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CONCLUSIONS
This study is concerned with the measurement of
efficiency for the rice growing farmers using both cost
Data Envelopment Analysis (DEA) and profit DEA as
well as focuses on the empirical investigation of the
factors affecting rice production efficiency in Malaysia,
using the Tobit analysis. The primary data is collected
from PPK KepalaBatas of Kedah, Malaysia with a number
of 70 farmers which are randomly selected to estimate cost
efficiency and profit efficiency using DEA. The technical
and scale efficiency estimates were made also for the rice
farmers in PPK KepalaBatas of Kedah, Malaysia.
According to profit efficiency, most of the farms are
performing on an average however and cost efficiency of
the farmers is below of the average. However, profit and
cost efficiency are showing that most of the farmers have
opportunity to increase their relative efficiency. Technical
efficiency is low, that is, it is possible to reduce inputs for
all the farmers to be efficient. Allocative efficiency is
better than technical efficiency; however, still the farmers
have opportunity to allocate the inputs according to their
cost. The result showed low level of efficiency in resource
utilization by the farmers. Majority of the farmers were
experiencing increasing returns to scale. By operating on
an optimal scale (CRS), input wastage could be reduced.
From the results, the scale efficiencies of rice
farmers at 0.85 above implied that essentially the average
farms are very close to optimal scale. The lower CRS and
VRS efficiency scores are compared to the scale efficiency
and it is suggested that the inefficiencies are mostly due to
inefficient management. Therefore, the analysis on factors
affecting the efficiency is conducted by Tobit regression
analysis. The determinants of efficiency scores are
regressed against the wage, disease control, production,
income, farming system, cost, used in farming operations
and discussed. Farming System has positive significant
impact on the efficiency under CRS, VRS and Scale while
wage has positive significant impact on Scale efficiency
only. Again cost has positive significant impact on
efficiency under both CRS and scale but Production has
positive significant impact on technical efficiency under
CRS only.
According to the theoretical assumption of the
DEA approach, the farm which possesses the highest
efficiency score is situated on the production frontier line
and so, the estimated results from DEA indicate that the
inefficient samples farmers can improve their rice
production efficiency to catch up the efficient sample
farmers in this northern region, Malaysia. This study
suggested that the existence of some inefficiency may be
reduced through policy interventions, adoption, and spread
of improved agricultural mechanization. In particular,
knowledge of factors driving rice production efficiency
and contributions of production efficiency to economic
performance could provide support for policy makers.
ACKNOWLEDGEMENTS
This study was supported by the Research
Acculturation Grant Scheme, S/O Code: 12715, Universiti
Utara Malaysia and Ministry of Higher Education
Malaysia.
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