=Paper=
{{Paper
|id=Vol-1498/HAICTA_2015_paper93
|storemode=property
|title=Technical Efficiency of Shrimp and Prawn Farming: Evidence from Coastal Region of Bangladesh
|pdfUrl=https://ceur-ws.org/Vol-1498/HAICTA_2015_paper93.pdf
|volume=Vol-1498
|dblpUrl=https://dblp.org/rec/conf/haicta/BegumHTP15
}}
==Technical Efficiency of Shrimp and Prawn Farming: Evidence from Coastal Region of Bangladesh==
https://ceur-ws.org/Vol-1498/HAICTA_2015_paper93.pdf
Technical Efficiency of Shrimp and Prawn Farming:
Evidence from Coastal Region of Bangladesh
Mst. Esmat Ara Begum1, Mohammad Ismail Hossain2, Maria Tsiouni3 and
Evangelos Papanagiotou4
1
Senior Scientific Officer, Bangladesh Agricultural Research Institute, Joydebpur, Gazipur,
Bangladesh, e-mail: esmatbau@yahoo.com
2
Associate Professor, Department of Agribusiness and Marketing, Bangladesh Agricultural
University, Mymensingh, Bangladesh, e-mail: ismailho12@yahoo.co.in
3
PhD candidate, Department of Agriculture Economics, Agriculture School of Aristotle
University of Thessaloniki, Greece, e-mail: mtsiouni84@yahoo.gr
4
Professor, Department of Agriculture Economics, Agriculture School of Aristotle University
of Thessaloniki, Greece, e-mail: papanag@agro.auth.gr
Abstract. Shrimp and prawn farming in Bangladesh have experienced
spectacular growth in response to expanding global demand and higher
economic return. In 2011, 180 shrimp and prawn farms were surveyed in
Bangladesh to estimate their production efficiency and determine factors
affecting the efficiency level. The results show that there are substantial
inefficiencies among shrimp and prawn farms. The technical efficiency ranges
from 55% to 97% (Mean±SD: 88±9%) for shrimp farms and from 39.56% to
99.79% (72.41±16%) for prawn farms, suggesting that shrimp and prawn
farms could increase their output by 12% and 27.59%, respectively. For a land
scarce country like Bangladesh this gain could increase income and ensure
better livelihood for farmers. The results of the stochastic production frontier
approach indicate that farmers could operate at an optimal scale for increasing
their product. Farmers’ education, training, age and water quality significantly
affect efficiency.
Keywords: Technical efficiency, shrimp and prawn, coastal region,
Bangladesh.
1 Introduction
Bangladesh is widely recognized as one of the most suitable countries in the world
for brackish water shrimp (marine crustacean) (Penaeus monodon) and freshwater
prawn (Macrobrachium rosenbergii) farming because of its favorable resources and
agro-climatic conditions. A sub-tropical monsoonal climate, low laying agricultural
land, saline water availability and a vast area of shallow water provide ideal
conditions for shrimp and prawn production (Ahmed et al., 2008a). Within the frame
of the agro-based economy of the country, the contribution of shrimp and prawn
production has been considered to hold good promise for creating jobs, earning
foreign exchange and providing protein to an undernourished population. During the
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last three decades development of shrimp and prawn farming has attracted
considerable attention due to its high export potential. The prawn and shrimp sector
is the second largest export industry after readymade garments, generating US$396
million annually and contributing by 5.7% to the total value of exports (DOF, 2013).
During 2012-2013, Bangladesh exported 50,333 tons of prawn and shrimp, valued at
US$ 337.62 million, 82% of which was shrimp and the remaining 18% was prawn
(Export Promotion Bureau, (EPB), 2013).
Bangladesh, like most tropical countries, derives fish from a large number of
complex natural systems. In 2012-13, 3,410,254 MT of total fish were produced in
Bangladesh of which 82.73% came from inland sources. Of the inland sources,
65.92% of the total catch came from the culture sector and the rest from the capture
fisheries. Four sources culture fishes are: baors, ponds and ditches, commercial
shrimp farms and semi-closed floodplains. Baors or oxbow lakes account for a
negligible number of fish catch. In 203, 88% of total inland culture fish came from
the ponds and ditches. Commercial shrimp farms account for about 11% of total
culture fish catch. Marine fisheries represented about 17.27% of total catch. Most of
it comes from marine artisanal source (87.60%).
In 2010, the total area under shrimp and prawn farming was estimated to be
around 275,274 hectares (Ministry of Fisheries (MOF), 2013) while in 1980 it was
20,000 hectares, indicating an average increase of 35% per annum (Department of
Fisheries (DOF), 2013. This level of expansion reflects the government's priorities as
shrimp and prawn farming are recognized as an essential component of economic
development for the country. Most shrimp and prawn farms (53%) are located in
southwest Bangladesh mainly in the districts of Bagerhat, Khulna and Satkhira, and
produce 46% of country’s total shrimp and prawn production (DOF, 2013). The
families of southwest Bangladesh having a high population density tend to be
resource poor, income poor and vulnerable to environment, climate and economic
variability (Bundell and Maybin, 1996; Muir, 2003). Shrimp and prawn farming
therefore creates prospects for increased income and sustainable livelihood for
farmers. The most spectacular boost of shrimp and prawn farming have taken place
in the Satkhira and Bagerhat districts where a large number of farmers have
converted their rice fields to profitable shrimp and prawn farms (Ahmed et al.,
2008b). In spite of the spectacular expansion of shrimp and prawn farms during the
last decades, as well as the adoption by some farms of semi-intensive systems that
produced higher yields, still the average yield is low compared to other Asian
countries. Moreover, the expansion of shrimp and prawn farms have been
accompanied by disease outbreaks and environmental degradation including
destruction of vegetation and social forests, reduction in crop production (especially
rice) and pasture land that have spread and threaten the sustainability of shrimp and
prawn production. Disease outbreaks and environmental degradation have resulted
from increased competition for limited resources linked to intensified production,
overuse of chemicals, absence of proper water treatment and degradation of water
quality. Besides its direct economic losses, long-term environmental degradation also
creates losses that are irreversible and irrecoverable. Therefore, new ways of
developing and expanding this sector in an economically viable and environmentally
sustainable manner need to be identified. In this respect, among many other factors,
increasing the efficiency of resource use in shrimp and prawn production at the farm
843
level stands as an attractive option because it has the potential to generate output
growth without increasing quantities of inputs generating negative environmental
externalities. Based on this promises, this paper estimates the level and the
determinants of technical efficiency in an attempt to modify the management strategy
and increase shrimp and prawn farming productivity in Bangladesh. The objectives
are pursued in parallel for two different production systems that both play important
roles in Bangladesh aquaculture. The first one corresponds to shrimp culture in
brackish water and the second one, corresponds to prawn culture in freshwater.
A number of studies have been conducted on shrimp and prawn farming in
Bangladesh, including, technical efficiency of shrimp farming (Begum et al., 2013),
economic analysis of shrimp farming (Alam et al., 2007), determinants of efficiency
in prawn farming, conversion of rice fields to prawn farms (Ahmed et al., 2010a),
and sustainability of freshwater prawn farming (Ahmed et al., 2010b). However,
there is a lack of studies on the production performance and resource use efficiency
of shrimp/prawn farming in Bangladesh, which is the major source of expansion of
the shrimp/prawn industry in the country. In this context, a stochastic production
frontier model is applied to investigate the level of technical efficiency as well as the
factors that have an effect on the estimated (in)efficiency of shrimp/ prawn farming
in Bangladesh. This study is expected to generate information that will be useful for
farmers in adopting best observed production techniques, in identifying and
eliminating inefficiencies, and in attaining the highest possible output within the
resource endowments.
2 Materials and Methods
2.1 Data and the Study Area
The empirical analysis is based on farm-level cross sectional data collected in
2011 from Shyamnagar upazila in the Satkhira district, in the brackish water area,
and Fakirhat upazila in the Bagerhat district, in the freshwater water area of
southwestern Bangladesh. Shyamnagar and Fakirhat upazilas were selected because
most of the brackish water shrimp and freshwater prawn farms are concentrated in
this area, farmers are experienced in shrimp and prawn farming and resources and
climatic conditions are favorable for shrimp and prawn farming. The shrimp and
prawn farms of the selected region account for the 33% and 15% of total country’s
shrimp and prawn farms, respectively (DOF, 2012). Three villages from each
upazilas were selected on the basis of shrimp and prawn farms concentration. A total
of 90 shrimp and 90 prawn farmers (30 farms from each village) were randomly
selected. A pre-tested questionnaire was used to collect technical and economic data
from the shrimp and prawn farmers, as well as socio-demographic and environmental
characteristics.
844
2.2 Theoretical Model: Stochastic Frontier Model
Farrell (1957) defined technical efficiency as the ratio between inputs per unit of
output at the production frontier and inputs per unit of output in the observed case. In
a more recent presentation, which is adopted in the present study, technical efficiency
of the firm, which produces output y with inputs x is given by y/y*, where y* is the
frontier output associated with the level of inputs x (Coelli et al., 1998).
Aigner et al. (1977) and Meeusen and Van den Broeck (1977) proposed a
stochastic frontier production function model with the following structure:
L n Y = f ( X i : β ) + εi (1)
εi = Vi – Ui, I = 1, … , N (2)
where Y denotes production level, Xi is input level and β is a vector of unknown
parameters to be estimated. εi is the composed error term and f is the Cobb–Douglas
function form. Vi are independently and identically distributed random errors, having
N (0, δν2) distribution while Ui are non-negative stochastic variables, called technical
inefficiency effect, associated with the technical inefficiency of production of
farmers involved.
According to Battese and Coelli (1995), technical inefficiency effects are defined
by
Ui = Zi δ + Wi , i = 1, ... , N (3)
where Zi is a vector of explanatory variables associated with technical inefficiency
effects, δ is a vector of unknown parameters to be estimated, Wi are unobservable
random variables, which are assumed to be identically distributed, obtained by
truncation of the normal distribution with mean zero and unknown variance σ2, such
that Ui are non-negative.
The stochastic frontier production function was estimated through the application
of the maximum likelihood approach, using the FRONTIER computer program
developed by Coelli (1994). The stochastic frontier technique can only handle one
single output. Therefore, the different outputs from shrimp and prawn production
were aggregated to a single output using the actual farm gate prices. The following
model specifications were used in the analysis:
(4)
where,
ln represents the natural logarithm (i.e., to the base e) and i refers to the ith farm in the
sample;
Yi represents geometric mean based on revenue share of multi-output (such as
shrimp/prawn production, other fine fish production, dike crops and rice production)
which is an ideal output variable in the production frontier analysis as suggested
Iinuma et al., 1999;
X1i represents the total area of land/gher1 size in hectares;
1
Gher is Bengali word used to describe coastal fisheries in the south-western region
of Bangladesh. Gher means encirclement of brackish water areas along the coastal
belts by building dwarf earthen dykes in order to hold tidal water containing shrimp
fries until they grow to marketable size.
845
X2i represents the human labor employed in man-days per hectare;
X3i represents total number of shrimp/prawn fingerlings released/stocked per hectare
per year;
X4i represents quantity of feeds in kg (pulses, oilcake and wheat bran) applied per
hectare per year;
X5i represents quantity of lime applied in kg per hectare per year;
X6i represents quantity of manure/fertilizer used in kg per hectare per year;
X7i represents quantity of pesticide used in kg per hectare per year/amount of cost
incurred for other inputs in Taka per hectare per year;
β1 - β7 are parameters to be estimated;
vi represents the random variations in output due to factors outside the control of the
farm operator such as: degree of water salinity, shrimp fry availability in the sea
water, disease of shrimp, existence of carnivorous (predator) fish species during the
entry of sea water in the farms.
Following Battese and Coelli (1995), it is further assumed that the technical
inefficiency distribution parameter, Ui is a function of various operational and farm
specific variables hypothesized to influence technical inefficiencies as:
(5)
where z1i denotes the age of the ith farmer in year;
z2i denotes the education (year of schooling) of the ith farmer;
z3i denotes the training received by the ith farmer (1 if received, 0 otherwise);
z4i denotes the involvement in fish farm associations of ith farmer (1 if involve, 0
otherwise);
z5i denotes share of non-farm income to total income of ith farmer in percent;
z6i denotes the family size of ith farmer in persons;
z7i denotes the distance of the farm from the canal of ith farmer (1 if less than 500
metres, 0 otherwise);
z8i denotes the water quality of gher of ith farmer (1 if good enough, 0 otherwise);
and
z9i denotes the proportion of lease area to total shrimp/prawn farm area of ith farmer;
δ1, δ2, δ3, δ4, δ5, δ6, δ7, δ8 and δ9 are unknown parameters to be estimated.
2.3 Sample characteristics
A summary of the sample data from the survey for the variables incorporated in
the stochastic frontier model is presented in Table 1. The table shows that
considerable variation exists among the farmers in terms of production practices and
the socioeconomic attainments. The average gher size of the sampled shrimp farms is
2.0 ha, ranging from 0.53 ha to 6.68 ha, while 28% of operations have a gher size of
less than 1.0 ha. The average gher size for the prawn farms is 1.96 ha, ranging from
0.20 ha to 6.32 ha and 30% of the farms have a gher size of less than 1 ha.
846
Table 1. Summary statistics for variables in the stochastic frontier production functions for
shrimp and prawn farmers of different farming types
Variables Farm Sample Standard Minimum Maximum
types mean deviation value value
Geometric mean of Shrimp 16125.68 7728.79 6485.89 44608.69
total return Prawn 9133.44 2325.93 4737.19 18206.71
(Taka/ha)
Land (Hectares) Shrimp 2.00 1.52 0.53 6.68
Prawn 1.96 1.37 0.20 6.32
Labour (Person- Shrimp 116.79 37.45 35.93 220.45
days/ha) Prawn 88.61 22.37 39.58 151.25
Labour (Person- Shrimp 116.79 37.45 35.93 220.45
days/ha) Prawn 88.61 22.37 39.58 151.25
Shrimp/Prawn Shrimp 8034.15 1191.71 4574.07 10977.78
fry/fingerlings Prawn 13052.84 677.62 11805.15 14250.00
(Number/ha)
Feed (kg/ha) Shrimp 134.55 116.61 0.00 428.13
Prawn 1086.23 160.15 833.30 1504.00
Lime (kg/ha) Shrimp 53.13 114.56 0.00 439.95
Prawn 168.31 74.19 67.33 301.00
Organic fertilizer Shrimp 168.49 61.37 44.85 274.44
(kg/ha) Prawn 170.32 72.38 75.74 290.00
Pesticide Shrimp 9.56 9.13 0.00 37.05
(kg/ha)
Prawn 5638.01 1476.89 3694.44 15607.11
(Other
cost Taka)
Education (years Shrimp 10.17 3.45 0.00 16.00
of schooling) Prawn 11.27 2.09 5.00 16.00
Age (years) Shrimp 45.36 10.16 25.00 70.00
Prawn 42.89 6.32 29.00 55.00
Nonfarm income Shrimp 44103.33 35847.67 0.00 150000.00
(Taka) Prawn 59455.56 32719.83 12000.00 150000.00
Family size Shrimp 5.16 1.39 2.00 10.00
(persons) Prawn 5.16 1.11 3.00 8.00
Proportion of lease Shrimp 6.76 19.33 0.00 100.00
area (%) Prawn 7.28 1.55 0.00 81.00
The average gher size of shrimp farming (2.0 ha) is comparatively larger
compared with prawn (1.96 ha) farming. Stocking density of shrimp farms on
average (number of fingerling released per ha) is appeared to be 8034.15 pieces
while stocking density of overall prawn farms is 13052.84 pieces on average, which
has considerable variation in the two farm types as prawn farmers used more
fingerlings compared with shrimp farmers. The average feed application in shrimps
847
is 134.55 kg/ha which is higher compare to earlier studies as shrimp is grown
naturally without any feed or little feed application. The average feed application in
the prawn system is 1086.23 kg/ha. In prawn farming farmers used more feed
compared to shrimp. Prawn farmers used more lime (168.31kg/ha) compared with
shrimp farmers (53.13 kg/ha). All the sample shrimp and prawn farmers apply
organic fertilizer for gher preparation and water treatment which ranges from 44.85
kg/ha to 274.44 kg/ha with a mean of 168.49 kg/ha and from 75.74 kg/ha to 290.00
kg/ha a mean of 170.32 kg/ha, respectively, indicating that farmers of both
production systems use almost the same quantity of fertilizer. The mean of nonfarm
annual income of the shrimp and prawn farmers are Tk. 44103.33 (US$ 543.48) and
Tk. 59455.56 (US$ 732.66) respectively. The average labor use in the shrimp and
prawn farming is 116.79 man-days/ha, ranging from 35.93 man-days/ha to 220.45
man-days/ha and 88.61 man-days/ha ranging from 39.58 man-days/ha to 151.25
man-days/ha, respectively. Although intensity of inputs use varies across gher, the
overall technology practice is largely improved extensive (33% of sample farmers)
(relying more on naturally food produced in the water body and to some degree on
supplementary inputs) to semi-intensive (67%, relying mostly on supplementary feed
and fertilizer). The average age of farmers vary from 45.36 years in shrimp to 42.89
years in prawn farming. Average general educational level is seemed to be moderate
varying from ten years in year round to eleven years in shrimp and prawn farming
(Table 1).
3 Empirical Results
3.1 Stochastic Frontier Results
The estimates of the stochastic frontier analysis which shows the best practice
performance, i.e., efficient use of the available technology, is presented in Table 2.
The empirical results in Table 2 indicate that the output elasticity with respect to gher
size in overall shrimp farming was estimated to be -0.281 and is significant at 1%
level which is unexpected but might be due to over use of input. This indicates that,
if the gher size of shrimp farms is increased by one percent, then the per hectare
return from shrimp is estimated to decrease by 0.281%. In the overall shrimp farming
the elasticity of output with respect to labor, fingerlings, organic fertilizer and
pesticide are estimated to be 0.104, 0.302, 0.149 and 0.063 respectively and
statistically significant. The elasticity of output with respect to fingerlings implies
that, if the number of shrimp fingerlings is increased by one percent, the shrimp
return is estimated to increase by 0.302%. The increase in the use of shrimp
fingerling is expected to have a positive effect on shrimp production, unless the
quality of fingerling is very poor or diseased.
In the case of prawn farming, elasticity of frontier production with respect to gher
size is -0.167 and significant at 1% level. This indicates that if the area under prawn
production is to be increased by one percent, the average return from prawn is
estimated to decrease by 0.167% which is wondering as land has some impacts on
848
production. It might be due to over use of inputs of the small farmers and less use of
inputs of the large farmers. Further, the elasticity of output with respect to
fingerlings, feed, organic fertilizer and pesticide are estimated to be 0.089 and 0.741,
0.297, 0.310, 0.162, respectively, and statistically significant.
Gher size may have some influence on production of output but we encountered a
negative sings for gher size both shrimp and prawn farming which are significant.
Whether small lands are more productive or not is still dilemma. No definite answer
is established as yet. Rahman (2005) found medium sized gher having the highest
yield. The small gher get intensively input fed since additional of a small quantity of
inputs adds very little to the overall cost that is not usually felt burdened. However,
this small addition of inputs might get proportionately higher than the gher requires.
It is likely that this might have happened beyond the knowledge of the farmers. On
the contrary, larger land owners also seldom add inputs proportionately with the gher
size because costs associated with the inputs application for bigger gher are high.
Therefore, they are likely to add proportionately less than the gher requires. This
feeling often results in proportionately higher input feeding for small ghers and lower
for larger ghers. This is general scenario in particularly the shrimp and prawn
farming system under the existing economic conditions of the farmers. Appearance
of a negative signs for the coefficient of gher is therefore not surprising.
3.2 Factors Explaining Inefficiency
The results indicate that the farm specific variables included in the technical
inefficiency model contribute significantly, both as a group and several of them
individually, to the explanation of the technical inefficiencies (Table 2). In overall
shrimp farming, education of the farmers, training, age and nonfarm income have
positive impact on technical efficiency (negative impact on technical inefficiency and
involvement in fish farm associations, family size, distance, water quality and lease
area have negative impact on technical efficiency (positive impact on technical
inefficiency).
Results indicate that education significantly improves technical efficiency of
shrimp farming, consistent with Asadullah and Rahman (2009) and Sharif and Dar
(1996) for Bangladeshi farms. The educated farmers are expected to follow the
shrimp management practices properly, which might have led to higher efficiency for
them. The age coefficient is positive and significant with technical efficiency in
shrimp farming which indicates that older farmers are more capable to take proper
decisions regarding farm management practices as they have many years of practical
experience. This confirms to the results obtained by Dey et al. (2000); Alam et al.
(2011) and Rhaman et al. (2011).
In the case of prawn farming factors such as nonfarm income, family size and
water quality were positively related to inefficiency while education, training, age,
involvement of fish farm associations, distance of the farm from the canal, and lease
area were negatively related to inefficiency. It is expected that the coefficient of
nonfarm income (not significant) to be positive however the findings of this study is
consistent with the findings of Haque (2011).
849
Table 2. Maximum Likelihood estimates of the stochastic frontier production function of the
shrimp and Prawn farming
Shrimp Prawn
Variables Parameters Standard Standard
Coefficients Coefficients
error error
Production frontier
Constant β0 6.580*** 1.303 12.474*** 0.955
Land (x1) β1 -0.281*** 0.032 -0.167*** 0.047
Labour (x2) β2 0.104* 0.058 0.089 0.081
Fingerlings 0.741*** 0.236
β3 0.302** 0.150
(x3)
Feed (x4) β4 0.004 0.009 0.297 0.258
Lime (x5) β5 0.008 0.009 -0.346** 0.165
Organic 0.310** 0.148
β6 0.149*** 0.048
fertilizer (x6)
Pesticide (x7) β7 0.063*** 0.0137 0.162*** 0.038
Inefficiency function
Constant δ0 -2.411 3.354 1.748*** 0.370
Education δ1 -0.166** 0.176 -0.096*** 0.026
Training δ2 -0.755 0.818 -0.103* 0.119
Age δ3 -0.016* 0.014 -0.014* 0.009
Involvement -0.015 0.198
of fish farm δ4 0.561 0.680
association
Non-farm 0.211 0.918
δ5 -0.005 0.008
income
Family size δ6 0.385 0.391 0.021 0.055
Distance δ7 0.183 0.306 -0.076 0.237
Water quality δ8 0.216* 0.209 0.154* 0.131
Lease area δ9 0.008 0.135 -0.129 0.825
Variance parameters
Sigma- 0.091*** 0.011
σ2 0.415 0.470
squared
Gamma γ 0.975*** 0.031 0.999*** 0.0006
Log likelihood 37.23 25.73
Mean TE index 87.84% 72.41
*** Significant at 1%, ** Significant at 5% and * Significant at 10%
The educated prawn farmers are expected to follow the prawn management
practices properly, which might have led to higher efficiency for them. This result is
consistent with the findings by Abdulai and Eberlin (2001), which established that an
increase in formal education will augment the productivity of farmers since they will
be better able to allocate family-supplied and purchased inputs, select and utilize the
appropriate quantities of purchased inputs while applying available and acceptable
850
techniques to achieve the portfolio of household pursuits such as income. The
training coefficient is positively significant with technical efficiency in prawn
farming, which consistent with Rashid (2002).
It is evident from Table 2 that the estimate of σ2 and γ are large and significantly
different from zero, indicating a good fit and the correctness of the specified
distributional assumption. Moreover, the estimate of γ, which is the ratio of the
variance of farm-specific technical efficiency to the total variance of output, is 0.98
of shrimp; and significant at 1% level. In the case of overall prawn farming the γ-
parameter associated with the variances in the stochastic production frontier is
estimated to be close to 1 (Table 2). This suggests that the technical inefficiency
effects are significant component of the total variability of shrimp output for
different farming methods. Therefore, the traditional production function with no
technical inefficiency effects is not an adequate representation of the data.
3.3 Efficiency Distribution
The mean technical efficiency of the shrimp farmers in Bangladesh is 88±9%
(Mean ± Standard deviation), ranging from 52% to 97% (Table 3). And the mean
technical efficiency of the prawn farmers in Bangladesh is 72.41±16% ranging from
39.56% to 99.79%. The implication is that, on average, shrimp and prawn farming
could generate 12% and 25% higher output, respectively by eliminating technical
inefficiency, which is substantial and could improve the competitiveness of the
Bangladesh shrimp and prawn farming. The indices of TE indicate that if the average
shrimp farmers of the sample could achieve the TE level of its most efficient
counterpart, then average shrimp farmers could increase their return by 9% [1-
(88/97)].
On the other hand, the indices of TE indicate that if the average prawn farmers of
the sample could achieve the TE level of its most efficient counterpart, then average
prawn farmers could increase their return by 27% [1-(72/99)]. Similarly, the most
technically inefficient prawn farmers could increase the return by 60% [1-(40/99)] if
he/she could increase the level of TE to his/her most efficient counterpart. Similarly,
the most technically inefficient shrimp farmers could increase the return by 46.39%
[1-(52/97)] if he/she could increase the level of TE to his/her most efficient
counterpart. For a land-scarce country like Bangladesh, these gains in return will
increase their overall income and ensure better livelihood for the farmers. The
distributions of the efficiency scores are quite similar at the higher of the efficiency
spectrum for farm types. About 4.44% of the shrimp farmers respectively are
producing at an efficiency level of less than 60% while 57.78% of the shrimp farmers
are producing respectively at an efficiency level of 90% and above, which are
encouraging (Table 3). About 8.89% of the prawn farmers are producing at an
efficiency level of less than 50% while 15.56% of the prawn farmers are producing at
an efficiency level of 90% and above.
The mean technical efficiency of shrimp and prawn farms is 87.84% and 72.41%
respectively, which is quite similar to the estimates of average agricultural farms
(aquaculture and livestock/dairy farms) in Bangladesh and/or elsewhere in the world
(Bravo-Ureta et al., 2007; Coelli et al., 2002; Wadud and White, 2000, Theodoridis
851
et al., 2009; Theodoridis et al., 2011). Haque (2011) found the TE of shrimp culture
to be 71%. Rashid (2002) found technical efficiency of extensive, improved
extensive and semi intensive shrimp farming were 82%, 85%, 93% respectively.
However, technical efficiency of shrimp farming in other countries appeared to be
higher than that found in Bangladesh. Studies on India conducted by Reddy et al.
(2008) estimated the TE of shrimp to be 93%. Other studies such as Alam et al.
(2011) found the TE of tilapia in Bangladesh farmers at 78%. Sharma and Leung
(2000) estimated the TE of carp polyculture in Bangladesh to be 47.5% for extensive
farming and 73.8% for semi-intensive farming. ICLARM (2001) found the TE of
carp polyculture at 70%. This wide inefficiency spectrum is not surprising and is
similar to those reported in the literature (Rahman et al., 2011; Alam et al., 2011;
Bravo-Ureta et al., 2007; Coelli et al., 2002; Wadud and White, 2000).
3.4 Tests of Hypotheses
A likelihood ratio test was conducted to test the null hypothesis that the Cobb-
Douglas production function could be replaced by the translog production function.
The test statistic H0: βjk = 0, H1: βjk ≠ 0, has a likelihood ratio value of 12.21 for
shrimp and 9.35 for prawn farms, implies a rejection of the null hypothesis at the 5%
significance level. In other words, the Cobb-Douglas production function is more
suitable to the shrimp and prawn farms survey data that adequately captures the
production behaviour.
Now we turn our attention to the tests of hypotheses for the study. Hypothesis (1):
the inefficiency effects are not present, symbolically,
H0: γ = δ0 = δ1 = δ2 = .......... = δ9 = 0; and
hypothesis (2): the coefficients of the explanatory variables in the inefficiency model
are equal to zero (and hence that the technical inefficiency effects have the same
truncated-normal distribution) i.e.,
H0: δ1 = δ2 = .......... = δ9 = 0
were tested using the generalized likelihood-ratio statistic, λ, defined by Equation 5.
Formal tests of hypotheses associated with the inefficiency effects (hypotheses (1)
and (2)) are presented in Table 4. It is evident from Table 4 that the null hypothesis
H0: γ = δ0 = . . . = δ9 = 0 is rejected for the shrimp and prawn farming indicating the
significant presence of inefficiency effects on shrimp and prawn farming. Thus the
traditional average response function is not an adequate representation for shrimp
and prawn production, given the specification of the stochastic frontier and
inefficiency model, defined by Equations (3) and (4).
The second null hypothesis H0: δ1 = δ2 = .......... = δ9 = 0 implies that technical
inefficiency effects follow a standard truncated normal distribution (Stevenson,
1980) as the null hypothesis is rejected at 5% level of significance for both categories
of farming. This indicates that the farm-specific variables involved in the technical
inefficiency model contribute significantly as a group to the explanation of the
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technical inefficiency effects in shrimp and prawn production although, based on
asymptotic t ratios, some slope coefficients are not significant individually.
Table 3. Distribution of technical efficiency scores
Estimates
Variables
Percent
Efficiency levels Shrimp Prawn
≤ 50 0.00 8.89
50 ≤ 60 4.44 11.11
60 ≤ 70 3.33 30.00
70 ≤ 80 3.33 13.33
80 ≤ 90 31.11 21.11
90 ≤ 100 57.78 15.56
Mean efficiency level 0.88 0.72
Minimum 0.52 0.4
Maximum 0.97 0.99
Standard deviation 0.09 0.16
Number of observations 90 90
Table 4. Generalized likelihood ratio tests of hypotheses of parameters
Test of null hypotheses Log-likelihood Test DF Critical Conclusion
(H0) value of the statistic χ2 value
reduced model (λ) at 95%
1. No inefficiency effects
(H0: γ = δ0 = δEd = …. = δFs = 0 )
Shrimp farming 27.26 19.95 11 19.045 Reject H0
Prawn farming 13.34 24.83 11 19.045 Reject H0
2. No effects of inefficiency factors included in the inefficiency model
(H0: δEd = …. = δFs = 0 )
Shrimp farming 27.22 20.02 9 16.274 Reject H0
Prawn farming 13.29 24.88 9 16.274 Reject H0
Note: The value of the log-likelihood function under the specification of alternative
hypothesis (unrestricted/full model) is 53.89. The correct value for the null
hypothesis of no inefficiency effects are obtained from Kode and Palm (1986).
The next issue of interest is to test the hypothesis (3): shrimp farms are equally
technical efficient with prawn farming operating under different farming types. A
simple t-test was administered for testing this hypothesis. Assuming Ho to be true,
the hypothesis can be written as, technical efficiency of shrimp farms = technical
efficiency of prawn farms;
H1 : Ho is not true.
853
Formal test of hypothesis (3) associated with the technical efficiency of farms is
presented in Table 5. The null hypotheses considered in Table 5, Ho : TE(sh) = TE(pr) is
rejected at 1% level of significance which indicated that there are evidence that the
mean of technical efficiency is significantly different.
Table 5. Statistics for test of hypothesis involving technical efficiency of the shrimp and
prawn farms types
Null Hypothesis Test Statistic t Critical Value (5%) Decision
Ho : TE(sh) = TE(pr) 7.870 1.654 Reject Ho
Note: sh = shrimp, pr = prawn.
4 Conclusions and Policy Implications
This study examines the efficiency of shrimp and prawn farming in Bangladesh.
The production data and several farm-specific data were collected from a sample of
shrimp and prawn farmers and analyzed using a stochastic production frontier,
including a model for the technical inefficiency effects. The parameters for the
production frontier and those for the technical inefficiency model are estimated
simultaneously using a ML estimation technique. The results indicate that there are
significant production inefficiencies among the sample shrimp and prawn farmers in
Bangladesh. The mean technical efficiency level of shrimp and prawn farming were
88% and 72% respectively implying that a substantial 18% and 28% of the potential
output from the shrimp and prawn farming system can be recovered by eliminating
inefficiency. Reductions in technical inefficiencies are unlikely to bring about large
productivity gains. Our estimates suggest that these efficiency gains could mainly
come from increased production intensity, from the improvement in the adoption of
management practices, and from making better use of other inputs. The key factors
of the management practices of brackish water shrimp and fresh water prawn farming
in Bangladesh are to be considered by farmers as feeding show improper application.
The quality, quantity of feeds, and frequency of feeding are important considerations
in shrimp and prawn farming management, which will enhance the productivity of
shrimp and prawn farming. In addition, fingerlings, fertilizer and pesticides are
significant factors contributing positively to the production of shrimp and prawn.
Finally, education, age of farmers, and water quality, are significant determinants of
technical inefficiency of shrimp and prawn farming. The study reveals that the level
of understanding of shrimp/ prawn farming technology is different across farmers,
particularly in terms of inputs application. The decision to add or not to add inputs
must be reasoned. It has to be judicious and this could help farmers to increase their
farm efficiency. Policies leading to the improvement of farm education would be
favourable for improving the technical efficiency of farmers. More investment in
education in rural areas through private and public partnerships, initiating progress to
encourage those at school-going age and ‘food for education’ programs may be
harnessed as a central ingredient in the development strategies. Moreover, the farmer
field schools (FFS) program, promoted by different development agencies may be
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rigorously implemented and practiced. This would help farmers develop their
‘learning by doing’ practices and improve their analytical and decision-making skills
that contribute to adapting improved farming technologies. These measures in the
long run may shift the farmers’ production frontier upward, which may in turn,
reduce technical inefficiency on the one hand and lead to raise income and standard
of living of the farming people on the other.
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