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American Journal Agricultural Science and Engineering Technology

ROLE OF SOCIO-ECONOMIC FACTORS TO DETERMINE TECHNICAL 
EFFICIENCY OF SHRIMP FARMERS OF BANGLADESH 

Sadika H.  1, Siegfried B.2, Madan M. D.3, Nazmul H.4 and Puran M.5 

Abstract 
This paper is an attempt to measure the technical efficiency of shrimp farmers in 

Bangladesh using a translog production frontier and inefficiency effect model. A 

sample of 185 farmers has been considered from the Paikgacha Upazila of Khulna 

district. The result shows high levels of technical inefficiency exists in shrimp 

cultivation. The mean efficiency is 71% which suggests that farmers could 

increase production by increasing technical inefficiency by 29%. The efficiency 

differences are significantly explained by farm size, education, share of non-farm 

income, training, distance from pond to water source, water quality etc. 

Key words: Shrimp, technical efficiency, translog stochastic production frontier. 

Introduction  
Shrimp production plays very important role in the economy of Bangladesh in terms of export and 

employment. Over the last two and half decades, from 1983 to 2008, the volume of shrimp 

cultivated has increased more than 14 times. The area of shrimp has also expanded from 20,000 ha 

in 1980 to 150,000 ha in 2001 (Karim, 2005) and this expansion has also been spurred by the 

introduction of 2002-03 shrimp export cash incentives and the government encouragement to 

farmers to increase shrimp production (Deb and Bairagi, 2009). Shrimp farming has also created a 

substantial volume of employment in the coastal regions of Bangladesh through its forward and 

backward linkages (i.e. processing, marketing and exporting). It is estimated that there are over 

600,000 people employed directly in shrimp aquaculture who support around 3.5 million 

dependents (USAID, 2006). But the problems of disease and environmental degradation have 

contributed to a decline in productivity, with many shrimp farmers experiencing negative profits, a 

decline in living conditions and economic losses. Disease outbreaks are results of unnecessary use 

of chemicals, lack of proper water exchange and degradation of water quality (Alam, 2007).  

By nature, the expansion of shrimp farming is dependent on tidal flow of common property 

brackish water which flows from water source to the main canals, main canals to sub-canals and 

the supply of water at the farms behind the sub-canals is usually done by using the others farm 

 Corresponding Author’s e-mail: sadikahaque@yahoo.com 
1, 2, 4 and 5 Justus Liebig University of Giessen, Germany  
3 Aquaculture/Fisheries Centre, University of Arkansas at Pine Bluff, AR 71601, USA. 



American J. Agril. Sc. Engr. Tech.  

land. Thus, problem of a head-enders and tail-enders arises. The water management is very easy 

for the head-enders and tail-ender farmers exchange water by using others’ private land and in 

most cases, they face restricted access to water exchange. Due to restricted access, the water 

quality degrades that affects shrimp production (Mazid, 2003). As a consequence, the tail-enders 

lease out land to the head-enders, leave farming occupation and engage in other non-farming 

activities. Literature informs us, different factors; such as institutional, social and economic issues 

contribute to increase productivity and efficiency as well as economic well-being. According to 

that, shrimp production can be increased by improving the technical and allocative efficiency, 

using existing resources and new technology. TE is more important where use of farm-produced 

inputs (not purchased from the market) is highly prevalent (Singh, et. al, 2009). 

Given the above stated situation, the main objective of this paper is to measure the level of 

technical efficiency of shrimp producers and its determinants in the south-west coastal areas of 

Bangladesh. The results are expected to provide information to the policy level how higher 

productivity could be gained by improving technical efficiency of shrimp farming in Bangladesh. 

The paper is organized as follows; the next section presents methodological issues. The results are 

presented and discussed in the third section and the last section draws conclusion and some 

implications for policy. 

Methodology 

Literature posits different methodologies to measure technical efficiency. Farrell (1957) pointed 

out that the frontier production function is appropriate for such analysis as it meets the theoretical 

definition of a production function. After his seminal paper, there has been a growing interest in 

methodologies and their applications to efficiency measurement. Among all the literatures, the 

frontier production function approach by Aigner, et. al, (1977) can generally be considered an 

appropriate method where they defined the random term is separated into two independent 

components as: Yi = f (Xi,β) + Vi-Ui; where Yi = output for observation i, β = vector of parameters. 

Vi is the two-sided symmetric, normally distributed random error {Vi~ N(0,σv
2)} representing the 

usual statistical noise found in any relationship and Ui ≥ 0 is a one-sided error representing 

technical inefficiency with a half normal distribution {Ui~ |N(0, σu
2)|}. Aigner, et. al, (1977) 

derived the log-likelihood function for the model as:  

LnL(Y/βλσ2) = N ln 

2

+ N ln σ-1 + 


n

i 1

ln [1-F(εiλσ-1)] – ( 22
1


)


N

i 1

2
1 ; in which εi = error 

term for observation i. The error term εi is made up of two independent components (Vi-Ui).  

Later Jondrow et al., (1982), Bravo-Ureta and Rieger (1991) and others expressed the likelihood 

function in terms of the two variance parameters, σ2 = σu
2 + σv

2 and λ = σu/σv. They interpreted λ to 

be an indicator of the relative variability of the two sources of the random error that distinguishes 

firms from one another. Here λ = σu/σv, is the ratio of the standard deviation of the non-negative 

error term Ui to the standard deviation of the two-sided symmetric error term Vi. If λ approaches 0 

then it implies σv very large or σu is close to zero. Similarly, when σv is close to zero, λ becomes 

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21



22
large and the one-sided error becomes the dominant source of random variation in the model 

(Rahman, 2002).  

The next step is to estimate the farm-specific technical efficiency for the individual observation. 

The farm-specific technical efficiency relative to the stochastic production frontier as the ratio of 

observed output to the frontier or expected output. That is: exp. (-Ui) = 
v

i

i

eXf
Y

)(
;  

The value of Ui is practically unobservable and the farm-specific technical efficiency is usually 

obtained by finding out the conditional expectation of Ui given εi. 

Based on the above mentioned literature, a stochastic Cobb-Douglas production frontier model 

may be written as: In Y = b0 + 


k

i 1
bi In Xi + Vi-Ui; where b0 = ln a. 

Though the Cobb-Douglas production function is the most widely used form for fitting agricultural 

production data, but it imposes certain limitations, such as: constant elasticity regardless of the 

input level; and the elasticity of substitution among inputs is unity whereas the translog production 

function does not impose these restrictions and it is a flexible functional form. However, the 

translog production function is more difficult to mathematically manipulate and it can suffer from 

degrees of freedom and multicollinearity problems (Coelli, et. al, 1998). The translog function has 

been used in stochastic production frontier studies by Wilson, et. al, (2001), Awudu and Eberlin 

(2001), Awudu and Huffman (2000), Rahman and Rahman (2005). The translog frontier 

production model could be defined as: 

In Yi = βo + 


n

j 1

βj ln Xji + 


n

j 1



p

k 1

 βj ln Xji ln Xki + Vi-Ui 

Where Yi represents the output on i-th farm; βo represents intercept term; βj is the effect due to the 

X input in the i-th farm; βjk is the interaction effect of the inputs Xj, and Xk, in the i-th farm; In this 

study, Y = output (kg) and Xis are area, labor, lime, fry, fertilizer and feed. The technical 

inefficiency model is defined by:  

Ui = δ0 +δ1 age +δ2 education+δ3 training +δ4 non farm income + δ5 (non farm income)2  +δ6 

family labor+ δ7 ownership + δ8 water quality+ δ9 distance + δ10 farm size + δ11 (farm size)2 +δ11

local + wi 

It is important to mention that the inefficiency effect model can only be estimated if 

inefficiency effects are stochastic and have particular distributional specification. 

Therefore, testing null hypothesis is important based on inefficiency effects are not 

present; H0:  = 0 = . . . = 10 = 0; the inefficiency effects are not stochastic, H0:=0; and 

the coefficients of the variables in the model for the inefficiency effects are zero, H0: 1 = 

2 = 3 =………= 10 = 0. Coelli (1995) suggested that the one-sided generalized 

likelihood-ratio test should be performed when ML estimation is involved because this test 

has the correct size (Rahman, 2002). The generalized likelihood-ratio test is a one sided 

test since γ cannot take negative values. The test statistic is calculated 

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American J. Agril. Sc. Engr. Tech. 
 

23

as:      1ln2 0 HLHLLR  ; where L (H0) and L (H1) are the values of the likelihood 

function under the specifications of the null and alternative hypotheses, respectively. 

Data and descriptive statistics 

As a data set, 185 shrimp farmers were interviewed using a structured interview schedule from 

Paikgacha Upazilla. Multistage random sampling technique was followed on the basis of distance 

from the water source (head and tail enders) and farm size (small, medium and large).  Farmers 

whose fields are adjacent to the canal are considered as head enders and if distance is ≥ 5 meter 

then, they are considered as tail enders. While selecting samples from head and tail-enders, farm 

size was also considered proportionately like as small, medium and large. In Bangladesh, farms 

with ≤1 hectare are considered as small; ≤ 2.5 hectares, medium and above 2.5 hectares, large. 

From them, 65 farmers were head ender and the rest 120 were tail-enders of which, 74 farmers 

were small, 59 were medium and 52 farmers were large. Data was collected in 2008.  

The descriptive statistics of all variables has been presented in Table 1 and in the 

inefficiency model (Table 2), Wis are unobservable random variables, which are assumed 

to be independently distributed with a positive half-normal distribution. The parameters of 

stochastic frontier and translog production function models are estimated by the method of 

maximum likelihood, using the computer program, FRONTIER Version 4.1. 

Table 1. Descriptive statistics (mean and standard deviation) of the variables 

Inputs and output variables (per hectare) 
Output 
(Kg.) 

Land (Area of 
one pond, hec.) 

Labor (Man-
days) 

Lime 
(Kg) 

Fingerlings 
(No.) 

Fertilizer 
(Kg.) 

Feed (Kg.) 

184.9 
(61.08) 

4.38 
(9.64) 

115.2 
(41.59) 

44.77 
(23.39) 

12848 
(3047) 

10.98 
(29.51) 

49.55 
(31.23) 

Technical inefficiency estimators 
Age of 
household 
head 

Education 
(Number of 

school years) 

NFI (Share of 
non-farm 
income) 

No. of adult 
family labor 

Distance (meter) 
from canal to 

pond 

Farm size 
(hectare) 

39.39 
(12.73) 

8.56 
(3.92) 

.452 
(.274) 

2.56 
(1.088) 

206.5 
(364.7) 

5.73 
(10.35) 

Training, ownership and water quality are dummy variables where 1 for trained farmer, owner 

operator and good quality water respectively, local is also a dummy variable where 1 is if farmers 

are living in farming community (within 10 km from the pond). 

Results and discussion 

Before analyzing the inefficiency effects, hypothesis was tested on the choice of functional form 

(Cobb–Douglas versus translog) and it was found that translog production function is a better 

representation of the production structure (Table 3). Therefore, this study focuses on the results of 

the translog production function. Table 2 shows results of the stochastic frontier model from the 

two different functional forms. Gamma (γ)  measures the level of the inefficiency in the var iance 

parameter which ranges between 0 and 1. For the translog model, it is estimated at 0.564, this can 

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24
be interpreted as: 56 percent of random variation in shrimp production is explained by 

inefficiency. 

Table 2. Maximum likelihood (ml) estimates for parameters of Cobb-Douglas and translog 
stochastic production frontier functions and technical inefficiency effect  

Variables Parameters Cobb- Douglas Model Translog model 
Coefficient Std. error Coefficient Std. error 

Intercept β0 4.9300** 0.0036 3.19802** 0.9861 
ln Land β1 0.3926** 0.1035 0.4153** 0.0049 
ln Labor β2 0.2614** 0.0015 0.2964** 0.0114 
ln Lime β3 0.0121** 0.0001 0.0108 0.0438 
ln Fry β4 0.1884** 0.0677 0.2047**  0.8650 
ln Fertilizer β5 0.0170 0.0090 0.0165 0.1064 
ln Feed β6 0.0081 0.0242 0.0093 0.9074 
½ ln Land2 Β11 0.0339 0.0295 
½ ln Labor2 Β22 0.0026** 0.0040 
½ ln Lime2 Β33 -0.2259 0.9789 
½ ln Fry2 Β44 0.5319 1.6392 
½ ln Fertilizer2 Β55 0.0056 0.0263 
½ ln Feed2 Β66 -0.0052 0.0109 
ln Land X ln Labor Β12 0.7968 0.8143 
ln Land X ln Lime Β13 0.0064 0.0058 
ln Land X ln Fry Β14 -0.2423 0.9535 
ln Land X ln Fertilizer Β15 -0.0025 0.0148 
ln Land X ln Feed Β16 0.0325 0.3637 
ln Labor X ln Lime Β23 2.546** 1.014 
ln Labor X ln Fry Β24 0.0077 0.0695 
ln Labor Xln Fertilizer Β25 1.7031* 0.9058 
ln Labor X ln Feed Β26 0.0186 0.0223 
ln Lime X ln Fry Β34 -0.0064 0.0158 
ln Lime X ln Fertilizer Β35 -1.5351 1.4113 
ln Lime X ln Feed Β36 -0.0174 0.0407 
ln Fry X ln Fertilizer Β45 0.0042 0.0268 
ln Fry X ln Feed Β46 0.3422 0.8836 
ln Fertilizer X ln Feed Β56 -0.5012 0.4918 
Technical Inefficiency Factors 
Intercept δ0 -0.0261 0.6549 -0.0478 0.2308 
Age δ1 0.0418 0.0704 0.0001 0.0086 
Education δ2 0.0391** 0.0040 0.0074** 0.0018 
Training δ3 -1.382** 0.1970 -0.0005** 0.0175 
Non-farm income δ4 -0.003** 0.0007 -0.0022** 0.0196 
(Non-farm income)2 δ5 0.0001** 0.0000 0.0001** 0.0000 
Family labor δ6 -0.0004 0.0000 -0.6032* 0.3473 
Ownership δ7 -0.0003* 0.0001 -0.0004** 0.0001 
Water quality δ8 0.0104** 0.0031 0.0104** .00426 
Distance δ9 0.0049** 0.0010 0.0123** 0.0022 
Farm size Δ10 -0.0001** 0.0000 -0.0010** 0.0002 
(Farm size)2 Δ11 0.0091* 0.0038 0.0074* 0.0031 
Local dummy Δ12 -0.103 0.2905 -0.1090* 0.0050 
Variance parameters 2 0.0159** 0.0022 0.0110**  0.0436 

γ 0.5999** 0.0652 0.5639**  0.0389 
The mean technical efficiency (TE) is computed and it is 71 percent for both models. Hypotheses 

have also been tested by using likelihood-ratio (LR) test statistic. The null hypothesis, there is no 

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American J. Agril. Sc. Engr. Tech.      

inefficiency is rejected at the 1% level of significance and establishes the presence of inefficiency 

effects and they are stochastic. In addition, the null hypothesis that the inefficiency effects are not 

present in the model (Ho: µ=γ= 0), is also rejected at the same level of significance. Thus, a 

significant part of the variability in production among farms has been explained by the existing 

differences in the level of technical inefficiency. 

The mean technical efficiency (TE) is computed and it is 71 percent for both models. Hypotheses 

have also been tested by using likelihood-ratio (LR) test statistic. The null hypothesis, there is no 

inefficiency is rejected at the 1% level of significance and establishes the presence of inefficiency 

effects and they are stochastic. In addition, the null hypothesis that the inefficiency effects are not 

present in the model (Ho: µ= γ = 0), is also rejected at the same level of significance. Thus, a 

significant part of the variability in production among farms has been explained by the existing 

differences in the level of technical inefficiency. 

Technical efficiency has been computed for each farm. Figure 1 shows a histogram of predicted 

technical efficiencies. The minimum estimated efficiency is 26.45 percent, and the mean is 71.01 

percent with a standard deviation of 18.04 percent. This suggests that, on an average, about 29 

percent of shrimp yield is lost because of inefficiency and in the short run; there is a scope for 

increasing shrimp production by 29 percent by improving socio-economic factors. 

Table 3. Hypothesis tests 

Null Hypothesis LR test statistic (χ2) d.f. p-value (Prob>χ2) Decision 
Choice of functional form: 
Cobb–Douglas versus translog 
model (H0: βjk = 0 for all jk) 

62.13 21 0.030 reject 

No inefficiencies present in the 
model (H0: µ = γ =0) 

87.12 3 0.000 reject 

No inefficiency effects 51.05 12 0.000 Reject 

Table 2 shows that farm size increases farmers’ efficiency, but the coefficient of (farm size)2 

confirms that very large farm size negatively affects the efficiency. So, how much land farmers are 

able to manage efficiently. Hoque (1988) found that farm size between 7 and 12 acres is the most 

efficient for Bangladesh agriculture. In the same line, five piece-wise regressions for five ranges of 

farm size have been considered and the results are presented in Table 4. Result shows that farm 

size and technical efficiency are  

0

5

10

15

20

25

30

35

upto 60 61-70 71-80 81-90 91-100
% of technical efficiency

%
 o

f f
ar

m
er

s

Figure 1. Frequency distribution of technical efficiency from translog 
model 

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positively related for less than 3 hectares and the regression coefficients are significant. This 

implies that technical efficiency increases with less than 3 hectares of land. On the other hand, the 

relationship is negative and significant for 3- <5.5 hectares. Table 5 also shows that average farm 

size for most efficient producers is 4.32 hectares. 

Though, the relationship is negative after 3 hectares of size, it sounds that optimum farm size may 

be found around 5 hectares. When all the farms are considered together, the relationship is positive 

but insignificant. Thus, piece-wise regressions give better indication about the relationship 

between farm size and technical efficiency. It has also been found that rich people are taking land 

in terms of lease from the absentee land owners and from those who cannot produce shrimp due to 

conflict with neighbors. Increasing the amount of land is a matter of reputation for them. There is 

competition among rich farmers, to be the owner of the highest amount of land rather than to 

maximise productivity or efficiency. Therefore, very large farmers are rarely capable to manage 

their farm. 

Non-farm income is a very critical issue in farming. Increased non farm income may reduce 

financial constraints, particularly for resource-poor households by enabling them to apply inputs in 

time; thereby increase productivity as well as efficiency. It is clear from figure 2 that with the 

increase of non farm income share, small farmers’ efficiency increases but medium and large 

farmers´ efficiency decreases. As small farmers’ land-man ratio is very less compared to other 

categories (Table 6), it seems, if some additional labors engage in non farm sector, production 

would be same. So, in that sense, farmers having more land should be attentive in farming. Since 

large farmers have a shortage of labor, additional small farm labors could be hired. 

0

10

20

30

40

50

60

70

<60 60 - <70 70 - <80 >80
Efficiency

%
 io

f s
hr

im
p 

in
co

m
e 

Small Medium
Large

Figure 2. Percentage of shrimp income to total income for different farmers 

Table 4. Socio-economic characteristics on the basis of different levels of technical efficiency 

Level of TE 
Different socio economic characteristics 

Mean 
Education 

(years) 

Training 
(yes) 

Proportion 
of shrimp 
income 

Proportion 
of Family 

labor 

Owner 
operato
r (%) 

Average farm 
size (hac) 

< 60 11 38.2 0.38 0.44 38 11.32 
60-< 70 7 70.5 0.57 0.42 53 4.26 
70- < 80 8 58.8 0.77 0.53 58 6.94 
80- < 90 8 79.6 0.79 0.53 73 3.55 
90-100 8 85.7 0.76 0.54 100 4.32 

Source: Authors’ calculation based on field survey data, 2008. 



American J. Agril. Sc. Engr. Tech. 27

Table 5. Regression results: Farm size is independent and TE is dependent variable 

Farm size (hectare) Reg. coeff. (std. error) R2 
<1 ha. 0.53** (0.24) 0.68 
1- < 3 0.29* (0.23) 0.53 
3- <5.5 -0.13 (0.08) 0.43 
>5.5 ha. -0.04 (0.15) 0.28 
All farms 0.008 (0.02) 0.23 

Source: Authors’ calculation  
Figures in the parentheses indicate standard error 

Table 6. Average land-man ratio of different farmers 

Types of farmers Small Medium Large 
Land man ratio (ha.) 0.108 0.355 4.442 
Source: Authors calculation based on field survey data, 2008. 

The positive coefficient of education reveals that efficiency decreases with the increase of farmers´ 

level of education. In the context of Bangladeshi culture and values, agricultural activities are 

reserved for the illiterate people; higher educated persons don’t want to be farmers. When farmers 

are more educated, they start to ignore farming responsibilities. Table 4 shows that efficiency 

decreases after secondary level of education which could be explained by the education system of 

Bangladesh. After secondary level of education, education becomes specialized and there remain 

very little scope to learn about farming. So, education system should be farming oriented and 

social values needs to be changed. Various studies have found a positive connection (like Ali and 

Flinn, 1989), while several others have reported no statistically significant relationship between 

these two variables (Bravo-ureta and Evenson 1994; Kalirajan 1984), and Haller (1972) found a 

negative relationship for education on agricultural productivity (Phillips, 1987). Coefficient of 

training is negative which means that trained farmers are more efficient than non-trained farmers. 

So, it could be said that improvement in farmer's managerial ability is a key factor for sustainable 

operation, which can be improved by providing adequate training. 

Conclusion and policy implications 

In this study, the translog specification was found more appropriate than the Cobb-Douglas for the 

data being analyzed. The result shows 29 percent technical inefficiency exists which can be 

attained by the efficient use of existing inputs; without any additional cost to the farmers. Such 

detailed information has important implications for formulating agricultural policy.  

Based upon the analytical result, this study suggests that around 5 hectares could be an optimum 

farm size for shrimp farming which is not a small amount in Bangladesh perspective. The present 

study bears an important message that there is ample opportunity to redistribute land from those 

having more than 5 hectares to those having less. For increasing efficiency, large landholdings 

could be discouraged through policy instruments. For instance, progressive rate of land tax could 

be charged for farmers having more land. This measure will have some monitoring and 

enforcement costs, but may also raise substantial revenues that can be used to finance 

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28
technological improvements that would reduce natural resource degradation normally induced by 

shrimp cultivation. 

As distance from pond to canal negatively affects farmer’s efficiency, the study recommends that 

narrow sub canals could be constructed for the tail end farmers; the proposal should be carefully 

examined focusing on the costs; and effects on the efficiency and production of farmers. Farmers 

could also be socially organized for collective water management. If farmers get timely supply of 

water, they will be able to produce shrimp along with one season rice and fresh water fish.  

Poor performance of farmers in shrimp farming with increased non-farm income indirectly implies 

that farming is becoming a secondary occupation and is incapable of providing sufficient returns 

from farming activities. Off farm income could be helpful for farming because it provides liquidity 

for agricultural expenditures and long-term investments; but if farmers operate farming as 

secondary occupation, then off-farm income reduces efficiency. However, the risk of disease 

epidemic in shrimp pushes small and medium farmers to engage in non farm income-earning 

activities. This study recommends that large farmers should be attentive in farming and they could 

hire additional labors from small farms.  

Shrimp farming is relatively new occupation for the farmers and they have little formal knowledge 

on framing technology. Therefore, this study recommends training for shrimp farmers. After 

completion of secondary level of education, the members of farm family become eligible to 

receive training. To encourage training, monetary incentives could be introduced for the trainees.  

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