Impact of socio economic and environmental hazards on the technical efficiency of shrimp farms at cam thinh dong commune, cam ranh district, vietnam

MINISTRY OF EDUCATION AND TRAINING
NHA TRANG UNIVERSITY

FOLORUNSO, EWUMI AZEEZ OLATUNJI

IMPACT OF SOCIO-ECONOMIC FACTORS AND ENVIRONMENTAL HAZARDS
ON THE TECHNICAL EFFICIENCY OF SHRIMP FARMS AT CAM THINH DONG
COMMUNE, CAM RANH DISTRICT, VIETNAM.
MASTER THESIS
Marine Ecosystem-based Management
and Climate Change

Major:
Topic Allocation decision
Decision on establishing the committee:
Defense date:
Supervisors:

07/06/2018
Prof. Arne Eide
Dr. Le Kim Long

Chairman

Prof. Kim Anh

Faculty

Faculty of Graduate Studies

KHANH HOA - 2018

i


ACKNOWLEDGEMENT
I would like to thank my international supervisor, Professor Arne Eide, Norwegian college
of fishery science, University of Tromsø, Norway, for his resourceful advice, support,
comments and understanding through the course of the research and writing.
I would also like to thank my local supervisor, Dr. Le Kim Long, department of Economics,
Nha Trang University, also for his resourceful advice, supports and guidance throughout
the course of the thesis.
My sincere gratitude goes to the entire board of NORHED (Norwegian Program for
Capacity Building in Higher Education) for making my sojourn here a successful one with
their financial and moral supports.
I would as well be grateful to my parent and siblings for their unending encouragement and
understanding throughout the course of my study.
My gratitude also goes to Cam Thinh Dong community committee for provision of
comfortable atmosphere and warm support that ensues successful field survey exercise.
I would also without reservation extend my utmost gratitude to the entire teaching staffs of
Nha Trang University and my classmates for their supports and encouragement.

Folorunso, Ewumi Azeez Olatunji
May 2018, Nha trang, Vietnam.

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TABLE OF CONTENT
ACKNOWLEDGEMENT ....................................................................................................ii
TABLE OF CONTENT ..................................................................................................... iii

LIST OF TABLES .............................................................................................................. iv
LIST OF FIGURES .............................................................................................................. v
LIST OF ABBREVIATIONS ............................................................................................. vi
ABSTRACT .......................................................................................................................vii
1.0 INTRODUCTION .......................................................................................................... 1
1.1 BACKGROUND INFORMATION .................................................................................................. 3
1.2 PROBLEM STATEMENT ............................................................................................................. 5
1.3 AIM ......................................................................................................................................... 6
1.6 TECHNICAL EFFICIENCY .......................................................................................................... 7
1.7 MEASUREMENT OF TECHNICAL EFFICIENCY .......................................................................... 11
1.8 STOCHASTIC PRODUCTION FRONTIER .................................................................................... 13
1.9 LITERATURE REVIEW ............................................................................................................. 14

2.0 DATA ........................................................................................................................... 20
2.1 DATA SAMPLING METHODOLOGY .......................................................................................... 20
2.2 PRIMARY SURVEY ................................................................................................................. 20
2.3 PRE-TESTING ......................................................................................................................... 20
2.4 DATA COLLECTION ................................................................................................................ 20
2.5 DATA SAMPLE AGGREGATE ................................................................................................... 22

3.0 METHOD ..................................................................................................................... 23
3.1 DATA PROCESSING AND ANALYSIS ........................................................................................ 23
3.3 EXPECTATION ....................................................................................................................... 23

4.0 RESULT ....................................................................................................................... 24
4.1 THE EMPIRICAL RESULT ....................................................................................................... 24
4.2 TEST OF HYPOTHESIS ............................................................................................................. 25
4.3 INEFFICIENCY MODEL ............................................................................................................ 30
4.31 Socio-economic factors ............................................................................................................................... 30
4.32 Environmental factors ................................................................................................................................. 32

5.0 DISCUSSION AND CONCLUSION .......................................................................... 36
5.1 DISCUSSION .................................................................................................................... 36
5.2 CONCLUSION.................................................................................................................. 40

REFERENCES ................................................................................................................... 42
APPENDIX ........................................................................................................................ 47
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LIST OF TABLES
Table 1-2: Summary statistics for some of the variables used in the model. ..................... 22
Table 1-4: Stochastic frontier and Inefficiency effects result ............................................ 24
Table 2-4: Hypothesis testing ............................................................................................. 25
Table 3-4: Technical efficiency distribution ...................................................................... 27
Table 4-4: Descriptive statistics of the technical efficiency............................................... 28
Table 5-4: Descriptive statistics of inputs .......................................................................... 29
Table 6-4: Farmers age distribution ................................................................................... 30
Table 7-4: Educational level of the shrimp farmers ........................................................... 31
Table 8-4: A chart showing farm size information ............................................................ 31

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LIST OF FIGURES
Figure 1-1: Map of Cam Ranh showing Cam Thinh Dong. ................................................. 6
Figure 2-1: Input-oriented measure of TE. ........................................................................... 8
Figure 3-1: Output-oriented measure of TE. ........................................................................ 9
Figure 1-4: Chart of TE in relation to numbers of farmers ................................................ 29
Figure 2-4: Chart showing the majorly encountered environmental hazard in the area .... 32

Figure 3-4: Chart showing the number of flood experienced by farmers .......................... 33
Figure 4-4: chart showing the majorly coping strategies ................................................... 34
Figure 5-4: Chart showing the different environmental impacts affecting shrimp farming
at Cam Thinh Dong ............................................................................................................ 35

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LIST OF ABBREVIATIONS
AE

Allocative Efficiency

EAS

Environmental Assessment system

EE

Economic Efficiency

DEA

Data Envelopment Analysis

GDP

Gross Domestic Product

GFDRR

Global Facility for Disaster Reduction and Recovery

FAO

Food and Agricultural Organisations

HRS

Hours

KG

Kilogram

LR

Likelihood Ratio

MLE

Maximum Likelihood Estimate

OLS

Ordinary Least Square

SFA

Stochastic Frontier Analysis

SPA

Stochastic Production Frontier

TE

Technical Efficiency

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ABSTRACT
This study employed stochastic frontier production function approach to investigate the
technical efficiency and factors affecting the technical efficiency of shrimp farmers. With
the aid of questionnaire, data were collected on farmers’ cost of major inputs (labour, seed,
feed and lime), socio-economic activities, age, education, experience, household size and
farm size, and on environmental hazards, flood and drought experience. The data was
analysed using a stochastic production frontier. The result obtained revealed a mean
technical efficiency of approximately 58%, reflecting that there exists a great potential for
improving the efficiency of the shrimp production. The input variables considered in the
model (feed, seed, labour and lime) were all found to be important factors for shrimp
production in the area. In the inefficiency model, age, education, experience, drought
experience and farm size were found to be positively related to technical efficiency. While
flood experience was found to be positively related to technical inefficiency, drought
experience was found to be negatively related to technical inefficiency. This study suggests
a huge impact of pond adjustment on farmers’ cost of production, as 98% of the farmers
reported to be adopting frequent pond adjustment and maintenance as an adaptation to
frequent flood events. On the other hand, number of drought experience a farmer has was
found to enhance his technical efficiency. Since all the inputs considered in this study were

found to be positively related to the technical efficiency, this study therefore suggests the
farmers be encouraged to increase their output by providing them the medium or platform
to learn the best input combinations in order to reduce cost while maximizing their profit.
Furthermore, since age, experience, and education are positively related to TE, this study
suggests establishment of or restructuring of community-based organisations and extension
services to create medium for interactions between the farmers in order to allow young and
less experienced farmers to learn from the older and more experienced ones. Lastly,
government should support farmers with sea dike structures to help curb flood impacts and
also provide history of drought patterns to help farmers plan against forecasted drought
events.
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1.0

INTRODUCTION

On a global basis, annual records of natural disasters or events amount to over 300, and in
2010 it cost global economy USD 109 billion from 106,891 fatalities (Guha sapir, et al.
2012). The fish farmers and their communities globally are particularly vulnerable to these
disasters because of their locations, their livelihood characteristics, total high levels of
exposure to natural hazards, shocks from their livelihood and impacts of climate change.
The impacts on economic, social and environmental structures are significant with
disproportionate effects in developing countries and on vulnerable groups, as 98% of the
262 million people are affected by weather and climate change-related disasters between
2000 and 2004 who lived in developing countries, and majority of which are dependent on
aquaculture and agriculture as livelihoods (FAO, 2012).
Lying in the tropical monsoon area of the north western pacific means that Vietnam is one
of the disaster-prone countries in the world, as it is affected by floods, storms, tropical
depression, storm surges, whirlwinds, coastline erosion, hail rains, drought and landslides,

destroying lives, assets and degrading cultural and socio-economic structures as well as
natural environments (Anh, 2016). Using national disaster database, Chin luu et al. (2017)
reported that apart from Mekong delta who has the highest number of flood fatalities, south
central and north central coast were the two most affected regions in flood fatalities
historically based on average per province per year in the regions investigated.
Aquaculture, which is perceived to be playing key role in economic growth, food security
and job creations in the country has been plagued by natural disasters such as flood, flood
flash and drought (Anh, 2016). The sector remains one of the major occupation of the
coastal population of Vietnam, accounting for 12% of total exports (about USD2.5billion)
and providing source of livelihood for about 4 million people. (GFDRR, 2011), and the fact
that the high concentration of human population and economic activity in coastal areas has
a heavy reliance on fishery and aquaculture sectors that account for 6.6% of Vietnam’s
GDP in 2008 (Bierbaum, et al. 2010), make aquaculture an important sector of the country’s
economy. Therefore, the environmental hazards and climate change impacts that befalls the
productivity of the industry may arguably be found producing an effect on the entire
1


economy of the country. Apart from the government’s effort to implement policies and
strategies to help prevent further damages and to assist residents to cope with these changes,
there have been limited studies that have evaluated how the adaptive strategies
(autonomous and non-autonomous) have decreased the profit-maximization of the farmers
and possibly forcing some farmers out of their livelihoods.
De Silva et al. (2009) concluded that impact of climate change on capture fisheries have
received more attention than in aquaculture and stressed the need to assess the
vulnerabilities of major aquaculture farming systems and proposed appropriate mitigation
and/or adaptation measures to maintain the viability of these systems. Number of researches
in agriculture have recently emerged looking up the impact of climate change on the
performance of agriculture outlets using technical efficiency. Oyekale (2012) considered
rainfall and temperature as climate change factors affecting the technical efficiency of

cocoa farms in Nigeria. Also in Bangladesh, both humidity and rainfall were found to
produce a positive impact on the technical efficiency of rice farm while temperature
produced a negative impact on the technical efficiency of the rice farm.
According to FAO (2015) report on the impact of natural hazards and disasters on
agriculture and food security and nutrition, one of the key findings is that there exist major
data gaps on the impact of natural hazards and disasters on the agriculture sectors in
developing countries. In aquaculture, significance numbers of studies have been carried out
on the impact of different factors such as farm size, farmer’s experience, farmer’s age and
some socio-economic factors such as household-size on the technical efficiency of that
farm, but few or limited researches have looked into the impact of environmental hazards
on the technical efficiency of farms. However, Auci and Vignani, (2014) in their research
considered the climate change impacts on technical efficiency of fish farm outlets in Italy .
Rainfall and minimum temperature were considered as one of the inefficiency factors in the
inefficiency model, and it was found that rainfall variable had a positive impact on the
efficiency while minimum temperature reduces the efficiency of harvested production.
Recently, Nguyen et al. (2017) considered the impact of climate change on the technical
efficiency of Pangasius species in the Mekong delta area of Vietnam, factors such as flood
2


effect, salt intrusion effects, farmers experience level in climate change and access to
trainings, using Data Envelopment Analysis (DEA). Their conclusion accepted with the
few existing literatures that farmer’s experience in climate change impacts contribute
positively to the technical efficiency of their fish farms and how to deal with the impacts.
In a bid to protect development investment and strengthen aquaculture resilience to
disasters, there is a need to understand the particular way the sector is being affected, the
magnitude at which it is affected in order to understand how to assign priorities to the
disasters during the course of planning against the disasters. Khanh Hoa province which is
located in the south-central region of the country and was once known a major supplier of
shrimp seeds in the region has been reported to have witnessed a drastic reduction in output

owing to unfavourable weather conditions, (Hoang Thu Thuy, 2008; Pham Xuan Thuy,
2004; Nguyen Thi Kieu Thao, 2012). There has been little or no investigation carried out
on the impacts of these environmental hazards on the efficiency of the shrimp farms. This
study will investigate the impact or effects of farmers experience on climate change, flood
effects, drought effects, level of education, age of the farmer, and the farm size on the
technical efficiency of the fish farms.
1.1

Background information

Cam Thinh dong is a commune in Cam Ranh, which is located in Khanh Hoa province,
south central of Vietnam. The province has a total area of 5,197 km2 and a provincial
coastline that spreads 385 km featuring numbers of creek mouths, lagoons, river mouths
and hundreds of islands and islets from Dailanh commune to the end of Cam Ranh Bay.
This province is contiguous to Phu yen province in the north and south-eastern border, Dak
lak province at the west, Ninh Thuan province at the southern border and the eastern border
with the south-china sea.
This province was known for its influential contribution to the development of shrimp
farming in Vietnam, with 1,019 farms and production of about 3.25 million ton of shrimp
seeds between 1995 and 2000, when it accounted for 40.8% of total shrimp production in
the country (Hoang Thu Thuy and Kim anh 2008). Favourable indicators of temperature,
humidity and rainfall were described as climatic factors that had produced great influence
3


on the development of Aquaculture in this province. (Hoang Thu Thuy, 2008). According
to Pham Xuan Thuy (2004), Shrimp production reduced drastically in Khanh Hoa province
between 2001 and 2002 despite the increase in farming area, which has been on the increase
since 1999, and increased to 53.2 km2 from 49.57 km2 in 2001. This is not in parallel to
production, as the latter fell short from 7,452 tons in 2001 to 6,275 tons in 2002. The author

described temperature and rainfall as the major factors that are behind any success of shrimp
farming in the province, therefore, extreme local fluctuation of the two weather factors in
this year was reported to be responsible for the reduction (Pham Xuan Thuy, 2004). This
went ahead to force farmers out of their livelihood and subsequently dealt a blow to the
production of fish seeds; as the number of farms has gone to decrease from 1,282 in 2003
to 507 in 2006; and fingerlings production decreased from 69,531 tons in 2004 to 41,410
tons. Specifically in Cam Ranh, number of farms decreased from 630 in 2003 to 120 in
2006 and production fell to 9,470 in 2006 from 38,430 tons in 2003. Also in Nicole Portley
(2016) report, Khanh Hoa province aquaculture production rose to be listed among the top
five exporter of shrimp in 2010 with a production of 15,912 tons, and gone on to decrease
from 2011 to 2014, where it only produced 11,540 tons. The weather factors such as
unsteady distribution of rainfall, has forestalled the potential success of aquaculture in
Khanh Hoa province (Hoang Xuan Huy, 2009) and the small reservoirs of underground
water in the province only get to supply for the small scale production in the coastal areas.
Furthermore, Nguyen & Fisher, (2014) from their research stated that production area in
Khanh Hoa province decreased from 2012 to 2013 and the output started decreasing from
2012 to 2014. 10,788 tons produced in 2012 was the highest in the whole of the three years,
when 8,850 tons and 7,912 tons were recorded respectively for the year 2013 and 2014.
Factors such as disease epidemics, unfavourable weather conditions and environmental
pollution were the stated causes of the reduced production.
The environmental condition of this region, which was considered to be of great advantage
to the successive aquaculture production in the region, has been savaging the aquaculture
operational activities recently. Hoang Thu Thuy (2009) reported the air temperature of the
major aquaculture-producing districts in his research to produce the evidence of the
4


decreasing production in the area. Nha trang has a highest temperature of 37 0C, and Cam
Ranh has 39.30C. The evidence of the impact of the environmental distress, which is
probably brought by climate change, continues with another record of low performance of

lobster farming, owing to sudden increase in temperature. Lobster, which normally survives
between 26- 290C in the summer and about 22-270C in winter, has been reported to be
facing low growth and mass mortality of the juveniles. The increase of 3-50C reported in
Cam ranh district causes mortality of the juvenile of this species and subsequently causing
a low output (Nguyen Thi Kieu Thao, 2012).
1.2

Problem statement

As indicated above, environmental stresses from environmental variation could lead to the
low output, forcing farmers out of their livelihood while reducing the profits of the existing
ones. The current reductions in output of shrimp production reported by Hoang Thu Thuy
(2009); Pham Xuan Thuy (2004) and Nguyen Thi Kieu Thao, (2012) in this region has in a
way or the other been attributed to unstable environmental conditions and previous studies
in this region has concentrated on the investigation of how socio-economic factors such as
age, level of education, farm-size, household size affect the farmers technical efficiency, as
observed in Hoan Thu Thuy (2008), who compared the performance of shrimp farms in
Cam Ranh, Nha Trang and Ninh Hoa districts. Also, Nguyen et al. (2017) recently
investigated the impact of environmental hazards (flood and salt water intrusion) along with
the common factors negatively affecting technical efficiency of Pangasius farms in the
Mekong delta region of Vietnam, and it was found that there was a positive effect on the
technical efficiency by farmers’ education level and experience in flooding and salt water
intrusion. Farms affected by salt-water intrusion had a lower scale of efficiency as they
reduce stocking rate and frequency. In this regard, there may be unresearched or
univestigated environmental factors or hazards affecting the technical efficiency of shrimp
farms in some areas in the country, as this result shows that environmental hazard could
also be hindering the optimal prioritisation of shrimp farming in Cam Thinh Dong, and
could be responsible for the reported output reduction in the region.

5

1.3

Aim

This study therefore intends to investigate the relative technical efficiency of the shrimp
farms in the region using stochastic production frontier by considering the input, output and
inefficiency factors. The study will further investigate the impact of the environmental
hazards (drought and flood) along with socio-economic factors (age, education, experience,
and farm size) on the technical efficiency of the shrimp farms in this commune. Hopefully,
the result obtained will help recognize the contributions of these factors on the economic
performance of the shrimp farms in this community. Recognizing and establishing the
magnitude of the effects of these factors on the shrimp productivity will help farmers and
policy makers to make headway on assigning or allocating priorities to these effects when
planning or preparing for adaptation and/or mitigation techniques.
1.5

Study site

Cam Thinh Dong commune is located in the south-western part of Cam Ranh, one of the
major aquaculture districts in Khanh Hoa province, which is located in the south central
region of Vietnam with more than 300 km of coastline running from 11040’ to 12050’
Northern latitudes. This commune has four villages including, Hiep thanh, My thanh, Hon
quy, and Hoa diem village.

Figure 1: Map of Cam Ranh showing Cam Thinh Dong

6

1.6

Technical Efficiency

Farell (1957), who defined technical efficiency as a reflection of the ability of a firm to
obtain maximum outputs from a given set of inputs, was reported as the first to study the
modern technical efficiency building on the foundation of the initial work by Debreu and
Koopmans, (1951). An efficiency can be input-oriented or output oriented. In the former, a
target point that maximizes the proportional reduction of inputs or produces a given level
of output from an optimal combination of outputs is formed and the latter finds a target
point which maximizes the proportional augmentation of outputs or produces the optimal
output from a given set of inputs.
Farell (1957) further proposed technical efficiency and allocative efficiency as two
components of efficiency after using a simple model with two inputs and single output
under constant return to scale. In his seminar paper, he stated that the observed input-perunit of output values for firms would be above or beyond the unit of isoquant curve. In his
explanation, he assumes a firm uses X1 and X2 inputs to produce Y output, in such a way
that the points defined by the input-per-unit of output ratios (X1/Y, X2/Y), are above the
unit isoquant curve. The unit isoquant curve defines the input-per-unit of output ratios
associated with the most efficient use of the inputs to produce the output involved.
Technical inefficiency of the firm was considered to be the deviation of observed inputper-unit of output ratios from the unit isoquant. So, if given isocost line AA' and isoquant
curve KK', at S when a farmer has used certain quantities of inputs to produce output (y),
technical efficiency is commonly measured by the ratio TE = OT/OS,

7

0

Figure 2: Input-oriented measure of TE. S is inefficient input combination to produce a unit of

output Y (line TS measured the amount which the input would have to be reduced to assume
technical efficiency), X1 and X2 are inputs, Y is output, T is technically efficient point, T' is
economically efficient point, AA' is isocost line, R is optimal input quantity and line KK' is isoquant
curve (efficient input combinations)

Using Farell’s model of constant return to scale, the isoquant curve KK' captures the
minimum combination of inputs per unit of output needed to produce a unit of output. In
this framework, every input combination along the isoquant curve are considered to be
technically efficient, while input combinations above the curve such as S are considered
inefficient because the input combination employed is beyond what is needed to produce
a unit of output. For this producer using input combination S, the distance TS measures the
technical inefficiency because this distance represents the amount of inputs which all inputs
can be divided without reducing the amounts of output. Ratio TS/OS measures the technical
inefficiency associated to input combination at point S while the technical efficiency of the
producer under this analysis (1-TS/OS) would be given by the ratio OT/OS. TE value
ranges between zero and one and a firm that is fully technically efficient has its technical
efficiency score to one and vice versa.
Isocost line AA' reflects input price ratio which can be assumed from a particular
behavioural objective such as cost minimization (Murillo-Zamorano, 2004). Using the line
8


segment RT, allocative inefficiency can be measured as SR/OR. This ratio is deduced from
the cost reduction that a producer would be able to reach if he intends to move from T, a
technically efficient but allocatively inefficient point, to T', a technical and allocatively
efficient point. Therefore, allocative efficiency (AE) that characterizes the producer at point
S is given as the ratio OR/OT. Farell (1957) defined what he referred to as the overall
efficiency (Economic efficiency) as a multiplicative interaction of both technical and
allocative efficiency.
EE = TE ×AE = OT/OS ×OR/OT = OR/OS

Figure 3: Output-oriented measure of TE. Q1 and Q2 are outputs, X is input, T is technically
efficient point, T’ is economically efficient point, S is inefficient output combination, line AA' is
isorevenue line, and line KK' is production frontier, where all output combinations are efficient.

The second is output-oriented measure that tends to increase output proportionally with the
same level of input, also under the assumption of constant return to scale. This approach
implies maximum radial expansion in all outputs that is feasible with given technology
(Murillo-Zamorano, 2004). In the illustration above, assume a farm produces two outputs
from a single input. If the input quantity is held constant at a particular level, the
technologies can be represented in two dimensions (Murillo-Zamorano, 2004). The farm
on the production frontier KK' are technically efficient and the farm operating below the
curve such as S is inefficient. While T is technically efficient point, higher revenue could
be achieved by producing at T', where marginal rate of transformation is equal to the price
ratio or slope of the isorevenue line AA'. Distance ST is the measure of technical
9


inefficiency or the amount of output that could be increased without increasing the inputs.
Therefore, technical efficiency is the ratio of OS/OT, the ratio by which returns may be
increased without affecting the inputs.
If input and output prices are available, an isorevenue line AA' could be drawn and
allocative efficiency (AE) = OT/OR, will indicate the reduction in production cost that
would occur if production were to occur at the allocatively and technically efficient point
T'. Though, technical efficiency may be 100% obtainable at T, but technical efficiency
would be obtained at point T' with lowest cost of input. As obtained in input-oriented
measure, overall efficiency or economic efficiency is the multiplicative interaction of
technical efficiency and allocative efficiency.
EE = TE ×AE = OS/OT × OT/OR = OS/OR
TE, AE and EE scores are bounded by zero (totally inefficient) and one (totally efficient).

In summary, the relationship that exist between an observed production and the best
practice production that a farm is able to put to use defines the level of technical efficiency
at which such farm operates. (Hai au, 2009).
For the purpose of this study, the efficiency of the farms will be estimated using inputoriented measure of technical efficiency, this is because, firstly, the research is
concentrating only on one output (shrimp) and secondly, because, mostly, the farmers only
have the ability and capacity to manipulate their inputs and they have less control over
output. The production technology with the input-oriented measure of technical
inefficiency can be expressed as:
Yi = f (Xi × Øi),

i = 1…….N

Where Yi is the scalar output of each shrimp farm, Xi is the vector of inputs (Feed, seed,
lime, labour) used to produce an output for a growing season. Ø is scalar technical
efficiency, calculated as Xeij/Xji ≤ 1, Xeij is input-vector in efficiency units. Input-oriented
technical inefficiency is measured as 1- Ø. While i represent the farms, j is the rate at which
all inputs could be reduced without reducing output. One-stage approach that express
10


inefficiency effects (Ui) as an explicit function of a vector of firm-specific variables and a
random errors was employed. This model specification may be expressed below:
Yit = χit β + (Vit – Uit)
Where Yit is the log of the production of a specific farm (i) and at a specific time (t)
Χi is a K X 1 vector of input quantities transformation of a specific farm i
βi is a vector of unknown parameter.
Vi and Ui are the components of error term, Vi accounts for measurement error and other
random factors that are beyond the control of the farms and the Ui is the inefficiency. Uit
represent technical cost efficiency and it must be positive.
Uit = /Uit/, where Uit ⁓ N (0,σ2u).

Vit are random variables that are assumed to be iid (independently and identically
distributed). Vit ⁓ N (0,σ2v) and it is independent of Uit .
Uit = (Ui exp (-η (t-T))),
Technical inefficiency in production are assumed to be iid and accounted for by nonnegative random variables, Ui.
η is a parameter to be estimated. Composed error is the sum of absolute value of inefficiency
(normally distributed variable) and a symmetric normally distributed variable.
1.7

Measurement of Technical efficiency

The approach of measuring technical efficiency was reported to have first been proposed
by Farell (1957). Building on the work of Farell (1957) were Charnes, Cooper, and Rhodes,
(1978) on the specific research on efficiency measured for production units. The work
receives contribution from Banker in 1984, which resulted into the proposal of two
approaches for the measurement of technical efficiency. Non-frontier and frontier
approaches were developed. Non-frontier approach measures technical efficiency by
comparing the actual output with the standard frontier estimated from the experimental
data. While this approach helps to separate and examine the conventional and non11


conventional inputs, it’s too expensive to conduct experimental study and the experimental
result may not portray the real situation in production. (Hai au, 2009).
Stochastic production frontier (Frontier approach) has been described as the appropriate
tool for economic sectors where randomness is important tool in the production system.
Frontier approach describes the maximum output that can be produced from any
combination of input by an efficient farm. Data Envelopment Analysis (DEA) and
stochastic production frontier are methods that are considered the best approaches for
measuring technical efficiency of aquaculture farms. (Xuan Huy, 2009)
Xuan Huy (2009) in her research referred to frontier and non-frontier approaches as
parametric and non-parametric. The latter is treated as a random variable due to the

existence of exogenous factors that affect the relationship that exists between the output
and the input, leading to the estimation of stochastic frontier that gives the expected values
of output using the conditional level of inputs.
The non-parametric approach makes use of linear computer programming that produces
stochastic production using piece-wise linear deterministic frontiers. According to Xuan
Huy (2009), this approach neither impose functional forms nor do they take into account
the randomness that exist in the parametric approach. Therefore, they are less prone to
misspecification and are not subsequently subject to the problems of assuming an
underlying distribution about error terms.
However, technical efficiency estimated when the data generating process is not
characterized as a full-frontier deterministic production model is negatively biased due to
the feature of DEA that allows the largest random frontier shock in the data to determine
the production frontier estimate (Sengupta, 1985). Also, the primary criticism of DEA
approach is that measurement errors can have a large influence upon the shape and
positioning of the estimated frontier. In other to account for the presence of measurement
error in production in the specification and estimation of frontier production functions,
Aigner, Lovell, and Schmidt, (1977), and (Meeusen and van den Broeck, (1977)
independently proposed the frontier production function (Battese, et al.1996).
12


1.8

Stochastic production frontier

Stochastic production frontier has been described as a parametric and econometric approach
that construct a production function based on average values of the observed data. (Hai au,
2009). It maps a production frontier, finds the locus of maximum outputs that are associated
with a given input levels and further estimates the farm-specific technical efficiency as a
deviation from the fitted frontier (Singh, et al. 2009). It was reported to have first been

proposed by Aigner, Lovell and Schmidt, and Meeusen and Van den Broeck in 1977. It was
described to have original specification that involved a production function that is specified
for cross-sectional data which had an error term that had two components, one to be take
account of the random effects and the other to take account of the technical inefficiency.
According to Färe and Lovell, (1978)), among the methods commonly used for measuring
technical efficiency, stochastic production frontier has been considered as the most
appropriate for assessing the technical efficiency of agricultural farms in the tropics because
it takes into account the measurement error and other stochastic factors such as weather
condition and diseases which often influences the data. This approach also takes into
account the stochastic variation which is important if the output is affected by random noise.
However, it requires specific functional forms such as Cobb-Douglas, translog or quadratic
functions in order to estimate the production function. In order to separate the inefficient
component from stochastic component, it’s important to have some distributional
assumptions. This method cannot be applied to production functions with multiple outputs.
The traditional approach to investigate the relationship between technical efficiency and
socio-economic variables is estimating the stochastic production frontier at the first phase,
providing the basis for measuring farm-level technical efficiency while the second phase
estimates technical efficiency as a function of the various attributes of the farms in the
sample using separate two-limit tobbit equations. (Kehar singh, 2009). This two-staged
approach includes a first stage that specifies and estimate the stochastic frontier production
function and the prediction of the technical inefficiency effects with the assumption that
these inefficiency are identically distributed and the second stage involves the specification
of a regression model for the predicted technical inefficiency effects that contradicts the
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assumption of identically distributed inefficiency effort in the stochastic frontier. However,
in the recent working paper of Coelli, (1996) on stochastic production frontier, the
pronounced author described this two-stage approach as being inconsistent regarding the
independence of the inefficiency and therefore, he suggested the approach may not provide

estimates that are as efficient as a single-stage approach estimation method proposed by
Reifschneider and Stevenson, (1991). The latter proposed stochastic frontier models in
which the inefficiency model are expressed as an explicit function of a vector of firm
specific variables and a random error.
It was argued that the socio-economic variables should be incorporated into the production
frontier model because such variables are capable of producing significant influence on the
production efficiency. Therefore, the simplicity, computational flexibility and ability to
examine the various farm-specific variables on technical efficiency in an economically
consistent manner has made the technical inefficient model Battese and Coelli, (1995)
popular among other models (Kehar singh, 2009).
1.9

Literature review

Numbers of literatures have explored the use of technical efficiency in agricultural farms
to investigate the factors that are having the positive or negative impact on the production
capacity of farms. In 2008, Kareem and Dipeolu in Nigeria investigated the factors affecting
production in 85 fish farms using stochastic production frontier approach based on the
cobb-Doulas production. The technical inefficiency function used were experience of the
farmer, age, education level and size of the household while the input factors used included
labour, time, fingerlings, feed and other materials. In the result obtained, the mean technical
efficiency of earthen ponds and concrete tank were 0.88-0.89 with no significant difference.
The experience of the farmers were found to have negative impact on the technical
inefficiency of the concrete pond.
Also, Islam, Tai, and Kusairi (2016) used farm level data in five regions in peninsula
Malaysia to investigate technical efficiency of brackish water fish cage farms were also
investigated. The study considered socio-economic factors such as age, education, and
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experience and production cycle as inefficient factors that might be affecting the technical
efficiency. The authors reported a mean technical efficiency of 0.37, a result that drove the
authors’ suggestion that potentials for increasing fish production exists in the entire area of
peninsula Malaysia. The result of the technically inefficiency model was reported to show
that farmers experience and the number of production cycles were negatively influencing
the efficiency of finfish cage culture in the study areas. Two important conclusions drawn
from the results by the authors were that efficiency of fish cage culture can be improved
through increase of production cycles and also that increasing the capacity building in the
practice will increase production.
In Bangladesh, in a two-output approach that involves a polyculture of prawn and carp, the
technical efficiency was measured using Data Envelopment Analysis (DEA) with a crosssection of 105 farms. The inputs considered included, fingerlings, labour, organic fertilizers
and feed. Pond size was found to have a positive impact on technical and cost efficiency,
and a negative relationship was found to exist between pond size and allocative efficiency,
feed application and technical, allocative and cost efficiency of the farms at the region.
However, Misraa & Misra (2014) research on establishing the systematic difference on the
technical efficiency of fish farms of different size-classes categorized on various socioeconomic conditions was reported to show that while experience, ownership and sole
proprietorship were found to be important determinants of technical efficiency, pond size
and education were reported to show no significant relationship with the technical
efficiency.
In order to compare the effects of technical inefficiency factors on different levels of
production (extensive, intensive and semi-intensive aquaculture), Nguyen and Fisher,
(2014) investigated the effects of pollution on intensive, extensive and semi-intensive
shrimp farms in Mekong river delta in Vietnam. Aside the comparisms of the efficiency of
the different practices, the study also compared the efficiency of downstream farms and
upstream farms using group frontier and meta-frontier analysis in a sample of 292 farms.
In their conclusion, they reported that extensive fish farms were more efficient than
intensive and semi-intensive shrimp farms which could be as a result of low cost of
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operation. They further reported that upstream farms were more efficient than downstream
farms in the region, siting pollution as the major environmental variable that influenced the
efficiency of the downstream region. In their elaborate discussion, while assessing the
impact of socio-economic factors, experience was found to be negative in relation to the
technical efficiency of intensive farms and positively related to the TE of the extensive
farms, while in the meta-frontier result, age of household head, farm size and experience
were found to be positive on the technical efficiency of the extensive farms.
In reference to numbers of literatures in the past, experience has seems to be an important
socio-economic factor that contributes positively to the increasing yield of agricultural
firms. However, this was not the case in the Kehar Singh et al. (2009) research when the
authors assessed the level of technical efficiency and its determinants of small-scale fish
production in the west Tripura district of the state of Tripura, India. The authors used both
one-stage and two-stage procedures of stochastic production frontier approach to analyze
the determinants. The study reported that TE ranges between 0.21 and 0.96, and the mean
technical efficiency is 0.66. The authors further confirmed Battese and Coelli (1995)
reported that one-stage procedure of stochastic production frontier gives reliable estimates
of the coefficients of stochastic frontier production function than that of the two-stage
procedure and that Cobb-Douglas functional form is more dependable than that of translog
form under the faming conditions in the west Tripura district of Tripura state. In the
technical inefficiency model, the authors found that there is a correlation positive between
age and experience of the farmers and a negative correlation between the age of a farmer
and education level as majority of the farmers were having a primary education or lower.
However, the author reported that the experience of farmers do not show positivity to the
quantity of output. Farmers’ age were reported to influence the practices either directly or
indirectly through labour, management and knowledge as young and middle-aged farmers
were more willing to adopt new technologies and old farmers were reported to be
conservative and risk aversive.
Aside these type of researches that investigate the factors that produce significant effect on
the production efficiency of the system using set of economic inputs, aggregate cost and
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output, there seems to be scanty literatures that consider looking into the impact of climate
change factors on the productivity of agricultural farms. However, Amin and Yacob,
(2012) considered the empirical analyses of the impact of climate change and climate
variability on the production efficiency and incomes at farm level in Kansas, Australia.
Climate variables, temperature and rainfall were considered in an approach that assumes
that climate variabilities affect the technical efficiency of farms and then farm incomes.
These variables were included in technical inefficiency model and used along with another
model, stochastic production frontier model which considered dependent variables such as
farm products, inputs, capital, labour, purchased inputs, time and precipitation. The third
model was fixed effect model that includes quadratic terms of weather variables, farm full
effects, vector of observed determinants of farm income which are time varying. The result
showed that climate variability produced significant effects on mean output elasticities with
respect to inputs, return to scale and technical efficiency. It was reported that purchased
inputs showed more responsiveness to climate variability than capital and labour. The
authors reported that 30 years climate projections from this research showed that farm
incomes will increase with a modest increase in mean maximum temperatures and decrease
with a modest increase in mean minimum temperatures, with the combination showing a
modest decline in average farm income that ranges from 0.2%-0.5%.
In 2014, Auci and Vignani investigated the efficiency of Agricultural farms in Italian
regions during the period of 2000-2010, when there was an observation climate change
impact was on the increase. Using stochastic production frontier approach, the effects of
production inputs such as labour, physical and human capital, from inefficient
meteorological factors such as temperature, and rainfall was analysed. The main objective
of the work was to analyze the economic impacts of climate change on the agricultural
sector in Italian regions. The analysis was concluded by ranking of the Italian regions on
the basis of these estimated technical efficiencies. The result obtained showed that
variability of rainfall had a positive impact on the production capacity while the average
minimum temperature produced a significant reducing effect on the quantity of harvested

production.
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In Chile, Roco et al. (2017) analysed the impact of climate change adaptation on
productivity for annual crops of 265 farms in the central part of the country using stochastic
production frontier approach. In order to measure climate change adaptation, set of 14
practices were adopted in three different specifications; binary variable, count, and index,
which represent decision, intensity and adaptation quality respectively. These variables
were used in three different stochastic production frontier models. The authors suggested
that the use of adaptive strategies had a significant and positive impact on productivity. The
practice with the highest impact on productivity was irrigation improvement. Empirically,
they concluded that there is a relevance of climate change on farmers’ productivity and it
is believed that this should facilitate discussions regarding the need to implement adaptation
measures.
Recently in Vietnam, impact of climate change on the technical efficiency of striped catfish
was carried out by Nguyen, et al. (2017) in the Mekong Delta. The study aimed at
identifying the major factors that affect the technical efficiency of Pangasius farms, while
exploring the relationship that exist between those factors and the impact climate change
has or would have on Pangasius farming. DEA-bootstrapping running in the Renvironment was described to be used to annul the overestimation of the technical
efficiency because the empirical sample is usually a fraction of the entire population of the
decision-making units; a problem that has been established with DEA approach. The
method employed multiple inputs to produce multiple outputs. The result showed that the
well-educated farmers and the more efficient farmers have perceived the impact of climate
change and have defined means of dealing with the influences. Salinity intrusion was found
to have reduced the scale of operation of the farmers located at the down-stream regions
while the farmers’ located at flood-prone areas of the upstream and midstream regions have
larger scale of operation. This success of the upstream and midstream may be attributed to
the higher level of education of the farmers and their huge experience at dealing with impact
of climate change as reported by the data. It may also be attributed to the fact that Pangasius

farming originated from this region and the farmers may possess quite better technical
know-how of the best operational practices. However, the higher TE found in the
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