Appril june issue of GJMMS pdf

Volume 1 Number 2, April-Jun,2015

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GLOBAL JOURNAL OF
MULTIDISCIPLINARY AND
MULTIDIMENSIONAL STUDIES
Dr. N.M.Lall
B.com, M.A.(Eco), Ph.D.
FRAS (LONDON)
Patron
Dr. A.K.Jha
PGDM, M.A.(Eco), Ph.D.
Managing Cum Chief Editor
Dr. Suresh Sachdeva Dr. Brajesh Mishra
M.A.(Eco), Ph.D. MOT
Prof. of Economics HOD (OT)
Govt. SLP College Smt.K.P.P.I.P.O
Gwalior (M.P.) Annand (GUJRAT)
Editor Editor
ISSN No.2394-8965
SHRUTAAYUSH PUBLICATION
GREATER NOIDA

II
Member of Editorial Board
----------------------------------------
Dr.V. D. Sharma
(M.Sc. M.A, B.Ed, PGDFM, Ph.D)
A Gandhian Professor,
Faculty of Management Studies & Ex Proctor
Gen. Secy, Rashtriya Shaikshik Mahsangh (University Campus)
VBS Purvanchal University Jaunpur-222003 (UP)
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Amity University Haryana, Gurgaon
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Gujarat University, Ahmedabad
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Dr. Dheeraj Pawar

III
Amity Institute of Telecom Engineering and Management,
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Aurangabad
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presentWestern Carolina University, Cullowhee, NC
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Marketing University Of Modern Science Dubai
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Nilesh Borde,
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Dr. Kiran Mehta,
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Bengal College of Engineering and Technology, Durgapur
Devanathan Elamparuthy
B.E.,M.B.A.,M.Phil.,P.G.D.P.E.,D.I.S.,(P.hd).,
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Ahsanullah Teachers’ Training College,Dhaka
SUDHASHREE PARVATI,
Lecturer,
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VI
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Adi Keih, Zoba: Debub, State of Eritrea, N.E.Africa
Dr. SHAUKAT ALI,
M.Con., M.Phil., Ph.D.
Associate Professor and Head, Commerce Department.
Anjuman-I-Islam’s Akbar Peerbhoy College of Commerce & Economics,
University of Mumbai, Mumbai
Indrani Ganguly,
M.A. B.Ed. (Geography),
Principal of Shri Shikshayatan School., Kolkata.
Nagori Viral Y.,
Assistant Professor
GLS Institute of Computer Technology (MCA), Ahmedabad .

VII
Editorial
-------------
The current changes and challenges experienced by the
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journal publishes Research papers, survey, articles, research findings,
book reviews, and annotations of new books.
Dr.A.K.Jha
Managing and Chief Editor
GJMMS

VIII
GLOBAL JOURNAL OF MULTIDISCIPLINARY
AND MULTIDIMENSIONAL STUDIES
Vol. 1 Issue No. 2 April-
June2015
1 ESTIMATION OF PRODUCTIVITY AND EFFICIENCY 1
OF COTTON FARMERS IN PAKISTAN: A CASE STUDY
OF DISTRICT DERA GHAZI KHAN
ABDUL HAMEED
2 IDENTIFICATION OF FACTORS INFLUENCING 24
PREFERENCES FOR GREEN PRODUCTS: A STUDY IN
AND AROUND KOLKATA (INDIA)
Prof. Sudipta Majumdar & Dr. Sukanta Chandra Swain
3 Performance of financial markets in Indian Economy 35
Dr.A.K.Jha & Viriender Pal Singh
4 DIFFICULTIES IN IMPLEMENTING IFRS: 44
A STUDY ON PERCEPTION OF CA STUDENTS
IN KOLKATA
Surajit Das
5 FINITELY GENERATED FREE ABELIAN 53
GROUPS AND THEIR APPLICATIONS
Simon Eze Ejiofor
6 Software of the Mind: Culture-Strategy Fit a 62
Trump card for Multinational Corporations
- A study
Mrs. Sheela Reddy

1
ESTIMATION OF PRODUCTIVITY AND EFFICIENCY OF COTTON FARMERS IN
PAKISTAN: A CASE STUDY OF DISTRICT DERA GHAZI KHAN
ISSN NO. 2394- 8965 GJMMS
Vol. – 1, Issue – 2, April- June-2015
ESTIMATION OF PRODUCTIVITY AND EFFICIENCY OF COTTON
FARMERS IN PAKISTAN: A CASE STUDY OF DISTRICT DERA GHAZI KHAN
ABDUL HAMEED
Pakistan institute of Development Economics
ABSTRACT
This paper investigates technical, allocative and economic efficiency of cotton farmers of
district Dera Ghazi Khan using data envelopment analysis technique. Structured
questionnaire is used to collect the data of cotton farmers. Data collection is carried out for
Kharif season of 2012. A stratified random sampling selection technique was used to
collect the data. The results reveal that mean total technical, pure technical, allocative,
economic and scale efficiencies are 0.67, 0.94, 0.57, 0.54 and 0.71 respectively. It also
shows that cotton farmer can produce average 22.5 mounds per acre seed cotton without
reducing the inputs and technology. It also concludes that education, experience and
contact with extension workers are significant determinants of technical efficiency of the
cotton farmers.
Keywords: Tchnical Efficiency, Allocative Efficiency, Economic Efficiency, DEA, Cotton
INTRODUCTION
Agricultural is a key sector in Pakistan. Its share in Gross Domestic Product
(GDP) is 21.4 percent with absorption of 45 percent of labor force. Population residing in
rural areas of country is directly or indirectly linked with agriculture sector, which is
almost 60 percent of total population (GOP, 2012). The major crops of Pakistan are wheat,
cotton, rice and sugarcane and these contribute 25.24 % to entire agricultural sector.
Cotton is the most important cash crop in Pakistan. It accounts for 7% in the entire
agricultural sector. It is sown in 2879 thousand hectares1
. In 2012, cotton production was
predicted at 14 million bales (GOP, 2012).
Figure 1 shows different sectors share in GDP. The overall agriculture share in
GDP decreased from 1960 to 2011 but still it is higher than manufacturing sector. In 1960
agriculture share in GDP was 46.2 percent and in 2011 it came at 21.6 percent (World
Bank, 2013). The area of cotton increased in Pakistan over the last three decades but every
time its yield has been threatened.
In developing countries majority of the population lives in rural areas
and faces extreme poverty. Pakistan is one of those countries where the huge
population is linked directly or indirectly with agriculture sector. In the
agriculture sector cotton is the one of major cash crops in Pakistan. It is very
important crop to earn the money and reduce the poverty level and in
1 Hectare: one hectare is equal to 2.471 acres

ABDUL AMEED
2
improving living standard in rural areas. But unfortunately cotton production has not
improved
significantly. Farmers practice subsistence and traditional farming with low productivity.
“This may be attributed to higher inefficiencies (technical and allocative) because farmers
have less access to resources and extension services to guide them for commercial
production” Javed, et al (2009). Cotton is also the most important and major crop of
District Dera Ghazi Khan. Recently, cotton crop decreased 6.61 percent due to the some
crop related and climate change problems (Pakistan Cotton Ginners Association).
Table-1 gives the cotton production of District Dera Ghazi Khan. It was 515000
bales in 2005-06 and 519000 bales in 2006-07. It was highest in 2006-07 and yet has not
reached again up to this level. The condition of cotton production in Dera Ghazi Khan is
similar to other districts of Punjab. Mostly increase in production is due to increase in
planted area (Directorate of Agriculture Crop Reporting Service, Punjab). It seems other
factors have not major role. Such conclusions cannot be drawn until we assume that
farmers are technically efficient.
This study selected cotton crop to find total technical, pure technical, allocative,
economic and scale efficiencies of its farmers. The efficiency indices computed will make
known the extent of technical and allocative inefficiencies among cotton farmers. It
would reflect existing potential for farmers to improve output without changing the
combination of inputs or produce the same output with fewer inputs than they are
currently using. Farm and farmer characteristics observed among efficient farmers will be
used to formulate policy recommendations that will help policy makers to develop

3
ESTIMATION OF PRODUCTIVITY AND EFFICIENCY OF
COTTON FARMERS IN PAKISTAN: A CASE STUDY OF
DISTRICT DERA GHAZI KHAN
Table-1: Cotton Production of District Dera Ghazi Khan
Years Production In „000‟ Bales
2005-06 515
2006-07 519
2007-08 464
2008-09 233.18
2009-10 356.16
2010-11 212.50
2011-12 344.25
Note: Bale=170 KG
Source: Directorate of Agriculture, Crop Reporting Service, Punjab
strategies that may help inefficient farmers. This will also be important in extension work
as it will highlight farm and farmer characteristics more likely to enhance productivity
among the farmers. It also analyzes the
determinants of technical efficiency. Furthermore, it helps in understanding the core
problems which are being faced by cotton farmers of District Dera Ghazi Khan. The other
reason of selection of cotton crop is its importance in textile, food of animals and edible
oil.
1. LITERATURE REVIEW
A number of studies are available in literature that present technical efficiency of cotton
and other agriculture crops like wheat, sugarcane, tomato etc. Sohail, et al (2012)
estimates the technical efficiency for wheat production of district Sargodha, Pakistan.
They use data envelopment analysis (DEA) methodology for estimation of efficiencies and
Tobit regression to find out the determinants of efficiency. The study finds that efficiency
varies from 0.6 to 1. It also examines dependence of efficiency on farm specific variable
such as experience, education, villages distance, household size and farm size. The results
show that farm size and village distance are negatively related with technical efficiency.
A similar study using DEA is conducted by Javed, et al (2009) estimates
technical, allocative and economic efficiencies of cotton and wheat farmers in Punjab,
Pakistan. Result shows that average technical, allocative and economic efficiencies are
0.87, 0.44 percent and 0.37 respectively. It also indicates that farmers‟ education and
extension agents are negatively related with inefficiency of cotton and wheat farming. Gul,
et al (2009) estimates the determinants of technical efficiency of cotton growing farms in

ABDUL AMEED
4
Turkey. They use DEA methodology to find efficiencies and Tobit analysis to analyze the
determinants of efficiency. Result shows that on average technical efficiency is 0.79. It
means 21 percent capacity is available for increasing cotton production without changing
inputs and technology.
Gwandi, et al (2010) estimates efficiency of cotton production in Gassol local
Government area of Taraba state, Nigeria. The stochastic frontier analysis (SFA) is used to
determine technical, allocative and economic efficiencies. The result shows that 82 percent
of the variation in output of cotton is explained by input factors. The farm size and family
labour have negative impact. The result also shows that resources are over utilized in
cotton production so farmers need more knowledge on input use. Sylvain, et al (2010) also
use the SFA to estimate determinants of the technical efficiency of cotton farmers in
Northern Cameroon. The result gives technical efficiency indices vary from 11 percent to
91 percent.
OGUNNIYI, et al (2011) “estimates the technical efficiency of tomato production in Oyo
state Nigeria”. The 150 random samples were collected by using multi-stage sampling
technique.
The DEA methodology used to estimate technical efficiency and Tobit analysis used to
determinants of efficiency. Result show that the technical efficiency varies 31 percent to
100 percent .The on average technical and scale efficiencies are 42 percent and 82 percent.
The study also shows that there is small scale inefficiency due to excess use for all inputs
especially for fertilizer, family and hired labor. The determinants of technical efficiency
are education, experience, marital status and gender. EBONG, et al (2009) “estimates the
determinants of technical efficiency of urban farming in Uyo metropolis of AkawaIbom
state, Nigeria”. A simple random sampling procedure was employed in the selection of 75
urban farmers from the four designated locations in the study area and Maximum
likelihood estimation (MLE) procedure was used. The SFA used to determents technical
efficiency of urban farming The result shows that the coefficients of farm size, capital,
labor and planting materials were all positive and significant with technical efficiency.
According to the inefficiency analysis age, farming experience, education, extension
contract and household size has influence the inefficiency of the farmer. The Farmers
technical efficiency index varies 0.10 to 0.95 and with on average 81 percent.
DLAMINI, et al (2010) “estimates the technical efficiency of small scale sugarcane
farmers in Swaziland state, Africa”. A stratified random sample size of 75 farmers was
obtained. The well structure questionnaire used to collect the data. The result of SFA and
inefficiency model indicated that elasticity of fertilizer variable for the VUVULANE small
scale sugarcane farmers was higher 0.536 and the labour, herbicides were positive, age
and land was negative influence.Overall technical efficiency mean is 73.6 percent to
86.7 percent.

5
.HAJIAN, et al (2013) “estimates the total factor productivity and efficiency in Iranian
crop productivity through the data envelopment analysis”. The study consists on the penal
data of 1995 to 2009. The result shows that productivity rise in this period. Technical
efficiencies level higher but allocative and
economic efficiencies are in lower level.
On other hand, Abid, et al (2011) conduct resource use efficiency analysis of small BT
cotton farmers in Punjab, Pakistan by using Cobb-Douglas production function. They find
that BT cotton production has an increasing return to scale with elasticity of production
1.16 of small farmers. All production inputs i.e. pesticides, irrigation; fertilizers and labour
were underutilized because ratio of MVP/ MFC2
was greater than unity i.e. 3.94, 2.01, 1.5
and 1.27, respectively.
In previous studies descriptive statistics analysis is used to describe socioeconomics
characteristics of cotton farmers while the DEA and SFA are commonly used to analyze
the productivity and efficiencies (technical, allocative and economic). These studies also
show that the determinants of inefficiency of productivity and technical efficiency are
estimated with the help of (Tobin, 1995) regression and Coelli, et al (2005) model. These
all studies help us in making the design of our study analysis.
2. MATERIAL AND METHODS
The Dera Ghazi Khan District is enclosed in the north by Dera Ismail Khan
District of Khyber Pakhtoonkhwa (KPK) and it‟s bordering Tribal Area, on the west by
Musa Khel and Barkhan districts of Baluchistan Province, on the south by Rajanpur and
on the east by Muzaffargarh and Layya.
Sample size and Sampling design
The District Dera Ghazi Khan was purposively (study area of cotton analysis)
selected for this study. The Dera Ghazi Khan consists of the 41 union councils. We
dropped 7 urban union councils and one Sakhi Sarwar union council because cotton crop
is not sown there because of arid area. A random sampling technique is used to select the
union councils, 08 union councils are randomly selected from 33 union councils. Fifteen
sample farmers from each union council are selected from randomly selected villages
based on the share of different categories small, medium and large farmers. Total 120
farmers are interview by stratified sampling technique. The data are collected for the crop
year 2012 (Kharif 2012). The cotton farmers are categorized as small, medium and large
farmers. The categories are given below:
1- Category (A): Small farmers 1 to 3 acres under cotton area 2- Category (B): Medium
farmers 3 to 6 acres under cotton area 3- Category (C): Large farmers above than 6 acres
under cotton area
2
MVP is marginal value of product is the value of additional unit of input is equal to the price of output multiplied by
marginal product of factor of production and MFC is marginal factor cost indicates how the total factor cost affected
by one or more change in inputs.

ABDUL AMEED
6
Source: Survey of Pakistan
Data Limitation
This study has some weaknesses related to survey interviews; data accurateness is
depended on respondent skill to remember earlier period information and to answer the
survey questions. In district Dera Ghazi Khan, most of the farmers are illiterate and they
do not keep the records of inputs and outputs. Therefore, after the first interview some
information was again collected by re-interviewing the farmers to minimize the errors.
However, some errors and inconsistencies are unavoidable in this kind of study.
3. ANALYSIS
This paper uses the data envelopment analysis (DEA) under assumption of
constant return to scale (CRS) and variable return to scale (VRS) to estimate the technical,
allocative and economic efficiencies and Tobit regression to find out determinants of
technical efficiency.
Objective: in ,
Subject to: yiY0
  xi  X  0
   0
represents the inputs vector of X1i, X2i . . . X8i
X1i represents the crop area of the ith farm in acres
X2i represents the total quantity of seed per acre used on the ith farm in kilogram
X3i represents the total quantity of nitrogen per acre used on the ith farm in kilogram.
X4i represents the total quantity of phosphate per acre used on the ith farm in kilogram

7
X5i shows the total tractor hours for all farm operations (which used in land preparation,
weeding, planting, etc)
X6i represents the total quantity of pesticides per acre used on the ith farm in litre. X7i
represents the total number of irrigation per acre used on the ith farm in hours.
X8i represents the total labour (family and hired) as the total number of man-days3
used on
the ith farm.
To estimate the pure technical efficiency DEA is used following, Coelli, et al (2005) with
assumption of VRS:
Objective in ,
Subject to yiY0
xi  X  0
N1  1
  0
Where:
N1 represents a convexity constraint which ensures that inefficient farm is only
benchmarked against farm of a similar size.
This paper also uses DEA cost minimization method following Coelli, et al
(2005) with assumption of VRS for estimation of cost efficiency:
Objective in ,
Subject to yiY0
xi

 X  0
N1  1
  0
Where:
WI is vector of input price w1i, w2i, w3i . . . w12i of the ith farm,
xi

Is the cost minimizing vector of input quantizes for the ith farm, N refers to total
number of farms in the sample,
W1i represents the per acre land cost of the ith farm in rupees,
W2i represents the total cost of seed per acre used on the ith farm in rupees,
W3i represents the total cost of nitrogen per acre used on the ith farm in rupees,
W4i represents the total cost of phosphate per acre used on the ith farm in rupees,
W5i shows the total cost of tractor hours for all farm operations (which used in land
preparation, weeding, planting, etc.),
W6i represents the total cost of pesticides per acre used on the ith farm in rupees, W7i
represents the total cost of irrigation per acre used on the ith farm in rupees,
3
Man-day is a number of labor days while one day equals to 8 hours

ABDUL AMEED
8
W8i represents the total cost of labors family and hired as the total number of man-days
used on the ith farm.
Cost efficiency is the ratio between the minimum possible cost and the observed cost.
CE  wixi
E
wixi
Allocative efficiency is estimated by dividing the cost efficiency with the technical
efficiency.
AE =
Scale efficiency is estimated by dividing the technical efficiency of constant return to scale
and technical efficiency of variable return to scale.
Scale efficiency score varies from zero to one, if scale efficiency equal to one
indicate efficiency and less than one indicate inefficiency. The scale efficiency less than
one due to increasing return to scale or decreasing return to scale and equal to one due to
constant return to scale. Technical, allocative, economic and scale efficiencies scores will
be estimated by using the computer software DEAP 2.1.
A second step regression model was applied to determine the farm specific
attributes in illumination efficiency in this study. Alternatively, the factors can be
integrated directly into the model and some external factors influence the technical
efficiency of cotton farmers so in order to investigate these external factors. The study
applied second step approach by using a Tobit regression.
i01122334455i
Where:
i represent the ith farm in sample,
i
Represent the technical efficiency of the ith farm,
1
Represents the education of the ith farmer in years of schooling,
 2
Represents the farming experience of the ith farmer in years, 
 3
Represents the farm size of the ith farm in acres, 
 4
Represents the access to extension services of the ith farmer in the cotton season, 
 5
Represents the distance of the ith farm from main market in kilometers, 
 ' s
are unknown parameters to be estimated, 
i
is the error term.
GRETL computer software will be used to estimate Tobit regression model.
4. RESULTS AND DISCUSSION
A review of key variables integrated in data envelopment analysis is given in table-A1.

9
The table-A1 is specified on per acre inputs quantities and per acre4
cost basis. These
results are calculated from 119 samples while one sample is dropped due to outlier. Total
technical, pure technical, allocative, economic and scale efficiencies are presented in
table-A2 and table-2. The estimation gives 0.67 mean of total technical efficiency of
sample farmer, which varies from 0.21 to 1.0. These results show that if sample farmer
operate at full efficiency level they can reduce, on an average, their inputs use by 33
percent to produce the same level of output. Decomposition of technical efficiency shows
that, on average, the sample farmers are more scale efficient than they are technically
efficient. The mean pure technical efficiency of sample farmer is 0.94, which varies from
0.64 to 1.0. The mean scale efficiency is 0.71, which varies from 0.26 to 1.00 and only 15
percent farmers are scale efficient while remaining 85 percent sample farmers are scale
inefficient. Ninety-nine percent of these scales inefficient farmers operate under increasing
returns to scale while remaining only 1 percent of these scale inefficient farmers operate
under decreasing return to scale. The mean allocative efficiency of sample farmer is 0.57,
with a minimum of 0.18 and a maximum of 1.00. The combined effect of technical and
allocative efficiencies shows that mean economic efficiency of is 0.54, with a minimum of
0.17 and maximum of 1.00.
The results show that cotton farmers are not fully efficient. Therefore, if the farmers
operate at full efficiency level they can reduce their cost of production by 46 percent
without reducing the level of output and with the existing technology because their
economic efficiency is 54 percent and allocative efficiency shows that the considerable
room is available to enhance the productivity of sample farmers because 43 percent cost of
inputs used in wrong direction and improve it. Frequency distribution of technical,
allocative and economic efficiencies estimates of sample farmers in cotton system are
given in figures 2 to 6 and appendix table-A1.
It is evident from figure 2 that total technical efficiency of the sample farmers varies from
0.21 to 1.00. Most of the farmers‟ (63% out of 119) total technical efficiency is less than
0.80 while only 23% have more than 0.90. The situation seems different in case of pure
technical efficiency (figure 3) here almost 90% farmers have pure technical efficiency
more than 0.90. The pattern of allocate and economic efficiencies are alike (figure 4 and
figure 5) with both average efficiencies around 0.55. Like other efficiencies the farmers
are not scale efficient too (figure 6).
Input Slacks Analysis and Number of Farmers Using Excess Inputs
Table-3 indicates that input slacks and number of cotton farmers using excess
inputs. It is evident that the farmers can reduce their cost on inputs by reducing the amount
of slacks without reducing the output. Slacks are observed in irrigation, pesticides,
nitrogen and labor. This is because farmers adopt traditional practices in using the inputs.
4
Acre: one acre is equal to 0.04046 hector.

ABDUL AMEED
10
Table-2: Total Technical, Pure Technical, Allocative, Economic and Scale
Efficiencies
5
5
TEcrs mean technical efficiency through constant return to scale, TEvrs mean technical
efficiency through variable returns to scale, AE mean allocative efficiency, EE mean economic
efficiency and SE mean scale efficiency
Efficiencies TEcrs TEvrs AE EE SE
Mean 0.67 0.94 0.57 0.54 0.71
Minimum 0.21 0.64 0.18 0.17 0.26
Maximum 1.00 1.00 1.00 1.00 1.00

11
They use inputs on the behalf of their father‟s, individual experience and illiterate
pesticides shopkeeper advice. Therefore, it is most important to create awareness about
new technologies and to give them training to improve the use of inputs.
Relationship between efficiencies estimates and cropping area
In order to investigated relationship among efficiencies and crop area. The crop area was
categorized into three groups on the basis of operational holdings of farmers. Farm size A
consists of 1 -3 acres under cotton crop considered as small farmers, farm size B consists
of 3-6 acres under cotton crop considered as medium farmers and farm size C consists of
above 6 acres under cotton crop considered as large farmers. The total technical, pure

ABDUL AMEED
12
technical, allocative, economic and scale efficiencies scores relative to the farm size in
cotton crop are presented in table-4
The total technical efficiency, pure technical, allocative, economic and scale
efficiencies of small sample farmers are 0.72, 0.96, 0.55, 0.53 and 0.75 respectively. The
medium sample farmers have total technical, pure technical, allocative, economic and
scale efficiencies are 0.65, 0.93, 0.61, 0.57 and 0.69 respectively. The large sample
farmers have technical, pure technical, allocative, economic and scale efficiencies 0.62,
0.92, 0.55, 0.51 and 0.67 respectively.
In the total technical, allocative, economic and scale efficiencies among cropping
categories, category A farmers are more efficient than category B and
Table- 3: Input Slacks and Number of Farmer Using Excess Inputs
Inputs
Number
of Mean Mean Input Excess
Farmers Slack Use Input Use
(%)
Cotton crop land in acres 13 0.70 6.91 10.09
Seed per acre in kg 15 0.15 5.96 2.58
Nitrogen per acre in kg 26 3.51 55.37 6.34
Phosphate per acre in kg 16 0.47 20.94 2.23
Per acre tractor hours 19 0.09 8.67 1.07
Pesticides per acre in
litres 28 0.64 8.31 7.72
No of irrigation per acre
in hours 34 1.92 13.15 14.59
Labor days per acre man-
days 28 1.68 19.53 8.58
C sample farmers and medium farmers are more efficient than category C farmers. The
small sample farmers total technical, allocative, economic and scale more efficient than
medium farmers because the small sample farmers use small unit, family labor, which all
time work in field and proper management of small unit, less inputs required, easily
control outside factor effect e.g. rain, weather. In the monsoon rain when the water stay in
the field of cotton crop so the small unit of cotton crop easily drain and support to plant
with different ways as compare to large and medium farm size.
In the total technical, allocative, economic and scale efficiencies among cropping

13
Table- 4: Means of Total Technical (TECRS), Pure Technical (TEVRS), Allocative,
Economic and Scale Efficiencies Estimates according To Farm Size in Cotton crop
categories, category A farmers are more efficient than category B and C sample farmers
and medium farmers are more efficient than category C farmers. The small sample farmers
total technical, allocative, economic and scale more efficient than medium farmers
because the small sample farmers use small unit, family labor, which all time work in field
and proper management of small unit, less inputs required, easily control outside factor
effect e.g. rain, weather. In the monsoon rain when the water stay in the field of cotton
crop so the small unit of cotton crop easily drain and support to plant with different ways
as compare to large and medium farm size.
Table- 5: Scale share in categories, CRS (scale efficient), IRS (increasing return to
scale) and DRS (decreasing return to scale) in cotton crop.
Categories CRS IRS DRS
A (1-3 acres) 20% 80% -
B (3-6 acres) 15% 83% 2%
C (up to 6 acres) 10% 90% -
As presented in table-4 and table-5, scale efficiency of category (A) is 0.75. The 20
percent sample farmers are on constant return to scale while remaining is on increasing
return to scale. It indicates that 80 percent of sample farmers need to increase operational
scale to enhance the productivity and efficiency. While, medium farmers (category-B)
having 0.69 scale efficiency. Among them only 15 percent are on constant return to scale
and remaining 83 percent are on increasing return to scale. The large farmers (category-C)
have scale is 0.67. The only 10 percent of sample farmers are on constant return to scale
and 90 percent are on increasing return to scale the results show that in all categories most
of the farmers are on increasing returns to scale i.e. they can increase their output by
changing their operational scale. It will also enhance their efficiencies
Estimates of Target Output in Cotton Crop
This study also presents target output estimates based on output orientation methodology.
This technique has an advantage of estimating the maximum possible production. Table-6
gives the summary of target output. The target refers to the amount of output the decision
making units should aim at producing given the available unit of inputs and technology.
Categories TEcrs TEvrs AE EE SE
A (1-3 acres) 0.72 0.96 0.55 0.53 0.75
B (3-6 acres) 0.65 0.93 0.61 0.57 0.69
C (up to 6 acres) 0.62 0.92 0.55 0.51 0.67

ABDUL AMEED
14
The minimum output target that some of the decision management unit (DMU) should aim
at producing the target output is 6.8 Mounds per acre. The maximum output target range is
36 Mounds per acre.
The average actual production is 16.46 Mounds per acre, but according to output
orientation analysis the sample farmers can produce on average 22.5 mounds per acre
without reducing or increasing their current level of inputs and technology. According to
this analysis based on actual available inputs and technology to formers 10.9 percent out
of total 119 sample farmers of cotton crop can produce the cotton seed range 6-10 Mounds
per acre, 10.1 percent 11-15 Mounds per acre, 29.5 percent 16-20 Mounds per acre, 11.8
percent Mounds per acre, 14.3 percent 26-30 Mounds per acre and 23.5 percent more than
30 Mounds per acre, respectively.
Table- 6: Frequency Distribution of Output Target in Cotton System
(Mound=40kg)
Range Frequency Percentage
1.00-5.00 0 0.00
6.00-10.00 13 10.9
11.00-15.00 12 10.1
16.00-20.00 35 29.4
21.00-25.00 14 11.8
26.00-30.00 17 14.3
>30 28 23.5
Total 119 100.0
Analysis of Determinants of Technical Efficiency
The socioeconomic factors are expected to affect the level of technical efficiency of
farmers. This study also makes an attempt to find out the sources of technical efficiency
and external factors of cotton crop in District Dera Ghazi Khan. The Tobit regression
model is used to estimate the determinants of technical efficiency and external factors.
Table-7 shows that the coefficients of farmers education (schooling years), experience and
contact with extension agents have positive signs as our priori expectations (positive
related to technical efficiency) and significant. The educated farmers are more technically
efficient than less/no years of schooling cotton farmers. The results are similar to Sohail,
et al (2012), Gul, et al (2009) and Ali and Flinn (1989) who argue that the educated
farmers have better access to information, technology and standard inputs. Moreover, they
can have effective dealing with financial issues. The experience is positive related and
statistical significant which is the same explanation of the Bravo-Uretta (1994), Sohail, et
al (2012), Ali and Flinn (1989) and Abid, et al (2011). It is indicating that

15
farmers experience an important effect on productivity and technical efficiency
of cotton farming. The experienced farmer can manage the farming uncertainty
and different practice in better way. The coefficient of contact with extension
agents has positive and significant effect on the technical efficiency of cotton
sample farmers. Result of this study is in the line with the result of Javed, et al
(2009) when farmers contact with extension agents then they get more
information about modern farming, weather condition, cropping preparation, information
about seeds, fertilizers and other requirements.
Table-7: Source of Technical Efficiency of Cotton System with Tobit Analysis
Note: *** significant at 0.01level. **significant at 0.05 level.* significant at 0.10 level
While the variable of farm size negatively related with the technical efficiency of cotton
crop but coefficient is very small. However, on the basis of technology available to
farmers of Dera Gazi Khan bigger farm size can be a cause of low efficiency as proper
management would not be easy. The most farmers used private Muzarey (labor) which are
also illiterate and have financial constraints so they cannot properly manage the large unit.
These distance. The roads and market infrastructure is highly related with the agriculture
production because the outputs are properly reached in market at the proper time and less
destroy with hardship weather.
5. CONCLUSIONS
The present study was designed to estimate technical, allocative and economic efficiencies
and also to investigate the determinants of technical efficiency of cotton farmers in District
Dera Ghazi Khan. The data were collected for the crop year 2012 from 120 respondents,
the one respondent drop due to outlier6
. The Data envelopment analysis technique used to
estimate the technical, allocative and economic efficiencies and the Tobit regression
analysis was used to estimate the determinants of technical efficiency. Result derived from
DEA models for the cotton crop farmers of District Dera Ghazi Khan indicated that mean
total technical, pure technical, allocative, economic and scale efficiencies were 0.67, 0.94,

ABDUL AMEED
16
0.57, 0.54 and 0.71 respectively. Findings also uncovered that if farmers could manage
optimal levels of inputs, they can reduce 33 percent inputs and 46 percent cost without
changing level of output and technology because the technical and allocative efficiencies
respectively, 67 and 54 percent. The small farmers are more technical, allocative and
economically efficient than category-B (medium) and category-C (large) farmers The
result of target output analysis shows that the sample farmers should produce on average
22.50 Mounds per acre output of seed cotton without reducing the inputs and technology
while the actual output of seed cotton in this study was 16.46 Mounds per acre. The result
of Tobit regression model shows that the education, experience, extension workers have
positive collision on technical efficiency while impact of farm size and market distance
was negative on technical efficiency of cotton crop.
Policy Recommendations
According to the finding there are some commendations, which enhance
agricultural efficiency and productivity in District Dera Ghazi Khan.
1-The majority comprehensible consequence is that there is required of echo plan to
encourage formal and technical education in rural area. This will allow situations create
many problems for productivity and efficiency. The variable market distance is used as the
proxy for development of road and market infrastructure. The distance from the village to
main market of agriculture inputs and output is negatively associated with the technical
efficiency. According to the (FAO, 2004) the purchasing of inputs would have been higher
in a developing country if the supply of inputs available at the walking the farmers to
make better technical decision about the farming and best allocation of resources.
2- The study shows that farmers used excess inputs as traditional behavior, individual
experience, believing their parents experience and the local village shopkeeper advice. The
Government should make broadcasting strategy for awareness about the use of agriculture
inputs and resources.
3-The study shows that the farmers having more contact with extension agents are more
efficient than the farmers having low contacts. It is, therefore, recommended that the
policy makers should focus on attractive farmers6
access to information via provision of
better extension services. Government should apportion more funds to make stronger the
extension department and expending net of extension services in the remote areas.
4-The Government should make the strong policy to remove without licenses agro-shops,
because mostly income of the illiterate farmers wastes into the flak seeds, fertilizers,
pesticides and herbicides etc.
5-The study also shows that the farmers located near to the market are more efficient than
the farmers located away from the market. It is, therefore recommended that the policy
makers should focus on the development of market and road infrastructure supply outlets
should be made closer to the farm gate.
6- The Government should issue the licenses to shops for purchase of cotton seeds at the
prescribed rate by government. Further government can generate new revenue way in the

17
form of license fee. This revenue can be used for the welfare of the farmers.
7- The Government should provide more funds for paka khal system to save the canal
irrigation water. Moreover, there should be proper monitoring by the agriculture
department to check the quality of water, soil, fertilizers, seeds, pesticides and herbicides.
REFERENCES
Abid, M., Ashfaq, M., Quddus, A., Tahir, A., & Fatima, N. (2011). A Resource Use.
Efficiency Analysis of Small BT Cotton Farmers in Punjab, Pakistan. Pakistan Journal of
Agriculture, 48(1), 75-81
ALi, M., & Filnn, J. (1989). Profit Efficiency in Basmati Rice Producers in Pakistan‟s
Punjab.
American Journal Of Agricultural Economics, 71, 303-310.
Banker, R.D., Charnes, A., and Cooper, W.W. (1984). Some Models for
Estimating Technical Efficiency and Scale Inefficiencies in Data Envelopment
Analysis. Management Science, 30, 1078-1092.
Bravo-Ureta, B. E., & Evenson, R. E. (1994). Efficiency in Agricultural Production: The
Case of Peasant Farmers in Eastern Paraguay. Journal of Agricultural Economics, 10, 27-
37.
Coelli, T. (1996). A Guide to DEAP Version 2.1 A Data Envelopment Analysis
(Computer) Program. Australian Journal of Agricultural Economic, 39, 219–245.
Coelli, T., Rao, D., & Battese, G. (2005). An Introduction to Efficiency and Productivity
Analysis. Springer.
Dlamini, S., Rugambisa, I., Masuku.B.M, & Belete.A. (2010). Technical Efficiency of The
Small Scale Sugarcane Farmers in Swaziland: A Case Study of Vuvulane and Big Bend
Farmers. African Journal of Agriculture Research, 5(9), 935-940.
Ebong, V., Okoro, U., & Effiong, E. (2009). Determinants of Technical Efficiency of
Urban Farming in Uyo Metropolis of Akwa Ibom State, Nigeria. Journal of Agriculture
and Social Science, 5, 89-92.
FAO. (2004). Fertilizer use by Crop in Pakistan, Land and Plant Nutrition Management
Service, Land and Water Development Division, FAO, Rome.
GOP. (2012). Economic Survey of Pakistan. Ministry of Finance,Govt of Pakistan.
Gul, M., Koc, B., Akbinar, G., & Parlakay, O. (2009). Determination of Technical
Efficiency in Cotton Growing Farms in Turkey: A Case Study of Oukurova Region.
African Journal of Agriculture Research, 4(10), 944-949.
Gwandi, O., Bala, M., & Danbaki, J. (2010). Resource Use Efficiency in Cotton
Production in Gassol Local Government Area of Taraba State, Nigeria. Journal of
Agriculture and Social Science, 87-90.
Hajian.Mohammadhadi, S. (2013). Total factor Productivity and Efficiency in Iranian
Crop. Research Journal of Agricultural and Environmental Management, 2(2), 033-043.
Javed, I., Adil, A., Hassan, S., & Ali, A. (2009). An Efficiency of Punjab Cotton-Wheat
System.
The Lahore Journal of Economics, 2(14), 97-124.
6 One sample drop which farm area is 200 acres due to this stander deviation is greater in table-AI

ABDUL AMEED
18
Ogunniyi, L., & Oladejo, J. (2011). Technical Efficiency of Tomato Production in Oyo
State Nigeria. Agricultural Science Research, 1(4), 84-91.
Sohail, N., Latif, K., Abbsa, N., & Shahid, M. (2012). Estimation of Technical Efficiency
and Investigation of Efficiency Variables in Wheat Production: A Case of District
Sargodha (Pakistan). Interdisciplinary Journal of Contemporary Research in Business,
3(10), 897-904.
Sylvain, B. N., C, J. N., & Cletus, N. (2010). The Determinants of The Technical
Efficiency of Cotton Farmers in Northern Cameroon. MPRA, 24814, 50-62.
Tobin, J. (1995). Estimation of Relationship for Limited Dependent Variable.
Econometrica, 26, 24-36.
APPENDICES
Table-A1
Variables Minimum Maximum Mean
Std.
Deviation
Output per acre in kg 240.0 1440.0 657.10 223.0153
Total farm land in acres 1.0 200.0 10.40 19.3541
Land under cotton crop in acres 1.0 40.0 6.91 6.9563
Seed per acre in kg 3.8 10.0 5.96 1.6712
Nitrogen per acre in kg7
9.0 147.0 55.37 23.4862
Phosphate per acre in kg 4.6 69.0 20.94 7.5348
Per acre tractor hours 4.0 14.0 8.67 1.5490
Pesticides per acre in litre 1.5 18.1 8.31 3.1337
No. of Irrigation per acre in hours 4.0 32.0 13.15 7.3619
Labor days per acre man-days 4.5 57.0 19.53 11.2650
Per acre land cost in Rs 5000.0 20000.0 10263.03 2259.1758
Per acre seed cost in Rs 400.0 3000.0 1437.60 574.3237
Per acre nitrogen cost in Rs 684.8 11184.8 4220.16 1792.6342
Per acre phosphate cost in Rs 727.0 9945.7 3052.40 1093.8497
Per acre tractor hours cost in Rs 1200.0 4800.0 2825.63 664.3738
Per acre pesticide cost in Rs 2200.0 13000.0 7058.10 2058.3371
Per acre irrigation cost in Rs 910.0 10533.3 3067.01 1621.5261
Per acre labor cost in Rs 1350.0 17100.0 5858.15 3379.7743
Source: Field survey by author 2012
7
Nitrogen & phosphate amount estimate from ratio of nitrogen in 50 kg bag

19
Table-A2
Efficiencies of Sample Farmers of District Dera Ghazi Khan
EFFICIENCY
RANGE TECRS TEVRS AE EE
SE
Freq % Freq % Freq % Freq % Freq %
0.01-0.10 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0
0.11-0.20 0 0.0 0 0.0 1 0.8 1 0.8 0 0.0
0.21-0.30 5 4.2 0 0.0 1 0.8 1 0.8 3 2.5
0.31-0.40 11 9.2 0 0.0 10 8.4 24 20.2 8 6.7
0.41-0.50 12 10.1 0 0.0 29 24.4 27 22.7 12 10.1
0.51-0.60 21 17.6 0 0.0 33 27.7 27 22.7 20 16.8
0.61-0.70 21 17.6 2 1.7 27 22.7 25 21.0 15 12.6
0.71-0.80 12 10.1 10 8.4 10 8.4 7 5.9 18 15.1
0.81-0.90 14 11.8 18 15.1 6 5.0 5 4.2 15 12.6
0.91-1.00 23 19.3 89 74.8 2 1.7 2 1.7 28 23.5
TOTAL 119 100 119 100 119 100 119 100 119 100

Majumdar & Swain
20
Identification of factors influencing Preferences for Green
Products: A study in and around Kolkata (India)
Prof. Sudipta Majumdar
Faculty Member & Research Scholar
ICFAI University Jharkhand, Ranchi,
India
E-mail: smajumdar2004@gmail.com
Dr. Sukanta Chandra Swain
Asst. Dean
ICFAI University Jharkhand, Ranchi,
India
E-mail: sukanta_swain@yahoo.com
Abstract
Since the concept of environmental consciousness has become a necessity to save the
mankind, promoting consumption of green products is the need of hour, owing the fact
that green products are environment friendly or sustainable products and are organic in
nature. It is evident that the feeling for the health of environment and consumers, the usage
of green products is emerging at the cost of traditional or conventional products. However,
the magnitude of usage of green products is much behind the ideal one to safeguard the
consumers and environment at large. Thus stretching the incidence and depth of usage of
green products is a must. In order to achieve the pious objective, it is necessary to know
the factors which insisted the users to go for the green products so that the same can be
ventilated to the masses for extending the consumer base for the green products. On this
backdrop, this study has been undertaken to collect responses from the green product users
in and around Kolkata to find out the significant factors, through factor analysis, which
contribute for the popularity of the Green products. The study also tries to find out the
impact of different psychographic variables with respect to popularity of green products.
The findings so obtained will definitely help in augmenting the usage of green products
and hence contribute to safeguard the health of consumers and environment at large.
Key Words: Green Products, Factors, Kolkata, Factor Analysis, Psychographic Variables
1. Introduction
From the last decade onwards people became more concerned about their health as a result
of which they are using more of green products. Green products can be stated as having
less of an impact on the environment and are less damaging to human health than
traditional products. Hence they are also called as sustainable or environment friendly
product. Green products are formed from recycled components, be manufactured in a
more energy-conservative way, or be supplied to the market in more environmental
friendly way [1]. Since people are becoming more aware about the concept of
environmental consciousness, the usage of traditional or conventional products are getting
reduced. Traditional products are those manufactured in the traditional way. They are not
being produced keeping environmental considerations in mind. In today‟s competitive
scenario green products are competing with the conventional or regular products (products
produced by traditional methods).But, this usage pattern is not applicable to all parts of the
society. Knowledge and awareness about the green products play a very vital role in
enabling the customers to use them. But, this awareness and knowledge do not exist, thus

21
Identification of factors influencing Preferences for Green Products: A study in and
around Kolkata (India)
restricting the usage of the green products. From the last decade onwards, we have started
using the green products and it will take time before it penetrates to all parts of the society.
The concept of green products is becoming more popular with the aspect of food items.
Since people are becoming more health conscious, they are giving more importance to the
consumable products. People started using more green food products to minimize their
health risk. But, here also like normal green products knowledge and awareness is not
there in all parts of the society. So, these are more being used by the more educated parts
of the society. Also, organizations and government are incapable of promoting the concept
of “Green”. But the best part is, the concept has started and it is penetrating to the society
at a very fast pace. If all the factors which contribute to the popularity of green products,
such as price of the product, its quality, customer‟s perception about the products,
awareness about them, are being handled carefully by the government and the
organizations, then they will become more popular in the society.
As we have been discussing, there are various factors which positively as well as
negatively influence the popularity of green products, both in food and nod-food sector. In
this context, it is important to examine various psychographic factors which influence the
usage of green products, specifically in cosmetics and food sector in Kolkata and around
Kolkata in West Bengal, India. The various psychographic variables, such as
Environmental Consciousness, Health Consciousness, Price Sensitivity, Product
Involvement and Innovation are selected from a through literature review. The consumers‟
perception about each psychographic variable is being understood using specific items.
This paper aims to provide a snapshot of consumers‟ belief about Green Products about
Psychographic variables in India (Kolkata).
2. Review of Literature
From the existing literature, psychographics is being defined as the study of personality,
values, attitudes, interests, and lifestyles (Senise, 2007). This mainly focuses on interests,
activities and opinions (IAO) of the customers. Hence psychographic variables can be
interpreted as combinations of demographic and psychological variables which impact
customer‟s attitude in an overall manner.
It was observed that there is a general perception about organic food products catering
mainly for higher social classes (Harper and Makatouni, 2002). It is further stated in the
same paper that people from those classes have an affordability as well as consciousness
regarding organic products, thus resulting in green food product consumption. Few authors
have also discussed about people‟s tendency towards safe and healthy organic products
intake influencing positively the customers‟ intention to purchase them(Ahmed and Juhdi,
2010). Also, (Davies et al, 1995; Lea and Worsley 2005) in their paper referred that green
consumers prefer buying organic food products for their health concern. So, health is an
important factor driving the customers for green food product consumption. Contradictory
results are also published in a paper by Pickett-Baker and Ozaki (Pickett-Baker and Ozaki,
2008), where authors fail to conclude any positive correlation between positive
environmental beliefs and propensity of the customers to go for buying more green
products.

Majumdar & Swain
22
Environmental knowledge and attitude play a significant role in customers‟ tendency for
green food product purchasing as reported in several papers. Many authors stated that
environmental consciousness generates more interest of the customers towards organic
products (Schlegelmilch et al, 1996). Kaiser et al (1999) in their paper reported that
environmental values and environmental knowledge are important factors which affect
ecological behavior intention ultimately helping in building customer‟s attitude towards
organic products. Also Ahmed and Juhdi (2010) referred that customers are positively
inclined towards environment friendly farming because of their environmental
consciousness and it leads to positive customer intention to buy organic products. Lockie
et al , (2002), said that the consumers‟ familiarity with the green products, generate more
interest to consume them. This is common to conventional consumer‟s behavior. They
also stated that the mood of the consumers, i.e., to keep him relax is positively correlated
with organic food consumption. The customers believe that consuming organic food items
make customers stress-free.
Apart from health consciousness and environmental belief, several other psychographic
variables are also tested in literature like customers belief towards information
authenticity, political motivation, skepticism etc. Kozup et al (2003) said that more proper
information from credible sources increase the consumption of organic food products
because of customers‟ environmental belief and authenticity of the information provided.
Similar observation was reported by Schlegelmilch et al (1996), by inferring that more
knowledge, i.e., detail factual information about the organic products improve the chance
of customers‟ buying them. Also , it was said that the customers‟ previous experience of
using some environmental brands i.e., the brands which produce the products in
environment- friendly way have an impact on their chances of selecting those brands only
for repeated usage(Pickett-Baker and Ozaki, 2008). In another paper, it is being stated that
recycling activities positively influences pro-environmental purchasing behavior for those
customers who can dedicate more time and effort(Schlegelmilch et al, 1996). Same papers
also stated that politically motivated activities act positively only for those customers who
are environmentally conscious. In the paper by Chang (Chang , 2011), it is being discussed
that perceived higher price, lower quality and skepticism negatively and perceived
emotional benefits acting positively will create more ambivalence attitudes of the
customers towards buying green products.
In addition to demographic and psychographic variables, the different product specific
variables affect the customers‟ attitude towards green products. The various variables
discussed in the literature are environmental brands, brand name, product type (Green vs.
non-green),preferences for green attributes for the products, green technology, energy
savings .Whereas, with respect to green food products, Heart healthy claim on food
products, nutritional information about the food products, nutritional content of the
alternative products, price, product types (fresh fruit, fresh vegetables, meat, milk and
dairy products, cereals and cereal products) were discussed in the literature.
In the paper by Pickett-Baker and Ozaki(2008), the author stated that environmental
brands, i.e., the brands which produces the products in environmental-friendly manner will
positively influences customers green product purchase decision. In his paper, Mobley et

23
al (1995) reported that only branded green products create positive impression in the
minds of the customers. Lin and Chang, 2012) had said that green or non-green products
affect the environmental conscious customers‟ usage amount for the products. Olson
(2012) stated that using green technology consumers use more products with energy
efficiency. He also stated that energy savings characteristics of the products positively
influences customers attitude towards green products.
Kozup et al (2003) stated in their paper that heart healthy claim, nutritional information on
the food products partially affect consumer‟s evaluation of the packaged food products.
Also, nutritional content of the alternative food items negatively influences consumer‟s
evaluation of packaged food items. In other papers the authors discussed about the
negative effect of price towards organic food consumption. So, price is a significant
barrier for customer‟s attitude formation towards green food products consumption
(Lockie et al, 2002).
In addition to the demographic, psychographics and product specific variables, there are
various external, i.e., environmental variables which leads to specific customer behavior.
From the reviewed literature it was found that customer‟s attitude towards green food
products s being affected by information people have about organic products, tasty,
availability, expensive, food value , natural content, animal welfare, convenience,
environmental protection, food production method, source of information, purchasing
place(hypermarket, supermarket, organic stores, farms), purchasing difficulties(difficult to
find, high prices, poor range of choice), word of mouth, marketing communications,
information about green products, claim Type.
Ahmed and Juhdi (2010) had discussed that information people have about organic food
products negatively influences customer‟s purchase intention towards the products. But
in another paper, the authors had reported that more information people have about the
products , the more customers will be interested to consume them(Chinnici et al ,
2002).Again,Lin and Chang (2012) stated that only the positive information about the
products influences positively user‟s perception of the effectivity of the green products .
Also, Pickett-Baker and Ozaki(2008) stated that effective marketing communications , i.e.,
communicating all the desired information about the product influences positively
consumers‟ green product purchase decision. He had also reported that word of mouth
communication is the most effective tool to convince the
customers about the positive aspects of green products. Chang (2011) had stated that the
claims organizations make about the products have a positive impact towards ad
believability only if they are from authorized sources. Lea and Worsley (2005) had
reported that organic food products tastes better than conventional products and
availability and expense customers have to bear for these acts as barriers towards creating
consumers belief about organic food items. Harper and Makatouni (2002) have concluded
that more environmentally friendly food production method generates positive customers‟
perception about the products. Again more food value creates more positive belief about
the products. More natural content for the organic food items , concern for animal welfare
and environmental protection creates more customers‟ interest towards these
products(Lockie et al , 2002). And the customers were buying more organic food items

Majumdar & Swain
24
from hypermarket, organic stores and farms where they are more motivated towards
buying them by the overall environment.
3. Methodology
The study was based on quantitative data on consumers‟ perception about green products.
Data was collected both in online and offline format. All the respondents were briefed
about the project before they respond.
In case of the online format, the data was collected with the help of mail-based
questionnaire. The questionnaire was sent to many respondents selected randomly. A
cover letter was also sent along with the questionnaire. A total of 100 respondents were
selected randomly and the questionnaires were sent to them. To improve the success rate,
the questionnaires were sent repeatedly to the prospective respondents. Approximately, 65
respondents sent back the filled questionnaires.
The survey was also carried on in the offline format. For that, the questionnaires were
distributed to the respondents selected randomly from the different parts of Kolkata, India
using Green products. The respondents were both green products buyer and non-buyers. A
total of 235 respondents were surveyed for their responses.
So, considering both the online and offline format, 300 respondents were surveyed for
their responses. The questionnaire was formulated from a review of literature based on the
following literatures (e.g. Sanchez, 2010; Hofmester-Toth,2010 ; Grewal, 2000).The
questionnaire‟s main objective is to study the impact of the various psychographic
variables, such as involvement with the product, respondent‟s opinion leadership etc. on
the respondents intention to purchase green products. The paper will be studying the
responses on only two types of green products, namely cosmetics products and food items.
The questionnaire is divided into eight parts. The first part is trying to measure the
environmental consciousness of the respondents with respect to the scales used in the
paper by Sanchez, 2010. The second part is measuring the price sensitivity of the
respondent with respect to the scale used in a paper by Goldsmith, 1991. In the third,
fourth and the fifth part, the respondent‟s opinion leadership, innovativeness and
involvement in buying green products will be studied based on a paper by Grewal, 2000.
In the sixth part, the respondent‟s health consciousness will be studied based on the
concept from the literature by Hong1990.In the seventh part, the respondent‟s reaction to
the different characteristics of the green cosmetics products are studied. The scales are
based on the literatures by Ahmad,2010 ;Chang2011;Davies,1995;Bamberg,2006 and
Lea2005. The eighth part is same as the seventh part. The only difference is that the
products considered here are green food products. The scales are based on the literatures
by Ahmad,2010;Kozup,2003;Davies,1995;Bamberg, 2006; Lin,2012; Chang,2011 and
Lea,2005.All the factors were measured on a seven point scale stating the following
things(1 = Very Strongly Disagree, 2 = Strongly Disagree, 3 = Disagree, 4 = Neither
Agree Nor Disagree, 5 = Agree, 6 = Strongly Agree, 7 = Very Strongly Agree). The socio-
demographic information of the respondents is collected in the ninth part.
The collected data for all the parts of the questionnaire is analyzed using Exploratory
Factor Analysis to to uncover the underlying structure of a relatively large set of variables.
The IBM SPSS (version 19) is used for the purpose.

25
Variables/Factors(used in the study) contributing for the popularity of Green products
Environmental Consciousness
Variable Description
v1 I support different measures to improve water management leading to water conservation
v2 I am aware about the issues and problems related to the environment
v3 I would be willing to pay higher prices for water
v4 It is very difficult for a person like me to do anything about the environment
v5 I believe that using recyclable materials for daily use will improve the environment
Price Sensitivity
v1 In general the price or cost of buying green products is important to me
v2 I know that a new kind of green product is likely to be more expensive than older ones , but
that does not matter to me
v3 I am less willing to buy a green product if I think that it will be high in price
v4 I don‟t mind paying more to try out a new green product
v5 A really good green product is worth paying a lot of money
v6 I don‟t mind spending a lot of money to buy a green product
Innovativeness
v1 I like to take a chance in buying new products
v2 I like to try new and different products
v3 I am the first in my circle of friends to buy a new product when it appears in the market
v4 I am the first in my circle of friends to experiment with the brands of latest products
Involvement
v1 I select the green products very carefully
v2 Using branded green products helps me express my personality
v3 You can tell a lot about a person from whether he/she buys green products
v4 I believe different brands of green products would give different amounts of satisfaction
Health consciousness
v1 I worry that there are chemicals in my food.
v2 I worry that there are chemicals in my cosmetic products
v3 I‟m concerned about my drinking water quality.
v4 I avoid foods containing preservatives.
v5 I read more health-related articles than I did 3 years ago.
v6 I‟m interested in information about my health.
v7 I‟m concerned about my health all the time.
v8 Pollution in food and cosmetic products does not bother me.

Majumdar & Swain
26
General characteristics about green cosmetic products
v1 Green cosmetic products are safer to use than non-green cosmetic products
v2 Green cosmetic products are of better quality than non-green cosmetic products
v3 Green cosmetic products are more effective than non-green cosmetic products
v4 Branded green cosmetic products are better than non-branded green cosmetic products
v5 Less knowledge about green cosmetic products prevent people from buying them
v6 Less information about green cosmetic products prevent people from buying them
v7 Less availability about green cosmetic products prevent people from buying them
v8 Green cosmetic products are expensive than non-green cosmetic products
General characteristics about green food products
v1 Green food products are safer than non- green food products
v2 Green food products are healthier than non-green food products
v3 Green food products have more nutritional value than non-green food products
v4 Green food products are tastier than non-green food products
v5 Less knowledge about green food products prevent people from buying them
v6 Less information about green food products prevent people from buying them
v7 Branded green products are better than non-branded green food products
v8 Green food products do not look good in appearance
v9 Less availability about green food products prevent people from buying them
v10 Green food products are expensive
4. Data Analysis and Findings
Environmental consciousness:
Rotated Component Matrixa
Component
1 2
v4 .692
v5 .662
v1 .761
v3 .792
v2 .771

27
Variable Description Components
v1 I support different measures to improve
water management leading to water
conservation
Environmental Sense(v1 , v2 and
v3)
Environmental Callousness (v4
and v5)
v2 I am aware about the issues and
problems related to the environment
v3 I would be willing to pay higher prices
for water
v4 It is very difficult for a person like me
to do anything about the environment
v5 I believe that using recyclable materials
for daily use will improve the
environment
v1 I support different measures to
improve water management
leading to water conservation
Environmental Sense(v1 , v2
and v3)
Environmental Callousness
(v4 and v5)
v2 I am aware about the issues and
problems related to the
environment
v3 I would be willing to pay higher
prices for water
v4 It is very difficult for a person like
me to do anything about the
environment
v5 I believe that using recyclable
materials for daily use will
improve the environment
From the above table, the variables v1 , v2 ,v3 had more loadings on component 2, thus
making it a Component which can be named as Environmental Sense. Likewise, variables
v4 and v5 have more loadings on component 1 and making it a part of component named
as Environmental Callousness From the above table, it can be stated that the variables v4
and v5 can be combined to be a part of component 1, named as Higher Price. The
variables v1 and v2 can be combined to be part of component 2 named as Price
Sensitivity. Likewise the variables v3 and v5 can be combined to form component 3
named as Price Barrier.

Majumdar & Swain 28
Price sensitivity
Component
1 2 3
v4 .855
v6 .823
v2 .704
v1 .650
v5 .812
v3 .440 .667
v1 In general the price or cost of buying
green products is important to me
Higher Price(v4 and v6)
Price Sensitivity(v1 and v2)
Price Barrier(v3 and v5)
v2 I know that a new kind of green product
is likely to be more expensive than older
ones , but that does not matter to me
v3 I am less willing to buy a green product if
I think that it will be high in price
v4 I don‟t mind paying more to try out a
new green product
v5 A really good green product is worth
paying a lot of money
v6 I don‟t mind spending a lot of money to
buy a green product
Innovativeness
Component
1 2
v1 .868
v2 .803
v3 .399 .386
v4 .935

29
v1 In general the price or cost of buying
green products is important to me
Higher Price(v4 and v6)
Price Sensitivity(v1 and v2)
Price Barrier(v3 and v5)
v2 I know that a new kind of green product
is likely to be more expensive than older
ones , but that does not matter to me
v3 I am less willing to buy a green product
if I think that it will be high in price
v4 I don‟t mind paying more to try out a
new green product
v5 A really good green product is worth
paying a lot of money
v6 I don‟t mind spending a lot of money to
buy a green product
For the case of Innovativeness, the variables v1, v2 and v3 can be combined to form a
component 1 named as New Product Initiative. The variable 4 alone will be forming
component 2 named as Experimental Attitude.
Involvement:
Component
1 2
v1 .868
v4 .803
v2 .399 .435
v3 .935
v1 I select the green products very
carefully
Satisfaction from
Branded Green products
(v1 and v4)
Branded green products
reveal personality(v2 and
v3)
v2 Using branded green products helps me
express my personality
v3 You can tell a lot about a person from
whether he/she buys green products
v4 I believe different brands of green
products would give different amounts
of satisfaction

Majumdar & Swain
30
From the above table, the variables v1 and v4 can be combined to form a part of
Component 1 , named as Satisfaction from Branded Green products . Likewise, the
variables v2 and v3 are combined to form component 2, named as Branded green products
reveal personality.
Health consciousness:-
In case of health consciousness of the respondents, the variables 2 and 5 can be combined
to form component 1 , named as Health Sensitivity . The variables v1 , v6 and v7 can be
combined to form component 2 named as Health Concern. Likewise the variable v4 alone
will form component 3 named as
Component
1 2 3 4
v2 .793
v5 -.686
v7 .758
v1 .629
v4 .837
v6 .785
v8 -.313 .378 .487
v3 .375 -.436 .447
Avoid Preservative Food. Lastly, the variables v3 and v8 are combined to form a part of
component 4 named as Food Pollution.
v1 I worry that there are chemicals in my food. Health Sensitivity(v2 and
v5)
Health Concern(v1, v6 and
v7)
Avoid preservative food(v4)
Food pollution(v3 and v8)
v2 I worry that there are chemicals in my
cosmetic products
v3 I‟m concerned about my drinking water
quality.
v5 I read more health-related articles than I did
3 years ago.
v6 I‟m interested in information about my
health.
v7 I‟m concerned about my health all the time.
v8 Pollution in food and cosmetic products
does not bother me.

31
Characteristics of green cosmetic products:
In case of the Green Cosmetic products, the variables v5 and v6 can be combined to form
component 1 which is named as Green Product Knowledge. The variables v3 and v4 are
combined to form component 2, which is named as
Component
1 2 3 4
v6 .890
v5 .859
v4 .757
v3 -.683
v1 .745 -.337
v2 .612 .437
v7 .434
v8 -.432
Branded Green Cosmetic Products. The third component 3 , component 3 is formed by
combining the variables v1, v2
food.
Health Sensitivity(v2 and
v5)
Health Concern(v1, v6 and
v7)
Avoid preservative
food(v4)
Food pollution(v3 and v8)
cosmetic products
v3 I‟m concerned about my drinking water
quality.
v5 I read more health-related articles than I
did 3 years ago.
v6 I‟m interested in information about my
health.
v7 I‟m concerned about my health all the
time.
v8 Pollution in food and cosmetic products
does not bother me.
and v7 and named as Reliability of Green Cosmetic Product . The remaining variable v8
forms the 4th
component , named as Green Products Expensive .
Characteristics of Green Food Products:-
In case of the Green Food products, the variables v3 and v4 are combined to form
component 1, named as Green Food Products Nutritional Taste. The variable v2 forms
component 2, which is named as Green Food Products are Healthier. The variables v5, v6

Majumdar & Swain
32
v1 Green cosmetic products are safer to use
than non-green cosmetic products
Green Product
Knowledge(v5 and v6)
Branded Green Cosmetic
Products(v4 and v3)
Reliability of Green
Cosmetic Product (v7 , v1
and v2)
Green Products
expensive(v8)
v2 Green cosmetic products are of better
quality than non-green cosmetic products
v3 Green cosmetic products are more
effective than non-green cosmetic products
v4 Branded green cosmetic products are better
than non-branded green cosmetic products
v5 Less knowledge about green cosmetic
products prevent people from buying them
v6 Less information about green cosmetic
v7 Less availability about green cosmetic
v8 Green cosmetic products are expensive
than non-green cosmetic products
and v9 are combined to form component 3 which is named as Lack of information and
availability of green Food Products. Likewise the variables v1 and v10 are combined to
form component 4 named as Green Food Products are safe and expensive. Lastly the
variables v7 and v8 are combined to form component 5, which is named as Branded Green
Food Products‟ Look and quality
5. Conclusion
In order to meet the purpose of the study as envisaged in the introduction part of the paper,
factor analysis is used to know important factors which insist buyers to go for green
products and also find out the impact of psychographic variables on the popularity of
green products.
On the basis of analysis done using Exploratory Factor Analysis, huge number of variables
used in the study, to be specific forty five variables, had been scaled down to twenty
variables. Concerning the facet - impact of Environmental consciousness towards
popularity of Green products, factors such as; Environmental Sense and Environmental
Callousness are the most important. Relating to relevance of price towards popularity of
green products, factors such as; Higher Price, Price Sensitivity and Price Barrier plays the
most important role. In the pretext of studying the innovation of the respondents‟ about
buying green products, it has been found that New Product Initiative and Experimental
Attitude are two important factors. Regarding involvement in buying process while
buying green products, factors such as; Satisfaction from Branded Green products and
Branded green products reveal personality are the key contributors. About
health consciousness of the respondents in buying green products, factors such

33
as; Health Sensitivity, Health Concern, Avoid preservative food and Food pollution play
the most important role.
Regarding general factors contributing for the popularity of green cosmetic products,
important factors are; Green Product Knowledge, Branded Green Cosmetic Products,
Reliability of Green Cosmetic Product and Green Products expensive.
Pertaining to general factors impacting green food products, factors such as;
Green Food Products‟ Nutritional Taste, Green Food Products are Healthier, Lack
of information and availability of Green Food Products, Green Food Products are
safe and expensive and Branded
Green Food Products‟ Look and Quality impact the respondents‟ decision for
buying green food products.
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4.139.58.2/ejournalver2/.../PavanMishraandMs.PayalSharma.pdf

35
Performance of financial markets in Indian Economy
Dr.A.K.Jha
Director, CMAT, Gr. Noida
&
Viriender Pal Singh
Research scholar, Mewar University
Abstract
In present circumstances the role of financial market is become larger than other
times. This paper is divided into the two parts one is related with the structures of
financial markets and other is related with performance of financial market.
Structure of financial market in Indian economy
Generally financial markets refers to a place where Buyers and sellers participate in the
trade of financial instruments like commercial bills, Commercial papers, treasury bills and
trade of assets such as equities, bonds, currencies and derivatives. It can be found in nearly
every country in the world. Financial markets is broadly classified into the
1. Capital Market
2. Money Market
3. Insurance market
4. Derivative market
5. Foreign exchange market
6. Commodity market
1. Capital Market: Capital market is the market where individual & Institutions
trades financial securities. It is also known as long term market. A capital market
provides all the facilities and institutional arrangements for borrowings and landings for

Jha & Singh
36
term funds. The capital market further divides into the two parts i.e. Stock market and
Money market.
(a) Stock markets: Stock markets allow investors to buy and sell shares in
publicly traded companies. They are one of the most vital areas of a market economy as
they provide companies with access to capital and investors with a slice of ownership in
the company and the potential of gains based on the company's future performance.
(b) Bond Market: A bond is a debt investment in which an investor loans
money to an entity (corporate or governmental), which borrows the funds for a defined
period of time at a fixed interest rate. Bonds are used by companies and governments to
finance a variety of projects and activities. Bonds can be bought and sold by investors on
credit markets around the world. This market is alternatively referred to as the debt, credit
or fixed-income market. It is much larger in nominal terms that the world's stock markets.
The main categories of bonds are corporate bonds, Government bonds, and Treasury
bonds, notes and bills, which are collectively referred to as simply "Treasuries.
This market can be split into two main sections: the primary market and the secondary
market. The primary market is where new issues are first offered, with any subsequent
trading going on in the secondary market. Indian capital market has grown rapidly during
the last two decade. It has played an important role of India‟s industrial growth.
2. Money Market: The money market is a segment of the financial market in which
financial instruments with high liquidity and very short maturities are traded. It means all
the financial assets or instruments which can be easily converted into the money traded in
this market. The money market is used by participants as a means for borrowing and
lending in the short term, from several days to just under a year. The money market is
further classified in the two parts i.e. Organized and Unorganized market.

37
Money market securities consist of negotiable certificates of deposit (CDs), banker's
acceptances, Treasury bills, commercial paper (Cps), government notes, currency and
repurchase agreements (repos). Money market investments are also called cash
investments because of their short maturities.
3. Insurance market: Insurance market is the market where insurance policy is
bought and sold. The insurance companies working o sale their product in this market.
4. Derivative market: Derivative Market is a market where derivative are
exchanged or traded. The derivative is a special type of security whose market price or its
value is derived from its underlying asset or assets. A derivative is a contract, but in this
case the contract price is determined by the market price of the core asset. Derivative
market can be classified into the tow broad categories i.e. over the counter and exchange
traded market and divided into fiver sub markets i.e. Credit derivative, future contracts,
option, swap and forward contracts.
5. Foreign exchange market: The Foreign exchange market is the financial system
and trading of currencies among banks and financial institutions, excluding retail investors
and smaller trading parties. While some interbank trading is performed by banks on behalf
of large customers, most interbank trading takes place from the banks' own accounts. It is
known as interbank transaction also.
6. Commodity market: A commodity market is a market where the primary
products are bought and sold under legal contracts. These primary products can be part of
the FMCG sector, Metal sector and energy sector etc. The instrument or commodity is
traded in this market by the using of spot trading, Future contracts, Hedging of funds etc.
The exchange can be done through derivative trading or physical exchange of commodity.

Jha & Singh
38
Role and Performance of financial markets in Indian economy
In India, we have traversed a long way since the economic reforms started in the early
1990‟s. The reforms of the early 90‟s were focused on three pillars – Liberalization,
Privatization and Globalization (LPG). The financial sector has also undergone significant
changes during the period to not only to support the rapid growth but also to do so without
disruptive episodes. In this study, The analysis is divided into the four areas. These areas
are
1. Bank based financial sector
2. Debt market based financial sector
3. Foreign exchange market
4. Financial inclusion
1. Bank based financial sector: Indian financial sector has traditionally been bank
based. The banking sector has so far played a seminal role in supporting economic growth
in India. The assets of the banking sector have expanded nearly 11 times from ` 7.5 trillion
at end-March 1998 to ` 83 trillion at end-March 2012. The non-food credit has expanded
by more than 14 times from ` 3.1 trillion 1998 to ` 45.30 trillion during the same period.
The credit to GDP ratio which stood at about five per cent in 1950-51 improved to about
25 per cent in 2000-01 and further to about 52 per cent at the end of 2011-12 (Chart 1).
The pre-emption by way of Statutory Liquidity Ratio (SLR) has declined considerably
from 38.5 per cent in 1991 to 23.0 per cent of the Net Demand & Time Liabilities (NDTL)
in 2013 (Chart 2). All the while, the banking sector has been robust, meeting all prudential
standards as per best international practice. During the recent global financial crisis and
slowdown in the global and domestic economy, the Indian banking sector has proved to be
resilient. There are, however, issues relating to liquidity, asset quality, capital adequacy in
the context of Basel III and earnings which have surfaced in the recent past mainly due to

39
economic slowdown and have to be tackled expeditiously for continued resilience of the
Indian banking system.
Chart 1: Bank credit as a percentage of GDP
2. Debt market based financial sector: The debt market in India has widely
divided into he government bond market and corporate bond market.
(A) Government bond Market: India is a country in which a large segment of
people lived in the underprivileged condition and government has a vital role for the
develop heir livelihood and standard of living. For the fulfillment of this cause
government of India have to be supported by a large government borrowing program. The
outstanding marketable government debt has grown from ` 4.3 trillion in 2000-01 to ` 29.9
trillion in 2012-13. The size of the annual borrowing of the central government through
dated securities has grown from 1.0 trillion to ` 5.6 trillion during this period. It is no
mean achievement to manage such large issuances in a non-disruptive manner in the post
Fiscal Responsibility & Budget Management (FRBM) regime and declining SLR. A large
number of initiatives have been taken over the years, such as, the Primary Dealer (PD)
system, Delivery Vs Payment (DVP), centralized clearing, anonymous dealing system
based on order matching, floating rate bonds, STRIPS, Inflation Index Bonds (IIBs), etc.
The liquidity in the secondary market has also increased significantly from a daily average
trading volume of ` 9 billion in February 2002 to ` 344 billion in March, 2013. The
development of the debt and the derivatives market in India needs to be seen from the
perspective of a central bank and a financial sector regulator which has a mandate to
(B) Corporate bond market: In india the role of debt market for corporate also have

Jha & Singh
40
a vital importance. For business expansion and developmental rolein the economy the
corporate need a huge amount of fund. The impending large scale expenditure for
improving our infrastructure critically depends on the debt market. It is difficult for the
public sector to finance development of a world-class infrastructure of the magnitude
envisaged because of its other commitments and the limitations imposed by fiscal
prudence. Though the banking sector has been playing an active role in infrastructure
finance, there are limitations imposed by Asset Liability Mismatch (ALM) mismatch,
exposure norms, and de-risking the financial system to provide stability to the financial
system. Development of government and corporate debt market may be approached within
a framework of seven key components, viz., Issuers, Investors, Intermediaries,
Infrastructure, Innovation, Incentives and Instruments – what I have called a 7i
framework. Sovereign securities dominate the fixed income markets almost everywhere.
In India too, the central and state governments remain the main issuers. The large supply
of securities, due to enhanced borrowings, has enabled creation of benchmark securities
with sufficient outstanding stock and issuances across the yield curve. The issuances
across the risk-free yield curve in turn, have provided benchmarks for valuation of other

41
bonds/financial assets and benefitted the corporate bond market. Fiscal consolidation
efforts of the Government of India and the State Governments enhance the quality of the
issuers. In the field of corporate bonds besides financial sector entities, large well rated
non-finance companies have also been issuers. The traditional investor base for G-Sec in
India comprised banks, provident funds and insurance companies with a dominance of
domestic investors and limited foreign participation. In the corporate debt market, investor
base is mostly confined to banks, insurance companies, provident funds, PDs and pension
funds. An approach of gradual opening of the domestic bond market to the foreign
investors has been adopted in India keeping in view the macro-economic risks involved in
providing unfettered access to them. Intermediaries play an important role in development
of the market by facilitating the transactions, providing value-added services and
increasing efficacy of the processes. In India, the major intermediaries are the PDs,
industry associations like Fixed Income Money Market & Derivatives
Association/Primary Dealers Association of India, Gilt Mutual Funds and the
Infrastructure Development Funds (IDFs). Infrastructure plays an important role in
development of markets and want of an efficient, transparent and robust infrastructure can
keep market participants away on one extreme or cause market crisis on the other. India
can boast of being one of the few emerging countries with such a state of the art financial
market infrastructure for the G-Sec market. A state-of-the-art primary issuance process
with electronic bidding and fast processing capabilities, dematerialized depository system,
DvP mode of settlement, electronic trading platforms (Negotiated Dealing Systems and
Negotiated Dealing Systems-Order Matching) and a separate Central Counter Party in the
Clearing Corporation of India Ltd. (CCIL) for guaranteed settlement are among the steps
that have been taken by the Reserve Bank over the years towards this end. Financial
innovation is an essential feature in the history of development of financial markets.
Innovations that are motivated by the need to match the needs of the investor and the
issuer or made possible by advancement in technology or knowledge are essential for
evolution of financial markets.
Incentives play a significant role in shaping the development, stability and
functioning of the financial markets. Reserve Bank has been trying to align incentives by
regulation and supervision though regulation itself may have created unintended
incentives/disincentives as in the case of requirement regarding Held To Maturity (HTM)
dispensation. In the process of development of new instruments, Reserve Bank‟s
endeavour has been to ensure calibrated and orderly development of the markets with
emphasis on prudent risk management and promotion of financial stability
3 Foreign exchange market:The foreign exchange market analysis is further
divided into the two parts one is related with current account deficit and second is related
to capital account management.
(A) Current Account deficit : In the context of foreign exchange market, one of the
most talked about issue is the rising current account deficit (CAD). In recent times, CAD
has been increasing and reached a historic high of 6.7 per cent during the quarter October-
December, 2012. It is of course not sustainable in the long run. Therefore, a structural shift
in the composition of our trade account – increasing exports and curtailing non-productive

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