Appril june issue of GJMMS pdf

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Second edition of Global journal of Multidisciplinary and Multidimensional studies

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Appril june issue of GJMMS pdf

  1. 1. Volume 1 Number 2, April-Jun,2015
  2. 2. I 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
  3. 3. 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) Dr. H.K.S.Kumar Chunduri Sr. Faculty Member, Department of Business Studies, Ibra College of Technology, IBRA, Sultanate of Oman Dr. Violetta Gassiy Associate professor, Public administration department, Kuban State Univer-sity, 149, Stavropolskaya st., Krasnodar Russia Prof (Dr) Ramesh Balkrishna Kasetwar (Retd Colonel) Ex professor Bharati Vidyapeeth University, New Delhi Vineet Jain, Asstt. Prof. (Mechanical) Amity University Haryana, Gurgaon Dr. Rushiraj Upadhyay, Asst. Professor, M.S.W Department, Gujarat University, Ahmedabad Deepak Pathak Assistant Professor, Mechanical Engg Dept., FET Agra College Agra Mahendra N. UmareAssociate Professor & HOD (Civil) at NIT, Nagpur ROB WOOD Department of Global Strategy & Management 2010 presentWestern Carolina University, Cullowhee, NC Judi Krzyzanowski B.Sc, M.SC., Environmental scientist Dr. Vijay Pithadia, PhD., MBA, Electronics Technician Director & Professor, SHG MBA Women college, Amreli Dr. Dheeraj Pawar
  4. 4. III Assistant Professor, Amity Institute of Telecom Engineering and Management, Amity University, Noida Raymond W. Thron, Ph.D Faculty College of Health Sciences, Walden University Dr. Mwafaq M. Dandan Associate Professor Department of Banking and Financial Sciences Amman University College for Banking and Financial and Sciences Albalqa applied universityJordan Professor (Dr) Rajesh Arora Director Dr D Y Patil Institute of Management Studies, Pune. Dr. L. Govinda Rao, PG in Mgt.(XLRI), Ph.D., Chairman & CEO, Matrix Institute of Development Studies, Kameswari Kuteer, Secunderabad 500 011 AP India. Shailkh.Shoeb Anwer Aurangabad Dr.C.B.Singh, Ph D, M A, (Economics), M Sc (Ag Eco.), MBA (FM, MM), Associate Professor Institute oF Economics & Finance Bundelkhand University, Jhansi 284128 (UP) India Dr. David Nickerson, Distinguished Professor, Department of Finance and Real Estate, Rogers School of Management, Ryerson UniversityToronto, Ontario M5B 2K3, Canada Dr Dilip Kumar Vinnakota Principal, Govt Junior College SATHUPALLY Khammam District, Telangana State Steve Pyser Fellow, Caux Round Table and Lecturer (PTL), Rutgers University School of Business - Camden Bocar Samba Ba (Research scholar Economics) 2 place viala, 34060 montpellier, France
  5. 5. IV Mahendra N. UmareAssociate Professor & HOD (Civil) at NIT, Nagpur Charles "Randy" Nichols, Ph.D., Louisville, KY, Professor of Management Author, Educator, Speaker Shabnam Siddiqui, Assistant Professor, FMS-WISDOM, Banasthali University, P.O. Banasthali Vidyapith 304022Rajasthan, INDIA, Monika Hudson, DM Assistant Professor, Director, Gellert Family Business Resource Center/ Public Service Internship Program, University of San Francisco Juan Carlos WANDEMBERG – Ph.D. WANDEMBERG Sustainable Development Quito - ROB WOOD Associate - Graduate Faculty; Department of Global Strategy & Management 2010 presentWestern Carolina University, Cullowhee, NC Dr. Mohammed Rizwan Alam, Assistant Professor Marketing University Of Modern Science Dubai Avil sinha Fellow (ECONOMICS), IIM, Indore Mary Manana University of South Wales Dr. Stefan Walter, Heidenrod, Germany (Economics and Management) C.H.Raj Marketing professional Noida Greg Benzmiller Ph.D. MA, MBA Colorado Springs, CO 80919 Hazra Imran (PhD) Post-Doctoral Fellow, Funded by MITACS Elevate (Canada), Athabasca University, Edmonton, AB, Canada Indrajit Bandyopadhyay, Registrar, Usha Martin Academy, Kolkata, India Rijo Tom, Asst. Professor , Dept. of ECE, Kalaivani College of Technology, Coimbatore
  6. 6. V S.Praveen – HR & Administration Executive – FDC International FZE (Dubai) Anil kumar. S Hagargi, Research scholar, Dept of Management Studies and Research, Gulbarga University,Gulbarga, Karnataka, Ihor Yaskal, PhD in Economics, Yuriy Fedkovych Chernivtsi National University, Ukraine Nilesh Borde, Assistant Professor at Goa University Dr. Kiran Mehta, Associate Professor (Finance), Chitkara Business School, Chitkara University Dr. Renuka Sharma, Associate Professor (Finance), Chitkara Business School, Chitkara University Pradeep Kumar Owner ASPIRE OVERSEAS CO, Noida Dr.prof.V.Raghu Raman, Senior Faculty (Business Studies), IBRA COLLEGE OF TECHNOLOGY, OMAN PAZIENZA, Department of Economics, University of Foggia, Foggia, Italy Dr. Tiyas Biswas, Assistant Professor Department of Business Administration 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)., Asst.Professor Business Administration, Annamalai University MUFTI MD. IBRAHIM, Faculty of Education ,Ahsanullah University of Science and Education. Ahsanullah Teachers’ Training College,Dhaka SUDHASHREE PARVATI, Lecturer, Department of English,
  7. 7. VI Adi Keih College of Arts and Social Sciences, 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 .
  8. 8. VII Editorial ------------- The current changes and challenges experienced by the contemporary world have been an inspiration for us in elaborating this new forum of dis-cussions on the real world issues affecting or having a meaningful impact on the different segment of society and on our lives. This is an attempt of boldly and unrestrictedly contributing to new Ideas through research findings and doing things differently, thereby providing quality and value. Scholars, re- searchers, young researchers worldwide are encouraged to join efforts in find-ing solutions for the common issues raised by the recent social and environ-mental changes. It aims to be a dialogue between the scientific community and the citizens, as a testimony of their concern to place the results of their work in the service of the society. A new orientation in research policy is imperative to respond to the new needs of the society to guarantee environ-mental sustainability and economic growth in the knowledge society. The purpose of the Global Journal of Multidisciplinary and Multidimenstional Studies is to make an area of free circulation of ideas and knowledge, of shar-ing experience and finding effective solutions for real-life problems, to under-stand their causes and foresee the consequences. While the society needs and calls for research, research needs to be accountable to society. To this end, the journal publishes Research papers, survey, articles, research findings, book reviews, and annotations of new books. Dr.A.K.Jha Managing and Chief Editor GJMMS
  9. 9. 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
  10. 10. 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
  11. 11. 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
  12. 12. 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
  13. 13. 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.
  14. 14. 5 ESTIMATION OF PRODUCTIVITY AND EFFICIENCY OF COTTON FARMERS IN PAKISTAN: A CASE STUDY OF DISTRICT DERA GHAZI KHAN .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.
  15. 15. 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
  16. 16. 7 ESTIMATION OF PRODUCTIVITY AND EFFICIENCY OF COTTON FARMERS IN PAKISTAN: A CASE STUDY OF DISTRICT DERA GHAZI KHAN 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
  17. 17. 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.
  18. 18. 9 ESTIMATION OF PRODUCTIVITY AND EFFICIENCY OF COTTON FARMERS IN PAKISTAN: A CASE STUDY OF DISTRICT DERA GHAZI KHAN 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.
  19. 19. 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
  20. 20. 11 ESTIMATION OF PRODUCTIVITY AND EFFICIENCY OF COTTON FARMERS IN PAKISTAN: A CASE STUDY OF DISTRICT DERA GHAZI KHAN 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
  21. 21. 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
  22. 22. 13 ESTIMATION OF PRODUCTIVITY AND EFFICIENCY OF COTTON FARMERS IN PAKISTAN: A CASE STUDY OF DISTRICT DERA GHAZI KHAN 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
  23. 23. 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
  24. 24. 15 ESTIMATION OF PRODUCTIVITY AND EFFICIENCY OF COTTON FARMERS IN PAKISTAN: A CASE STUDY OF DISTRICT DERA GHAZI KHAN 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,
  25. 25. 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
  26. 26. 17 ESTIMATION OF PRODUCTIVITY AND EFFICIENCY OF COTTON FARMERS IN PAKISTAN: A CASE STUDY OF DISTRICT DERA GHAZI KHAN 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
  27. 27. 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
  28. 28. 19 ESTIMATION OF PRODUCTIVITY AND EFFICIENCY OF COTTON FARMERS IN PAKISTAN: A CASE STUDY OF DISTRICT DERA GHAZI KHAN 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
  29. 29. Majumdar & Swain 20 ISSN NO. 2394- 8965 GJMMS Vol. – 1, Issue – 2, April- June-2015 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
  30. 30. 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.
  31. 31. 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
  32. 32. 23 Identification of factors influencing Preferences for Green Products: A study in and around Kolkata (India) 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
  33. 33. 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.
  34. 34. 25 Identification of factors influencing Preferences for Green Products: A study in and around Kolkata (India) 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 Variables/Factors(used in the study) contributing for the popularity of Green products Environmental Consciousness 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.
  35. 35. Majumdar & Swain 26 Variables/Factors(used in the study) contributing for the popularity of Green products Environmental Consciousness 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
  36. 36. 27 Identification of factors influencing Preferences for Green Products: A study in and around Kolkata (India) 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 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 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.
  37. 37. Majumdar & Swain 28 Price sensitivity Rotated Component Matrixa Component 1 2 3 v4 .855 v6 .823 v2 .704 v1 .650 v5 .812 v3 .440 .667 Variable Description Components 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 Rotated Component Matrixa Component 1 2 v1 .868 v2 .803 v3 .399 .386 v4 .935
  38. 38. 29 Identification of factors influencing Preferences for Green Products: A study in and around Kolkata (India) Variable Description Components 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: Rotated Component Matrixa Component 1 2 v1 .868 v4 .803 v2 .399 .435 v3 .935 Variable Description Components 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
  39. 39. 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 Rotated Component Matrixa 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. Variable Description Components 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. 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.
  40. 40. 31 Identification of factors influencing Preferences for Green Products: A study in and around Kolkata (India) 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 Rotated Component Matrixa 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 Variable Description Components 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. 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. 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
  41. 41. Majumdar & Swain 32 Variable Description Components 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 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 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
  42. 42. 33 Identification of factors influencing Preferences for Green Products: A study in and around Kolkata (India) 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. Bibliography 1) Ahmad, S. & Juhdi, N.Organic Food: A Study on Demographic Characteristics and Factors Influencing Purchase Intentions among Consumers in Klang Valley, Malaysia International Journal of Business and Management, 2010, Vol. 5(2), pp. 105-118 2) Bamberg, S.How does environmental concern influence specific environmentally related behaviors? A new answer to an old question Journal of Environmental Psychology, 2003, Vol. 23, pp. 21–32 3) Chang, C. Feeling Ambivalent About Going Green Implications for Green Advertising Processing The Journal of Advertising, 2011, Vol. 40(4), pp. 19-31 4) Chinnici, G.and D'Amico, M. & Pecorino, B. A multivariate statistical analysis on the consumers of organic products British Food Journal, 2002, Vol. 104(3/4/5), pp. 187-199 5) Davies, A., Titterington, A. & Cochrane, C. Who buys organic food? A profile of the purchasers of organic food in Northern Ireland British Food Journal, 1995, Vol. 97(10), pp. 17-23 6) Harper, G. & Makatouni, A. Consumer perception of organic food production and farm animal welfare British Food Journal, 2002, Vol. 104(3/4/5), pp. 287-299 7) Kaiser, F., Wolfing, S. & Fuhrer, U. Environmental attitude and Ecological behavior Journal of Environmental Psychology, 1999, Vol. 19, pp. 1-19 8) Kozup, J., Creyer, E. & Burton, S. Making Healthful Food Choices: The Influence of Health Claims and Nutrition Information on Consumers' Evaluation of Packaged Food Products and Restaurant Menu Items Journal of Marketing, 2003, Vol. 67, pp. 19-34 9) Lea, E. & Worsley, T. Australians’ organic food beliefs, demographics and values British Food Journal, 2005, Vol. 107(11), pp. 855-869 10) Lin, Y. & Chang.C.A. Doubie Standard: The Role of Environmentai Consciousness in Green Product Usage Journal of Marketing, 2012, Vol. 76, pp. 125-134 11) Lockie, S., Lyons, K., Lawrence, G. & Mummery, K. Eating ‘Green’: Motivations Behind Organic Food Consumption in Australia European Society for Rural Sociology, 2002, Vol. 42(1), pp. 23-40 12) Mobley, A., Painter, T., Untch, E. & Unnava, H. Consumer Evaluation of Recycled Products Psychology & Marketing, 1995, Vol. 12(3), pp. 165-176
  43. 43. Majumdar & Swain 34 13) Moisander, J. Motivational complexity of green consumerism International Journal of Consumer Studies, 2007, Vol. 31, pp. 404-409 14) Olson, E. It’s not easy being green: the effects of attribute tradeoffs on green product preference and choice J. of the Acad. Mark. Sci., 2012 15) Pickett-Baker, J. & Ozaki, R. Pro-environmental products: marketing influence on consumer purchase decision Journal of Consumer Marketing, 2008, Vol. 25(5), pp. 281- 293 16) Raghunathan, R., Naylor, R. & Hoyer, W. The Unhealthy = Tasty Intuition and Its Effects on Taste Inferences, Enjoyment, and Choice of Food Products Journal of Marketing, 2006, Vol. 70, pp. 170-184 17) Schlegelmilch, B., Arizona, B., Bohlen, G. & Diamantopoulos, A. The link between green purchasing decisions and measures of environmental consciousness European Journal of Marketing, 1996, Vol. 30(5), pp. 35-55 18) Razzaque, M. (1995), 'Demographics, Psychographics and Consumer Value Dimensions: a Study of Consumers in a Traditional Asian Society', European Advances in Consumer Research 2, 183-192. 19) (http://www.enviro-news.com/glossary/green_products.html) 20) http://www.inc.com/encyclopedia/green-marketing.html 21) http://escholarship.org/uc/item/49n325b7 22) http://www.slideshare.net/f098/green-marketing-5596884 4.139.58.2/ejournalver2/.../PavanMishraandMs.PayalSharma.pdf
  44. 44. 35 Performance of financial markets in Indian Economy ISSN NO. 2394- 8965 GJMMS Vol. – 1, Issue – 2, April- June-2105 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
  45. 45. 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.
  46. 46. 37 Performance of financial markets in Indian Economy 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.
  47. 47. 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
  48. 48. 39 Performance of financial markets in Indian Economy 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
  49. 49. 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
  50. 50. 41 Performance of financial markets in Indian Economy 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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