Volume 11 June 2017
SAMIKSHYA (DE&S Journal on Socio-economic Issues)
11th STATISTICS DAY 2017
Special Issue on
“Administrative Statistics”
DIRECTORATE OF ECONOMICS AND STATISTICS,ODISHA 4th Floor, Heads of Department Building, Bhubaneswar, Khordha, Odisha Pin – 751001, India Ph: 0674-2391295, e-mail ID: [email protected], [email protected] Website: desorissa.nic.in
The Physicist - Mathematician turned Statistician – Economist of highest order who gave Indian statistical system and Indian planning process a global recognition with his visionary synergy, intellect and contribution in applied statistics and economics.
Prof Prasanta Chandra Mahalanobis ( 1893-1972)
Sri D.V.Sadananda.Gauda, Hon’ble Minister,MoSPI; Smt Usha Devi, Hon’ble Minister P&C ; Dr TCA Ananta, Secretary MoSPI, Sri R.Balakrishnan, DC-cum- ACS,Odisha at Inaugural session of IHSN conference at Bhubaneswar - A historic moment for DES,Odisha.
Economics and statistics go together to build up an edifice of trustworthy governance and development streams.
Volume 11 June 2017
SAMIKSHYA
(DE&S Journal on Socio-economic Issues)
11thStatistics Day, 2017
Special Issue on Administrative Statistics
Directorate of Economics and Statistics, Odisha 4th Floor, Heads of Department Building, Bhubaneswar, Odisha Pin – 751001, India Fax: 0674-2391897, Ph: 0674-2391295, Email : [email protected], [email protected] Website: desorissa.nic.in
Bhubaneswar
Smt. Usha Devi. Minister Planning & Convergence Skill Development and Technical Education, Odisha
Message
I am glad to know that the Directorate of Economics & Statistics, Odisha is observing its 11th Statistics Day on 29th June 2017 at Ravindra Mandap, Bhubaneswar and a Technical Journal “SAMIKSHYA, 2017” will be released on this occasion.
The role of statistics assume paramount importance in the development issues and policy decisions of the State as well as Nation. It is elemental in the information technology development of the Country. Directorate of Economics and Statistics organize a seminar on the Statistics Day theme “Administrative Statistics” on the occasion.
I wish the observation of “Statistics Day, 2017” and release of journal “SAMIKSHYA,2017” great success.
( Usha Devi )
Bhubaneswar
Sri R. Balakrishnan, I.A.S. Development Commissioner-cum- Additional Chief Secretary, Odisha
Message
I am delighted to learn that Directorate of Economics & Statistics, Odisha is organizing a one day State level seminar on theme “ Administrative Statistics” and brings out the 11th issue of DES annual journal “ SAMIKSHYA” on the occasion of observation of 11th Statistics Day on 29th June 2017. Statistics plays the crucial role in the development paradigm of a welfare State like Odisha. DE&S plays the key role in providing the quality, reliable and adequate statistics for effective formulation of plans ,policy decisions and implementation of development programmes. The State Government has taken special initiatives to strengthen and modernize the administrative statistical system and machinery of the State.
I wish the function and DES journal “SAMIKSHYA 2017” all success.
Sri Dushasan Behera Director Economics and Statistics, Odisha
Foreword
The 11th issue of “SAMIKSHYA 2017” is placed before you with the sublime blessings of LORD JAGANNATH. This is the mirror journal of DES, Odisha that embodies research-based and resourceful analysis of productive statistics. Like every year, Statistics Day observation on 29th June,2017 motivates the producer and user of data with more productivity linked activities. The release of SAMIKSHYA 2017 symbolizes such motivation. The current year theme of Statistics Day 2017 i.e. Administrative Statistics finds its priority obligation in the contents of SAMIKSHYA 2017. The theme assumes crucial importance in the context of overwhelming dependency syndrome on administrative statistical system by the implementing Agencies of widely diversified development streams and related socio economic demographic activities in the State. The workshop organized on the occasion of Statistics Day 2017 is an appropriate forum to think globally and act locally in the area of quality statistical products.
I acknowledge with sincere thanks the valued contributions of Paper Writers and concerted efforts of the Members of the Editorial Board in preparing and releasing the Annual Journal of DES “SAMIKSHYA 2017” with in short span of time period.
I wish “ SAMIKSHYA 2017” and “ Statistics Day 2017” a great success.
(Dushasan Behera) Director
Editorial Board
Advisor : Sri Dushasan Behera
Chief Editor : Dr. Subhakanta Pattnaik
Editors : Dr. Dillip Ray Dr. Bigyanananda Mohanty Dr. Sujata Priyambada Parida
Editors’ Pen…
SAMIKSHYA 2017 attempts to deliver the desired outcome of economic and statistical fields with research insights. It provides new opportunities and possible solutions with new area of activities and interventions on different sectoral issues. Its contents transcend to explore new horizon of administrative statistics and economic analysis with prudence. We extend our sincere appreciations to the enriched contributions of expert faculties & professionals in the field of economics, statistics and other social sciences. The constructive views of esteemed Readers to improve the quality of the Journal are humbly welcome.
Editorial Board
Contents
Sl. No Subject Page 1 Observation of 11th Statistics Day,2016 : Concept Paper 1
2 Prof. P. C. Mahalanobis – A Planner and Economist 5 Dr. Dillp Ray
3 Genesis and Major Development of Official Statistics in India 6 Smt. Indira Mishra
4 Resource Diversification for the Challenging Need 9 Dr. Subhakanta Pattnaik
5 Sectoral Link and economic growth in Odisha – A Causal Analysis 13 Sri Rajballav Kar Md. Feroz Khan
6 A Study of Fiscal Management in Odisha -1995 - 2017 23 Sri Gopinath Mohapatra
7 Education, Information and Administration 45 Sri Pradeep Kumar Sarangi Sri Sarat Chandra Sahoo
8 Status of Aged Persons in Odisha 54 Sri Rashmi Ranjan Kanungo
9 Consumer Price Index – Measuring for Odisha 60 Miss Prabhatirani Pradhan Smt. Anita Dash
10 Quality of Primary Education in India 68 Sri Sridhar Sahoo
11 Sectoral Linkage and Economic Development in Odisha 76 Dr. Kalpana Sahu Dr. Narayan Sethi
12 Use of Administrative Data in the Context of Kalahandi District 85 Sri Bimbadhar Sethy
Sl. No Subject Page
13 Migration in Odisha : Factors and Impacts 95 Smt. Indira Garnaik
14 Missing Women in Labour Market of Odisha – A Statistical Profile 100 Smt. Sanghamitra Mohanty
15 Consumption Pattern of Odisha 107 Smt. Parbati Barla
16 Inflation : An Odisha Experience 111 Smt. Anita Dash
17 Water Shortage and Remedial Measures in Ganjam District 119 Sri Rama Krushna Satapathy
18 Road Accidents : Everybody’s Concern 125 Sri Tapan Kumar Mishra
19 District Level Poverty Elimination from Rural Odisha – A critical 129 Analysis Dr. Sujata Priyambada Parida
20 Odisha Performs : India Cherishes 137 Smt. Jayashree Rath
21 Administrative Statistics 138 Sri Ramesh Chandra Panda
22 Changing Pattern of Sex Ratio in Odisha 145
Dr. Bijaya Bhushan Nanda
23 Gender Construction through Electronic Media : An Assessment by 151 Television Viewers of Odisha Dr. Aliva Mohanty
Paper Writers and their Affiliations
Sl. No Name Address 1 Dr. Subhakanta Pattanaik Deputy Director DES, Odisha, Bhubaneswar Email: [email protected]
2 Dr. Dillip Ray Deputy Director DES, Odisha, Bhubaneswar Email: [email protected]
3 Smt. Indira Mishra Deputy Director DES, Odisha, Bhubaneswar Email: [email protected]
4 Dr. Kalpana Sahu, Ex Research Officer, Department of Social Sciences, NIT, Rourkela
5 Dr. Narayan Sethi Department of Humanities and Social Sciences, NIT, Rourkela
6 Dr Bijaya Bhusan Nanda Deputy Director, Regional Institute of Planning, Applied Economics & Statistics, Odisha, Email: [email protected]
7 Md. Feroz Khan Deputy Director, DES, Odisha, Bhubaneswar Email: [email protected]
8 Miss Parbhatirani Pradhan Deputy Director DES, Odisha, Bhubaneswar Email: [email protected]
9 Dr. Sujata Priyambada Parida Assistant Director DES, Odisha, Bhubaneswar Email: [email protected]
Sl. No Name Address 9 Sri Rajballav Kar Assistant Director DES, Odisha, Bhubaneswar Email: [email protected]
10 Sri Pradeep Kumar Sarangi Statistical Investigator DES, Odisha, Bhubaneswar Email: [email protected]
11 Sri Sarat Chandra Sahoo Statistical Assistant, DES, Odisha, Bhubaneswar Email: [email protected]
12 Smt. Indira Garnaik Statistical Assistant, DES, Odisha, Bhubaneswar Email: [email protected]
13 Sri Tapan Kumar Mishra Assistant Director, State Transport Authority, Cuttack Email: [email protected]
14 Sri Bimbadhar Sethy Deputy Director, DPMU,Kalahandi Email: [email protected]
15 Sri Ramakrusna Satapathy Assistant Director, District Rural Development Agency, Ganjam, Berhampur Email:[email protected]
16 Sri Sridhar Sahoo Assistant Director School and Mass Education Department, Odisha, Bhubaneswar Email: [email protected]
17 Smt. Jayashree Rath Assistant Director, DES, Odisha, Bhubaneswar Email: [email protected]
Sl. No Name Address 19 Smt Anita Dash Statistical Assistant, DES, Odisha, Bhubaneswar Email: [email protected]
20 Sri Rasmi Ranjan Kanungo Statistical Assistant, DES, Odisha, Bhubaneswar Email: [email protected]
21 Smt Parbati Barla Statistical Investigator DES, Odisha, Bhubaneswar Email: [email protected]
22 Smt Sanghamitra Mohanty Statistical Assistant, DES, Odisha, Bhubaneswar Email: [email protected]
23 Sri Ramesh Panda Statistical Assistant District Planning & Monitoring Unit Ganjam, Berhampur Email: [email protected]
24 Dr. Aliva Mahanty Faculty, School of Women’s Studies Utkal University, Odisha, Email: [email protected]
25 Sri Gopinath Mohapatra Assistant Director Statistics Cell Planning & Convergence Department, Email: [email protected]
Editors Summary Note on “SAMIKSHYA 2017”
SAMIKSHYA 2017 provides a sound interface between producer and user as a research and informative annual journal of Directorate of Economics & Statistics, Odisha. The esteemed Paper Writers showed inspiring intellect and interest in offering their valued observations and recommendations on development & policy issues of the State. A brief summary of their contributions is placed as follows:
Dr Dillip Ray gives a brief account of phenomenal contribution of Renaissance Man and Genius Statistician Prof P.C. Mahalanobis particularly in the field of Indian Planning process and new economic model.
Smt. Indira Mishra gives a historical perspectives of Indian Statistical System.
Dr Subhakanta Pattnaik makes an advocacy on promoting more calorie centric production in crops, livestock and marine fish sectors in Odisha. Resource diversification from traditional production process to calorie based production will not only be cost effective and affordable but also help in poverty reduction.
Sri Pradeep Kumar Sarangi & Sri Sarat Chandra Sahoo make an empirical analysis of stock of diversified educational statistics of Odisha to explain the elemental role of administrative data in the policy decisions and development programmes implementation in education sector.
Sri Raj Ballav Kar & Md.Firoz Khan make effort to establish the relationships between structural linkages ( among broad sectors of agriculture, industry and services ) and growth rates of Odisha with application of appropriate econometric models. Dr Kalpana Sahu & Dr. Narayan Sethi investigates the sectoral linkage with growth trajectory of Odisha with time series macro aggregates and application of econometric models.
Sri Bimbadhar Sethy presents a series examples of administrative data ( both physical & financial) particularly in road transport, agriculture, livestock, revenue & excise, industry, forest, health sectors being collected, compiled and maintained in Kalahandi district administration. These data have enabling effects on development policy decisions of the district.
Smt Indira Garnaik focuses on major social, economic, political and natural risk components as the pull and push factors attributable to perpetual migration in rural and urban areas ofOdisha.
Smt Sanghamitra Mohanty makes concern over the stagnated rather depleting trend of women labour force participation in unorganized sector of Odisha with domestic, social and occupational barriers & compulsions ingrained in it.
Sri Rasmi Ranjan Kanungo assess the structure and composition of the aged with respect to age, sex, their healthcare and wellbeing in Odisha. He finds possible domestic & social front and social security measures to address the problem area of aged community.
Smt Parbati Barla analyses the changing pattern of consumption in food and non food items with economic class distribution of Odisha.
Sri Gopinath Mohapatra makes wide range of fiscal analysis to establish the far reaching impacts of stable state finances of Odisha since the beginning of decade 2000. He addresses on the issues of increasing capital outlays, revenue generation and quality expenditure with empirical analysis.
Sri Ramakrusna Satpathy highlights on the problem areas of ground water and surface water reserves, availability, shortages and water qualities & management etc with special reference to Ganjam district Miss Prabhatirani Pradhan shows the variability of prices on food and non- food items among the districts of Odisha with the application of statistical model of weighting diagram.
Sri Sridhar Sahoo makes a brief analysis on need for quality primary education in India with historical perspectives.
Smt. Anita Dash explains the trend of CPI based inflation (food and non-food) in Odisha by applying weighted diagram process.
Smt. Jayashree Rath presents a brief highlights of sectoral performance of Odisha economy in 2015-16
Dr. Sujata Priyambada Parida analyses the district wise level of poverty elimination form rural Odisha using NSS data.
Sri Ramesh Chandra Panda describes the conceptual aspects of Administrative Statistics for better understanding on the crucial component of statistical system.
Dr. Bijaya Bhushan Nanda analyses the changing pattern of sex ratio in Odisha in terms of its trend, spatial pattern, rural-urban and caste dimensions and comparison of the situation with all India level.
Dr. Aliva Mohanty makes an empirical assessment of perception on women aptitude and attitude in the context of women decency through electronic media.
Observation of 11th Statistics Day, 2017
Concept Paper Backdrop
Directorate of Economics & Statistics, Planning & Convergence Department, Government of Odisha observes 11th Statistics Day on 29th June 2017. The Statistics Day 2017 is observed and celebrated to redefine the three core values of Statisticians i.e. noble mission, stronger integrity and optimistic professionalism. The Day is observed on 29th June every year to honour the Birth anniversary of Prof. Prasant Chandra Mahalanobis, who is regarded as the founder father of Administrative Statistics in India. The Day is celebrated to commemorate the remarkable strides in the field of administrative statistical system that gained momentum both at regional and national level in last 67 years. Observation of ‘Statistics Day’ is an international annual event. By the declaration of United Nation’s Statistics Division, different nations observe the Statistics Day on different dates of the year as per the subject specific significance or recognition in their respective countries.
Objectives
1. Remembrance of visionary contribution of Prof Mahalanobis, the founder father of statistics, 2. Promotion of statistical literacy and capacity among civil servants, technocrats, academia, civil society organization and general public etc. 3. Symposium on the National theme of the Statistics Day. The national theme of 11th Statistics Day 2017 is “ Administrative Statistics ”.
Objectives in brief
Visionary contribution of Prof Mahalanobis:
i. Prof Mahalanobis ( 1893-1972 ) was born on 29th June 1893in West Bengal. ii. Completed degree in Mathematics & Physics from Cambridge University. iii. Turned to statistics being inspired by his Tutor W.H. Macaulay on ‘Biometrika’. iv. He was mathematician, Physicist and Scientist by academics, but statistician by profession.
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v. His major contribution to the world of statistics: 1st paper on statistics: Anthropological observation on anglo Indians of Calcutta- published in records of Indian Musium. Meteorological statistics. Discovery of probable errors in agricultural experiments, Working with R.A.Fisher, the global theorist on statistics, Working on prevention and solution of flood problems with new statistical parameters and methods, Working on Karl Pearson’s coefficients to measure biological affinities, Prepared seminal Paper on D Square statistic, He established large scale sample survey on crop yield estimation and National sample survey. He founded Indian Statistical Institute, Kolkota. He was 1st Director, ISI, Kolkota He was Member,newly constituted Planning Commission, India The 2nd 5-year plan was based on his 4 sector growth model, called Mahalanobis model of growth, He was the first Chief statistical Advisor to Govt of India Honourary President & Fellow to many national and International statistical organizations. He received Padmabhibhusan award for his phenomenal contributions to world of statistics and economic planning. vi. He died in 28th June 1972
2. Promotion of Statistical literacy
Interactive discussions and debate on importance of statistics in socio-economic- cultural-political issues and development, Evaluate the operation & performance of all the central & state sponsored administrative statistics & economics schemes and surveys under the Directorate of Economics & Statistics, Odisha, Promote Producer – user interface on statistical data through dissemination and round table discussions etc,
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Find out new solutions to advanced statistics by linking information technology and remote sensing technology etc.
3. Symposium on the National theme of the Statistics Day. The current year national theme is “ Administrative Statistics”, A State level workshop will be conducted on the theme, Statisticians, economists, researchers, planners, agricultural & environmental scientists, academia and other experts will participate in the deliberations, A special issue of Annual Socio-economic Journal of DE&S, Odisha “SAMIKSHYA” relating to the current year theme will be released and discussed.
DE&S ,Odisha: Fact file
Established in 1958. The apex government organization and nodal authority of all administrative statistics system of Odisha. About 30 technical statistics and economics Divisions relating to statistical surveys, schemes, capacity buildings, macro economics, economic statistics, statistical infrastructures, IT & networking etc are functioning under the Directorate with field functionaries at district and block level. Major Schemes of DE&S: Obligatory uses and strategic implications are as follows : State Income: DES is the only competent & apex Authority of the State to estimate GSDP every year. It is the key parameter in State finance ,fiscal reforms and devolution of central fund etc. It identifies principal drivers of growth. It helps in fixing sectoral priorities in development streams. It ranks districts & States in terms of material standard of living of people. Economic Survey: Evaluates State economy in depth and is placed before Budget session of OLA every year. Crop Survey : The land use, area, yield rate and production of 13 principal crops including major crop paddy estimated by EARAS scheme every year are accepted as official estimates of State Government of Odisha which not only decide the agriculture sector growth as well as overall growth rate of the State but also used in policy issues. Crop survey is also made at GP level to assess the extent of crop loss in natural risks for payment of insurance claims by the affected farmers under PMFBY. National Sample survey: The results of different round surveys of NSS on different social & economic activities ( both central, state and pooled reports ) are used in Samikshya-2017 3
GSDP estimates; state & district level poverty estimates ( CE NSS surveys rounds) and different sectoral & sub sectoral policy issues. Annual survey of Industry: Results of annual ASI and quarterly IIP are used in GSDP estimates and evaluate the trend and extent of industrialization. The results are used for industrial policy issues. Price: The monthly prices collected for agricultural and non agricultural products and the CPI prepared are used for production plan, policy decisions, cost of living and GSDP estimates every year. Publications :A series of statistical publications with time series data and analysis are useful tools for policy issues of government and corporate agencies as well as for researchers, planners etc. Economic census: The only census that captures the number, types,structural characteristics of establishments, workers characteristics and employment in unorganised sector of the State economy. The findings are used for policy issues of welfare and development of unorganized workers. Minor irrigation census: Major users of MI Census data are Central Ground Water Board, Central Water Commission, State Department Water Resources and Central Ministry of Water Resources , River Development and Ganga Rejuvenation, Govt of India etc. Agriculture census : The census captures the number and area of operational holdings as per size classes & social groups( the only source to generate such data ), land use, cropping pattern, irrigation, tenacy particulars and characteristics of agriculture inputs etc. The results are used for production plan, policy decisions and crop sector priorities. Computer networking : Plays supporting roles to e-governance in DES from filed level to head quarters level. Ensure better vertical as well as lateral coordination in data flow. Capacity building at Regional Institute of planning, applied economics &Statistics(RIPAES) for quality, timely and reliable data production, storage. analysis and dissemination. New interventions on major schemes like Business Register, Local Body Analysis ( RLB & ULB), BSLLD, Construction of Block Statistics Office buildings in all 314 blocks etc enhanced the scope and coverage of administrative statistical system in Odisha. Statistics no more remains other man’s concern, let us make it every body’s concern.
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Prof. P.C. Mahalanobis: A Planner and Economist Dr. Dillip Ray
The 20th century India saw a renaissance man in Professor Prasanta Chandra Mahalanobis. He was an institute by himself. He was a rare phenomenon in the academia field of statistics and economics. He belonged to a revolutionary family that showed the path of Bengal renaissance as founder activist of Bramha Samaj. This Physicist- Mathematician turned Statistician laid the strong edifice of Indian administrative statistical system with his unending innovative and phenomenal contributions to the theories, principles and applied side of statistics at local, regional, national and international level between 1912 and 1970. That made him rarest visionary in the field of Statistics. That gave him global recognition with numerous honour of highest order as Statistician at national and international level. His vision, innovation, reformation and contribution put him as the Founder Father of Indian Statistical System. He was the first Chief Statistical Advisor to Government of India.
The journey of Prof. Mahalanobis to visionary reformation did not end here. He had an rare intellect in the field of applied economics which was equally shining and par excellent. His striking contributions to economic development and planning received national recognition. As a Member of newly constituted Planning Commission in 1951, Prof Mahalanobis was assigned the uphill task of developing clear strategy for second five year plan to attain the pre identified plan objectives of rapid economic growth & employment; reduction of income & wealth disparities; prevention of concentration of economic power and creation of values & attitudes for equal society. With no clear strategy for first 5-year plan, Prof Mahalanobis was the real architect to introduce basic strategy for 2nd 5-year plan by Russian experience. He made strong advocacy on the strategy of increased investment in heavy industries for accelerated industrialisation and economic growth. To him, this would ensure rapid capital formation, more supply of capital goods, make India independent of import of producers goods, raise purchasing power of people, increased expenditure on health education and welfare activities etc. Realising that heavy industry strategy alone can not attain all the underlined objectives of Plans, Prof Mahalanobis introduced 4 sector growth model putting both capaital goods and consumption goods sectors in due priorities. His adoption of growth model and strategy in 2nd plan was highly applauded by Planning Commission and the then Prime Minister Pandit Jawaharlal Nehru and was followed till 5th 5-year plan. As first Director of ISI,Kolkota, Prof Mahlanobis’s academic excellence, vision, dynamic faculties, advanced statistical laboratory etc seemingly influenced the then 2 visiting Premiers of China who later sent series of teams of China’s professional statisticians and economists to ISI to acquire skills, knowledge and plan experiences for their country’s interest. This was the magnanimity, synergy and rare intellect of Professor Prasant Chandra Mahalanobis as an Economist and Statistician. We salute this towering compete personality with humble respect. Promote statistical literacy, capacity and raise statistical concern.
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Genesis and Major Development of Official Statistics in India
Smt. Indira Mishra
The British Administration laid down the foundation of Indian statistical system. The Provincial Governments published their annual administration reports on the relevant statistics collected from the district offices in uniform forms.
Year Development
1850’s District Gazetteers of major provinces were released with all relevant administrative statistics of district level recorded.
1862 The first Statistical Committee was constituted in for the preparation of forms to collect statistical information on different subject areas.
1862 A Statistical branch was created in Finance Department of Govt of India.
1868 The first Statistical Abstract of British India (1840‐1865) was published based on such information provided by the Provinces.
1881 Agriculture Departments were opened in various provinces inter alia for collection of Agricultural Statistics .
1881 The population census was conducted in India
1886 The first publication on Agricultural Statistics of British India, was brought out.
1895 The Statistical Branch of Finance Department was converted into a full‐fledged Statistical Bureau with the task of compilation & dissemination of commercial intelligence.
1905 Statistical work of the Bureau was extended to data on demography, crop production and prices, rainfall, industrial production, education, health and hygiene, mining, roads and communications, and other subject matters.
1914 Directorate of Statistics was established in Finance Department. Govt of India
1916 to 2018 Department of Industries in provinces collected information on industries by the recommendation of Indian Industrial Commission (1916‐1918).
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1924-1925 The Royal Commission on Agriculture in India recommended for extensive collection & coordination of agricultural statistics in provinces and districts.
1925 Directorate of Statistics & Department of Commercial intelligence merged into one organization called DG of Commercial intelligence & Statistics.
1925 The Indian Economic Enquiry Committee was set‐up. It recommended to create one central statistical Bureau that will supervise both the Central and Provincial Governments in all statistical matters.
1932 Indian Statistics Institute was set up with Prof Mahalanobis as first Director.
1934 Committee under Messrs. Bowley and Robertson was set up. It recommended for whole time Statistician in each province who would cooperate with the Central Director of Statistics as nearly independent of departmental control as administrative requirements permitted.
1942 United Provinces Government was the first to set up a Department of Economics and Statistics.
1945 Inter Departmental Committee headed by Economic Advisor formed. It recommended for creation of CSO, Statistical cadre and State Statistics Bureaus.
1946 The Government of Bombay established its Bureau of Economics and Statistics.
1948 Registrar General and Census Commissioner was created.
1949 Prof P.C. Mahalanobis, was the first statistical adviser to the Cabinet, Government of India in January 1949. He was the architect of the statistical system of independent India. Prof P. V. Sukhatme, was Statistical Adviser to the Ministry of Agriculture.
1949 Central Statistical Unit( CSU) (1949)was established.
1949 National Income Committee was formed.
1950 NSSO was established.
1951 CSU was converted to CSO. Department of Statistics under MOSPI, GoI set up.
1954 The National Income Unit was transferred from the Ministry of Finance to the CSO.
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1957 The subject of Industrial Statistics was transferred from the Ministry of Commerce and Industry to the CSO.
1961 The Department of Statistics was set up in the Cabinet Secretariat and the CSO became a part of it.
1972 A Computer Centre in the then Department of Statistics was set up.
1973 The Department of Statistics became a part of the Ministry of Planning.
1999 The Department of Statistics and the Department of Programme Implementation were merged and named as the Department of Statistics and Programme Implementation under Ministry of Planning and Programme Implementation.
1999 The Department of Statistics and Programme Implementation was declared as the Ministry of Statistics and Programme Implementation (MoS&PI).
What is growth for if not to help ordinary people thrive.- W. Biyanyma, Executive Director, Oxfam International
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Resource Diversification for the Challenging Need
Dr. Subhakanta Pattnaik
Abstract
The paper makes comparative evaluation of the production scenario in crop, livestock and fishery sector in Odisha. The analysis advocates on “calorie centric” production of major crops, livestock products and marine fishes in the State for better health prospects, cost effectiveness and poverty reduction.
Introduction
In India, the decennial population growth rate during 1911 was 5.75 as compared to 24.80 in 1971, 24.66 during 1981, 23.87 during 1991 , 21.52 during 2001 & 17.72 during 2011 over previous decennial census. With the constraints of decreasing trend in net area sown countrywide due to rapid industrialisation, there is alarming trend to feed the growing population during next two decades. Although the per capita food grain availability in India has marginally varied during 1961 to 2011, there is need to explore diversification of food habits in order to maintain the need based calories of food. It is further observed that, in India 19.36% of people were cultivators during 1951 as compared to 9.81% during 2011, with the significant decrease in percentage of cultivators countrywide & lacking interest towards crop cultivation, there is need for diversification to augment the calorie based food to feed the growing population. During 2016, 251.57 million tonnes of food grains are produced to feed nearly 128 crore people of the country. With the constraint of resources & productivity per unit area, it seems incredulous to meet the growing demand. Further, due to lack of sufficient water resources & irrigation channels, yield increasing technologies seems to gradually become a failure.
It is therefore imperatives to lay more emphasis on livestock &fisheries to supplement the food demand. Moreover, thrust emphasis to augment livestock & fishery activities would provide lucrative income to the farm enterprises. At the same time, it would provide the required calories needed for the human beings. India has the greatest peninsular region in the world and the vast marine potential available food scope can be explored to maintain the required calories for the people.
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In the national level, it is significantly observed that during 2015-16, 2.90% increase was recorded in food grain production over 2010-11 whereas there was 31.15% increase in fish production during the same period. It reveals shifting of interest towards fish production from crop production. Although there was 4.60% increase in fertiliser consumption in the national level from 2014-15 to 2015-16, there was fall of around 0.44% in food grain production during the same period. In spite of application of increased dose of fertiliser, as there was low return, the crop cultivators were constraint and inclined towards diversification in the national level. Further, due to natural calamities and non-availability of manpower needed for crop cultivation, the farmers are gradually loosing interest from crop cultivation. During the period 2015-16 there was only 0.15% increase in cereals production and a decrease of 4.66% in pulses production in the national level over 2014-15, whereas there was 6.29% increase in milk production, 5.61% increase in egg production and 3.49% increase in fish production during the same period. It reveals sustained diversification towards milk, fish & egg production. As this shifting provides remunerative price for the producers and increases demand of the consumers, the sustained trend in the national level would provide a healthy situation. Further, the per- capita increase in availability of food grains, cereals and pulses do not keep pace as that of milk, fish & egg during the same period in the national level.
Although, per capita net availability of food grain has marginally varied from 468.7gms. per day during 1961 to 486.8 grams per day during 2016, the availability of food grains seems grim due to the increasing constraints in the crop production and farm enterprise environments. The percentage increase in food grain production during 2015-16 is 2.90% over 2010-11. Whereas, the percentage increase in milk production is 27.67% during the same period. Similar trend in egg and fish production in the same period are observed. The egg production is 31.59% increase during 2015-16 over 2010-11. It would ideally and substantially provide nutritional required calories of food to sustain healthy life for the coming generation. It would further contribute to improve the poverty line of the existing population in a developing country like India.
The protein supplement with adequate calorie based nutritional diet in the national level is provided by pulses, milk, fish& egg. The production figure during 2015-16 provides a satisfactory nutritional diet in the existing scenario of constraint agriculture.
In the eastern zone of the country, West Bengal contributed maximum food grain production (16.51 million tonnes) during 2014-15 associated with maximum cereals production (16.33 million tonnes). The projected population of Bihar would also rank first
Samikshya-2017 10 during 2000 A.D. (10.46 crores) and 2010 A.D. (12.92 crores). The trend is followed by Bihar, Odisha, Assam& Jharkhand. But there was reverse trend in pulses production. Odisha is the major state in pulses production in Eastern India. In Odisha, there was 4.76% increase in pulses production from 2013-14 to 2014-15. Hence, in the eastern zone, Odisha would contribute significantly in pulses production during coming years. The pulses, which contribute substantial protein based diet, needs more attentive cultivation through diversification of crops. The growers share in pulses cultivation also provide remunerative and substantial return at low cost.
Asia plays the more significant roll in the world aquaculture production. Fish farming is a traditional and age old practice in almost every Asian countries. Over 80% of the total aquaculture production globally comes from Asia. Hence, aquaculture continues to grow in economic importance. The supply and demand gap for calorie based food with the increasing population is widen due to the fact that the crop production does not keep pace. Therefore, the aquaculture fulfils and continues to narrow down the supply and demand gap.
India’s share in the world fish production was 3.64% during 1989. It has increased from 752 thousand tonnes during 1950-51 to 10795 thousand tonnes during 2015-16. Although, India has got greatest peninsular region in the world, the marine fish production has increased only 6.71 times as compared to 14.36 of the total fish production during the period.
With the constraint & limited resources, the farmers in the eastern zone can substantially increase their income exploring the marine potential. As compared to the inland aquaculture, the marine aquaculture potential is very vast in the eastern zone. Moreover, it would need less of manpower & inventory resources.
In the eastern zone of India, West Bengal contributes the highest fish production (16.17 lakh tonnes) followed by Bihar (4.79 lakh tonnes), Odisha (4.70 lakh tonnes) & Assam (2.83 lakh tonnes) during 2014-15. As compared to (-)3.34% growth of food grain production, the growth of fish production in West Bengal was 2.32% from 2013-14 to 2014-15. Similarly in Odisha state although there was growth of 7.30% for food grains, the total fish production grew at 13.48%. Hence, in the eastern zone, there was significant exploring of acqua resources in the sphere of fish production. We have also significantly observed that the total fish production in Odisha has increased from 0.71 lakh tonnes during 1981 to 4.70 lakh tonnes during 2014-15. As compared to food grain production, there is sky rise increase in the fish production in Odisha. In the sphere of marine fish also similar trend is observed.
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In order to narrow down the supply and demand gap in the sphere of calorie based required food to meet the challenging need of the growing population, the exploration of marine resources is essential for the next two decades. The natural & vast resources of coast-land would help to meet the growing demand. With the limited inventory resources& manpower, the net family income of the household can be substantially increased. Moreover, the marine prawn fetches lucrative price through exports in the international market. The real resources thrust diversification or supplementation through aquaculture farm enterprise would considerably augment the income resources. Thereby in addition to significant increase in the farm enterprise, it would considerably increase the production of calorie based required food for the nation as a whole. The said diversification towards marine activities would further improve the poverty line to increase per-capita availability of food in calories in a developing country like India. It would prepare the coming generation to meet the challenging need at low cost. The nation as a whole can earn substantial foreign exchange through intensive growth of such diversification.
As the poverty line of the people in the eastern zone of India used to lag behind as compared to other parts of the country, such marine diversification would ascertain substantive increase in poverty eradication. Further, the eastern zone is lagging behind in the sphere of industries & factual implementation of agricultural developed technology due to scarce inventory, manpower and literacy. Such diversification towards marine activities would strengthen the financial status of the people in the eastern zone.
Strengthen vertical and lateral coordination of administrative statistical system for
quality and timely data flow.
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Structural Link and Economic Growth in Odisha: A Causal Analysis
Sri Rajballav Kar Md. Feroz Khan Abstract
The purpose of this paper is to study the pattern of economic growth and sectoral shifts in Odisha from the period from 1950-51 to 2009-10 (2004-05 prices). It also investigates relationship between economic growth and the broad sectors: Agriculture, Industries and Services using Granger causality test under Vector Error Correction Model (VECM) framework for Odisha. The Co-integration analysis suggests that there are long run relationships among the variables. The present analysis points out that Industries sector could cause economic growth more compared to other sectors. However, importance of Agriculture may not be overlooked as it also lead to growth in Industries as evident from the study.
Key Words: Economic Growth, GDP, GSDP, Co-integration, Granger Causality Test, Vector Error Correction Model, Unit root
Introduction
Gross Domestic Product (GDP) is a very strong measure to gauge the economic performance of a nation. It comprises of money value of all goods and services produced in a country and thus used as an indicator at Government level for planning and policy decision. The Gross State Domestic Product (GSDP) of Odisha has been experiencing faster rate of growth in recent years and has even crossed national growth rate. With regard to composition of GSDP, the shares of various sectors have largely changed. The share of agriculture in the total GSDP has declined, on the other hand, the percentage share of services sector in the GSDP is increasing. With this shift, the state economy has become predominantly service based which accounts for 54 percent of GSDP at 2004-05. The national economy also exhibits the same pattern with rising service sector share of 55 percent in GDP. With this changing phenomenon in the composition of GSDP, it becomes important to understand the nature and direction of relationship between economic growth and its sectors like Agriculture, Industries and Services.
The Objective of the study
To study pattern of changes in growth as well as sectoral shifts in Odisha and Indian economy over a period of time
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To study long run relationships between economic growth and the broad sectors using co-integration technique. To study direction of relationships between economic growth and Agriculture, Industries and Services sectors using Granger causality test under Vector Error Correction Model (VECM) framework.
The outline of the paper is as follows: Section-2 describes on Data and Methodology. Section-3 deals in Empirical analysis with four sub-sections like 3.1. Pattern of economic growth and sectoral shifts in the economy, 3.2. Stationary time series and unit root tests, 3.3 Test of co-integration and 3.4. Causal relationship in Odisha economy: Granger causality test under VECM, Section-4 depicts Summary and Conclusion.
Data and Methodology
The paper uses annual time series real gross value added data of Agriculture, Industries, Services and GSDP covering period from 1950-51 to 2009-10(2004-05 prices) to examine causal relationship among variables for Odisha. The Agriculture covers animal husbandry, fishery and forestry sectors.
As regards methodology, since we use time series data, first step is to check stationarity of data. So unit root tests at level and first difference of the data under log specifications are conducted. All the variables are found to be I(1) or stationary at first difference. Hence it is necessary to proceed to test the possibility of co-integration or long run relationship among the variables. Using Johansen co-integration test, it is found that there is one co-integrating equation, which means the time series variables have a long run relationships and, are co-integrated.
Since, the variables are non-stationary at levels and co-integrated, Vector Error Correction Model (VECM) is used to test Granger causality among the co-integrated variables.
Granger causality test is basically a statistical test to offer a formal test of the direction of causality between variables. It uses data to find patterns of correlation between current value of one variable and past values of others. It does not mean changes in one variable cause changes in another. By using chi-square test under VECM environment, Granger causality between variables can be determined.
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Empirical Analysis
Pattern of economic growth and sectoral shifts in the economy
A rise in GSDP shows that there is more value addition in the economy. Thus the economic growth is measured with percentage of increase in GSDP. Although the absolute value of GSDP may be rising over time, but in terms of growth, it shows fluctuations. The real GSDP of Odisha has grown by an annual average rate of 4.2 percent during the period from 1951 to 2009, while all India growth is averaging at 4.9 percent during the same period. While analyzing 5-yearly growth rate, the figure-1 clearly shows rising trend in growth rate of Odisha economy from 2000 onwards. There seems to be high growth phase in Odisha as it surpasses national growth rate from 2005 onwards. During 1990’s, the economy of Odisha was losing momentum as it could not take advantage of the benefits of reforms. The degree of dispersion of growth rate of Odisha is more than all India as evident from the figure-1. Although the growth of GSDP for Odisha reflects more accelerations and decelerations compared to all India, it achieves strong growth of above 7 percent from 2005 to 2009-10.
10 20 9 8 7 15 6 5 gsdp 4 10 ag_allied gdp 3 indus 2 1 5 services
0
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2010 0
1955
1960
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Figure-1: 5-yearly growth rate: Odisha and India Figure-2:Sectoral5-yearly Growth rate of Odisha Economy Source: State Income Division, DES, Odisha/CSO Source: State Income Division, DES, Odisha
In sectoral approach, both agriculture and industries sectors growth rate showed fluctuations, while service sector exhibited stable pattern of rising trend from 1975 onwards. The agriculture sector growth declined negatively in 1980’s and 2000’s due to natural shocks. The industries sector growth was negative during 1970’s, but it showed rising trend during 2005’s onwards after witnessing a slow growth in 1995’s and 2000’s.
The last two decades have witnessed major shifts in the composition of GSDP. The share of industries and services sectors have recorded continuous increase. But, when compared, this increase has been higher in services sector, which is 44.94 percent during 2000-09 from 36.15 percent in 1990’s. The industries sector witnessed upward trend in 1990’s at 33.32 percent from 26.01 percent in 1980’s. But, it recorded moderate increase
Samikshya-2017 15 during 2000-09 at 33.73 percent. The share of agriculture and allied was 62 percent in 1950’s and showed a continuous decline and finally remained at 21.33 percent during 2000- 09. Thus, it can be concluded that service sector has become the most attractive sector of the state economy (figure-3).
Figure 3 : Percentage share of GSDP by activity from 1950’s to 2000’s (2004-05 prices)
70.00
62.01 59.17
60.00 55.20 48.37
50.00 44.94
40.00 36.15
33.88
33.73
33.32
29.42
28.87
28.68
28.28
26.54 26.01
30.00 25.87
21.33 19.30 20.00
10.00
0.00 1950`s 1960`s 1970`s 1980`s 1990`s 2000-13
Agril_allied indus services
Source : State Income Division, DES, Odisha Figure 4 : Economic Growth pattern in Odisha Mean Growth Rate Year Agril_allied Indus Services GSDP 1950-70 3.60 7.64 3.08 3.64 1971-90 1.16 5.73 3.98 2.96 1991-2000 1.14 3.11 6.31 4.29 2001-2009-10 4.70 8.85 9.46 8.02 Variation in Growth rate 1950-70 15.40 10.57 6.00 6.45 1971-90 15.91 11.09 4.35 10.36 1991-2000 10.10 8.69 3.17 5.47 2001-2009-10 9.81 9.96 2.45 4.63
Source :Computation by Authors Scatter plots for gross value added of gsdp and Agriculture, gsdp and industries, gsdp and services sector in log specifications haves been plotted in figure-5. This shows how just a quick view on the data can support a positive relation between GDP with other sectors.
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16.4 16.4 16.0 16.0 15.6 15.6
P 15.2 D
P
15.2 S
D G
S
N 14.8
G L
N 14.8
L 14.4 14.4 14.0 14.0 13.6 13.6 11 12 13 14 15 13.2 13.6 14.0 14.4 14.8 LNINDUSTRIES LNAGRIL
16.4
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P 15.2
D S
G
N 14.8 L
14.4
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12.5 13.0 13.5 14.0 14.5 15.0 15.5 16.0 LNSERVICES
Figure-5: Scatter Plot for GSDP(LNGSDP), Agriculture(LNAgriculture), Industries (LNIndustries) and Services sector (LNServices)
Stationary time series and Unit root tests
Broadly speaking, “a time series is said to be stationary if its mean and variance are constant over time. If a time series is not stationary in the sense just defined, it is called a non-stationary time series. In other words, a non-stationary time series will have a time - varying mean or a time - varying variance or both. Non-stationary time series may be of little practical value. If we have two or more non stationary time series, regression analysis involving such time series may lead to the phenomenon of spurious or nonsense regression (Gujarati, 2011).
The annual time series data under study covers period from 1950-51 to 2009-10. In the first step, we examine the stationary properties of data series under in log specifications. The study uses Augmented Dickey Fuller (ADF) test (Dickey and Fuller, 1979, 1981) to perform the unit root tests. The unit root tests for all variables in levels and first difference can be seen in the Table-2 below.
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Table 2: ADF Unit root test of GSDP, Agriculture, Industries and Services Sectors at levels
Null Hypothesis: LNGSDP has Lag Length: 0 D(LNGSDP), Lag Length : 0 LNGSDP has a unit a unit root (Automatic has a unit (Automatic root Exogenous: based on SIC, root based on SIC, Constant & MAXLAG=10), Exogenous: MAXLAG=10), linear trend in level Constant & in level linear trend t-Statistic t-Statistic (level) Prob.* (1st diff) Prob.* Augmented Dickey- Fuller test statistic -0.437024 0.9838 -11.67394 0.0000 Null Hypothesis: LNAGRICULTURE has a unit root DLNAGRICULTURE) has a unit root Augmented Dickey- Fuller test statistic -2.309166 0.4223 -14.57829 0.0000 Null Hypothesis: LN INDUSTRIES has a unit root D(LN INDUSTRIES) has a unit root Augmented Dickey- Fuller test statistic -1.267803 0.8861 -6.030023 0.0000 Null Hypothesis: LNSERVICES has a unit root D(LNSERVICES) has a unit root Augmented Dickey- Fuller test statistic -0.555989 0.9779 -6.375513 0.0000 Source: Authors’ Analysis using E-Views
The test result confirms that the series are indeed non-stationary at levels as probability value is greater than 5% for GSDP and all three sectors. So, we accept null hypothesis and reject alternative hypothesis that the series have unit roots. But, in 1 st difference, the series are stationary as probability value is less than 5%. We reject null hypothesis and accept alternative hypothesis that the series have no unit roots i.e., stationary. The results reflects the I(1) state of the variables.
Testing for Co-integration
Economically speaking, two variables are co-integrated if they have a long run or equilibrium, relationship between them. In more technical terms, if we have two non- stationary time series X and Y that become stationary when differenced (these are called integrated of order one series, or I(1) series; random walks are one example) such that some linear combination of X and Y is stationary (aka, I(0)), then we say that X and Y are co- integrated.
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Time series data are itself non-stationary. Regression with non-stationary time series data gives spurious result. If they are co-integrated and become stationary, one can do proper forecasting or inference with stationary time series. Since all variables under study are stationary at first difference, it appears that they are co-integrated. However, we have several tests of co-integration, out of which we apply Johansen test of co-integration to establish long run relationship among the variables.
The Johansen test can be seen as a multivariate generalization of the augmented Dickey-Fuller test. It provides estimates of all co-integrating vectors. The Johansen tests are likelihood-ratio tests. There are two tests: 1. Trace test, and 2. Maximum eigenvalue test. For both test statistics, the initial Johansen test is a test of the Null Hypothesis of no co- integration against the alternative of co-integration. The tests differ in terms of the alternative hypothesis. Results based on Johansen co-integration test is given in Table-3.
Table 3: Result based on Johansen Cointegration test
Included observations: 57 after adjustments Trend assumption: Linear deterministic trend (restricted) Series: LNGSDP LNAGRIL LNINDUSTRIES LNSERVICES Lags interval (in first differences): 1 to 2 Unrestricted Cointegration Rank Test (Trace) Hypothesized Trace 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.426103 77.18052 63.87610 0.0026 At most 1 * 0.376093 45.52811 42.91525 0.0268 At most 2 0.184667 18.63811 25.87211 0.3026 At most 3 0.115582 7.001077 12.51798 0.3444 Unrestricted Cointegration Rank Test (Maximum Eigenvalue) Hypothesized Max-Eigen 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None 0.426103 31.65241 32.11832 0.0569 At most 1 * 0.376093 26.89000 25.82321 0.0361 At most 2 0.184667 11.63703 19.38704 0.4497 At most 3 0.115582 7.001077 12.51798 0.3444 Note: The result is calculated using E-Views, Hypothesis testing at 0.05 level Table 3 shows the results of co-integration tests under the null hypothesis of no co- integration. Under Trace test, None* indicates there is no co-integration (Null Hypothesis). Since p-value is 0.0026, which is less that 0.05, we can reject null hypothesis and accept alternative hypothesis that there is co-integration. At one* indicates there is one co- integration among variables (Null hypothesis). Since p-value is 0.0268, which is less that 0.05, we can reject null hypothesis and accept alternative hypothesis that there is one co-
Samikshya-2017 19 integrating equation. Similarly we examined Max eigen value test and found that there is at least one co-integration among the variables. The existence of co-integration signifies that the variables GSDP, Agriculture, Industries and Services have long run relationship. Hence, Vector Error Correction Model (VECM) is used for all the series to test for causality to establish sectotal linkages in Odisha economy.
Causal Relationship in Odisha economy: Granger Causality test under VECM
Although regression analysis deals with the dependence of one variable on other variables, it does not necessarily imply causation. In other words, the existence of a relationship between variables does not prove causality or the direction of influence. For example, if event A happens before event B, then it is possible that A is causing B. However, it is not possible that B is causing A. In other words, events in the past can cause to happen today or can affect event of today. This is roughly the idea behind Granger causality test. Given the results of co-integration test, one has to estimate VECM/VAR to determine the direction of causality between the variables. Since co-integration exists, the Granger causality test is performed under vector error correction methodology.
To explain Granger test, we have considered four variables: GSDP, Agriculture, Industries and Services. Suppose we want to answer the question: Is GSDP causes Agriculture (GSDP->Agriculture) or Agriculture causes GSDP (Agriculture->GSDP)? To answer the question we apply Granger test under VECM, where the possible permutations of the two variables (GSDP and Agriculture) are:
Unidirectional Granger causality from variable GSDP to variable Agriculture, Unidirectional Granger causality from variable Agriculture to variable GSDP Bi-directional casualty, No causality
Before conducting multivariate Granger causality tests under VECM framework among the variables, selection of lag length is determined using Vector Auto Regression model (VAR). We run the VAR model and choose maximum lag of seven. The results of Akaike Information Criteria (AIC), Schwarz Information Criteria (SC) and Hanna-Quinn Criteria (HQ) are compared in Table-4. We choose lowest AIC criteria and lag order is found 7(seven) as marked with star. Since we use VECM, our lag order will be 6(six) and
Samikshya-2017 20 accordingly, we run VECM using lag order of 6 with number of co-integration equation as 1 under linear trend assumption.
Table 4: Lag order Selection VAR Lag Order Selection Criteria Endogenous variables: LNGSDP LNAGRIL LNINDUSTRIES LNSERVICES Exogenous variables: C Date: 06/17/17 Time: 10:33 Sample: 1950 2009 Included observations: 53 Lag LogL LR FPE AIC SC HQ 0 125.8566 NA 1.18e-07 -4.598365 -4.449664 -4.541182 1 312.7110 338.4532 1.88e-10 -11.04570 -10.30219* -10.75978* 2 329.2331 27.43293* 1.86e-10* -11.06540 -9.727090 -10.55075 3 345.88582 25.13614 1.87e-10 -11.09003 -9.156915 -10.34665 4 358.3403 16.91931 2.25e-10 -10.95624 -8.428317 -9.984122 5 369.4315 13.39313 2.96e-10 -10.77100 -7.648274 -9.570150 6 388.5284 20.17786 3.04e-10 -10.88786 -7.170333 -9.458281 7 410.1069 19.54285 3.04e-10 -11.09838* -6.786039 -9.440060 * indicates lag order selected by the criterion
The results of Granger Causality test under VECM frame work is presented in Table- 5. The dependent variable is taken as GSDP and it is evident from the result that causality is running from industries sector to GSDP rather than GSDP to Industries. So, the direction is unidirectional. Under four variables of VECM frame work, it is found that Agriculture, Industries and Services can collectively lead to higher growth, although Agriculture and Services sectors individually do not lead to higher growth. The direction of causality from Agriculture to GSDP and Services to GSDP is termed as independent.
Table 5: VEC Granger Causality/Block Exogeneity Wald Tests
Sample: 1950 2009, Included observations: 53, Dependent variable: D(LNGSDP) Direction of Excluded Chi-sq df Prob.(p-value) Causality D(LNAGRIL) 4.003536 6 0.6762 Independent D(LNINDUSTRIES) 13.46702 6 0.0362 Unidirectional D(LNSERVICES) 8.814392 6 0.1843 Independent All 30.69080 18 0.0312 Unidirectional Dependent variable: D(LNAGRIL) Excluded Chi-sq df Prob. D(LNGSDP) 0.822337 6 0.9915 Independent D(LNINDUSTRIES) 4.493168 6 0.6103 Independent D(LNSERVICES) 6.678786 6 0.3516 Independent All 17.03700 18 0.5206 Independent Dependent variable: D(LNINDUSTRIES) Excluded Chi-sq df Prob. D(LNGSDP) 9.815712 6 0.1326 Independent D(LNAGRIL) 13.32854 6 0.0381 Unidirectional D(LNSERVICES) 7.480771 6 0.2787 Independent All 30.96736 18 0.0290 Unidirectional Dependent variable: D(LNSERVICES) Excluded Chi-sq df Prob. D(LNGSDP) 7.693649 6 0.2614 Independent D(LNAGRIL) 7.808826 6 0.2524 Independent D(LNINDUSTRIES) 8.335712 6 0.2145 Independent All 17.41939 18 0.4945 Independent Samikshya-2017 21
When Agriculture is taken as Independent variable, it is observed that GSDP, Industries and Industries do not cause Agriculture. Similar pattern is observed in case of Services sector taken as dependent variable. But, it is evident that Agriculture can cause higher growth in Industries, while Agriculture alone cannot boost economic growth. Moreover, GSDP, Agriculture and Services can jointly boost growth in Industries.
Summary and Conclusion
It appears from the study that Odisha economy has undergone structural shifts. A higher rate of growth is observed in Industries and Services sectors. It is empirically evident that the direction of causality is from Industries sector to economic growth individually and Agriculture, Industries, Services sector to economic growth collectively. There is no causality from economic growth to Agriculture, Industries and Services sectors. It is also evident that Agriculture can cause Industries individually, while economic growth, Agriculture and Services can jointly lead to growth in Industries sectors.
Thus, the results indicate that Industries sector plays a key role in driving economic growth in Odisha. Besides Agriculture sector is also responsible for boosting growth in Industries sector. Therefore, in order to foster rapid, sustained and broad based growth in Odisha, Government should give priority for the development of Industries as well as Agriculture sector.
References: DrD.Gujrati and Sangeetha: Basic Econometrics Vijay Subramaniam and Michael Reed: Agricultural Inter-Sectoral Linkages and Its Contribution to Economic Growth in the Transition Countries: Ramesh Jangili: Causal Relationship between Saving, Investment and Economic growth for India. Mirza Md. Moyen Uddin: Causal Relationship between Agriculture, Industry and Services Sector for GDP Growth in Bangladesh: An Econometric Investigation: D.K. Behera: Economic growth and sectoral linkages: Empirical evidence from Odisha TomislavGelo: Causality between economic growth and energy consumption in Croatia Dhiraj Jain, K. Sanal Nair and Vaishali Jain: Factors Affecting GDP (Manufacturing, Services, Industry): An Indian Perspective S.K. Bhaumik: Principles of Econometrics
Noble services, stronger integrity and assured professionalism are the three core values of Statisticians.
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A Study of Fiscal Management in Odisha 1995 – 2017 Sri Gopinath Mohapatra Abstract
The State finances in Odisha remain near stable and sustainable in the last decade. The enactments of MTMRP and FRBM Act 2005 in particular, by the State Government made visible impacts on the fiscal disciplines. Major fiscal measures to cut down unproductive expenditure and restructuring of debt added to the turnaround of the fiscal situation in post FRBM Act period and high growth trajectory in the State. The paper makes empirical analysis of vital fiscal parameters on revenue generation, expenditure & debt management with time series data that explain the extent of fiscal sustainability in the State.
Introduction
Odisha is endowed with rich natural resources in the form of vast mineral deposits, forest, fertile land, surface and ground water, long coastline, tourist potentials and young human resources. But high incidence of un-productive expenditure was one crucial factor that kept Odisha’s economy lagged behind many other States. After meeting the committed expenditure very little room was available for productive/ welfare expenditure.
Objectives
The prime objectives of the paper are outlined as follows: 1. Examine the trends in state finances of Odisha from 1995 – 2015. 2. Study the fiscal scenarios in the pre and post MTFRP & FRBM Act 2005 periods by analyzing major fiscal responsibility indicators. 3. Identify the major drivers of efficient and effective management of revenue, expenditure and debt pattern over the last decade from the fiscal variables.
Methodology adopted
Scope & Coverage – The universe of the study is Odisha State. Data – Collected and compiled from different secondary published sources such as State Finance Department, RBI, CAG, CII, DES, NIPFP, World Bank Reports etc. Periodicity – 1995-2017 Methodology – Trend analysis with time series data. Variables – All fiscal variables & GSDP of Odisha Technique of interpretation - Use of rates, ratio, percentage, trend
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Limitations - The study has not taken into account other economic and political conditions and non-quantifiable parameters, even they have impact on overall fiscal scenario of the State. The policy level changes like revision of mining royalty, impact of increasing tax rate in particular have not been taken into consideration.
Fiscal situation in brief
The White Paper on Odisha State Finances” placed before Odisha Legislative Assembly on 29th March 2001, states that “the ever-increasing revenue expenditure, inadequate central devolution and absence of adequate generation of revenue receipt by State Government commensurate with revenue expenditure have aggravated the financial position of the State”. As per the report, Government revenue was stagnant and inadequate to meet the increasing expenditure and thereby created a serious mismatch between daily receipts and expenditures. As a result Government had to resort to frequent overdraft, almost 360 days in a year. To overcome this financial crises, State Government took steps to cut down unproductive expenditure, restructuring of debt, implementation of Medium Term Fiscal Reform Plan (MTFRP) and enactment of Fiscal Responsibility and Budget Management (FRBM) Act, 2005. By these steps, Government was able to manage state finances by reducing deficits, managing debt and liquidity position, which resulted in a turnaround of the fiscal situation post 2004-05 and Odisha economy became in higher growth trajectory. According to 2015-16 RE and 2016- 17 BE, Odisha’s outstanding debt was 15.75 percent and 16.96 percent of GSDP respectively, which is below the level of 25 percent recommended by the 13th Finance Commission.
A country can be said to achieve external debt sustainability if it can meet its current and future external debt service obligations in full, without recourse to debt rescheduling or the accumulation of arrears and without compromising growth (World Bank Report, 1998). The external debt sustainability can be obtained by a country by bringing the net present value of external public debt down to about 150 percent of a country’s exports or 250 percent of a country’s revenues (IMF, 2001). High external debt is believed to have harmful effects on an economy (Wikipedia, 2012). The economy of Odisha is one the fastest growing state economies in India. According to Odisha Economy Survey, 2015-16, Odisha grew impressively during 2014-15 with a real growth rate of 6.24 percent and expects a real growth rate of 6.16 percent during 2015-16. Odisha has an agriculture-based economy which is in transition towards an industry and service-based economy. According to Dun & Bradstreet, the GSDP is expected to grow at a rate of 8.1 percent during 2015 – 2020. Odisha is also one of the top FDI destinations in India. In the fiscal year 2011-12, Odisha received investment proposals worth Rs. 49,527 crore (US $9.296 billion). According to the Reserve Bank of India, Odisha received Rs. 53,000 crore (US $8.33 billion) worth of new FDI commitments in the 2012-13 fiscal (Wikipedia, 2015).
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Data Analysis
There are three key fiscal parameters – revenue, fiscal and primary deficits – indicate the extent of overall fiscal health of government finances during a specified period. The deficit in the government account represents the gap between its receipts and expenditure. The nature of deficit is an indicator of the prudence of fiscal management of the government. The trends, nature and magnitude of these deficits and also the assessment of actual levels of revenue and fiscal deficits during the period from 1995-96 to 2016-17 BE are presented below.
Trends in Deficits/Surpluses: Trends in deficits/ surpluses for the period from 1995 – 2016 and their percentage in respect of Gross State Domestic Product (GSDP) are given in the Table 1 and Figure 1 below.
Table 1: Deficit Indicators of Odisha from 1995-96 to 2016-17 BE (Rs. In Lakh) Financial Gross Revenue Fiscal Primary Revenue Fiscal Primary Year State Deficit(-)/ Deficit(-)/ Deficit(-)/ Deficit to Deficit to Deficit to Domestic Surplus(+) Surplus(+) Surplus(+) GSDP (%) GSDP (%) GSDP (%) Product 1995-96 3200294 -80710 -207149 -114223 -2.52 -6.47 -3.57 1996-97 3162811 -83049 -400460 -292523 -2.63 -12.66 -9.25 1997-98 3830064 -90314 -468768 -339595 -2.36 -12.24 -8.87 1998-99 4255150 -226250 -634221 -485737 -5.32 -14.90 -11.42 1999-2000 4789168 -257419 -805589 -681819 -5.38 -16.82 -14.24 2000-01 4841484 -193196 -922854 -694173 -3.99 -19.06 -14.34 2001-02 5170371 -283375 -1140567 -857071 -5.48 -22.06 -16.58 2002-03 5480111 -157591 -1216781 -928223 -2.88 -22.20 -16.94 2003-04 6610014 -142092 -1331298 -1045270 -2.15 -20.14 -15.81 2004-05 7772943 -52230 -506912 -173710 -0.67 -6.52 -2.23 2005-06 8509649 48119 -131405 238304 0.57 -1.54 2.80 2006-07 10183947 226060 -102755 216088 2.22 -1.01 2.12 2007-08 12927445 424392 132313 264764 3.28 1.02 2.05 2008-09 14849071 341989 -33404 81317 2.30 -0.22 0.55 2009-10 16294643 113863 -226537 -70990 0.70 -1.39 -0.44 2010-11 19753000 390822 -65775 32018 1.98 -0.33 0.16 2011-12 22528348 560678 62176 319819 2.49 0.28 1.42 2012-13 25527261 569935 362 281085 2.23 0.00 1.10 2013-14 27727069 332910 -463364 -174545 1.20 -1.67 -0.63 2014-15 30980722 586214 -547862 -266835 1.89 -1.77 -0.86 2015-16 RE 33232913 682902 -993187 -579543 2.05 -2.99 -1.74 2016-17 BE 38322800* 368333 -1453240 -988240 0.96 -3.79 -2.58
* Note: Based on the methodology recommended by 14th Finance Commission in para‐ 14.66 of their report, the estimated GSDP of Odisha comes to Rs.3,83,228 crore for 2016‐17. This represents a nominal growth of 15.32 percent over 2015‐16 (Statement presented along with the Annual Budget 2016-17 under the Odisha FRBM Rules, 2005).
Samikshya-2017 25
Figure 1 : Trends in key deficit indicators relative to GSDP (in %)
5.00 Key Deficit Indicators to GSDP
0.00
Percentage
-5.00
1996-97 1995-96 1997-98 1998-99 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2014-15
2013-14
1999-2000 2016-17 BE 2016-17 RE 2015-16 -10.00
-15.00
-20.00
Financial Year -25.00 Revenue Deficit Fiscal Deficit Primary Deficit Linear (Fiscal Deficit to GSDP to GSDP to GSDP to GSDP)
From 1995-96 to 2004-05, the fiscal, revenue, primary deficit to GSDP were in critical condition. Over the period of study, the fiscal deficit to GSDP and the primary deficit to GSDP were -22.20 and -16.94 respectively in 2002-03, revenue deficit to GSDP was -5.48 in 2001- 02, which are the highest. The liner trend line of fiscal deficit to GSDP shows in Figure 1 improving trend over the period of the study. The fiscal surplus to GSDP was 1.02 in 2007-08, became highest fiscal surplus over the period of the study and fiscal deficit to GSDP in year 2012-13 became zero. Similarly, revenue surplus to GSDP was 3.28 in 2007-08, i.e., became highest revenue surplus over the period of the study and the State became revenue surplus to GSDP since 2005-06 and continues till now. The primary surplus to GSDP was 2.80 in 2005- 06, became highest primary surplus over the period of the study. In view of above, till 2004- 05 Odisha Economy was in bad shape and made a turnaround from 2005-06 and continues to be in excellent condition in regard to key deficit indicators to GSDP.
Figure 2: Key Deficit Indicators
1000000 Key Deficits 500000
0
Amount -500000
2011-12 1995-96 1996-97 1997-98 1998-99 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2012-13 2014-15 2013-14
-1000000 1999-2000 2015-16 RE 2015-16 BE 2016-17 -1500000 Financial Year -2000000 Revenue Deficit(-)/ Surplus(+) Fiscal Deficit(-)/ Surplus(+)
Samikshya-2017 26
Revenue deficit was -Rs. 2,83,375 lakh in 2001-02 and revenue surplus was Rs. 6,82,902 lakh in 2015-16, both are highest in absolute terms over the period. The revenue deficit became revenue surplus from the year 2005-06 and continues till now (Figure 2 above).Fiscal deficit is projected at Rs. 14,53,240 lakh in 2016-17 and fiscal surplus was Rs. 1,32,313 lakh in 2007-08, both are highest in absolute terms over the period. However, if the impact of UDAY scheme for Rs. 1,19,618 lakh is taken out, the deficit comes down to Rs. 13,33,622 lakh which is 3.48 percent of GSDP against the FRBM limit of 3.5 percent (Figure 2 above).Primary deficit was -Rs. 1,04,52,270 lakh in 2003-04 and primary surplus was Rs. 31,19,819 lakh in 2011-12, both are highest in absolute terms over the period. The primary deficit became primary surplus between the year 2005-06 and 2012-13 except 2009-10 (Figure 2 above).
Figure 3: Gross State Domestic Product of Odisha
Gross State Domestic Product of Odisha 50000000 (Rs. in lakhs) 40000000
30000000
20000000
10000000
0
Amount
1999-…
2004-05 1996-97 1997-98 1998-99 2000-01 2001-02 2002-03 2003-04 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2014-15 2015-16… 2016-17… 1995-96 Financial Year 2013-14
GSDP for the base year 2011-12 is estimated as Rs. 2,25,28,348 lakh. Nominal GSDP or GSDP at current prices for the year 2012-13 is estimated as Rs. 2,55,27,261 lakh while that for the year 2013-14 is estimated as Rs. 2,77,27,069 lakh and for the year 2014-15 is estimated as Rs. 3,09,80,722 lakh exhibiting a growth of 13.31 percent, 8.62 percent and 11.73 percent during 2012-13, 2013-14 and 2014-15 respectively (Figure 3 above). Revenue Deficit/ Surplus
Revenue deficit for the year 2001‐02 was (‐) Rs. 2,83,375 lakh, which was reduced to (‐) Rs. 52,230 lakh in 2004‐05. In the year 2005‐06, after a gap of 22 years, the State was able to achieve revenue surplus of Rs. 48,119 lakh. From the year 2005-06, Odisha is able to attain revenue surplus and continued to be a revenue surplus till 2015-16. Thus after achieving revenue balance, a surplus of Rs. 3,68,333 lakh is projected in the budget estimates for 2016‐ 17. In 2014-15, revenue surplus was Rs. 5,86,214 lakh, which is 1.89 percent of GSDP. In 2015-16, the revised estimate is Rs. 6,82,902 lakh and in 2016-17, the budget estimate is Rs. 3,68,333 lakh, which are 2.05 and 0.96 percent of GSDP respectively (Table 1). Thus the
Samikshya-2017 27 revenue surplus of 0.96 percent of GSDP is adhered to the FRBM limit of zero revenue deficits. The trend line of revenue deficit/ surplus of the State is reflected in Figure 4 and trends of revenue deficit/ surplus relative to GSDP is given in Figure 5 below.
Figure 4: Trends of Revenue Deficit/ Surplus
1000000 Revenue Deficit(-)/ Surplus(+) (Rs. in Lakh) 500000
0
-500000
Revenue Deficit(-)/ Surplus(+) Linear (Revenue Deficit(-)/ Surplus(+))
Figure 5: Trends of Revenue Deficit/ Surplus relative to GSDP (in %)
Revenue Deficit to GSDP 4.00
2.00
0.00
-2.00
1996-97 1995-96 1997-98 1998-99 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2014-15
-4.00 2013-14
1999-2000 2015-16 RE 2015-16 BE 2016-17 -6.00 Revenue Deficit to GSDP Linear (Revenue Deficit to GSDP)
Fiscal Deficit/Surplus
Fiscal Deficit for the year 2003‐04 was (‐) Rs. 13,31,298 lakh which was 20.14 percent of the GSDP was reduced to (‐) Rs. 1,02,755 lakh in 2006-07 which was 1.01 percent of the GSDP. For the first time the State has been able to generate fiscal surplus to the tune of Rs. 1,32,313 lakh in 2007‐08 which was 1.02 percent of the GSDP. During 2008‐09, 2009‐10 and 2010‐11, the fiscal deficit was contained at Rs. 33,404 lakh, Rs. 2,26,537 lakh and Rs. 65,775 lakh and again a marginal fiscal surplus of Rs. 62,176 lakh and Rs. 362 lakh were generated in 2011‐12 and 2012‐13. The fiscal deficit during 2013‐14 was Rs. 4,63,364 lakh and Rs. 5,47,862 lakh during 2014‐15. From 2005-06 to 2015-16 RE, the fiscal deficit was well within 3 percent
Samikshya-2017 28 of GSDP as per the State’s FRBM (Amendment) Act, 2012 target of fiscal deficit. The estimated fiscal deficit of Rs. 14,53,240 lakh for 2016‐17 would be 3.79 percent of GSDP. If the impact of Ujwal DISCOM Assurance Yojna (UDAY) scheme for Rs. 1,19,618 lakh is taken out, the fiscal deficit will come down to 3.48 percent of GSDP which is within the proposed enhanced borrowing ceiling of 3.5 percent of GSDP (earlier it was 3 percent of GSDP). Because, UDAY scheme envisages that the taking over of the debt of State DISCOMs shall not count towards the fiscal deficit of the State during those years. The State Government intends additional fiscal space of 0.5 percent available to the State for higher capital investment and enhanced borrowing limit to participate in UDAY scheme. The trend of fiscal deficit/ surplus of the State is reflected in Figure 6 and trends of fiscal deficit/ surplus relative to GSDP is given in Figure 7 below.
Figure 6: Trends of Fiscal Deficit/ Surplus
Fiscal Deficit(-)/ Surplus(+) 400000 (Rs. in Lakh)
200000
0
-200000
1995-96 1996-97 1997-98 1998-99 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2014-15
-400000 2013-14
1999-2000 2015-16 RE 2015-16 BE 2016-17 -600000
Amount -800000
-1000000
-1200000
-1400000
-1600000 Financial Year
Figure 7: Trends of Fiscal Deficit/ Surplus relative to GSDP (in %)
Fiscal Deficit to GSDP 10.00
0.00 -10.00
-20.00 -30.00
Fiscal Deficit to GSDP Linear (Fiscal Deficit to GSDP)
Samikshya-2017 29
Primary Deficit/ Surplus
Primary deficit represents the fiscal deficit less interest payment. It represents the net borrowing to meet the expenditure excluding the interest payment. The State continued to be in primary deficit from 1995-96 to 2004-05 and made a turnaround in 2005-06 and in primary surplus from 2005-06, which was continued up to 2012-13 except 2009-10. Its share as a percentage of GSDP varies from -2.23 to +2.80 within the period of 10 years from 2004-05 and 2014-15. Primary deficit for the year 2003-04 was (‐) Rs. 10,45,270 lakh, which was reduced to (‐) Rs. 1,73,710 lakh in 2004‐05. In the year 2005‐06, after a gap of 22 years, the State was able to achieve primary surplus of Rs. 2,38,304 lakh. From the year 2005-06, Odisha is able to attain primary surplus and continued to be in primary surplus till 2012-13 except 2009-10. The primary deficit during 2013‐14 was Rs. 1,74,545 lakh and Rs. 2,66,835 lakh during 2014‐15. In 2015-16, the revised estimate is Rs. 5,79,543 lakh and in 2016-17, the budget estimate is Rs. 9,88,240 lakh, which are 1.74 and 2.58 percent of GSDP respectively. The trend of primary deficit/ surplus of the State is reflected in Figure 8 and trends of primary deficit/ surplus relative to GSDP is given in Figure 9 below.
Figure 8: Trends of Primary Deficit/ Surplus
500000 Primary Deficit(-)/ Surplus(+) (Rs. in Lakh) 0
Amount
-500000
1995-96 1996-97 1997-98 1998-99 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2014-15
2013-14
1999-2000
2015-16 RE 2015-16 2016-17 BE 2016-17 -1000000
-1500000 Financial Year Figure 9: Trends of Primary Deficit/ Surplus relative to GSDP (in %)
5.00
0.00
-5.00
2003-04 1995-96 1996-97 1997-98 1998-99 2000-01 2001-02 2002-03 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2014-15
2013-14
1999-2000 2015-16RE -10.00 2016-17BE
-15.00
-20.00 Primary Deficit to GSDP Linear (Primary Deficit to GSDP)
Samikshya-2017 30
Expenditure Management
Government of Odisha has managed the state finances through the policies of revenue generation and expenditure contraction. The expenditure policy involves rationalization of expenditure to contain the rise in salary expenditure, interest payment and pension payment, increase plan expenditure, enhancing capital expenditure, reduction of debt servicing liabilities through debt swap and buy‐back of high cost loan, reduction of net borrowing, shifting the focus to low cost and concessional borrowing from external agencies through Government of India, improving quality of public expenditure in order to ensure better output and outcome. The extent of effective expenditure management can be deduced from the Table 2 below.
Table 2: Revenue Receipts, Own Revenue, Revenue Expenditure, Interest Payments and Revenue Deficit(-)/ Surplus(+) from 1995-96 to 2016-17 BE. (Rs. In Lakh) Revenue Own Revenue Interest Revenue Deficit Financial Receipts Revenue Expenditure Payments (-)/ Surplus(+) Year (Rs.) (Rs.) (Rs.) (Rs.) (Rs.) 1995-96 389071 175542 469782 92926 -80710 1996-97 428676 182382 511725 107937 -83049 1997-98 463203 196267 553517 129174 -90314 1998-99 455440 204462 681690 148484 -226250 1999-2000 588464 242056 845883 123770 -257419 2000-01 690202 286950 883399 228681 -193196 2001-02 704799 315864 988173 283496 -283375 2002-03 843877 383301 1001468 288558 -157591 2003-04 944024 439627 1086116 286028 -142092 2004-05 1185019 552212 1237249 333202 -52230 2005-06 1408471 653417 1360352 369710 48119 2006-07 1803262 865318 1577202 318843 226060 2007-08 2196719 950967 1772327 316948 424392 2008-09 2461001 1117135 2119012 288981 341989 2009-10 2643021 1219454 2529158 304381 113863 2010-11 3327616 1597303 2936794 306146 390822 2011-12 4026702 1988573 3466024 257643 560678 2012-13 4393691 2311216 3823756 280723 569935 2013-14 4894685 2527019 4561775 288822 332910 2014-15 5699788 2789916 5113574 281027 586214 2015-16 RE 7156918 3070000 6474016 413644 682902 2016-17 BE 7812671 3302293 7444338 465000 368333
Own Revenue to Revenue Expenditure Ratio
Own revenue to revenue expenditure (ORRE) ratio measures the amount of revenue expenditure covered by own revenue, that is State’s own tax and own non-tax. Revenue expenditure is establishment related and maintenance/ housekeeping related expenditure. It includes salary, pension, interest payment, subsidy, old age pension, electricity, water charges, motor vehicle, contingent expenditure and maintenance of capital assets like roads, buildings,
Samikshya-2017 31 irrigation works etc. The committed expenditure consists of salary, pension and interest payment. These committed expenditures have first charge on the resources of the government.
Table 3: Revenue Expenditure, Own Revenue and Own Revenue to Revenue Expenditure from 1995-96 to 2016-17 BE. (Rs. In Lakh) Financial Revenue Expenditure Own Own Revenue to Revenue Year (Rs.) Revenue(Rs.) Expenditure(%) 1995-96 469782 175542 37.37 1996-97 511725 182382 35.64 1997-98 553517 196267 35.46 1998-99 681690 204462 29.99 1999-2000 845883 242056 28.62 2000-01 883399 286950 32.48 2001-02 988173 315864 31.96 2002-03 1001468 383301 38.27 2003-04 1086116 439627 40.48 2004-05 1237249 552212 44.63 2005-06 1360352 653417 48.03 2006-07 1577202 865318 54.86 2007-08 1772327 950967 53.66 2008-09 2119012 1117135 52.72 2009-10 2529158 1219454 48.22 2010-11 2936794 1597303 54.39 2011-12 3466024 1988573 57.37 2012-13 3823756 2311216 60.44 2013-14 4561775 2527019 55.40 2014-15 5113574 2789916 54.56 2015-16 RE 6474016 3070000 47.42 2016-17 BE 7444338 3302293 44.36
Figure 10: Own Revenue and Revenue Expenditure
8000000
7000000
6000000
5000000 4000000 3000000 2000000 1000000 0 -1000000
-2000000
1998-99 2011-12 1996-97 1997-98 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2012-13 2013-14 2014-15 1995-96
Revenue 1999-2000 Expenditure Own Revenue 2015-16RE 2016-17BE
Samikshya-2017 32
Figure 11: Own Revenue to Revenue Expenditure Ratio
70 60 50 40 30 20 10 0
Year
1998-99 2005-06 1996-97 1997-98 2000-01 2001-02 2002-03 2003-04 2004-05 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 1995-96
Own Revenue to Revenue Expenditure
1999-2000 2016-17BE 2015-16RE Linear (Own Revenue to Revenue Expenditure)
Own revenue to revenue expenditure (ORRE) ratio varies between 28 and 60 percent over the period of study (Table 3 and Figure 11). It is a good trend that own revenue is increasing to cover the revenue expenditure. In this scenario it is presumed that economic activities of the State are increasing thereby State is generating more tax and non-tax revenue. From 2005-06 onwards, the industrial and economic activities picked up so that half of the revenue expenditure covered by State’s own tax and non-tax revenue. It is noteworthy that in 2012-13, own revenue covers 60.44 percent of revenue expenditure (Figure 10).
Interest Payments to Revenue Expenditure Ratio
Interest payment is a committed expenditure and part of the revenue expenditure. Interest payment to revenue expenditure (IPRE) ratio represents how much interest is the part of revenue expenditure. This specifies the amount of debt service on account of outstanding debt to be part of committed expenditure.
Table 4: Revenue Expenditure, Interest Payments and Interest Payment to Revenue Expenditure from 1995-96 to 2016-17 BE. (Rs. In Lakh) Financial Revenue Expenditure Interest Payments Interest Payment to Revenue Year (Rs.) (Rs.) Expenditure(%) 1995-96 469782 92926 19.78 1996-97 511725 107937 21.09 1997-98 553517 129174 23.34 1998-99 681690 148484 21.78 1999-2000 845883 123770 14.63 2000-01 883399 228681 25.89 2001-02 988173 283496 28.69 2002-03 1001468 288558 28.81 2003-04 1086116 286028 26.33 2004-05 1237249 333202 26.93 2005-06 1360352 369710 27.18 2006-07 1577202 318843 20.22 Samikshya-2017 33
Financial Revenue Expenditure Interest Payments Interest Payment to Revenue Year (Rs.) (Rs.) Expenditure(%) 2007-08 1772327 316948 17.88 2008-09 2119012 288981 13.64 2009-10 2529158 304381 12.03 2010-11 2936794 306146 10.42 2011-12 3466024 257643 7.43 2012-13 3823756 280723 7.34 2013-14 4561775 288822 6.33 2014-15 5113574 281027 5.50 2015-16 RE 6474016 413644 6.39 2016-17 BE 7444338 465000 6.25
Figure 12: Interest Payments and Revenue Expenditure
8000000 7000000
6000000 5000000 4000000 3000000 2000000 1000000
0
Year
2010-11 1996-97 1997-98 1998-99 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2011-12 2012-13 2013-14 2014-15
1995-96
1999-2000 2016-17BE Revenue Expenditure Interest Payments Linear (Interest Payments)2015-16RE
Figure 13: Interest Payments to Revenue Expenditure Ratio
35 30 25 20
15 10 5 0
Year
2007-08 1996-97 1997-98 1998-99 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15
1995-96
1999-2000 2015-16RE 2016-17BE Interest Payment to Revenue Expenditure Linear (Interest Payment to Revenue Expenditure)
Samikshya-2017 34
Interest payment to revenue expenditure (IPRE) ratio varies between 5.50 and 28.81 percent over the period of study and constantly below 20 percent between 2007-08 and 2010- 11 and below 10 percent since 2011-12 (Table 4, Figure 12 and Figure 13). It is a good trend that the committed expenditure, i.e., interest payment relative to revenue expenditure is decreasing because of the State Government policy to reduce the burden of interest payment through pre-payment of high cost market borrowing and also through debt swap. According to the policy of pre-payment of high cost market loan and debt swap, during the years from 2002‐ 03 to 2012-13, high cost debt of Rs. 3869.67 crore have been swapped, which resulted in interest relief of Rs. 199.71 crore.
Interest Payments to Revenue Receipts Ratio
Interest payment to revenue receipt (IPRR) ratio represents to the extent revenue receipts finance the interest payment on account of outstanding debt. The IPRR ratio of Odisha may be seen from the Table 5 below.
Table 5: Revenue Receipts, Interest Payments and Interest Payment to Revenue Receipts from 1995-96 to 2016-17 BE. (Rs. In Lakh) Financial Revenue Receipts Interest Payments Interest Payment to Revenue Year (Rs.) (Rs.) Receipts (%) 1995-96 389071 92926 23.88 1996-97 428676 107937 25.18 1997-98 463203 129174 27.89 1998-99 455440 148484 32.60 1999-2000 588464 123770 21.03 2000-01 690202 228681 33.13 2001-02 704799 283496 40.22 2002-03 843877 288558 34.19 2003-04 944024 286028 30.30 2004-05 1185019 333202 28.12 2005-06 1408471 369710 26.25 2006-07 1803262 318843 17.68 2007-08 2196719 316948 14.43 2008-09 2461001 288981 11.74 2009-10 2643021 304381 11.52 2010-11 3327616 306146 9.20 2011-12 4026702 257643 6.40 2012-13 4393691 280723 6.39 2013-14 4894685 288822 5.90 2014-15 5699788 281027 4.93 2015-16 RE 7156918 413644 5.78 2016-17 BE 7812671 465000 5.95
Samikshya-2017 35
Figure 14: Interest Payments and Revenue Receipts
9000000 8000000 7000000 6000000 5000000 4000000
3000000
2000000 1000000 0
Year
1996-97 1997-98 1998-99 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15
1995-96
1999-2000 2015-16RE Revenue Receipts Interest Payments Linear (Interest Payments) 2016-17BE
Figure 15: Interest Payments to Revenue Receipts Ratio
45 40 35 30 25 20 15 10 5 0
Year
1995-96 1996-97 1997-98 1998-99 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15
1999-2000 2015-16RE Interest Payment to Revenue Receipts Linear (Interest Payment to Revenue Receipts) 2016-17BE
The 12th Finance Commission recommended that the IPRR ratio should be brought down to 15 percent to achieve debt sustainability. Odisha had been able to achieve IPRR ratio at 14.43 percent during the year 2007‐08 and the ratio has been constantly below 15 percent level since then and below 10 percent since 2010-11. It is evident from the Table 5, Figure 14 and Figure 15 above that IPRR brought down to 4.93 percent in 2014‐15 and estimated to be 5.95 percent during 2016‐17, which is much below FRBM limit of 15 percent. Thus, State Government have been able to achieve debt sustainability. This has been achieved due to prudent debt management policy of the State Government as to reduce the burden of interest payment through pre-payment of high cost market borrowings and through debt swap. Samikshya-2017 36
Accordingly, during the years from 2002‐03 to 2004‐05, high cost debt of Rs. 2,543.62 crore have been swapped, which resulted in interest relief of Rs. 144.47 crore. During 2006‐07, high cost market borrowing amounting to Rs. 394.61 crore has been prepaid, which resulted in interest relief of Rs. 27.74 crore. During the year 2007‐08, the State Government have made prepayment of high cost loan of Rs. 356.16 crore (NSSF loan of Rs. 199.72 crore and market loan of Rs. 156.44 crore), which resulted in interest relief of Rs. 10.27 crore. During the year 2012‐13, prepayment of high cost loan of Rs. 575.28 (HUDCO loan of Rs. 251.04 crore and REC loan of Rs. 324.24 crore) have been made, which resulted in interest relief of Rs. 17.23 crore.
Revenue Deficit/ Surplus to Revenue Receipts Ratio
Revenue deficit/surplus to revenue receipt (RDRR) ratio represents that the percentage of revenue receipt not able to cover the revenue expenditure over revenue receipts. The RDRR ratio of Odisha may be seen from the Table 6 below.
Table 6: Revenue Receipts, Revenue Deficit (-)/ Surplus(+) and Revenue Deficit to Revenue Receipts from 1995-96 to 2016-17 BE. (Rs. In Lakh) FinancialYear Revenue Revenue Deficit (-)/ Revenue Deficit to Receipts Surplus(+) Revenue Receipts (Rs.) (Rs.) (%) 1995-96 389071 -80710 -20.74 1996-97 428676 -83049 -19.37 1997-98 463203 -90314 -19.50 1998-99 455440 -226250 -49.68 1999-2000 588464 -257419 -43.74 2000-01 690202 -193196 -27.99 2001-02 704799 -283375 -40.21 2002-03 843877 -157591 -18.67 2003-04 944024 -142092 -15.05 2004-05 1185019 -52230 -4.41 2005-06 1408471 48119 3.42 2006-07 1803262 226060 12.54 2007-08 2196719 424392 19.32 2008-09 2461001 341989 13.90 2009-10 2643021 113863 4.31 2010-11 3327616 390822 11.74 2011-12 4026702 560678 13.92 2012-13 4393691 569935 12.97 2013-14 4894685 332910 6.80 2014-15 5699788 586214 10.28 2015-16 RE 7156918 682902 9.54 2016-17 BE 7812671 368333 4.71
Samikshya-2017 37
Revenue deficit/ surplus to revenue receipt (RDRR) ratio varies between -49.68 and 19.32 percent over the period of study (Table 3.6 above, Figure 16 and Figure 17 below). During 1998-99 to 2001-02, the RDRR was very high due to heavy expenditure on salary and pensions on account of implementation of 5th Central Pay Commission recommendation in respect of State Government employees as well as rehabilitation and reconstruction activities due to Super Cyclone of 1999. However, due to concerted effort of State Government, RDRR is constantly become positive from 2005-06 onwards and 19.32 percent in 2007-08, which is the highest over the period of study. It is a good trend that the State is able to finance its committed expenditure and leaving surplus balance in this account for giving more space for developmental needs/ projects. This happens because government is able to collect more tax and non-tax revenue due to increased industrial, service and allied sector activities in the State.
Figure 16: Revenue Deficit/ Surplus and Revenue Receipts
10000000 Revenue Receipts Revenue Deficit (-)/ Surplus(+) 8000000
6000000
4000000 2000000 0
-2000000 Year
1995-96 2009-10 1997-98 1998-99 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2010-11 2011-12 2012-13 2013-14 2014-15
1996-97
1999-2000 2016-17BE 2015-16RE
Figure 17: Revenue Deficit/ Surplus to Revenue Receipts Ratio
40
20
0
-20 Year
2003-04 1996-97 1997-98 1998-99 2000-01 2001-02 2002-03 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15
1995-96
1999-2000 2016-17BE -40 2015-16RE
-60 Revenue Deficit to Revenue Receipts Linear (Revenue Deficit to Revenue Receipts)
Samikshya-2017 38
Debt Management
The temporary mismatch in revenues and expenditure can be bridged by loans. However, if the mismatch persists over a long period of time and grows in volume and the revenue receipts are to be inadequate to cover the interest liabilities, it leads to growing revenue and fiscal deficits. This, in turn, results in unsustainable debt. The debt sustainability level can be derived from debt-GSDP ratio and interest payment as percentage of GSDP ratio.
Debt-GSDP Ratio
The debt-GSDP ratio represents the outstanding debt as percentage of GSDP. The ratio measures the financial leverage of an economy. A low debt-GSDP ratio indicates an economy that produces and sells goods and services sufficient to pay back debts without incurring further debt.
Table 7: Debt, GSDP and Debt-GSDP Ratio from 1995-96 to 2016-17 BE. (Rs. In Lakh) Year Debt GSDP Debt-GSDP (Rs.) (Rs.) (%) 1995-96 921991 3200294 28.81 1996-97 1049236 3162811 33.17 1997-98 1238609 3830064 32.34 1998-99 1475115 4255150 34.67 1999-2000 1810078 4789168 37.80 2000-01 2100188 4841484 43.38 2001-02 2403360 5170371 46.48 2002-03 2780119 5480111 50.73 2003-04 3163396 6610014 47.86 2004-05 3405118 7772943 43.81 2005-06 3645645 8509649 42.84 2006-07 3724951 10183947 36.58 2007-08 3631161 12927445 28.09 2008-09 3643054 14849071 24.53 2009-10 3773004 16294643 23.15 2010-11 3913691 19753000 19.81 2011-12 3858937 22528348 17.13 2012-13 3798014 25527261 14.88 2013-14 3866624 27727069 13.95 2014-15 4327338 30980722 13.97 2015-16 RE 5233084 33232913 15.75 2016-17 BE 6499183 38322800 16.96
From the above Table 7, it is seen that in 2002-03, debt -GSDP ratio touched to the high of 50.73 percent and over the periods it has been declined steadily and touch to as low as 13.97 percent in 2014-15. This reflects, sound fiscal management, growth of GSDP due to increased economic activities and impact of debt relief mechanism that incentivized States’ adherence to a rule-based fiscal regime. The IMF and World Bank said 40 percent debt-GSDP ratio is the
Samikshya-2017 39 ideal limit for developing States and crossing the limit will have adverse impact on fiscal sustainability and suggested debt-GSDP ratio be brought down to 40 percent by 2030. In this respect Odisha is the leading State to reach the level much ahead of the dateline.
State debt is classified by the Comptroller and Auditor General of India under the three broad categories of (i) internal debt which, inter alia, includes ways and means advances and overdrafts from the Reserve Bank of India (ii) loans and advances from the Central Government and (iii) small savings, provident funds and obligations like reserve funds and deposits, both interest and non-interest bearing. In the budgets for 2012-13 and 2013-14 it is, proposed to carry forward the fiscal consolidation, in line with the recommendation of the 13th Finance Commission. Accordingly, the debt-GSDP ratio continued to decline from 2002-03 onwards. The liabilities of the State consists mainly of internal borrowings loans and advances from the Government of India and receipts from the public accounts and reserve funds. After 2006-07, Government have accumulated cash balances and liquidated the past liabilities and also made significant improvement in fiscal balances. The State has successfully overcome the debt-trap of the past years and improved debt sustainability through fiscal prudence. The per-capita liability has been reduced to Rs. 10,852 as compared to Rs. 20,182 for all States average during 2014-15. The composition of States’ outstanding liabilities reveals decrease in dependence on market borrowings to finance the fiscal deficit, since the year 2005-06. Market borrowing which was 27 percent of total liability in 2005-06 was reduced to 10.55 percent during 2014- 15. The States’ dependence on loans from the centre has come down to around 42 percent of the total liability. The share of State provident funds had risen from the year 2005-06 (36 percent) to 2014-15 (37.96 percent). The following two Figure 18 and Figure 19 reflect the clear improvement in debt-GSDP ratio over the period of study.
Figure 18: Debt and GSDP from 1995-96 to 2016-17 BE
50000000
40000000
30000000 20000000
10000000
0
-10000000
1995-96 1996-97 1997-98 1998-99 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2014-15
2013-14
1999-2000
2015-16RE 2016-17BE Debt
Samikshya-2017 40
Figure 19: Debt-GSDP Ratio
60.00 50.00 40.00 30.00 20.00 10.00
0.00
Debt - GSDP Ratio Linear (Debt - GSDP Ratio)
Interest Payment as percentage of GSDP Ratio
Interest payments as percentage of GSDP (IPPG) ratio reflects the amount of interest needs to be paid as percentage of GSDP, which depends on the amount of outstanding debt and interest rate. IPPG is a guiding factor in management of debt and gives an idea about affordability of debt. Higher interest rates will increase the cost of future borrowing.
Table 8: Interest Payments, GSDP and Interest Payment as Percentage of GSDP from 1995-96 to 2016-17 BE. (Rs. In Lakh) Year Interest Payments GSDP Interest Payment as (Rs.) (Rs.) Percentage of GSDP (%) 1995-96 92926 3200294 2.90 1996-97 107937 3162811 3.41 1997-98 129174 3830064 3.37 1998-99 148484 4255150 3.49 1999-2000 123770 4789168 2.58 2000-01 228681 4841484 4.72 2001-02 283496 5170371 5.48 2002-03 288558 5480111 5.27 2003-04 286028 6610014 4.33 2004-05 333202 7772943 4.29 2005-06 369710 8509649 4.34 2006-07 318843 10183947 3.13 2007-08 316948 12927445 2.45 2008-09 288981 14849071 1.95 2009-10 304381 16294643 1.87 2010-11 306146 19753000 1.55 2011-12 257643 22528348 1.14 2012-13 280723 25527261 1.10 2013-14 288822 27727069 1.04 2014-15 281027 30980722 0.91 2015-16 RE 413644 33232913 1.24 2016-17 BE 465000 38322800 1.21
Samikshya-2017 41
Interest payment as percentage of GSDP (IPPG) ratio varies between 0.91 and 5.48 percent over the period of study and well above 2 percent of GSDP till 2007-08, which is alarming (Table 8). This happened mainly due to high cost borrowings. From 2008-09, the IPPG has been declining because of the State Government policy to reduce the burden of interest payment through pre-payment of high cost market borrowing and also through debt swap. However, in 2015-16 RE and 2016-17 BE, the IPPG projected to be increasing minimally due to open market borrowings of the Government in 2014-15 (open market loan: Rs. 3,000 crore + ways and means advance from RBI: Rs. 1082.05 crore), 2015-16 RE (open market loan: Rs. 5709.62 crore) and 2016-17 BE (open market loan: Rs. 7979.03 crore).
Figure 21: Interest Payment as percentage of GSDP Ratio
6.00 4.00 2.00 0.00
1999-…
1996-97 2001-02 2006-07 2011-12 2016-17… 1995-96 1997-98 1998-99 2000-01 2002-03 2003-04 2004-05 2005-06 2007-08 2008-09 2009-10 2010-11 2012-13 2014-15 2015-16… 2013-14 Interest Payment as Percentage of GSDP Linear (Interest Payment as Percentage of GSDP) Linear (Interest Payment as Percentage of GSDP)
The liner trend of interest payments as percentage of GSDP is declining over the period of study as evident from the Figure 21 above.
Conclusion and Policy Prescription
Due to sound fiscal management in post-FRBM period from 2004-05, the overall fiscal scenario of the State has improved considerably and consistently. The improvement in fiscal parameters is linked to growth in GSDP. Due to revenue surplus through the policies of revenue generation and expenditure contraction over the period State Government has been able to finance higher capital outlays in development sector. The capital expenditure is a major indicator of growth and priority has been given by State Government for increasing this expenditure. The growth in GSDP exceeds the growth in debt and the Debt-GSDP ratio is declining over the period. Higher growth rate in GSDP compared to growth in debt and low cost of borrowings have made the fiscal policy of the State sustainable. FRBM legislation as a fiscal policy tool has helped to achieve stability in State finance and efficiency in expenditure allocation. Revenue surplus with low fiscal deficit and low debt-GSDP ratio has created more reasonable space for additional borrowings. Now that the State’s finances are considerably stable, the tasks of further improving the quality expenditure, regular expenditure review, Samikshya-2017 42 expanding the coverage of public services and of investing in social and physical infrastructure are very critical to achieve higher inclusive growth rate on sustainable basis. In order to fulfill this objective the State should go for higher capital receipts to fund capital outlay in developmental sectors as there is capacity to sustain additional debt burden and the State economy has reached a stage wherein it can absorb higher capital outlay after the fiscal stabilization.
Fiscal Space and Priorities
Considering, the positive impact of debt on the growth of GSDP, the State Government resorted to fresh borrowings and made higher allocation in developmental sector for creation of capital assets as well as maintenance of the existing assets and increased the productivity of the state economy. In 2014-15 the State was able to achieve revenue surplus at 1.89 percent relative to GSDP and reduced the debt burden substantially. The outstanding debt burden was only 13.97 percent of the GSDP, well below the 30.2 percent level recommended by 13th Finance Commission. The compression of expenditure leads to revenue surplus and reduced fiscal deficit. Due to higher revenue generation, the dependence on market borrowing has been reduced and high cost loans have been discharged to bring down the debt liability. The accumulated large cash balance invested with Reserve Bank of India, which generates revenue for the State. The revenue surplus has used for capital formation, expenditure in priority sectors and infrastructure creation. The improvement in fiscal position of the State Government and emergence of comfortable resource position leads to larger allocations and better utilization of funds in priority sectors.
In this paper it is established that the policies of revenue generation and expenditure contraction has succeeded because of improvement of all fiscal parameters from 2005-06 onwards, i.e., post-FRBM period.
Policy Prescription and Way Forward
The State Government should continue with the higher capital outlay because of revenue surplus, since the capital outlay on the growth of GSDP has have multiplier effect. The improvement in capital outlay and capital productivity will induce inclusive growth. The State will earn more revenues due to rise in industrial activities and leads to be in higher growth trajectory. Further, the FRBM Act, 2005 has completed more than ten years in operation. A committee should be formed to review the effectiveness of the Act in achieving its objectives. Further, the committee should look into the prudence of prescribing a fixed fiscal deficit target (percentage of GSDP) or fiscal deficit range.
Samikshya-2017 43
References
1. Various issues of Odisha Budget at a Glance prepared by Finance Department, Government of Odisha. 2. Various issues of Odisha Economic Survey prepared by Planning and Convergence Department, Government of Odisha. 3. Various issues of State Finances: A Study of Budgets by Reserve Bank of India. 4. Various issues of Report on State Finances by the Comptroller and Auditor General of India. 5. White Paper on Odisha State Finances (March 2001), Finance Department, Government of Odisha. 6. Odisha Vision Document @ 2022 by Confederation of Indian Industry (CII), Odisha State Council, Bhubaneswar. 7. Causal Link Between Government Spending and Revenue: An Empirical Analysis on Odisha State Finance by Dr. Asit Mohanty and Vasuki.N. Mannem in Odisha Review January, 2014. 8. Effectiveness in Management of State Finance: An Empirical Analysis for Odisha by Dr. Asit Mohanty in Odisha Review February – March, 2014. 9. Evolving Paradigms in Odisha State Finance: An Empirical Analysis by Dr. Asit Mohanty in Odisha Review June, 2014. 10. Fiscal Development in Odisha: Problems and Prospects by Sri S.S. Rath, Department of Economics, Sambalpur University, Odisha. 11. State Fiscal Reform of Odisha: Programmes and Prospects by Dr. Kshiti Bhusan Das, Reader P.G. Department of Commerce, Utkal University, Bhubaneswar in Odisha Review April, 2008. 12. Review of Compliance to Odisha FRBM Act – 2012-13 by Dr. Pratap Ranjan Jena and Dr. Tapas Kumar Sen, National Institute of Public Finance and Policy, New Delhi. 13. Review of Compliance to Odisha FRBM Act – 2013-14 by National Institute of Public Finance and Policy, New Delhi, March, 2015. 14. Odisha has transformed itself from a seriously lagging State to a State in transition, Nandita Roy, The World Bank, 20 May 2008. 15. Odisha in Transition: From Fiscal Turnaround to Rapid and Inclusive Growth, The World Bank, 2007
Statistics conceals what is important and reveals what is essential.
Samikshya-2017 44
Education, Information and Administration Sri Pradeep Kumar Sarangi Sri Sarat Chandra Sahoo Abstract
Education is the prime social capital that contribute significantly social and economic development raise social inclusion, improve human development. The paper examines the key variables of dropouts, enrollment, and teacher pupil ratio to highlight the progress of education at elementary level in Odisha. The advocacy on five dimensions of holistic education are placed for better interface
Key words: Elementary education, dropouts, enrollment, teacher pupil ratio
Introduction
Appropriate integration of available human and material resources for achieving the effective and purposeful delivery .Administration does not confined to a mono act ,rather an umbrella encompassing planning, organistion, direction, coordination, control and evaluation of performances. Thus, educational administration being a non-profit business its element needs integration of both physical and human resources. The physical resource s constitute building equipment’s institutional materials and the human resources include pupils, teachers, and supervisors, administrators and parents alike. Besides this another essential element of education inter-alia includes various aspects, such as educational theory and practice, including philosophy and objective of education, curriculum, technique, discipline, role of teachers, rules and regulations etc. The jurisdiction of educational administration encompasses all the levels of education starting from the primary level to tertiary education. The system of educational administration therefore covers all forms of general education like formal and non-formal education, adult education, vocational education ,special education, teacher’s education , integrated technical and professional education including engineering ,medical and management education. As such, the planning is to be prepared carefully keeping in view the activities to be undertaken vis-à-vis the objectives.
Objectives
i. To study the status of elementary education in Odisha for educational administration. ii. To analyse the Teacher-Pupil Ratio during last 15 years. iii. To analyse the social group-wise and sex-wise dropout rate of students at primary and upper primary level.
Samikshya-2017 45
Data Source and Methodology
The secondary data of elementary education for the period from 2000-01 to 2015-16 collected by OPEPA was used for analysis. Time series analysis was made.
Results and Discussion
The present paper focused on elementary education which serves as the pillars of educational system in the State. The elementary education includes primary education and upper primary level of education.
Primary Education
The status of primary education in Odisha during the period from 2000-01 to 2015-16 has been presented in Table-1.The scenario of primary education in the State prevailed during the period of independence(1947-48) and in the beginning of the planning process in the country(1950-51) has also been added in the table to visualise start up in primary education. It may be summarized as follows;
Table 1 : Status of Primary Education in Odisha (1947-48 to 2015-16) Year Number of No. of Primary Teacher Enrollment Gross Net primary Teachers school / Pupil (‘000) Enrollment Enrollment schools 100 sq. Ratio Ratio (%) Ratio (%) km. 1947-48 6814 16520 4.40 1:16 255 NA NA 1950-51 9801 16525 6.30 1:20 315 NA NA 2000-01 42104 114791 27.00 1:41 4710 NA NA 2001-02 42824 116231 27.50 1:41 4769 98.27 87.25 2002-03 42824 83652 27.50 1:56 4608 101.78 90.98 2003-04 44416 97175 28.50 1:54 5214 103.48 91.51 2004-05 45700 99079 29.30 1:53 5215 104.26 93.13 2005-06 45890 115351 29.50 1:40 4602 83.59 78.58 2006-07 46722 114105 30.01 1:39 4485 93.49 92.02 2007-08 48402 123765 31.10 1:37 4513 96.66 84.23 2008-09 50062 125434 32.10 1:37 4587 97.48 92.72 2009-10 52972 137833 34.00 1:33 4493 98.04 92.88 2010-11 54144 136407 34.80 1.32 4489 99.60 91.83 2011-12 55106 133262 35.40 1:30 4433 99.69 93.27 2012-13 37056 134578 35.00 1:28 4341 99.96 93.61 2013-14 36399 121193 23.00 1:35 4278 99.20 93.85 2014-15 36550 122214 23.50 1:26 4224 92.74 91.01 2015-16 36760 133541 24.00 1:25 4111 91.62 90.23
Source :OPEPA
Samikshya-2017 46
Figure 1 : Number of Primary Schools in Odisha
60000
50000 40000 30000 20000 10000
0
Accessibility to education that counts most.
The progress in primary education during 1947-48 started with just more than four schools within 100 sq km and two teachers per school
During 1950-51, the number of primary schools increased by 43% with addition of two more schools within 100 sq km.
Figure2 : Number of Primary Schools per 100 sq. kms in Odisha
40 35 30 25 20 15 10 5 0
Nearly six folds increase in number of schools with almost seven folds increase in number of teachers and four times additional schools per 100 sq.kms. is noticed over the period of 60 years.
The Pupil Teacher Ratio and Enrollment improved over the decades amidst some temporary setback in last decade.
Inconsistent growth in Gross Enrolment Ratio(GER) and Net Enrolment Ratio(NER) browbeats the desired progress.
Qualitative improvement in the place of quantitative progress need be the right choice for betterment of primary education.
Samikshya-2017 47
Figure3 : Gross and Net Enrollment Ratio in Primary level (2001-02 to 2015-16)
120 100 80 60 40 20 0
Gross Enrolment Ratio Net Enrolment Ratio
Drop out in Primary Education
Figures transpired in Table-2 may be on dropout rates in the primary schools during the period from 2000-01 to 2015-16 signifies the progress of achievements so far made in this direction.
Table 2 :Dropout Rates in Primary Schools in Odisha by social groups
Year All Categories Scheduled Castes Scheduled Tribes Boys Girls Total Boys Girls Total Boys Girls Total 2000-01 42.30 41.40 41.80 50.50 54.30 52.40 61.70 66.50 64.10 2001-02 42.00 40.00 41.00 50.00 52.00 51.00 61.00 65.00 63.00 2002-03 32.30 36.50 34.40 35.80 38.70 37.20 49.30 57.40 53.30 2003-04 31.90 35.40 33.60 34.60 36.60 35.60 48.20 56.60 52.40 2004-05 31.40 32.70 32.00 34.00 35.60 34.80 48.00 56.00 52.00 2005-06* 18.12 18.86 18.49 19.09 19.82 19.46 12.44 24.34 23.32 2006-07 10.34 10.72 10.53 15.91 18.02 16.97 18.70 27.05 22.88 2007-08 7.76 7.83 7.79 11.93 13.16 12.54 14.03 19.75 16.89 2008-09 5.00 4.89 4.95 7.70 8.22 7.96 9.05 12.34 10.69 2009-10 2.57 3.10 2.83 4.06 4.36 4.21 6.27 6.66 6.46 2010-11 2.35 2.86 2.60 3.08 3.89 3.38 4.12 5.35 4.85 2011-12 0.25 0.62 0.43 2.15 2.68 2.41 3.51 2.80 3.10 2012-13 0.73 0.22 0.37 2.36 2.42 2.39 2.76 2.85 2.97 2013-14 2.08 2.05 1.97 2.41 2.38 2.39 2.68 2.77 2.71 2014-15 1.58 1.78 1.63 0.93 1.34 1.13 3.57 3.93 3.75 2015-16 2.87 2.92 2.82 3.66 2.77 3.30 6.88 7.26 7.07
(* 2005-06 year based on Odisha Child Census, 2005) Sources: OPEPA Down ward trend in the dropout rates may not be a solace for educational administration. Persistent need be leveraged to minimize the ratio.
Samikshya-2017 48
Figure4 :Dropout Rates in Primary Schools in Odisha
200.00 150.00
100.00
50.00 0.00
General SC ST
Over all lower dropout rates of boys, but the girls in the scheduled caste categories overtake their boy’s counterparts indicate awareness among the communities. Dropout rates at elementary level fell sharply except in few years between 2010-11 and 2014-15. Developed environmental school condition may distract the dropout rates. Arrangement of Participatory interaction with the parents/guardians may ameliorate the condition. Upper Primary Education
Status of upper primary education over the period from 2000-01 to 2015-16 with a reference to that of 1947-48 and 1950-51 depicted along Table-3 reveals growth story in the upper primary education in the State. The contents may be enumerated in the following texts.
Table 3 :Status of Upper Primary Schools in Odisha Year No. of No. of UP school Teacher- Enrol Gross Net schools teachers /100 sq. pupil ment Enrollment Enrollment km. ratio (000’) Ratio (GER) Ratio (NER) 1947-48 286 1483 0.18 1:26 32 NA NA 1950-51 501 2569 0.32 1:16 40 NA NA 2000-01 12406 40706 7.69 1:26 1057 NA NA 2001-02 11510 38914 7.14 1:27 1055 NA NA 2002-03 11510 41375 7.14 1:23 953 73.96 58.64 2003-04 14233 49786 9.09 1:27 1363 79.37 65.96 2004-05 15893 31393 10.00 1:44 1383 81.29 69.04 2005-06 15737 32985 10.00 1:37 1225 83.3 71.84 2006-07 17322 39832 11.11 1:47 1817 100.31 73.11 2007-08 18224 49413 11.11 1:40 1997 104.28 76.62 2008-09 19057 55832 12.50 1:38 2128 99.06 85.52 2009-10 22209 56758 14.20 1:37 2128 104.11 85.68 2010-11 24377 53994 15.70 1.39 2090 105.45 83.84 2011-12 23239 53264 15.70 1:25 2087 104.63 90.84 2012-13 21289 53791 15.60 1:24 2081 101.83 91.57 2013-14 21945 62570 14.00 1:34 2110 104.44 91.38 2014-15 22497 74647 14.50 1:25 2163 108.99 82.49 2015-16 22795 72472 15.00 1:23 2217 107.07 81.79 Source: OPEPA
Samikshya-2017 49
Figure 5 : Number of Upper Primary Schools in Odisha
30000
25000 20000
15000
10000 5000
0
Prior to 2000-01 and in between 1947-48 to 1950-51, a double score in infrastructural development and managerial strength hardly convert the Teacher Pupil Ratio and Enrolment position.
A marginal and inconsistent improvement in the Teacher Pupil Ratio noticed over last 15 years.
Remarkable achievement in establishment of 15 upper primary schools within a radius of 100 sq.kms. over the period of last 15 years.
Figure 6 : Number of Upper Primary Schools per 100 sq. kms in Odisha
18 16 14 12 10 8 6 4 2 0
Increase in number of schools by 84% and 78% in teaching staff over the period hardly managed to improve constant enrolment ratio.
Samikshya-2017 50
Figure 7 :Gross Enrollment and Net Enrolment Ratio in Upper Primary Schools
120 100 80 60 40 20 0
Gross Enrolment Ratio Net Enrolment Ratio
Strategically devised mechanism need be evolved to arrest the bottlenecks in the upper primary education sector.
Dropout in Upper Primary Education
Dropout rates in Upper primary school level during the last 15 years shown along Table-4 signifies social group-wise and sex-wise picture of students in the following lines.
Table 4 :Dropout Rates in Upper Primary Schools in Odisha by social groups
Year All Categories Scheduled Caste Scheduled Tribe Boys Girls Total Boys Girls Total Boys Girls Total 1 2 3 4 5 6 7 8 9 10 2000-01 52.9 61.1 57 49.7 69.7 59.7 70.9 77.1 74 2001-02 52 60.5 56.2 49 68 58.5 70 76 73 2002-03 57.7 60.5 59.1 45.7 49.2 47.5 75 80.3 77.7 2003-04 56.5 58.6 57.5 60.9 65.3 63.1 73 78.5 75.8 2004-05 48.2 50.1 49.1 47 63 55 67 72 69.5 2005-06 27.86 28.96 28.39 28.46 30.21 29.33 35.89 38.46 37.07 2006-07 17.63 18.47 18.05 23.71 27.46 25.59 29.91 34.97 32.44 2007-08 13.05 13.49 13.27 17.55 20.05 18.8 22.13 25.53 23.83 2008-09 8.42 8.43 8.42 11.32 12.53 11.92 14.28 15.96 15.12 2009-10 8.13 8.24 8.19 8.64 9.61 8.89 8.47 6.82 9.72 2010-11 7.15 7.31 7.23 6.86 6.05 6.21 7.18 6.96 7.85 2011-12 3.85 2.23 3.07 2.20 1.23 1.73 3.20 6.31 4.70 2012-13 2.45 2.38 2.36 2.44 2.39 2.41 3.95 3.29 3.38 2013-14 2.71 2.08 2.40 2..84 2.24 2.58 3.48 3.89 3.63 2014-15 4.31 4.25 4.21 4.39 4.50 4.45 4.14 5.91 5.03 2015-16 4.20 3.52 3.87 5.46 4.11 4.80 9.38 8.27 8.82 Sources: Director, Elementary Education and Director, OPEPA
Samikshya-2017 51
Phenomenal decrease in dropout rates all categories indicates some improvement during last 15 years.
Figure 8 :Dropout Rates in Upper Primary Schools in Odisha
250.00 200.00 150.00 100.00 50.00 0.00 General SC ST
Lower rate of dropout in girls compared to their boy’s counterparts indicates creation of awareness among the girls. Girls in the scheduled caste and scheduled tribe communities seem to have followed the benefits available for upper primary education. Retention of the girl’s child at the upper primary level may augur expansion of literacy among the mass living aside the mainstream.
The five dimensions of holistic education, i. Education need be informative like history, geography and other subjects to create awareness among the students. ii. Human guidance intervention need be explored for simplified models of complicated theories. iii. Education plays a vital role in art of living. It helps relief from stressful academic pressure. Let us not forget the language of laughter or else, it may fore go the career. iv. The vast domain of education lives in art, creativity, painting, music, craftsmanship, poultry, masonry etc. The activities of interest may be ingrained in the mind of the individual to develop creativity for existence. v. Yoga and meditation are the powerful instruments of individual’s concentration which can open the doors of life inside.
Samikshya-2017 52
Conclusion
Education, Information and administration are the triajuncta in uno of an equilateral triangle. Meaningful synergy among the three lines is a sine qua non for effective and purposeful delivery. The foundation of educational pyramid rests on elementary level. No stone should be left unturned to strengthen the elementary education. The systems of educational administration need be adhered to pro pupil approach .Besides timely compilation of appropriate and realistic information may help the administrator to plug the loop holes with the help of the indicators of development.
Reference
OPEPA, Odisha Economic Survey, Statistical Abstract Odisha, 2012
Statistics is the universal tool of inductive inference, natural & social science and technological application.
Samikshya-2017 53
Status of the Aged Persons in Odisha
Sri Rashmi Ranjan Kanungo
Abstract
The main focus of the study is to assess the structure and composition of the aged with respect to age and sex, and their health and wellbeing in Odisha. Various indicators like economic independence, dependence ratio and mobility of aged persons are also discussed in this paper.
Key words: NSS, Aged persons, Elderly persons, Dependency Ratio, Physical Mobility, Economic Independence
Introduction
Population ageing is an inevitable consequence of the demographic transition experienced by all countries in different degrees. Declining fertility and increasing longevity during the process of development have already resulted in increased proportions of elderly persons (60 years and above) in almost all developed and developing countries. Population ageing has profound social, economic and political implications for a country. The increasing number of older persons put a strain on health care and social care systems in the country. Old age comes with lot of ailment and diseases. In case of large number of elderly persons in the population, the country needs more and more health and medical services, facilities and resources. A large majority of the elderly lives in rural areas and there is an increasing proportion of the old-oldest age (80 years and above) with women living longer than men in older ages. This process of feminization of ageing poses a particular challenge as many such elderly women are widowed and experience prevailing discriminatory practices longer during their lifetime.
Objective
To analyse the economic independence and mobility by different age group of aged population in Odisha.
Source of Data
Data of population census 2001 and 2011 and secondary data from published central sample report of 60th round (during the period January 2004 to June 2004) and 71st round (during the period January 2014 to June 2014) of NSSO has been used in this paper.
Samikshya-2017 54
Results and discussion Share of elderly in total population
The total population of Odisha has been divided into three major age groups, i.e.age in years 0-14, 15-59 and 60 & above the percentage share of each major groups to the total population of Odisha is presented in figure 1.
Figure 1: Specific age wise percentage distribution of population in Odisha
census 2001, 8.26
census 2011, census 2001, 9.49 33.17
census 2011, 28.77
census 2001, 58.40
census 2011, 61.45 0-14 15-59 60+
The share of children (age 0-14) is decreasing from 33.17% in census 2001 to 28.77 % in census 2011.On the other hand the proportion of population in the working age group (age 15-59) and the aged (60 years & above) both are increasing. The share of aged population increase from 8.26 percent in census 2001 to 9.49 percent in census 2011.
Figure 2: Specific Age wise aged persons in Figure 3: Specific Age wise aged persons in Rural Odisha Urban Odisha
240000 1400000 200000 1200000 1000000 160000 800000 120000
600000 Population
Population 80000 400000
40000 200000
0 0 60-64 65-69 70-74 75-79 80+ 60-64 65-69 70-74 75-79 80+
2001 2011 2001 2011
Source-population census 2001 & 2011
Samikshya-2017 55
The aged persons with five years interval for rural and urban Odisha according to population census 2001 and 2011 has been presented in figure 2 and figure 3. During the year 2004 there is a decreasing trend with increasing age up to age group 75-79 both for rural and urban Odisha. If we compare between 2001 and 2011 census, there is an increase of aged persons of age 75 or more for rural and urban sector.
Figure 4 : Sex wise composition of aged population in Odisha (2001 and 2011 Census)
800 800 600 600
400 400 200 200
0 0 60-64 65-69 70-74 75-79 80+
2011 Male 2011 Female 2001 Male 2001 Female
Source-population census 2001 & 2011
Figure4 represents the distribution of age group wise aged persons for census 2001 and 2011.It shows that, during census2001 female population was more than male in age group60- 64 and 65-69, and near to equal in 70-74 and 75-79 age group where it was less in age group 80+ .During 2011 census female population mark as less than male in age group 60-64 and 80+, where in the age group 65-69 and 70-74 female population was more than male and near to equal in age group 75-79.
Dependency Ratio
The dependency ratio is an age-population ratio of those aged persons typically not in the labour force (the dependent part) and those persons typically in the labour force (the productive part). It is used to measure the pressure on productive population group and is normally expressed as a percentage. Generally, persons aged 15 to 59 years are supposed to form the population of working ages and at age 60, people generally retire or withdraw themselves from work. Thus, old age dependency ratio is defined as the number of persons in the age-group 60 or more per 100 persons in the age-group 15-59 years. At all Odisha level dependency ratio is an increasing trend over period of time. Dependence ratio shows upward trends over time for urban and rural sector. At all Odisha level during the year2001dependence ratio was 14 percent, where it changed to 15 percent during the year 2011.Similarly in rural Odisha the old-age dependence ratio during the year 2001 was 15 percent ,where it changed to 16 percent during the year 2011,this figure for urban Odisha changed from 10 percent to 12 percent during the census 2001 to 2011.
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Figure 5: Sector wise Old-Age Dependence Ratio in Odisha
18
12
6
0 Rural Urban Odisha
2,011 2,001
Source-population census 2001 & 2011
Economic Independence
The economic independence reveals the associated problem of day-to-day maintenance of livelihood of the elderly. Figure 6 and 7 represent the percent distribution of the aged persons NSS 60th and 71st round survey for both rural & urban sector in Odisha. As many as 55.3 per cent of the aged male and nearly 92.5 percent female had to depend on others either fully or partially for their day to-day maintenance in rural Odisha during NSS 60th round (2004), similarly for urban it had 49.8 and 93.8 percent male and female aged person respectively had to depend on others either fully or partially for their day to-day maintenance for the same period. The situation was even worse during 71st round (2014) for elderly male and females. In this respect, males were much better off – 33.8 percent in rural and 42.4 percent in urban sector among them during 71st round NSS (2014) did not fully depend on others for their livelihood.
Figure 6 : Percentage Distribution of Aged persons Economically Independace in Rural Odisha
100 36.1 32.4 80 69.8 60 20.6 77.4 30.1 40
20 20.8 44.7 33.8 12.4 0 9.4 7.5 male female male female 71st 2014 60th 2004
Not dependant on others Partially dependant on others Fully dependant on others Source-Published NSS report 60th &71st round
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Figure 7 : Percentage Distribution of Aged person Economically in Urban Odisha
100 90 26.1 80 33.3 70 60 31.5 85.6 15.3 84.0 50 40 30 50.2 20 42.4 10 5.5 9.8 0 8.9 6.2 male female male female 71st 2014 60th 2004
Urban Not dependant on others Urban Partially dependant on others Urban Fully dependant on others
Economic Support Providers
As mentioned above a large proportion of elderly are economically dependent either fully or partially on others for their livelihood. It is therefore very important to know those person who provides financial support to these elderly.
Figure 8: Sector wise Percentage Distribution of Financial Support Provider to Economically Dependent Aged Persons
100 90 80 70 86.5 60 82 50 40 76.3 80.1 30 14.4 1.8 11 20 4.7 9.7 0.4 3.4 11.6 10 3.6 5.7 2.5 6.4 0 Spouse own children Grand Others Spouse own children Grand Others children children Rural Urban 60th 71st
Source-Published NSS report 60th&71st round
In Odisha during the year 2004,76.3& 80 percent aged persons were dependent on their own children in rural and urban area. This figure changed in the year 2014 as 82% and 86.5% respectively. Aged persons dependent on their own grand children in rural and urban sector Samikshya-2017 58 shows very negligible percent. 14.4% and 11.0% aged persons depend on their spouse during the year 2004,in rural and urban area respectively this figure changed to 11.6 % & 9.7% in rural and urban Odisha respectively during the year 2014.
Conclusion
According to this study the proportion of the elderly in Odisha is higher than the national average. There is considerable poverty and vulnerability in the state, frequently exacerbated by natural disasters. Coping with these difficult living conditions is often beyond the physical and psychological capacity of the elderly, particularly the women. They often experience higher levels of trauma than younger members of the family and community. In Odisha dependency ratio is higher than national average. The elderly in Odisha prefer the support of children in their old age, irrespective of economic status and sex.
Socio-economic conditions in Odisha and frequent natural disasters pose a serious challenge to the government in coping with increasing demand for care, safety and security of the elderly persons. The state government implements SOAP scheme since four decades ago in 1975. The social security schemes and programmes in the state are provided by the Department of Women and Child Welfare. The old age schemes and programmes implemented in the state are according to the provisions of the NPOP, 1999 and Maintenance and Welfare of Parents and Senior Citizens Act of 2007.
References
PCA data of Population Census 2001 and 2011 Report of 60th Round NSSO, 2004 Report of 71stRoundNSSO, 2014
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Consumer Price Index : Measuring for Odisha
Miss Prabhati rani Pradhan Smt. Anita Dash
Abstract
The paper makes a cross sectional presentation of prices of food and non-food items by following weights diagram principles to observe the variability of prices among the districts of Odisha State
Introduction
Consumer Price Index Numbers are constructed to study the effect of changes in the prices of a basket of goods and services on the purchasing power of a particular class of people during current period as compared with some base period. In other words, CPI is a statistical estimate constructed using the prices of a sample of representative items whose prices are collected periodically. -In order to ensure better reflection of retail price movement and to help the Reserve Bank of India for taking effective monetary policy steps dealing with inflation, the Central Statistical Office in the Ministry of Statistics and Programme Implementation, Govt. of India has developed its methodology to compile C.P.I. at micro level on monthly basis. Globally, C.P.I. is also taken as the key indicator to measure health of the economy. It is used as an adjustment factor for indexation of wages, social security benefits and other payment. Similarly, for formulating state level policies and reaching the masses at the grass root level, it is also necessary to have district level inflation rates. In this context, Directorate of Economics & Statistics (DE&S), Odisha has taken steps to compile new series of C.P.I. at district level with effect from January 2018.
Data Source : 68th Round NSS, Central Sample Data on Household Consumer Expenditure
Weighting Diagram
Weighting diagram gives the share of each item considered in the total consumption expenditure. The consumption patterns (weighting diagram)for compilation of CPI have been derived on the basis of average monthly consumption expenditure of an Urban/Rural household obtained from the consumer expenditure survey conducted by the NSSO during 2011-12(68th round NSS). After exclusion of non-consumption expenditure (like legal expenses) from the average monthly consumption expenditure, the remaining items have been classified into several consumption groups and sub-groups considering classification of individual consumption according to purpose(COICOP), the standard international classification with certain modifications as recommended by Technical Advisory Committee on SPCL, CSO,
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Govt. of India for preparation of item basket. Due to small size in some districts, 22 regions instead of 30 regions have been formed for construction of weighting diagram by taking 15 districts as independent regions and rest 15 districts into 7 regions (through clubbing 2 to 3 neighbouring districts having more or less similar consumption pattern).The items in the basket have been classified into six(6) groups and twenty one(21) sub-groups.
In order to make the basket size optimum covering all sections of items in a more representative way, multiple norms have been designed by the CSO to include administered items, items having reasonable share of expenditure and items consumed by most of the households in the State. Accordingly, four fold criteria like i) Include all PDS items ii) Include all items accounting for 1% or more of total expenditure at sub-group level. iii) Include all items accounting for more than specified percentage of total expenditure of all consumption items as given below and iv) Include all items for which more than 75% households have reported consumption. have been adopted.
Group Group Description Specified percentage out of total expenditure Gr.I, Gr.II, Gr.IV, Food and Beverages, Pan, Tobacco and Intoxicants, Gr.VI Housing and Miscellaneous excluding ‘Bedding’ > 0.04% Gr. V Fuel and light >0.03% Gr. III Clothing and footwear, Section ‘Bedding’ under >0.02% subgroup ‘Household Goods and Services’
As per the consumption pattern followed in Odisha, a total of 300 nos. of items have been selected in the Final Weighting Diagram to be included in the Item Basket for the Compilation of the District Level CPI. The Group/sub-group wise total no. of items along with their percentage share is given in Table I.
Table 1: Groups and Sub Group wise item baskets included in the weighting diagram for the CPI Group Sub Group COICOP code Description Total Items Percentage share to total items I 01 Food and beverages 1 01.1.1 Cereals and products 18 6.00 2 01.1.2 Meat and fish 5 1.67 3 01.1.3 Egg 1 0.33
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Group Sub Group COICOP code Description Total Items Percentage share to total items
4 01.1.4 Milk and products 5 1.67
5 01.1.5 Oils and fats 6 2.00
6 01.1.6 Fruits 20 6.67
7 01.1.7.1 Vegetables 21 7.00
8 01.1.7.2 Pulses and products 11 3.67 9 01.1.8 Sugar and 7 2.33 confectionery 10 01.1.9 Spices 10 3.33 11 01.2 Non-alcoholic 5 1.67 beverages 12 11 Restaurants and 10 3.33 hotels Sub-Total 119 39.67
II 02 Pan, tobacco and 16 5.33 intoxicants Sub-Total 16 5.33 III 03 Clothing and footwear 1 03.1 Clothing 26 8.67 2 03.2 Footwear 6 2.00
Sub-Total 32 10.67
IV 1 04.1 to 04.4 Housing 6 2.00 Sub-Total 6 2.00 V 04.5 Fuel and light 11 3.67 Sub-Total 11 3.67 VI Miscellaneous 1 05 Household goods and 46 15.33 services 2 06 Health 11 3.67 3 07 and 08 Transport and 20 6.67 communication 4 09 Recreation and 17 5.67 amusement 5 10 Education 6 2.00
6 11 Personal Care & 16 5.33 Effects Sub-Total 116 38.67
Total 300 100.00
Figure - 1 depicts the Percentage share of items included in each group for construction of weighting diagram. It reflects that maximum numbers of items are included in the “food and beverages” group followed by “miscellaneous,” “clothing and foot wear”, “pan, tobacco and intoxicants”, “fuel and light” and “housing group” respectively.
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Figure 1 : Percentage share of group wise items in the item basket
38.67 39.67
10.67 3.67 5.33 1 Food and beverages2.00 2 Pan, tobacco and intoxicants 3 Clothing and footwear 4 Housing 5 Fuel and light 6 Miscellaneous Among the total consumption of 300 items, 109 numbers of item are commonly consumed in all districts /regions. Table – 2 reflects the group wise common items, its share to total common items in all groups and its share in the group itself. It is found that around 36% of total items are commonly consumed by the consumers of Odisha. Besides, 48% of common items belong to “food and beverages” group followed by “miscellaneous” group with 27 %.
Table 2 : Group wise common items, its share to all common items and share to total items in the group itself Group Group_Name Total no. of Percentage Share Percentage share Code Common Items in to total common of common items each group items in all groups to total items of the group I Food and beverages 52 47.71 43.69 II Pan, tobacco and 3 2.75 18.75 intoxicants III Clothing and footwear 17 15.60 53.12 IV Housing 3 2.75 50.00 V Fuel and light 5 4.59 45.45 VI Miscellaneous 29 26.61 25.00 All Groups 109 100.00 36.33 While comparing the composition of common items within the group it is found that the clothing and footwear item group shares maximum percentage of common items within the group followed by Housing, Fuel and light, Food and Beverages, Miscellaneous and Pan, Tobacco and Intoxicants.
District wise share of consumption expenditures of the items
The resultant expenditure on each item, which is finally retained for pricing, is expressed as a percentage of the total expenditure accounted for by all the items included in the sub-group/group to yield the final weight of the items within the respective sub-
Samikshya-2017 63 group/group. The weight of each sub-group was obtained by expressing the total expenditure on the sub-group as a percentage of the total expenditure on all sub-groups of the same group. Similarly, weight of each group was obtained by expressing the total expenditure on the group as a percentage of the total expenditure on all groups. The Table: 3 given below represents the district/region wise annual average expenditure share of the different groups of items in the Basket of CPI (Combined) along with standard deviation(SD) of the share of items.
Table 3 :Group Wise Share of weights of different districts/regions Dist/ Dist_Regn_Name Food and Pan, Clothing Housing Fuel Miscellan Regn/ beve tobacco and and eous CD rages and footwear light intoxicants 1 Angul 50.59 3.04 8.98 6.73 8.12 22.54 (max.) 2 Balasore 50.16 2.63 6.06 9.11 10.33 21.72 3 Baragarh 53.12 1.24 7.47 3.29 10.05 24.83 4 Bhadrak 57.33 3.45 8.39 4.55 9.21 17.08 5 Cuttack 49.26 1.83 6.62 9.50 8.17 24.62 6 Dhenkanal 54.02 3.51 7.03 5.13 7.93 22.36 7 Jagatsinghpur 56.28 4.11 7.39 3.74 7.72 20.76 8 Jajpur 51.53 2.90 7.13 3.93 9.12 25.41 9 Kendrapada 55.13 4.31 5.42 3.97 10.50 20.67 (max.) (min.) 10 Keonjhar 52.90 2.41 5.77 6.39 11.36 21.17 11 Khorda 37.78 1.20 5.99 22.41 6.35 26.27 (min.) (min.) (max.) (min.) (max.) 12 Mayurbhanj 59.75 3.61 6.53 2.80 12.89 14.42 (max.) 13 Nayagarh 54.34 3.05 6.23 4.45 10.88 21.06 14 Puri 55.66 1.85 6.27 4.41 8.92 22.90 15 Sundargarh 47.48 2.42 6.79 11.86 8.23 23.21 16 Balangir_Subpur_ 52.16 1.46 7.90 2.77 9.49 26.22 region (min.) 17 Baudh_Kandhamal 62.78 2.78 7.53 3.34 9.44 14.13 _Reg (min.) 18 Sbp_Jhar_Deog_ 45.83 3.39 7.16 10.60 8.09 24.94 Region 19 Ganjam_Gajapati 57.25 2.86 7.59 4.26 8.93 19.11 _Reg 20 Kalahandi_Npada 60.73 2.51 5.48 3.14 10.81 17.33 _Region 21 Koraput_Rayagada_ 58.97 3.50 6.53 5.78 10.53 14.69 Region 22 Malkangiri_Nabpur_ 63.73 2.92 5.49 3.25 10.21 14.40 Region (max.) Average Weights of 53.94 2.77 6.81 6.15 9.42 20.90 Odisha Standard Deviation 5.93 0.86 0.95 4.48 1.48 4.01
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Figure 2 :Average Group wise Final Weights of Odisha
20.90
9.42 53.94
6.15 6.81 2.77 I Food and beverages II Pan, tobacco and intoxicants III Clothing and footwear IV Housing V Fuel and light VI Miscellaneous
Analysis and Findings
The Table – 3, above indicates that the Average percentage share of weights of Odisha is maximum in the Food and Beverages Group (53.94) followed by Miscellaneous (20.90), Fuel and Light (9.42), Clothing and Footwear (6.81), Housing (6.15), and Pan, Tobacco and Intoxicants group (2.77) respectively. This implies that the average retail consumers of Odisha give more importance to the items in the Food & Beverages Group as a whole than the other groups and likewise.
The standard deviation in different weight groups of Odisha indicates that more variation are observed in food and beverage group (5.93) followed by housing (4.48), miscellaneous (4.01), fuel and light(1.48), clothing footwear (0.95), and least variation in pan tobacco and intoxicant group(0.86).
While comparing the weights given by the districts/regions to different consumption groups it is found that the “food and beverages” group gets maximum weightages in Malkangir-Nawarangpur region(63.73) and minimum weightage in Khorda district(37.78). Regarding the group pan tobacco and intoxicants, it gets maximum weightage in Kendrapara district(4.31) and minimum weightage in Khorda district(1.2). So far the “clothing and footwear” is concerned the Angul district has the maximum weightage(8.98) and Kendrapara district with minimum weightage(5.42). In both the groups of “housing and miscellaneous”, maximum weightage is given by Khorda district with group weightage of 22.41 and 26.27 respectively, while Balangir- Subarnpur region and Boudh-Kandhamal region respectively attach minimum weightage of 2.77 and 14.13 to the above two groups.
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So far the group “fuel and light” is concerned the Mayurbhanj district gives maximum weightage (12.89) among all districts/regions while Khorda attaches minimum weightage of 6.35.
Conclusion
During the study of Price behaviour and consumption pattern of consumer of Odisha in the broad groups of consumption namely Food and Beverages, Pan, tobacco and intoxicants, clothing and foot wear, housing. Fuel and light and miscellaneous, the following observations were made.
1. The consumer of Odisha on and above, incur maximum share of their expenditure in food and beverages items followed by miscellaneous, fuel and light, clothing and foot wear, housing and pan, tobacco and intoxicants.
2. While studying the weights of different districts/regions on different item groups, it is found that the consumers of Khorda district have given minimum weightage to items on Food beverages, Pan Tobacco and Fuel and light and maximum weightage to items on housing and miscellaneous groups, compared to other districts/regions.
3. The Khurda district attaches maximum weightage to housing group in Odisha which is nearly 10 times higher than weightage of the region Balangir- Subarnpur with minimum weights. This implies that people living in the Urban areas of Khurda district are incurring a big share of expenditure in the items of housing group in comparision to other urban areas of the state and this is least in case of Balangir-Subarnpur region.
4. Regarding weights of food and beverages group, maximum weightage to this group are assigned by southern regions of Odisha namely, Malkangiri- Nawarangpur region followed by Boudh- Kandhamal and Kalahandi- Nuapada with more than 60% of total weights in all groups.
5. In studying the extent of variation within different groups it is found that more variations are observed in consumption of items coming under food and beverages group(5.93)followed by housing(4.48), miscellaneous(4.01), fuel and light (1.48), clothing and footwear(0.95)with least variations in pan tobacco and intoxicants(0.86).
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6. Making a comparison of weights of different sub-groups in miscellaneous group in Khorda district with maximum group weightage, it is found that the average consumers of Khorda district are incurring a relatively a large share of their expenditure in the items of sub group namely Transport and Communication as well as in Education.
7. Thus, it can be concluded that because of some variability of the share of the items among the Districts/Regions, the overall price behaviour of the consumers of entire Odisha varies according to the districts as reflected in the concerned Item Basket derived from the Final Weighting Diagram for the compilation of the Sub-State level CPI.
Reference Weighting diagram for compilation of sub-state level consumer price index of Odisha - prepared by Price division, D. E &S., Odisha
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Quality of Primary Education in India
Sri Sridhar Sahoo Abstract
Expansion of primary education is the priority area of State intervention to improve the quality of life of people. But improving quality of education is equally important to be addressed with high priority. The paper makes a brief account of historical perspective and clinical role of quality primary education in India.
Introduction
The key concern about education, in any formal educational system, of all time, has been its quality. Every stake holder, direct or indirect, of education is concerned about its quality. Guardians or parents, irrespective of their socio-economic status, want to educate their children with best quality education which would add better value to the degrees their wards acquire subject to the budget constraints. But what do we mean by ‘quality education? How this quality is being assessed? What is the status of the quality of education in India? This paper seeks to focus on these issues with respect to the quality of primary education in India.
There is no universally accepted definition for ‘quality of education’. In education, perception of quality is around students (Mukhopadhyay, 2001). The performance of the students like examination results, learning achievements, ability to apply learned knowledge in practical life-- exhibit the quality of an education. For some, “Quality of education” means value addition in education (Feigenbaum 1951); excellence in education (Peters and Waterman 1982); for others, fitness of education outcomes and experience for use (Juran and Gryna 1988).
In this study we measure the quality of primary education on the basis of student learning achievement (i.e., ability to read, write and do mathematics).
Primary Education: Universality vs Quality
The universal declaration of human rights (1948) considered primary education as the basic human right of all people. Accordingly, all nations prioritized universal access to education. The developed and developing countries have attained universal or near universal access to primary education. There is unanimous agreement among educationists to-day, that the quality of primary education in almost all parts of our country is poor. Now the focus is on the quality of student learning. The quality concern is not uniform across the nations. The developing and poor nations are still striving for expansion of educational access. It has been established that access to education and its quality are not sequential elements. At the sub-
Samikshya-2017 68 regional meeting of South Asian Ministers in Kathmandu in April 2001, quality education was unanimously identified as a priority area. All participants were in agreement that there was an urgency to develop the quality of education to meet the intermediate target and education for all by 2015. Because how well pupil are taught and how much they learn can have a crucial impact on how long they stay in school and how regularly they attend. Further whether parents send their children to school at all is likely to depend on the judgment they make about the quality of teaching and learning. Based on this perception, parents decide whether attending school is worth the time and cost for their children and for themselves. The World Bank (1997) suggested that ‘the best way to improve access is to improve quality which would make coming to school or staying in school a more attractive option from the perspective of parents as well as children. Moreover, efforts to improve quality will tend to increase the efficiency of the public expenditure and will encourage parents to contribute children education’.
In the year 1950, when the Constitution of India was adopted, education was recognized as a basic individual right. Directive Principles of State Policy, Article 45, states that “the state shall endeavour to provide within a period of ten years from the commencement of this Constitution, for free and compulsory education for all children until they complete the age of fourteen years” (The Constitution of India). In line with the commitment of the country to provide elementary education to all children, educational facilities have got tremendously expanded during the post-independence period, especially in primary stage. The number of primary school in India has increased from 2.2 lakhs in 1950-51 to 8.47 lakhs in 2014-15. As a result, the illiteracy rate and drop-out rate at school stage have come down. The national literacy rate has increased from 64.8 per cent in 2001 to 73 per cent in 2011(Census, 2011) .The average annual drop-out rate has also declined from 64.9 per cent in 1960-61 to 4.34 per cent in 2013-14 (NUEPA). Although the literacy rates for both males and females have increased, the latter still continues to lag behind the former. However, there has been a narrowing of the male-female gap in literacy from 21.6 percentage point in 2001 to 16.3 percentage point in 2011(Educational Statistics at a Glance ,2016).
Primary education is the first stage of formal education. The main objective of quality primary education is to inculcate basic knowledge about reading, writing and arithmetic among the children. It is expected that after the successful completion of the primary level of education, a pupil should be able to read, write, and solve simple arithmetic problem.
A study conducted by the ASER Centre has revealed a very disappointing scenario of primary education in India. The study was conducted in primary schools in all the States of India (excluding Union Territories, Goa, Sikkim) to measure the learning achievement (quality) of students in language and mathematics. This study found that in India after completion of
Samikshya-2017 69 primary level education, among children in Std-V, 6.0 per cent student were not able to read letters of mother tongue, 13.3 per cent can read letters but not words or higher, 14.2 per cent can read words but not Std-I level text or higher, 18.6 per cent can read Std-I level text but not Std-II level text and only 47.8 per cent can read Std-II level text (Table).
Similarly, among children in Std-V, 11.9 per cent student were not able to read capital letters of English, 13.7 per cent can read capital letters but not small letters or higher, 25.6 per cent can read small letters but not words or higher, 24.3 per cent can read words but not sentences and only 24.5 per cent can read sentences (Table).
Table 1 : State wise percentage children by grade V and reading level in mother tongue, English and arithmetic State % Children by grade-V and reading level All children 2016 Reading Mother Tongue Nothing Letters Word St-I level text St-II level text
1 Andhra Pradesh 4.5 7.3 11.5 21.6 55.1 2 Arunachal Pradesh 0.4 11.9 34.5 27.8 25.5 3 Assam 5.5 11.6 22.7 22.2 38.0 4 Bihar 9.3 17.5 15.4 15.8 42.0 5 Chhattisgarh 4.1 12.9 9.1 17.9 55.9 6 Gujarat 4.0 9.0 14.8 19.3 53.0 7 Haryana 2.4 5.0 6.8 17.6 68.3 8 Himachal Pradesh 0.4 4.4 7.1 17.6 70.5 9 Jharkhand 7.2 22.3 17.4 16.8 36.4 10 Jammu & Kashmir 1.7 9.6 24.8 32.4 31.6 11 Karnataka 5.6 8.3 20.1 23.9 42.1 12 Kerala 0.9 2.8 10.3 16.7 69.2 13 Madhya Pradesh 8.5 20.2 14.3 18.3 38.7 14 Maharashtra 4.2 6.9 8.8 17.6 62.5 15 Manipur 0.7 2.8 9.1 16.7 70.7 16 Meghalaya 0.4 1.1 12.2 38.4 47.9 17 Mizoram 0.4 1.1 20.9 31.6 46.0 18 Nagaland 1.9 2.7 22.8 22.6 50.1 19 Odisha 5.3 10.0 15.5 17.7 51.6 20 Punjab 1.6 5.1 7.8 16.4 69.1 21 Rajasthan 3.9 12.5 12.3 17.1 54.1 22 Tamil Nadu 3.5 6.3 18.4 26.6 45.2 23 Telangana 3.1 9.5 16.9 23.4 47.1 24 Tripura 2.3 16.3 13.9 16.4 51.0 25 Utter Pradesh 8.3 19.0 12.3 17.2 43.2 26 Uttarakhand 4.7 6.2 9.8 15.5 63.7 27 West Bengal 4.0 12.2 16.4 17.3 50.2 All India 6.0 13.3 14.2 18.6 47.8
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Table 1 : State wise percentage children by grade V and reading level in mother tongue, English and arithmetic (Contd.)
State % Children by grade-V and reading level in English All children 2016 Reading and comprehension in English Not even Capital Small letters Simple Easy capital letters Words sentences letters
1 Andhra Pradesh 5.8 2.8 15.3 28.8 47.3 2 Arunachal Pradesh 0.4 0.6 15.1 50.4 33.4 3 Assam 10.7 14.4 25.4 27.2 22.3 4 Bihar 18.1 12.2 23.4 28.2 18.1 5 Chhattisgarh 12.4 16.1 41.5 13.8 16.3 6 Gujarat 20.9 28.1 25.9 17.7 7.4 7 Haryana 3.6 6.6 10.4 24.6 54.8 8 Himachal Pradesh 1.5 4.8 12.4 18.2 63.2 9 Jharkhand 11.9 20.8 29.4 23.2 14.8 10 Jammu & Kashmir 1.8 4.6 8.6 36.8 48.3 11 Karnataka 8.1 14.1 26.4 26.7 24.8 12 Kerala 1.6 3.3 7.4 19.2 68.5 13 Madhya Pradesh 18.4 18.5 34.3 16.3 12.6 14 Maharashtra 10.2 11.1 25.1 25.7 27.9 15 Manipur 0.1 0.0 3.0 11.9 85.0 16 Meghalaya 0.4 3.6 5.4 39.0 51.6 17 Mizoram 0.4 1.1 7.6 52.5 38.5 18 Nagaland 2.2 0.4 2.9 29.5 65.0 19 Odisha 9.3 11.7 24.3 30.0 24.8 20 Punjab 2.4 4.0 11.0 23.6 59.1 21 Rajasthan 9.6 15.2 29.8 25.3 20.2 22 Tamil Nadu 4.1 5.8 21.1 31.8 37.2 23 Telangana 5.2 5.3 22.1 23.2 44.1 24 Tripura 3.0 11.2 28.0 33.1 24.7 25 Utter Pradesh 15.0 18.9 28.2 19.5 18.4 26 Uttarakhand 5.8 9.9 20.8 25.2 38.3 27 West Bengal 9.1 11.3 31.6 25.3 22.8 All India 11.9 13.7 25.6 24.3 24.5
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Table 1 : State wise percentage children by grade V and reading level in mother tongue, English and arithmetic (Concld.)
State % Children by grade-V and arithmetic level All children 2016 Arithmetic Not even 1-9 Number recognition Subtraction Division 1-9 10-99 1 Andhra Pradesh 2.7 1.8 26.6 31.7 37.2 2 Arunachal Pradesh 0.0 1.8 42.9 36.4 19.0 3 Assam 4.0 16.4 37.6 28.4 13.6 4 Bihar 5.6 16.2 26.6 19.0 32.6 5 Chhattisgarh 1.5 21.6 31.4 22.5 23.0 6 Gujarat 3.9 18.4 35.0 26.7 16.1 7 Haryana 1.7 6.9 15.4 27.2 48.9 8 Himachal Pradesh 0.1 5.5 15.8 24.9 53.7 9 Jharkhand 3.6 24.3 28.7 20.0 23.5 10 Jammu & Kashmir 2.1 7.9 33.2 35.1 21.8 11 Karnataka 3.2 5.6 33.4 38.1 19.7 12 Kerala 1.0 1.9 32.5 26.0 38.6 13 Madhya Pradesh 6.7 24.2 30.6 19.1 19.4 14 Maharashtra 2.7 12.9 34.8 29.3 20.3 15 Manipur 0.0 0.6 13.7 33.2 52.5 16 Meghalaya 0.6 1.3 50.6 36.9 10.7 17 Mizoram 0.2 1.1 11.2 59.8 27.7 18 Nagaland 1.6 1.3 25.6 50.4 21.2 19 Odisha 3.7 14.3 28.5 26.8 26.6 20 Punjab 1.2 5.8 17.5 27.6 48.0 21 Rajasthan 3.0 17.7 27.0 24.1 28.2 22 Tamil Nadu 1.5 3.9 36.0 37.3 21.4 23 Telangana 2.2 2.4 26.9 38.1 30.4 24 Tripura 0.2 14.9 31.5 33.5 19.9 25 Utter Pradesh 5.9 23.7 29.2 18.7 22.6 26 Uttarakhand 2.2 11.6 25.6 23.6 37.0 27 West Bengal 3.2 13.0 36.0 18.8 29.0 All India 4.0 15.5 30.0 24.6 25.9
The condition of students learning achievement (quality) is extremely poor in mathematics compare to language (Mother tongue). This study found that after the successful completion of the primary level education, among children in Std-V, 4.0 per cent children could not recognize numbers 1-9, 15.5 per cent children can recognize numbers up to 9 but cannot recognize numbers up to 99 or higher, 30 per cent children only can recognize numbers up to 99 but cannot do subtraction, 24.6 per cent children can do subtraction but cannot do division and only 25.9 per cent children can do division (Table).
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This is the scenario of primary education in India where students are not in a position to read, write and do a simple arithmetic problem even after completing the primary level education. A majority of our children in rural and even urban areas are left with no option but to attend poor quality and often dysfunctional schools. The learning achievement of the student is very poor in all Indian states whether it is educationally backward or forward.
The National Knowledge Commission (2007) has been emphasizing on ‘quality primary education’ for making India as a knowledge society in the world. The quality of education mainly depends upon physical infrastructure, qualified and competent teachers, curriculum and instructional materials, support materials and equipments, teaching learning strategies, comprehensive and continuous evaluation and effective management. Unfortunately in our country, where 90 per cent of our primary schools are run by government, most of them are situated in rural areas, lack the average criteria for quality education. The low learning achievement problem in India mostly due to the combination of following factors: Socio-economic status of family Parental education and father’s occupation Home environment Influence by a social category Size of the family Medium of teaching Health and nutritional status of children Availability of infrastructure in the schools Management of school Availability of instructional materials Quality teacher Relationship between student and teacher Size of the class School’s academic climate
For quality improvement, we have to re-think about the idea, structure and system of primary education which have very crucial motivating effects on the students. The teaching–learning environment and quality of education are so unsatisfactory particularly in government controlled primary schools that teachers of those schools themselves prefer to arrange education for their children in a better run school in private sector. The elite and rich groups of the society or village enroll their children in private schools where quality is presumed to be far better than the government schools. So the clients of these public schools are the poorest of the poor, economically and socially downtrodden people. The
Samikshya , 2017 73 stakeholders of these schools being weak and vulnerable cannot ensure accountability of the system for quality education. The elite groups are not concerned about this, as their children are safe in private schools. The marginal groups of the society or villages are not in a position to enroll their children in private schools which provide quality education. Due to the economic factor they have to enroll their children in government run primary schools where quality is very poor. So the children belonging to marginal groups are deprived from good quality education and as a result they can hardly climb ladder of development.
For improving quality, the concerned authorities need to emphasis on the following issues:
Early childhood education is extremely important and must be universalized. A relevant curriculum. Accurate assessment of learning outcomes. Participatory management of education system. Engaging local communities. Using ICT in teaching learning process Training of teachers and administrators of ICT. District Institute of Education and Training (DIETs) and State Council of Educational Research and Training (SCERT) need to be more strengthened and undergo structural changes. Establishment of a National Primary Education Assessment system like National Assessment and Accreditation Council (NAAC) for Higher education. Strengthening the teacher training institution. Reform in examination system. Proficiency in English is widely perceived as an important avenue for employment and upward knowledge, which also greatly facilitates the pursuit of higher education. English should be incorporated into the curriculum of primary schools as a language subject. Special strategies needs to be devised to ensure greater access to schools for children in backward regions, remote locations, children belonging to SC/ST and Muslim communities and other backward caste (OBC). Substantially higher public spending is required for quality primary education. There should be transparent, norm-based and straightforward procedures for the recognition of private schools. Community participating in monitoring the primary education. Samikshya , 2017 74
Parent-teacher association. Rigorous implementation of the inspection system. Optimizing the pupil teacher ratio. Developing new teaching learning strategies. Developing the infrastructure of primary education. Appointment of duly qualified teachers in primary level. Appointment of female teachers and teachers in disadvantaged and rural areas. Local qualified applicants need to be preferred. District based educational planning and implementation body needs to be formed. Conclusion
It appears from the empirical study that quality of primary education has been compromised for its universality in India. Operation Black Board, District Primary Education Project, National Literacy Programme, Sarvha Shiksha Abhiyan—all aim at universalizing. It is time that the nation pays heed to the quality dimension. As it has already been pointed out, quality indirectly helps in making the quantity. As a result, quality improvement programmes need to be devised for all levels—national, state and district.
References
1. Odisha Economic Survey, 2016-17 2. Annual Status of Education Report (Rural), 2016 3. Education Statistics at a glance, 2016, MHRD, GoI 4. Feignbaum, A.V. (1983). Total quality Control. McGraw Hill: New York 5. The Government of India, (As modified up to the 1stdec/07), The Constitution of India., Ministry of Law and Justice. New Delhi. 6. World Bank. (1997). Pakistan towards a Strategy for Elementary Education. Report No. 16670 - Pak.
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Sectoral Linkages and Economic Development in Odisha
Dr. Kalpana Sahu Dr. Narayan Sethi Abstract
The present study tries to examine the contribution of all the three major sectors on economic development of Odisha. It employs Ordinary Least Square (OLS) technique to examine the impact of all the sectors on economic development of Odisha using the secondary data for the period of 1980-81 to 2014-15. The empirical findings state that industrial sector has shown more significant positive impact on economic development of Odisha which is followed by agriculture and service sector during the study period. All the sectors hold the key of overall development of the economy by creating employment, generating income, ensuring self- reliance in food production and food security, providing tools and equipment to other sectors and foreign exchange earnings. The study concludes that agriculture is necessary for survival whereas both industry and service sector are important for higher economic growth in Odisha. All the sectors have their own contribution for Odisha economy which can’t be ignored or substituted by each other. In the light of the empirical findings and review section, this study suggests that to attend a higher level of growth with desire level of development, all the sectors should develop simultaneously in Odisha.
Keywords: Agriculture, Industry, Service Sector, Economic Development, OLS test, Odisha
Introduction
Economic development is necessarily a holistic process for the survival of an economy. It is a multidimensional phenomenon. Achieving a higher rate of economic development is the ultimate objective of all the national plans, programs and foreign policy of an economy. It is a long term process and policies by which a nation improves the economic, political and social wellbeing of its people. According to Amartya Sen, “Economic Growth is one aspect of the Process of Economic Development’. Central Statistical Organization (CSO) divides the economy into three major basic sectors such as Primary sector (agriculture), Secondary sector (industry) and Tertiary sector (service). The importance of these sectors can be evaluated by their contribution to output production, income and employment generation (Sahoo & Sethi, 2012). Agriculture is necessary for survival, industry is necessary for growth and service sector is essential for the future development of an economy. The former sector helps to meet the basic needs of people necessary for survival; and the other two sectors help to fulfill multiple wants which enable one person to live a healthy life with dignity. There are several studies exist
Samikshya , 2017 76 between the sectoral linkages and their interdependence particularly relating to Indian economy. Baradwaj (1966) and Hazari (1970) are considered as the founder of these studies relating to the sectoral interlink among various sectors.
Odisha economy is considered as an agrarian economy as the ratio of cultivators and agricultural labourer is 61.80 percent (2011) with respect to its total work force (Economic Survey 2014-15). Nearly 3/4th of Odisha population is living in villages having agriculture and allied activities as their prime occupation. Until the industrial revolution the majority of the population depended in agriculture for their survival. Agricultural growth of an economy is directly linked with the economic development of the economy. Cross-country studies estimate that for every 1 percent increase in agricultural output, employment is increased by between 0.3 and 0.6% (Mellor, 2001a). The role of agruiculture is described by Dorward (2002) as follows:
“Increasing agricultural productivity is essential for capital investment in agriculture itself and then for the steady releases of surplus capital and labor to other sectors of the economy. It is also the major source of export earnings and of food, plays a major role in keeping food process down, and is the major source of domestic income and hence stimulus for demand for local goods and services”.
Agriculture and industrial sector consider as two important determinants of economic development process of an economy as they provides employment opportunities and make an addition to the real output production. In the era of globalization and economic integration, service sector plays an important role for both economic growth and development of an economy. The growth of the service sector is considered as an indicator of economic progress of a nation. Service sector includes those actions which are produced first, then traded, bought or sold and then finally consumed to fulfill the existing needs of the common people. Currently the world economy is broadly characterized as a service economy due to its higher contribution towards output production, income and employment generation in the economies of most developed and developing countries. Economic history tells us that all the developing countries have experienced a shift from agriculture to industry and then to the service sector as the mainstay of the economy.
There is no doubt that all the sectors have their own contribution towards Odisha economy. The question arises which sector contributes more to Odisha economy. The question is in debate since last few years. It is very essential to keep an adequate balance among all sectors with the aim to maintain this current growth rate. And to cope with the changing world, one must have to move towards more industrialization because it will bring the possibility of higher growth for the economy in future. Presently, the importance of agriculture is growing
Samikshya , 2017 77 very fast. To ensure the food security and development of the society, the economy depends on agriculture. In the context India, some studies found that sectoral distribution of growth rates has a strong impact on poverty reduction (Tyler et al, 1993; Ravallion and Datt, 1996). But we need to re-look in our approach about agriculture as means of providing sustainable employment and wealth to people especially at cost of industry. The educated youth of the farmers’ family who would become unemployed due to the mechanization of the agricultural sector with the aim to produce more output with lesser time and manpower, should get proper training to get themselves employed through alternative possibilities.
The question arises that if industrialization and growth of service sector is meant for greater development then why do people go against it? It’s because in Odisha, industrialization and growth of the service sector mostly takes place in an unplanned way without considering the aspect of sustainable development. The government’s mainly focusing to increase the country’s GDP growth rate. It feels that industries and service sector are needed to meet the growing demands of the growing population as its share to Gross State Domestic Product (GSDP) of Odisha are 33.4 and 52.1 respectively (Economic Survey, 2014-15) . But it forgets that these farmers are also a part of this population and their needs cannot be overlooked in the name of higher growth. Loss of agricultural land might lead to food inflation which is widespread in many industrially developed countries. When industrialization takes place without a proper understanding of the needs of these people, it benefits only few groups of the society, namely the companies, the investors and industrial workers. Industries prefer to employing people who have prior knowledge of working in factories instead of inexperienced farmers. Hence, the country faces many internal protests by the local people when any industrial development takes place with no measures for ensuring rehabilitation and securing job opportunities for the farmers. The recent spread of violence in Ratnagiri over the Jaitapur Nuclear Power Plant project and the protests and resulting massacre in Nandigram and Singur indicate people’s frustration and anger at having their agricultural lands taken away for non- agricultural purposes (Youth Ki Awaz, 2011).
Odisha economy is diversifying at a faster rate that is happened due to transformation in the economy from Agricultural based to Industries and Service Sector Driven (Economic Survey, 2014-15). Odisha’s economy has been following a high growth path in last few years in terms of higher gross state domestic product (GSDP). The evidence clearly shows that the economy is balanced for a take-off to a high growth phase, almost similar to that at the national level (Panda, 2008). Before examining the contribution of all the three sectors, it is essential to know the contribution of these sectors towards Odisha economy. The diagram-1 shows the GSDP and its sectoral share in Odisha in 2014-15, which clearly depicts that the share of service sector is highest which is followed by industry and agriculture.
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Figure 1: GSDP and Its Sectoral Share in 2014-15
15.4
51.2 33.4
Agriculture Industry Service
Source: Economic Survey of Odisha 2014-15 Agriculture is the foundation of the industry and service sector. All the three sectors are inter-dependent with each other. Agriculture provides raw materials to other sectors. Currently, due to establishment of new industries, and infrastructural development; the demand for raw materials has shown a rising trend. The growing demand for raw materials can only fulfilled by raising the agricultural productivity. All the sectors contribute to each other in many ways. The following table-1 shows their inter linkage:
Table 1: Inter linkages between Agriculture, Industry and Service Sector Agriculture to Industry and Service Industry and Service to Agriculture Provides market for manufacturing products and Provides Seeds, Fertilizers, Pesticides, infrastructure Provides Raw Materials Provides Instruments needed for Irrigation and funds Provides Manpower to Industry and Service Provides Materials for infrastructure which helps in Marketing and Storage
Provides Food Security to Workers engaged in Provides Modern Equipment for Cultivation such as industry and service sector tractors Source: Economic Survey of Odisha (Various Issues)
If we observe last few years Sectoral composition data of Odisha economy, then it is observed that the service sector dominates the state economy which constitutes more than half i.e. 51.2 percent of Odisha Gross State Domestic Product (GSDP) in comparison to 33.4 percent share of industry and 15.4 percent share of agriculture in 2014-15 (Economic Survey of Odisha, 2014-15). The present study aims to empirically investigate the impact of three major sectors on economic development of Odisha. The remaining part of this paper is organized into four sections including introduction. Section 2 describes the nature, sources and methodology of the study. Section 3 presents the analysis of the empirical results and its discussion. Section 4 presents the summary and conclusion of the paper.
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Data, Variables and Methodology of the Study
The present study empirically examines the contribution of all the three major sectors on economic development of Odisha from 1980-81 to 2014-15. This study uses some selected macroeconomic variables like real Per-capita Net State Domestic Product (PcNSDP, as an indicator of economic development), and monetary valuation of all the three major sectors i.e. Agriculture (Primary), Industry (Secondary) and Service Sector for its empirical analysis. The whole study is based on the secondary annual time series data which is collected from the Economic Survey of Odisha 2014-15 published by the Government of Odisha. All the variables are expressed in terms of their real prices. This study uses the regression technique i.e. Ordinary Least Square (OLS) test to carry out the empirical analysis.
Before going to use OLS technique first it is essential to check the stationary properties of the variable in case of time series data. As our data is time series in nature, the study needs to test stationary property of the variables using unit root test, namely Augmented- Dicky Fuller (ADF) test to avoid the spurious regression results.
In the light of the above discussion of the literature review and variables definition section, the following equation is used as the basic model to find out the impact of three major sectors on economic development of Odisha. Here real Per-capita Net State Domestic Product (PcNSDP, is considered as the indicator of economic development. PcNSDP = f {Agriculture, Industry, Service} Here, PcNSDP is considered as the dependent variable and other three variables are taken as independent variables. The following model is specified to find out the impact all sectors on growth of Odisha economy by using the ordinary least squares (OLS) techniques which can be written as:
(dt) = 0 + 1 Agr t + 2 Indu t + 3 Ser t + ut ------(1) Here, d t = Economic development (Real per-capita NSDP) during the time period t
Agr t = Agricultural production in terms of their monetary value during the time period t
Ind t = Industrial production in terms of their monetary value during the time period t
Ser t = Service sector contribution in terms of their monetary value during the time period t ut = White Noise Error term
Analysis of Empirical Results
This section deals with the analysis of the empirical results and its discussion. The empirical result is calculated by using the simple regression analysis such as OLS test. It has
Samikshya , 2017 80 examined the stationary property of the time series data of 35 years which contains some trend. Before using any econometric tools in time series data, the first step is to identify whether the series is stationary or not. Unit root test is used to test the stationary property of the variables. Stationarity of the variables are very much desired as non-stationary series will produce spurious regression estimates and the resulting outcome will be of no practical relevance. Unit root test is a pre-requisite of testing long run relationship between two or more time series data (Granger, 1981). Augmented Dickey-Fuller (ADF) test is widely used in empirical research. Here, to test the stationarity of the variables we conducted all the three tests. The result of unit root test has shown in the following table-2. The test result suggests that all the variables are stationary in ADF and KPSS test.
Table 2: Unit Root Tests Result
Variables ADF ADF (1st Difference) Without Trend With Trend Without Trend With Trend PcNSDP 1.16 -1.4 -7.12* -7.96* Agriculture -3.41** -1.31 -8.33* -9.31* Industry 0.89 -1.55 -5.54* -5.63* Service 6.29 1.31 -2.27*** -4.51*
Notes: -*, ** and *** indicate significance at 1%, 5% and 10% level respectively.
Next, we examined equation-1 with applying the Ordinary Least Square (OLS) method. Here we calculate the simple regression to find out the impact of all sectors on economic development of Odisha where real per capita NSDP is considered as the indicator of economic development. The following table-3 shows the result of the simple regression test. Here PcNSDP is considered as the dependent variables. Table 3: Simple OLS results - Impact of Three Major Sectors on Economic Development of Odisha Dependent Variable: PcNSDP Time Period: 1980-81to 2014-15 Method: OLS Test Variable Coefficient t-Statistic Prob. C 49.31 14.57* 0.0000 Agriculture 0.0015 3.91* 0.0005 Industry 0.0034 9.38* 0.0000 Service 0.0002 2.84* 0.0007 R-squared-0.974, Adjusted R-squared-0.97, Durbin-Watson Statistics- 1.74, Prob. (F-statistics – 0.00)
Notes: -* and ** indicate significance at 1% and 5% level respectively. Source: E-views 5.0 Software
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From the above table 3, it is clear that the result which is drawn from the simple OLS technique is considered for analysis as it satisfies all the criteria of a good model. The values of both R2 and adjusted R2 are nearer to 1 which shows a good fit of the model (Gujarati, 2004). The value of the Durbin-Watson Statistics is 1.74 which is nearer to its ideal value 2. This Durbin-Watson Statistics shows the lower chances of the auto-correlation problem in the model. R2 and adjusted R2 values are nearer to 1 which shows that the economic development of Odisha is completely explained by the independent variables included in the model. Therefore, we consider the regression results of above table 3 for the analysis of equation 1, as the estimated regression results satisfy all the criteria for a good model.
(dt) = 0 + 1 Agr t + 2 Indu t + 3 Ser t + ut ------(1)
After putting the values of both coefficients and t-statistics in the equation-1, we will get,
(dt) = 49.31 + 0.0015 Agr t + 0.0034 Indu t + 0.0002 Ser t + ut ------(2) t-statistics (14.57*) (3.91*) (9.38*) (2.84*)
From the above regression results it is found that, the coefficients of agriculture, industry and service sector have shown significant positive impact on economic development of Odisha during the study period such as 1980-81 to 2014-15. The estimated coefficients of three major sectors i.e. agriculture, industry and service sector have shown positive and statistically significant which indicate that all the three sectors have positive contributes to real per capita net state domestic product of Odisha during the study period. Among them, industrial (secondary) sector has shown highest significant positive impact on PcNSDP of Odisha which is followed by agriculture and service sectors respectively. This may cause due to slowly transformation of Odisha economy from agricultural led economy to Industrial based economy which is clear from their share in GSDP. Though the share of service sector is highest in GSDP of Odisha but its contribution towards per-capita income is low in comparison to other two sectors. The coefficient of determination (R2 = 0.97) is quite high and reveals the goodness of fit of the model. This indicates the proportion of total variation in economic development (PcNSDP) explained by the explanatory variables included in the model and the rest will be explained by the external factors included in the error term.
Conclusions
The main objective of this study is to analyze the impact of three major sectors on economic development of Odisha using the OLS test over the period 1980-81 to 2014-15. The empirical result shows that all the three major sectors have been considered as a significant determining factor of economic development in Odisha during the study period. The empirical
Samikshya , 2017 82 test result states that among all the variables industrial sector has shown highest significant positive impact on economic development of Odisha during the study period in comparison to the other two sectors. Among all the three sectors, service sector has shown lowest significant impact on economic development of Odisha during the study period. If we compare its share on GSDP then it is highest among all the three sectors which may cause due to remarkable structural transformation of Odisha economy after the new economic reforms. Agriculture is necessary for survival whereas both industry and service sector are important for higher economic growth in Odisha. From the whole analysis, it can be observed that the contribution of all the three major sectors have their own importance towards the development of Odisha economy which can’t be ignored or substitute by each other.
Agriculture is and will remain the mainstay for a large proportion of rural population in the coming years, many of which are living below poverty line. Agriculture occupies a prominent position in Odisha policy-making not only because of its contribution to GSDP but also because of the large proportion of the population that is dependent on the sector for its livelihood. The fact for Odisha is that without agriculture, it cannot exist and without industry and service sector, it cannot develop. It’s necessary for Odisha government to make proper plans for the development of all the three sectors simultaneously. The following are some policy implications which will help to maintain a balance in all sectors. First, government should try to set up industries in those lands, which are either unproductive for agriculture or very low productivity or barren. Second, government must ensure that the people displaced due to industrialization and infrastructural development should be compensated adequately. Third, agro based industries should be encouraged as they used agricultural products in terms of their raw materials. Forth industries used labour intensive techniques should also be encouraged and aims to provide employment to the local people. Lastly, commercialization of agricultural sector is also one effective major to raise the income level of the farmers (Sahoo and Sethi, 2012). However, the study is not without its limitations. The study is constrained due to the unavailability of time series data of certain variables like data on employment and capital generation by all the three sectors.
References Bharadwaj, K. (1966). A Note on the Structural Interdependence and the Concept of a Key Sector. KYKLOS, 19. Dorward, A. et al. (2002). “Seasonal Finance for Staple Crop Production: Problems and Potentials for Rural Livelihoods in Sub Saharan Africa”, Working Paper, DFID Working Paper Rural Livelihoods in Sub Saharan Africa”, Working paper, DFID Policy Research Programme project 'Diverse income sources and seasonal finance for smallholder agriculture: applying a livelihoods approach in South Africa'. Wye, Imperial College.
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Economic Survey of Odisha (2014-15), published by Government of Odisha. Gujarati, Damodar, N. (2004). Basic Econometrics. Tata McGraw Hill. Granger, C.W.J. (1981). Some Properties of Time Series Data and Their Uses in Econometric Model Specification. Journal of Econometrics, 16. Hazari, B. (1970). Empirical Identification of Key Sectors of the Indian Economy. Review of Economics and Statistics, 52(3), 173-195. Mellor, J. (2001a). Reducing Poverty, Buffering Economic Shocks – Agriculture and the Non- Tradable Economy. FAO, Rome. Panda, Manoj (2008). Economic Development in Odisha: Growth Without Inclusion?, Working Paper Series No-2008-025, Indira Gandhi Institute of Development Research, Mumbai, [available from http://www.igidr.ac.in/pdf/publication/WP-2008-025.pdf] Ravallion M. and Datt G. (1996). How Important to India’s Poor is the Sectoral Composition of Economic Growth?. The World Bank Economic Review, 10, 1-25. Sahoo, K., & Sethi, N. (2012). Investigating the Impact of Agriculture and Industrial Sector on Economic Growth of India. OIDA International Journal of Sustainable Development, 5(5), 11- 22, ISSN 1923- 6662 (online). doi: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2185418 Tyler G, El-Ghonemy R. and Couvreur Y. (1993). Alleviating Rural Poverty through Agricultural Growth. Journal of Development Studies, 29, 358-64.
Administrative statistics show the way to effective policy decisions
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Use of Administrative Data in the Context of Kalahandi District Sri Bimbadhar Sethy Abstract
The paper makes an attempt to place a series of sectoral data stock being collected, compiled and maintained at district administration of Kalahandi district. The data are being widely used for administrative purpose, policy decisions and implementation of development programmes
Introduction
Administrative records containing various data collected by different Government agencies are useful for the purpose of official monitoring and evaluation of different programmes. These data are useful for research analysis. The information collected by different Government agencies is maintained regularly and updated. More over these data can be treated reliable as these are collected and recorded by Government agencies. Administrative data are regularly updated and there is a set of system for data collection and hence requires no additional cost for collection of data. It helps researchers for programme evaluation and understanding the socio economic scenario of a particular area or sector. Several administrative data usually used for statistical purposes. For the statistical purpose administrative data regarding Land revenue, excise revenue, small savings, number of vehicles registered, month wise annual rainfall, irrigation potentialities, fertilizer use and seed replacement, cooperative structure and credit status, banking credit, data of live stock and animal husbandry, fishery , Industry and mines sector, education and health are collected from the respective department and compiled for analysis. The analysis help Government to know the status and for future planning to improve.
In this paper administrative data of different line departments of Kalahandi district are used for analysis of the status of different topics/sectors for the period of two decades.
Kalahandi District at a Glance Total Geographical Area (Sq. Kms) 7920 Literates 802036 No. of Subdivisions 2 Male literates 484177 No of Blocks 13 Female literates 317859 No. of Gram Panchayats 272 Literacy rate 59.20 No. of Villages 2253 Female literacy rate 46.70 Total Population (2011 Census) 1576869 Density sqkm 199 Total Male Population (2011 Census) 787101 Sex ratio 1003 Total female Population (2011 Census) 789768 Child sex ratio(0-6) 957 District Headquarter Bhwanipatna,451Kms away from State capital Bhubaneswar connected through roadways and railways Name of Scheduled Tribes Kandha, Gond, Shabar, Lodha, Paroja Tourists places in Kalahandi district Manikeswari,Belkhandi Temple, Phulijharan , Dokarichanchara, Amapani Hills Major crops Paddy, Maize, Ragi, Bajra, Kangoo and Ulsi etc.
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Transport Sector (Vehicle Position)
The data of different classes of vehicle registered in different period help us to know the growing economic status. Bullock carts were playing as an important vehicle for transportation of goods both in rural and urban pockets till seventies except in Dangarla areas of Kalahandi district. Cycle was commonly used by most of the people during this period. In 1975-76, there were only 108 motor vehicles registered in the district which includes 69 motor cycles and scooters, 10 station wagons and jeeps, 6 cars, 2 taxies and 13 tractors and trolleys. Bullock carts are now rarely seen and its use is on the verge of extinction. The numbers of different types of vehicles registered after 1975-76 till 2015 have gone up many folds in the district.
Table 1 : Registration of different categories of vehicles (1975 to 2015) Number of Number of Vehicle Class Vehicles Vehicle Class Vehicles Registered Registered Motor cycle 59033 Ambulance 33 Moped 5944 School Bus 26 Scooter/Moped 3494 LMV (VAN-P) 21 Tractor (Commercial) 2297 Deluxe Bus 20 Trailer (Commercial) 2193 Motor Cycle (IMP) 12
L.M.V.(Car-P) 1774 Car Taxi 9
L.M.V.(Jeep/GYPSY-CC) 1481 Maxi Cab 8
Light Goods Vehicle 1193 LMV (VAN-CC) 5 Three Wheeler (Passenger) 574 Bus (CC) 5 Heavy Goods Vehicle 436 Mini Bus (SC) 2 L.M.V. (LEP/GYPSY-P) 382 Private Service Vehicle 2 Medium Goods Vehicle 178 Crane (PVTY.) 2 Tractor (Agriculture) 126 Tractor Trailer 2 LMV(CAR)(CC) 91 Motor Cycle with side car 2 Pickup VAN 68 Light Commercial vehicle 1 Three Wheeler (Goods) 59 Crane 1 Bus(SC) 59 Vehicle fitted with compressor 1 Tanker 1 Source- RTO, Kalahandi
It is observed that there is a considerable growth in the numbers of vehicles registered in the district. This indicates the growing economic trend of the district.
Agriculture Sector
Now coming in to Agriculture and allied sector from the administrative data we see that, in Kalahandi there is substantial increase of marginal farmers, small farmers and total numbers of farmers from 1984 to 2014-15, whereas the number of big farmers (possessing one full ceiling of land) has decreased during this period. The number of marginal farmers is 64751 in 2014-15 as against 35917 in 1984, the number of small farmers is 42516 and 28657 and the
Samikshya , 2017 86 number of big farmers is 54820 and 63534 respectively. Thus the total numbers of farmers has reached 162087 in the year 2014-15 as against 128108 in the year 1984. Interestingly doing this period the number big farmers have been reduced substantially, whereas the numbers of small & marginal farmers have increased. This trend may be due to implementation of land reform and tendency to keep small holdings.
In the field of Agriculture there is a trend of growth in Kalahandi. Keeping pace to it, farmers have adopted changes and there is gradual shift from Paddy to Non Paddy cash crops.
Table 2 : Changes of crop area over the period from 1994 to 2014. (Area in Hect.) Crops 1994 2014 Paddy 1,84,736 1,90,000.00 Maize 8,862 19,545.00 Millets 17,082 2,539 Pulses 95,432 83,341 Oilseeds 41,589 16,268 Vegetable 19,052 29,702 Cotton 1,358 44,677 Fiber 1,631 734 Spices 2,227 2,531 Sugarcane 2,996 2,017 Total 3,74,965 3,91,354
From the above table it is observed that there is increase in cultivated area of maize and cotton during 1994 and 2014. The vegetable cultivated area has also increased during last two decades. The increased area of the above crops is replacing the cultivable area of millets, pulses and oilseeds. The total cultivable area for the year 2014 includes the area of intercropping. An area of 19,628 ha is under cultivation of different fruit crops in the district. There are about 62 varieties of local & high breed scented rice were cultivated in an area of about 500 ha during 1994, which is reduced to about 100 ha during 2014-15. This is due to low yield of the crop and also due to lack of marketing facilities.There is also systematic attempt for seed replacement to increase the crop production in the district and official data reveal that 1.71 % seed replacement in the year 1994 has reached to the stage of 66 % during kharif season, 2016- 17.
Animal Husbandry Sector
Kalahandi district is having 21 veterinary hospitals and 129 livestock aid centers against 118 in 2009-10. There has been declined in the indigenous cattle population from 463427 in the year 2001, 291320 in 2007 to 277240 in 2012. Similarly indigenous buffalo population has declined from 95534 in 2001, 55724 in 2007 to 43764 in 2012. On the contrary cross breed cattle
Samikshya , 2017 87 increased from 17629 to 33635 in the said period. The population of pig, poultry is 6447 and 1000218respectively in 2007.The total meat production (MT) in 2010-11 is 159.15(sheep), 1831.36(goat), 210.36 (pig) totaling to 2670.84. There is only one poultry layer farm with bird capacity of 85000 and egg production is 1500000 per month. There are 30 private poultry broiler farms as on 31/10/2013. Similarly there are 6 private dairy farms for cross breed cattle and buffaloes with average daily milk yield ranging between 65 to 110 liters. The annual turnover of milk and egg production is 5243000 MT and 8.59 Million respectively. There is only one cattle and poultry feed unit with capacity of 50 quintals per day. The above administrative data available from the veterinary office reveal that despite potentialities in the district there is gradual reduction of livestock population and thus there is need for attention and special planning in this sector.
Employment Scenario
The District Employment Exchange was started at the district headquarters, Bhawanipatna, on the 17th September 1958.As regards the employment seekers generally Graduates in Arts, Science and Commerce; under graduates, matriculates, unskilled workers and typists registered their names in the Employment Exchange show the number of registration, placement and the position of the live register for the period 2009 to 2014.
Table 3 : Employment position
Registration Placement Year S.C S T Others Total S C S T Others Total 2009 1670 1135 3465 6270 28 21 26 75 2010 2184 1702 3350 7236 12 27 26 65 2011 2289 1484 5128 8901 10 18 28 56 2012 1450 919 3028 5397 24 31 49 104 2013 1897 1456 3839 7192 3 2 17 22 2014 1488 1200 2767 5455 9 2 28 39
Year Live Register S C S T Others Total 2009 7358 4176 17270 28804 2010 7272 4480 16809 28561 2011 8060 5106 18625 31791 2012 8247 5192 19186 32625 2013 8365 5287 19915 33567 2014 7511 4708 20185 32404 Source:- Database of Employment Exchange, Bhawanipatna
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Live register shows the social group wise accumulated figures of registration over a period of time. The official data maintained by district employment exchange indicate the employment scenario of the district which is not encouraging. This is due to the absence of industrialization, mining activities and growing commercialization.
Revenue Collection from Different Sources
The district contributes good revenue in various sectors to the state income. The Excise and Forest Corporation from its sale proceeds from timber, NTFP (Non-Timber Forest Product) and Kendu leaves and the revenue generated by RTA in the district shows a healthy growth. The corporate social responsibility of M/s. Vedanta Alumina limited also adds to the economic development potential of the district.
Table 4 : Revenue collection from excise (1988-89)
Year Collection (In Rs.) Year Collection (In Rs.)
1988-89 1,27,79,991/- 2002-03 4,43,75,902/-
1989-90 1,50,23,629/- 2003-04 4,56,43,812/-
1990-91 1,60,58,290/- 2004-05 5,33,59,740/-
1991-92 1,90,50,799/- 2005-06 5,96,26,947/- 1992-93 2,82,20,665/- 2006-07 6,14,19,573/-
1993-94 3,00,91,411/- 2007-08 7,05,99,305/-
1994-95 95,50,521/- 2008-09 7,72,49,893/-
1995-96 1,30,28,844/- 2009-10 8,83,64,765/-
1996-97 2,61,25,550/- 2010-11 10,41,72,099/-
1997-98 3,56,47,423/- 2011-12 11,81,24,961/-
1998-99 3,83,50,644/- 2012-13 13,30,77,517/- 1999-2000 3,18,62,680/- 2013-14 15,55,51,492/- 2000-01 4,29,91,449/- 2014-15 20,77,14,103/- 2001-02 3,82,37,093/- 2015-16 up to December-2015 14,65,64,265/-
Source:- District Excise Office, Kalahandi
The district contributes the highest amount of revenue in excise to the state. The collection of revenue from Excises Department indicates that there is a phenomenal rise in the revenue (3.48 times between 2005 and 2015). The revenue has increased by about 12 times whereas the population has increased by 3 times during the last 30 years. It may be due to increase of price and higher consumption of liquor per person in the district..
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Table 5 :Timber and Non-Timber Revenue (Rupees in lakh) Year Sale proceeds from sale of round Year Sale proceeds from sale of round timber timber & Kenduleaf & Kenduleaf Timber Firewood Kenduleaf Timber Firewood Kenduleaf 1980-81 ------198.24 1998-99 49.15 4.07 4,157.78 1981-82 ------162.23 1999-2000 52.66 8.02 2,639.87 1982-83 10.1 0.62 231.95 2000-01 67.3 14.07 3,180.72 1983-84 54.91 13.71 309.13 2001-02 83.95 9.46 4,042.95 1984-85 56.91 11.45 266.93 2002-03 112.89 7.28 4,157.46 1985-86 26.38 13.31 435.7 2003-04 34.54 6.4 3,591.00 1986-87 45.85 13.83 677.17 2004-05 85.96 11.07 1,345.19 1987-88 37.4 3.27 1,706.77 2005-06 97.66 9.98 1,330.76 1988-89 53.39 9.65 1,063.83 2006-07 257.19 30.89 2,647.42 1989-90 9.63 11.37 2,210.31 2007-08 290.52 33.7 2,888.62 1990-91 0.97 0.3 1,594.91 2008-09 218.26 23.88 2,804.48 1991-92 16.91 7.37 2,299.80 2009-10 293.77 32.91 3,359.68 1992-93 34.36 4.36 2,665.17 2010-11 241.53 40.78 3,631.34 1993-94 123.41 3.44 1,567.13 2011-12 188.6 58.43 4,604.34 1994-95 53.68 2.48 1,417.78 2012-13 265.52 37.35 4,482.08 1995-96 46.43 0.59 2,639.87 2013-14 187.95 21.08 3,988.60 1996-97 134.14 5.22 3,180.72 2014-15 273.52 38.22 4,283.08 1997-98 112.07 12.89 4,042.95 Source:- OFDC, Bhawanipatna, Kalahandi
The information shows that there has been an increase in the amount of sale proceeds from timber, fire wood and Kendu Leaf during the period from 1980-81 to 2014-15(21.6 times). However, so far as the revenue from Kendu Leaf is concerned there is a fluctuating trend in the revenue.
Table 6 : Revenue collection from Road Transport (In Rupees) Financial Year Target Collection amount % achieved 2006-07 6,30,00,000 5,40,18,980 85.74 2007-08 7,31,00,000 5,60,00,290 76.60 2008-09 6,74,00,000 6,32,92,164 93.00 2009-10 8,00,00,000 7,53,33,878 94.00 2010-11 9,04,00,000 7,69,27,655 85.00 2011-12 9,30,00,000 9,24,81,560 99.44 2012-13 11,44,00,000 10,48,94,885 92.00 2013-14 11,58,00,000 10,94,53,782 94.51 2014-15 12,44,00,000 12,57,22,696 101.06 2015-16 (up to 15.02.2016) 15,80,00,000 11,11,84,655 70.37
Source:- Regional Transport Office, Kalahandi
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The Regional Transport Authority, Kalahandi indicate a sizable growth of revenue collection from 2006-07 to 2015-16 (2.06 times). In percentage terms it is also satisfactory which shows that the economy is on a path of growth in terms of the standard of living of the people.
Table 7 : CSR Expenses, Vedanta Aluminium Limited Lanjigarh
Focus Area Financial year wise expenditure (Rs. in lakhs)
Year 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10
Health 0.50 2.55 1.54 15.00 28.23 32.73 49.62
Education 1.55 5.75 23.14 47.17 101.74 194.41 202.79
Culture & Sports 0.60 0.69 2.13 3.52 20.01 24.40 22.04
Sustainable Livelihoods 0.00 0.25 0.18 4.84 9.64 17.94 3.47
Environment value addition / 0.00 0.50 23.00 5.00 21.00 56.50 4.07 Social Forestry Total A 2.65 9.74 49.99 75.53 180.62 325.97 281.99
Rehabilitation colony 0.00 10.30 12.88 38.76 59.33 37.10 12.41
Infrastructure Development 441.98 1048.37 615.67 0.00 0.00 0.00 0.00 through RAC Infrastructure Development 1.18 31.45 34.29 142.75 1679.91 2349.68 307.28 by Company Infrastructure recommend- 0.00 0.00 29.34 7.00 0.00 535.59 0.00 ation of district administration
Contribution to LPADF 0.00 0.00 0.00 0.00 0.00 1570.80 0.00
Total B 443.16 1090.12 692.18 188.51 1739.24 4493.17 319.68 IVDP through a national 0.00 0.00 0.00 0.00 0.00 0.00 0.00 NGO Impact / Social study by 0.00 0.00 0.00 0.00 0.00 0.00 0.00 External Agency Misc.- Special strategic CSR 0.00 0.41 2.26 26.71 8.08 217.33 10.54 Projects
Administration Expenses 0.00 4.39 9.70 18.51 51.19 86.38 78.37
Mines 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Total C 0.00 4.79 11.96 45.22 59.27 303.91 88.91
Grand Total (A+B+C) 445.81 1104.65 754.14 309.26 1979.12 5122.85 690.59
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Table -7 : CSR Expenses, Vedanta Aluminium Limited Lanjigarh (Concld.) Focus Area Financial year wise expenditure (Rs. in lakhs) Total Year 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 Amount Health 108.64 191.90 202.10 206.44 185.73 160.11 1185.10 Education 370.67 264.58 167.63 203.64 423.96 176.48 2183.51 Culture & Sports 46.31 0.00 0.00 0.00 0.00 0.00 119.69 Sustainable Livelihoods 29.80 11.66 2.96 6.92 13.07 73.68 174.40 Environment value addition / Social 1.16 0.00 0.00 0.00 0.00 0.00 111.23 Forestry Total A 556.57 468.14 372.70 417.01 622.76 410.27 3773.94 Rehabilitation colony 5.00 9.03 49.50 8.42 22.10 0.00 264.83 Infrastructure Development through 0.00 0.00 0.00 0.00 0.00 0.00 2106.02 RAC Infrastructure Development by 164.62 0.00 0.00 0.00 6.11 60.78 4778.05 Company Infrastructure recommendation of 0.00 0.00 0.00 0.00 0.00 0.00 571.93 district administration Contribution to LPADF 0.00 0.00 0.00 0.00 0.00 0.00 1570.80 Total B 169.62 9.03 49.50 8.42 28.21 60.78 9291.63 IVDP through a national 0.00 8.42 2.81 0.00 0.00 0.00 11.23 NGO Impact / Social study by 0.00 0.00 0.63 0.00 2.90 10.78 14.32 External Agency Misc.- Special strategic 42.36 6.15 1.39 4.83 126.40 36.82 483.27 CSR Projects Administration Expenses 165.04 163.43 109.91 85.94 116.58 64.47 953.91 Mines 0.00 0.00 0.00 0.00 0.00 33.55 33.55 Total C 207.40 178.00 114.74 90.78 245.88 112.07 1462.72 Grand Total (A+B+C) 933.59 655.18 536.93 516.20 896.86 583.12 14528.29 Source:- Vedanta Alumina Limited, Lanjigarh, Kalahandi
The data of CSR investment reveals that there is a substantial contribution to different sector by this industry. Incidentally Vedanta is the only large industry in the district which is struggling due to non availability of raw material. With the increase in the field of industrialization the district can be benefited.
7. Health Sector
The health system caters to about 16 lakh population of the district which includes Rs.4.5o lakh and 2,.86 lakh SC (48% approximately) living in an area of 8364.89 sq,kms in 13 blocks of the district. Both curative and preventive majors are undertaken by the district administration with the following health infrastructure Samikshya , 2017 92
1. DHH- (District Head Quarter Hospital) - One 2. SDH (Sub-Divisional Hospital) - One 3. CHC (Community Health Center) - Sixteen 4. PHC(N) (Primary Health Center New) - Forty-three 5. Other Hospital (Police Hospital & Jail Hospital) - Two 6. Sub Center (in number) - 241. in 310 Gram Panchayat and 2236 Villages 7. Anganwadi Centers (in number) - 1830 8. ASHAs (Accredited Social Health Activities) - 1727
The position of manpower sanctioned and available in 2016 is given in table underneath
Table 8 : Medical Personnel in Kalahandi District
Sl. No Name of the Post Sanctioned In Position Vacancy 1 Doctors 193 111 69 2 Staff Nurse 127 93 34 3 Pharmacist 75 75 0 4 MPHW(M) 178 176 2 5 MPHW(F) 322 321 1 6 MPHS (M) 59 59 0 7 MPHS (F)/ LHV 45 32 13 8 L.T. 36 27 9 9 Radiographer 7 5 2 10 Staffs under NHM 535 467 68
The total numbers of doctors include 155 Allopathic Doctors, 16 Homeopathic Doctors, and 17 Ayurvedic Doctors and 83 Nurses, 11 Homeopathic Assistants, and Ayurvedic Assistants plus 10 Additional Contractual Doctors.At present the total bed strength is 217 and 100 bedded MCH center is under construction within the campus.
Table - 9 :Vital statistics of Kalahandi (2008 to 2014-15)
Indoor Out Door Dist. total Dist. Total Total Infant Maternal Year Patient Patient Birth Death Death Death 2008-09 136924 1132235 30469 9641 1028 23 2009-10 122659 1336970 31548 9715 1187 27 2010-11 108695 215113 26763 9780 1131 17 2011-12 124204 252212 28393 9866 808 7 2012-13 132408 254723 28867 11033 829 2 2013-14 146306 266405 26647 10501 707 5 2014-15 NA NA 28560 11586 869 1
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Thus considering the population and requirement, the district is lacking adequate man power and infrastructure in health sector which need improvement and focus.
Conclusion
From the above it is onbserved that arious data available from administrative records are immensely helpful to know the trend of development in different sectors of the district. Besides, helpful to research scholars and law enforcement authority, on proper analysis and evaluation these data is also helpful for future planning process. There is ample scope to improve the administrative data collection system and converting the information to data sets appropriate for research and analysis. However there is need to be cautious while using such data due to lack up quality control over the data in some cases, possibility of missing item or records etc. which need proper scrutiny.
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Migration in Odisha: Factors and Impacts
Smt. Indira Garnaik
Abstract:
This paper is based on the result of the state report of NSS (National Sample Survey) 64th round published by Directorate of Economics and Statistics, Odisha. .It focused on the reasons of migration in rural Odisha. Migrations from rural to urban are caused by different factors. The major factors of migration are Marriage, Employment and Education etc. Urban towns provide scope for employment in various sectors and also offer modern facilities of life. Thus, they act as ‘magnets’ for the migrant population and attract people from outside. In other words, cities pull people from rural areas. This is known as “pull factor”. People also migrate due to ‘push factors’ when they do not find means of livelihood in their home villages, natural disaster, social\political problem and displacement by development projects they are ‘pushed’ out to the nearby or distant towns. Millions of people are migrated from their far-off villages to the cities.
Key Words :NSS, Migration, UPR, Population Census, Out migration
Introduction
Official statistics refer to public information which is produced for the benefit of the society. It provide the quantitative basis for the development and monitoring of Government’s social and economic policies and decision making. In the field of official statistics migration has its special importance. AS we know, migration is one of the most important demographic component to determining the size, growth and structure of population of a particular region, besides fertility and mortality. For a state like Odisha, the study of movement of population in different parts of the state helps in understanding the dynamics of the society and societal change better. We all know that, urbanization is taking place at a faster rate in Odisha .Due to the adoption of mixed economy, private sectors are growing more. As per census 2011 the total Population of Odisha is 4,19,74,218, of this rural population is 3,49,70,562 and the urban population is 70,03,656.In percentage terms, the rural population constitutes 83.3% and urban population 16.7% of the total population. There has been an increase of 1.7% in the proportion of urban population in the last decade. Odisha is the least urbanized state in comparison to other developed states. Rural to Urban migrations are caused by a variety of factors including economic, social and political factors. In nutshell major factors of such migration are Marriage, Employment, Education and Lack of Security and ‘Pull’ and ‘Push’ Factors.
Samikshya , 2017 95
Objectives
The main objectives of this paper are as follows: To analyze the causes of rural to urban Migration. To examine the factors effecting rural to urban Migration. To examine the impact of Migration To suggest some policy measures.
Source of data and methodology
This paper is based on secondary data collected from the state NSS (64thround, July 2007-June 2008) t on “Migration in Odisha” published by Directorate of Economics & Statistics, Government of Odisha. percentage distribution of migrants by reason for migration has been used to show the factors and impact of rural migration in Odisha.
Results and Discussions
The analysis of causes of migration in Odisha has been carried out by using the 64th round NSS data. In the present paper further analysis was made on the basis of the following reasons. (i) For employment: in search of employment, in search of better employment and to take up employment / better employment: (ii) To start a business in a new place of shifting of the existing business. (iii) Transfer of service/ contract (iv) Proximity to place of work (v) For studies (vi) Due to natural disaster like earthquake, drought, flood, cyclone and tsunami. (vii) Due to social/ political problem such as riots, terrorism, political refugee, law and order. (viii) Due to displacement by development project such as construction of dams, power plants, or starting of a new factory, etc. (ix) By acquisition of own house/ flat (x ) Due to housing problems: (xi) For better health care facilities (xii) Post retirement stay in native place. (xiii ) Due to marriage in case of women (xiv) Due to migration of parent/ earning member of the family
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Percentage distribution of households reporting out migration by former members:
Figure -1 gives information on Fig:1 Percentage distribution of rural households of Odisha reporting migration whether any former member of the rural household had out migrated anytime in migrated the past. In rural Odisha the percentage of 33% not households having outmigration is 33% migrated whereas the percentage of households not 67% having out migration is about 67%.
Distribution by reason of migration
In Odisha the percentage distribution of the out-migrants, by reason for outmigration, is presented in Table-1.Majority of the out-migrants from the rural Odisha had migrated out marriage reasons which accounted for nearly 61% employment related reasons accounted for nearly 30 percent of the out-migrants from the rural areas and about 2 per cent of the out- migrants from the rural areas was due to other reasons. Other reasons include proximity to place of work, social\political problem, displacement by development project, acquisition of own house/flat, post retirement and housing problem etc. .Majority of the out-migrants from the rural Odisha had migrated out for Marriage reasons which accounted for nearly 61% .employment related reasons accounted for nearly 30 percent of the out-migrants from the rural areas and about 2 per cent of the out-migrants from the rural areas was due to other reasons. Other reasons include proximity to place of work, social\political problem, displacement by development project, acquisition of own house/flat, post retirement and housing problem etc.
Table 1 : Percentage distribution of persons who have out migrated by the reason for migration in rural Odisha Reason for out-migration Percentage Employment related reason 30.15 Marriage 61.25 Business 1.99 Transferor service/contract 1.21 Studies 1.02 Migration of parent/earning member of the family 2.43 Others 1.95
It is seen that the reasons for migration for males and females showed distinct pattern. Majority of the male out-migrants from both the rural areas of Odisha had migrated out for employment related reasons which accounted for nearly 98 per cent of the out-migrants from the rural areas. For female out-migrants from the rural areas, the reason for out-migration was predominantly for marriage, which accounted for nearly 99 per cent. Itis found that a
Samikshya , 2017 97 significantly higher proportion of males (81%) from the rural Odisha had migrated out for study compared to the female (19%) out-migrants.
Table 2: Sex wise percentage distribution of migrants by broad reason for migration (Rural Odisha) Reason for Migration Percentage Male Female Employment related reason 97.96 2.04 Studies 80.74 19.26 Social/Political/displacement by projects & Housing related reason 23.89 76.11 Marriage 1.33 98.67 Migration of parent/earning member of the family/Post retirement 27.5 72.5 Others 95.17 4.83
Present place of residence of the out-migrants
For each of the out-migrants from the selected households information was collected regarding the present place of residence of the out-migrant. The distribution of the out-migrants by present place of residence has been presented at the all-Odisha (rural) level .It may be seen that a higher percentage (76.08) of out-migrants, from the rural areas of Odisha, took up residence within the State. The present place of residence for 23.59 percent rural out-migrants was outside the state.
Table 3: Percentage distribution of persons who migrated out by their present place of residence Present place of Residence of persons who migrated out Percentage Within the same state 76.08 Outside the State 23.59 Another country 0.31 Not known 0.02 All 100
Factors affecting Migration from Rural to Urban
There are various problems affecting massive distressed migration of people from rural areas to urban areas which causes unbalanced urbanization .Due to mass migration poverty, unemployment and underdevelopment increases. Firstly, poor, landless, illiterate and unskilled agricultural labours and poor farmers from backward rural areas moves to the urban towns, which fails to give them minimum employment. Such migration patterns, leads to urban slums and footpath dwelling and very poor level of living. Secondly, unskilled migrants coming into urban areas, where there is limited employment-generation capacity under industrialization, the migrants are paid less wages and mostly they are exploited due to lack of knowledge. Although
Samikshya , 2017 98 such migration helps to avoid starvation but it does not improve their economic conditions. Thirdly, such urban towns are filled with slums with poor development and more economic inequality. This will lead to extreme social disorder, severe class conflict, crimes.
Impact of Rural to Urban Migration:
With the liberalization in full swing and its impact on all sections of economy we are witness to the inexorable urbanization of the country. Our development programs have been geared towards economic growth and GDP growth with the belief that once GDP growth occurs there would be a spinoff in all areas like employment, health, education and living conditions Our planners work for 9% growth while agriculture cannot grow faster. As a sequel either the villages get relatively poor and disadvantaged and/or large-scale rural-urban migration continues. If this trend is not reversed quickly the rural income would become a small fraction of urban income. The urban population will grow beyond manageable levels with most living in slums.
Conclusion
About 33%of rural households had reported out-migration of its former members. This paper concludes that the migration from rural to urban is increasing with industrialization and modernization. The main reason for migration for rural male is employment related (97.96%). This shows that female usually migrates to accompany with male members with several other factors like after marriage (98.67%) or migration of parent\earning member of the family. It was found that a higher percentage (76.08) of rural out-migrant’s present place of residence was within the State and 23.59 per cent of out-migrant’s present place of residence was outside the state.
For the villagers to overcome poverty, villages should provide economic opportunities in non-agricultural sector need to be promoted for raising poor villagers income. All basic amenities like roads, electricity, safe drinking water, health facilities (health centres), electronic communication facility, job opportunities in business and service sectors should be develop by the government
Reference: 1. Report on Migration in Odisha (2007-08), published by, DE&S,Odisha 2. Census-2011
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Missing Women in Labour Market of Odisha: A Statistical Profile
Smt. Sanghamitra Mohanty Abstract This article highlights on the causes behind the recent decline in female labour force participation in Odisha and identify the factors underpinning the long-term stagnation in female participation. Increased attendance in education and higher household income levels are the possible causes of rapid economic development. The limitations lies in the difficulty of differentiating between domestic duties and contributing family work. Substantial numbers of women who are not counted in the labour force are, as described in the official statistics, ‘attending to domestic duties’ in their own households.
Key words: Labour force, participation rate, out of labour force, economic activity, usual activity status.
Data Source & Methodology
This article is prepared using thedata available in the report “District Level Employment and Unemployment Situation of Odisha”(Based on Central & State Pooled Data of68th Round NSS (2011 - 12), Govt. of Odisha).
Introduction
Female work participation is considered as an important indicator of women’s involvement in economic activities. As per the 1971 census the percentage of women workers in Odisha to total workers was 10.85 and increased to 31.35 by 2001, and rural work participation of women was at a higher 33.47 % while urban was only 15.45% as per 2001 Census. But, it exhibited a steady decrease to 20 % for rural female and 11% for urban females in 2011-12. Why is Odisha’s female labour participation rate so low? Part of the answer lies in the shift of economy from agriculture to service sector and partly in the methods employed to measure women’s work. Gender inequality in unpaid care work is the missing link in the analysis of gender gaps in labour outcomes, such as labour force participation, wages and job quality
The decline in agricultural employment, while perhaps desirable in the process of economic development, has not been sufficiently compensated by an increase in jobs in other sectors. The biggest losers of this phase of “jobless economic growth” have been women workers. Thus, along with jobless growth, women workers have to contend with poor quality, Samikshya , 2017 100 insecure jobs with a higher risk of harassment. These, along with some other factors, suggest that conditions on the demand side of the labor market are very undesirable for women workers. On the other hand women’s reduced participation in the labor force coincides with an increase in working-age women’s participation in “domestic and other allied activities.” These consist of production for household consumption—such as processing one’s own food or caring for animals that produce milk and meat for the household. It also includes cooking, cleaning, and caring for one’s own family. These activities increase the consumption of total goods and services by the household, but do not counted as an economic activity, and do not get reported in the national income statistics. This is unlike the case of services by a paid domestic help, which is considered an economic activity and is counted in the national income. However, society undervalues these immense contributions made by the ‘missing women' in labour market. This article provides a summary update examining the nature and causes of the observed trends in women, remaining out of labour force in Odisha over different NSS rounds.
Results and Discussion
Trends in female remained out of labour force over rounds, sectors and districts
Table 1: District wise per 1000 distribution of females remained out of labour force according to usual status over different rounds Rural Odisha.
Rural Urban Rural Urban District District 66th 68th 66th 68th 66th 68th 66th 68th Baragarh 741 685 759 846 Nayagarh 885 921 810 896 Jharsuguda 781 667 757 913 Khurda 916 948 878 924 Sambalpur 709 705 844 852 Puri 927 959 906 689 Deogarh 630 668 750 784 Ganjam 763 696 867 762 Sundargarh 638 567 803 885 Gajapati 711 666 738 846 Keonjhar 786 809 772 860 Kandhamal 755 663 828 838 Mayurbhanja 745 775 925 936 Boudh 792 614 658 796 Balasore 875 854 911 895 Sonepur 755 664 798 827 Bhadrak 944 961 949 909 Bolangir 694 711 787 915 Kendrapara 870 905 864 922 Nuapada 882 852 920 834 Jagatsinghpur 854 889 862 922 Kalahandi 742 902 738 908 Cuttack 831 881 840 957 Rayagada 610 845 847 898 Jajpur 884 876 874 888 Nawrangapur 675 613 820 884 Dhenkanal 920 885 874 909 Koraput 632 827 740 910 Angul 833 800 901 838 Malkangiri 684 739 799 839 All Odisha 781 798 844 881
The above table shows that compared to 2009-10, in 2011-12, the number of female out of labour force per 1000 female population, increased for both rural (by 1.7 percentage point)
Samikshya , 2017 101 and urban (by 3.7 percentage points) at all Odisha level according to usual status (PS+SS). That is, a staggering numbers of women have withdrawn from the labour force and attend to domestic duties over the years. The issue of the missing women in the state’s population has a parallel consequence in the problem relating to the missing women in workforce. Rayagada registered a maximum increase of 23.5 percentage point against Boudh which exhibited a 17.8 percentage points decrease in the same in rural Odisha. In the case of urban Odisha, Koraput and Kalahandi showed a highest increase of female out of labour force (17 percentage points) over the year 2009-10.Conversly Puri district reported that, there is a decrease of the rate by 21.7 percentage points. Above facts are represented graphically in figure 1-2 below.
Figure 1: District wise per 1000 distribution of females remained out of labour force according to usual status over different rounds Rural Odisha
1000
900 798 800 781 700
600
500
All
Puri
Jajpur
Angul
Boudh
Khurda
Cuttack
Ganjam
Koraput
Sonepur
Gajapati Bhadrak
Deogarh
Balasore
Bolangir
Nuapada
Baragarh
Keonjhar
Nayagarh
Rayagada
Kalahandi
Dhenkanal Sambalpur
Malkangiri
Jharsuguda
Sundargarh Kandhamal
Kendrapara
Mayurbhanja Nawrangapur 68th 66th Jagatsinghpur Figure 2: District wise per 1000 distribution of females remained out of labour force according to usual status over different rounds Urban Odisha
1000 881 900
800 844 700
600
500
68th 66th
Samikshya , 2017 102
Prevalence of not in labour force among male and female of age 15-24 in 2011-12 in usual status (ps+ss)
The increase in the number of female out of labour force per 1000 female population, over that of male was nearly the same (36.6 and 36.5 percentage points) for both rural and urban at all Odisha level according to usual status (ps+ss). That is, a voluminous number of women have withdrawn from the labour force in the age group of 15 to 24 years. This may be due to mass participation in the education. If this is the cause then what about male counter parts? Are they not participating in education. Rather, it suggests that women of this age group were pushed back into the undervalued and invisible domestic spheres.
Table 2 : District & sex wise per 1000 distribution of 15 to 24 years age group persons remained out of labour force in usual status (ps+ss) during 2011-12 in Odisha.
Rural Urban Rural Urban District District Male Female Male Female Male Female Male Female Baragarh 352 690 260 774 Nayagarh 180 757 343 942 Jharsuguda 302 664 488 963 Khurda 423 898 724 953 Sambalpur 313 670 502 877 Puri 426 905 109 942 Deogarh 425 770 424 936 Ganjam 367 678 618 786 Sundargarh 182 377 475 908 Gajapati 172 480 518 955 Keonjhar 336 694 503 755 Kandhamal 228 529 633 924 Mayurbhanja 433 793 879 937 Boudh 340 577 435 895 Balasore 359 852 621 726 Sonepur 426 618 412 825 Bhadrak 486 954 255 871 Bolangir 364 669 656 960 Kendrapara 798 849 683 979 Nuapada 509 622 433 933 Jagatsinghpur 437 875 542 949 Kalahandi 423 811 469 862 Cuttack 386 939 512 954 Rayagada 282 683 800 960 Jajpur 486 862 285 910 Nawrangapur 173 366 455 908 Dhenkanal 409 853 401 865 Koraput 203 835 609 923 Angul 401 877 475 797 Malkangiri 228 532 616 756 All Odisha 387 753 533 898
The following figures-3 -5 throw light over the district wise information of the above situation in both rural and urban scenario of Odisha labour market. According fig-3, the condition of urban Odisha in this regard is much alarming than rural. In Rural sector, Koraput district reported the highest female to male differential (63.2 percentage points) while Kendrapara district showed the lowest (5.1 percentage points). In Urban sector, Puri district topped with 83.3 percentage points and Mayurbhanj was placed at the bottom (5.8 percentage points).
Samikshya , 2017 103
Figure 3 : District wise per 1000 distribution of females of 15 to 24 years age group remained out of labour force in usual status during 2011-12 in Rural Odisha
1000 900 800 700 600 500 400 300
Rural Urban
Figure 4 : District & sex wise per 1000 distribution of 15 to 24 years age group remained out of labour force in usual status in 2011-12, Urban Odisha
1000 800 600 400 200 0
15-24_Male 15-24_Female
Figure 5 :District & sex wise per 1000 distribution of 15 to 24 years age group remained out of labour force in usual status in 2011-12, Urban Odisha
1000 800 600 400 200 0
15-24_Male 15-24_Female
Samikshya , 2017 104
Comparison of Female labour force participation rate (FLFPR) to females remaining out of labour force.
Table 3 presented that Bhadrak district in rural and Cuttack district in urban Odisha had the highest number of female population missing from the labour force ( both nearly 96 percent) registering an all Odisha value nearly 80 percent in rural and 88 percent in urban sector in 2011- 12.
Table 3 : District & sector wise per 1000 distribution of female labour force participation rate and out of labour force in usual status during 2011-12. District Rural Urban District Rural Urban FLFPR Out of FLFPR Out of FLFPR Out of FLFPR Out of Labour Labour Labour Labour Force Force Force Force Baragarh 315 685 154 846 Nayagarh 79 921 104 896 Jharsuguda 333 667 87 913 Khurda 52 948 76 924 Sambalpur 295 705 148 852 Puri 41 959 311 689 Deogarh 332 668 216 784 Ganjam 304 696 238 762 Sundargarh 433 567 115 885 Gajapati 334 666 154 846 Keonjhar 191 809 140 860 Kandhamal 337 663 162 838 Mayurbhanja 225 775 64 936 Boudh 386 614 204 796 Balasore 146 854 105 895 Sonepur 336 664 173 827 Bhadrak 39 961 91 909 Bolangir 289 711 85 915 Kendrapara 95 905 78 922 Nuapada 148 852 166 834 Jagatsinghpur 111 889 78 922 Kalahandi 98 902 92 908 Cuttack 119 881 43 957 Rayagada 155 845 102 898 Jajpur 124 876 112 888 Nawrangapur 387 613 116 884 Dhenkanal 115 885 91 909 Koraput 173 827 90 910 Angul 200 800 162 838 Malkangiri 261 739 161 839 All Odisha 202 798 119 881 Figure 6 : District wise per 1000 distribution of female labour force participation rate and out of labour force in usual status during 2011-12. Rural Odisha.
100%
80%
60%
40%
20%
0%
FLFPR Out of labour force
Samikshya , 2017 105
Figure 7 : District wise per 1000 distribution of female labour force participation rate and out of labour force in usual status during 2011-12. Urban Odisha. 100% 80% 60% 40% 20% 0%
FLFPR Out of Labour Force
Conclusion Development scholars and policymakers often assume that economic growth lead to unshackle women from the confines of the domestic sphere, increase their social status, and allow them to participate in economic and political decision-making as equals. The women’s participation in the labor market has continued into the current period during which India has experienced robust economic growth. Withdrawal from the labor market does not allow women to engage in leisure activities; it has instead pushed them back into the undervalued and invisible domestic sphere. From this perspective, female LFPR can be expected to depend on the availability of suitable jobs such as farming, which are flexible and mainly ‘at home’ also the flexibility offered by working in the farm or in casual jobs will be highly valued. Inclusion of domestic and other allied activities in the calculation of women’s labour force participation rate is necessary for higher value addition by women labour.
References: 1. “District Level Employment and Unemployment Situation of Odisha”-68th Round NSS (2011 -12) 2. “Report On Employment and Unemployment Situation of Odisha-District level results”- NSS 66th Round 2009-10 World bank group-Policy Research Working Paper 8024 –“Precarious Drop, Reassessing Patterns of Female Labor Force Participation in India”. 3. ILO Research Paper No. 10-“Why is female labour force participation declining so sharply in India? “ 4. India Census 2011. Provisional Population Totals. 5. Odisha Review-February - March – 2012 6. “Unpaid Care Work: The missing link in the analysis of gender gaps in labour outcomes”- OECD Development Centre, December 2014 7. Berniell, M.I. and C. Sánchez-Páramo (2011), “Overview of Time-use data Used for the Analysis of Gender Differences in Time Use Patterns”, Background paper for the WDR 2012. 8. Vol. 51, Issue No. 44-45, 05 Nov, 2016 » Domestic Labour and Female Labour Force Partici.ation
Samikshya , 2017 106
Consumption Pattern of Odisha Smt. Parbati Barla
Abstract
Increasing level of personal income influence the consumption pattern of the people. The constantly rising income level has seemingly influenced the consumption pattern and consumption expenditure of Odisha. The food and non-food expenditure in the context of economic classes have been statistically explained in the paper.
Introduction
Consumptoin have been expanded and diversified in view of rising income and standard of living. The standard or quality of life can be accessed from the consumption pattern .If the percentage share of expenditure on non-food items like education,medical etc is more than food items than we can say the standard of living is high and vice-versa.
Source of Data and Methodology
The state sample results of National Sample Survey (NSS) 68th round has been used in this paper. The data has been collected on “Household Consumer Expenditure” by Directorate of Economics and Statistics (DE&S), Odisha during July’2011 to June’2012. An attempt has been made to show the living standard of people in Odisha by comparing the percentage share of households consumption expenditure on food and non-food item.
Objective
To compare the stectorwise average household size by different economic classes in Odisha; To examine the consumption pattern of different economic classes in Odisha
Results and Analysis
Monthly Per capita Consumer Expenditure (MPCE)
Economic Class of population may be referred to simply as “0-5%”, “5-10%”, “10- 20%”, … 80-90%, 90-95%, 95-100% showing 12 economic classes. Estimates of population characteristics are often generated separately for population in different economic classes in order to portray the variation of such characteristics with variation in MPCE. Economic classes wise MPCE have been formed separately for rural and urban sector of Odisha.
Samikshya , 2017 107
Figure 1 : Sector wise average household size across economic classes of Odisha
5.5 5 4.5 4 3.5 3
2.5 Average size Average household 2 1 2 3 4 5 6 7 8 9 10 11 12 Economic class Rural Urban
It is observed that, the average household size decline steadily as MPCE level increases in rural sector. The trend is not so clear up to 6th economic class in urban sector, however it decreases towards upper classes. It is highest in economic class-3 in urban sector. There exists a difference between the minimum household sizes of rural to urban sectors; the differential being 0.8.
Figure 2 : Sector wise average number of children across MPCE fractile classes of Odisha
2.5
2
1.5
1 Average childrenAverage no.of 0.5 rural urban 0 1 2 3 4 5 6 7 8 9 10 11 12
MPCE fractile class
On the other hand average no of children is highest in 3rd economic class in urban sector. If we divided the total economic class into two parts, the lower part i.e. economic classes 1 to 6 show the average no of children is higher in poorest classes. In the upper half of the economic classes i.e. from 7th to 12th, the average no of children is lower in richer classes. At the 11th economic class, both the sectors show nearly equal level of average no of children.
Samikshya , 2017 108
Consumption pattern
The main indicator of standard of living is monthly per capita expenditure (MPCE), within the same consumption level the pattern of consumption would differ because of differences in need and preference. As such the pattern of consumption expenditure have been divided in to two groups i.e Food and Non-food items.
Figure 3: Percentage share of food in Consumer Expenditure of Odisha
30 25 Economic classes 20 15 10 5 0 1 2 3 4 5 6 7 8 9 10 11 12
cereals pulses & pulse prdcts milk & milk products: edible oil vegetables
It is observed that, percentage share of cereals and vegetables decline steadily as MPCE level increases. The share of pulses & pulse products and edible oil are more or less parallel. The share of milk & milk products is in increasing order as MPCE level increases. If we divided the total economic class into two parts the lower part i.e from economic class 1 to 6 shows that the share of cereals and vegetables are highest in poorest class. This satisfies Engel’s law which states that as income rises, the proportion of income spent on food falls, even if absolute expenditure on food rises and vice versa.
The upper half of the economic class i.e from 7th to 12th show the comparatively richest class the share of cereal and vegetable are lowest. It is observed that those people who are at lower economic class spending more on essential commodities like cereals and pulses to sustain and unable to afford for milk, vegetables etc. due to their low income. On the other side those people at the rich classes are comparatively higher group and afford all the things. For the higher economic group the percentage share of consumption of cereals and pulses are low. Whereas the percentage share of consumption of milk & milk products are in an increasing trend towards the higher economic class. This shows a better standard of living of people who are in the higher economic class then lower class. Samikshya , 2017 109
Figure 4 : Percentage share of Non_food items in Consumer Expenditure of Odisha
16 Economic classes 14 12 10 8 6 4 2 0 1 2 3 4 5 6 7 8 9 10 11 12
fuel and light clothing & footwear education medical (institutional) Conveyance durable goods
For non- food items the fuel, light and clothing are the basic necessity of people. It is observed that percentage share of expenditure on fuel light and clothing& footwear is more among the lower economic class people. On the other hand it is less for people belonging to higher economic class. Education is important for the development of knowledge and efficiency of a person. Here it is observed that percentage share of expenditure on education is more in higher classes. A wide gap is found among lower and higher economic class in expenditure on education.
Conclusion
This paper shows expenditure pattern of people living in Odisha. With the growth in MPCE an increase in expenditure on non-food items i.e education, medical and durable goods is observed. It is also observed that if income of a household increases, the proportion of expenditure on food item decreases while the proportion spent on other goods increases. On the other hand the proportion of expenditure on food items increases when income decreases.
Reference 1. Level and pattern of consumer expenditure of Odisha, 68th round NSS, 2011-12 2 https:// en.wikipedia.org.
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Inflation: An Odisha Experience
Smt. Anita Dash Abstract
Inflation has direct bearing on purchasing power, standard of living and economic growth of the State and Nation. The paper makes in depth analysis on sub-group wise (food and non- food) CPI trend to evaluate the inflationary situation in Odisha with appropriate weighted diagram.
Introduction
Inflation is defined as a sustained increase in the general level of prices for goods and services in a county, and is measured as an annual percentage change. In other words, Inflation is raise in price of goods and services, which decrease the purchasing capacity of the people. When the general price level rises, for each unit of the currency fewer goods and services can be purchased. Consequently, the purchasing power of customer would gradually decrease. That means the standard of living of the people would also be deteriorated. In this situation the real value of the currency would less. It is known that Small amount of inflation is regarded as a healthy sign for an Economy and controlled inflation is a sign of growth. Small increase in the price of goods and services over a period of time helps the Economy move. Inflation makes the goods more competitive in the international market. It helps increase wages, salaries etc. which thereby increases the purchasing power of the consumer and eventually increasing the demand for goods. It’s a chain reaction which does not lead to stagnation of an Economy.
According to some great Indian economists and scholars, the acceptable range of inflation rate in India lies between 3-7 percent. (Loksabha Secretariat Chronicle). Therefore, the persistence increase of the year on year inflation rate beyond the above prescribed limit is hazardous for the overall economic as well as social development of our country. India has experienced persistently high inflation in recent years, despite a period of below-trend economic growth. As a result, controlling inflation has become a key objective for policymakers. The two main indicators of inflation in India are the wholesale price index (WPI) and the consumer price index (CPI). Although the WPI has traditionally been the most widely used measure for assessing inflationary pressures, the CPI based inflation is being adopted as per the recommendation of the Reserve Bank of India (RBI) with the advantage that,
The CPI places a much larger weight on food items than the WPI, since it is weighted on the basis of household expenditure. Samikshya , 2017 111
Food, beverages and tobacco have a combined weight of nearly 50 per cent (compared with around 25 per cent in the WPI). Also, the CPI includes housing and services which are notable omissions from the WPI. Thus, now Central Statistical Office (CSO) of the Ministry of Statistics and Programme Implementation has started compiling a new series of CPI for the entire urban population, entire rural population and consolidated CPI for Urban + Rural is being compiled based on above two CPIs. These new indices are now compiled at State / UT and all India levels.
Objective of the Study
To give a brief idea about Inflation of our economy. To investigate the present scenario of Inflationary trend of Odisha in a brief and its impact on the common man of the society. Data Sources
The monthly time series data on Consumer Price Index (Base=2012) of General level as well as of all groups and sub-groups from the month of January 2011 to December 2016 of Odisha and the Group & Sub group wise Weights of the Items included in the Market Basket of CPI from the Official Website of Central Statistical Organisation (CSO), MOSPI, GOI.
Findings First Stage
The Table 1 represents the Yearly Average General Inflation rates of (Rural, Urban & combined) of Odisha from the year 2012 to 2016.
Table 1 :Yearly Inflation Rates of Odisha
Year Rural Urban Combined 2012 8.13 8.76 8.30 2013 10.60 9.36 10.24 2014 7.46 6.71 7.26 2015 6.95 3.76 6.06 2016 7.56 3.69 6.52
Samikshya , 2017 112
Figure 1 : Yearly Inflation rates of Odisha
12.00 10.24 10.60 10.00 8.76 7.56 8.00 8.13 9.36 7.46 6.95 8.30 7.26 6.00 6.71 6.52 6.06 4.00
2.00 3.76 3.69 Rural Urban Combined 0.00 2012 2013 2014 2015 2016
Figure 1 is the graphical representation of the above table. It may be seen from the figure that that the line showing the Inflation of Rural areas of Odisha is higher than the other two lines (depicting Urban and combined Inflation rates) in all the Years expect 2012. All the three lines reach at their maximum in the year 2013, i.e., 10.60 points, 10.24 points & 9.36 points for the Rural, Urban as well as the combined (Rural+Urban) areas respectively. The overall inflation rates (combined) of Odisha is higher than the acceptable limits in these five years, where as it is worse in case of Rural areas which is not good for the health of the economy as a whole. It can be visualised from the above picture that the trend of the combined inflation rates of Odisha varies in depending more on the inflation of Rural areas than the urban Odisha which is not good for the welfare of our state as a major concentration of the population of our state is in Rural areas.
Second Stage
To be more precise, we have to analyse the details about it in the context of Rural areas. As inflation measures the percentage change of the Average CPI derived from the fixed weighted Market Basket, hence, it is more important to know about the sub group / group wise items’ share (Weights) in the Item Basket of CPI. This is because, a small amount of rise in the prices of the items having relatively more amount of share of weights give rise a larger amount of increase in the overall CPI of the concerned society which in turn raises the level of Inflation and vice versa.
Table 2 :Group wise Weights of Rural & Urban Areas of Odisha in CPI (2012 Base)
Group Group Name Rural Urban Weight_percet Weight_percet_U _Rural rban 1 Food and beverages 1.7104 0.51631 58.39 39.47 2 Pan, tobacco and intoxicants 0.08987 0.02065 3.07 1.58 3 Clothing and footwear 0.21103 0.07837 7.20 5.99 4 Housing 0.28632 0.00 21.89 5 Fuel and light 0.3061 0.08262 10.45 6.32 6 Miscellaneous 0.61163 0.32378 20.88 24.75 General Index (All Groups) 2.92903 1.30805 100.00 100.00
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The Table: 2 reflects the group wise percentage share of the items of Rural as well as Urban areas of Odisha. It shows that the total as well as the group wise weights of the Items of the Rural CPI basket are more in than the Urban areas. The total Rural Basket Share of Odisha in India is 2.92903 which is more than double the Share in Urban Odisha (1.30805). That means, the Rural CPI contributes more than the Urban CPI to the rise and the fall of the Combined CPI of Odisha as a whole.
Figure 2 : Group wise rural weights
20.88 58.39 10.45 7.20
3.07
Food and beverages Pan, tobacco and intoxicants Clothing and footwear
Figure: 2 is the pictorial representation of the group wise percentage Share of the items weights of the CPI Rural of Odisha. This shows that the items share in the Food and beverages group is maximum (58.39) following the Miscellaneous, Clothing & footwear, Fuel & Light, and Miscellaneous groups respectively. This implies that the General level inflation rates of CPI Rural is mostly effected by the rise & fall of the prices of the items in the Food group having more than 50% weights in this followed by Miscellaneous, Clothing & footwear, Fuel & Light, and Miscellaneous groups respectively.
Table 3 : Group wise Yearly Inflation rates (CPI) for Rural Odisha
Group code Group Name 2012 2013 2014 2015 2016 1 Food and beverages 7.84 10.32 7.12 7.33 8.63
2 Pan, tobacco and intoxicants 12.82 8.39 6.40 13.12 11.90
3 Clothing and footwear 11.23 9.69 7.49 9.75 7.89
5 Fuel and light 12.62 12.06 7.76 5.83 2.88
6 Miscellaneous 7.73 6.68 5.90 5.73 6.75
General Index 8.13 10.60 7.46 6.95 7.56
Table 3 depicts group wise yearly inflation rates of rural Odisha from the year 2012 to 2016 along with the General Inflation rates.
Samikshya , 2017 114
Figure 3 : Group wise as well as general yearly inflation of rural Odisha
7.56 6.95 General 7.46 10.60 8.13 6.75 5.73 5.90 Miscellaneous 6.68 7.73 2.88 5.83 7.76 12.06 Fuel and light 12.62 7.89 9.75 Clothing and footwear 7.49 9.69 11.23 11.90 Pan, tobacco and 13.12 6.40 intoxicants 8.39 12.82 8.63 Food and beverages 7.127.33 10.32 7.84 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00
Figure 3 is the graphical representation of the above table which depicts the yearly Overall inflation rates along with its different Groups in respect of the Rural areas of Odisha. It can be seen that the general Inflation rate varies from 6.95 (2015) to 10.60 (2013) in these five years. Whereas the Miscellaneous group inflation rate varies from 5.73 (2015) to 7.73 (2012), Fuel and Light group from 2.88 (2016) to 12.62 (2012), Clothing and footwear group from 7.49 (2014) to 11.23 (2012), Pan, tobacco and intoxicants group from 6.40 (2014) to 13.12 (2015) and the Food and beverages group from 7.12 (2014) to 10.32 (2013) in these years. In the year 2013, the Overall inflation rate of Rural Odisha reaches at the maximum (10.60) accompanied with the Food and beverages rate by 10.32 points, Miscellaneous group rate by 6.68 points, Fuel and light by 12.06 points, Clothing and footwear group by 9.69 points and lastly, the Pan, tobacco & intoxicant group rate by 8.39 points. The general inflation rate (6.95) is minimum in the year 2015 with the fall in the Fuel & light and Miscellaneous group trends. Whereas, the Food & the Clothing & Foot wear inflation points are still more than the normal.
It is a matter of regret that, the inflationary trend in respect of the Food group of Rural Odisha is more than 7% in these five years which is more pathetic as more people of Rural Odisha are poor enough to pay for these. Here, it is worth mentioning that, in case of Odisha with a large share of population that is poor, food price inflation can be particularly important. This is because poor spend a large proportion of their income on food (over 50%) based on 61stround NSS survey, analysed in the planning commission report). These poor people are typically net buyers of food and their incomes are tend to be fixed.
Samikshya , 2017 115
It is now essential to analyse the sub group components responsible for the resultant hike of the Food inflation in these five years. Before analysing sub group wise the inflationary trends of the Food group, it is important to look deeply into the Sub group weights which contributes more to the Food group to be responsible enough for its changes.
Table 4 : Sub group wise weights of food group of rural Odisha – in CPI (2012 base)
Sub Group Sub Group Name Rural Sub Group wise Code Weights of Food Group
1.1.01 Cereals and products 0.54 31.29 1.1.02 Meat and fish 0.17 9.70 1.1.03 Egg 0.01 0.86 1.1.04 Milk and products 0.08 4.43 1.1.05 Oils and fats 0.10 5.96 1.1.06 Fruits 0.07 3.86 1.1.07 Vegetables 0.33 19.19 1.1.08 Pulses and products 0.10 5.63 1.1.09 Sugar and confectionery 0.04 2.20 1.1.10 Spices 0.08 4.94 1.2.11 Non-alcoholic beverages 0.03 1.70 1.1.12 Prepared meals, snacks, sweets etc. 0.18 10.24 1 Food and beverages 1.71 100.00 Table 4 depicts the sub group wise items’ weights along with their percentage of the Food Group.
Figure 4 :Sub-Group wise Rural Weights of Food Group
35.00 30.00 31.29 25.00 20.00 19.19 15.00 9.70 10.24 10.00 5.96 5.63 4.43 4.94 3.86 2.20 5.00 0.86 1.70 0.00
Samikshya , 2017 116
The Figure 4 is the graphical representation of the above table which reflects that the weight is maximum (31.29) in Cereal & product sub group, followed by Vegetable (19.19), Prepared Meals & snacks (10.24), Meat & Fish (9.70), Oils & Facts (5.96), Pulses & products (5.63),Spices(4.94), Milk & Product (4.43) & Fruits Sub group (3.86) points and likewise.
Table 5 : Yearly Inflation Rates of Sub groups contributing more to Food Group
Year Cereals and Meat and Vegetables Prepared meals, Milk & Pulses & products fish snacks, sweets etc. Products Products
2012 4.36 9.79 7.54 10.76 8.02 2.40
2013 7.93 13.76 25.81 9.45 9.78 7.89
2014 8.41 8.94 9.32 9.90 9.12 6.34
2015 7.76 5.90 -0.33 11.69 10.31 23.95
2016 6.70 8.05 8.79 6.64 8.68 19.22
Table 5 shows the yearly inflation rates of some sub groups of the Food Group having major Weights contributing a lot for the hike of the Food Inflation. The inflation rates of above sub group are the chief determinant of the Food inflation as shown in the table.
Figure 5 : Yearly food inflation of rural Odisha with its sub-groups
8.63 7.33 Food Group 7.12 10.32 7.84 19.22 23.95 Pulses & Products 6.347.89 2.40 8.68 Milk & Products 9.1210.31 8.02 9.78 6.64 Prepared meals, snacks, sweets etc. 9.9011.69 9.4510.76 -0.33 8.79 Vegetables 9.32 25.81 7.54 5.90 8.05 Meat and fish 8.94 13.76 6.70 9.79 7.76 Cereals and products 8.41 7.93 4.36 -5.00 0.00 5.00 10.00 15.00 20.00 25.00 30.00 2016 2015 2014 2013 2012 Figure 5 is the pictorial representation of the table 5 reflecting the Food group inflation rates along with six important Sub group rates (having more weights in the food group) of Rural Odisha from the year 2012 to 2016.This graph reflects the year wise variability as well as the abnormal hike of the
Samikshya , 2017 117 inflation rates of these important subgroups of the Food group in these five years in respect of Rural areas of Odisha. The inflation rates in respect of Cereal and product, Meat and fish, Vegetables, Prepared meals, snacks, sweets etc, Milk & Products and Pulses & Products varies from 4.36 to 8.41, 5.90 to 13.76, -0.33 to 25.81, 6.64 to 11.69, 8.02 to 10.31 and 2.40 to 23.95 respectively in these five years contributing a lot for the yearly Food group inflation rates which in turn is mostly responsible for the determination of the variations of the General Inflation rates of Rural Odisha. As a result, reflecting the Combined (Rural +Urban) yearly General Inflation rates of Odisha a lot from the year 2012 to 2016.
Conclusion
It can be concluded that the higher Food inflation rates of Rural Odisha affecting more to people of Odisha as more than 50% population of this segment are of poor families. Inflation affects the poor very badly. As it forces them to spend almost their entire income for the very basic necessities of survival like food, clothing and shelter in that order.
1. The poor can not afford to spend anything for educating themselves or their children. This inability to acquire qualifications/ skill sets, prevents them and their children from improving their earning capacity/ income levels to at least partially overcome the bad effects of inflation. Thus inflation stunts their growth and condemns them to perpetual poverty for generations together.
2. The poor can not acquire or own any assets, which again prevents them from getting at least the positive benefits of inflation due to the increase in value of these assets in future.
3. The poor can not have the basic required nutrition, hygienic living environment and can not have basic minimum health care, and so become easy victims to malnutrition and disease. Thus the inflation condemns the poor to a life time of bad health and worse living conditions.
5. Finally, with passage of time, the inflation starts gradually diminishing the ability of the poor to spend even on their basic necessities and takes away their human dignity by rendering them homeless and sometimes even forcing them to beg.
“Inflation is bad. To be poor is worse. But the combination of both is the worst.”
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Water Shortage and Remedial Measures in Ganjam District
Sri Ramakrusna Satpathy General Concepts
Two thirds of the earth's surface covered by water, it is obviously clear that water is one of the most important compounds responsible for life. It is not only very important for nourishment of life, but equally essential for socio-economic development. Water and mountains are the unique gift of God and the fundamental requirement of life. The global environment is changing every day due to the high pleasure and utilization of human being with nature. As a result, the water resources are reducing gradually. The fresh water shortage issues related to the abundant of population by shifting of fresh water from agriculture to other more pressing uses. The State is facing the worst ever crisis of water shortage for last many years, as water available for any given use has become increasingly scarce. The rapidly growing population, expanding of irrigation areas, urbanization and industrialization which are putting more stresses on water resources. The problem of water shortage is crucial .
Introduction
Although large scale water resources development has been taken up in the Ganjam district, but still majority of people shall do not have enough water for drinking and irrigation.
Ganjam district is the second largest district of Odisha and it is situated in south-east bounded by Nayagarh in north, gajapati is in south, Puri and Khurdha are in east and Kandhamal in the west. The Climatic condition ishot and high humidity during summer. The distribution of water supply systems for drinking purpose is quite very bad. In ancient and old proverb in Oriya there is a common saying, “Jalabihunesrustinasha, jalabahulesrustinasha” which means water both in abundance and in scarcity pose a threat to the existence of life on earth. The summer season and scarcity of water are identical. Both the rural and the urban area go through a water stressed condition during these months. The rural women have to bear the brunt of water scarcity, as they have to go a distant place to fetch water, which has an adverse effect on their health. It’s a pity that rural women are not still aware of water conservation methods to fight the scarcity. Role of women in water resources management and conservation has been recognized from the beginning of history. The National Water Policy 2002 while stressing on participatory approach in water resources management specifically provides for necessary legal and institutional changes to be made at various levels for the purpose of ensuring appropriate
Samikshya , 2017 119 role for women. The Ministry of Water Resources, while issuing guidelines in April 1987, specifically emphasized the States to consider representation of women in Water Users Associations at all levels. As a consequence, many State Governments have amended their Irrigation Acts or have come out with specific Acts on the Participatory Programme in Irrigation. Some of the States have gone further and have made specific provisions for women. One can categories the women in relation to water as rural and urban. The approach for water of a rural woman is different from her counterpart in an urban area.
The Rushikulya River is one of the major/important rivers of state and covers entire catchment area in Kandhamal and Ganjam districts of Odisha. The Rushikulya originates at an elevation of about 1000 metres from Daringbadi hills. The place from where the river originates, Daringbadi is called the ' Kashmir of Odisha '. The river lies within the geographical coordinates of 19.07 to 20.19 north latitude and 84.01 to 85.06 east longitude. It meets the Bay of Bengal at Puruna Bandha of Chhatrapurblock. Its tributaries are the Baghua, the Dhanei, theBadanadi etc. The river flows from the Daringbadi hill station of Kandhamal and in Ganjam districts it flows through Surada, Dharakote, Aska, Pitala, Purusottampur, Taratarini, Pratappur, Alladigam, Brahmapur, Ganjam and finally at Chhatrapur block. It is 165 km long with the total catchment area is 7700 sq.km. The villages near the mouth are Pali Bandha, Puruna Bandha, GokharaKuda and KantiaPada, where one can find the nesting sites of the olive ridley turtles. These villages basically are fishermen's villages. Berhampur is only situated in the basin and important towns are Chhatrapur, Ganjam, Aska, Bhanjanagar , Bellaguntha and Surada. A number of large scale industries have been set up in the basin. Among them are M/s Jayashree Chemical Industries. Aska Co-operative Sugar Industries Ltd. Nuagam, Aska Spinning Mills, Monorama Chemical Works Ltd., Odisha Tubes Pvt. Ltd., etc. There are about 3360 numbers of small scale industries of different categories mainly food and allied, forest & wood based, rubber and plastic products and glass and ceramics. There is enough scope for setting up forest based industries. The basin is rich in mineral wealth. The major economic minerals are clay, lime stone, manganese, sand talc, black sand and grinding materials.
The downstream people are not suitably protected against flood or disaster hits due to the improper management systems. The threat of floods in major rivers has been appeared large as rain continued to thrash the Ganjam district in many instances. Water from the rivers like Rushikuya and Bahuda has discharged into the sea.All precautionary measures have been taken to tackle flashfloods in the district by discharging water from almost all reservoirs into the sea. Despite these measures, the water level in the major rivers rose due to rain. The water level of Rushikuya at Sorada is at 80.41 metres. Similarly, the water levels of the river at Aska and
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Purushottampur are 52.42 and 14.16 metres respectively. As a result, it is not possible to properly maintain the water disaster during the summer season.
Causes: The five number of main important causes of water shortage are given below:
I. Population Expansion: The actual population of Ganjam district has increased more than three-fold from 9.57 lakhs in 1901 to 35.29 lakhs in 2011. As per 2011 census, the decadal growth rate of the district was 11.66 percent. It may be observed that larger family sizes and access to better health care and lifestyles which means that use of wholesome water for drinking, cleaning, cooking and sewage has tripled. We waste more water than ever before.
There was 17786 number of working tube wells, 386 sanitary wells and 707 piped water supply in 6,151 villages. There are 415 villages/ hamlets without any source of safe drinking water facilities during the period 2009-10.Similarly, As per the MIS report of Panchayati Raj Deptt. for the period from 06.06.2017 to 12.06.2017, Out of 26656 number of tube wells,26301 tube wells are in functioning condition and 133 number of Tube wells are defunct. Similarly, out of 1387 number of PWSs, 1365 number of PWSs are functioning and 22 number of PWSs will be repaired. Most of the PWSs are damaged due to Road widening cases.From the DJRC primary survey, it was found that 83.16 percent households have access to safe drinking water. The Block wise number of Functioning Tube Wells and PWSs during the year 2009 and 2017 are depicted in figure-1 and figure-2.
Figure 1: Number of functioning Tube wells
30000 25000 20000
15000 10000 5000 0
Year-2017 Year-2009
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Figure 2: Number of functioning PWSs
1600 1400 1200 1000 800 600 400
200
0
Year - 2009 Year -2017
II. Lack of Awareness ::Water is important for nourishment of life .Without which nobody can live. Every body must be aware about the limitation of water and the same should be utilised proportionately as per requirement. They must be aware about the shortage of drinking water.
III. Urbanisation: Fourteen number of Municipalities as well as NACs like Berhampur,Aska, Rambha ,Chatrapur, Bhanjanagar, Khallikote, Buguda and Gopalpur towns etc. are growing and expanding more than ever before. Cities and towns also tend to hold more people than villages. Educational Instituations, Markets, Slaughter houses and purchase of land , Number of Vehicles are growing more and more. This means thereis an increased need to take care of sewage, cleaning, construction and manufacturing.
IV. Pollution: Sewage, oil discharges from industries, waste dumping into water bodies, waste materials from mining activities as well as dirty water from sanitation work in hospitals, hotels, oil companies, mining, schools and restaurants all end up polluting our water.
V. Deforestation: Trees help prevent excessive evaporation of water bodies. They also enrich and condition the climate. This means the destruction of forests by fire, logging and farming have exposed soil moisture and water bodies to the sun’s intense heat, leaving them dried out.
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As a result, people are in starvation and affected on various water-borne diseases due to shortage of water and forced to drink low quality water from streams and fallen to die off. A lot of water is essential to grow food and care for domestic animals; otherwise, they will die within short span. No economic activities will happen due to shortage of drinking water.
In order to streamlining the water preservation in management systems in Ganjam district, Some necessary precautionary steps has been adopted for making pre plan for avoiding water scarcity towards safe drinking as well as irrigation, industrialization and other purposes, which will be taken up very shortly without discharging the rain water into the sea. Water Resources Department should have to make keen interest on creating a breach on the dam near by Kansari Ganda which is nearly six Kms from Chatrapur block. Irrigation through canals is the main stay in district’s economy, around 60% of total agriculture output is totally dependent on irrigation. It is very clear that water resources have played very important role in the land development as well as water storage system. If we do not acquire the right attitude towards water, it is only a matter of time and one day there will be a shortage. Keep the tap off when not in use. Talk about it with family and friends. Look out for television, various news Papers, Reports, journals and facts on water scarcity and its crisis areas. If you understand a problem, you are in a better position to have a solution.
Conclusion
When we go to the backs’ history, we see that poor rural communities depend heavily on exploitation of local natural resources for their livelihoods due to the lack of education, interferences of different political parties and mismanagement of the Drinking water systems. Create awareness among the public through all possible means. The problem of water shortage should be investigated up to its grass root level and then only, an effective solution will be implemented. In order to maintain balance between the growing urban concentration and industrialization along the rivers on one side led to the significant abstraction of fresh water and on other side put negative impacts on water quality. Therefore water balance, water quality and water accounting is a must for sustainable water supply for all possible uses.
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Table 1 : Drinking water facilities available in different Blocks of Ganjam District Sl. Block No. of 2009 2017 No. Villages No. of No. of % of No. of No. of % of No.of /Hamlet functi functio HHs functi functi HHs water s oning ning with oning oning with scarcity T/Ws PWSs safe T/Ws PWSs safe villages/ D/W D/W (P) wards 1 Aska 342 948 34 78.75 1333 57 81.25 0 2 Beguniapada 282 888 35 71.25 1257 67 72.17 0 3 Bellaguntha 189 788 17 70.00 1066 30 70.35 01 4 Bhanjanagar 207 724 22 90.00 1602 56 91.25 0 5 Buguda 533 1160 31 66.25 1172 59 67.35 0 6 Chatrapur 278 849 18 80.00 1020 68 82.17 0 7 Chikiti 172 624 33 87.50 844 47 89.00 0 8 Dharakote 240 784 30 91.25 1117 47 91.25 03 9 Digapahandi 340 773 21 82.50 1318 80 83.45 05 10 Ganjam 262 918 45 88.33 699 84 89.05 0 11 Hinjilicut 190 477 34 93.75 1131 56 94.25 0 12 J.N.Prasad 179 478 32 93.75 1896 108 94.35 0 13 K.S.Nagar 568 1305 37 93.75 944 42 95.05 0 14 Kholikote 93 583 22 73.75 1117 82 79.15 03 15 Kukudakhandi 229 809 46 68.33 1205 63 72.35 0 16 Patrapur 410 675 32 92.50 1009 55 92.50 0 17 Polosora 150 779 25 68.75 1204 39 69.75 0 19 Rangeilunda 214 889 66 76.25 1299 121 78.25 0 20 Sanakhemundi 332 771 32 86.25 1166 42 78.29 0 21 sheragada 247 762 28 90.00 1141 64 87.27 0 22 Soroda 553 987 30 78.75 1541 48 91.05 01 GANJAM 6151 17786 707 83.16 26301 1365 92.67 13 Source: DHDR & DCHB, Ganjam
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Road Accidents : Everybody’s Concern
Sri Tapan Kumar Mishra Abstract
Mobility and accidents are synonymous. Faster growth of vehicle population in Odisha lead to sharp increase in road accidents in recent years. Odisha remains low profile among States in terms of number of vehicles and accidents. But its fatality rate is a serious concern. The State implements some laudable remedial measures to prevent road accidents.
Introduction
Road transport is a critical structure for economic development of a country. It influences the pace, structure and pattern of development. India is experiencing one of the highest motorization growth rate in the world accompanied by rapid expansion in road network and urbanization over the years. Generally speaking, this phenomenon has significantly contributed in raising the road accident rate resulting into injuries, fatalities, disabilities and hospitalization. All of these, in turn cause severe socio-economic costs to the country. Traffic hazards, exposures and risk factors have also increased over the years. Road accidents create negative impact on the economy, public health and the general welfare of the people. Road accidents are human tragedy involving tremendous human suffering in terms of premature deaths, injuries and loss of productivity, etc. The loss of main bread earner and head of households due to death or disability can be catastrophic, leading to lower living standards and poverty in addition to the human cost of bereavement of the highest degree. Thus, road safety has become an issue of national as well as international concern.
Table 1 : Road Accidents scenario in India and Odisha
Year Odisha India No. of accidents No. killed Fatality Rate No. of accidents No. killed Fatality Rate
2012 9285 3701 39.86 4,90,383 1,38,250 28.2
2013 9680 4062 41.96 4,86,476 1,37,572 28.3
2014 9648 3931 40.74 4,89,400 1,39,691 28.5
2015 10542 4303 40.82 5,01,423 1,46,133 29.1
2016 10532 4463 42.38 - - -
N:B; Fatality Rate – No. of death per 100 accidents
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Comparison between India and China
China has been able to reduce road accident deaths from98,738 in 2005 to 65,225 in 2010, whereas the accidents and deaths are in an increasing trend in India.
Table 2 : Road Accident status in India and China (2005 to 2010)
Year No. of Accidents (Lakh) No. of Deaths India China India China 2005 4.39 4.50 94,968 98,738 2006 4.60 3.78 1,05,749 89,455 2007 4.79 3.27 1,14,444 81,649 2008 4.84 2.65 1,19,860 73,484 2009 4.86 2.38 1,25,660 67,759 2010 4.99 2.19 1,34,513 65,225 Source – Ministry of Road Transport & Highways.
Share of road accidents in major States (%)
More than 400 people are killed every day on our roads in road accidents. In India, 53.8 per cent road accident victims are in the age group of 15 to 34 years, who are the bread earners of the family. The number of persons injured in the road accidents in India have a fluctuating trend between 2012 & 2015. During 2015, 5 lakh people were injured on roads accidents in India.
Figure 1: Percentage Share of Road Accidents
Odisha Haryana 2% Tamil 2% Others West Bengal 11% Nadu 3% Chhattisgarh 14% Maharashtra 3% 13% Telegana 4% Gujarat 4% Madhya Pradesh Rajasthan 11% 5% Andhra Pradesh Karnataka 5% Uttar Pradesh Kerala 9% 6% 8%
Source – Ministry of Road Transport & Highways, GoI, STA, Odisha
Road accidents in Odisha – An Analysis ,2015
41% of the road accidents occurred on National Highways. The length of National Highways is less than 2% of the total road network of the State Samikshya , 2017 126
32% of the accidents occurred in open areas 38% of the road accidents occurred in good weather condition Two wheelers are involved in 30% of the road accidents followed by 24% goods vehicles. Most of the accidents are occurred by new vehicles i.e. in 65% of the accidents vehicles under the age of 6 years are involved. 1359 Hit & Run cases were reported in the year 2015. 98% of the accidents occurred due to fault of drivers 39% of the accidents occurred due to excess speed followed by 9.22% due to drunken driving 72% of the road accidents victims are under the age of 44 years
Occurence of accidents on types Accidents According to Responsibility of Drive
19% 1% 37% 39% 41%
39%
9% 5% 5% 5% Excess Speed Consumption… NH
2% Types of vehicles involved
16% 30% 5% 6%
17% 24%
2 Wheelers Goods Vehicles Car / Taxi/Jeep Bus Auto rickshaws Other Vehicles Other Objects
Remedial measures
Administrative Steps Formulating State Road Safety Policy formulated with a target to reduce road accident fatality by 20% by the year 2020. Road Safety Action Plan has been developed involving various Departments like Health & F.W., Home (Police), Excise, Works, NHAI, H & UD, School& Mass Education etc. Samikshya , 2017 127
State Road Safety Council has been constituted. The Council prepares plans for implementation of road safety activities A Lead Agency on Road Safety has been constituted to co-ordinate among different Departments for implementation of road safety activities. Separate Fund for Road Safety has been created. 20% of the fines collected for various M.V. offences are placed as fund for road safety.
In addition to the above Institutional arrangements following Engineering, Enforcement, Education and Emergency care measures have been taken up as per the direction of the Supreme Court Committee:
Engineering:
256 Black Spots have been identified, where accidents are frequently occurring. Remedial Measures are being taken to rectify the defects Road Safety Audit being conducted by the Road owning Departments Enforcement: Strict enforcement being conducted for detection of drivers violating Rules. Up to December, 46768 D.Ls. have been suspended by the RTOs. Driving Licenses are being suspended for Drunken Driving, over speeding, carrying overload, using mobile phone while driving. Education
Road safety as a topic in the curriculum of Classes V to VIII has been included. Refresher Training being imparted to drivers. Driving training being imparted to youths. Awareness camps being organized for drivers. Emergency Care:
Accident Helplines have been opened Good Samaritan Policy notified for protection of the people who help the road accident victims From the Road Accident analysis it is quite clear that, most of the accidents are occurred in good roads i.e. National highways and in good weather condition. If the road users will be careful road accidents can be minimised in the state.
Road accidents no more remains other man’s concern, let us make it every body’s concern
Samikshya , 2017 128
District level Poverty Elimination From Rural Odisha: A Critical Analysis Dr. Sujata Priyambada Parida Abstract
This paper has analysed the district wise poverty incidence along with BPL population showing position of districts in rural Odisha. The monthly per-capita poverty gap and reduction of poverty from 2004-05 to 2011-12 for each districts has been displayed in this paper. Some suggestion has been given to policy makers to make Odisha free from poverty.
Key words: BPL (Below Poverty Line), NSSO (National sample survey Office), DES (Directorate of Economics and Statistics), HCR (Head Count Ratio), PGR (Poverty Gap Ratio), PCPG (Per-capita Poverty Gap).
Introduction
The pre-stage of poverty elimination from a particular geographical region is poverty reduction.But poverty reduction can be measured through two major dimensions i.e Humanitarianism and economy. According to Humanitarianism poverty reduction is the philosophical belief in movement toward the improvement of the human race towards the activities relating to human welfare specifically. But according to economy, it is the progressive improvements in income and consumption of goods and services of basic necessities like food, clothing, shelter, health and education etc.
But before thinking about poverty reduction Poverty estimation is the fast and foremost step before design and implementation of any appropriate anti-poverty policies. Two basic ingredients of poverty estimation are Poverty Line and Data on size distribution of consumption or income. Poverty line is a cut-off point separating the poor from the non-poor.
For India ,The Planning Commission of India (presenltly renamed as NITI Ayog), had been estimating official poverty line for all states as well as India using Household Consumer Expenditure data of NSS (National Sample Survey) up to 2011-12.According to the latest measure, poverty in rural Odisha has declined by 25.1 percentage points that is from 60.8% to 35.69% between 2004-05 and 2011-12.All these result had been estimated using the central sample data of NSSO, Govt of India. But on behalf of Govt of Odisha, Directorate of Economics and Statistics has been participating in NSS in equal matching sample basis to Govt. of India. The sample surveyed by DES, Odisha is called as state sample. Using both samples DES, Odisha has taken an initiative to obtain district level poverty estimates at district level.
Samikshya , 2017 129
This paper has analysed the incidence and gap of poverty for each districts of rural Odisha. Also poverty reduction in percentage has been shown.
Objective
To analyse the position of districts in rural Odisha according to incidence, gap and reduction of poverty and to suggest some solutions.
Sources of data
NITI Ayog, NSSO, population census 2011 and DES, Odisha.
Tools and methodology
(1) HCR (Head Count Ratio): It is the percentage of population lying below poverty line.
풎 푯푪푹 = × ퟏퟎퟎ 풏
Where m=population lying below poverty line & n= total population
(2) PGR (Poverty Gap Ratio): It is defined by the mean distance below the poverty line expressed as a proportion of the line, where the mean is taken over the whole population, counting the non-poor as having zero poverty gaps. This measure reflects both incidence and gap of poverty. This provides information regarding how far off households are from the poverty line. The mathematical expression is as follows:
풎 ퟏ (풁 − 풀풊) 푷푮푹 = ( ∑ ) × ퟏퟎퟎ 풏 풁 ퟏ
Where Yi is the expenditure of the ith individuals who are poor. ‘Z’ is the poverty line, ‘n’ represents total population and ‘m’ represents number of poor.
(3) PCPG (Per-Capita Poverty Gap): It measures the per-capita gap of the poor population from the poverty line considering the gap of each individual poor ∑풎 (풁 − 풀풊) 푷푪푷푮 = 풊=ퟏ 풎 Where Yi is the expenditure of the ith individuals who are poor. ‘Z’ is the poverty line and ‘m’ represents number of poor.
Samikshya , 2017 130
Using Household consumer expenditure data of NSS state and central sample of 61st round (2004-05) and 68th round (2011-12) respectively and official poverty lines of NITI Ayog for rural Odisha (Rs. 407.78 for 2004-05 and Rs. 695 for 2011-12) in the above tools the results have been computed.
Result and Analysis i. Measures for Rural Odisha
During 2004-05 and 2011-12, the rural poverty has been reduced by 25.1 percentage points that is from 60.8% to 35.69% which is shown in HCR part of table 1 . But the PGR has been reduced by 10 percentage points between 2004-05 and 2011-12.As PGR is the measure of depth of poverty and it is a superior measures to HCR the high rate of poverty reduction have comparatively slow impact on poverty gap for the poorer people suffering severe poverty (table1).
Table 1 : Poverty measures of Rural Odisha
Year HCR PGR
2004-05 60.80 17.37
2011-12 35.69 7.01
Source: Social Statistics Division, MOSPI,Govt of India ii.District Level Poverty Measures for Rural Odisha
The district level HCR (%) varies from 14%(for Khurda) to 78%(Koraput) in all thirty districts of rural Odisha.Poverty percentage is below 20% in six districts i.e Khurda, Cuttack, Dhenkanal, Bhadrak ,Jagatsighpur and Puri but more than 70% in Rayagada, Gajapati and Koraput districts(Figure1). But the position of districts are different due to size of population. As an example ,HCR is highest for Koraput but BPL population is highest in Mayurbhanj as for its population(figure2).
Samikshya , 2017 131
Figure 1 : District wise Poverty Head Count Ratio(%)
100 90 78 80 75 67 67 70 70 61 61 62 62 56 59 60 52 47 47 49 50 42 40 33 35 29 29 30 30 22 25 17 17 18 20
20 14 15 16 HCR(%) 10 0
DISTRICT NAME
Figure 2 :District wise Estimated BPLPoulation* ('000)
1600
1400 1419.37
1200 972.58
1000 899.71
727.88
712.89 704.78
800 690.26
634.66 620.17 600 574.66
465.9
393.48
389.89
380.36
357.32
344.51
343.84
338.42 291.41
400 283.26
260.86
258.08 235.3
234.8
224.48
194.39
173.57
172.05 163.43 200 101.02 0
(*BPL Population has been estimated using poverty HCR of figure1 and census 2011 population)
The higher PGR (Poverty Gap Ratio) of the districts indicates that poverty is more severe. As PGR is highest (figure3), the depth of poverty is highest in Malkangiri than Koraput where as Koraput has highest poverty as per the HCR (figure-1).Position of almost all of the districts of figure-1 have been changed in figure-3.Again Koraput is in second position as per PGR and first as per HCR. So Koraput is in critical situation in both cases.
Samikshya , 2017 132
Considering the monthly expenditure gap of the BPL population from poverty line the PCPG (Per-capita Poverty Gap) has been computed. The PCPG varies from Rs.69 to Rs.228.81.The PCPG is also highest for Malkangiri i.e. Rs. 228.81 which means that to cross the poverty line monthly average needs for each poor person of rural Malkangiri is Rs.228.81. (figure-4)
Figure-3 : District wise Poverty Gap Ratio(%)
60.00
50.00
40.00 34.09
30.00 25.63
22.89
17.06
16.76 15.75
20.00 15.61
12.54
12.17
10.46
10.08
10.08
9.40
9.04
8.55
7.74
6.83
6.41
5.04
4.91
4.82
4.57 4.16
10.00 4.09
3.14
3.02
2.97
2.46 2.24
1.73 0.00
PURI
ANGUL
JAJPUR
BOUDH
GANJAM KHURDA
CUTTACK
BARGARH GAJAPATI KORAPUT
BHADRAK NUAPADA
DEOGARH
BALANGIR
BALASORE
KEONJHAR RAYAGADA
NAYAGARH
KALAHANDI
SAMBALPUR
DHENKANAL
MALKANGIRI
KANDHAMAL
SUBARNAPUR
KENDRAPARA MAYURBHANJ
JHARSUGUDA
SUNDARGARH NABARANGPUR
JAGATSINGHPUR
Figure-4 : District wise Monthly per-capita Poverty Gap in Rs(0.00)
228.81
212.79
198.43
178.95
178.13
176.89
170.85 163.43
154.99
148.63
140.7
139.01
136.05
135.2
134.41
132.09
127.74
127.56
123.87
122.69
117.54
114.76
113.73
113.5
112.97
99.84
98.09 90.68
89.67 69
…
…
…
… …
… … … …
PURI
ANGUL
BOUDH
JAJPUR
GANJAM
KHURDA
CUTTACK
KORAPUT
KENDRAP
SUNDARG
BARGARH NUAPADA DEOGARH
GAJAPATI
JAGATSIN
NABARAN
BHADRAK MALKANG
MAYURBH
KANDHAM
BALANGIR
JHARSUGU
BALASORE KEONJHAR
SUBARNAP
NAYAGARH RAYAGADA
KALAHANDI SAMBALPUR DHENKANAL
Comparing the district wise Poverty HCR of 2011-12 to that of 2004-05 it is seen that poverty has been reduced in all districts except Boudh and Bolangir(figure -5.3).Poverty has been reduced in high rate(more than 20%) in sixteen districts (figure-5.1).The reduction rate is medium (10-20%) in eight districts(figure-5.2).The reduction rate is very slow(less than 10%) in four districts(figure-5.3).
Samikshya , 2017 133
Figure 5.1 : Districts with high rate poverty reduction(more than 20%)
100 89
86
79
78 70
80 69
64
63
63 63
62
56
53
52
52 50
47 49
60 48
48
47 46 39 36 35 42
33 35
30 30 33 29 29 28 29 40 29 27 24 23 23 25 21 21
22
18
17 16 20 14 0
2004_05 2011_12 % of poverty declined
Figure 5.2 : [Districts with MEDIUM (10-20%) poverty reduction]
83
90 80
74
80 71
67
61 61
70 59
60 58 47 50 45
35
40 30 26 30 16 15 13 12 15 11 20 19 18 17 10 11 0
2004_05 2011_12 % of poverty declined
Figure 5.3 : (Districts with slow(less than 10%) poverty reduction
84
90 82
78
75 73
80 70
67 62
70 61 59 60 50 40 30 25 20 20 4 3 10 9 5 -6 0 -3 -10 -20
2004_05 2011_12 % of poverty declined
Samikshya , 2017 134
Conclusion and Suggestion
According to the latest official measure of NITI Ayog(Planning Commision),Poverty HCR of rural Odisha was about 35.7% for 2011-12.But according to the result of this paper, povery HCR is more than 40% in sixteen districts(incluing all KBK districts) of rural Odisha.Koraput,Rayagada and Gajapai were having poverty incidence more than 70%. The HCR is below 20% in six districts i.e Khurda, Cuttack, Dhenkanal, Bhadrak and Jagatsighpur.
Similarly according to the official measure, rural Odisha has maximum poverty reduction i.e about 25percentage points from 2004-05 to 2011-12.But this paper has reflected very disimilarity in poverty reduction at district level. Although the Poverty reduction is more than 20% in sixteen districts (with highest reduction 47% in Angul district) ,there is very slow reduction in Rayagada,Koraput,Jajpur and Gajapati). Again the high rate poverty reduction from over all rural Odisha has no impact on two disricts i.e Bolangir and Boudh. The poverty has been inclined in these two districts during 2004-05 to 2011-12. If this trend will continue in this space poverty elimination from rural Odisha will only be a dream. Accoring to the measure PGR for rural Odisha is about 7 where as the PGR is more than 7 in sixteen districts .The PGR is more than 20 in three districts i.e. Malkangiri , Koraput and Gajapati(with highest 34.09 in Malkangiri).The districts having more PGR indicates more depth in poverty i.e the BPL populaion consists of poorer people suffering from severe poverty.
It is suggested to the policy makers that a lot of steps to be taken up before planning to eliminate poverty from Odisha. The thirty districts of Odisha should be classified in to three separate classes giving weightage to large poverty gap (according to PGR)and slow(or negative) reduction of poverty.According to the propertionate necessity, planning for poverty elimination should be implemented at district level.Again at district level, selection of BPL households is one of the vital step towards poverty elimination. The norm of selection should be prepared on the base of poverty gap of the households. If a common plan or norm will be applied for all people lying below poverty line then poverty can’t be eliminated. Rather the condition of the people suffering from severe poverty may be remaining unchanged.Hence more facility to given to the bottom level.
Monthly PCPG computed for each districts can give the minimum cost reqired of eliminating poverty from the BPL households of the respective districts. Free meals should be provived to the distress and helpless old people living without any family members at old-age. But the people suffering severe poverty with physical ability to work should not be provided
Samikshya , 2017 135 any free facilities.It is suggested to make them employed in such sustainable economic activities that, they can able to cross the poverty line by earning the required monetary amount for ful-filling their basic necessities. They should be sensitized regarding their ability to be engaged in profitable economic activities to eliminate poverty from their level.
References:
Social Statistics Division, MOSPI, Govt. of India 2015:MDG,India country report
Planning commission, Government of India. (2011): Report of export group to review the methodology for estimation of poverty under the chairman ship of prof S. Tendulkar.
Directorate of Economics and statistics, Govt of Odisha(2017):Depth and Severity of poverty in Rural Odisha ,A District Level estimation.
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Odisha Performs, India Cherishes
Smt. Jayashree Rath Better performer of State economy( 7.94% growth rate) Structural shift of State economy in par with India. Public Debt burden lessens over years ( now Debt-GSDP ratio of 16%)
Poverty falls sharply ( 32.6% in 2011-12) HDI improves over decade ( 0.442 in 2011) Rising women participation in organized sectors (16%) New born child remain more safe ( IMR falls to 40 & institutional delivery rise to 85% ) Elementary school children drop out at lowest ebb ( 2.6 % dropout rate) High dependency syndrome on crop and livestock sector ( 62% workforce in agriculture) Upswing in farm credit ( more than Rs 10,000 crore)
Richly endowed with mining resources( chromite, iron ore, coal, bauxite etc) Impressive forest area, resources and biodiversity( 37.4%) 480 nautical kms coastal length,12 perennial rivers, abundant ground water reserves Financial inclusion in take off stage.
A strong Administrative statistical system with both vertical and horizontal coordination, MIS with secondary, census, surveys data sources etc play the decisive role in identifying the major drivers, performance and profile of the State economy. The advocacy on administrative statistics always remain a pre-condition to build up economic power and performance.
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Administrative Statistics
Sri Ramesh Chandra Panda In Hindu mythology the rulers or kings used to collect different data and information through the spies and self espionage . By using these information and data they do the best job for the betterment of the state.
During the reign of Chandragupta Maurya once a boy rearing the goats in a grazing land on the bank of a river, by that way three persons one Economist, one Statistician and one Statesman passed to him , as per the wish of the boy the statistician counted the no of goats and bifurcated the herd into male, female, kid, full grown goat and colour etc; the economist estimates the meat production and requirement of grazing land of the country by way of sampling .The team of three to cross the river filled with flown water. The boy warned the team and not to cross it because water level of flown water as three meter. The statistician calculated the average water level for one persons as one meter and basing on the average figure the economist suggested for easy crossing of water. The statesmen Chandragupta Maurya himself called the boy and told him to bind a log boat and enquired about his experience about his sailing. The boy replied he can cross the river to them one by one through the log boat as per its capacity. Ultimately kings’ order executed and all of them crosses the river safely. Then the king choose the boy as his minister and appointed him as such.
From the above legend tale why the boy rewarded to the post of minister but neither the statistician nor the economist ? Both of them had the professional knowledge but neither of them had practical knowledge .Thus statistics survey and economic estimation to go together as per the practical requirement of the state. This is the things of past. With the march of civilisation , new innovation and scientific temper led human being and nation into a sophisticated life style to adopt . The concept of the state is now changed, it is no more a police state rather it is a welfare state, the more and more its citizens enjoy, the more and the more the sate develops. This all possible due to spread of knowledge and technology at grass root level. Behind it Statistics and Economics plays a vital role.To day statistics is no more the concern of the sate it is every body’s concern.
Administrative Statistics – generally collected by State Governments; consisting of statutory administrative returns and data derived as a by-product of general administration; and other important sources namely, censuses and sample surveys.
Administrative Statistics are very much needed for effective planning of censuses and sample surveys. The state of the Indian Statistical System thus depends largely on the state of functioning of the Administrative Statistical System. In case of the system of direct data
Samikshya , 2017 138 collection through sample surveys, the main failure had been in timely processing of data and release of results. But, with effective computerisation, the problem of delays in publication of results of sample surveys has, by and \ large, been resolved
Official statistics are statistics published by government agencies or other public bodies such as international organizations as a public good. They provide quantitative or qualitative information on all major areas of citizens' lives, such as economic and social development, living conditions, health, education, and the environment.
During the 16th and 17th centuries, statistics were a method for counting and listing populations and State resources. The term statistics comes from the New Latin statisticum collegium (council of state) and refers to science of the state. According to the Organization for Economic Cooperation and Development, official statistics are statistics disseminated by the national statistical system, excepting those that are explicitly not to be official". Of course, governmental agencies at all levels, including municipal, county, and state administrations, may generate and disseminate official statistics. This broader possibility is accommodated by later definitions. For example:
Almost every country in the world has one or more government agencies (usually national institutes) that supply decision-makers and other users including the general public and the research community with a continuing flow of information . This bulk of data is usually called official statistics. Official statistics should be objective and easily accessible and produced on a continuing basis so that measurement of change is possible.
Official statistics result from the collection and processing of data into statistical information by a government institution or international organisation. They are then disseminated to help users develop their knowledge about a particular topic or geographical area, make comparisons between countries or understand changes over time. Official statistics make information on economic and social development accessible to the public, allowing the impact of government policies to be assessed, thus improving accountability.
Administrative records are data collected for the purpose of carrying out various non- statistical programs. For example, administrative records are maintained to regulate the flow of goods and people across borders, to respond to the legal requirements of registering particular events such as births and deaths, and to administer benefits such as pensions or obligations such as taxation (for individuals or for businesses). As such, the records are collected with a specific decision-making purpose in mind, and so the identity of the unit corresponding to a given record is crucial. In contrast, in the case of statistical records, on which no action concerning an
Samikshya , 2017 139 individual or a business is intended or even allowed, the identity of individuals/businesses is of no interest once the database has been finalized.
Using administrative records presents a number of advantages to a statistical agency and to analysts. Demands for statistics on all aspects of our lives, our society and our economy continue to grow. These demands often occur in a climate of tight budgetary constraints. Statistical agencies also share with many respondents a growing concern over the mounting burden of response to surveys. Respondents may also react negatively if they feel they have already provided similar information (e.g. revenue) to administrative programs and surveys. Administrative records, because they already exist, do not incur additional cost for data collection nor do they impose a further burden on respondents. Advancements in technology have permitted statistical agencies to overcome many of the limitations caused by processing large datasets. For all these reasons, administrative records are being used increasingly for statistical purposes.
“A data holding containing information collected and maintained for the purpose of implementing one or more administrative regulations.” “Data collected by sources external to statistical offices.” The narrow and wider definitions can be shown graphically as follows:
Figure 1 : Narrow definition Figure 2 : Wider definition
Advantages and disadvantages of using administrative data in research
Administrative datasets are typically very large, covering samples of individuals and time periods not normally financially or logistically achievable through survey methods. Alongside cost savings, the scope of administrative data is often cited as its main
Samikshya , 2017 140 advantage for research purposes. Other advantages include relieving the burden on survey respondents and providing data on individuals who would not normally respond to surveys.
The criticisms levelled at these resources relate to the lack of control the researcher has during the data collection stage and how this affects what can be done with the data. More general concern has also been voiced about the lack of well established theory and methods to guide the use of administrative data in social science research.
The table below summarises some of the general advantages and disadvantages of using administrative data. As with any method of data collection, when deciding whether and when to use administrative data it is necessary to weigh up the pros and cons in relation to the specific research situation.
Advantages of administrative data Disadvantages of administrative data Already collected for operational purposes and Information collected is restricted to data required therefore no additional costs of collection for administrative purposes – limited to users of services and administrative definitions. (though costs of extraction and cleaning). Collection process not intrusive to target population. Lack of researcher control over content. Regularly (sometimes continuously) updated. Proxy indicators sometimes have to be used. Can provide historical information and allow May lack contextual/background information. consistent time-series to be built up. Collected in a consistent way Changes to administrative procedures could change definitions and make comparison over time (if part of national system) problematic. Subject to rigorous quality checks. Missing or erroneous data. Near 100% coverage of population interest. Quality issues with variables less important to the administrator e.g. address details may well not be updated. Reliable at the small area level. Metadata issues (may be lacking or of poor quality). Counterfactuals and controls can be selected post Data protection issues. hoc. Captures individuals who may not respond to Access for researchers is dependent on support of surveys. data providers. Potential for datasets to be linked to produce Underdeveloped theory and methods. powerful research resources (see below).
Limitations of Administrative Data
Administrative data are collected to manage services and comply with government reporting regulations. Because the original purpose of the data is not research, this presents several challenges.
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The administrative data only describe the families using a service and provide no information about similar families who do not use a service
The potential observation period for any subject being studied (e.g., a person, a family, a child care program) is limited to the period of time that the subject is using the service for which the data are being collected
Only those services that are publicly funded generally are described in the administrative data. In most states, it would be impossible to rely on subsidy data to learn about non-subsidized forms of child care being used to augment child care that is subsidized
Many variables used in administrative data are not updated regularly, so it is important to learn how and when each variable is collected. For instance, an "earnings" variable in administrative data for subsidized child care generally is entered at the time that eligibility is determined and then updated when eligibility is predetermined. There is no way to know, using administrative data alone, whether the "earnings" amount in the data is a family's earnings in the months between eligibility determination and redetermination
Important variables needed for a particular research study may not be collected in administrative data
Researchers interested in using administrative data for the purpose of research should expect to invest considerable time learning about the details of the administrative data system, the specific data elements being used, the data entry process and standards, and changes in the data system and data definitions over time. It also takes time to transform administrative data into research datasets that can be used in statistical analyses.
Official Statistics Presentation
Official statistics can be presented in different ways. Analytical texts and tables are the most traditional ways. Graphs and charts summarize data highlighting information content visually. They can be extremely effective in expressing key results, or illustrating a presentation. Sometimes a picture is worth a thousand words. Graphs and charts usually have a heading describing the topic.
Example-1 .Let us examine the complaints against e-commerce cos top list at national consumer helpline through analytical texts and tables
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Table 1 : Top 4 sectors by user Grievance (In Percentage) Period E-commerce Telecom Products Banking 2015-16 15.42 16.70 21.09 5.04 Apr,2016 18.00 16.00 23.00 5.00 Aug,2016 17.00 18.00 21.00 9.00 Sep,2016 17.00 11.00 19.00 9.00 Jan,2017 21.00 11.00 11.00 9.00 Mar,2017 15.00 11.00 7.00 7.00
The national consumer helpline (NCH),a joint incentive of the consumer affair department and Indian Institute of Public Administration(IIPA), tied up with 35 e-commerce companies received about 3.5 lakh grievances annually. This is only fraction of complaints registered by consumers, as there are other avenues for dispute redressal , but NCH said complaints relating to e-commerce over took all other sectors since September. Due to increased penetration of internet and more companies pushing for online sale of their products, the number of complaints also increased. The rising number of consumer grievances has not gone unnoticed by the government, in June last year PM Modi had flagged concerns over the large number of consumer complaints relating to e- commerce including booking of tickets and hotel reservation.
Example-2 .There are different types of graphic but usually the data determine the type that is going to be used.
Figure 3 : E- Commerce Annual Global financing history in billion $
18000 16456 1000 15653 15000 813 775 795 800 12071 737 743 12000 9440 600 9000 5368 400
6000 4800 Noof start ups
funding Disclossed 3000 190 200
0 0
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Administrative Statistics Sources
As discussed in the previous paragraphs, the potential range of administrative sources that could be used for statistical purposes is large and growing. The following list is not meant to be exhaustive; instead it aims to show range and types of potential data sources, as the final step towards arriving at an operational definition of administrative sources.
Tax data Electoral registers Personal income tax Register of farms Value Added Tax (VAT) Local council registers Business / profits tax Building permits Licensing systems e.g. television, sale of Property taxes restricted goods Import / export duties Published business accounts Internal accounting data held by Social security data businesses Contributions Private businesses with data holdings: Benefits Credit agencies Pensions Business analysts Health / education records Utility companies Registration systems for persons / Telephone directories businesses / property / vehicles Identity cards / passports / driving Retailers with store cards etc. licenses
Official statistics are part of our everyday life. They are everywhere: in newspapers, on television and radio, in presentations and discussions. For most citizens, the media provide their only exposure to official statistics. Television is the primary news source for citizens in industrialized countries, even if radio and newspapers still play an important role in the dissemination of statistical information. On the other hand, newspapers and specialized economic and social magazines can provide more detailed coverage of statistical releases as the information on a specific theme can be quite extensive. Official statistics provides us with important information on the situation and the development trends in our society.
References Times of India Source: Smith, G., Noble, M., Anttilla, C., Gill, L., Zaidi, A., Wright, G., Dibben, C and Barnes, H. (2004) The Value of Linked Administrative Records for Longitudinal Analysis, Report to the ESRC National Longitudinal Strategy Committee.
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Changing Pattern of Sex Ratio in Odisha
Dr. Bijaya Bhushan Nanda Abstract
Declining sex ratio (females per ‘000 males) and more specifically the child sex ratio is a matter of serious concern. The paper seeks to analyse the changing pattern of sex ratio in Odisha in terms of its trend, spatial pattern, rural-urban and caste dimensions. Besides, it attempts a comparison of the situation with all India level. The data from different census counts have been analysed for the purpose.
Introduction
The Sex Ratio (females per 000’ males) in Indian population is 933 (1). This is strikingly below the world average of 990 (2). The proportion of women in Indian population has rapidly declined over the census counts since 1901. Any satisfaction drawn from a marginal 6 point rise of sex ratio of overall population to 933 in 2001 from 927 in 1991 will have to be looked in the face of a 18 point drop in the child (0-6 year) sex ratio from 945 to 927 during the same period.
District level analysis of sex ratios among children in the age group 0-9 or 0-6 years age by some scholars had established a clear division of the Indian landscape into the northwestern and southwestern parts (3, 4, 5). Sex ratios in the northwestern parts of the country have been distinctly masculine than those in the southeastern parts, which is attributed to the male dominated cultural practices in the northwestern regions and relatively female friendly practices in the southeastern regions (6). But this divide is going to break, given the declining trend of sex ratios in the southeastern region as well and some scholars have expressed concern over it (7, 8, 9). Odisha’s population, which was characterized by female preponderance in the early nineties, has registered a continuous and accelerated decline in the proportion of females and turned out to be a sex ratio adverse to female over the last 40 years. In this backdrop this paper attempted a detail analysis of different aspects of sex ratio of Odisha such as its trend, spatial pattern, rural-urban composition and variation by caste.
Trend of sex ratio of Odisha and India Fig. 1 Trend of sex ratio of Orissa & India: Census 1901-2001
1,100 1,086
1,067
1,056
1,053
1,050 1,037 The trend of sex ratio over the period has been 1,022
1,001
988
1,000 981
972 972
971
964
955
950
945 946
941
Fig. 1(10). Odisha’s population was once 934 933 analysed in 950 930
927 characterised by preponderance of female. But due to 900 850 continuous decline the scenario becomes reverse in 1971. Males Females 1000 per 800 1901 1911 1921 1931 1941 1951 1961 1971 1981 1991 2001
During the period 1901-2001 the sex ratio of Odisha & Orissa India Linear (Orissa) Linear (India)
India has shown rapid declining trend, which is much
Samikshya , 2017 145 faster for Odisha. However the millennium census has registered a six-point rise in the sex ratio of India and only one point for Odisha. The comfort drawn from this increase in sex ratio immediately vanishes when we look at the scenario of child sex ratio. Analysis of child sex ratio (0-6 years) provides better insight because it is not affected by sex selective migration and indicated recent trend in the population.
The child sex ratios for the State of Odisha Fig.2 Sex ratio of Children (0-6 years) of Orissa and India: Census 1961 -2001 and country as a whole have continuously declined 1050 1035 1020 over the census period 1961-2001. In the year 1010 995 976 964 962 967 970 953 1961 it was 1035 for Odisha, which declined to 945 927 953 in 2001, and the corresponding figure for India 930 890
declined from 976 to 927 (Fig. 2) (11). The decline males Females per 000' 850 in child sex ratio for Odisha was much faster than 1961 1971 1981 1991 2001 Orissa India Linear (Orissa) Linear (India) the all India level. This declining trend is intriguing and if it continues it will have a serious repercussion on the overall sex ratio.
The sex ratio trend in rural and urban areas of Fig. 3 Trend of sex ratio of Orissa & India in rural and uban areas: Census 1901-2001 Odisha and India has been illustrated in Fig.3(12). 1,100 1,050
The trend in rural areas both for Odisha and India 1,000 follows similar pattern as that of the overall sex ratio. 950 900
The trend in urban areas depicts a different pattern 850 Femalesper1000 Males
than the rural areas. The sex ratio in urban areas is 800
1901
1911
1921
1931
1941
1951
1961
1971
1981
1991 2001 significantly lower than that of the rural areas. Urban Orissa(Rural) Orissa(Urban) India(Rural) India(Urban) sex ratio was exhibiting a declining trend from 1901- 1961 and thereafter it has registered a sharp increase both for Odisha and India as well. The reversal of trend in the sex ratio indicated a shift in the pattern of migration to the urban areas. At one point of time influx to the urban areas was gender specific and dominant area of males. But with the change in the social attitude, female education etc., more and more females are also coming to the urban areas for pursuing education and in search of livelihood. And another important factor is that, earlier the male members were alone migrating to the urban areas for the purpose of livelihood, but the current trend being the migration for such purpose is along with the families in many cases.
Trend of sex ratio by caste
The scheduled castes and scheduled tribes belong to the socially, economically and educationally backward sections of the population. A study of sex ratio among scheduled castes and scheduled tribes vis-à-vis others is important to infer the impact of socio-economic condition on the sex ratio. The analysis revealed an interesting and intriguing scenario. The
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Fig. 4 Sex Ratio Trend (females per 000' Males) of scheduled tribes have the highest sex ratio and other SCs, STs, Others for Orissa:1961-2001
caste have the lowest sex ratio and scheduled castes 1020 1016 1012
1010 1007
1003 1015 come in between during the period 1961-2001 (Fig. 4) 1002
1000 993 991
(13). The rate of decrease during this period is the 990 988
979 979
980 975 969
highest for the other caste and lowest for the scheduled 970
960 959 960 tribes. This throws a million-dollar question whether the Females1000per Males 950 prosperity is linked with female deficit and as we go on 1961 1971 1981 1991 2001 Census Years progressing socially, economically and educationally SC ST Others more and more women are likely to disappear. In a number of analysis by different researcher (Premi,2001; Agnihotri,2002) also have expressed concern over the pattern of adverse sex ratio among more prosperous regions and segments.
Spatial pattern of sex ratios
According to 2001 Census Gajapati has the highest sex ratio of 1031 and Khurda the lowest of 902 and there is a wide spread inter district variation in the sex ratios. Ten districts namely Gajapati, Rayagada, Kendrapara, Kandhamal, Nuapada, Kalahandi, Koraput, Ganjam, malkangiri and Nabarangpur have high sex ratio in the range of 1031 to 991. Twelve districts namely, Khurda, Cuttack, Nayagarh, Angul, Jharsuguda, Balasore, Sundargarh, Dhenkanal, Jagatsinghpur, Sonepur, Puri and Sambalpur have low sex ratio in the range of 902 – 969. Rural sex ratio is higher than the urban in all districts.
Child sex ratio in the districts
It is more relevant to study the distribution of child sex ratio, Map 1 District-wise Sex Ratio of 0-6 year Children in Orissa : Census 2001 which is not affected by migration and provide the recent Sundargarh Mayurbhanj Jharsuguda
Deogarh Keonjhar Sambalpur Balasore trends in the population. The district of Nayagarh has the Bargarh Sonepur Angul Bhadrak Dhenkanal Jajpur Boudh a Bolangir Cuttack
d Kendrapara
a
p -
a Nayagarh
a u lowest child sex ratio of 904 and Nabarangpur the highest of h
Khurda Jagatsingpur
N d
l
n
a
a
r Puri
K m
u
p
g Kalahandi
n
a
r
b Ganjam a
999. The spatial distribution of overall child sex ratio depicts N Rayagada
Gajapati Koraput a clear cut geographical pattern in the State of Odisha (Map Child Sex Ratio (0-6 year): Total 904 - 944 Malkangiri 945 - 966 1). (14). A conspicuous sex ratio deficit zone has emerged 967 - 999 comprising of 12 districts in the central and eastern coast of Map 2 Rural - Urban difference in Child (0-6 Year) Odisha. These districts are Nayagarh, Dhenkanal, Sex Ratio in Orisaa: Census 2001 Sundargarh
Mayurbhanj Jagatsinghpur, Khurda, Puri, Angul, Jajpur, Cuttack, Ganjam, Jharsuguda Deogarh Keonjhar Sambalpur Balasore Bargarh
Sonepur Angul Bhadrak Kendrapara, Bhadrak, Balasore and have the child sex ratio in Dhenkanal Jajpur
a Bolangir Boudh
d Cuttack Kendrapara
a
p -
a Nayagarh
a
u
h d
N Khurda Jagatsingpur
l
n
a
the range of 904 to 944. Similarly a better child sex ratio zone a
r Puri
K m
u
p
g Kalahandi
n
a
r
b Ganjam
a N is prominent in south western belt comprising of 9 districts Rayagada Gajapati Koraput Rural_Urban difference in Child Sex Ratio namely Malkangiri, Koraput, Rayagada, Nabarangpur, -16 - 9 10 - 29 Malkangiri 30 - 70
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Kalahandi, Nuapada, Kandhamal, Bolangir and Sonepur. And Sundargarh in the north also joins this group. These districts have sex ratio in the range of 967 - 999.
Rural-Urban difference in the child sex ratios
The rural-urban differences in the child sex ratio throw deeper insight to the problem of deficit of girl children in the urban vis-à-vis rural areas. The deficit of girl child is prominent in the urban areas of Odisha. At the State level the child sex ratio is 22 point lower in urban areas than the rural areas. The difference is 30 or more in 12 districts. These 12 districts namely Sundargarh, Jharsuguda, Sambalpur, Angul, Bolangir, Boudh, Nayagarh, Kandhamal, Rayagada, Gajapati, Ganjam and Nabarangpur formed almost a contiguous band extending from north to south (Map 2) (14). The urban pockets in these districts are emerging as girl child deficient zone in Odisha. The syndrome in the urban pockets may be attributed to availability and access to the modern facility of sex determination through amniocentesis and ultra- sonography leading to systematic elimination of girl child in the mother’s womb and this also may propagate to the rural areas in the future, unless some measures are not taken to prevent it.
Decline in child sex ratios across the districts
The overall decline in child sex ratio is 14 point in Odisha during 1991 to 2001. Across the districts, it is seen that in 28 districts child sex ratios have declined while in one district namely Nabarangpur it has remained the same and in Sundargarh a marginal increase of 3 point has occurred. The highest decline of 46 point has occurred in the district of Nayagarh. Ten districts namely, Kandhamal, Balasore, Khordha, Rayagada, Angul, Nuapada, Dhenkanal, Koraput, Boudh, Nayagarh have experienced very high decline in child sex ratio in the range of 20-46 and eleven more districts namely, Bhadrak, Ganjam, Puri, Bargarh, Sambalpur, Jagatsinghpur, Gajapati, Malkangiri, Jharsuguda, Keonjhar and Kalahandi have experienced decline in the range of 10-19-points (15). Summary and Conclusion
Odisha’s population once characterised by the preponderance of females has turned in to a population with sex ratio adverse to female since 1971. The State has witnessed faster decline in sex ratio than the all India average. The socially and economically backward STs and SCs have relatively better sex ratio and lower rate of decline in it than the other castes. This caste dimension of sex ratio indicated inverse relationship between sex ratio and prosperity. Child sex ratio in the 0 – 6 year age group also have shown steep declining trend. The sex ratio and so also child sex ratio have shown contiguous geographic pattern across the districts of the State. Extremely low child sex ratio zone occurred in the relatively advanced coastal belt and the adjacent districts in the central parts. Relatively, better child sex ratio regions appear in the less advanced south western tribal belts. Child sex ratio is striking low in urban areas than in rural areas. Urban pockets in 12 districts in a narrow band extending from north to south is
Samikshya , 2017 148 emerging as girl child deficit zone. Decline in child sex ratio during 1991 – 2001 is even more than 20 point in 10 districts.
The above issues are of serious concern and unless some corrective remedial policies and measures are taken, the situation may worsen in the near future. These issues need immediate attention of planners, administrators and researchers. In-depth research on the above aspects is required to bring out the causes and consequences and suggest corrective measures and policies.
The continuous disappearance of females over the census count along with the development in Odisha is a matter of significant concern and puts a million dollar questions. Is the progress contributing negatively to the status of women-even a threat to their survival. It is worth dwelling in the possible reasons. The preference for sons is strongly embedded in the psyche of the population of Odisha. It is now abetted by scientific technology of ultra- sonography and amniocentesis, which has helped to eliminate the unwanted girl child in the bud i.e. at embrynical or foetal stage. This is happening in the urban and semi urban areas and systematically percolating to the rural areas. Strikingly low child sex ratio i.e. specially in the urban areas is a pointer to this. Another important reason for strikingly low child sex- ratio could be the differential access to the health and nutrition against the girl child due to discrimination. In a economically backward State like Odisha, the access to improved health facilities and nutrition is difficult for the large mass of poverty affected people. But the house - hold makes extra effort to ensure the health and nutrition of the male children, while the girls become the natural victim.
In order to prevent the worsening situation, some corrective measures and policies are suggested, which may go a long way. The education of girl child may be universalized and free of cost. Social, political and economic empowerments of women need to be given top priority. The health facilities like user fees etc. in the Government Hospitals should be made free for the girl children up to the adolescent age. Mass awareness on the social implications of declining sex ratio should be made through print and electronic media, educational system and through Govt. and non-Govt. work force working in the field.
Reference
1. Census of India 1991: Final Population Totals: Paper 2 of 1992 . 2. World Fact Book (2003): www.cia.gov/cia/publications/factbook 15th March 2006 3. Sopher, D.E (ed) .(1980) : An exploration of India , London , Longman. 4. Miller, B.D (1981) : The Endangered Sex, Ithaca, N.Y: Cornell University Press.
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5. Agnihotri Satish B (2000) : Sex ratio Patterns in Indian Population – A fresh Exploration . New Delhi: Sage. 6. Dyson, T and Moore, M(1983) : “ On Kinship Structure, Female autonomy and demographic balance. Population and Development Review, pp:35-60. 7. Miller, B.D (1989) : Changing patterns of juvenile sex ratios in rural India, 1961 to 1971. Economic and Political Weekly, 24 (22): 1229-1235. 8. Caldwell, JC and Cladwell, P, (1990), Gender implications for survival in South Asia , Health transition working paper No. 7, Canberra, NCEPH, Austrialian National University. 9. Agnihotri Satish B (2002) : Changes in Sex Ratio Patterns in Odisha : 1991-2001, Is there an Epi-centers of Female Deficit?, Demography India, pp 179-194. 10. Census of India (2001) : Series – 22 Paper 2 of 2001, ‘Provisional Population Totals”. 11. Derived from Age Tables of Census of India from 1961 to 2001 12. Census of India (1991): Final Population Totals: Paper 2 of 1992, Govt. of India P.102 13. Derived from Social & Cultural Tables relating to SCs and STs of Census 1961, 1971, 1981, 1991 and Primary Census abstract on SCs and STs of 2001 Census 14. Census of India 2001, Census CD on Age Data, Registrar General of India, Govt. of India, 2001. 15. Derived from Census CD on Age Data, Registrar General of India, Govt. of India, 2001 and Census of India (1991): Final Population Totals: Paper 2 of 1992, Govt. of India P.102
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Gender Construction through Electronic Media: An assessment by the Television Viewers of Odisha
Dr Aliva Mohanty Abstract
The Television has played pluralistic role in the image construction of women. It has neglected the real, basic and deeper issues of women’s empowerment, equality, justice and has consequentially reinforced the continued image of women albeit in different discourses, symbols and modes of representation. Television thrive on salability not substance, on market forces not morals. There is a shift presentation, representation and signification which is geared to commodification and not centrality of concerns. In this paper attempt has been made to see the attitude of the Television viewers regarding the indecent representation of women.
Key Words : Commodification, Thrive, Indecent, Consequentially, Pluralistic
Introduction
Media plays an important role in development communication through circulation of knowledge, providing forum for discussion of issues, teach ideas, skills for a better life and create a base of consensus for stability of the state . The history of development communication in India can be traced to 1940’s when radio broadcast was done in different languages to promote development communication through various programmes, like—Programs for Rural Audience, Educational Programs and Family Welfare Programs. Today Television in our country is also used as a medium for social education, weapon against ignorance and awareness among the people through different programs like Educational Television (ETV), Countrywide Classroom (CWC), Teleconferencing etc. Experiments in Satellite technology has been conducted in recent years.
Television in India
Television has come to the forefront only in the past two decades and more so in the recent past. There were initially two ignition points: the first in the eighties when colour television was introduced by state-owned broadcaster, Door Darshan (DD) timed with the 1982 Asian Games which hosted by India. It then proceeded to install transmitters nationwide rapidly for terrestrial broadcasting. In this period no private enterprise was allowed to set up television stations or to transmit television signals.
In the multimedia world of today , television has emerged as an important instrument of transmission of knowledge and information . It is working as a complementary agent to Samikshya , 2017 151 other sources , television has enhanced the process of change by providing timely information about education , hygiene , health , customs and so on . The purpose of Television is to inform , educate and entertain its viewers. Television because of its predominance of visual movement has the capacity to bringing the world into the living room with great authenticity and efficacy.
There is clearly a big gap between urban elites and rural masses in the state of Odisha. Issues like lack of healthcare and education facilities for women and children, increasing crimes against SCs and STs, and the like are crying for more attention. More than half of the population of Odisha belong to rural areas and they are normally poor and illiterate. It is very difficult for them to get the information regarding new policies, schemes and awareness generation programmes of the government. But the tremendous popularity of television and its ability to reach a vast audience with illiteracy being no barrier led to the idea of using television as a channel for information on development was adopted by the Govt. of Odisha.
On the Television of Odisha the T.V. channels like Tarang T.V. which has popular serials like Sahanai , Uaanshi kanya, Kichi luha peejae otha, swabhiman etc,in Sarthak T.V. the popular serials are To aganara Tulashi mu, Badhu, Parietc. Tye famous T.V. serials of E. T.V. are Tapasyaa, Gayatri, Appa, Rajakanya etc. From the above titles of the serials , it can easily be observed that how all the entertainment programmes revolving around women and their traditional and stereotyped life and role. The most interesting fact is that these type of serials are getting the highest number of Television Rating Point.
Television is a powerful channel of media which easily disseminate the messages and information to the people as well as it reflects the current situation of the society. So television can be used for providing information about people in difficult circumstances, different problems related to women, children, weaker section and about other areas which needed immediate attention of the policy makers.
But there is no exclusive focus on women’s problems and their development. In the absence of a comprehensive media policy in India, television content emphasising entertainment has grown to such an extent that today all television channels are oriented towards commercialisation with not even semblance of public education or service. Though with the a vowed goals of education, modernisation and development, today’s satellite channels have done more damage to the cause of women’s development by regressing to highly negative values that impede women’s empowerment .Television programmes have made use of cultural stereotypes to reinforce subservient role model for women that are major obstacles in changing social prejudice and traditions affecting women.
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The UNESCO report on “Women and the Media” states that the perspective of women reflected in messages disseminated by the mass media express male concept of women. The commercial media produce message systems and symbols which create or structure prevailing images of social reality which show women as housewives, consumers and sex objects, women in advertising are always young and attractive, they are frequently depicted as sexual objects and they are seldom shown as intelligent people.
In advertisements, whether it is detergent, cosmetic, automobile or any other product, youthful and beautiful images of women are frequently used to sell the products. At another level, women are strictly shown as being household being solely responsible for household task. The daily soaps are also the same; today’s soaps follow a new package formula that emphasises personal relationships, family conflicts, emotions that are generally confined in a domestic space. In most serials, the soap story lines remain female centred in a stereotypical pattern focusing on women’s emotions and relationships and men figuring peripherally in many ways to the bonds between women.
There are several mechanisms through which television may affect the women's status. For example, television may affect fertility by providing information on family planning services or changing the value of women's time or women may be given more freedom to do things outside of the home like going to the market because the value of men's leisure is increased by television. However, one plausible mechanism is that television exposes rural households to urban lifestyles, values and behaviours that are radically different than their own and that households begin to adopt or emulate some of these, as suggested by many anthropological and ethnographic studies of television in India (Mankekar 1993, 1998; Fernandes 2000; Johnson 2001; Scrase, 2002).
Women and T.V. Serials
Satellite Television has offered the Indian viewers a variety of channels and genres of programming along with a heavy dose of family oriented serials for women viewers.
According to the National Readership Survey (NRS) cable and satellite subscription has now penetrated to 50 % of all T.V. homes. Only 8% of the total telecast time today is devoted to serials but that accounts 30% of viewership (The Hindu, Business Line). One of the main features of the phenomenal growth of satellite T.V. has been the media focus and as main protagonists. The entry of private T.V. channels has increased the visibility of women. Rating of some of the more recent prime time soaps indicates a very high percentage of women viewers. It has also been found that women are more regular in T.V. viewing than men which means women are vivid viewers of modern Soap Operas. Samikshya , 2017 153
The soap operas affect women more simply because the women are watching more serials then men. Men spend maximum time outside whereas women are confined to the household (Jyotin 2002). Sex stereotyping is also very much evident in television portrayal of men and women in their appointed roles. Invariably, masculine personality attributes are emphasised and women in the world of television are presented in role of domestic help, a wife, a mother etc. and they are portrayed as submissive and engrossed in common family affection and duties as against this, men are depicted as employed, competitive. Women shown in similar competing roles with men are far less in number and are considered to be deviations from norm, trait wise though there is a stereotype portrayal of women being congenitally much more than men. Even when women are presented as power holders, the patriarchal context is unmistakably present. In fact, the attributes of power and aggressiveness is portrayed as something unnatural to a woman and a challenge to the male ego.
Table 1 Sl. Responses Respondents No Men (N1=60) Women (N2=60) N % N % 1 Domesticated 40 66.6 30 50.0 2 Business women 7 11.6 6 10.0 3 Fashionable & glamorous 40 66.6 32 53.3 4 Emancipated women 4 6.6 5 8.3
Statistics of a research conducted in Jammu by Shashi Kaul and Shradha Sahni Department of Community Resource Management and Extension, Govt. College for Women Parade, Jammu, Jammu and Kashmir, India
The images portrayed in TV serials have a definite impact on the thought patterns of society. Women on television entertainment programmes are projected as non-thinking, sacrificing, and suffering beings while educated and motivated , women are seen as the scource of the patriarch order of the society (Desai 1990). Media is an important tool for change. Women need to ensure that media reflects images that create positive role models of men and women in society, which will alter damaging stereotypes .This negative attitude towards women in real life is very much reflected in way media represents them as well. Media representations of Indian women reveal that they are less accepted and respected as persons and more looked upon as objects. She has three projected roles biological, domestic and decorative. Media hardly challenges the gender attitudes promoted and perpetuated by the society.
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Objectives
1. To assess knowledge and attitude of the T.V. viewers regarding the Portrayal of women in Television. 2. To study the impact of T.V. programmes on the behavioural pattern of the respondents. 3. To analyse the reason for the negative portrayal of women through T.V. 4. To suggest appropriate strategies for improving the portrayal of women through Television 5. To analyse the consequences of portrayal of women on the real life of the T.V. viewers.
Research Methodology
The purpose of this study is to examine the attitude of the T.V. viewers regarding the portrayal of women in Television with special reference to the TV viewers of Bhubaneswar. The methods used in conducting the study are as follows:
Sample Design: A simple random sampling is used to select the samples. All total 100 respondents from different age group of women TV viewers of Bhubaneswar are selected to conduct the study.
Data collection: The data for this study are collected from both primary and secondary sources.
Primary Data: At the initial stage the primary data was collected by personal visit to the respondents with a set of scheduled questionnaires.
A scheduled questionnaire a set o 43 questions were prepared by the investigator herself to collect information regarding portrayal of women through electronic media.
Secondary data: secondary data were collected from Nielsen report , journals , magazines, media related books and other reports related to media.
Findings of the Study
The socio-economic profile of the respondents shows that about 46% of the respondents belong to middle age group. Around 38% of respondents belong to general category. Almost all the respondents are highly qualified. About 54% of respondents are living in joint family. About 63% of middle aged house wives and 31% of elderly persons are sparing more time in watching T.V due to their non-engagement in any type of work. The study reveals that about 34% of young respondents are watching T.V. in the night. Whereas 44% of elderly respondents Samikshya , 2017 155 are watching T.V. So age has a positive impact on watching T.V. in different sessions. The young masses are sparing more time in night for watching different entertainment programmes in T.V. But the elderly persons are preferring evening hours for watching serials as per their interest. The study shows that maximum percentage of young respondents are watching different T.V. channels like zee T.V., Sony T.V., Colours and Discovery channels for their recreation. Around 19% of elderly respondents are prefering odia channels like O.T.V, Sarthak T.V.etc. About 47% of young respondents, around 48% of middle aged respondents and 56% of elderly respondents have shown their preference for different programmes in T.V. But 38% of elderly respondents have shown their preference for news items. Regarding the reason for watching T.V. about 30% of young respondents are watching T.V to pass their time. whereas32% of elderly respondents are watching T.V to avoid loneliness and 28% of middle aged respondents are watching T.V. for relaxation. So the age has a differential impact on the reason for watching T.V. The highest 94% of elderly age group women watch soap operas where as 24% of young respondents are watching reality shows in T.V. About 32% of young age group women watch the serials like Diya aur bati hum and 30% of middle aged respondents watch the serial Pyar ka dard hei mitha mitha on T.V. In case of Jodha Akbar the percentage of elderly persons is more i.e.25%. In case of Pavitra Rista the percentage of middle aged respondents is also more i.e 20%.So the respondents of different age group have differential preference for different programmes. Highest 41% of middle age group women have pointed out that there is commodification of women’s body through soap operas. Around 41% of middle age group women say there is commodification of women’s body through soap operas .Almost all the respondents of all the age group have reported that in order to attract the viewers the emphasis is given on negative portrayal. Whereas maximum percentage of respondents of all age group are also focusing on the stereotypical representation of women in T.V. Around 46% of middle aged women have reported that the women are indecently represented through soap operas and 13% of elderly respondents have focused on beautiful representation of women in T.V. Whereas 32% of young respondents have highlighted about the traditional representation of women in T.V. The study indicates that almost all the respondents of all age groups are putting emphasis on the intention behind the stereotypical representation is to make profit. Maximum percentage of elderly respondents i.e.31% reported that the people are enjoying the stereotypical representation. The study focused on the differential attitude of respondents regarding the negative portrayal of women. About 37% of young and middle aged respondents are putting emphasis on the sexy look of women in T.V. Maximum percentage of respondents of all age group are focusing on the exposure of body of the women in T.V. Regarding the advertisements on T.V. the study reveals that around 34% of young aged women watch beauty products on T.V. during watching programmes and about 17% of middle aged respondents are watching the detergents products because they are using the detergents in their daily life. About 33% are watching the advertisements regarding the undergarments. Some of the respondents are also watching the kitchen appliances in T.V. This states that age do not has any impact on watching the advertisements in T.V. Around 48% of
Samikshya , 2017 156 women of middle age group pointed out that T.V has an influence in their dressing style and44% of elderly respondents have reported that it is influencing their attitude towards the family. The respondents of all age group have been influenced by T.V. About 73% of young aged women are facing eve-teasing/ sexual violence outside home for negative representation of women on T.V. and 11% of young age group and middle age group women are facing domestic violence. So the respondents of all age group have faced violence in their life due to the negative portrayal of women in T.V. About 48% of middle age group women want to see women as Modern professional/ successful women roles on T.V. and about 41%of middle aged respondents want to see the women in action role. So almost all the respondents are preferring modern role of women. Around55% of young age group women have reported that violence against women increased due to negative representation of women by T.V. and about 41% of middle aged respondents are pointed out that men are becoming disrespected towards women. So respondents of all age group are pointed out that negative portrayal of women have a negative impact on the society. Around43% of middle age group focused on the fact that misbehavior increased towards elderly women from the youth by negative representation of women by T.V. and about 29% of youth have reported that they are neglected by their family members. Around 37% of middle aged respondents have become burden for the family due to the negative portrayal. About 33% of middle aged women have reported that that the adolescents and small children imitate the violent behaviour from the serials and about 31% of elderly respondents reported that the adolescents want to reflect it in their behaviour. Regarding the impact of T.V. on women 38% of elderly women reported that the children are spending their time unnecessarily. About 37% of middle aged respondents reported that the children are not responding to their elders. So maximum percentage of respondents are reporting that T.V. has a negative impact on children. Regarding the impact of negative portrayal of women on status of women the study reveals that 39% of young aged women reported that the subordination of women has increased due to negative representation of women by T.V and about 37% of young respondents are pointing that women are facing more violence due to the negative portrayal. Around 38% of elderly respondents are pointing out that the women are loosing their respect. About75% of young age group women have reported that steps should be taken by policy makers and 31% of elderly respondents want to consult activists for the removal of negative portrayal of women.
Conclusion
In the present society everyone is dependent on Television. One can see that in every household in Bhubaneswar there are two to three T.V. sets and DTH services. From the study it was reflected that most of the women in all age groups are spending most of their time in watching different Serials or Soap operas, News , Reality shows, Cookery shows, Music videos, etc. They also watch different types of advertisements of beauty products, under garments ,kitchen appliances, cleaners, etc. It is also found from the study that how the T.V. programmes Samikshya , 2017 157 has its impact on the real life of the T.V. viewers. Some women are facing problems like lack of confidence due to their ugly and dark faces and how the other persons are reacting on the women outside their home. Some other women are facing many health related issues like under weight, blood pressure, trauma etc.
Again the Television also putting bad impact on the small children and the adolescent girls. The children are not responding and misbehaving to their parents and spending much more time than necessary on viewing Television. They are also getting inattentive in the class and are getting punishments for not doing their home work. Whatever they learn from the Television they try to apply on their parents or on their siblings.
The adolescent girls also facing problems due to the portrayal of women in T.V. programmes and advertisement. They are spending a lot of time and money to get a sexy outfit , they have also change their dressing styles, they are wearing those dresses which will make them look sexy so that the male will be attracted towards them and which is a big cause for the sexual violence and molestation in the society.
Therefore the negative portrayal should be banned and at that place positive images of women should be shown in the films, serials and advertisements which will make easy the work of up-liftment of women in the society and which is only possible by the coordinated effort of policy makers , policy implementers, producers of movies , serials ,Television advertisements as well as the common man.
References:
1. Desai. R (1990) Television in Transition London, British Film Institute 2. Fernandes. S. (2000) The Soap Opera London Sage Publication, New Delhi. 3. Johnson.M (1990) Television and Women’s Culture Sage Publication, New Delhi 4. Jyotin C. (1991),Women and Soap Opera, Polity Press. Cambridge 5. Mankekar.G (1993) Living with Television, Bloomington, Indiana University Press. 6. Rase.S.C.. (2002) The Effects of Television, London, Panther Books.
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New Releases of DES Publications in 2016- 2017
Odisha Economic Survey Odisha at a Glance Districts at a Glance Compendium of Environment 2016-17 2016 2017 Statistics, Odisha,2016 2016
SAMIKSHYA Annual Report, DES Estimated GSDP of Advance Estimate 2016 2015-2016 Odisha of GSDP
Fact Book on Manpower District Statistical Hand District Statistical Hand District Statistical Hand Book, Khordha Book, Ganjam Book, Jagatsinghpur
Un-incorporated non- Consumer expenditure Employment and Un- District Level Poverty agricultural enterprise in Pattern employment Estimate using SAE Odisha
Scientific advance and human welfare must be ingrained in the defined purpose
of statistical system
Revered Guests Prof. S.N.Pasupalak, Vice Chancellor, OUAT; Sri N.K.Nayak, IAS(Retd) OSD,P&C Dept and Sri P.K.Biswal, IAS, Additional Secretary, P&C Dept, Sri D. Behera, Director, E&S, Odisha released SAMIKSHYA 2016 on 10th Statistics Day2016 at Bhubaneswar on 29th June 2016 – A proud occasion for DES, Odisha
New Office Building “ARTHANITI O’ PARISANKHYAN BHAVAN” of DES Head Quarters at HOD Campus, Bhubaneswar under completion