Curriculum Vitae
Pulakesh Chandra Maiti
Professor of Statistics, Indian Statistical Institute
E-mail: pulakesh@isical.ac.in, maiti.pulakesh@gmail.com
1. Personal Details:
1.
Name
: Pulakesh Chandra Maiti
2.
Date of birth
: 21st October, 1950
3.
Citizenship
: India
4.
Present position/designation: Professor, Indian Statistical Institute
5. Address:
(a) Office 203, B.T. Road, Kolkata – 700108
Phone: 91 33 25752612
E-mail: pulakesh@isical.ac.in, maiti.pulakesh@gmail.com
(b) Residence: Krishna Apartment
Flat – 3A
8/1B, Panditia Road
Kolkata – 700029
Phone: 91 33 2476 4170 (Land); +919432304116 (Mobile)
2. Academic and Professional Details:
1. Qualifications:
Degree/Diploma
Statistician’s
Diploma
Ph.D
Subject
Statistics
Institution
Indian Statistical Institute
Year
1974
Sampling
Indian Statistical Institute
1983
3. Permanent Positions:
(a) Technical Assistant, Sankhya, Indian Journal of Statistics, Indian Statistical
Institute, July 78 – April 1983.
(b) Consultant, Market Research Survey, Operations Research Group (ORG),
Vadodara, April 14, 1983 – October 23, 1984.
1
(c) Publication Officer, Sankhya, Indian Journal of Statistics, Indian Statistical
Institute, October 23, 1984 – November 30, 1990.
(d) Lecturer, Economic Research Unit, Indian Statistical Institute, November 30, 1990
– June 1, 1995.
(e) Associate Professor, Economic Research Unit, Indian Statistical Institute, June 1,
1995 – September 1, 2014.
(f) Professor, Economic Research Unit, Indian Statistical Institute, September 1, 2014
till date.
4. Visiting Positions:
(a) Visiting Scientist (August 3 – August 15, 2014)
The Institute for Mathematical Research (INSPEM)
University Putra, Malaysia.
(b) Visiting Professor (April 10, 2013 - May 02, 2013)
Department of Statistics
University of Rajshahi, Bangladesh
(c) Visiting Professor (August 28, 2006 – September 8, 2006)
UNIVERSIDADE NOVA DE LISBOA
Lisbon, Portugal
(d) Senior Consultant (May 22, 2000 – May 21, 2001)
National Council of Applied Economics Research
New Delhi.
(e) Visiting Professor (1996 – 1997)
PG Department of Statistics
The MEDUNSA
Pretoria, South Africa
(f) Chief Research Specialist (Sampling): (January 27, 1995 – December 15, 1996)
Centre for Statistics
Human Sciences Research Council (HSRC)
134, Pretorious Street
Private Bag X41
Pretoria, South Africa
(g) Guest Lecturer (April 1, 1993 – March 1995) University of Calcutta, Kolkata.
2
5. Honorary Positions:
(a) In Charge of One Year Evening Course in Statistical Methods and Applications
(1990 – 93) Indian Statistical Institute held at different Centres, Kolkata, Delhi,
Bangalore in India.
(b) Editorial Secretary, Sankhya Indian Journal of Statistics, Indian Statistical
Institute, Kolkata (1991 – 1993).
(c) Member, Working Group 69th Round NSS (July 2012 – December, 2012),
National Sample Survey, Appointed by National Statistical Commission,
Government of India.
(d) Member, Working Group, 73rd Round NSS (June 2015 – June 2016 ) National
Sample Survey, Appointed by National Statistical Commission, Government of
India.
(e) Member, Working Group, Drug Abuse Survey (November 18, 2014 – April 10,
2015), Ministry of Statistics & Programme Implementation, National Statistical
Commission, Government India.
(f) Member, Expert Group, 11th Five Year Plan, (April 2007 – March 2012),
Preparation of Annual Perspective Plan (Under the Process of Decentralised
Planning) District of Howrah, West Bengal, India.
(g) Analyst of village resource mapping. (2014) Directorate of Micro and Small Scale
Industries, Government of West Bengal, India.
(h) Member, “Technical Assistance Piloting below poverty line”, (May 2010 – 2011):
(Census) Panchayat & Rural Development, Government of West Bengal, West
Bengal, India.
(i) Collaborative Scientist (May 17 – August 16, 2010), Department of Economics
and Statistics (DES), Tata Service Limited, Mumbai, India.
(j) Member, (2006) Uganda Bureau of Statistics, Uganda.
6.1. Services to Government of India:
•
Serving as a sampling expert on the working group of 69th round of NSS, which
was devoted to Drinking Water, Sanitation, Hygine and Housing conditions
(including slums), sampling design, methodology and strategies to reduce nonsampling errors were developed. Sampling design, schedules of enquire and
procedures for data collection were finalized at all India Training of Trainers
(AITOT) New Delhi, during March 15 – 16, 2012.
3
•
A special estimation procedure of estimating the number of slums in the absence
of a list of slum was developed. The sampling methodology thus developed
suggests the number of slums can be estimated unbiasedly from the survey with
the help of an additional information on the number of blocks linked to a slum of
which the sample blocks are found to be parts and the methodology thus
developed was deployed in the 69th round. Number of slums at the state as well as
at all India level was estimated.
•
On the pilot basis, field visits were made during /and after the NSS survey at the
states namely Tripura, West Bengal, Jammu and Kashmir, Gujarat, Andaman &
Nicobar islands to make an assessment of related field problems including
problems of non-sampling errors in connection with 69th round NSS. The report
was submitted to the office of National Statistical Commission, New Delhi.
•
Served as a Chairman of the subcommittee of 69th round on imputation of rental
values, and the report was submitted to the Commission.
•
Provided knowhow on methodology, sampling design, and development of
enterprise schedule in conducting nationwise surveys of NSS 73rd round devoted
to surveying un-incorporated non-agricultural enterprises in manufacture, trade
and other services (excluding construction). For the first time, Enumeration
Block(EB) in urban areas was suggested as the first state unit (FSU).
•
Provided knowhow on Drug abuse survey as a member of the working group. A
dual approach i.e., household based survey for commonly used (licit) drugs and
Respondent Drives Sampling Design (RDS) technique for illicit drugs was
recommended in conducting drug abuse survey.
6.2. Services to Government of West Bengal:
•
Created Data Base and for Creating the RDBMS, such studies as System
Requirement Study (SRS), Context Analysis and Design (CAD), Data Flow
Design (DFD) were made.
•
Provided know how on Design and Development of Application Software
programmes for processing and generating statistical distribution of the
concerned variables. This lead to development of 105 software programmes.
•
Development 157 booklets of Resource Mapping for 157 Gram Panchayats of
the District Howrah, West Bengal.
•
Prepared fourteen Human Development Reports at the block level of the
fourteen blocks entitled “Block wise Human Development Report in the
District of Howrah West Bengal.
4
•
Provided guidance to the Directorate of Micro and Small Scale Enterprises
regarding the fields to be considered for identifying potential resources in
specific areas of different districts with the help of village resource
information as obtained from Different District Industries Centre (DIC). This
may ultimately facilitate industrilisation in the state.
•
Provided know how on imparting training and supervision for the field work
in conducting nationwide surveys, data analysis and inference in connection
with the project entitled “Assistance of Piloting Below Poverty Line (BPL)”.
•
Provided know how on projection of Demand of thirty two items of
consumption under the project of the Department of Economics and Statistics
(DES), Tata Services Limited, Mumbai.
•
Provided expertise on Stochastic Modeling of Buying Bachviour of Indian
Customers to Business Research and Corporate Planning Group of Hindustan
Lever Limited and know how on Stochastic modeling of discovery of Hydro
Carbon as needed by Oil & Natural Gas Commission, Government of India.
•
Provided know how as a member in the consulting team for price-water
house project “Strengthening Local Government in MP”. The project was
highly appreciated so much so that it was reported in the ‘News Letter’
Section of the American Statistician.
•
Provided expertise on statistical assistance to the Uganda Bureau of Statistics
with respect to possible methodologies that could be implemented by the
Uganda Bureau of Statistics, Uganda in such identified areas as (i) the
estimation of yield rates of crops such as rice, potatoes and various fruits and
(ii) estimation of total production in enterprises owned and operated by
Government of West Bengal, (iii) estimation of consumption and expenditure
on household items using Sample Survey Methodology.
6.3. Services to the Industry Houses:
•
Provided knowhow on methodology and applications of Statistics to Hindustan
Lever Limited, Tata Group of Consultancy Services in conducting nationwide
surveys, data analysis and inference.
The project through which the above are realized are mentioned in detail under 4 of
section 18.
7. Awards and Appreciations:
1. Received the “Leading Scientists Award, 2013” from International
Biographic Centre, Cambridge, UK.
2. As a mark of appreciation, works on the projects entitled “Strengthening
Local Government in Madhya Pradesh India (1998 – 1999), “Statistical
5
Information System for local planning by local bodies with creation of
computerized data base for Howarh, West Bengal, India (April 2007 – March
2012)” and “Development of Consensus Development plan for the GramPanchayat Amardah under the Block Shyampur – II of the District of Howrah
of West Bengal” have been kept in the Prasanta Chandra Mahalanobis
(PCM) memorial Archive and Museum for preserving the historical
heritage of the Institute for posterity.
3. The first ever excise in the country on decentralized planning at the Gram
Panchayat Level entitled “Statistical Information System for local level
planning” was carried out.
“Development of Concensus Development plan for the Gram Panchayat
Amardah under the block Shyampur – II of the District of Howrah, West
Bengal, India” has been preserved at the State Planning Board,
Government of West Bengal, India.
4. Books on Human Development Report have been preserved in the
National Library Kolkata under rule 3 of the delivery books (Public
Libraries) Act, 1954 (27th May , 1954).
8. Invited Talks:
1. At the Tata Consultancy Service, Tata Group of Industries, Mumbai,
(March 18, 2015).
2. At the International Conference on Recent Advances in Mathematics,
Statistics and Computer Science (ICRAMSCS – 2015), Central University
of South Bihar, (May 29 – 31, 2015).
3. At the International Conference on ‘Celebrating Statistical Innovation and
impact in a world of Big and Small data, Department of Statistics,
Sabitribai Phula, Pune University and International Indian Statistical
Association, (December 20 – 24, 2015).
4. At the International Conference on Statistics and related areas for Equity,
Sustainability and Development (SRAESD – 2015) & XXXV Annual
Convention of Indian Society for Probability and Statistics (ISPS),
University of Lucknow, (November 28 – 30, 2015).
5.
At the National Seminar on ‘Poverty, Inequality and Health in India with
Special reference to North East India’, North Eastern Hill University, and
Indian Statistical Institute, (October 8 – 10, 2015).
6
6. At the 9th International Triennial Calcutta Symposium on Probability and
Statistics, Department of Statistics, University of Calcutta and Calcutta
Statistical Association, (December 28 – 30, 2015).
7. At the National Conference on Agricultural and Rural Development issues
in Eastern India, Indian Statistical Institute, Giridih, (March 12 – 13,
2015).
8. At the 68th Annual Conference of Indian Council of Agricultural Research
(ICAR), New Delhi (February 23 – 25, 2015).
9. At the National Conference “National Conference on Recent Trends and
Development in Statistics, Department of Statistics, NCRTDS”, M.D.
University, Hariyana, (February 21 – 23, 2015).
10. At the 17th Annual Conference of the Society of Statistics, Computer and
Applications (SSCA) on ‘Statistics and Informatics for Smart decisions in
Management Resources: issues and challenges, Birla Institute of
Management Technology, (February 23 – 25, 2015).
11. At the International Conference on Recent Advances in Mathematical
Statistics and its application in Applied Sciences, Department of Statistics,
Gauhati University, (December 31 – January 1 -2, 2014).
12. At the National Conference on Recent Advances in Statistical and
Mathematical Sciences and their Applications (RASMSA – 2014),
Kumayun University, (October 14, 2014).
13. At the 12th Conference of Indian Association of Social Sciences and
Health (IASSH), GL Gupta Institute of Public Health, University of
Lucknow, (November 21 – 23, 2014).
14. At the 2nd ISM International Statistical Conference 2014, with
Applications in Sciences and Engineering (ISM – II, 2014), Pahang,
Malaysia, (August 12 – 14, 2014).
15. At the Department of Statistics, Lucknow University, India (November 16
– 17, 2014).
16. At the International Conference on Recent Advances in Mathematical
Statistics and its applications in Applied Sciences, Dhaka University,
Bangladesh, (December 31, 2012 & January 1 – 2, 2013).
17. At the 1st International Conference on Information, Operations
Management and Statistics (ICIOMS 2013), Kuala Lumpur, Malaysia,
(September 1 – 3, 2013).
18. At the International Conference on Statistical Data Mining for Bioinformatics, Health, Agriculture and Environment, Rajshahi University,
Bangladesh, (December 22 – 24, 2012).
19. At the 99th Session of Indian Science Congress Association, KIIT,
Bhubaneswar, India(January 3 – 7, 2012).
7
20. At the International Workshop on Sample Surveys with special emphasis
on Large Scale National Sample Surveys towards promoting national
development, Department of Statistics, Rajshahi University, Bangladesh
(October 18 – 19, 2012).
21. At the Department of Statistics, Rashtrasant Tukadoji Maharaj Nagpur
University, Nagpur (January 10 – January 12, 2008).
22. At the conference of Central and State Statistical Organisations
(COCSSO), Delhi (April 10 – 11, 2007).
23. At the Universidade Nova De Lisboa, Lisbon, Portugal (August 29, 2006
to September 01, 2006).
24. At the SCRA 2006 Conference, Lisbon, Tomar, Portugal (September 1,
2006 – September 4, 2006).
25. At the workshop on Demography and Population Dynamics with emphasis
on Survey Sampling Methodologies, Tejpur University, Tejpur (October
26 – 28, 2006).
26. At the International Triennial Calcutta Symposium on Probability and
Statistics, Department of Statistics, University of Calcutta and Calcutta
Statistical Association (December 29 – 31, 2006).
27. At the national conference on ‘Recent Trends in Estimation and
Optimizations”. Also to celebrate fifty years of Professor V.P. Godambe’s
revolutionary theorem on unified theory of sampling, Institute of Science,
Nagpur; Golden Jubilee (January 1 -2, 2006.
28. At the UGC sponsored refresher course on ‘Decentralisation, Planning and
Participatory Development, Department of Economics with Rural
Development, Didyasagar University, (December 12 – 31, 2005).
29. At the Training Programme Organised by Operations Research Guide
India, ORG, India CA 242, Salt-Late, (December – 17, 2005).
30. At the Department of Economics, Jadavpur University, (December 31 –
January 3, 2005).
31. At the national seminar ‘Information Support for rural development,
Indian Association of Special Libraries & Information Centres (IASLIC),
Golden Jubilee, (September 2004 – August 2005).
32. At the Tenth Course/Workshop on Sampling Design and Establishment
Surveys, jointly with CSO, Ministry of Statistics and Programme
Implementation and UN Statistical Institute for Asia and the Pacific,
Japan, (October 18 – November 12, 2004).
33. At the joint IMS-SRMS Mini Meeting on current topics in Survey
Sampling and Official Statistics, Calcutta, India, (January 2 – 3, 2004).
34. At the 275th IASLIC Study Circle meeting, (June 17, 2004).
8
35. At the eleventh International conference – SCRA 2004 – FMXI, Forum
for Interdisciplinary Mathematics, Institute of Engineering and
Technology, Lucknow, India (December 27 – 29, 2004).
36. At the UGC-sponsored national seminar on Participatory Decentralised
Planning: Issues of Functions and Functionaries, Economics with rural
development, Vidyasagar University, India (2003).
37. At the International conference ‘Recent Statistical Techniques in Life
Testing, Reliability, Sampling Theory and Quality Control, Department of
Statistics, Faculty of Science, Banaras Hindu University, India (December
29 – 24, 2003).
38. At the two day UGC sponsored national seminar on Decentralised
planning, Department of Economics with rural development, Vidyasagar
University, India (March 20 – 21, 2003).
39. At the Business Research Group and Corporate planning, Hindustan Lever
Limited, Mumbai, India (August 12 – 13, 2003).
40. Keynote address speaker at the two-day national seminar on information
support for rural development, conference hall of the Central Library,
Vidyasagar University Campus, India (November 18 – 19, 2003).
41. At the seminar talk on Decentralised planning, Department Research
Support Scheme (DRS), Vidyasagar University, India (November
07,2002).
42. At the XXI Indian Social Science Congress, Tamil University,
Thanjavour, India (December 06, 1997).
43. At the Programme of Seminars, Department of Statistics, University of
South Africa, First Semester, 1995, Pretoria, South Africa.
44. At the University of Transkei, Umtata, Transkei, South Africa, (March 28
– March 29, 1995).
9. Serving as a Resource Person:
a. On a three day University Grants Commission (UGC) sponsored short
time course on “How to Write Research Project”, for the college and
university teachers organized by Academic Staff College, Sambalpur
University, Orissa, India, (January 22 – 24, 2013).
b. On a UGC-sponsored workshop in Mathematics, Nabadwip Vidyasagar
College, Nadia, West Bengal, India (January 10, 2012).
c. On a UGC-sponsored workshop on Statistical methods, University
College of Raiganj, Dinajpur, West Bengal, India (February 10 – 17,
2011).
d. On a UGC-sponsored refresher course (Statistics), UGC Academic Staff
College, Sambalpur University, Orissa, India (December 4 – 24, 2008).
9
e. On a workshop on Large Scale Sample Survey and Regression Technique,
Department of Statistics, (March 12 – 14, 2014).
f. On a weeklong workshop at the Institute of Regional Planning,
Bhubaneswar, (June 9 – 14, 2014).
g. On a workshop on “Survey Methodology”, “Basic Principles of Survey
Sampling”, Centre for Statistics, HSRC, Pretoria, South Africa (March 23
– 29, 1996).
h. On a workshop on Advanced Sampling Techniques, Centre for Statistics,
HSRC, Pretoria, South Africa (August 14 – 18, 1995).
i. On a workshop on Statistical Distribution, Department of Economics and
Statistics, Tata Services Limited, Bombay House, Mumbai (May 16 – 18,
2005).
j. On a workshop on how to use data collected through the Base line Survey
Programme of the District of Howrah, West Bengal, India, (July 27, 2010
– October 13, 2010).
10. Dissertation Supervision:
Supervised Masters dissertation in different curricula under different Universities.
11. Services to the Universities:
a.
Rasthrapati Tukadori Maharaj Nagpur University:
•
b.
Appointed as the External examiner for the Practical Examination in Pr-I
(Statistics) at the MA/M Sc. II examination, 2006, 2007.
University of Burdwan:
•
Appointed as a Post-Publication Review Examiner in paper VIII/I (Stat.&
Eco.) for reassessing the scripts of MA/M.Sc. Economics, Part II
examination, 2003, 2004, 2005, 2006 and 2007.
•
Appointed as a Review Examiner in part II examination, paper VIII/1 (Stat. &
Econ.) for MA/M.Sc. examination, 2003, 2005, 2006.
c. Vidyasagar University:
•
Appointed as a moderator in Economics, MA/M.Sc. Part II examination
1998, 2000, 2001,2004, 2005, 2006.
•
Appointed as a re-examiner of Economics, paper VIII for the M.Sc.
examination 2003.
•
Appointed as a paper setter for the Special paper XV (New) for the
MA/M.Sc. part II examination 2004.
•
Appointed as a re-examiner on economics paper VI for the MA/M.Sc
examination, 1998, 1999, 2001, 2002.
10
12. Academic Administration:
1. Member convener of the organizing committee for International
Conference on Environmental and Ecological Statistics with Applications
as a part of Platinum Jubilee of the Indian Statistical Institute, March 21 –
23, 2007.
2. Member, JRF Selection Committee for the Academic Year 2003 2004.
3. Expert member of the Selection Committee to the post of Assistant
Professor for Visva-Bharati University, Central University, (2012), India.
4. Expert member on Research Methodologies for Mahatma Gandhi National
Rural Employment Guarantee Act (2013), Ministry of Rural Development,
Government of India.
13. Member of Scientific Bodies:
(a) Life member, Sankhya, Indian Journal of Statistics (L/5222);
(b) Life member, Calcutta Statistical Association;
(c) Life member, Asiatic Society (M00250);
(d) Life member, Indian Association of Social Science and Health (IASSH);
(e) Life member, Indian Science Congress Association (418904).
14. List of Publications:
14.1. Journal of Publications:
1. Interactive Linear Models in the Context of two Stage Sampling, To appear in
Volume 14, No. 1, March 2015.
2. Interactive Linear Models in Survey Sampling (2013) (with B.K. Sinha), JSTA,
Volume 13, No. 3. pp. 263 – 272.
3. Estimation of Nonsampling Variance Components Under the Linear Model Approach
(2009). Statistics in Transition, New Series, Vol. 10, No. 2, pp 193 – 222.
4. Some improved variance estimators from a bivariate non-normal population (2009). (with
T.P Tripathi). Pakistan Journal of Statistics, Vol XX(X) pp 165 – 194
5. A note on optimum inclusion probabilities in WOR-sampling scheme based on super
population model (2009). (with T.P Tripathi), Pakistan Journal of Statistics, Vol
XX(X), pp 221 – 226.
6. Existence of the BLUE for finite Population Mean under multiple imputation (2008)
Statistics in Transition – new series, Journal of Polish Statistical Association, Vol 9,
pp 233 – 258
7. Stochastic Modeling of Buying Behaviour of Indian customers (2008), Calcutta
Statistical Association Bulletin, Vol 60, pp 111 – 112.
11
8. Some Shrinkage-Type Estimators for Variances of Univariate Populations (2007): To
Appear in JISPS (Journal of Indian Society of Probability and Statistics) in 2007 issue.
9. Unbiased Estimation of a Finite population: Case of Multiple Indirect Identifiability of
Population Units (with B. K. Sinha and S. Sengupta) (2006): JSTA (Journal of
Statistical Theory and application), Volume 5, 1, pp. 81-90.
10. Information Support for Planning and Rural Development (2004): IX, Journal of
Economics, Volume IX, Vidyasagar University.
11. Statistical Enquiry in identifying some specific castes as OBC’S in West Bengal, India,
(2000): Bharatiya Samajik Chintan, XXII, 1-2.
12. Stochastic Modeling and Forecasting of Discovery, Reserve and Production of
hydrocarbon with an Application (with J.K. Ghosh, et. al.) (1997): Sankhyā, Series B,
Volume 59, Part 3, pp. 288-312.
13. An Econometric Model of Exploration and Exploitation of Hydrocarbon (with M. Pal)
(1997): Journal of Quantitative Economics, Volume 13, No. 2, pp. 29-44.
14. Evolution of Statistics in India (with J.K. Ghosh et. al.) (1997): International Statistical
Review, Volume 67, 1, pp. 13-34.
15. On Minimax Allocation of Stratified Random Sampling when only the order of Stratum
Variances is known (with M. Pal) (1994): Statistics and Decisions, vol 12, pp 195 –
201.
16. Estimating unknown dimensions of a binary matrix with applications to estimation of the
size of a mobile population (with M. Pal and B. K. Sinha) (1992): Statistics and
Probability, Edited by S. K Basu and B. K Sinha.
17. Some results on T1-class of linear estimators (1988): Jour. Indi. Society of Agri.
Statistics, vol LX, No.1, pp 1 – 8.
18. Estimation of Lorenz Ratio from a finite population (with M. Pal) (1988): Anvesak, vol
18, Nos. 1 – 2, June – Dec, pp 29 - 62.
19. On some estimates of Poverty Measures (with M. Pal) (1988): Calcutta Statistical
Association Bulletin, vol 37, March & June, Nos 145 – 146, pp 81-90.
20. A unified approach to estimation of Lorenz Ratio from a finite population (with M. Pal)
(1988): Sankhya, Series B, vol 50, part 2, pp 215 – 223.
21. Use of prior information on some parameters in estimating population mean (with T.P
Tripathi, S.D Sharma) (1983): Sankhya, Series A, vol 45, part 3, pp 372 – 376.
22. A note on the Estimation of Mean Square Error (1982): The Aligarh Journal of
Statistics vol 2, pp 38 - 40.
23. Some T2-class of estimators better than H-T estimator (1981): The Aligarh Journal of
Statistics vol 1, No.1, pp 52 - 58.
14.2. Papers under Communication:
1. Revised version of the paper entitled “The Indian Official Statistical System
Revisited” by P. Maiti., T.J. Rao and J.K. Ghosh: Submitted to Sankhya Series B.
12
2. Mean Square Error Decomposition Model under two-Stage Sampling: Estimation
of Population Mean: by P. Maiti and Mahendra S. Shiton. Submitted to Statistics
in Transition, New Series.
3.
14.3.
Estimation of Measurement Variance Under two stage sampling: Estimation of
Population Mean by P. Maiti. Submitted to Statistics in Transition, New Series.
Published in Proceedings of Conference:
1. A Generalised Horvitz-Thompson Estimator for Population total with an
application in estimating number of species in the Region of Silent Valley in
India (2015): National Conference on Recent Advances in Statistical and
Mathematical Sciences and their Applications (RASMSA – 2014), Kumayan
University, India, October 2014.
2. Estimation of Measurement Variance in the Context of Environment Statistics
(2014), 2nd ISM International Statistical Conference 2014, with applications in
Sciences and Engineering (ISM – II 2014), Pahang, Malaysia, August 12 – 14,
2014.
3. Estimation of variance of Horvitz-Thompson estimator in the presence of
measurement error: The linear model approach (2013): 1st International Conference
on Information Management and Statistics (ICIOMS), September 1 – 3, 2013, Kuala
Lumpur, Malaysia.
4. Some Aspects of Decentralised Planning International Conference on Recent
Advances in Mathematical Statistics and its Applications in Applied Sciences in
Collaboration with Indian Statistical Institute, Kolkata; Department of Statistics,
Gauhati University DST FIST and UGC(SAP) DRS-I, Department, Gauhati 781014,
Assam, India, December 31, 2012 – January 1 -2, 2013.
5. Interactive linear model in survey sampling (2013): 7th Annual Conference on
Statistics, 17 – 20 June, 2013, Athens, Greece.
6. Estimation of finite population total: Interactive Model in Survey Sampling with
Bikash Sinha, International Conference on Statistical Data Mining for Bioinformatics, Health, Agriculture and Environment , December, 21 – 24 2012,
Proceeding Department of Statistics, University of Rajshahi, Bangladesh.
7. Information created and developed for decentralized planning to two different blocks
of the district of Howrah, West Bengal (2012): International workshop on large scale
national surveys pp. 20 – 28, Department of Statistics, University of Rajshahi,
Bangladesh ISBN 978-984-33-7470-77.
8. Information for Decentralized Planning in the session of Mathematical Sciences: case
studies and surveys in the 99th Indian Science Congress during January 3 – January 7,
2012, held at KIIT University, Bhubaneswar, Orissa, India.
13
9. A Tale of Two Externally Funded Project Reports: M. P. Experience (Jointly with J.
K. Ghosh and B. K. Sinha) (2007), Presented at the Conference of Central and
State Statistical Organizations (COCSSO) held during April 10-11, 2007.
10. Some aspects of unbiased estimation of size of tree species in the Western Ghats of
Western India (2007): Presented at Platinum Jubilee Conference on Environment
and Ecological Statistics, March 21 – 23, 2007, Indian Statistical Institute, Kolkata.
11. Stochastic Modeling of Life Data (2005): Presented as an invited lecture in
Department of Economics and Statistics, Tata Services Limited, Mumbai, May
16-18, 2005.
12. Non-Sampling Errors – Classification, Quantification and Decomposition of MSE of
a Survey Based Estimator (2003). Presented at 5th International Calcutta Symposium
on Probability and Statistics, December 28-31, 2003.
13. Improved variance estimators from bivariate normal population (with Dr. T. P.
Tripathi) (2003): Presented at the International Conference on Recent Statistical
Techniques in Life Testing, Reliability, Sampling Theory and Quality Control,
Benaras Hindu University, December 29-31, 2003.
14. Combined use of Method of Moments and of Generalised Least Squares in
estimating the parameters under inverse sampling (1997): Presented at the
Conference on Recent Advances in Statistics and Probability, Dec 29, 1997 – Jan
1, 1998, Indian Statistical Institute, Kolkata.
15. Sampling and Estimation Procedures for Inverse Multinomial sampling associated
with single, multiple and joint events (with H. S Styen) (1996): Presented at the
Annual Conference of SASA, at the University of Stellenbosch, Nov. 6 to Nov. 8
1996.
16. Asymptotic Behaviour of two estimates of Poverty Measures (1995): Presented at
the Annual Conference of the South African Statistical Associations (SASA),
Nov. 5 1995, University of Orange Free State (OFS).
17. The Literacy program in India and its evaluation (1995): Presented at the Department
of Statistics, Unversity of Transkei, March 29.
18. My experiences with Practical Sampling (1995): Presented at Department of
Statistics, University of Transkei, March 28, 1995 and University of South Africa
(UNISA), Feb 21, 1995.
19. A Stochastic modeling on the discovery of Hydro-carbon with application to Indian
data (1993): Presented at the Annual Conference of Operation Research Society
of India, Calcutta.
20. The process of Exploration and Exploitation of Hydro-carbon (1993): Presented at
P. C. Mahalanobis Birth Centenary Celebrations Symposium on Sample
Surveys: Theory and Methods, Dec 15 – 17, Indian Statistical Institute, Kolkata.
14
21. An econometric approach to the estimation of hydro-carbon (1992): Presented at the
Annual Conference of Operation Research Society of India, Ahmedabad, India.
22. Some results on T1-class of estimators (1980): Presented at the Conference of
Mathematical Statistics and Probability Theory in Honour of Prof. C. R. Rao to mark
his 60th Birthday, New Delhi, India.
23. Use of prior information on some parameters in estimating population mean (with T.
P Tripathi) (1978): Presented before 33rd Annual Conference of the Indian
Society of Agricultural Statistics, Trichur, India.
24. The use of multivariate auxiliary information in the selecting the sampling units (with
T. P Tripathi) (1976): Proceedings of the Symposium on Recent Developments in
Survey Methodology. Indian Statistical Institute, Kolkata.
14.4. Books Published:
a) Some Aspects of Complex Design in Survey Sampling (1996):
HSRC/RGN Publishers; ISBN 0-7969-1857. A brief summary has been
provided.
b) Sampling and Estimation Procedures for Inverse Multinomial
Sampling Associated with Single, Multiple and Joint Events (with
H.S. Steyn) (1997): HSRC/RGN publisher, 134, Pretorious Street,
Pretoria 0002, ISBN 0-7969-1822-8. A brief summary has been
provided.
c) Sampling and Estimation Procedures for Inverse Multinomial Sampling
Associated with Single, Multiple and Joint Events by H. S. Steyn and P.
Maiti, HSRC / RGN Publishers; ISBN 0 – 7969 – 1822 – 8.
d) Basic Principles of Survey Sampling (1996): CENSTAT, HSRC/
RGN, Pretoria, South Africa.
e) Survey Methodology (1996): CENSTAT, HSRC/RGN, Pretoria, South
Africa.
f) A Unified Set up for Probability Sampling (1996): CENSTAT,
HSRC/RGN, Pretoria, South Africa.
g) Statistical Information System for Decentralized Planning by Local
Bodies in the District of Howrah (2008). Howrah Zilla Parishad, Govt.
of West Bengal.
15
15.1. Summaries of the Research Papers
(Published in referred journals):
(1981-1994 &1995 onwards):
1. Interactive Linear Models in the context of two-stage sampling. To appear in
Volume 14, No. March 2015.
Summary: Following Sinha and Maiti (2014), we continue our investigations
along a linear interactive model by incorporating a two-stage sampling design.
Expressions for an unbiased estimator of the finite population total along with its
unbiased variance estimator have been derived. Essentially, it incorporates second
level of randomness.
2. Interactive Linear Models in Survey Sampling (2013) (with B.K. Sinha), JSTA,
Volume 13, No. 3, pp. 263 – 272.
Summary: Considered is a liner interactive model in the context of survey
sampling. The situation arises when investigator and/or supervisor interventions
are contemplated in the responses. An appropriate linear model is introduced to
represent the response profiles(s) arising out of each respondent-cum-supervisor
combination as per the planned ‘design lay out’. Two situations (blinded and
unblended submission of responses) are differentiated and corresponding data
analysis techniques are discussed. Variance components are assumed to be known
in the study.
3. Estimation of Nonsampling Variance Components Under the Linear Model
Approach (2009). Statistics in Transition, New Series, Vol. 10, No. 2, pp 193 –
222.
Summary: The importance of nosampling or measurement errors has long been
recognized. [for numerous references see e.g., the comprehensive papers by
Mahalanobis (1946), Hansen et.al. (1961), Bailar and Dalenius (1970), Dalenius
(1974)]. Attempts have been made for estimating components due to nonsampling
errors. The work in this area starts developing surveys, specifically designed to
incorporate features which can facilitate the estimation of non sampling
components such as reinterviews and/or interpenetrating samples. However most
of the survey designs so far developed, though few, are very complex in nature
[Fellegi (1964, 1974), Biemen et al. (1985), Folsom(1980), Nelson(1974)]. Here,
a very simple survey design as well as a simple estimation procedure have been
developed for the purpose of estimating simple as well as correlated response
variances, namely interviewer variance and superviser variance.
4. A note on optimum inclusion probabilities in WOR-sampling scheme based on
super population model (2009). (with T.P Tripathi), Pakistan Journal of
Statistics, Vol XX(X), pp 221 – 226.
Summary: This paper deals with the problem of obtaining a set of optimum
inclusion probabilities{πi;i = 1,2,…….N},optimum in the sense of having
smallest average(δ-model based) mean square(design based) of the HorvitzThompson estimator for the population total Y. Since π ,s are dependent on model
16
parameters,a near optimum solution based on estimates of model parameters
have been proposed.
5. Some improved variance estimators from a bivariate non-normal population
(2009). (with T.P Tripathi). Pakistan Journal of Statistics, Vol XX(X) pp 165 194.
Summary:Given paired observations {(xi ,yi); i =1,2,…,n} on two variables X
and Y on a random sample s from some bivariate non normal population.This
paper considers an improvement of the customary estimator of population
variance.A mixture (i.e.,a weighted combination) of the customary estimator and
a suitably chosen statistic t is proposed. It has been shown that under certain
conditions ,the improvement has been shown to be significant.
6. Existence of the BLUE for finite Population Mean under multiple imputation
(2008): Statistics in Transition – new series, Journal of Polish Statistical
Association, Vol 9, pp 233 – 258
Summary: Missing values not only mean less efficient estimates because of
reduction in the sample size ,but also mean that the standard complete data
methods can not be immediately used to analyse the data. Imputation, single or
multiple, is a compensatory method for handling non responses and takes care of
the fact that once the values have been filled in, standard complete data methods
of analysis can be used. Here in this paper ,using multiple imputation technique,
an estimator for the finite population mean in the presence of unit non response
has been proposed and the estimator so proposed has been found to be theBLUE.
A very general non-linear cost model has been developed and discussed in the
presence of nonresponse and an optimal solution of sample size for a given
number of imputations or of number of imputations for a given sample size has
been determined..
7. Stochastic Modeling of Buying Behaviour of Indian customers (2008), Calcutta
Statistical Association Bulletin, Vol 60, pp 111 – 112.
Summary:A collaborative research on a problem of common interest and of
immediate concern which Hindustan Lever Limited (HLL)-a multinational
company was facing or likely to face in the near future was undertaken at the
Indian Statistical Institute, Kolkata. The problem was to explain the purchase
behaviour of frequently bought branded consumer products using stochastic
models .For this ,the panel data after being coded to ensure anonymities were
supplied to the ISI and on the basis of available data, modeling of the buying
behaviour was made. To begin with, some descriptive measures were calculated
to understand the data and finally, Dirichlet multinomial model was used for
explaining the buying behaviour of the customer in the soecific segment with
respect to the specific group of commodities.Because of not-so-wide coverage of
the data and not-well-validated assumptions on the underlying distributions,the
result failed to reveal much of the consumer behaviour pattern.
8. Some Shrinkage-Type Estimators for Variances of Univariate Populations (2007):
JISPS (Journal of Indian Society of Probability and Statistics) in 2007 issue.
17
Summary:Here in this paper,an attempt has been made to improve upon the usual
estimator for the variance σ2 by bringing in the role of some other statistics in
addition to the sampling variance.The paper also discusses Bayes estimators for
the parameter(s) of one parameter and multiparameter exponential families in
general and in particular,,Bayes estimators for normal population. It also
considers Bayes estimators for the parameters of Gamma population.
9. Unbiased Estimation of a Finite population: Case of Multiple Indirect
Identifiability of Population Units (with B. K. Sinha and S. Sengupta) (2006):
JSTA (Journal of Statistical Theory and application), Volume 5, 1, pp. 81-90.
Summary: Considered is the problem of estimation of population size and
population mean in situations wherein the survey statisticians do not have direct
access to the ultimate population units. Instead, there are available intermediate
reference units and a network connection through which the ultimate units are
linked. We develop a general procedure to tackle this problem and illustrate it via
an example.
10. Information Support for Planning and Rural Development (2004): IX, Journal of
Economics, Volume IX, Vidyasagar University.
Summary : This paper discusses the need of information net work - both
electronic and others, for planning of rural development. It also discusses how
information should flow from top to the bottom-the ultimate users especially to
the farmers in rural areas.
11. Statistical Enquiry in identifying some specific castes as OBC’S in West Bengal,
India, (2000): Bharatiya Samajik Chintan, XXII, -21.
Summary: At the instance of West Bengal Backward Class Commission, a study
was undertaken at the Indian Statistical Institute to address the issues of social,
educational and economic levels of some selected communities of West Bengal.
.Based on primary data, an attempt was made to make an objective assessment of
the Socio-Economic Profile of the five communities .Several indicators were
used in this study to identify the relative social position of the individual
communities .The present paper is based on some of the quantitative findings
from the report.
12. Evolution of Statistics in India (with J.K. Ghosh et. al.) (1997): International
Statistical Review, Volume 67, 1, pp. 13-34.
Summary: This is a brief history of the evolution of official and academic
statistics in India which focuses mainly on the period 1930 to 1960 but traces its
origins in antiquity and recent history. We also comment on how statistics has
continued to evolve since the 1960s.This is a history of both institutions and
people,who built and shaped them, and of ideas.
13. An Econometric Model of Exploration and Exploitation of Hydrocarbon (with M.
Pal) (1997): Journal of Quantitative Economics, Volume 13, No. 2, pp. 29-44.
Summary: An econometric model of exploration and exploitation of hydrocarbon
for estimation of discovery and production costs has been presented in this paper.
18
This is one of the two approaches developed for the purpose and is based on
certain econometric relationships estimated on the basis of time series data on
cumulated values of relevant values like reserves, costs of exploration and
development drilling etc.
14. Stochastic Modeling and Forecasting of Discovery, Reserve and Production of
hydrocarbon with an Application (with J.K. Ghosh et. al.) (1997): Sankhyā,
Series B, Volume 59, Part 3, pp. 288-312.
Summary: The primary objective of this paper is to present some results in
connection with forecasting of discovery and production of hydrocarbon in a
partially explored basin. The focus in this article will be on stochastic modeling of
the purpose of discovery, based on the idea of subjective probability or
superpopulation. The results of a particular basin in India have been presented
here.
15. On Minimax Allocation of Stratified Random Sampling when only the order of
stratum variances is known (with M.Pal) (1994): Statistics and Decisions, Vol. 12,
pp. 195-201.
Summary: This paper proposes the minimax criteria for obtaining the sample
sizes to different strata when only the ranks of the stratum variances, apart from
the stratum sizes, are known and obtains a very simple and elegant solution to this
problem
16. Estimating unknown dimensions of a binary matrix with applications to
estimation of the size of a mobile population (M.Pal and B.K. Sinha) (1992):
Statistics and Probability Edited by S.K. Basu and B.K. Sinha.
Summary: For two fixed and positive integers N and T, Let ∆ ((aij ) ) be a matrix
of order N × T with elements aij s assuming values 0 and/or 1 with the restriction
that for every i.
T
M i = ∑ aij ≥ 1, 1 ≤ i ≤ N
j =1
However,
T
N i = ∑ aij ≥ 0, 1 ≤ j ≤ T
i =1
With
∑
M i = ∑ N j = ∑∑ aij ≥ N
We considered a situation where T is known in advance but N is unknown. The
problem we address is that of estimation of N unbiasedly. We propose to develop
a reasonable theory for this problem after formulating the same in the frame work
of Finite Population Inference.
19
17. Some results on T1 class of linear estimators (1998): Jour. Indi. Society of Agri.
Statistics, Vol. LX, No. 1, pp. 1-8.
Summary: T1 class of linear estimators is examined to obtain a biased sub class of
estimators better than the sample mean y .
18. Estimation of Lorenz Ratio from a finite population (with M.Pal) (1988):
Anvesak, Vol. 18, Nos. 1 -2, June – December, pp. 29 – 62.
Summary: To provide an unbiased estimator for Lorenz Ratio without assuming
any distribution of the population, a sample-theoretic approach has been taken to
estimate Lorenz Ration from a finite population under a general sampling design.
It has also been observed that the usual estimators for LR are biased under simple
random sampling.
Also an effort has been made to provide an estimate of Lorenz Ration in case of
rank of i th observation is known. Uusing this knowledge of rank, two alternative
estimators have been proposed under PPSWR – scheme.
19. On some estimates of poverty measures (with M.Pal) (1988): Calcutta Statistical
Association Bulletin, Vol. 37, March and June, Nos. 145 – 146, pp. 81 90.
Summary: There are now a number of poverty measures available in the
literature. Some of the measures are alternative to each other and some claimed to
be superior in some sense to many others.
When significant work has been done in developing the alternative measures, not
much attention has been paid to the problem of estimation of these indices.
Estimation does not pose very serious problems in the large sample, but when are
deals with a small sample, which may typically be the case in reality, situations
become quite different. In fact usual estimators become biased for some of the
indices. In this paper, alternative estimators for these cases have been proposed.
Other properties of the estimators and some other relevant issues have also been
examined.
20. A Unified Approach to estimation of Lorenz Ratio from a finite population (with
M.Pal) (1988): Sankhya, Series B, Vol. 50, part 2, pp. 215-223.
Summary: Most of the results relating to estimation of Lorenz Ration (LR) are
based on the assumption of some distribution of the population. Not much
attention has been paid in the literature to provide a design based unbiased
estimate of LR. To the best of Author’s knowledge, Taguchi’s (1978) is the only
one which however can hardly be used in practice. In case Y , the population
mean is known, an effort, in this paper, has been made to provide some unbiased
estimator of LR together with estimates for the variance of estimators. The
relevant expression have been found under the general sampling design and then,
in particular, the cases of SRSWR and SRSWOR have been discussed.
20
21. Use of Prior information on some parameters in estimating population mean (with
T.P. Tripati, S.D. Sharma) (1983): Sankhya, Series A, Vol. 45, part 3, pp. 372376.
Summary: We consider the problem of estimating population mean Y of a
character y, using information on some other parameters of y . A class of
estimators which are linear functions of y and a suitably chosen statistic t is
presented. General properties of this class are studied and the optimum weights
and the resulting optimum mean square error is found. A general technique of
generating estimators better than sample mean y and Searl’s estimator (1964) is
given and a number of such biased estimators are identified for some choice of t,
under very moderate conditions depending on the prior knowledge of the
quantities which are smaller or greater than the actual values of some population
parameters.
22. A Note on the Estimation of Mean Square Error(1982): The Aligarh Journal of
Statistics, Vol. 2, pp. 38 -40.
Summary: In an earlier paper, Maiti and Tripathi (1981) obtained a biased
subclass in T2 -class of linear estimators, where the well known HorvitzThompson estimator fails to be better, better in the sense of having smaller mean
square error than one belonging to the biased subclass, of course, under some
moderate conditions. In this paper, we present the conditions under which the
estimates of mean square error will be non-negative.
23. Some T2 - class of estimators better than H-T estimator (1981): The Aligarh
Journal of Statistics, Vol. 1, No. 1, pp. 52-58.
Summary: The T2 class of estimators for the population total of a character y, in
case of general sampling design is revisited and a subclass of biased estimators
from T2 better than H-T estimator YˆH −T is identified. It is found that in case of a
class of sampling designs, we may always generate estimators better than Yˆ .
H −T
We also study another biased subclass of estimators T2* = λ ∑ y i / pi where
i∈s
N
pi = xi / ∑ xi i = 1, 2,  , N , x being an auxiliary character and λ is a suitably
i =1
chosen constant. Some members from T2* are shown to be better than YˆH −T under
a super population model.
15.2.Abstracts of some of Research Papers Appeared in the Proceedings
of the Conferences:
1. A Generalised Horvitz-Thompson Estimator for Population Total with an Application
in estimating number of species in the Region of Silent Valley in India (2015):
21
National Conference on Recent Advances in Statistical and Mathematical Sciences
and their Applications (RASMSA 2014), Kumayun University, India, October 2014.
Abstract: considered is the problem of estimation of a population parameter, say
total, where the survey statisticians do not have direct access to the ultimate
population units, as may be the case of a mobile population. On the other hand, there
may be some intermediate reference units and a well defined unique net work
connection through which the ultimate units may be reached. Making use of such
intermediate reference units, a Generalised Horvitz-Thompson estimator has been
defined and deployed in estimating number of species of a real life population.
2. Estimation of Measurement Variance in the Context of Environment Statistics (2014)
2nd ISM International Statistical Conference 2014 with applications in Science and
Technology (ISM-II 2014), Pahang, Malaysia, August 12 – 14, 2014.
Abstract: Timely, reliable and comparable data are needed for any statistical
analysis. The field of environmental statistics has no single overreaching
internationally agreed classification for statistical purpose. Lack of proper and
uniform definitions, unambiguous classifications pose serious problems to procure
qualitative data causing measurement errors – errors in coverage as well as in
technique. For example, for obtaining statistics related to an area under a crop,
perimeter bias causes not to have accurate information. We consider the problem of
estimating measurement variances, so that some measures may be adopted during the
field work to improve upon the quality of data on environmental goods and services
and on their valuation in economic terms, which is necessary for policy formulation
to achieve overall sustainable development of the society.
The measurement technique considered here is that of employing personal
interviewers and sampling design adopted is that of a two-stage sampling.
3. Estimation of Variance Horvitz-Thompson estimator in the presence of measurement
error: The linear model approach. 1st International Conference on Information
Management and Statistics (ICIOMS) during September 1 to September 3, 2013,
Kuala Lumpur, Malaysia.
Abstract: The importance of identifying and finding ways and means to control the
non-sampling errors has long been recognized [Mahalanobis (1946); Hansen etal
(1951), (1961), Bailer and Dalenius (1970), Dalenious (1974)]. But not much work
compared to controlling sampling errors has progressed so far. Attempts have been
made here for estimating the two components of the variance, namely sampling
variance and measurement variance under the linear model approach. Primary need to
estimate measurement variance is to obtain repeat measurements and to follow the
principle of randomization i.e., an appropriate survey design needs to be developed.
In this paper, a survey design based on Symmetric Balanced Incomplete Block
Design (SBIBD) has been developed and deployed in estimating non-sampling
variance component of the Horritz-Thompson H-T estimator. The sample design
adopted is that of cluster sampling and the estimator used is the H-T estimator.
22
4. Interactive linear model in survey sampling:
Appeared in the conference series as well as in conference volume of 7th Annual
Conference on Statistics during 17 – 20 June, 2013, 2013, Athens, Greece.
Abstract: Considered is a linear interactive model in the context of survey sampling.
The situation arises when investigator and/or supervision interactions are
contemplated in the responses. Blinded situations have been discussed here in.
5. Some Aspects of Decentralised Planning International Conference on Recent Advances in
Mathematical Statistics and its Applications in Applied Sciences in Collaboration with Indian
Statistical Institute, Kolkata; Department of Statistics, Gauhati University DST FIST and
UGC(SAP) DRS-I, Department, Gauhati, Assam, India, December 31 2012 – January 1-2,
2013.
Abstract: A seminal talk on the theme of the decentralized planning was delivered. It
discussed the decentralized administration, additional responsibility assigned by 73rd
amendment of the constitution, India for rural administration and need for strengthening local
Government, which inturn needs the Development of Statistical Information System (SIS).
The SIS discussed has specified such major components as (i)items of information needed for
local level planning, (ii) designing input output formats amenable to computerization, (iii)
method of data collection, collation, and (iv) software developed for creating RDBM
(Relational Data Based Management) and for processing the data.
It also discussed how a consensus development plan for one Gram Panchayat could be
developed on the basis of information created by the SIS.
6. A tale of two Externally Funded Project Reports: The MP Experience, Pulakesh
Maiti, J.K. Ghosh and B.K. Sinha. Presented at the conference of Central and State
Statistical Organsation (COCSSO), April 10-11, 2007, New Delhi – 110012.
A rudimentary Statistical System began in Indian during the Hindi-Budhist period. It evolved
into a more mature system when the Moghuls ruled India. A rapid growth took place during
the British Period. Further growth and modernization with focus on the country’s socioeconomic programmes occurred after India became independent in 1947. Currently, the
system has some problems, but also it has, on its record, many achievements and much
promise.
An appraisal of the present Indian Statistical System suggests some improvements needed in
the statistical framework both at the state and at the centre. Report on National Statistics
Commission (August, 2001) has emphasized, among other issues, on the adequacy, reliability
and timeliness of the information generated by the system. The State Statistical Bureaus
(SSD/DES/BAES) have been required to be strengthened with respect to the above goals.
At the same time, the 73rd and 74th amendments of the constitution have squarely laid the
responsibility of local planning to local bodies. Local level planning will need local level
statistics with respect to decentralized planning. The Commission has also pointed out that I
the future, the new self-managed local bodies in cities and rural areas created by virtue of the
above two amendments, will be in a position to supply lots of data.
Evaluation of the performances of two externally funded projects, viz., one ISI-PWI Project
(1999) and the other JP Associates (P)-Project (July, 2006) have been made, keeping in mind
the present need from the existing systems. It may be observed that the project undertaken by
JP-Associates has mainly focused on identifying specific requirements for strengthening
23
SSB;s and, hence, no attempts have been made on identifying items of information needed
for local planning. Although, the primary concern of the ISI-PWI project was to build up the
Statistical Information System (SIS) for local level planning, with respect to decentralized
planning, it has also investigated in to the strength and weakness of the Directorate of
Economics and Statistics (DES) of the then undivided M.P.
In effect, the work involved in the ISI Project is two- fold: Development of SIS for
Strengthening Local Government in M.P. as well as strengthening the DES, though in the
later domain, it is not as detailed as in the report of JP Associates. The presentwrite-up has
made a comparative study of the contributions from the two projects with respect to both
strengthening SSB’s and Local Government in undivided M.P. . It has also provided an out
line of the volume of work, cost/time frame work for generating data base for basic statistics
for local development.
7. Estimation of a Finite Population Total: Interactive linear models in Survey
Sampling, Bikas K. Sinha , (with Pulakesh Maiti); An invited talk: International
Conference on Statistical Data Mining for Bio-informatics, Health Agricultural and
Environmental: 21 -24 December. Proceedings, Department of Statistics, University
of Rajshasi, Bangladesh.
Abstract: Considered is the set up of simple i.e., direct response on a quantitative
response variable Y in the context of a finite lablelled population of size N. In actual
surveys, it so happens that we need investigators and often supervisors as well. We
depict a situation where in there are possibilities of investigator effect and/or
supervisor effect on the response profile finally received by the data collecting
agency. Of course, there effect may be assumed to be random, having mean zero,
non-interactive within and between the two sets of people. The problem is to
unbiasedly estimate the finite population total of the response Y by incorporating a
fixed size (n) sampling design and by administering the sampling design in a situation
where the above two types of random effects are likely to be present.
8. Information created and developed for Decentralised planning to two different blocks
of the district of Howrah, West Bengal (2012), Pulakesh Maiti, Proceedings of
International Workshop on large scale National Surveys, pp. 20-28, Department of
Statistics, University of Rajshahi, Bangladesh, ISBN 987-984-33-7470-7.
Abstract: To help one Gram Panchayat (GP) prepare a data based local level plan on
the basis of the principle of Decentralisation, discussions have been made on how the
information collected on items as listed in 11th and 12th schedules of 73rd and 74th
Amendment of the constitution were of great help in identifying most needy areas
with respect to (i) infrastructure facilities, (ii) health, (iii) education, (iv) drinking
water facilities, (v) rural electrification, (vi) other living conditions, namely daily
marketing facility, availability of medical shops etc., which would require immediate
attention for development in the areas. As an illustrative example, two blocks of the
district of Howrah, West Bengal, India have been considered – one for from the city
and the other being adjacent to the city.
24
9. The National Sample Survey Organization (NSSO) and the quality of its data – A
Brief Review (with J. K. Ghosh) (2004). Presented as an invited paper at Joint IMSSRMS meet on current trends in survey sampling and official statistics, Calcutta,
India, January 2-3, 2004. Appeared on the web as University of
Maryland/IMS/Papers
Abstract: After a description of the history and salient features of the surveys
undertaken by the NSSO, we focus on quality of its data in recent times. We
summarize some common criticisms as well as a couple of analytical studies in the
literature. We also present our tentative view that except Annual Survey of Industries
(ASI), both thin and large rounds provide reliable data.
10. The Indian Statistical Systems at Cross Roads – An Appraisal of Past, Present and
Future (with J. K. Ghosh) (2004): An invited paper at Joint IMS-SRMS meet on
current trends in survey sampling and official statistics, Calcutta, India, January 2-3,
2004.Appeared on the web as University of Maryland/IMS/Papers.
Abstract: A rudimentary statistical system began in India during the Hindu-Buddhist
period. It evolved into a more mature system when the Moghuls ruled India. A rapid
growth took place during the British period. Further growth and modernization with
focus on the countries socio-economic progress occurred after India became
independent in 1947. Currently it has many problems but also many achievements
and much promises. We traced the history of the system and end with an appraisal of
its present status and future prospects and much promises. We traced the history of
the system and end with an appraisal of its present status and future prospects.
11. The 275th Indian Association of Special Library and Information Centre (IASLIC)
study circle lecture “Information System on Decentralized Planning for Rural
Development”, Presented as an invited lecture on June 17, 2004.Proceedings of
the Conference of the Golden Jubilee Celebration ,September 2004-August,2005.
Abstract: Planning for development involves four different types of activities,
formulation, implementation, monitoring during implementation and evaluation on
completion. To carry out each of these activities, relevant, reliable and timely
information is needed at every stage. The above presentation makes an effort to
determine the items of information needed for strengthening rural decentralization
12. Information Support for Planning and Rural Development :Key-Note Address
Proceedings of the Conference on “National Seminar on Information Support
for Rural Development” organized by Dept. of Library and Information Science,
Vidyasagar University: Nov. 18-19, 2003:
Abstract: Information have been collected and used in the Indian subcontinent from
antiquity, but major changes in collection and use took place during the British period
(1757-1947) in Indian history. New imperial needs dictated some of the changes, but
much of it took place indirectly as a result of western education and a spirit of
scientific curiosity and experimentation. Interest in rapid social, economic and
technological development changed the face of information need of the country and
25
added a new dimension to information system after India’s independence in 1947,
especially in planning rural development.
15.3. Papers under Communication:
1. Revised version of the Indian Official Statistical System Revisited by P.
Maiti., T.J. Rao and J.K. Ghosh: Submitted to Sankhya Series B.
Abstract: We first discuss some emerging problems and then focus on what
seems to be most important as well as puzzling among these problems, namely the
divergence between survey based estimate and estimate based on national
accounts. A couple of simple implementable strategies have been proposed to
capture sparsely scatteredness of the informal sector, which is suspected to be the
most important single factor causing the aforementioned divergence.
2. Mean Square Error Decomposition Model under Two-Stage Sampling:
Estimation of Population mean by Pulakesh Maiti and Mahendra S. Shiton.
Submitted Statistics in Transition, Poland.
Abstract: The problem is to estimate unbiasedly the finite population total/mean
of a response variable Y by incorporating a two-stage sampling design and by
administering the the sampling design in a situation where the intervention due to
the investigators are likely to be present. An estimator for the population mean
has been defined taking care of investigator’s effect. General expressions for
measurement variance as well as sampling variance have been provided
considering the mean square decomposition model.
3. Estimation Measurement Variance Under Two-Stage Sampling by Pulakesh Mait,
Communicated to Statistics in transition, New Series, Poland.
Abstract: Considered is the mean square error decomposition model for
estimation of population mean and the problem of estimation of measurement
variance under two-stage sampling. The measurement technique considered here
is that of employing personal interviewers for data collection. The sample mean
has been defined taking care of interviewers effects and the measurement variance
associated with the estimator has been derived with an effort to estimating the
measurement variance.
16. Book Chapters:
1. Interactive Liner Models in Survey Sampling (2014): Essays on Mathematics
and Statistics, Edited by Codrufa Stoica, Athens Institute for Education and
Research (ATINER), pp. 141 – 145.
26
2. Indian Statistical Institute – Numbers and Beyond (1931-1947) (with J. K. Ghosh
and Anil Bera) (2007): Appeared in Project of History of Indian Science,
Philosophy and Culture (PHISPC) Volume 1, “Science and Modern India: An
Institutional History C ‘1784-1947’ edited by Professor Uma Dasgupta.
3. Development of Statistical Information System (SIS) for Decentralized Planning
(2004), pp. 198-248: Participatory Decentralized Planning in India: Issues of
Finance and Statistics Information,Edited by Professor Sau , FIRMA KLM
Publication, Calcutta.
17. List of Projects:
1. A Collaborative Research Project (with Rajshahi University, Bangladesh)
entitled Study of Quality Education in Some Districts of Bangladesh, (2012).
2. The externally funded project entitled Diagnostic Study of the Registered
Closed Units (2012-13), Indian Statistical Institute, Calcutta. Nature of
Participation: Principal Investigator
3. The externally funded Project on Socio-Economic Conditions of five minority
communities in West Bengal (2008 -10 ).Indian Statistical Institute, Calcutta.
Nature of Participation: Principal Investigator.
4. The externally funded Project on Development of Data Base for Decentralized
Planning in Howrah District (2006 - 2007).
Nature of Participation: Member Statistician
5. ISI-HLL Collaborative Project on Business Research (October 1999-August
2000 : Indian Statistical Institute, Calcutta, India.
Nature of Participation: Co-Principal Investigator.
6. ISI-PWI Project on Strengthening Local Government in Madhya Pradesh, India
(May 1998- April 1999). Indian Statistical Institute, Calcutta and P.W.I.,
Calcutta.
Nature of Participation: Co-Principal Investigator.
7. Mid-Term Review of IPP-VIII in Calcutta Metropolitan Area (1997-1998):
Indian Statistical Institute, Calcutta, India.
Nature of Participation: Member Statistician.
8. The Socio-economic-demographic and cultural pattern of the female labour force
participation of the North West and The Cape (June 1995-December 1996):
CENSTAT, HSRC, South Africa.
Nature of Participation: Principal Investigator.
9. Survey of family and community life in the selected communities of The Cape
Peninsula of The Republic of South Africa (January 1995-December 1996):
CENSTAT, HRSC, South Africa.
Nature of Participation: Principal Investigator.
10. Community attitudes and preferences pertaining to cemetery and cremation
related issues in the East Rand in Republic of South Africa (May 1995December 1996): CENSTAT, HSRC, South Africa.
27
Nature of Participation: Principal Investigator.
11. Identification of Other Backward Classes (OBC) in West Bengal (1994): Indian
Statistical Institute, Kolkata.
Nature of Participation: Member Coordinator
12. Evaluation of Total Literacy Campaign (TLC) in West Bengal (1990 - 91):
Indian Statistical Institute, Kolkata.
Nature of Participation: Member Statistician.
13. ISI-ONGC Collaborative Research Project (1985 – 1990): Indian Statistical
Institute, Kolkata.
Nature of Participation: Member Statistician.
14. On Domestic Tourists survey in Orrisa (1988 – 89): Indian Statistical Institute,
Kolkata.
Nature of Participation:Member Coordinator
15. On Medical Facilities available in Calcutta Metropolitan Development Area
(CMDA) (1977 – 78): Indian Statistical Institute, Kolkata.
Nature of Participation: Project Assistant.
16. Calcutta Urban Poverty survey (1976): Indian Statistical Institute, Kolkata.
Nature of Participation: Project Assistant
17. Cost Benefit Analysis of Rural Electrification (1976 - 77): Indian Statistical
Institute, Kolkata.
Nature of Participation: Project Assistant
18. On the study of Job Oriented Education based on Regional Demand survey (1975
- 76): Department of Mathematics, Indian Institute of Technology, Kharagpur,
West Bengal, India.
Nature of Participation: Research Scholar.
18. Project Reports (1975-76 - ):
1. The externally funded project “Diagnostic Study of Registered closed units:
(2012-2013). The Indian Statistical Institute.
Nature of Participation: Principal Investigator
Summary: The study was to identify and classify registered closed units by such
reasons of closures as (i) financial, (ii) administrative and (iii) marketing. The
Indian Statistical Institute under the leadership of Dr. Pulakesh Maiti have devised
the project and used the expertise in analyzing the data including
(i)
designing and testing of the questionnaire;
(ii)
training programme for the investigators;
(iii)
supervision of the field work;
(iv)
data entry and validation;
(v)
development of the tabulation programme, and the appropriate estimators;
28
(vi)
preparation of the Report.
The findings have helped the Government specify the different causes of the
closures of the units with their identification the government has also
identified amounts of lands under closed units, which could be utilized for
other Micro and Small Scale Enterprises who were eager to make the
industrial venture.
2. A Collaborate Research Project ‘Study of Quality Education in some
Districts of Bangladesh (2012--)
Nature of Participation: A member Statistician
Summary: The study has such specific objectives as to
(i)
observe infrastructure and environment of the schools; \
(ii)
examine some essential aspects of quality education;
(iii)
identify the success and short comings and examine all the relevant issues
and constraints in the attainment of quality education.
3. The externally funded Project on Socio-Economic Conditions of five minority
communities in West Bengal (2008 - 2010).Indian Statistical Institute,
Calcutta.
Nature of Participation: Principal Investigator.
Own Activity:
The sampling design developed was that of a three stage stratified sampling
one with ultimate sampling units being the households. The excersises on
arriving at the above sampling design and of determination of toal sample
size and their allocation into different hierarchical units were carried out by
myself. Representation of sampling units from rural as well as urban areas of
the district has been made possible through the above design.
The findings of the study helped the Government make assessment of the
socio-economic condition of the minority communities and take appropriate
measures from the view of development policies.
4. The externally funded Project on Development of Data Base for
Decentralized Planning in Howrah District (2006 - 2007).
Nature of Participation: Member Statistician
Summary: The terms of reference of the project was as follows:
(a) Designing of formats for data collection and collation;
29
The variables should be identified and assimilated into formats keeping in
view that each of them should have properties like (i)easy to understand,
(ii)concise, (iii) amenable to computerization;
(b) Imparting training to the investigators;
(c) Conducting the sample checks of the data.
Two days of classroom training program on 28-02-07 and on 01-03-07 were
preceded by five days of pilot study during the field work of March 2007.
An intensive scrutiny program was developed and the data were critically
examined.
Information thus created for decentralized planning helped the District
Planning Committee of the District Howrah for local level planning. In
fact a data based local level plan was developed for one Gram Panchayat
Amardaha of the district. The data base created under this project helped
the district planners identified the vulnerable areas with respect to (i)
informative facilities on health, education, (ii) drinking water facilities,
(iii) rural electrification and other living conditions namely daily
marketing facility and availability of medical shops etc.
Own Activity:
I was responsible for designing the schedule in English, which got translated later
on in Bengali by some other team members in order to facilitate data collection
job in the villages of the distict. Trainings were imparted to the investigators by
myself and a pilot study was undertaken under my supervision to finalise the
format of data collection and to estimate the manpower needed. With the help of
the scrutiny schedule developed by myself, data were critically examined through
an intensive scrutiny program.
I did all sorts of lianson activities—academic as well as administrative, with the
district officials as well as with the elected functionaries of the Zilla Parishad. A
number of meetings were held in the offices of the District Magistrate and of
Sabhadipati to finalise the data base.
Information this created for decentralized planning helped the District Planning
Committee for smooth running of government at the local level. Data base created
helped the policy makers identify the areas which would need immediate attention
for planning.
5. Indian Statistical Institute–Hindustan Lever Limited (ISI–HLL)
Collaborative Project on Business Research (October 1999 – August 2000):
Nature of Participation: Co-Principal Investigator.
Summary: There are many models of buying behaviours available in the existing
literature on Marketing Science. Among these, the Ehrenberg Bayesian model
seems to have given good results. One area of interest which was important to the
HLL was to this Ehrenberg model, if this could be applied to consumer panel data
in Indian Context. For this, we needed to study the validity of the Ehrenberg
30
model in Indian cross sectional data. The appropriateness of the distributional
assumptions of the model namely,
(i) assumption of the negative binomial distribution on the number of purchases
and
(ii) assumption of the Beta priors on pj , the choice probability of the jth brand
in a product group were examined for a given product.
The study helped the company understand the extent of demand of the
goods; it also helped the company understand the buying behavior of the
customers and the market share of the different commodities. This also
helped the policy makers arrive at the appropriate decisions.
Some Technical novelties of the work were as follows:
(i) The data used for the purpose of analysis consisted of, among others, the
information on household identification number, brand code, month number
and quantity of the product purchased. The number of purchases was the
basic input of the model. Thus for the model verification exercise, the data on
the quantity (in grammes) purchased of each brand by the households had to
be converted into the number of units. This requires a knowledge about the
standard size of each brand. In the absence of such information precisely and
also for the sake of rendering flexibility to our analysis, we used, alternative
sizes, viz, 125 grammes, 250 grammes, 500 grammes and 1000 grammes
with proper rounding off to obtain the number of units. By this procedure, we
generated four sets of data on number of purchases of each brand made by
each household in each of the months. The data on number of purchases
based on 1000 grammes was in agreement with the experiences as realised by
the practitioners of the HLL.
(ii) “Empirical Bayesed” as well as “Hierarchical Bayesed” estimates for the
model parameters were obtained;
(iii) Data failing to support the assumption of negative binomial distribution on
the number of purchases, insisted us to make some further studies, for each
of income group separately, as if, the data resulted from a mixed distribution
with some mass p at n = 0 (n being the number of purchases) and a binomial
of (3, 1-p);
(iv) It was interesting to observe that there had been a good fit on the distribution
of the number of brands. The distribution appeared to follow a truncated
geometric distribution (truncated at Zero);
(v) Assumption of Beta prior on pj, the choice probability for the jth brand also
appeared to be not tenable in the Indian Data Context;
(vi) The reasons behind the model for not being fitted to the Indian data were
tried to be found out. One of the reasons was that unlike the European family,
the number of decisions makers in an extended family in a country like India
may be many. The model was revised by introducing into it a new stochastic
variable namely, the number of decision makers at the household level to
examine if the revised model was supported by the Indian data.
31
(vii) The households were classified according to family size and income class
and the buying behaviour across such classes were examined to detect any
patterns of similarity.
Nature of my work:
The above exercises were carried out by myself along with my other co-team
members especially with Professor Manoranjan Pal.
In addition to the work made and indicated in the earlier section, as the coprincipal investigator, I had to liason with Dr. Siddhartha Roy, from HLL.
This involved a lot of administrative work. In particular, maintaining project
account was also one of the major responsibilities entrusted to me.
6. Indian Statistical Institute – Price Water House India (ISI - PWI) Project on
Strengthening local Government in Madhyay Pradesh, India (May 1998April 1999):
Nature of Participation: Co-principal investigator
Summary: Planning for development involves four different types of activities,
formulation, implementation, monitoring during implementation and evaluation
on completion. To carry out each of these activities, relevant, reliable and timely
information is needed at every stage.
The 73rd and 74th Amendments of the constitution made by the Government of
India have squarely laid the responsibility of local planning on local bodies. It
stands to reason that information needed for such planning should also be
collected and managed by local bodies and hence the Government of India had
requested Asian Development Bank (ADB) for technical assistance to strengthen
local Government of Madhya Pradesh as part of the reform agenda of the state.
At the request of Price Water House India, who was one of the prime contractor to
the Bank for this purpose, an inter-firm agreement between PWI and ISI was
made to develop the Statistical Information System (SIS) as a part of the project
assignment. The SIS was envisaged to be a statistical database for rational
decision making. It was expected to address the information needed for planning
at Panchayat, Janpad, district and higher levels. This was developed in accordance
with the 73rd /74th amendment of the constitution of India, 1992.
The statistical information system which comprises of such major
components as
(i)
(ii)
(iii)
Computer hardware forming he continues of statistical information;
Computer software to process the information;
Statistical data, the actual context of the system;
helped to make resource mapping of a given area, which in turn, helped
develop a development plan with respect to the principle of decentralized
planning.
32
Highlights of some of the Technical Features:
(i) Extensive surveys of the areas of activities as listed in the 11th schedule of 73rd
and in the 12th schedule of 74th amendment and of the lists of items prepared
by the expert committee on small area statistics and also of the report by
Hanumanth Rao Committee were made.
(ii) A survey on identification of the availability of required information with an
analysis of the existing data gap was made.
(iii)Twelve (12) rural and thirty one (31) urban schedules with indications of
respective sources of data serving as SIS input manual were developed.
(iv) The appropriate method of data collection was suggested.
(v) Designing the output format for (a) general information on variables
considered for decentralised planning, (b) quarry based information, (c) report
based information and for (d) summarised information, was made.
(vi) The following two research papers have been prepared out of the above
project.
(a) Development of Statistical Information System (SIS) for decentralised
planning by P. Maiti.
(b) Information Support for planning a development – with reference to
Rural Development by P. Maiti.
Own Activity:
The work involved in developing the SIS consisted of (a) identification of
required data items, (b) designing of formats of data collation, collection and
compilation, (c) specification of output formats, amenable to computerized data
base and (e) organization of workshops in collaboration with the state government
to finalize the methodology of data collection and data formats. I was stationed at
Bhopal, Madhya Pradesh and executed the above work during 1998-99.
The SIS developed for decentralized planning has the following major
components:
(i) Computer hardware forming the container of Statistical Information;
(ii) Computer software to process the information;
(iii) Statistical data, the actual content of the system;
In addition to the work as mentioned in the “Highlights of Some of the
Technical Features”, the following works need special attention.
Estimation of Work Load:
The volume of information for each village was calculated and appeared to
be approximately 8000-10000 alphanumeric characters or bytes and for each
urban local body (ULB) about 10000-15000 bytes. The total volume of
information for the sate as a whole was about 1000 million alphanumeric
characters or one Gigabytes (GB). The total number of documents was 72000 for
the rural sector and only about 400 for the urban sector.
33
It was estimated that to collect the information level and prepare a
document for a village, 3 mandays would be needed and for each document in the
urban sector 6 mandays would be needed.
To key in the data on to floppies, the total input volume was thus about 1
GB. At the rate of 5 KB per hour, a total of about 100 man-years of data entry
effort was required.
For the work, I had to stay at Bhopal most of the time during the project
period.
Strengthening Local Government in M.P., PWI & the Award :
The project was a part of the Technical Assistance provided to the
Government of Madhya Pradesh (MP) by the Asian Development Bank
through Price Water House India. On behalf of the PWI, the Statistical
Information System was developed by the Indian Statistical Institute,
Kolkata through ISI – PWI collaborative research project. The training
component of the project was assigned to some other agency.. Some of the
video programmes produced from the project report by the PWI has
bagged SONY/ICD – 1999 Award in Japan.
34
A Description of the work involved in development of SIS
METHODOLOGY
Additional Responsibility Assigned by 73rd & 74th
Amendment of municipality Act and Panchayati Raj Act
Assess data Requirements
Match with Existing Data (Raw & Published
Gap Analysis
Data Collection
RequirementFeasibility &
Scope of System
Client
Approval
Select from
Existing Data
Data Formatting for
Computerisation
Scope of
Implementation
Design Computerised
Database
Test & Deliver
Client
Approval
Training
If any new
data collected
data Take on
Pilot Implementation
at HO/Bhopal District
Role on Implementation Plan
Including Data Take on of
Remaining & New data
Client
Approval
Project Completion
Review
35
7.
Mid term Review of Indian Population
Project VIII in Calcutta
Metropolitan area (CMA) (1997-1998):
Nature of Participation: Member Statistician.
Summary: The objective of this project was to study on different facets of IPPVIII. One important component was to assess the contributions made by IPP-VIII
Project in spreading family limitation practices and in improving maternal and
child health care among the beneficiary households. Several satellite enquiries
were also conducted to assess (i) the role, capability and motivation of IPP-VIII
personnel, namely those of health officers, chairpersons, municipalities, (ii) the
effectiveness of training programmes and IEC programme organised by Calcutta
Metropolitan Development Authority (CMDA), and (iii) the qualitative and
quantitative progress of civil construction works undertaken for the project IPPVIII. Finally, evaluations on health facilities such as the physical ones with their
mentainance and repair, on the availability of drugs and equipment and on the
staffing pattern were made.
On the basis of the findings of the study on “assessment of IPPVIII project in
spreading family limited practices and in impressing material and child
health care”, government took the appropriate measures on extending health
facilities such as physical ones with their maintenance and repair, on the
availability of drugs and equipment and on the staffing pattern etc.
The followings were some of the technical novelties.
On estimates of some of the non-sampling errors:
(i)
An estimate of the over coverage error due to inclusion of non-population
units because of imperfections in the frame was found to be around 15%;
(ii)
The rate of non-availability of the responding beneficiary households was
found to be around 3.7%
On Sampling Design:
In developing the sampling design, the following considerations were made.
(i) All the programmes in Health Administrative Units (HAU’s) did not start
functioning at the same time. Some started before April ’95 and some, since
April ’95. Therefore a stratified multistage design was adopted, stratification
being made on the basis of the time of start of functioning of the different
programmes. Simple random sampling was adopted at different hierarchical
stages, namely at HAU’s, at subcentrers, and at blocks, except at the ultimate
stage of selecting the beneficiary households, where linear systematic
sampling was used.
Because of the variation in fertility among married couples with respect to
their ages, before selecting the sample households, all the households in the
selected sample blocks were further classified into four sub-strata on the
basis of presence of a married couple and the age of the wife in the
household. The required number of sample households from each substratum was selected by systematic sampling. Thus a multistage stratified
36
sampling with further deep stratification was developed for selecting the
ultimate respondents.
(ii) Since the project aimed to make an assessment of the impact of IPP-VIII
programme, the sampling design should have been repetitive in nature at
least over two periods to produce longitudinal data. But since this was not
possible, the problem was taken care of by properly designing the
questionnaire, where the respondent was asked during the course of data
collection to produce information on present overall demographic
characteristics, attitudes and practices of family planning along with those
within five years from the date of the survey. Since information related to
vital events like births and deaths within the family, not much recall lapse
error was reported. The error due to recall lapse was within control.
Estimation Procedure:
i: Subscript for a block,
t: Subscript for a stratum,
j: Subscript for a household,
k: Subscript for a sub-centre,
l: Subscript for a HAU,
H(U): Number of HAUS in the u-th stratum (u= 1,2).
h(u): Number of sample HAU’s selected from the uth stratum (u=1, 2).
S l(u ) : Number of sub-centres in the lth HAU of the uth stratum (u= 1, 2).
sl(u ) : Number of sample sub-centres selected from lth HAU of the uth stratum.
Blk(u ) :
Number of blocks in the kth sub-centres of the lth HAU in the uth stratum.
blk(u ) : Number of sample blocks selected from the kth sub-centres of the lth HAU
in the uth stratum.
T: Total number of sub-stratum ( T=4).
(u )
: Total number of households in the tth substratum of the ith block selected
N lkit
from the kth subcentre with lth HAU in the uth stratum.
(u )
: Number of sample households selected from the tth substratum of the ith
nlkit
block selected from the kth subcentre with lth HAU in the uth stratum.
(u )
Ylkitj
: Value of the study variable for the jth sample household in the tth
substratum of ith block selected from the kth subcentre of lth HAU.
Yu: Total value for the uth stratum ( u = 1, 2)
Unbiased estimates of Yu and Y were computed as
(u ) (u )
Yˆu = ∑∑∑∑∑ Wlkitj
Ylkitj
l
k
(u )
l
(u )
l
i
(u )
lk
(u )
lk
S
B
H
.
.
(u )
h
s
b
and Yˆ = Yˆ(1) + Yˆ( 2 ) .
(u )
where, Wlkitj
=
(u )
Ylkitj
(u )
t
j
.
N lkit
is the multiplier associated with the value
nlkit
37
Nature of my work:
In addition to above work executed by myself in developing sampling design,
estimation procedure and some error analysis as outlined above, special mentions
need to be made on my following work also.
(i) On selection procedure: Simple Random Sampling (SRS) was adopted at all the
different hierarchical stages except at the ultimate stage,where linear systematic
sampling was employed to select the ultimate stage units.
(ii) The stratified multistage design as described above was developed and deployed
to collect data, and consequently, the multipliers were calculated.
(iii) Combined use of Probability and Non-Probability Samples: In addition to the
probability sample of the beneficiary households which constituted the population
under study, use of non-probability/purposive selection of chair persons of
municipalities and knowledgeable persons were made for their assessment and
perceptions of health functionaries who were entrusted with the job under IPPVIII population project.
(iv) On Questionnaire Design: Mid term evaluation of the IPP-VIII project was
meant to make an assessment of its impact on the beneficiary households. For this
purpose, the sampling procedure should have been repetitive in nature to produce
longitudinal data for comparison. But this was not possible. As an alternative, the
problem was taken care of by properly designing the matching schedules in such a
way that the respondents might provide on their present overall attitude on family
planning practices along those within five years from the date of survey.
Questionnaire design was also made to avoid possible “recall-lapse error” due to
the time lag.
(v) Appropriate measures were taken on the choice of investigators to reduce the
gender bias.
(vi) The following exercises were made on the “item non-response” for the items,
namely “effectiveness of teaching”, “use of text books”, “use of teaching aids”
and use of “Black Board”.
Table 7A: Percentage distribution of item response indicating “Effectiveness of
Teaching” (IPP VII Project , 1997-98):
Items
Response
Non-response
Sufficient time to take notes
Sufficient examples used in teaching
Opportunity to interact/ask question
Satisfied with the answers provided
Yes
83.2
93.7
100.00
92.6
No
16.8
2.1
0
3.2
0
4.2
0
4.2
Table 7B: Percentage distribution of item response responding “use of text books”
(IPP VII 1997-98) :
Text book
Response
Non-response
Item
Receipt of text book
Easy to understand
Need to more pictures in the text-book
Yes
92.6
91.6
86.3
No
7.4
3.3
6.3
0
5.1
7.4
38
Table 7C: Percentage distribution of item response regarding use of “teaching Aids”
(IPP VII 1997-98):
Response
Teaching Aids
Non-Response
Used
Not used
Pictures
Charts
Models
Projections
Video films
90.5
86.3
22.1
34.7
72.6
4.2
6.3
51.6
41.1
16.8
5.3
7.4
26.3
24.2
10.5
Table 7d: Percentage distribution of item responses regarding “Use of black board”
(IPP VII 1997-98)
Blackboard
Response
NonResponse
Frequently
occasionally
Blackboard used
77.9
21.1
1.0
Difficulties in understanding writings in
6.3
93.7
0.0
the blackboard
Enough time for taking notes
85.3
14.7
0.0
New and technical words written on board
71.5
27.4
1.1
Drawing of pictures
69.5
30.5
0.0
8. The Socio-economic-demographic and cultural pattern of the female labour
force participation of the North West and The Cape (June’95 –December ’96):
Nature of Participation: Principal Investigator.
Summary: This research project was undertaken jointly at the Institute of
development research, University of North West and at the Institute for child and
family development, University of the Western Cape. Because, women are the first
educators of the society, because they bring up their own children by imparting their
family value system, female employment has shown to affect the qualities of life and
of development of the children. This study aimed in understanding the phenomenon
of female labour force participation by examining its socio-demographic and cultural
patterns and the concomitant problems associated with this process. (Ref: Some
Aspects of Complex designs in Survey Sampling by P. Maiti HSRC Publishers,
Pretoria, South Africa, pages 37 to 44). The study being exploratory in nature had no
explicit hypothesis to test. However, the analysis was done along the following lines.
(i) A woman’s decision and commitment to work outside the home is affected by a
combination of material and non-material factors;
(ii) Because of their accumulation of cultural capital stock such as job education
and job training, white women will have higher levels of labour force
participation;
(iii) Because of high family responsibilities, married women and mothers will have
lower levels of labour force participation than will single women and nonmothers.
39
Findings on the women participation on the basis of the study helped the
Government of South Africa take certains measures for maintaining
quality of family life in the face of increasing rate of participation of women
in the labour force.
Own Activities Associated with the Project:
Determination of Sample Size and Its Allocation:
Determination of the sample size and its allocation into different strata were
based on an extensive analysis of the data on population of the two provinces.
Data on urban female population were available from the Census. Distribution
of the actual total urban female population into two provinces played a crucial
role in determining the corresponding sample sizes for the two provinces.
Finally, the required samples were drawn following a stratified multistage
design.
The work involved in the project consisted of
(a) development of the sampling scheme;
(b) determination of the total sample size with its distribution into the provinces;
(c) providing training for the people to participate in the data gathering operation;
(d) preparing instruction manuals;
(e) development of the schedules, of tabulation programme and of the blank
formats for data acquisition and presentation of summarised information;
(f) suggesting the form of the estimators for the relevant parameters; Actual
computation procedures for calculation of multipliers and estimators were
suggested.
The Sampling Design:
The sampling design developed was that of a multistage stratified one with
districts, towns, suburbs, households forming the different hierarchical units. The
urban female population in the Western Cape Province were stratified into a
number of strata, population size being used as the stratification variable.
The Questionnaire Design:
While designing the schedule, enough care was taken of both current users
as well as non-users to obtain information on the basis of matching sample.
Exercises were made on the effect of Call-backs on the responses as well
as on error analysis due to non-response were made.
The Error Analysis:
Table 8A: Interviews Completed in Percentage by the Number of Attempts/Call-Backs
No. of Calls
1
2
3
Substitution Proxy
Total
Socio-economic
Demography
67.32
18.91
2.00
4.90 1
93.13
1
‘Time of interview’ was not appropriate for some respondents and hence even in spite of 3 Call-backs,
some households had to be substituted.
40
Table 8B: Percentage Distribution on Unit Non-Response
Non-response by reasons
Contacted, but not
Solicited,
Response Ineligible possible to be solicited but refused Total
Socio-economic
demographic and
93.13
1.09
5.35
0.43
6.87
cultural patterns
9. Survey of family and Community life in the selected communities of the Cape
Peninsula of the Republic of South Africa. (January, 1995-December, 1996).
Nature of participation: Principal Investigator.
Summary:: This research project was undertaken at the Institute of child and family
development, University of Western Cape. On a request from the Institute to the Centre
for Statistics (CENSTAT) at the HSRC, I was associated with the Project as the Principal
Investigator to help them formulate the problem, develop the sampling scheme and
prepare the schedules. (Ref. Some Aspects of Complex designs in Survey Sampling by P.
Maiti, HSRC Publishers, Pretoria, South Africa, pages 44 to 47).The study aimed to
develop the demographic profiles for the selected communities with a view to
understanding the family and the related problems. There were many technical issues on
the concepts and definitions of the problem. After several rounds of discussions with the
social scientists at the HSRC, the problems were identified and efforts were made to
arrive at unambiguous definitions. For example, the meaning of the concept ‘family’
varied greatly from one racial group to the another. This needed standardisation.
The study was to address the critical problem of lack of knowledge about the
family and then to develop a data base for systematic analysis of certain family issues,
with a view to help many policy makers implement important Government programmes
such as Reconstruction Development Programme (RDP) etc. This study became
extremely helpful in generating data on many demographic and socio-economic
characteristics of the families.
This study helped many policy makers in the government implement many
government programmes such as Reconstruction Development programme (RDP)
etc. This study become extremely helpful in generating data on many demographic
and socio-economic characteristics of the families.
Nature of my work:
Development of Schedules:
Since most of the questions were of very sensitive in nature, special care was taken
in specifying the form and language of the words. Different unstructured forms such as
both ‘open-ended’, both ‘closed ended’ or ‘open-closed’ or ‘closed-open’ etc., were
used for different items. Since the responses were expected to be arising out of
respondent’s belief and faith on different items of information, unstructured questions
were used to increase spontaneity in the response of the informants.
The Sampling Design
A stratified three-stage design with extensions, clusters, visiting points which formed
different hierarchical units was developed and deployed. A non-probability sample was
41
also drawn for understanding (a) structure issues such as public services, their quality and
co-ordinations, (b) the acceptability of education, (c) health care, (d) child welfare, (e)
employment etc.
The Error Analysis:
Table 9A: Interviews Completed in Percentage by Number of Attempts/Call-backs
Number of Calls
1
2
3
Substitution Total
Survey of Family and
61.32 25.95
3.05
7.19
97.51
Community Life
Table 9B: Percentage Distribution on Unit Non-Response
Respons
Non-Response by Reasons
Contacted, but
Solicited,
Ineligible
not possible to
but Refused
be solicited
Survey of
Family and
97.51
0.96
1.53
Community
Life
Total
2.49
10. Community attitudes and preferences pertaining to cemetery and cremation
related issues in the East Rand in RSA. (May ’95 – December ’96).
Nature of Participation: Principal Investigator.
Summary: At the request of the Eastern Gauteng Services Council (EGSC), this research
project was undertaken as an academic support programme at the University of the
Witwatersrand. As the outreach programme of the Council, I was requested to assist the
EGSC as the Principal Investigator in formulation and evaluation of the project. With
ever increasing demand of land for burial of the deceased, land was becoming scarce
especially in urban areas and also due to an uncertain economic climate, the problem to
find a suitable land for cemetery development was becoming a matter of great concern.
An alternative to burial in certain communities is the cremation, which unlike to
burial, is less land demanding and probably economic also in the long run.
Before the council proceeded to undertake the task of building a public
crematorium in the western urban side of Eastern Gauteng, it was therefore necessary to
find not only the attitude of the people, but also to get an estimate of the percentage of the
people from the community accepting it. (Ref: Some Aspects of Complex designs in
Survey Sampling by P. Maiti; HSRS Publishers, Pretoria, South Africa, Pages 54 to 63).
The study helped the policy makers find not only attitude of the people, but also get
an estimate of the percentage of people accept the concept of building a public
cremation in the Western urban side of Eastern Gauteng.
Nature of my Work:
Combined use of a probability and a non-probability sample:
It has been observed on many occasions, that a probability sample alone can not help in
drawing valid conclusions on some issues. In this case too, a need was felt on the
realisation of a non-probability sample from community based social groups of burial
42
societies, church groups, old generation CBO’s as well as new generation CBOs, and of
government-officials. Hence both a probability sample and a non-probability sample
were drawn to obtain relevant data.
Sampling Design:
The probability sample was realised through a stratified multistage design.
The sampling design developed was that of two stage,one with suburbs as first stage units
and enumeration blocks forming the second stage units. Different racial groups formed
the natural strata for allocation of a total of 480 households – firstly into different racial
groups, and subsequently allocations to within particular group were made into different
areas.
Use of aerial photographs were made for selecting the ultimate units.
Extensive analysis of the available data on population of different racial groups was
made to allocate the total sample size first into different racial groups and then for a
particular group into different areas. In some situations, strict proportionality was not
possible to be maintained, as the sample size in those cases turned out to be too small to
be considered.
Questionnaire Design:
Three categories of questionnaires of the open-closed-type were developed and used for
collecting data.
(a) household schedules (a probability sample);
(b) moderate schedules from community based social groups selected purposively (a
non-probability sample) and
(c) another set of moderate schedules for key informants such as leaders in the Transval
Legislative Council (TLC), Provincial Government, the EGSC, and prominent
religious leaders etc. selected purposively (another non-probability sample).
Exercises were made to examine the effect of Call-backs over the response and
also an error due to non-response was analyzed.
The Error Analysis:
Table 10A: Interviews Completed in Percentages by Number of Attempts/Call-backs
Number of Call-backs
1
2
3
Substitution Proxy Total
Community Attitudes 69.05 20.52 6.15
95.72
Table 10B: Percentage Distribution on Unit Non-Response
Respons
Non-Response by Reasons
Ineligible
NonSolicited,
Unable to
Contacted but Refused Answer
Community
95.72
0.79
0.89
0.96
0.64
Attitude
Others
Total
1.00
4.28
43
11. Identification of Other Backward Class (OBC) in West Bengal (1994):
Nature of Participation: Member Coordinator.
Summary: At the request of the backward commission for West Bengal, this research
project was undertaken at the Indian Statistical Institute, Kolkata to provide an
independent evaluation on the class of different communities. The evaluation was made
on the basis of a household survey with the following major criterion.
(i) The degree of economic affluence of the specified communities;
(ii) The extent of participation in different levels of education from the above
communities, and
(iii)The extent of involvement in different respected professions from such
communities.
The study helped the Government of West Bengal identify who could be included in the
other backward classes. This eliminated the confusion on some of the commodities,
whether they should be considered as belonging to other backward class.
On Sampling Design:
A three stage stratified sampling design with subdivisions as the strata was adopted.
Within each stratum, different hierarchical stages were as follows:
(i) Police station, as the first stage unit;
(ii) Village as the second stage, and
(iii)Households as the ultimate or final stage units.
At each stage of selection, the required number of sampling units at that stage was drawn
by simple random sampling.
Estimation Procedure:
The sample having been drawn in a probabilities manner, the estimates of the population
characteristics were obtained through appropriate statistical formulae.
Consider one subdivision (stratum); let
K = no. of police stations in the subdivision, and
k= no. of police stations in the sample, drawn by SRSWOR.
Next, suppose the no. of villages in these sample police stations are
N1 , N 2 ,, N k
and we have selected
n1 , n2 ,  , nk
villages, from these police stations by SRSWOR.
44
Finally, suppose that from the j th sample village, of i th police station, we have taken n'ij
households from nij households of the l th caste/community (l = 1, 2, …,5), then the
multiplier or probability weight of any sample household of the l th caste/community of
the j th sample village of the i th p.s. is
K N nij
.
M ij = . i .
k ni n'ij
Such multipliers were computed for all sample households drawn from all the strata.
Now, consider any characteristic of the (i, j , ) th sample household, like number of
females participating in a gainful work or number of matriculates in the household.
Denote any such characteristic of the household y ij
then
Yˆ = ∑∑∑ M ij y ij
i
j

gives the estimate of the total of y in the stratum and adding over strata, we get estimates
for districts or for all districts combined.
12. Evaluation of Total literacy campaign (TLC) in West Bengal (1990-91):
Nature of Participation: Member Secretary.
Summary: The objective of internal evaluation was two fold: a) evaluation of literacy
attainment of the learners of the district in terms of the norms of National literacy
Mission (NLM) and b) general observations on other aspects of TLC in the district (such
as motivational, organizational, technical etc.,).
On the basis of the study, Government of India was able to declare a district as a
literate one and helped the government understand, if her objective of achieving
total literacy has been ful filled or not.
On Sampling Design: The internal evaluation of ‘Total Literacy Programme’ sought to
assess the current state of attainment of literacy among the learners. This was done
principally through representative, scientifically designed and conducted sample
investigation of the literacy status.
On consideration of an efficient sampling design, a two stage stratified cluster sampling
design was adopted; the four subdivisions of the district formed the four rural strata and
similarly, nine municipalities constituted nine strata for urban areas.
The Gram Panchayat (GP)S were considered as first stage units and the learning centers
within the GPS formed the second stage or ultimate stage units. At both the stages,
Simple Random Sampling was adopted.
45
Estimation Procedure.
In major cases, the design was self weighting. In the remaining cases, disproportionate
sampling was introduced to compensate for differences in sample rates. These weights
were mostly based on the original probabilities of selection. This is evident from the
following formulations.
Let
d = Subdivision (d=1,2,3,4; 1 = Barasat, 2 = Barrackpore, 3 = Basirhat, 4=Barrackpore)
t= Sector (t=1,2; 1=Rural, 2 = Urban)
h= GP (h = 1,2; Gd ; Gd being the number of GPS in subdivision d)
k=l.c. (learning centre) (k=1,2,… Lh ; Lh , the number of learning centers in h th GP)
S= Six (S=1,2; 1 = Male, 2 = Female)
p= a learner (p=1,2,… nks ; s=1,2, k=1,2,…, Lh )
Let
dt
y hkp
stand for the value of the characteristic y associated with p th learner in k th learning
centre from h th GP in the sector t of the subdivision d.
 1 for s = 1 (given d , t , h and k )
dt
Defining y hkp
(S ) = 
0, otherwise (given d , t , h and k )
One can have
i
nks
∑∑
S =1 p =1
dt
y hkp
( S ) = Number of male learners from K th learning centre belonging to h th
G.P. of the sector t within a subdivision d .
Similarly, the number of female learners from the above classification can be obtained,
obviously,
z
nk = ∑
s =1
∑
dt
dt
(S ) =1, for both s = 1, and 2)
y hkp
( S ) ; ( yhkp
p
denote the total number of learners in the k th learning centre from the h th G.P. in the
sector t within a subdivision d.
46
Let
l h = number of learning centres sampled from h th G.P.;
Lh = total number of learning centres in the h th G.P.
Now the estimate of the total number of learners in a selected (G.P,t) classification
Lh
lh
lh
∑
k =1
 z
∑
 s =1

∑
z
nks
nks
p =1

dt
y hkp
(s) 


Writing,
lh
n
d (t )
h
∑∑∑
k =1
s =1
p =1
dt
y hkp
(s) ,
We have an estimate of total number of learners for the sector t in a subdivision d as
Gd
gd
gd
∑
h =1
Lh d
nh (t )
lh
and finally the estimates of the total number of learner for the sector t, in all the
subdivisions was found to be
4
∑
d =1
Gd
gd
gd
∑
h =1
Lh d
nh (t )
lh
Putting t = 1,2, estimates of total number of learners were obtained for rural and urban at
different levels of aggregation.
13. ISI-ONGC Collaborative Research Project (1985-1992)
Nature of Participation: Member Statistician:
Summary: At the request of Oil and Natural Gas Commission (ONGC) of India, the
Indian Statistical Institute evaluated the economic and physical consequences of various
strategies for action in different basins in India. Both economic and stochastic models
were used for estimating reserve and the per unit cost of hydrocarbon. (Ref. Estimation of
Discovery and Production costs of Hydrocarbon with some application to Indian Data,
Indian Statistical Institute, Calcutta).
Transformation of the real life problem into the statistical one required a series of
discussions with the technical experts working at the different levels of the organization.
In fact, the project formulation was not a routine work: instead, definitions and other
related concepts were defined, developed and redefined into the frame work of the
47
present problem. For example, the ‘reserve in place’ was distinguished from the
‘recoverable reserve’.
On the basis of the findings on discovery of hydrocarbon as well as production cost
of oil and gas, Government of India was able to revise per unit of oil and gas prices.
The following were the technical novelties among others:
(a) The available primary cost data were in the form of well-wise cost. The well-wise
cost figures were measured at current prices, and therefore, the cost data for
different years were non-comparable. In order to make them comparable, it was
necessary to deflate them using a suitable index number of well-drilling cost. No
such index number was available which could be used for the purpose of deflation
of cost figures measured at current prices and for this, a new index was
constructed and applied to the given data;
(b) The question of how to aggregate and what economic models to choose had to be
resolved;
(c) To test on the constancy of success ratio in hydrocarbon exploration, data were
examined through a number of statistical devices some of which were based on
graphical representations, while the other explored standard statistical techniques.
(d) A fully Bayesian Hierarchical method which provides better estimates for errors
in estimation and prediction was sought for. But because of analytical
complications, an empirical Bayesian view was taken in predicting the (n+I)th
discovery, given the data on past discoveries. Two types of simulation estimates
were provided (i) one based on the assumption of an approximate Gamma
distribution of the field sizes where as (ii) the second alternative used a Gamma
population and employed the classical method of “importance sampling” for
adjustment. Both methods involved novel methods of simulation developed by us.
14. Domestic Tourists Survey in Orissa (1988-89):
Nature of Participation: Member Coordinator
Summary: One of the major objectives was to determine the factors that could be
ascribed to the promotion of tourism of India. Apart from determining the extent of
revenue the country could earn, this study also aimed to investigate certain sociological
problems related to the tourism industry. Along with the development of methodology, it
became necessary also to observe.
(i) Socio-economic distribution of tourists in India;
(ii) Regional distribution of tourists across the country;
(iii)The existing infrastructure facility available in terms of accommodation,
transport, medical help etc.,
(iv) Average cost incurred by a tourist during his/her travels for travelling, boarding
and lodging etc.
(v) Influx of tourists during a period at a particular tourist spot.
48
The need of developing such infrastructure facilities as different types of places of
accommodation, connectivity to road, rail and of general administration was felt by
the Government of India on the basis of the findings of the study. It suggested the
government on the type of hotels affordable to the middle, and upper class of people,
as it was found that the major contributors to the flow of tourists were the middle
class and/or upper middle class people.
Tourists were distributed in different towns and within a town, they stayed in different
types of dwellings as 1: Hotels; 2= Dharmasalas; 3 = Circuit House;
4=Irrigation
Bungalows; 5 = Forest Department Bungalows;
6= Railway Bungalows; 7=Youth
Hostels; 8=Open air.
. With a view to the proper representation of all types of categories, an appropriate
scheme was designed and adopted for drawing samples of tourists.
Some technical novelties of the work were as follows.
(i) Among other objectives enquiries were also directed towards finding availability
of existing infrastructure facilities in terms of accommodation, transport (road,
rail, air), medicine etc. This required redefinition of a tourist. Normally, a
tourist by definition is a person who visits places of historical monuments,
pilgrimages etc. According to the objective of the study, any person for any
reason whatsoever, requiring accommodation to spend at least one night
should be considered as a tourist and hence because a member of the target
population. Therefore, the usual definition of a tourist became unusable and
was defined according to the objective of the study. Otherwise, target
population considered could have been under coverage.
(ii) For a stratified random variable, choice of the stratification variable must be
correlated with the study variable; that the administrative zones should not
always form the different strata has been examined in this case; Degree of
concentration has appeared here to be the stratification variables.
(iii)A multistage sampling design was developed and deployed. It may be interesting
to note that and was stratified into six strata
From each stratum, a probability sample of a period of 10 days was selected for visiting
these time points. Towns/cities formed the second stage units, while
dwelling/accommodation places within the selected town and the tourists within a
particular type of dwelling place served as the third and fourth stage units respectively.
At all the stages except at the final stage, simple random sampling was introduced in
selecting the pre-determined number of units from that stage.
49
At the final stage, a random sequence like D,S,S,M,M was generated and each
investigator was asked to follow strictly the random sequence thus generated for selecting
the room of a particular type in each selected hotel, where D = double room, S = single
room, M = multiple (Dormitory).
15. Costs and Benefits of Rural Electrification REC Project (1975-76):
Nature of participation: Project Assistant.
Summary: Primary objectives of the study were (i) quantification, as far as possible, of
all relevant costs and benefits of the selected REC project with reference not only to the
time, when the study was undertaken and when the projects were only in their second or
third years of life, but also to the future costs and benefits for each of the remaining years
of the life span of the projects (ii) identification of factors that facilitate or obstruct full
realization of benefits flowing from electrification in rural areas.
It because easy for the Government to decide, if the existing projects under Rural
Electric Corporation could be extended further on the basis of cost-benefit analysis
of rural electrification or not.
Subsidiary objectives:
(i) Assessment of share of benefits enjoyed by different socio-economic groups in
terms of proportions or percentages;
(ii) Assessment of socio-economic impact, for example, changes in employment,
forming techniques and migration to urban centers in terms of rates,
proportions etc.
Some Technical novelties of the work:
On Sampling Design:
Household /enterprises were sampled according to two-stage sampling scheme, using
probability samples. In the first stage all the villages were listed and certain particular
relating to use of electricity were tabulated. This enabled stratification of the villages into
a number of homogenous groups.
At the second stage, from the elected villages, household/enterprises were again
classified depending on the type of use of electricity.
On stratification:
On examination of the village wise lists of connections, a definite structure was found to
be present in the distribution of connections by villages.
A small number of villages were intensely electrified and accounted for approximately
half of all connections of each type.
50
With this pattern in distribution, a random sample often, twenty or even 50 percent
villages is unlikely to yield a reasonable number of connections of all types. Moreover,
the intensely electrified villages were to display conditions which favoured utilization of
electricity. It was therefore deemed essential that the villages be grouped into a number
of strata according to the intensity and type of use of electricity.
For the second stage of sampling, an unorthodox schemes was adopted. Instead of
selecting the households/enterprises from each of the villages selected in the first stage,
all such households/enterprises were collated together. From this single list of second
stage units, the required number of samples was drawn at random. This sampling was
obviously less efficient from the point of view of sampling error, but was necessary to
ensure adequate representation.
16. Calcutta Urban Poverty Survey (1976):
Nature of participation: Project Assistant.
Summary: The objective of the survey was to study the nature, extent and the causes of
poverty of the segment of population living in Calcutta pavements, which might be
different from rural poverty, since there is a sharp difference in the livelihoods of rural
and urban people.
The important features identified for investigations were
(i) Type of living unit;
(ii) Employment;
(iii)Migration,
and
(iv) Education.
Other than the above important features, several more were studied viz., type of shelter,
type of toilet facilities, per capita weekly income etc., as certain aspects revealing
standards of living.
The study helped the government agency understand the nature, extent and the
causes of poverty of the pavement dwellers. It also determined distribution of
pavement dwellers by socio-economic class, by their origin of domicile, from where
they migrated to the city and residing in the pavements. This helped the government
prepare the plans for improving the rural economy to resist migration towards the
city from the rural sector of the neighbouring districts.
(i) Coverage:
The survey extended over the whole of Calcutta Corporation area excepting 9
inaccessible blocks.
51
(ii) Sampling Design:
The broad design was a two-stage one with blocks as first stage units and
pavement dwellers’ households as second stage units.
(iii)Stratification:
Phase I: The 5127 NSSO (National Sample Survey Organisation of Government
of India) blocks were visited and the approximate number of pavement dwellers
residing in each was obtained through local enquiries.
These blocks were then grouped into 8 zones according to their geographical locations.
Then the blocks were further sub-divided into 4 strata according to the approximate
number of pavement dwellers in each. Thus the 5127 blocks were stratified into 32 strata
of 8 geographical zones and 4 size classes of the “number of pavement dwellers.
Phase II: The blocks were grouped into 4 geographical zones, combining two
contiguous zones of phase I. Thus 5127 blocks were stratified into 16 strata of 4
geographical zones and 4 size classes.
Sample Selection:
Phase I: 64 blocks were selected by SRSWOR, two from each stratum. These
sample blocks were divided into two half samples.
Phase II: 32 blocks were selected by SRSWOR, two from each stratum. Next 8
more blocks were sub sampled from the 64 samples blocks of phase I.
Estimation Procedure:
The following notations were used for obtaining various estimates.
r: Subscript for r th zone;
S: Subscript for s th stratum ;
i: Subscript for i th surveyed block;
j: Subscript for j th household;
m: number of surveyed blocks;
h: number of households ;
M: total number of blocks ;
y: value of a characteristic under study;
Y: total of y for all the subsamples combined;
Yˆ : estimates of the total of y for all the sub-samples combined.
52
Estimate of Total
An estimate for the total of y is given
Mrs Mrs hrsi
Yˆ = ∑ ∑
∑∑ y rsij
mrs mrs j =1
s
r
Mrs
is known as the multiplier.
mrs
Pooling of phase wise estimates:
n Yˆ + n2 Yˆ2
, ni is the number of blocks surveyed in the i th phase (i =1, 2)
Yˆ = 1 1
n1 + n2
17. Health and Socio-Economic Survey of Calcutta Metropolitan Development Area
(1976-77); Conducted by Metropolitan Development Authority in Collaboration
with the Department of Health, Government of West Bengal.
Nature of Participation: Project Assistant.
Objective: Government of West Bengal entrusted Calcutta Metropolitan Development
Authority with responsibility of improving medical facilities in the Calcutta Metropolitan
Development area. For this, it became necessary for them to know what facilities were
available, what more would be required and where they should be located. The
Government and CMDA thought, it was wise to carry out a fact finding study of the
available medical facilities in the area.
The Indian Statistical Institute was requested to obtain a survey based estimates of the
stock of the existing medical facilities and their functioning in the Calcutta Metropolitan
Development Area. This also aimed to assess the health condition of the population living
there in with reference to their social and economic conditions with a view to estimate the
demand on medical service of the different disciplines.
Sampling Design: The sampling design used was that of a stratified simple random
sampling without replacement of medical care institutions. A stratified multistage
sampling was also adopted for drawing a sample of households.
The study helped policy Decisions Committee to improve upon the medical facilities in
the Calcutta Metropolitan Development Area. On the basis of the findings, the policy
makers were able to know what medical facilities were available, what more were
required and where they should be located in Calcutta Metropolitan Development area.
This helped the government in extending medical facilities in the area.
53
19. Important Professional Work:
1. Worked as a chair person of the sessions of the two – day UGC sponsored
national seminar on decentralised planning, Department of Economics,
Vidyasagar University, March 20 – 21, 2003.
2. Worked as a chair person in the panel discussion on information support system,
Golden Jubilee Conference of Indian Association of special libraries and
Information Centre (IASLIC), December 31st 2004.
3. Worked as a member of the advisory committee for the two – day national
conference on “Recent Trends in Estimation and Optimization: Theory and
Applications” to celebrate platinum jubilee of the Institute of Science, Nagpur,
India, to be held during January 1 to January 2, 2006.
4. Worked as a member of the Advisory Committee on Geological Information
System (GIS) to assist in (a) developing the method by which data could be
generalised on a geographical basis, (b) developing sampling frames and drawing
samples, (c) upgrading the human resources at the CENSTAT HSRC, Pretoria,
South Africa with new techniques of sampling and to take part (d) in different
research programmes for the centre of statistics.
5. Worked as a member of the technical committee set up for the project “Human
Resources Survey of Black Professionals in South Africa”. The followings
were the responsibilities of the technical committee.
- To design the scheme and study according to the guidelines and
definitions set up by the steering committee;
- To train the supervisers and field workers to collect data from the
respondents;
- To provide all the technical details in connection with the project
including time scheduling, questionnaire design, sampling design and
weighting the sample data for calculation of the estimates.
6. Dr. P. Maiti and the Pslam Programme:
(An extract from the minutes of September 12, 1995 CENSTAT, HSRC/RGN,
PRETORIA, South Africa)
“….. Dr. Maiti will contact Professot Stoker to try and get the exact sampling
design --- where upon he will develop formulae to test the data……… The
Computer Centre will write the Pslam programme……”
The project was executed according to the above guideline. Exercises were
carried out to examine,
(a)
if different response rates existed in various categories/subgroups which
were formed with respect to (i) certain demographic variables such as
geographical location, degrees of urbanisation and population group and/or (ii)
with respect to certain biographical variables such as sex, age-group, marital
status, occupational group and level of education.
(b)
if these varying proportions resulted in skewness in the realised sample in
respect of such variables. This was done by comparing the sample data with
published statistics from census. The procedure followed was that of calculation
54
of proportion of units belonging to the different subgroups from the available
census data and then multiplying them by the sample size n, to obtain a set of
frequencies which were compared with the frequencies of the sample data.
No significant deviations between the sample data and census data were detected.
While calculating the weights, the factors of unequal probability sampling, nonresponse and variation of the varying weights were taken into account.
7. Conducted an workshop on Sampling (April ’95 – December ’95):
As a specialist consultant in Survey Methodology at the Centre of Statistics of
the Human Sciences Research Council, my expertise was rendered to HSRC
statisticians, researchers and other stakeholders in investigating their various
theoretical and practical problems on sampling. Workshops on ‘Sampling
Theory and Practice’ were conducted at the centre for statistics every two weeks
during the whole of 1995, according to the following time table.
April: 19/4/95; May 3/5/95 and 17/5/95; June 14/6/95 and 28/6/95,
July: 12/7/95; August 2/8/95 and 30/8/95; September 13/9/95 and 27/9/95.
October: 11/10/95 and 25/10/95, November 8/11/95 and 12/11/95; December
6/12/95.
55
20. Teaching Activities:
Teaching: (1995 -
)
Courses
Centre of Statistics, Human Sciences
Rresearch Council, Pretoria, South
Africa
Lecture
Series
on
Survey
1:
Methodology and Basic Principles of
Sampling: March 25-29, 1996
2: Lecture Series on Sampling
Methodology: April 14-May 15, 1996
3: Lecture Series on Advanced
Sampling Techniques: August 14-18,
1996
Indian Statistical Institute, Kolkata
1: Master of Science in
Quantitative Economics(MSQE)
Subjects:
Number of Lectures
Theory
Practical
Sampling
-
-
Statistics
50
20
Economic Statistics and
Official statistics
34
Sampling
Sampling
(1997-1988), (1998-1999), (1999-2000),
(2000-2001), (2001-2002), (2003-2004),
(2004 – 2005), (2005 – 2006),(2006-2007)
(2007-2008),(2008-2009)
2.: B. Stat.
(1996-1997), (1997-1998)
Statistical Quality Control
3.: PG Department of Statistics
The MEDUNSA, Pretoria, South
Africa (1996-1997)
4.:INTERNATIONAL STATISTICAL (a) Sampling
(b) Economic Statistics II
EDUCATION CENTRE: (ISI).
(1998-1999), (1999-2000), (2000-2001), (c) Economic Statistics III
(2001-2002), (2002-2003), (2003-2004), (d) Sampling
at
the
(2004-2005),
(2005-2006) (2006-2007),
Specialisation
Course
under
(2007-2008) (2008-2009), (2009-)
UNDP
(e) Sampling
at
the
Specialisation course in the
regular course
(f) Probability and statistical
methods in the regular
course
5:
Tenth
Course/Workshop
on Sample Size Determination in
Sampling Design for Household and Survey Sampling (Prepared a
training handbook for this
Establishment
Surveys
(organized
purpose)
jointly
by
Central
Statistical
Full one
academic
year
30
35
35
45
15
15
15
15
40
--
20
--
56
Organization, Ministry of Statistics and
Programme Implementation, Govt. of
India and United Nations Statistical
Institute for Asia and the Pacific
(UNSIAP), Japan): 18 Oct. – 12 Nov.
2004, ISI, Kolkata
6:
The ISI sponsored three day
workshop
on
Demography
and
Population Dynamics with emphasis on
Survey Sampling Methodologies at the
Department of Mathematical Sciences,
Tejpur University: 26 Oct.-29Oct.2006.
7:UGC-Sponsored Refresher Course on
“Decentralisation,
Planning
and
Participatory Development” organized
by Dept. of Economics with Rural
Development, Vidyasagar University:
Dec. 12, 2005 – Jan. 06, 2006.
8: UGC-Sponsored Refresher Course in
Statistics organized by UGC Academic
Staff College, Sambalpur University:
Dec. 04-24, 2008.
Nonsamplig
Errorsclassification,
And quantification (Prepared a
training handbook for this
purpose)
Development of Statistical
Information System (Prepared
a training handbook for this
purpose)
Sampling
Methodologies
(Prepared a training handbook
for this purpose)
57
21. Authored Text Books:
About the Books
Some Aspects of Complex Design in Survey Sampling
HSRC / RGN Publishers; ISBN 0 –7969 – 1857
Pulakesh Maiti
Abstract
The two major features of any survey design are the sample design and the
schedule/questionnaire design. Only occasionally, we do come across a book that deals with
different complex designs when applied to real life situations. Many difficulties, especially those
that arise in actual field conditions, make it almost impossible to use standard results and hence a
compromise between the theory and Practice is very much needed. The purpose of this write up is
to show in a practical manner how to arrive at a sample design for a complex survey after giving
due considerations to practical constraints such as time and cost and to develop a schedule or a
questionnaire in an efficient way. The approach is essentially practical.
The material therefore emphasizes on (i) how a complex survey design is developed, (ii)
how one can compute multiplier from each record / piece of information to build up estimator,
and how one can make an assessment of the reliability of the estimators in a multistage design
with minimum effort. Extensive use has been made of some of the projects, undertaken at the
Indian Statistical Institute, Kolkata and at the Human Sciences Research Council, Pretoria, South
Africa.
Since I believe that a survey practitioner needs to be exposed to basic principles of
sampling, there follows a brief discussion, not so much on providing proofs, but on understanding
why results are true, what makes these work, and how they are applied. It is hoped that
researchers in various different disciplines may find this useful when designing surveys for their
own research problem. This write up is intended to help non-specialists in sampling in their own
work.
FOREWORD
Dr. Pulakesh Maiti has written a fine monograph on the theory, practice and historical evolution
of survey sampling. He has drawn on existing theory, including his own work, as well as his
experience and expertise gained through practical work at the Indian Statistical Institute on such
different aspects of surveys as design, collection, scrutiny, editing and analysis of data.
Altogether he presents a very balanced view of the subject that includes some of the latest
methodologies. Being a Bayesian, I myself would have liked to see a bit more of the Bayesian
approach to these problems. But it is true that most practitioners of survey sampling are still not
Bayesians though they are now more sympathetic to Bayesian ideas than before.
I was also very pleased to see many references to surveys done in South Africa.
This is a indeed very appropriate because this way Dr. Maiti has validated the basic principles of
survey sampling over a bigger set of applications and drawn attention to important country
specific problems.
I am sure Dr. Maiti’s application-oriented but scholarly approach to his subject will gain him
many readers with very diverse backgrounds.
I like to think of this monograph as indicative of great things that can happen if developing
countries share resources and participate in innovative activities of mutual concern.
Professor J. K. Ghosh
Jawaharlal Nehru Professor, Indian Statistical Institute
President, International Statistical Institute (1993 – 1995)
58
Sampling and Estimation Procedures for Inverse Multinomial
Sampling Associated with Single, Multiple and Joint Events.
H. S. Steyn and P. Maiti
HSRC / RGN Publishers; ISBN 0 – 7969 – 1822 – 8.
Summary
In traditional sampling surveys the total size of a sample to be drawn from a
statistical populations is normally considered as a given parameter. However, the
situation changes when the focus is on adequate representation of a specific subgroup in a
population. It is often necessary to introduce different kinds of randomisation procedures
to ensure adequate representation of a specified group among the subgroups of a
population. This type of target sampling is known in statistical literature as inverse
sampling. The inverse sampling techniques introduced and used in this monograph ensure
a representative sample containing an adequate predetermined number of units possessing
a specified character or belonging to a subgroup of special importance within the
population.
It briefly introduces the existing theory on inverse sampling and these expands the
theory to include multiple events (i.e., events that can occur more that once per trial) as
well as joint events. An effort has been made to keep the mathematical statistical
procedures contained with text on a level that will be accessible to researchers in the
Human Sciences with a limited background in mathematical statistics. Some
mathematical derivations are included as appendicies. Some ideas on sampling from
labelled population units are mentioned briefly, but because of their more advanced
mathematical nature, be dealt in a separate publication.
22. Book-Chapters:
1. Interactive Linear Models in Survey Sampling (2014): Essays on
Mathematics and Statistics, Edited by Codueta Stoica, Athens Institute of
Education and Research (ATINFR) pp. 141 – 145.
Considered is a liner ‘interactive’ model in the context of survey sampling. The
situation arises when investigator and/or supervisor interventions are
contemplated in the responses. An appropriate linear model is introduced to
represent the response profiles(s) arising out of each respondent-cum-supervisor
combination as per the panned ‘design lay out’. Two situations (blinded and
unblended submission of responses) are differentiated and corresponding data
analysis techniques are discussed. Variance components are assumed to be known
in the study.
2. Indian Statistical Institute – Numbers and Beyond (1931-1947) (with J. K.
Ghosh and Anil Bera) (2007): To appear in Project of History of Indian
Science, Philosophy and Culture (PHISPC) Volume 1, “Science and Modern
59
India: An Institutional History C ‘1784-1947’ edited by Professor Uma
Dasgupta.
The project to study the history of Indian Science, Phylosophy and Culture from
the earliest times to 1947, funded by the Ministry of Human Resource
Development, Government of India, has been taken up with the objective to
launch a comprehensive and interdisciplinary study of scientific, philosophical
and cultural heritage of the Indian civilization from the past to the present times.
The volume on “Science and Modern India: An Institutional History C ‘17841947” includes our article‘Indian Statistical Institute – Numbers and Beyond
(1931-1947)’ in its second section, which has been made up of chapter histories
on the major science institutes that were founded in those years which were
contributing silently or overtly to the nation building effort.
The present essay is essentially in two parts, one on Mahalanobis (the early
years), the other on both Mahalanobis and Indian Statistical Institute (ISI) during
the period under review. Part 3 suppplements parts 1 and 2 and adds details on
rthe first two decades of the ISI as well as some earlier and later events.
Part 1: Prasanta Chandra Mahalanobis – the Early Years (1897 – 1931).
Part 2: The Statistical Laboratory and the ISI.
Part 3: Supplements and the Appendices.
3. Development of Statistical Information System (SIS) for Decentralized
Planning (2004), pp. 198-248: Participatory Decentralized Planning in India:
Issues of Finance and Statistics Information,Edited by Professor Sau ,
FIRMA KLM Publication, Calcutta.
The Government of India had requested Asian Development Bank for technical
assistance to strengthen local Government of Madhya Pradesh. The then Price
Water House India (PWI)-presently Price Water House Cooper was one of the
prime contractors to the Bank for this purpose. An inter-firm agreement between
Price Water House India and the Indian Statistical Institute was made to assist for
data requirement, data procurement needed for planning and efficient decision
making by local bodies. The Statistical Information System (SIS) developed at the
Indian Statistical Institute at Kolkata as a part of the assignment entrusted to the
Institute by PWI is presented here. The SIS has been developed in accordance
with the 73rd and the 74th Amendments of the Constitution of India.
23. Summaries of Major important professional activity:
Under Major Important Professional Activity :
1. Serving as a member of the working group of 73rd round of NSS (June 2015 –
June 16)
On being inducted as a sampling expert in the working group of 73rd round of
NSS on an invitation from National Statistical Commission, expertise on the
development of sampling design, designing the enterprise schedule and in the
selection of first stage units in rural and urban areas was provided. For the first
60
time, it was suggested that Census Enumeration Block (EB) should be taken as
the first stage units in urban areas.
2. Serving on the working group of Drug Abuse Survey (November 18, 2014 –
April 10, 2015)
Knowhow on the development of sampling design was provided. The sampling
design was a dual approach i.e., household based survey for commonly used (lied)
drugs and an alternative survey method known as Respondent Driven Strategy
(RDS) technique to capture illicit drugs. For this, licit and illicit drugs were
identified. Clarifications on the concept of ‘Drug Abuse’ and ‘Drug use’ were
sought for. Suggestions were offered on tackling the issue of multiplicity of
respondents across household based survey and RDS technique. A new estimation
procedure in case of the frame having multiplicity problem was suggested.
3. Serving as a member of the working group of 69th round of NSS
(June 2012 – December 2012 )
The National Statistical Commission (NSC) invited me to serve as the sampling
expert in laying down sampling design, methodology and strategies to reduce non
sampling errors for 69th round of NSS. The National Statistical Commission
(NSC) was set up by an Apex body by an Act of Parliament Government of
Imdia and empowered in monitoring statistical activity in the Country. The
following contributions have been made towards
(i)
Estimation of number of slums: A sampling methodology has been
developed in estimating number of slums in the face of having no frame
for the slums. The sampling methodology thus developed suggests the
number of slums can be estimated unbiasedly from the survey with the
help of an additional information on the number of blocks linked to a slum
of which the sample blocks is found to be a part. If there are more than
one slums intersecting a sample block, then additional information needs
to be collected in respect of each of the slums.
Serving as the Chairman of the sub-committee on
(ii)
Imputation of rental values: The back ground is the issue of compilation
of house rent component in the consumer Price Index for rural areas on
similar lines as is being done in respect of CPI for urban areas. The issue
was discussed in the Technical Advisory Committee on statistics of Price
and cost of living Index. The TAC recommended to refer the matter to
NSC for exploring a possible methodology.
Accordingly, the issue was placed before NSC. The recommendation of
the NSC in this regard was as follows.
61
“With regard to the reference made by the Central Statistical Organisation
(CSO) (NAD-PCL unit) on the issue of imputing house rent in respect of
owner occupied houses in rural areas, it was felt that relevant data needs to
be collected by the NSSO for the purpose of including weights on the item
in the price collection module by the CSO. It was decided that the matter
be referred for consideration in detail by the 69th round working group of
NSS”.
As recommended by NSC, the working group of 69th round of NSS may therefore
consider the above issue and explore a simple alternative methodology for
imputation of rent in respect of self-owned houses in rural areas so that the same
may be adopted in the next quinquennial round of consumer expenditure survey
by field staff of NSSO.
As subcommittee was formed and the following documents were prepared.
(i)
Development of a methodology/number of methodologies for imputing
rental values was/were made; Different imputation methods were
discussed;
(ii)
A document with underlying mathematical justification was prepared and
the methodology proposed there in was used to obtain an unbiased
estimate of the number of slums in the face of having no frame for the
slums;
(iii)
A document on the estimation of coverage error was prepared;
(iv)
A document dealing with the problem of non-response was prepared. This
aimed in estimating the response probabilities.
(v)
A document has been prepared in identifying causes of non-sampling
errors in the NSS-field work.
On the Recommendation on Selection of Urban Frame Survey UFS-blocks:
As per earlier guidelines, a norm of about 600-800 by population size was used
for formation of UFS-blocks. Since the blocks were supposed to be of more or
less of same size, it had been the convention for the NSS to select sample blocks
by SRS.
In 2007-2012 Urban Frame Survey (UFS), it was not possible to maintain said
norm strictly at the time of formation/ updating of UFS blocks. A study of 61st
round sample blocks reveals that as high as 28% of the sample blocks had a
population of size 400 or less. On the other hand about 14% of sample blocks had
a population size of 1000 or more. In other words, the UFS blocks, the way they
were being formed, do vary substantially in size.
62
Therefore, since under 2007-2012 UF Survey, the UFS blocks would vary in size,
it has been recommended that the UFS blocks for NSS surveys should be selected
by PPSWR and not by SRSWOR, as it is the present practice.
Field visits: Field visits were made on the sample basis by myself according to
the following schedule; This helped understand the causes of different kinds of
non-sampling errors. A document has been prepared in this direction.
NSS-field visits August 2012 – December 2012:
Field visits were made in the following states according to the following schedule.
State
District
Tripura
West-Tripura
West Bengal
Darjeeling
Jammu and
Jammu
Village/town/city
Duration of field visits
Braspur
Badharghat
(IV unit 0001)
August 21, 2012 – August 24, 2012
Pumong tea garden
Siliguri MC
(IV unit 0003)
September 11, 2012 – Sept. 14, 2012
Jandyal
Jammu
(IV unit 0039)
Kashmir
Delhi
North West
Delhi MC
September 25, 2012 – Sept. 28, 2012
October 29, 2012 – October 31, 2012
(IV unit 0038)
Gujarat
Vadodara
Andaman and
Nicobar Islands
Carnicobar
South Andaman
West Bengal
North 24 Parg.
Kolkata
Dunad
Vadodara MC
(IV unit 0086)
November 20, 2012 – Nov. 23, 2012
Malacca
Port Blair
(IV unit 0005)
Chandpara
Kolkata MC
December 04, 2012 to Dec. 07, 2012
December 17, 2012 to Dec. 17, 2012
4. Working as an Analyst (2011-12):
Appointed as the Analyst of village Resource Mapping by the Directorate of
Micro & Small Scale Enterprises, Government of West Bengal, India, and
rendered my expertise to guide the Director at regarding the fields to be
considered for identifying potential resources in specific areas of different
districts which might ultimately facilitate industrialization in the state.
63
5. Working as a collaborative Scientist for some of the Projects undertaken by
the Department of Economics and Statistics (DES), Tata Services Limited
(May 17, 2010 – August 16, 2010):
Projection of Demand of thirty two items:
Methodology on the determination of the projected market size for consumption
of items for the determined period 2015 and 2020 have been suggested. This has
involved estimation of the Engle Curve/Demand function of each of the thirty two
items and that of the total expenditure based on the data on rural consumption for
the year 2004-2005. Before actual fitting distributions, few tests on graphical
representation were carried out. However the distributions fitted to the given data
on different items of consumption appeared to follow long normal distribution.
Projection case 1: σ 2 , which indicates the measure of inequality remains
constant and the elasticity is also of constant type. The Engle Curve/demand
function is of the form
E ( y x ) = ax b ,
y and x being items wise and total per capita expenditure.
It was shown that,
y t = y 0 e b ( µt − µ0 )
Where
y t = Average demand at the future time point ‘t’
y 0 = Average demand at the present time point ‘0’
µ t = Mean of the distribution of x, at time point ‘t’
µ 0 = Mean of the distribution at present time point ‘0’
With the given data, demands for thirty two items, some of which were luxary and
necessary type and some of which were of inferior goods were projected at a future time
point.
Case 2: σ 2 undergoes changes.
From the relation
t F1 = t F − σ
64
Where,
F1 ( x) = Proportion of aggregate income through all those earners whose income is ≤ x
units.
F(x) = Proportion of earners having income ≤ x .
t F1 and t F are the points abscissa of the normal probability curve up to the areas F1 and F
respectively.
The above relation has helped in answering the querries of the following type.
Suppose “top 10% has 30% of the income in 2010” changes to “to 10% has 35% of the
income in 2015”, what happens to σ 2 .
6. Providing Technical Assistance for Piloting Below Poverty Line (BPL)
Census (May 2010 – 2011):
At the request from Principal Secretary to the Government of West Bengal,
Panchayat and Rural Development Department, Government of West Bengal,
India I was deputed by the director to provide technical assistance for piloting
BPL census for 11th five year plan. The following assistances have been provided.
1. Training has been given to the field workers for collecting data in different
districts of West Bengal;
2. Supervision of the field work in selected villages of the districts of North
Bengal was made;
3. A manual entitled “The BPL census: Questionnaire Design, Survey
Methodology, Scoring Method, Cut off score – visited” was prepared and
given to the paychayat and rural development department. This provided
many suggestions on improvement of the schedule develop by Ministry of
Rural Development for the Socio-Economic Survey 2010 and remedies of
many problems to face in the field work.
4. For cost valuation of the collected data, a random sample from the
respondents were drawn and given to them for revisits.
5. The following suggestions for analysis of data were made;
An outline of the possible analysis of the data:
Poverty alleviation programmes can be described as belonging to one of the
programmes (i) programmes whose beneficiaries are self-selected (for
example, MG National Rural Employment Guarantee Programme) or (ii)
programme that are meant exclusively for predetermined target groups (for
example, the Public Distribution System (PDS) for the provision of fair
priced food and the Indira Awas Yojana etc.,). The criterion of targeting is,
65
most often, whether or not a household is below poverty line (BPL).
Identifying such BPL households is crucial to implementation of targeted
antipoverty schemes. Hence, BPL data and its quality are very much
important and one should find ways and means to obtain relevant, reliable
and timely information, necessary for identifying the BPL households.
Exclusion and Inclusion Errors:
The important type of errors in BPL census one faces, involve errors that exclude poor
household from the category of the poor and inclusion errors that include non-poor
households in the category of poor. Some of the reasons behind such errors were
identified as belonging to one or more of the following categories.
(i) Errors involved in the survey:(a) In the questionnaire and in the investigation process; for examples,
respondents belonging to the joint family tend to report joint families as
separate nuclear families in order to qualify separately for benefits. Thus, if
benefits are to accrue, they will accrue not just to one household, but to
different segments of undivided family, if the households were to be defined
BPL. Such a situation may lead to inclusion of non-poor family in the poor
category, if one can find that household size and total scores are
correlated.
(b) Though the poorest people lack assets and are unable to borrow because of
their poverty, the “type of indebtedness” parameter gives the highest score to
a household that is not indebted. Such a household receives a higher score
than the score assigned to a rich land lord who borrows only from commercial
banks. Thus, a poor family has a chance of being excluded from the “poor
category”. Distribution by types of funding agency may help in this direction
for all those, but not for the poor family who does not get loans. This is
possibly because of the existing system in the aggregation of 13-parameters to
establish the absolute and relative position of each household with respect to
poverty status in a village: An alternative method of developing aggregate
score may be tried out as an exercise.
(c) Selection of the indicators and the scoring scheme for each parameter are also
crucial in identification of the BPL household. One should examine and
identify the variables who are likely to be (i) misspecified or (ii) under
specified or vague by and to the respondents. Replacement of the variables
should be made, if possible, through examination of the available data. One
may also find the contingent nature of the parameters like “status of
children”, reasons for migration” which are not applicable to all households.
One should try to for reconciliation of the kind of contingent happenings
though some exercise.
66
(d) Another serious cause for exclusion and/or inclusion errors is the way cut off
scores are set for each state, district and village. The state level cut off may be
set at the level of the official planning commission poverty line. The
determination of cut off scores for administrative divisions within the state
(district, block, village for exampled is left to the state government. The
aggregate cut off scores for the determination of BPL households could vary
across the administrative entities. Therefore, it is necessary to make exercises
on the determination of region-specific cut off scores.
Exercise of comparison of BPL census 2002 and the current BPL census 2007.
A) With respect scoring parameters:
A comparison of data on scoring variables for each household in a village for the BPL
censuses is difficult. It may be therefore advisable to compare aggregates at the village
level such as number of households and its distribution by indicators to identify serious in
consistencies in the two data sets, if any. To mention a few.
(i) Number of households and its distribution by size group of operational holding
land;
(ii) Number of households and its distribution by type of dwelling unit;
(iii) Number of household and its distribution by ownership of consumer durables;
(iv) Number of households and its distribution by literacy rate;
(v) Number of households and its distribution by child status;
(vi) Number of households and its distribution by type of indebtedness;
(vii) Number of households and its distribution by caste;
(viii) Number of households and its distribution by average monthly income etc.,
B) With respect to Non-scoring parameter: (i) Examination of relationship between household size and the total score, (for each
BPL, 2002, 2007);
(ii) Distribution of households by Social Group (separately for each BPL census,
2002 and 2007);
(iii)Distribution of households by land tenure and by size of operational land
holdings; (separately for each BPL, 2002 and BPL 2007);
(iv) Distribution of households by average monthly income (separately for each BPL
census);
Summary:(i) An exercise to examine the relationship between household size and total score;
(ii) An exercise for developing alternative method of aggregation;
(iii)An exercise on the identification of “misspecified”, underspecified” and vague
variables;
(iv) To estimate proportion of households having the contingent parameters like
“reason of migration” etc.,
67
(v) Determination of the cut off scores at different hierarchical units and
methodology;
(vi) Exercises of integration as mentioned in A and B above.
7. Participated at the 32nd Meeting of the National Statistical Commission
(April 24, 2010):
At the invitation of National Statistical Commission at an interaction session with
the commission at Kolkata. The following suggestions were offered.
(i)
The system, though capable of capturing the wide variety of data generated on
a given horizon and up to a given vertical distance of administration, it can not
reach at the grass root level, as we shall see in the case of data requirement for
local level planning.
(ii)
Because of growing diversified requirement in view of the expanding economy
leaning towards liberalization, and because of the change in the state-private
sector mix, the country’s information need characteristic’ is changing and at
the same time, because of the system’s dependence on the traditional recordsmainly by a product of the administration of the age old administrative set up,
data gap between the perceived need and the availability of it is gradually
widening. Thus, the system is now partially unable now to produce relevant,
comprehensive, accurate statistical information in some sectors.
(iii)
The civil registration system should be extended for planning health and
family welfare programme at the local level as required by the 73rd and 74th
amendment of the constitution.
(iv)
Data collection and compilation work on Environment statistics is in a very
nascent state, and as such, efficient system for the collection, collection and
compilation of data on environment statistics and development of
environmental indicators are heart felt needs.
(v)
Health sector requires, with respect to each disease, identification of major
chronic diseases, not only different types of cancer, heart-disease,
hypertension, diabetes and Aids, but a strong desire of a statistical study is
needed. The study would involve (i) data base for mortality, (ii) data base for
prevalence and incidence; (iii) epidemiological and intervention studies; (iv)
data base and literature on clinical disease; (v) statistical modeling at a
population or molecular level..
(vi)
The private sector is now replacing the public sector as a dominant force in the
economy and the statistical system must be redesigned to better meet its
information needs.
68
(vii)
Service Sector Activities are increasing and hence data must be collected on
this tertiary sector of the economy.
(viii) There is a need for development of a computerized Data Base for service
sector along with appropriate methodology of data collection.
(ix)
Legal/Legislative obstacles for collection, and collection of core statistics
needs to be discussed.
(x)
The system producing health statistics is totally decentralized and still
relatively week by Indian standards on incidence or prevalence of major
diseases at the national level. It needs major overhaul and improvement.
(xi)
The system being partly centralized, and partly decentralized, dependent on the
state government, state statistical agencies and other line departments, there
must be provision of strong coordination between the statistical authorities at
the state and at the centre. Regular meetings of the COCSSO must be held.
(xii)
It may be seen that under the system, there exist multiple agencies for
collecting data on the same subject. For example, in case of employment,
unemployment and under employment data can be made available through
seven sources. In case of price statistics also, one may have different sources
both at the state and at the centre level. If a data user faces the multitude of
sources, he quite often finds that – (i) figures on the same subject differ by
source; and (ii) often concepts, definitions, coverage and classifications made
by different agencies for the same subject do not match. Therefore in such
situations, one care must be taken to find some ways and means for
reconcillation of the figures produced by different agencies. There should
evolve some mechanism to control such differences between figures produced
by different agencies.
(xiii) Recognizing the importance of environmental statistics as an emerging area
and it being multidisciplinary in nature, needs of standardization of relevant
terminologies and concepts are felt so that people from different disciplines
working together both at the state and at the center may be familiar with the
uniform concepts and definitions. Otherwise, estimating number of species,
for example, in biodiversity, may land up to an unreliable value. Here we will
face new and peculiar difficulties such as the same plant having different
names in different parts of the country. These have to be sorted out before
biodiversity indices can computed. The same situation will apply to other
sectors of the economic activity. A standardization programe should be under
taken as an action programme.
69
(xiv)
In surveys, non-sampling errors (e.g., under or over coverage, non-response)
are more important than sampling errors. Non-response problems have
worsened over the years. There is some theoretical research on rectification of
these (e.g. Judith Ressler).
(xv)
With regard to coverage problem, it has been observed that in case of ASI
frames, the coverage problem both in terms of under and over coverage exists,
list of the population units being maintained by chief inspector of factories.
With a recent learning towards a system of economic liberalization and with
free flow of market research through the process of economic reforms, the
industrial firms have lost some of the incentives to report their production to
the Government of India. Some activity should be directed towards updating
the ASI frame. The same problem of under coverage and over coverage exists
in other surveys also. This problem should be addressed immediately.
(xvi)
In a multistage design like that of NSSO, because of the quick changes in the
rural nature, one unit may turn out to be a non-respondent. Similar problems
of non-response occur in many surveys. Existing methods of some imputation
techniques may be put to use in handling non-sampling errors. Measurement
errors also occur in many surveys; Existing techniques may be put to use in
handling such errors. Along with the standard error of the estimate, estimation
of non-sampling variance is also necessary.
(xvii) As the village and urban block level data on the number of enterprises and
workers as per the Economic Census (EC) are used as the sampling frame for
selection of villages and urban blocks in the follow up enterprise surveys,
necessary measures must be taken in the Economic Census (EC) to enhance
the quality of data. A few years back, at the request from National Advisory
Board of Statistics (NABS), a sampling methodology alternative to Economic
Census was developed by a team of ISI scientists, namely Professor J.K.
Ghosh, Professor Shibdas Bandyopadhyay, and Dr. Pulakesh Maiti for
estimating certain parameters related to unregistered manufactures with high
mortality rate. This method may be revisited and put to use in practice.
(xviii) In fact during April 1998 – July 1999, just before Rangarajan Commission
report came out, a research project entitled ‘strengthening local government in
MP” was undertaken jointly with Price Water House India at the ISI. The
project identified the items of information for local level planning, developed
method of data collection, and developed a computerized data base. However
a significant new follow up study has been made by Dr. Pulakesh Maiti.
70
The follow-ups were as follows:
(a) Creation of a computerized data base for decentralized planning in the
district of Howrah, i.e., collection and use of statistics at gross roots levels
for planning and decision making at the levels of panchayats;
(b) Development of a useful software for a computational data base the
usefulness of the software was demonstrated to users;
(c) Preparation of fourteen different block wise Human Development reports
and one report for rural sector of the district;
(d) Preparation of one data based Gram Pnchayat Plan under the principal of
decentralized planning.
(xix)
While working under the above two projects, some additional basic statistics
other than those identified through 11th and 12th schedules of the 73rd and 74th
amendments of the constitution at local level planning were needed to be
created. They are as follows:
(i) The nine fold classification of land use should be slightly enlarged to
cover two or three more categories such as social forestry, marshy and
water logged land, from the point of decentralized planning;
(ii) The yield rate estimates based on scientifically designed crop cutting
experiments under the general crop estimation survey (GCES) are not
adequate to provide estimates below the district level; For
decentralized planning and also with the introduction of National
Agricultural Insurance Scheme (NAIS), a need is felt for assessment of
yields at the level of block/teshil even at the panchayat level.
8. Working as Member of Expert Group in preparation of Annual and Perspective
Plan of the District of Howrah under 11th 5 year Plan (April 2007-March 2012)
In reply to the letter from the District Magistrate (Memo No.1283, dated 20.10.2006), I
was nominated by the Director as a Member of the Expert Group in Preparation of
Annual and Perspective Plan of the Howrah District, under 11th 5 Year Plan
(No.D.O/12216, 26.11.2006)
A Review of the work as a Member of the Expert Group can be summarized as
follows.
Planning for development invites 4 different types of activities – Formulation,
Implementation, Monitoring during implementation and Evaluation on completion. To
carry each of these activities, relevant, reliable and timely information is needed at every
stage.
The central idea of decentralization of planning is to make an effort to bridge the gap
between the availability of resources at the grass root level and the subsequent planning
process is built up on the basis of these information. Decentralization of planning needs
71
to be assessed from the perspective of functional, administrative and financial devolution
and the extent of people’s participation. Additional responsibility has been assigned to
the local Governments by the 73rd and 74th Amendments to the Constitution of India.
Writing a Monograph:
The monograph entitled Statistical Information System for Decentralised Planning in
the district of Howrah written by myself has discussed such issues for development of
database for decentralized planning as
a) identification of the items of information needed for decentralized planning;
b) the level at which data may be required;
c) designing of the formats of data collection, collation and compilation;
d) methodology of data collection;
e) identification of data collecting agency and
f) specification of output formats amenable to computerised database.
It has also discussed a review of the existing information gathering systems including the
Statistical Information System developed at the Indian Statistical Institute, Kolkata under
the ISI-PWI project [Maiti et al (1999)].
Volume of Information:
Volume of information needed for creating the database, different types of work, the
necessary man power, time and cost has also been indicated in the same monograph. A
few recommendations on
a) the present data recording system,
b) need for availability of electronic media at the level of different local bodies,
c) need of training programme to be imparted at the local level resource persons
for updating data
were suggested there. A description of the Statistical Information System to be
developed for the district of Howrah was also outlined in the monograph.
Description of Error Analysis:
Assessment in the quality of data is needed to avoid creating an undue expression that data so
unreliable as to be of no use. It is necessary to provide some idea of the reliability of results. Care
was taken to setup controls through proper :
a) schedule design;
b) survey design;
c) training to the survey management group and
d) to data processing and other personnel involved in the operation.
It became still necessary to examine that controls were effective and results with desired accuracy
have been achieved. Identification of the errors were made through
a) internal evidence and external administrative check;
b) internal evidence and subjective expert views; and
c) internal evidence alone.
The errors detected and corrected appeared to be belonging to one of the following categories:
a) unacceptable because the information appears to be impracticable;
b) unacceptable because of logical inconsistency;
72
c) unacceptable because of conceptual mistakes;
d) unacceptable because of they remaining blank; and
e) unacceptable because of misspecification of identity of the basic units.
Copying errors while entering raw data to MS Excel file and further loading the MS Excel file
data into the RDBMS were detected and corrected
Creation of the database:
As startup activities for creating the RDBMS the following studies were made:
1. System Requirement Study (SRS);
2. Context Analysis and Design (CAD);
3. Data Flow Diagram (DFD).
RDBMS is a computerised linkage in third normal form and consists of the tables which
are broadly classified into two categories namely, item-wise tables and hierarchy-wise
tables. The criteria considered when designing tables in the RDBMS included:
a)
c)
e)
g)
i)
k)
information rule;
systematic treatment of null values;
view updating rule;
physical and logical data independence;
distribution independence;
data non-redundancy;
b)
d)
f)
h)
j)
l)
guarantee of access rule;
online catalogue;
high level insert, update;
integrity independence;
non-subversion rule;
consistency in data stored
Uploading the data:
Before uploading the data in to the database, the following necessary work were done:
a) manual correction of Area Master names in the accumulated MS Excel files;
b) rectification of cell formats in the MS Excel file from text to number and vice
versa, percentage to number, decimal to number, accounting to numberetc.;
c) correction of area names as per Area Code List (ACL);
d) the cell formats were corrected for extraction of data without error from MS Excel
file to the tables of RDBMS;
e) the uniqueness in names was guaranteed to maintain relational integrity among
the tables of the RDBMS;
f) MS Excel data files contained some alphanumeric values, which were converted
to numeric values before loading into the database, as the basic criteria of
uploading the data is that data should be numeric in nature only;
However, some corrections were not possible to be made as data in dates, fractions and
scientific formats are in irreversible form. Data were stored in the tables of the RDBMS
and for that necessary instructions were developed to append the data into the tables of
RDBMS.
Preparation of 157 booklets of Resource Mapping for 157 Gram Panchayats
of the district(Data Content of the Statistical Information System):
73
For processing the data and compilation of the above booklets required development of
an application software. Fundamental work in preparation of the proposed application
software included :
a) designing of forms by adding objects like grid view, frames, text boxes, list
boxes, combo boxes, commands buttons on them;
b) layout/design of data entry forms in consistent with Area Master hierarchy
was made.
Separate data entry forms were developed for different items of information to be filledin from the manual survey schedule.
Development of the application software model needed separate entry forms for 12
blocks of the survey schedule.
With the help of the developed application software, Gram Sansad wise 34 basic reports
were prepared. They were as follows:
i) Educational Amenities I
ii) Educational Amenities II
iii) Medical Amenities
iv) P & T and Internet Amenities
v) Communication Amenities
vi) Financial Institution Amenities
vii) Market/Haat Amenities
viii) Office Amenities
ix) Service Centre Amenities
x) Power Connection Amenities
xi) Power Disconnection Amenities
xii) Power Non-connection Amenities
xiii) Drinking Water Amenities I
xiv) Drinking Water Amenities II
xv) Irrigation Water Amenities I
xvi) Irrigation Water Amenities II
xvii) Both Irrigation & Drinking Water
xviii) Both Irrigation & Drinking Water
Amenities I
Amenities II
xix) Stock of Animal Husbandry
xx) Educational Institutions Faclity
xxi) Development Project Beneficiaries
xxii) Land and Water Body Use – I
xxiii) Land and Water Body Use – II
xxiv) Cultivated Land – Main Crops
xxv) Cultivated Land – Pulses
xxvi) Cultivated Land – Oil seeds
xxvii) Cultivated Land – Other Produce
xxviii) Cultivated Land – Spices
xxix) Cultivated Land – Vegetables
xxx) Cultivated Land – Cash crops
xxxi) Health Service Available
xxxii) Health Scheme Beneficiaries I
xxxiii) Health Scheme Beneficiaries II
xxxiv) Non-conventional Energy Source
Preparation of Human Development Reports:
The 73rd and 74th Amendments to the Constitution in India has given rise to acceptance of
decentralized planning process and speaks of the bottom-up approach to planning, where the felt
needs of the people from the grass root level are assessed. For this, Inter-Block and Intra-Block
variation need to be brought out.
The work for preparing Human Development Reports started with the creation of above database
and completion of compilation of 157 above booklets of resource mapping for 157 Gram
Panchayats.
Each of the 14 Blocks of the district was separately examined from the point of its attainment in
the level of human development based on the above primary data collected and processed at the
different levels of hierarchy including Gram Sansad, the lowest level.
74
In the basic reports the extent of land use and cropping pattern, the stock of animal husbandry,
sources of drinking water and water for irrigation, rural electrification etc. have been displayed
for each of the 14 blocks. These have made possible to prepare 14 Block-wise Human
Development Report separately entitled Block-wise Human Development Report in the
district of Howrah, West Bengal. Also has come-up the single Human Development Report for
the rural sector of the district as whole entitled Inter-Block variation with respect to Human
Development in the Rural Sector of the district of Howrah.
Reports Published Under the Above Activities as a Member of Expert Group in
Preparation of Annual and Perspective Plan of the District of Howrah Under 11 Five Year
Plan:
The following are the total number of publications under the above project:
1. Statistical Information System for local level planning by local bodies in the District of
Howrah. A monograph published by Howrah Zilla Parishad;
2. 157 booklets of Resource Mapping for each of 157 Gram Panchayats of the District of
Howrah. published by Howrah Zilla Parishad;
3. Inter-Block variation with respect to Human Development in the Rural Sector of the
district of Howrah. The theme conceived and supported by Howrah Zilla Parishad;
4. 14 Human Development Reports at the Block level for each of the 14 Blocks entitled Blockwise Human Development Report in the district of Howrah, West Bengal. The theme
conceived and supported by Howrah Zilla Parishad.
Design and Development of Computer Software as a Member of the Expert
Group in Preparation of Annual and Perspective Plan of the District of
Howrah under 11th Five Year Plan:
Startup Activities:
1) System Requirement Study (SRS) was made to gather an in-depth knowledge about
the proposed system i.e Statistical Information System for Rural Decentralised
Planning (SISRDP).
2) Context Analysis Design (CAD) was prepared to depict the information inflow and
out-flow from the proposed system (SISRDP).
3) Preparation of Data Flow Diagram (DFD) to figure out the processes required to
generate the outputs (reports etc) from the given inputs (Area Master Names and
Codes, detailed survey data etc.).
4) Demarcation of Automation Boundary in the developed DFD.
5) Development of Area Master form ( to enter Area Master names and their codes) and
detail data entry screens for filling detailed data from the manual schedule (survey
form).
Fundamental Work in preparation of proposed Application Software:
1) Forms were designed by adding objects like grid view, frames, text boxes, combo
boxes, list boxes, check boxes, command buttons on them. The properties of these
objects are then bound to each and every data entry form (both in Add and Update
75
mode), report generation screen. Logical codes were written to populate objects like
combo boxes for effective date, list boxes for various Area Master names like Block
(Administrative Block), Gram Panchayat, Gram Sansad etc. Grid view to show data
fetched from tables of the database. The Area Master names are filtered basing on
names selected in upper hierarchy. For example, when a Block is selected from the
drop down box (combo box), only Gram Panchayats of that Block get populated.
2) Layout/design of data entry forms are developed in consistent with Area Master
hierarchy (like Gram Sansads under Gram Panchayats which is under
Block/Panchayat Samiti). Separate data entry forms were developed for different
items of information to be filled-in from the manual survey schedule.
Important Feature of the Application Software:
1.The application software has been so developed that in future new survey data can be
accommodated, edited/updated and the reports (basic, summarized as well as query
based) can be generated without any further modification in the database (RDBMS) and
the application software (SISRDP). This would help one append, revise, correct and
generate reports according to their requirement.
2.SISRDP can be made dynamic in the sense that it can be applied for another
hierarchical stage, say for another District, with some modifications, as an additional
hierarchy for the district has to be provided.
3The programs developed needs revision to reach the additional hierarchy i.e. the
District. All programmes such as
• Area Master form/screen;
• detailed survey data entry forms in addition/updation mode;
• reports (basic reports and summarised) as well as other reports at the
Block and District level
need to be modified so that District can be counted as a member in the area hierarchy.
4.This additional work to make the application software workable (for any district)
should not take more than six months.
Development of the application software using VB6, VB6 SP6, MS Projects includes
separate Area Identification Masters for the following:
i)
Sub-division
ii)
Block/Panchayat Samity
iii)
Thana
iv)
Gram Panchayat
v)
Mouza
vi)
Village
vii)
Gram Sansad
and separate Entry Forms for 12 blocks of the Survey Form.
76
They are as follows:
i)
ii)
iii)
iv)
v)
vi)
vii)
viii)
ix)
x)
xi)
xii)
Area Identification through selection of Sub-division name, Block name, Thana, Gram
Panchayat name, Mouza name, Village name and Gram Sansad from their respective
drop down box. This also includes no. of families, males, females and children residing
in the Gram Sansad.
Infrastructure Available – here also Area IDs need to be selected from available drop
down list boxes, then distance from the respective Gram Sansad to the Available
Infrastructure in KM are to be mentioned.
Land & Swamp Use – Area IDs are to be selected first. Then area of land and swamp
under different uses within the Mouza can be mentioned in hectors.
Domestic Animal/Bird - Area IDs are to be selected from respective drop down boxes.
Then number of domestic animals/birds in the category of male, female and calf/kid in
the Gram Sansad can be entered.
Irrigation & Drinking Water Source - Area IDs are to be selected from respective drop
down boxes. Then number of irrigation and drinking water source in the category of
only irrigation, only drinking water and source under both use within the Gram Sansad
are to be mentioned.
Land under different Crops produce - Area IDs are to be selected from respective drop
down box. Then area of land under Principal Crops, Pulses, Oil seeds, Other Produce,
Vegetables, Masala and Cash Crops categorized into Kharif, Rabi and Summer crop
land within the Mouza are to be entered.
Educational Institutions - Area IDs are to be selected from respective drop down boxes.
Then educational institutions available within the Gram Sansad can be entered in such
categories as, name of school, type of school as per level, type of school as per structure,
number of boys and girls students and number of male and female teachers in it.
Health Indicators – here also Area IDs are to be selected from available list in drop
down boxes. Then data on Health Sub-centre, Homeopathy Centre and Ayurvedic
Centre each categorized into name and number of beds in each centre within the Gram
Panchayat can be entered.
Health Scheme Beneficiaries – select Area IDs from available list in drop down boxes.
Then number of beneficiaries under Family Planning, T.B Control, Leprosy Prevention,
Blindness Prevention, Malaria Prevention and different sub categories of Mother and
Child Welfare Project within the Block can be entered.
Development Project Beneficiaries - here also Area IDs are to be selected from
available list in drop down boxes. Then number of beneficiaries in different
development projects under such categories as name of Project, Amount Allotted for the
project, Actual Expenditure made, Project Start Date, SC, ST, OBC, General and total
beneficiaries within the Gram Sansad can be entered. Each type of beneficiaries are
further sub categorised into Male, Female and total beneficiaries.
Renewable Energy Source – first Area IDs are to be selected from available list in drop
down boxes. Then number of Bio-gas, Solar Energy and Wind Mill within the Block are
to be entered.
Electric Connections – first the required Area IDs are to be selected from respective
drop down boxes. Then number of electrified, previously electrified presently not, nonelectrified and total of them under categories Domestic, Commercial, Cottage Industries,
77
Very Small Industries, Medium Industries and Irrigation in the Gram Sansad are to be
entered.
During system requirement study it was observed that if the distance from Gram Sansad
to nearest Primary School is 0 (in Infrastructure Available), then there must be one entry for
primary school in Educational Institutions within Gram Sansad. However,such cross validations
were not carried out since such inconsistent data were modified later through Edit option.
The following programs were designed and developed in the Application Software:
Design and development of Sub-division Area Master form with necessary template for data
addition/updation and retrieval.;
Design and development of Sub-division Area Master form with necessary class (template) for
data addition and retrieval.;
Design and development of Block/Panchayat Samiti Area Master form with necessary class for
data addition and retrieval;
Design and development of Thana Area Master form with necessary class for data addition and
retrieval.; Design and development of Gram Panchayat Area Master form with necessary class
for data addition and retrieval;.
Design and development of Mouza Area Master form with necessary class for data addition and
retrieval.;
Design and development of Village Area Master form with necessary class for data addition and
retrieval.
Design and development of Gram Sansad Area Master form with necessary class for data
addition and retrieval;
Design and development of Integrated Area Master form with automatic data populating,
addition and updation facilities. The previously developed class files were used with some
changes made to them to incorporate thana name in Block master class file and village name in
Gram Sansad Master class file.
Changes in Area Master hierarchy from Sub-division – Block – Thana – Gram Panchayat –
Mouza – Village – Gram Sansad to Sub-division – Block – Gram Panchayat – Gram Sansad
were made making thana optional and Village independent in database tables and Area Master
form.
Design and development of Detailed Surveyed information entry form with 12 tabs for 12 blocks of
the manual survey form:.
The Area Identity exists in the tab named “Area Identification”; for such other tabs as respective
Gram Sansad, Mouza, Gram Panchayat, Block name gotautomatically displayed. Like when
entry will be made in “Infrastructure Available” tab, name of the respective Gram Sansad will
automatically be shown in the top left corner heading;Similarly, when an entry will be made in
the “Land and Swamp Use” tab, name of the respective Mouza will automatically get displayed
in the top left corner heading
78
Some Operational Features:
a) Area Master selection from drop down box (where changes have been made in area master
hierarchy);
b) no. of families in a Gram Sansad, no. of males, no. of females, no. of children and automatic
calculation of population in a Gram Sansad.;
c) Distance from respective Gram Sansad to 51 infrastructural facilities segregated into 7
distinct groups (Education, Financial Institutions,
Communication, Health, Market/Haat,
P&T and Internet and Office and Service Centre);
d) Area under different Land & Swamp use Mouza wise in hectors;
e) No. of different domestic animals & birds Gram Sansad wise;
f) Gram Sansad wise no. of different source of irrigation and drinking water;
g) Mouza wise land under Principal crops, Pulses, Oil Seeds, Other
Produces, Vegetables,
Spices, Cash crops in Khariff, Rabi and Summer seasons;
h) Educational Institutions in Gram Sansad mentioning details of same of school, type of
institutions, structure of institution as per tier and as per construction, number of Boys & Girls
students and number of male and female teachers;
i) Gram Panchayat wise Health Indicators mentioning Type of Centre (which will any one of
Health Sub-centre, Homoeopathy Centre and Ayurvedic Centre), Name of Centre and No. of
Beds in the center;
j) Block wise Number of Health Beneficiaries which has been segmented into 3 categories viz
a) Beneficiaries under Family Planning Program,
b) Beneficiaries under different disease prevention projects like TB
Control, Blindness Prevention, Malaria Prevention, Leprosy
Prevention; and
c) Mother and Child Welfare Program in which beneficiaries under
DPT, TT, Polio, BCG, Chicken Pox, Anemia, Vitamin ‘A’ deficiency
and Total Sanitation Project are mentioned.
k) Gram Sansad wise Development Project Beneficiaries in which name of Development
Project, allotted amount, actual expenditure made, project start date, beneficiaries under
different caste like SC,ST,OBC general and total of beneficiaries under different caste are
mentioned
l) Block wise number of renewable energy sources like Bio-Gas, Solar Energy and Wind Mill
are mentioned
m) Gram Sansad wise number of Electrical Connections, Disconnections and no-connections in
domestic, commercial, cottage Industries, small scale industries, medium scale industries and
in irrigation are mentioned.
Preparation of Statistical Distributions of a number of Statistical variables:
A number of Application Software Programmes have been developed to process and
generate the statistical distributions of different variables. This has resulted in to develop ment of
105 software programmes.
79
24. A Plan of Future Research Work in the Next Following Years:
The survey process consists of the processes namely (a) sample selection,(b) response
from a sample member,(c) measurement of those agreeing to participate and (d) imputation
for those selected, but unwilling to participate, which are assumed to be stochastic,
Another source of survey error occurs when the sampling frame is imperfect. Imperfection
may be thought of as the presence of certain kinds of matching problem between the set of
frame population and target population elements. The match up of frame and target
populations is established by the indicator variable, which is viewed as deterministic rather
than stochastic, since sampling is often conditioned on the existing sampling frame, thus
making the linkage predetermined and fixed in practice.
Given enough evidence of different sources of error with their interrelationships, it is
important to develop total error model for survey estimates. Attempts to reduce or control
errors of one type may have adverse effects on some other components of total error.
It is interesting to examine the nature of total error model, i.e., those that accommodate
several sources of error in estimating more complex parameters than the total or mean only.
Development of appropriate cost models would also be necessary in the context of total
survey design. The study of non-sampling errors and its effect on different estimators –
linear as well as non-linear would worth studying to the coming days.
80