DOCSLIB.ORG

Disaggregate-Level Disparity in the Incidence of Poverty in Chhattisgarh, India

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Disaggregate-Level Disparity in the Incidence of Poverty in Chhattisgarh, India Agricultural Economics Research Review 2020, 33 (1), 23-33 DOI: 10.5958/0974-0279.2020.00003.8 Disaggregate-level disparity in the incidence of poverty in Chhattisgarh, India Priyanka Anjoy* and Hukum Chandra ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110 012, India *Corresponding author: [email protected] Abstract Poverty is widespread in India, especially in far-flung rural areas, and disparities exist among and between states and social groups. To design programmes that alleviate poverty, policymakers need disaggregated data. The small area estimation (SAE) technique using the hierarchical Bayes model generates representative, micro-level estimates of poverty incidence. The results of a study in Chhattisgarh show that the SAE-based estimates are precise, and can help the government formulate micro-level anti- poverty strategies. Keywords Sustainable Development Goal (SDG), National Sample Survey Office (NSSO), hierarchical Bayes model, small area estimation (SAE) technique, poverty JEL codes C810, C890 India’s economic growth looks strong, but poverty is spatial heterogeneity at higher levels of spatial still deeply rooted and persistent. In 2011–12, the disaggregation, monitor and evaluate parameters within poverty ratio was 25.7% in rural areas and 13.7% in and across local administrative units, and design urban areas; the poor numbered about 269 million programmes and strategies to alleviate inter-personal people, and 216.5 million (around 80%) lived in rural and inter-regional inequalities. areas(https://niti.gov.in/planningcommission.gov.in/ docs/news/pre_pov2307.pdf, Planning Commission). In the social hierarchy, Scheduled Tribes (ST) and A disaggregated analysis finds that urban poverty is Scheduled Castes (SC) are the most marginalized and caused by urban settlements and the assimilation of deprived; Other Backward Classes (OBC) are in migrants (Dubey and Tiwari 2018). Rural poverty may between the SCs and STs and the General category. be caused by hunger, undernourishment, education, Using the countrywide Household Consumer health, or unemployment. The distribution of, or Expenditure Survey (HCES) of the National Sample reduction in, poverty varies by socio-economic Survey Office (NSSO), researchers have tried to category (Thorat and Dubey 2012). capture the poverty level in India by estimating the macro-level poverty ratio or head count ratio (HCR). If policy is to alleviate rural poverty, it needs concrete, Chaudhuri and Gupta (2009) use the HCES 2004–05 disaggregated statistics on rural poverty that lays out data of the NSSO to estimate poverty by district, but a disparities by region and group. National and state- problem in their approach is that sample sizes generated level statistics mask local variations, and with the recent large standard errors in the estimates of several districts. emphasis in India on decentralizing governance and economic planning, the need and demand has increased Chauhan et al. (2016) study the intra- and inter-regional for micro-level, disaggregated statistics on socio- disparities in poverty and inequality using three economic conditions, infrastructure, and institutions. quinquennial rounds of HCES data of NSSO over two Policymakers need such statistics to target social and decades (1993–2012), and Mohanty et al. (2016) report 24 Anjoy P, Chandra H the district-level estimates within the region. Both widely used to estimate a small area mean or total (Fay studies use the traditional direct estimation and and Herriot 1979). The Fay–Herriot model and its synthetic estimation approach in measuring the poverty various extensions have been applied to solve small proportions at disaggregate or local levels, while domain estimation problems under both the frequentist estimating the poverty indicators by fitting the and Bayesian paradigms (Chandra et al. 2011; Chandra regression-based fixed effects model, and therefore 2013; Portar et al. 2014; Pratesi and Salvati 2016; their estimates fail to represent the dissimilarities across Chandra et al. 2017; Chandra et al. 2018b; Anjoy et al. areas. 2018). The small area estimation (SAE) technique considers In most practical applications, however, the target the random area-specific effects and potentially variable is either binary or count, rather than continuous explores the variability between areas (Anjoy et al. (Chaudhuri and Gupta 2009; Chandra et al. 2011), and 2018). This study uses the SAE technique to estimate the aim is to estimate small area proportions or count. disaggregate-level poverty in the state of Chhattisgarh. It would not be appropriate to apply the Fay–Herriot We chose Chhattisgarh because it is the poorest state model in these cases, and a potential alternative is the in India: 40% of the people live below the poverty line generalized linear mixed model, which is the logistic (World Bank 2016); large parts of the state are conflict- normal mixed model for binary data and the log normal affected, or they lie in remote areas, and are excluded mixed model for count data. Again, estimation from the benefits of development (Gebert et al. 2011); problems involving small area predictors for proportion and the progress of the large numbers of the socially or count often ignore the underlying sampling marginalized poor is limited. mechanism, but small area models that do not allow to incorporate the available survey information may Little reliable data is available from districts or smaller produce biased estimates (Liu et al. 2014; Hidiroglou administrative units on poverty or its impact and You 2016; Anjoy et al. 2018).Therefore, we prefer (Chaudhuri and Gupta 2009). Measuring poverty at modeling the survey-weighted estimator. Specifically the micro or local level using the SAE technique will attempt is to model survey-weighted proportions— help policy analysts draw acceptable inferences for poverty proportions (HCR)—through the logistic representative small areas, target interventions and normal mixed model to draw needful small domain develop policy, and decentralize planning and inferences. In this paper, we delineate two alternative monitoring for reducing poverty. models of the logistic normal mixed model under the Bayesian framework, and we apply these to produce Theoretical background reliable estimates of poverty among social groups in The SAE technique links the target variable from a the rural districts of Chhattisgarh by linking data from survey with the auxiliary information available for the HCES 2011–12 of NSSO and the Population small domains or areas from other data sources (such Census 2011. as the record of a census or other administrative source). Assume U denotes the finite population of interest of The technique tries to improve domain predictions by size N, which is partitioned into M disjoint small areas, borrowing strength from related small areas under a and a sample s of size n is drawn from this population mixed modeling framework, and its estimates are much with a given survey design. The set of population units more precise than that of the traditional direct in area i is denoted as U with known size N , such that estimation technique (Chandra et al. 2018a; Rao 2003). i i Based on the availability of unit-level auxiliary and . Where Ni and ni respectively information, small area models are classified into area- being the population and sample size from small area i level models, which use aggregated auxiliary and target (i=1,…,M), the units making up the sample in small information, and unit-level models, which are based on unit-specific variable information. area i are denoted by si, so that and . Let, y be the binary response for unit j (j=1,…,n ) in Obtaining unit-level auxiliary information may involve ij i additional expense, therefore, small area models are small area i. We wish to estimate the small area mostly considered. The Fay–Herriot model is most proportions, . The “small areas” or Disaggregate-level disparity in the incidence of poverty in Chhattisgarh 25 simply “areas” refer to the districts classified by the sampling with districts as strata; villages are the first social groups of Chhattisgarh. The direct survey stage units and households are the ultimate stage units. estimator for the proportion of poor households in area The HCES 2011–12 surveyed 1,435 households in 18 i (i=1,…,M) is defined as, districts of Chhattisgarh. We use the monthly per capita expenditure (MPCE) data of the HCES to estimate the poverty numbers. The estimates of state-wise poverty lines for both rural where wij denotes the survey weight attached to the sampling unit (household) j in the area i. The estimate and urban areas as well as percentage and number of persons those below poverty line are also specified of variance of piw can be expressed as, based on MPCE. The variable of interest at the unit (household) level is binary; if a household is poor, the value is 1 or 0, and if a household is not poor the value The basic structure of the logistic normal mixed model is 0. A household is categorized as poor if its MPCE is has two components: the sampling model for the direct below the state’s poverty line. The Planning survey estimates, and the linking model to incorporate Commission (http://planningcommission.nic.in/news/ area-specific auxiliary variables through a linear press_pov2307.pdf ) set the poverty line for rural areas regression framework. For estimating the small area in Chhattisgarh at INR 738 for the year 2011–12. proportions P , the sampling model for p is i iw The parameter of interest is the HCR, or proportion of piw = Pi + ei; i = 1,...,M poor households, by district and category. Hereabouts Table 1 presents the sample sizes of social groups in where, ei’s are independent sampling error assumed to σ2 the HCES 2011–12. The sample size ranges from 8 to have zero mean and known sampling variance ei. The linking model of P can be written as, 160, and the average of districts is 80; further i categorization into social groups makes the sample Tβ logit(Pi) = xi + vi; i=1,...,M sizes too small, even zero for certain caste groups.
Load more

Recommended publications
  • Year 18 September 1964 Maladies Quarantenaires
    Relevé épidém. hebd. ) 1964, 39, 453-464 N** 38 Wkly Epidem. Ree. | ORGANISATION MONDIALE DE LA SANTÉ WORLD HEALTH ORGANIZATION GENÈVE GENEVA RELEVÉ ÉPIDÉMIOLOGIQUE HEBDOMADAIRE WEEKLY EPIDEMIOLOGICAL RECORD Notifications et infoimations se rapportant à l’application Notifications under and information on the application of the du Règlement sanitaire international et notes relatives à la International Sanitary Regulations and notes on current incidence fréquence de certaines maladies of certain diseases Service de la Quarantaine internationale Internationai Quarantine Service Adresse télégraphique; EPDDNATIONS, GENÈVE Telegraphic address: EPIDNATIONS, GENÈVE 18 SEPTEMBRE 1964 39® ANNÉE — 39«* YEAR 18 SEPTEMBER 1964 MALADIES QUARANTENAIRES ■ QUARANIÎNABLE DISEASES Territoires infectés an 17 septembre 1964 ■ infected areas as on 17 September 1964 Notifications reçues aux termes du Règlement sanitaire international Notifications received under the International Sanitary Regulations relating concernant les circonscriptions infectées ou les territoires où la présence to infected local areas and to areas in which the presence of quarantinable de maladies qiuirantcnaires a été signalée (voir page 414). diseases was reported (see page 414). ■ « Circonscriptions ou territoires notifiés aux termes de Tarticle 3 à la ■ = Areas notified under Article 3 on the date indicated. date donnée. Autres territoires où la présence de maladies quarantenaires a été notifiée Other areas in which the presence of quarantinable diseases was notified aux termes des articles 4, 5 et 9 a): under Articles 4, 5 and 9 (a): A = pendant la période indiquée sous le nom de chaque maladie; A =: during the period indicated under the heading of each disease; B — antérieurement à la période indiquée sous le nom de chaque maladie; B = prior to the period indicated under the heading of each disease; • = territoires nouvellement infectés.
    [Show full text]
  • “Being Neutral Is Our Biggest Crime”
    India “Being Neutral HUMAN RIGHTS is Our Biggest Crime” WATCH Government, Vigilante, and Naxalite Abuses in India’s Chhattisgarh State “Being Neutral is Our Biggest Crime” Government, Vigilante, and Naxalite Abuses in India’s Chhattisgarh State Copyright © 2008 Human Rights Watch All rights reserved. Printed in the United States of America ISBN: 1-56432-356-0 Cover design by Rafael Jimenez Human Rights Watch 350 Fifth Avenue, 34th floor New York, NY 10118-3299 USA Tel: +1 212 290 4700, Fax: +1 212 736 1300 [email protected] Poststraße 4-5 10178 Berlin, Germany Tel: +49 30 2593 06-10, Fax: +49 30 2593 0629 [email protected] Avenue des Gaulois, 7 1040 Brussels, Belgium Tel: + 32 (2) 732 2009, Fax: + 32 (2) 732 0471 [email protected] 64-66 Rue de Lausanne 1202 Geneva, Switzerland Tel: +41 22 738 0481, Fax: +41 22 738 1791 [email protected] 2-12 Pentonville Road, 2nd Floor London N1 9HF, UK Tel: +44 20 7713 1995, Fax: +44 20 7713 1800 [email protected] 27 Rue de Lisbonne 75008 Paris, France Tel: +33 (1)43 59 55 35, Fax: +33 (1) 43 59 55 22 [email protected] 1630 Connecticut Avenue, N.W., Suite 500 Washington, DC 20009 USA Tel: +1 202 612 4321, Fax: +1 202 612 4333 [email protected] Web Site Address: http://www.hrw.org July 2008 1-56432-356-0 “Being Neutral is Our Biggest Crime” Government, Vigilante, and Naxalite Abuses in India’s Chhattisgarh State Maps........................................................................................................................ 1 Glossary/ Abbreviations ..........................................................................................3 I. Summary.............................................................................................................5 Government and Salwa Judum abuses ................................................................7 Abuses by Naxalites..........................................................................................10 Key Recommendations: The need for protection and accountability..................
    [Show full text]
  • * RPO Guwahati Covers Five Other North-Eastern States Also at Present
    * RPO Guwahati covers five other North-Eastern States also at present. **RPO Chandigarh covers parts of Punjab and Haryana. ***RPO Delhi covers parts of Haryana. @RPO Kolkata covers Sikkim and Tripura. ANNEXURE-III STATE-WISE LIST OF POST OFFICE PASSPORT SEVA KENDRAS (POPSKs) S.No. Locations State/UT Passport Office 1 Anantpur Andhra Pradesh Vijayawada 2 Bapatla Andhra Pradesh Vijayawada 3 Chittoor Andhra Pradesh Vijayawada 4 Gudivada Andhra Pradesh Vijayawada 5 Guntur Andhra Pradesh Vijayawada 6 Hindupur Andhra Pradesh Vijayawada 7 Kadappa Andhra Pradesh Vijayawada 8 Kodur Andhra Pradesh Vijayawada 9 Kurnool Andhra Pradesh Vijayawada 10 Nandyal Andhra Pradesh Vijayawada 11 Narasaraopet Andhra Pradesh Vijayawada 12 Nellore Andhra Pradesh Vijayawada 13 Ongole Andhra Pradesh Vijayawada 14 Amalapuram Andhra Pradesh Visakhapatnam 15 Eluru Andhra Pradesh Visakhapatnam 16 Kakinada Andhra Pradesh Visakhapatnam 17 Rajamundry Andhra Pradesh Visakhapatnam 18 Srikakulam Andhra Pradesh Visakhapatnam 19 Vizianagaram Andhra Pradesh Visakhapatnam 20 Yelamanchili Andhra Pradesh Visakhapatnam 21 Changlang Arunachal Pradesh Guwahati 22 Khonsa Arunachal Pradesh Guwahati 23 Barpeta Assam Guwahati 24 Dhubri Assam Guwahati 25 Dibrugarh Assam Guwahati 26 Goalpara Assam Guwahati 27 Golaghat Assam Guwahati 28 Jorhat Assam Guwahati 29 Karbi Anglong Assam Guwahati 30 Karimganj Assam Guwahati 31 Kokrajhar Assam Guwahati 32 Mangaldoi Assam Guwahati 33 Nawgong Assam Guwahati 34 North Lakhimpur Assam Guwahati 35 Silchar Assam Guwahati 36 Tezpur Assam Guwahati 37 Tinsukia
    [Show full text]
  • List of NSH / ICH Intra Circle / L2 Hub Circle Name NSH / L1 Hub Name Name Telangana WARANGAL NSH KARIMNAGAR ICH Khammam ICH
    List of NSH / ICH Intra Circle / L2 Hub Circle Name NSH / L1 Hub Name Name Telangana WARANGAL NSH KARIMNAGAR ICH Khammam ICH Telangana HYDERABAD NSH NIZAMABAD ICH NALGONDA ICH ANDHRA PRADESH KURNOOL NSH ANANTAPUR ICH ANDHRA PRADESH TIRUPATHI NSH Cuddapah ICH NELLORE ICH ANDHRA PRADESH VIJAYAWADA NSH GUNTUR ICH ONGOLE ICH ELURU ICH ANDHRA PRADESH VISAKHAPATNAM NSH RAJAHMUNDRY ICH SRIKAKULAM ICH Vizianagaram ICH ASSAM Dibrugarh NSH Jorhat ICH Tinsukia ICH ASSAM GUWAHATI NSH NAGAON SILCHAR TEZPUR BIHAR BARAUNI NSH – BIHAR MUZAFFARPUR NSH – BIHAR PATNA NSH CHHAPRA KIUL BHAGALPUR BIHAR GAYA NSH – CHATTISGARH RAIPUR NSH BILASPUR RAIGARH DURG JAGDALPUR ICH DELHI DELHI NSH – GUJRAT AHMEDABAD NSH HIMATNAGAR MEHSANA PALANPUR BHAVNAGAR BHUJ Dhola ICH GUJRAT RAJKOT NSH JAMNAGAR JUNAGADH SURENDRANAGAR GUJRAT SURAT NSH VALSAD GUJRAT VADODARA NSH BHARUCH GODHARA ANAND HARYANA GURGAON NSH FARIDABAD ICH REWARI ICH HARYANA KARNAL NSH – HARYANA ROHTAK NSH HISAR ICH HARYANA AMBALA NSH SOLAN MANDI HIMACHAL PRADESH SHIMLA NSH SOLAN ICH HIMACHAL PRADESH PATHANKOT NSH KANGRA HAMIRPUR JAMMUKASHMIR JAMMU NSH – JAMMUKASHMIR SRINAGAR NSH – JHARKHAND JAMSHEDPUR NSH JHARKHAND RANCHI NSH DALTONGANJ HAZARIBAGH ROAD JHARKHAND DHANBAD NSH B. DEOGHAR KARNATAKA BENGALURU NSH BALLARI ICH TUMAKURU ICH KARNATAKA BELAGAVI NSH – KARNATAKA KALABURAGI NSH RAICHUR ICH KARNATAKA HUBBALLI-DHARWAD NSH BAGALKOT ICH KUMTA ICH VIJAYAPURA ICH KARNATAKA MANGALURU NSH – KARNATAKA MYSURU NSH – KARNATAKA ARSIKERE NSH – KERALA KOCHI NSH Kottayam ICH KERALA THRISSUR PALAKKAD ICH KERALA TRIVANDRUM
    [Show full text]
  • Durg District, Chhattisgarh
    For official use GOVERNMENT OF INDIA MINISTY OF WATER RESOURCES Nawgarh CENTRAL GROUND WATER BOARD Bemetara Saja Berla Dhamdha GROUND WATER BROCHURE OF DURG DISTRICT, CHHATTISGARH 2012 Durg -2013 Patan Gunderdehi Dondi Lohara Balod Gurur Dondi Regional Director North Central Chhattisgarh Region, Reena Apartment, IInd Floor, NH-43, Pachpedi Naka, Raipur-492001 (C.G.) Ph. No. 0771-2413903, 2413689 E-mail: rdnccr- [email protected] DISTRICT AT A GLANCE DURG DISTRICT) By J.R.Verma, Scientist “B” 1. GENERAL INFORMATION i) Geographical area (Sq. km) 8701.80 ii) Administrative Divisions (As on 2009) a) Number of Tehsil/ Block 11/12 b) Number of Panchayat/ Villages 998/1176 iii) Population as on 2011 Census 1316140 iv) Annual Normal Rainfall (IMD,2008) 1142 mm v) Average Annual Rainfall (1994-12) 1055.56mm 2. GEOMORPHOLOGY i) Major Physiographic Units Two; Chhattisgarh Plain ii) Major Drainages Mahanadi, Seonath. 3. LAND USE (Sq. km) As on 2009 i) Forest Area 709.11 ii) Net Area Sown 5469.61 iii) Double cropped Area 2392.76 4. MAJOR SOIL TYPES Red & yellow soil, Black soil 5. AREA UNDER PRINCIPAL CROPS, in Rice: 2325.95, Pulses:555.28 Sq. km (As on 2011) Wheat: 186.90, 6. IRRIGATION BY DIFFERENT SOURCES (2011) (Areas in Sq. km. and Numbers of Structures) i) Dugwells 1458/16.69 ii) Tubewells/Borewells 33938/917.94 iii) Canals 296/1272.24(1788 km) iv) Ponds 306/27.29 v) Other sources 126.15 vi) Net Irrigated Area 2360.31 vii) Gross Irrigated Area 3174.33 7. NUMBERS OF GROUND WATER MONITORING WELLS OF CGWB (As on 31.3.2012) i) No of Dugwells 39 ii) No of Piezometers 25 8.
    [Show full text]
  • Plundered Histories, Forgotten Terrains & Entangled People
    Plundered Histories, Forgotten Terrains & Entangled People ** (A critical search on Planning and Commission on Mining) Goldy M. George I. Conflict in Interest: Planning, Commissioning and Development In the early 1950s India embarked on a program of planned industrial development. Borrowing planning concepts from the Soviet Union, the government tried to stimulate 1 development through massive investment in the public sector, imposing a system of tight controls on foreign ownership of capital in India and playing a highly interventionist role in all aspects of economic policy. The private sector was allowed to continue to operate in agriculture and in a wide range of ‘non-essential’ industrial sectors.1 The steady economic growth of industries with active support from the state machinery is directly proportional to the unchecked exploitation of masses. Several instances of eviction, evacuation etc. had put before a long array of questions on the very edifice of mining and development. Marginalised communities such as Dalits, Adivasis, women, working class, etc. are the first victims of this. Though during the independence struggle “factory to the workers” prominently came on to the national agenda, nowhere in India it was implemented in the post independence era. Resultant displacement, migration, repercussion of workers, loss of land and livelihood, pilfering state revenue, forest resources, etc. has outgrown to monstrous magnitude. Decolonisation of erstwhile colonies invariably saw the elite take control of political power. Naturally they were inclined to capitalism preferring to inherit the colonial state – its laws, structure and character – rather than to transform it fundamentally in ways to respond to the most urgent needs of the oppressed sections.
    [Show full text]
  • DSO List of Chattisgarh
    DSO list of Chattisgarh Sl.no Distt Name Designati Phone Mobile Address Email Email2 on 1 Koriya ShriC.Kerketta D.P.S.O. 07836‐ 9165836983 Room No.9‐10, deskoriya.cg@nic. topposanjay@gmail. (Baikunthpur 234450, Collectorate in com, ) 233405 premises, Koriya nikhilbhardwaj343@ 497335 gmail.com 2 Surguja Shri Suresh D.P.S.O. 07774‐ 9754658788 Collectorate ‐ sureshsingh.jkd@gm (Ambikapur) Kumar Singh 220422, premises, ail.com 220421 Surguja 497001 3 Jashpur Shri S. D.P.S.O. 07763‐ 9479944643 Collectorate dpsojashpur@gm shrivastava 220648 premises, ail.com Jashpur 496331 4 Raigarh Shri S.R. D.P.S.O. 07762‐ 9827960973 Collectorate dpsoraigarh@gm [email protected] Bhaskar 223241, premises, ail.com .in 223044 Raigarh 496001 5 Korba Mrs. D.P.S.O. 07759‐ 9977737783 Collectorate ‐ [email protected] MadhuSahu 227875, premises, Korba om 222886 495677 6 JanjgirCham Shri S. K. D.P.S.O. 07817‐ 9826769460 Collectorate ‐ [email protected] pa Kashyap 222773, premises, 224103 JanjgirChampa 7 Bilaspur Smt. D.P.S.O. 07752‐ 9907901797 Collectorate dpsobilaspur@gm PayalPandey 222251, premises, ail.com 223993 Bilaspur 495001 8 Kabirdham Smt. D.P.S.O. 07741‐ 8251029531 Collectorate dpsokwd@gmail. sadarrsuankurre@ya UshaKiran 232306 premises, com hoo.com Sharma Kabirdham 491995 9 Rajnandgaon Shri S.K. Dutta D.P.S.O. 07744‐ 9893310668 Room No.‐19, desrajnandgaon.c [email protected] 224608 Collectorate [email protected] om premises, Rajnandgaon 491441 10 Durg Mis. Prachi D.P.S.O. 0788‐ 9827402874 Collectorate dpsodurg@yahoo prachiemishra@india Mishra 2323137 premises, Durg .in times.com 491001 11 Raipur Shri B.L. D.P.S.O.
    [Show full text]
  • Bastar District Chhattisgarh 2012-13
    For official use only Government of India Ministry of Water Resources Central Ground Water Board GROUND WATER BROCHURE OF BASTAR DISTRICT CHHATTISGARH 2012-13 Keshkal Baderajpur Pharasgaon Makri Kondagaon Bakawand Bastar Lohandiguda Tokapal Jagdalpur Bastanar Darbha Regional Director North Central Chhattisgarh Region Reena Apartment, II Floor, NH-43 Pachpedi Naka, Raipur (C.G.) 492001 Ph No. 0771-2413903, 2413689 Email- [email protected] GROUND WATER BROCHURE OF BASTAR DISTRICT DISTRICT AT A GLANCE I Location 1. Location : Located in the SSE part of Chhattisgarh State Latitude : 18°38’04”- 20°11’40” N Longitude : 81°17’35”- 82°14’50” E II General 1. Geographical area : 10577.7 sq.km 2. Villages : 1087 nos 3. Development blocks : 12 nos 4. Population : 1411644 Male : 697359 Female : 714285 5. Average annual rainfall : 1386.77mm 6. Major Physiographic unit : Predominantly Bastar plateau 7. Major Drainage : Indravati , Kotri and Narangi rivers 8. Forest area : 1997.68 sq. km ( Reserved) 390.38 sq. km ( Protected) 2588.75 sq. km (Revenue ) Total – 4976.77 sq.km. III Major Soil 1) Alfisols : Red gravelly, red sandy &red loamy 2) Ultisols : Lateritic,Red & yellow soil IV Principal crops 1) Rice : 2024 ha 2) Wheat : 667ha 3) Maize : 2250 ha V Irrigation 1) Net area sown : 315657 sq. km 2) Net and gross irrigated area : 9592 ha a) By dug wells : 2460 no (758 ha) b By tube wells : 1973 no (2184ha) c) By tank/Ponds : 102 no (1442ha) d) By canals : 15 no ( 421 ha) e) By other sources : 4391 ha VI Monitoring wells (by CGWB) 1) Dug wells
    [Show full text]
  • Statistical Report General Election, 1998 The
    STATISTICAL REPORT ON GENERAL ELECTION, 1998 TO THE LEGISLATIVE ASSEMBLY OF MADHYA PRADESH ELECTION COMMISSION OF INDIA NEW DELHI Election Commission of India – State Elections, 1998 Legislative Assembly of Madhya Pradesh STATISCAL REPORT ( National and State Abstracts & Detailed Results) CONTENTS SUBJECT Page No. Part – I 1. List of Participating Political Parties 1 - 2 2. Other Abbreviations And Description 3 3. Highlights 4 4. List of Successful Candidates 5 - 12 5. Performance of Political Parties 13 - 14 6. Candidate Data Summary 15 7. Electors Data Summary 16 8. Women Candidates 17 - 25 9. Constituency Data Summary 26 - 345 10. Detailed Results 346 - 413 Election Commission of India-State Elections, 1998 to the Legislative Assembly of MADHYA PRADESH LIST OF PARTICIPATING POLITICAL PARTIES PARTYTYPE ABBREVIATION PARTY NATIONAL PARTIES 1 . BJP Bharatiya Janata Party 2 . BSP Bahujan Samaj Party 3 . CPI Communist Party of India 4 . CPM Communist Party of India (Marxist) 5 . INC Indian National Congress 6 . JD Janata Dal (Not to be used in General Elections, 1999) 7 . SAP Samata Party STATE PARTIES 8 . ICS Indian Congress (Socialist) 9 . INLD Indian National Lok Dal 10 . JP Janata Party 11 . LS Lok Shakti 12 . RJD Rashtriya Janata Dal 13 . RPI Republican Party of India 14 . SHS Shivsena 15 . SJP(R) Samajwadi Janata Party (Rashtriya) 16 . SP Samajwadi Party REGISTERED(Unrecognised ) PARTIES 17 . ABHM Akhil Bharat Hindu Mahasabha 18 . ABJS Akhil Bharatiya Jan Sangh 19 . ABLTC Akhil Bhartiya Lok Tantrik Congress 20 . ABMSD Akhil Bartiya Manav Seva Dal 21 . AD Apna Dal 22 . AJBP Ajeya Bharat Party 23 . BKD(J) Bahujan Kranti Dal (Jai) 24 .
    [Show full text]
  • Raigarh, Chhattisgarh
    District Profile Raigarh, Chhattisgarh Raigarh is a major district in the state of Chhattisgarh in India. The headquarters of the district is located in the city of Raigarh. The total area of the district is 7086 sq. km. Raigarh district is divided into 10 Tehsils/ CD Blocks: Raigarh, Pusaur, Baramkela, Kharsiya, Gharghoda, Tamnar, Lallunga, Dharamjaigarh, Sa- rangarh. DEMOGRAPHY As per Census 2011, the total population of Raigarh is 14,93,984 which accounts for 5.85 percent of the total population of State. The percentage of urban population in Raigarh is 16.49 percent, which is lower than the state average of 23.24 percent. Out of the total population there are 750,278 males and 743,706 females in the district. This gives a sex ratio of 991.24 females per 1000 males. The decadal growth rate of population in Chhattisgarh is 22.59 percent, while Raigarh reports a 18.02 percent decadal increase in the population. The district population density is 211 in 2011. The Scheduled Caste population in the district is 15 percent while Scheduled Tribe comprises one third of the total population. LITERACY The overall literacy rate of Raigarh district is 73.26 percent while the male & female literacy rate is 83.49 and 63.02 percent respectively. At the block level, a considerable variation is noticeable in male-female literacy rate. Dharamjaigarh has the lowest literacy rate 62.43 percent, with 74.09 per- cent men and 50.90 percent women being literate. Raigarh block, subse- quently, has the highest literacy rates– among both males and females.
    [Show full text]
  • Recognition of Community Rights Under FRA in Madhya Pradesh and Chhattisgarh
    Recognition of Community Rights under FRA in Madhya Pradesh and Chhattisgarh ChallengesChallenges andand WaysWays ForwardForward Study conducted by Samarthan Supported by UNDP Objectives • Review implementation of the Act in relation to the provisions of Community Rights • Understand the reasons for low or high claims and rejection of claims • Identify constraints and explore ways forward Methodology • 10 Districts ( 6 from MP and 4 from CG) • Selected on Community Claims and tribal population basis • 2 blocks with highest number of community claim • 12 villages from each block (1 where community claim was made and 1 where community claim was not made from each of below categories) – High Tribal Population (>60%) – Low Tribal Population (<20%) – Near Block HQ (Within 10 – 15 Kms) – Far from Block HQ (>40 Kms) – Near Forest (<10 Kms) – Far from forest (>20 Kms) Selected Districts – MP& CG Sample of Respondents Respondent Category Target Sample in MP CG Total One Unit Community Members 10 (Each Village) 720 480 1200 FRC Members 5 (Each Village) 360 240 600 Panchayat Secretary 1 (Each Village) 72 48 120 Official Who Conducted Gram Sabha 1 (Each Village) 72 48 120 SDLC Members (Non – Official) 3 (Each Sub-division) 36 24 60 SDLC Members (Officials) 3 (Each Sub-division) 36 24 60 DLC Members (Non-official) 3 (Each District) 18 12 30 DLC Members (Official) 3 (Each District) 18 12 30 Methods Used • Semi structured questionnaire • Focused Group Discussions • Participatory Appraisals • Interviews with stakeholders • Case Studies Defining Community
    [Show full text]
  • Department Related Parliamentary Standing Committee (RS)
    Department Related Parliamentary Standing Committee (RS) Committee on HUMAN RESOURCES DEVELOPMENT Rajya Sabha Member : 6 Vacant : 4 S.NO. Member Name State Party 1 Shri Vishambhar Prasad Nishad Uttar Pradesh Samajwadi Party 2 Shri Derek O Brien West Bengal ALL INDIA TRINAMOOL CONGRESS 3 Shri Sasmit Patra Odisha Biju Janata Dal 4 Dr. Vinay P. Sahasrabuddhe Maharashtra Bharatiya Janata Party 5 Shri Gopal Narayan Singh Bihar Bharatiya Janata Party 6 Shri Akhilesh Prasad Singh Bihar Indian National Congress Lok Sabha Member : 21 Vacant (LS) : 0 S.NO. Member Name State Constituency Party 1 Shri Rajendra Agrawal Uttar Pradesh Meerut BJP 2 Dr. Dhal Singh Bisen Madhya Pradesh Balaghat BJP 3 Shri Santokh Singh Chaudhary Punjab Jalandhar INC 4 Shri Lavu Sri Krishna Devarayalu Andhra Pradesh Narasaraopet YSR Cong.Party 5 Shri Sangamlal Kadedin Gupta Uttar Pradesh Pratapgarh BJP 6 Shri S. Jagathrakshakan Tamil Nadu Arakkonam DMK 7 Shri Sadashiv Kisan Lokhande Maharashtra Shirdi SS Dr. Jaisiddeshwar Shivacharya 8 Maharashtra Solapur BJP Mahaswamiji 9 Shri Asit Kumar Mal West Bengal Bolpur AITC 10 Ms. Chandrani Murmu Odisha Keonjhar BJD 11 Shri Balak Nath Rajasthan Alwar BJP 12 Dr. T. R. Paarivendhar Tamil Nadu Perambalur DMK 13 Shri Chandeshwar Prasad Bihar Jahanabad JD(U) 14 Shri T.N. Prathapan Kerala Thrissur INC 15 Shri Ratansinh Magansinh Rathod Gujarat Panchmahal BJP 16 Shri Jagannath Sarkar West Bengal Ranaghat BJP 17 Dr. Arvind Kumar Sharma Haryana Rohtak BJP 18 Shri Vishnu Dutt Sharma Madhya Pradesh Khajuraho BJP Bhiwani- 19 Shri Dharambir Singh Haryana BJP Mahendragarh 20 Shri S. Venkatesan Tamil Nadu Madurai CPI(M) 21 Shri Ashok Kumar Yadav Bihar Madhubani BJP Department Related Parliamentary Standing Committee (RS) Committee on INDUSTRY Rajya Sabha Member : 9 Vacant : 1 S.NO.
    [Show full text]