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Growth Week 2011: Country Session 4 – India-Bihar

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  • 1. Role of ICTin Improving the Quality of School Educationin Bihar Chirashree Das Gupta and Haridas KPN Presentation for IGC Growth Week: 19-21 September, 2011
  • 2.  Background Method Design A Few Preliminary Results Flagging Issues in Programme Delivery
  • 3. Identification of Hard Spots Category Sample Distribution Percentage of sample No hard spot 1 0 One Subject 3237 82 More than one subject 327 8 Missing data 264 7 Inconsistent response 131 3 Total 3960 100
  • 4. Identification of Hard Spots-Subject wise Subjects Number Percentage Percentage of Percentage Percentage Percentage of of Sample Students of students of Boys of Girls Students having having having difficulty only difficulty in Difficulty in one other in subject or more along with1-Hindi 92 2 54 46 54 462-Urdu 150 4 82 18 54 463-Eng 1026 26 75 25 42 584-Sans 2070 52 88 12 46 545-Maths 388 10 76 24 35 656-Science 149 4 63 37 49 517- Other 77 2 … … 55 45
  • 5. Role of e-Samarth in Addressing Hard Spots Perception Gaps on Role of e-Samarth Performance Analysis (Exam score) -Perception/ Performance School Authority Teachers Comparison of 3 year exam scoresIncreased Interest in learning 88 76Increase in attention span 76 65Increase in classroomparticipation 88 48Increase in classroom interaction 68 63Correct answers/response 72 46More clarity on topics taughtthrough CDS 60 39Improved examinationperformance 64 44 No significant improvementImproved understanding of thesubject 56 41Increase in enrolment (studentschanging schools) 15 Note: All figures are in percentages
  • 6. Role of e-Samarth in Addressing Hard SpotsStatus of Trained Teachers in e-samarth Trained under CAL Trained Outside/ self trainedTrained Teachers 85 15Training HoursNot sure 715 hours 725 hours 430 hours 5435 hours 1340 hours 250 hours 11126 hours 2 Note: All figures are in percentages
  • 7. Role of e-Samarth in identifying Hard SpotsStatus of Trained Teachers in e-samarth Usage of Computer (Days in a week) 7 11 6 20 5 9 4 13 3 4 2 9 1 2 Sometimes 22 Never 11 Usage of Computer/Kyan (computer aid) for Teaching Yes 43 No 57 Note: All figures are in percentages
  • 8. e-Samarth and PerformanceAnalysis of exam scores
  • 9. e-Samarth and PerformanceAnalysis of exam scores
  • 10. Some Preliminary Observations on Operational Status of e-Samarth School Level Operational Status of e-Samarth Type of modelClassification Total BEP BOOT ILFSCAL programme operational on paper 1 2 13 16CAL programme not operational 2 5 2 9Total 3 7 15 25CAL programme operational based on 1 2 11 14observations on the day of visit
  • 11. Some Preliminary Observations on Operational Status of e-Samarth District wise Operational Status of e-Samarth Districts Bhojpur Muzaffarpur Samastipur Saran Gaya Total BEP 1CAL programme operational on BOOT 2 16 paper ILFS 1 5 3 3 1 BEP 1 1CAL programme BOOT 1 2 1 1 9not operational ILFS 1 0 0 1 Total 5 5 5 5 5 25CAL programme BEP 1 operational based on BOOT 2 14observations on the day of visit ILFS 1 5 2 2 1
  • 12. Thank You
  • 13. Karthik Muralidharan & Nishith Prakash Introduction Motivation BackgroundCycling to School: Increasing High Policy GoalsSchool Enrollment for Girls in Bihar Empirical Strategy Methodology Data Data Karthik Muralidharan & Nishith Prakash Thank You Thank You University of California-San Diego & Cornell UniversitySeptember 19, 2011 / IGC Growth Week - LSE
  • 14. Motivation Karthik Muralidharan & Nishith Prakash Introduction Increasing school attainment of girls is one of the Motivation Millennium Development Goals Background Policy Improving female education directly contributes to Goals “Inclusive Growth”: Empirical Strategy Growth - by increasing human capital of labor Methodology Data force Data Inclusive - by allowing people to participate in the Thank You growth process Thank You Returns to schooling is approximately 7-10% in India (Duraisamy, 2000; Agrawal, 2011) Despite high economic returns to education in developing countries, there are: Low school completion rates High drop-out rates Students absenteeism
  • 15. Education in Bihar Karthik Muralidharan & Nishith Prakash Introduction Motivation Large gender gap in schooling in developing Background countries (for e.g. enrollment, attendance, Policy attainment, dropout etc.) Goals Empirical Strategy In rural Bihar, currently 63% girls are enrolled Methodology Data against 81% boys in the age category 10–14. For Data the age category 15–19, only 27% girls are Thank You admitted against 40% boys (Azam, 2011) Thank You In urban Bihar, currently 81% girls are enrolled against 86% boys in the age category 10–14. For the age category 15–19, only 55% girls are admitted against 57% boys (Azam, 2011) Low attendance and attainment among girls in Bihar
  • 16. Policy Intervention Karthik Muralidharan & Nishith Prakash Introduction Motivation Background In April 2006, the Government of Bihar headed by Policy the Chief Minister Mr. Nitish Kumar decided to Goals provide bicycles to all girl students studying in Empirical Strategy Methodology Class IX & X Data Data Approximately Rs. 2000 (45 USD) per girl student Thank You was allocated to purchase bicycles Thank You This scheme was called “Mukhyamantri Balika Cycle Yojana” and later “Mukhyamantri Cycle Yojana” Policy Questions Does Cycle Scheme increase girls enrollment? Does Cycle Scheme affect learning outcomes?
  • 17. Policy Intervention Karthik Muralidharan & Nishith Prakash Introduction Motivation Background In April 2006, the Government of Bihar headed by Policy the Chief Minister Mr. Nitish Kumar decided to Goals provide bicycles to all girl students studying in Empirical Strategy Methodology Class IX & X Data Data Approximately Rs. 2000 (45 USD) per girl student Thank You was allocated to purchase bicycles Thank You This scheme was called “Mukhyamantri Balika Cycle Yojana” and later “Mukhyamantri Cycle Yojana” Policy Questions Does Cycle Scheme increase girls enrollment? Does Cycle Scheme affect learning outcomes?
  • 18. Outcome Measures Karthik Muralidharan & Nishith Prakash Introduction Motivation Enrollment Background Does this reduce gender inequality? Policy Does this reduce gap across caste and religion? Goals Empirical Strategy Methodology Learning outcomes (for e.g. share of students Data passing 10th grade, passing with 3rd division, 2nd Data division, 1st division, distinction) Thank You Thank You Increased enrollment may reduce mean scores, but may increase absolute number of girls at higher levels of attainment Possibility of a follow-up survey: Female Empowerment- Use of bicycles has been considered a sign of self-confidence and empowerment in India
  • 19. Difference in Difference Approach Karthik Muralidharan & Nishith Prakash Introduction Motivation Background Policy Difference in Difference Approach: Goals Single Difference = [(Enroll)Girls Post − (Enroll)Girls ] Pre Empirical Strategy Boys Boys Methodology D-D Bihar = A = [(Enroll)Girls − Post (Enroll)Girls ] − [(Enroll)Post Pre − (Enroll)Pre ] This will control for changes in income, tastes and government policies that was Data targeted towards school going children Data Thank You Thank You Triple Difference Approach: Boys Boys D-D Jharkhand = B = [(Enroll)Girls Post − (Enroll)Girls ] Pre − [(Enroll)Post − (Enroll)Pre ] D-D-D = [A - B] This will control for remaining bias from differential time trend Jharkhand is particularly compelling as it was part of Bihar till 2000 Boarder districts share similar socio-economic conditions
  • 20. Map of Bihar Karthik Muralidharan & Nishith Prakash Introduction Motivation Background Policy Goals Empirical Strategy Methodology Data Data Thank You Thank You
  • 21. Difference in Difference Design Karthik Muralidharan & Nishith Prakash Start with D-D type strategy Introduction Motivation Background Policy Goals Empirical Strategy Methodology Data Data Thank You Thank You Enrollment - Boys C D Enrollment/Test Scores B IMPACT A Comparison group trend Enrollment-Girls Pre- Cycle Scheme Post- Cycle Scheme Year = 2006/07 Year = 2009/10
  • 22. Enrollment in Bihar: Class 9 Karthik Muralidharan & Nishith Prakash Introduction Motivation Background Policy Goals Empirical Strategy Methodology Data Data 240,000 Thank You 220,000 Thank You 200,000 180,000 160,000 Enrollment 140,000 120,000 100,000 80,000 60,000 40,000 20,000 0 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 Enrollment (Class 9) Boys Enrollment (Class 9) Girls
  • 23. Enrollment in Bihar: Class 10 Karthik Muralidharan & Nishith Prakash Introduction Motivation Background Policy Goals Empirical Strategy Methodology Data Data 200,000 Thank You 180,000 Thank You 160,000 140,000 120,000 Enrollment 100,000 80,000 60,000 40,000 20,000 0 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 Enrollment (Class 10) Boys Enrollment (Class 10) Girls
  • 24. Enrollment in Bihar & Jharkhand: Class 9 Karthik Muralidharan & Nishith Prakash Introduction Motivation Background Policy Goals 240,000 220,000 Empirical Strategy 200,000 Methodology 180,000 Data 160,000 Data 140,000 Enrollment 120,000 Thank You Thank You 100,000 80,000 60,000 40,000 20,000 0 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 Enrollment (Class 9) Boys_JH Enrollment (Class 9) Girls_JH Enrollment (Class 9) Boys_Bihar Enrollment (Class 9) Girls_Bihar
  • 25. Enrollment in Bihar & Jharkhand: Class 10 Karthik Muralidharan & Nishith Prakash Introduction Motivation Background 200,000 Policy Goals 180,000 Empirical Strategy 160,000 Methodology 140,000 Data 120,000 Data Enrollment 100,000 Thank You Thank You 80,000 60,000 40,000 20,000 0 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 Enrollment (Class 10) Boys_JH Enrollment (Class 10) Girls_JH Enrollment (Class 10) Boys_Bihar Enrollment (Class 10) Girls_Bihar
  • 26. Data work so far Karthik Muralidharan & Nishith Prakash Introduction Ministry of HRD, Government of Bihar Motivation We have enrollment data for class 9 and 10 from Background 26 districts (2 incomplete) in Bihar, and 9 districts Policy Goals (3 incomplete) in Jharkhand from 2002/03 to Empirical Strategy 2009/10 Methodology District names in Bihar that have not sent Data data: Aurangabad, Begusarai, Bhojpur, Data Gopalganj, Khagaria, Kaimur, Lakhisarai, Patna, Thank You Thank You Purnea, Muzaffarpur, Saran, Siwan District names in Bihar with incomplete data: Vaishali, Dharbhanga District names in Jharkhand with incomplete data: Sahibganj, Palamu, Godda Examination Board Data from Bihar and Jharkhand Detailed test scores data at individual level, school level, and district level from 2004 to 2010
  • 27. Thank You Karthik Muralidharan & Nishith Prakash Introduction Motivation Background Policy Goals Empirical Strategy We are grateful to the IGC-Bihar for providing Methodology Data financial support Data We are grateful to Government of Bihar and Thank You Thank You especially Ministry of HRD without whom we could not have started this project
  • 28. Introduction Existing Evidence Research Question Empirical Strategy Data Trend Trend Women Reservation in Bihar and Children’s Health Outcomes Santosh Kumar & Nishith Prakash University of Washington & Cornell University Sep 19, 2011 /IGC Growth Week (LSE) India-Bihar Country Session
  • 29. Introduction Existing Evidence Research Question Empirical Strategy Data Trend Trend Motivation • About 50 percent of world’s population are women • However, their participation in political process is far below than parity • As per the latest estimate, women are accounted for approximately 18.4% of parliamentarians worldwide (IPU, 2008) • Barriers to political participation includes:Institutional barriers; Cultural norms; Voter discrimination; Low education
  • 30. Introduction Existing Evidence Research Question Empirical Strategy Data Trend Trend Motivation • Many countries have adopted electoral gender quotas to prevent the political under-representation of women • Decentralization of governance • Gender or minority reservation of political elected positions is to improve targeting of developmental and welfare programs to women and vulnerable groups.
  • 31. Introduction Existing Evidence Research Question Empirical Strategy Data Trend Trend Context • In 1993, India introduced quota-based political reservations for women in rural areas (73rd Constitutional Amendment) • One of the broad objective was- • To promote gender equality in human development by making rural service provision and local governance “inclusive” and “responsive” to the needs of women
  • 32. Introduction Existing Evidence Research Question Empirical Strategy Data Trend Trend Efficacy of Gender quotas • The efficacy of these policies is still disputed by many policy makers around the world • Pro: • Such policies needed to correct pre-existing gender inequalities • Better targeting of development programs • Against: • Undemocratic, less effective leaders, and elite capturing • More evidence needed to truly evaluate the impact of affirmative policies
  • 33. Introduction Existing Evidence Research Question Empirical Strategy Data Trend Trend Existing Evidence • Chattopadhyay & Duflo - Women leaders are more likely to invest in drinking water facilities across rural India • Some recent papers report public good investments by female leaders either on non-water related goods (Munshi and Rosenzweig, 2008) • Bardhan et al. (2010) exploit within-village (over time) variation in reservation in West Bengal and find no impact of female reservation • Beamen et al. insignificant effect on the quality of public good (water, education, transport, fair price shop, public health facilities)
  • 34. Introduction Existing Evidence Research Question Empirical Strategy Data Trend Trend Research Question • Does women reservation in panchayats in Bihar improved health outcomes? • Studies the effect of political reservations in local governments in favor of women • Specifically, do districts with more female leaders perform better compared to districts with fewer female leaders? • Why Bihar? • Geographic coverage: No other study has covered Bihar so far; and it is important to examine whether findings of existing studies are specific to their respective geographic contexts.
  • 35. Introduction Existing Evidence Research Question Empirical Strategy Data Trend Trend Women Reservation in Bihar • Bihar has been a laggard in implementing 73rd Constitutional Amendment • The first panchayat election was held in April 2001 after a gap of 23 years • Fifty per cent seats are reserved for women since the 2006 panchayat election • No reservation in 2001 panchayat election for ”Ekal” or ”Solitary” position
  • 36. Introduction Existing Evidence Research Question Empirical Strategy Data Trend Trend Outcome Measures • Most of the existing studies have analyzed availability of public goods and services as the outcomes measure • Very few of them have really looked at utilization of these public goods and services as outcomes • Outcomes analyzed in this study: • Health care utilization: Ante-natal care (ANC 4) • Children vaccination (DPT3, Measles), Institutional deliveries
  • 37. Introduction Existing Evidence Research Question Empirical Strategy Data Trend Trend How to measure the causal impact? • Policy was implemented state-wide, in all districts of Bihar • All districts are treated, none in control • Use Jharkhand districts or UP border districts as control • Use the variation in the program intensity
  • 38. Introduction Existing Evidence Research Question Empirical Strategy Data Trend Trend Empirical Strategy- DID Design 1: Employ double-difference (DID) 2007-08 (DLHS 3) 2001-02 (DLHS 2) Difference Jharkhand (Control) A B A- B Bihar (Treatment) C D C-D Difference C-A D-B DID: C-D- (A-B) Design 2: Exploit the variation in policy intensity
  • 39. Introduction Existing Evidence Research Question Empirical Strategy Data Trend Trend Data • Household survey: 2nd and 3rd rounds District level household survey • DLHS 2 was conducted in 2001-02 (Pre-program period) • DLHS 3 was conducted in 2007-08 (Post-program period) • Panchayat-level will be collected from Ministry of Panchayati Raj, Govt of Bihar
  • 40. Introduction Existing Evidence Research Question Empirical Strategy Data Trend Trend Trend in health outcomes in Bihar • ANC utilization, Children’s Immunization and Institutional deliveries have increased tremendously from 2002- 2008 in Bihar • Percent increase vary across districts
  • 41. Introduction 10 20 30 40 50 0 Bihar Araria Aurangabad Banka Existing Evidence Begusarai Bhagalpur Bhojpur Buxar Darbhanga Gaya Gopalgunj Jamui Jehanabad Kaimur Katihar Khagaria Kishangunj Lakhisarai Madhepura Madhubani Research Question Munger Muzaffarpur Nalanda NawadaWest Champaran PatnaEast Champaran Purnea Rohtas Saharsa Samastipur ANC utilization Saran Sheikpura Sheohar Sitamarhi Empirical Strategy Siwan Supaul Vaishali Data Women receiveing at least Women receiveing at least three visits for ANC DLHS 3 three visits for ANC DLHS 2 Trend Trend
  • 42. Introduction 10 20 30 40 50 60 70 80 0 Bihar Araria Aurangabad Banka Begusarai Existing Evidence Bhagalpur Bhojpur Buxar Darbhanga Gaya Gopalganj Jamui Jehanabad Kaimur Katihar Khagaria Kishanganj Lakhisarai Madhepura Madhubani Research Question Munger Muzaffarpur Nalanda NawadaPashchim Champaran Patna Purba Champaran Purnia Rohtas Saharsa Samastipur Saran Sheikhpura Full Immunization Sheohar Sitamarhi Empirical Strategy Siwan Supaul Vaishali Full Full Data DLHS 3 DLHS 2 Immunization Immunization Trend Trend
  • 43. Introduction 0 10 20 30 40 50 60 70 Bihar Araria Aurangabad Banka Existing Evidence Begusarai Bhagalpur Bhojpur Buxar Darbhanga Gaya Gopalganj Jamui Jehanabad Kaimur Katihar Khagaria Kishanganj Research Question Lakhisarai Madhepura Madhubani Munger Muzaffarpur Nalanda NawadaPashchim Champaran Patna Purba Champaran Purnia Rohtas Empirical Strategy Institutional Deliveries Saharsa Samastipur Saran Sheikhpura Sheohar Sitamarhi Data Siwan Supaul Vaishali DLHS 3 DLHS 2 l Delivery l Delivery Institutiona Institutiona Trend Trend
  • 44. Introduction Existing Evidence Research Question Empirical Strategy Data Trend Trend Future work • To establish the causal effect of women reservation on these health outcomes • To identify the causes of heterogeneous performance of districts in Bihar
  • 45. Introduction Existing Evidence Research Question Empirical Strategy Data Trend Trend Thank You • We are grateful to the IGC-Bihar for providing financial support
  • 46. Education Policies and Practices: What Have We Learnt and the Road Ahead Nishith Prakash & Priya Ranjan Cornell University & University of California-Irvine September 19, 2011 / IGC Growth Week - LSE
  • 47. Objective of the Paper• Survey the literature on the effectiveness of education policies adopted in different parts of the world to improve both the “quantity” and “quality” of education.• Survey the policies adopted by the government of Bihar towards improving educational outcomes in the state. – Place these policies appropriately in our broader survey framework to make this work a contextual survey.• Identify best practices in education policies and make policy recommendations for Bihar
  • 48. Status of Education in Bihar: Quantity measures Out of School Rate (source: ASER) Gross Enrollment Ratio (DISE) Net Enrollment Ratio (DISE)In all graphs-Dashed lines – minimum and maximum of all states with non-missing dataSolid black line – median of all states with non-missing dataSolid red line – Bihar
  • 49. Status of Education in Bihar Out of school rate, by gender Male Female.3.2.10 2007 2008 2009 2010 2007 2008 2009 2010
  • 50. Status of Education in Bihar Out of school rate, by age group 5 to 7 8 to 10.3.2.10 2007 2008 2009 2010 2007 2008 2009 2010 11 to 13 14 to 16.3.2.10 2007 2008 2009 2010 2007 2008 2009 2010
  • 51. 20 03 0 50 100 150 200 250 - 0420 04 - 0520 05 - 0620 06 - 0720 07 - 0820 08 - 0920 Gross enrolment ratio, primary 09 - 10 Status of Education in Bihar
  • 52. 20 03 0 50 100 150 - 0420 04 - 0520 05 - 0620 06 - 0720 07 - 0820 08 - 09 Net enrolment ratio, primary20 09 - 10 Status of Education in Bihar
  • 53. Status of Education in Bihar Gross enrolment ratio, upper primary 150 100 50 0 04 05 06 07 08 09 10 - - - - - - - 03 04 05 06 07 08 0920 20 20 20 20 20 20
  • 54. Status of Education in Bihar Net enrolment ratio, upper primary 80 100 60 40 20 0 04 05 06 07 08 09 10 - - - - - - - 03 04 05 06 07 08 0920 20 20 20 20 20 20
  • 55. Summary of Evidence on Quantity• Out of school rate higher than the median, but declining over time and converging to the median – Gap with the best performing states significant• Enrolment ratio at primary level above the median starting in 2006-07 – Near universal primary enrolment• Enrolment ratio at upper primary level still very low (right at the bottom in India)
  • 56. Status of Education in Bihar: Quality measures Can read long paragraph, Can solve division problem (Source: ASER)
  • 57. Status of Education in Bihar Can read long paragraph, by gender Male Female0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 2007 2008 2009 2010 2007 2008 2009 2010
  • 58. Status of Education in Bihar Can read long paragraph, by class Std I Std II Std III Std IV0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 2007 2008 2009 2010 2007 2008 2009 2010 2007 2008 2009 2010 2007 2008 2009 2010 Std V Std VI Std VII Std VIII0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 2007 2008 2009 2010 2007 2008 2009 2010 2007 2008 2009 2010 2007 2008 2009 2010
  • 59. Status of Education in BiharCan solve division problem, by gender Male Female0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 2007 2008 2009 2010 2007 2008 2009 2010
  • 60. Status of Education in Bihar Can solve division problem, by class Std I Std II Std III Std IV0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 2007 2008 2009 2010 2007 2008 2009 2010 2007 2008 2009 2010 2007 2008 2009 2010 Std V Std VI Std VII Std VIII0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 2007 2008 2009 2010 2007 2008 2009 2010 2007 2008 2009 2010 2007 2008 2009 2010
  • 61. Summary of evidence on quality• In “reading” Bihar slightly below the median – Looking at reading by class, Bihar seems to be above the median in all classes• In math skills, Bihar very close to the median – Again, looking at math skills by class, Bihar seems to be above the median for all classes• In both reading and math skills, the gap with the best performers is substantial – Some evidence of narrowing of gap in recent years – In absolute terms, not very satisfactory
  • 62. Proximate Determinants of Low Schooling Attainment: Schooling Inputs pupil-teacher ratio, student-classroom ratio, no. of teachers per school, % schools with common toilet, % schools with girls’ toilet, % schools with drinking water facility Source: DISE
  • 63. Summary of evidence on schooling inputs• Primary schools – Highest pupil-teacher ratio as well as student- classroom ratio among Indian states – Number of teachers per school low, but has become higher than the median – % of schools with toilets or separate girls toilet well below the median – Surprisingly, % of schools with drinking water facility has gone down from above median to below it• Somewhat similar story for upper primary schools
  • 64. Overall summary• Bihar has made substantial progress on the “quantity” front at primary level• Enrolment at upper primary level still very low• In reading and math, Bihar’s performance satisfactory in relative terms, but weak in absolute terms – For example, 30% of students in class VI could not read a paragraph taken from a class II textbook – 50% of class V students cannot solve a simple division problem• Record on the schooling input front weak in both relative and absolute terms• Quantity – Quality trade off?
  • 65. 20 03 0 20 40 60 80 100 - 0420 04 - 0520 05 - 0620 06 - 0720 07 - 0820 08 - 09 Pupil-teacher ratio, primary20 09 - 10 Schooling Inputs: Primary Schools
  • 66. Schooling Inputs: Primary Schools Student-classroom ratio, primary 80 100 60 40 20 0 04 05 06 07 08 09 10 - - - - - - - 03 04 05 06 07 08 09 20 20 20 20 20 20 20
  • 67. Schooling Inputs: Primary Schools No. of teachers per school, primary 15 10 5 0 04 05 06 07 08 09 10 - - - - - - - 03 04 05 06 07 08 09 20 20 20 20 20 20 20
  • 68. Schooling Inputs: Primary Schools Schools with common toilets, primary (%) 1 .8 .6 .4 .2 0 04 05 06 07 08 09 10 - - - - - - - 03 04 05 06 07 08 09 20 20 20 20 20 20 20
  • 69. Schooling Inputs: Primary Schools Schools with girls toilets, primary (%) 1 .8 .6 .4 .2 0 04 05 06 07 08 09 10 - - - - - - - 03 04 05 06 07 08 09 20 20 20 20 20 20 20
  • 70. Schooling Inputs: Primary Schools Schools with drinking water facility, primary (%) 1 .8 .6 .4 .2 0 04 05 06 07 08 09 10 - - - - - - - 03 04 05 06 07 08 09 20 20 20 20 20 20 20
  • 71. IGC India – State of Bihar Development Priorities of the Government of Bihar andConsequent Research Possibilities Aishani Roy IGC India – State of Bihar
  • 72. IGC India – State of Bihar Objective• Acquaint us with the objectives and goals stated by the government in their mission documents.• Try and identify principle areas of research that might be of interest to the government.• Match the identified areas with the appropriate themes as stated by the IGC.
  • 73. IGC India – State of Bihar Themes WaterState Capabilities - Power Roads Resources Urba Agriculture Rural nizati and Allied Non Migration on Farm Health Governance Service Delivery & Social Inclusion - Food Education security
  • 74. IGC India – State of Bihar Agenda• Agriculture – Low productivity – role of institutional challenges, minimum support price, credit availability, diversification of livelihood patterns• Infrastructure and Urbanization – Decentralized Renewable Energy (Husk Power Systems, Barefoot Solar Engineers), Captive Power Policy, Roads• Natural Resources – Water Resources Management, Irrigation• Human Capital – Health, Education, Migration• State Capabilities – Rural Development, Food Security• Governance
  • 75. Food Security - some numbers2005 2011• National Sample Survey • Ratio of Purchases to and Food Corporation Entitlements (2011) of India data on Offtake Diversion Rate • Nationwide Sample Average – 84%• Best – Tamil Nadu 7% • Best – Chhatisgarh,• National Average – 54% Andhra Pradesh - >90%• Bihar – 92% • Bihar – 45%
  • 76. IGC India – State of Bihar Objectives of Bihar Government• A state level BPL commission will be constituted which will identify all BPL families and redress their grievances with this respect.• All the BPL families will be provided with food grains or equivalent cash in its lieu.• By running the procurement activities through all the Primary Agricultural Cooperatives (PACSs) of the state, the produce of farmers will be procured easily, paying the minimum support price to them.• By strengthening the institutions involved in the activity of procurement as well as the PDS, their working capacity and coverage will be adequately enhanced.• Developing the storage capacity in the state, concrete shape will be given to full potential of procurement
  • 77. IGC India – State of Bihar Who are the players?Clients Providers State• BPL families Intermediaries through which the •Regulation grains reach the targeted• BPL + APL households. •Monitoring andfamilies accountability •Individual suppliers through state• Everyone instituted Fair Price Shops •Ensure that grains(Universalized reach the targetedPDS) •Community based supply models households from the FCI (Primary Agricultural godowns Cooperatives) •Ensure that the •Private sellers beneficiaries are correctly identified
  • 78. IGC India – State of Bihar Client• How will the government ensure proper identification of the beneficiaries? – Recommendations of the N.C Saxena Committee – Is there a reliable way to identify poor households based on proxy indicators?• Is targeting divisive? – Prevents emergence of united public demand for a functional PDS – Tamil Nadu – Universalized PDS – consistent good performer• In the absence of adequate identification measures – what are the arguments regarding the feasibility of universalizing the PDS? – Chhattisgarh – 80% coverage – Estimated cost – 1 lakh crore (1.5% of GDP)
  • 79. IGC India – State of Bihar ProviderProblems MeasuresDuplicity of Coupons are being bar coded. Bar code will be afood coupons mix of Customer BPL card,Unique coupon, Dealer shopIllegal Sale at • Coupons for the whole year will be distributedthe in camps with tight surveillance (Century Ricedistribution Festivals – Bihar)level • Strict accountability measures for errant officials.Quality of Coupons can be used to buy essentialfood grains commodities from any shop
  • 80. Provider - Questions• Who will be the final seller of subsidized essentials to minimize diversion? – Single owners through Fair Price Shops ? – Community based models (Primary Agriculture Cooperatives)? Will procurement and distribution of essentials be less prone to leakage, diversion and scams? – Private stores ? Food coupons can be used to purchase from any shop – will it ensure quality of grains?
  • 81. State : Chhattisgarh ModelRole PracticeCorrectly identify •Not using UID but entire beneficiary databasebeneficiary digitized by NIChouseholds and •Bogus cards are being eliminated through door toensure effective door physical verificationtargetingEnsure that grains • Does not allot distribution to individuals but to areach the targeted PAC or self help groupshouseholds from •Use vehicles for transportation of foodgrainsthe FCI godowns directly to PDS shops and message would be circulated to targeted people via sms. •Does not use food coupons
  • 82. IGC India – State of Bihar State - Questions• The finance minister of Bihar talked about emulating the Chhattisgarh model – will that mean a shift from the current system of Food coupons?• Success of conditional cash transfers ( Bicycle Yojana, Uniform Scheme, kerosene) – replace subsidy with cash transfers? (As mentioned in the manifesto – ‘’All BPL families will be provided with subsidized food grains or equivalent cash in its lieu.” )