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Growth Week 2011: Country Session 10 - India-Central

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  • 1. Foreign Investors under stress Ajay Shah Ila Patnaik Nirvikar Singh September 15, 2011Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 1 / 32
  • 2. Part I QuestionsShah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 2 / 32
  • 3. The financial globalisation question While many emerging markets have removed capital controls, a large mass of the developing world continues to have significant capital controls. While capital account liberalisation has many attractive features, policymakers in many developing countries have certain important concerns. Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 3 / 32
  • 4. Concerns of policy makers in developing countries 1 Behaviour under crisis is what really matters. A focus on extreme days, not on average behaviour. Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 4 / 32
  • 5. Concerns of policy makers in developing countries 1 Behaviour under crisis is what really matters. A focus on extreme days, not on average behaviour. 2 “When we have a domestic crisis, will foreign investors make it worse by exiting? Are foreign investors fair weather friends?” Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 4 / 32
  • 6. Concerns of policy makers in developing countries 1 Behaviour under crisis is what really matters. A focus on extreme days, not on average behaviour. 2 “When we have a domestic crisis, will foreign investors make it worse by exiting? Are foreign investors fair weather friends?” 3 “Are foreign investors big fish in a small pond? Can their trades (regardless of the motivation) kick off short-term price distortions?” Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 4 / 32
  • 7. Concerns of policy makers in developing countries 1 Behaviour under crisis is what really matters. A focus on extreme days, not on average behaviour. 2 “When we have a domestic crisis, will foreign investors make it worse by exiting? Are foreign investors fair weather friends?” 3 “Are foreign investors big fish in a small pond? Can their trades (regardless of the motivation) kick off short-term price distortions?” 4 “Will my country get hit with selling for no fault of ours when there is a crisis in the foreign investors’ home country?” Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 4 / 32
  • 8. Concerns of policy makers in developing countries 1 Behaviour under crisis is what really matters. A focus on extreme days, not on average behaviour. 2 “When we have a domestic crisis, will foreign investors make it worse by exiting? Are foreign investors fair weather friends?” 3 “Are foreign investors big fish in a small pond? Can their trades (regardless of the motivation) kick off short-term price distortions?” 4 “Will my country get hit with selling for no fault of ours when there is a crisis in the foreign investors’ home country?” 5 “Is the behaviour of foreign investors asymmetric, where bad days are punished but great positive news is not?” Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 4 / 32
  • 9. These questions are a little different from thoseemphasised in the literature The international finance literature has emphasised the different question: “Are foreign investors stabilising?” The key identification problem : foreign investors and stock market indexes both respond to news. Difficult to identify cause and effect. But this debate is a different one, compared with what concerns policy makers. Example: Suppose exit by foreign investors is an efficient and rational response to a domestic crisis, and helps the local prices find their efficient level. That is, foreign investors are ‘fair weather friends’, but they are still stabilising.We take the three questions seriously and try to obtain evidence on them,even though some of the reduced form results have multiple theoreticalinterpretations. Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 5 / 32
  • 10. Part II Measurement strategyShah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 6 / 32
  • 11. Three key ideas 1 How to focus on tail events? Analogous to a tail beta: Focus on the extreme days. Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 7 / 32
  • 12. Three key ideas 1 How to focus on tail events? Analogous to a tail beta: Focus on the extreme days. 2 How to identify impacts? Treat a tail event as a shock, and use the event study methodology to trace out impacts. Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 7 / 32
  • 13. Three key ideas 1 How to focus on tail events? Analogous to a tail beta: Focus on the extreme days. 2 How to identify impacts? Treat a tail event as a shock, and use the event study methodology to trace out impacts. 3 Implement this using high frequency (daily) data for aggregative purchase/sale by all foreign investors (put together). Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 7 / 32
  • 14. Measurement strategy The 5% of days in left tail has roughly 12 worst days of the year Treat these as events Use the event study methodology to measure the reduced form impact upon a series of interest There is event clustering: Hence identify windows (a week before and after) in which there is exactly one extreme value and treat that as an uncontaminated event. Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 8 / 32
  • 15. Part III DataShah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 9 / 32
  • 16. Data Variables NIFTY Index, S&P 500, VIX , daily FII flows, NIKKEI Source Daily FII flows obtained from Custodian reports to Government of India Period 05 January 1999 – 30 August 2011 Frequency DailyRenormalisation Express FII flows as percent to the overall domestic market capitalisation. Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 10 / 32
  • 17. Summary statistics: Event on NIFTY returns Shocks 1.5% 2.5% 5% Negative Shocks 51 84 167 No Contamination 16 25 46 Positive Shocks 51 84 167 No Contamination 31 39 69 Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 11 / 32
  • 18. Summary statistics: Event on S&P 500 Shocks 1.5% 2.5% 5% Negative Shocks 51 84 167 No Contamination 18 35 54 Positive Shocks 51 84 167 No Contamination 17 37 69 Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 12 / 32
  • 19. Summary statistics: Event on FII Shocks 1.5% 2.5% 5% Negative Shocks 51 84 167 No Contamination 21 31 53 Positive Shocks 51 84 167 No Contamination 31 45 77 Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 13 / 32
  • 20. Summary statistics: Event on VIX Shocks 1.5% 2.5% 5% Negative Shocks 51 84 167 No Contamination 34 46 64 Positive Shocks 51 84 167 No Contamination 25 39 70 Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 14 / 32
  • 21. Summary statistics: By year5% tails with event window of 5 1999 2000 2001 2002 2003 2004 S&P 500 23 41 28 52 18 0 NIFTY 35 41 25 6 11 22 FII 13 53 26 10 31 28 VIX 28 25 19 23 5 15 2005 2006 2007 2008 2009 2010 2011 S&P 500 1 2 17 73 54 22 3 NIFTY 5 30 24 76 49 6 4 FII 29 24 28 47 18 22 5 VIX 18 20 48 51 23 41 18 Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 15 / 32
  • 22. Part IV MethodologyShah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 16 / 32
  • 23. Event study methodology Application of event study methodology Bootstrap inference. By focusing on extreme events, we produce results that describe the scenarios of interest to policy makers – e.g. the overall VAR is not that interesting. Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 17 / 32
  • 24. Part V ResultsShah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 18 / 32
  • 25. Q1: How do FIIs behave on extreme days of Nifty The domestic stock market does extremely badly; how do FIIs behave? Skeptics about financial globalisation worry that on and immediately after, there is capital flight by FIIs. This would give reduced domestic asset prices and potentially generate difficulties for exchange rate pegging (when that’s present). Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 19 / 32
  • 26. Extreme event on NIFTY and response of FII Very good (by Nifty) Very bad (by Nifty) 0.10 0.10 q q q q (Cum.) change in FII (Cum.) change in FII 0.05 0.05 q q q q q q q 0.00 0.00 q q q q q q q q q q q −0.10 −0.05 −0.10 −0.05 −4 −2 0 2 4 −4 −2 0 2 4 Event time (days) Event time (days) Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 20 / 32
  • 27. Very good days on Nifty are associated with buying by FIIs bothbefore and after the event date.Very good days on Nifty are generally positive news days: so FIIscould be responding either to the news or to Nifty.Relatively modest effects: Total buying of 0.06 basis points of Indianmarket capitalisation.Asymmetry: Such evidence is not found on bad days.Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 21 / 32
  • 28. Q2: What happens to Nifty on extreme events by FIIs Domestic or global motivations give an extreme day on FII inflow/outflow. What happens to Nifty? Skeptics worry: Foreigners are a big fish in a small pond, there is overshooting and then gradually the market finds its correct level. Or, if the domestic market is liquid enough, there would be an immediate impact (extreme events by FIIs are likely to be linked to news!) but after that the response would be flat. Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 22 / 32
  • 29. Q2. Extreme event on FII and response of NIFTY Very good (by FII) Very bad (by FII) q q 2 2 (Cum.) bps of NIFTY (Cum.) bps of NIFTY q q q q q q q q q q q q q q 0 0 q q q q q q −2 −2 −4 −2 0 2 4 −4 −2 0 2 4 Event time (days) Event time (days) Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 23 / 32
  • 30. Very good / bad days for FII investment are likely to be associatedwith news.It is hence not surprising to see Nifty being higher or lower on eventdate.But there is no evidence of overshooting. After the event is digested(on event date), Nifty is flat in the following period.Holds for extreme events of both kinds.Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 24 / 32
  • 31. Q3: What happens to foreign investors on an extreme dayfor the S&P 500 Skeptics worry: Crisis in the United States, investors pull money from emerging market funds. Recent research has brought out the role of fire sales by foreign investors when they face redemptions at home. Rational explanation: Bad news for the S&P 500 is bad news for all globalised economies, so what we are seeing is partly business cycle correlations. Are such effects present? Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 25 / 32
  • 32. Q3. Extreme event on S&P 500 and response of FII Very good (by SP500) Very bad (by SP500) 0.05 0.05 q q q (Cum.) change in FII (Cum.) change in FII q q q q q q q q q q q q q q q q q 0.00 0.00 q q −0.05 −0.05 −4 −2 0 2 4 −4 −2 0 2 4 Event time (days) Event time (days) Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 26 / 32
  • 33. FIIs seem to buy more Nifty when there is good news on the S&P 500But no such effects in the other direction.Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 27 / 32
  • 34. Part VI Summary and conclusionsShah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 28 / 32
  • 35. Concerns of developing country policy makers 1 Behaviour under crisis is what really matters. A focus on extreme days, not on average behaviour. Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 29 / 32
  • 36. Concerns of developing country policy makers 1 Behaviour under crisis is what really matters. A focus on extreme days, not on average behaviour. 2 “When we have a domestic crisis, will foreign investors make it worse by exiting? Are foreign investors fair weather friends?” Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 29 / 32
  • 37. Concerns of developing country policy makers 1 Behaviour under crisis is what really matters. A focus on extreme days, not on average behaviour. 2 “When we have a domestic crisis, will foreign investors make it worse by exiting? Are foreign investors fair weather friends?” 3 “Are foreign investors big fish in a small pond? Can their trades (regardless of the motivation) kick off short-term price distortions?” Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 29 / 32
  • 38. Concerns of developing country policy makers 1 Behaviour under crisis is what really matters. A focus on extreme days, not on average behaviour. 2 “When we have a domestic crisis, will foreign investors make it worse by exiting? Are foreign investors fair weather friends?” 3 “Are foreign investors big fish in a small pond? Can their trades (regardless of the motivation) kick off short-term price distortions?” 4 “Will my country get hit with selling for no fault of ours when there is a crisis in the foreign investors’ home country?” Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 29 / 32
  • 39. Concerns of developing country policy makers 1 Behaviour under crisis is what really matters. A focus on extreme days, not on average behaviour. 2 “When we have a domestic crisis, will foreign investors make it worse by exiting? Are foreign investors fair weather friends?” 3 “Are foreign investors big fish in a small pond? Can their trades (regardless of the motivation) kick off short-term price distortions?” 4 “Will my country get hit with selling for no fault of ours when there is a crisis in the foreign investors’ home country?” 5 “Is the behaviour of foreign investors asymmetric, where bad days are punished but great positive news is not?” Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 29 / 32
  • 40. Innovations of our approach Treat extreme events (uncontaminated) as pure shocks and watch what happens Event study methodology Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 30 / 32
  • 41. Results 1 Foreign buying seems to go with extreme +ve days for Nifty. Modest sized effects. Asymmetry: no such impact on extremely bad days. 2 Foreign investors are not big fish in a small pond: Even on their extreme days (for whatever reason), there is no overshooting on either side. 3 Extreme and positive days for the S&P 500 are associated with greater foreign buying Modest sized effects. Asymmetry: no such impact on extremely bad days. 4 In all cases, the effects are relatively small. Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 31 / 32
  • 42. Conclusion Results paint a relatively benign picture of India’s engagement with financial globalisation Future work: Scale this up to more countries, try to go down into cross-sectional variation by firms. Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 32 / 32
  • 43. Thank you. Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 33 / 32
  • 44. Motivating Agents to SpreadInformation: The Role of Explicit Incentives and Social Identity- Matching India Central session, 21 September, LSE Growth Week 2011 Erlend Berg (Oxford), Maitreesh Ghatak (LSE), R Manjula (ISEC), D Rajasekhar (ISEC) and Sanchari Roy (Warwick)
  • 45. Motivation• Human capital is viewed as a key driver of growth• Many government programmes are broadly aimed at boosting human capital – Publicly funded education, health care• But poor delivery of public services has the potential to jeopardise the gains from these investments• Research on public service delivery in developing countries has focused on supply-side problems – Teacher and health worker qualifications and absence, red tape, corruption, inefficient judiciary
  • 46. Motivation, continued• The demand-side is relatively under-studied – Evidence from the US shows low take-up of food stamps and public health insurance – Take-up of major public schemes in LDCs is often low – In India, enrolment in the National Rural Employment Guarantee is low in some of the poorer states• Barriers to take-up of welfare schemes – Social stigma (less relevant in developing countries?) – Cumbersome sign-up procedures – Lack of awareness• What can be done to increase awareness of government schemes?
  • 47. Summary• Research questions: – Does recruiting and paying local women (‘agents’) to spread awareness about a public health insurance programme increase knowledge and take-up? – Does the payment structure (flat versus incentive pay) matter? – What role does social identity play?• Findings in brief: – Hiring agents has an effect on awareness of and knowledge about the scheme – The effect is driven by agents on incentive-pay contracts – Agents perform better vis-à-vis households that share their social identity (caste and religion)
  • 48. The National Health Insurance Scheme (RSBY)• RSBY is a central government initiative aimed at the below- poverty-line (BPL) population of India, launched in 2008• Covers hospitalization and surgical procedures for a specified list of health problems • pre-existing conditions covered; outpatient services not covered• Total cover of up to Rs 30,000 (640 USD) per year per family• Administered by insurance companies selected in state-wide tender processes• Annual registration fee: Rs 30 (0.64 USD) paid by household to insurance company• Insurer receives agreed annual premium per enrolled household from central (75%) and state (25%) governments
  • 49. RSBY, continued• A network of empanelled hospitals, both public and private, provide healthcare services under RSBY• “Cashless” service linked to beneficiary smart cards• Hospitals reimbursed from insurance company according to a fixed ‘menu’ of treatments and prices• In Karnataka, five districts were selected for the initial phase of the rollout: Bangalore Rural, Belgaum, Dakshina Kannada, Mysore and Shimoga• In Karnataka, the scheme commenced in February-March 2010
  • 50. Experimental design• 220 selected villages in Bangalore Rural and Shimoga districts of Karnataka were randomly assigned to 3 treatment and 1 control groups• A local woman was recruited as an agent in each treatment village – Task: to spread information about RSBY over 1 year period• All agents paid, but experimental variation in contract:  Flat pay: Agent paid Rs 400 every three months  Knowledge pay: Agents paid a fixed Rs 200, plus a bonus depending on the level of knowledge about RSBY amongst the eligible households in the village, based on a knowledge test  Utilisation pay: Agents paid a fixed Rs 200, plus a bonus depending level of utilisation (frequency of hospital treatments booked on RSBY cards) in the village
  • 51. Experimental design, continued• Average pay was designed to equal Rs. 400 across all treatment groups – But some deviation in practice• This would help isolate the incentive effect of contract structure from “income effect” of the average payment size• Payment structure revealed to agent after recruitment – Payment structure in a sealed envelope, so that even our field staff was not aware of it until after the agent had been recruited – Purpose: to separate any selection effect of the contract from the incentive effect – No agent quit after being told the payment structure
  • 52. Data• Two rounds of surveys conducted post intervention on a random sample of 3638 and 2955 households respectively (with overlap) in the sample districts• Surveys designed to test the level of knowledge of eligible households about RSBY and measure level of utilization, awareness and take-up of RSBY (primary outcome variables)  used to pay the agents based on their performance and monitor project progress• Household characteristics for a subsample of these households, in treatment villages only, were obtained from an earlier baseline survey
  • 53. Outcome variables• The main outcome variables are awareness of RSBY, enrolment into the scheme, score in knowledge test and utilization of RSBY• In each survey, the knowledge test consisted of 8 questions about the RSBY scheme• Each answer was recorded and later coded as being correct or wrong• The number of correct answers, divided by eight, gives each interviewed household a knowledge score between 0 (least knowledgeable) and 1 (most knowledgeable)• Questions in each round of test are different – Scores in round 1 and 2 cannot be directly compared
  • 54. Effect of awareness-spreading agents (1) (2) (3) (4) Heard of Have enrolled Knowledge Have utilised RSBYAgent in village 0.00542 0.0197 0.0550*** 0.0000676 (0.0231) (0.0417) (0.0189) (0.00187)Bangalore Rural 0.0133 -0.0159 0.00836 0.0000520 (0.0249) (0.0353) (0.0177) (0.00143)Second survey 0.0524*** 0.0432*** -0.0135 0.00256* (0.0128) (0.0162) (0.0144) (0.00134)Constant 0.833*** 0.668*** 0.306*** 0.000824 (0.0224) (0.0411) (0.0179) (0.00189)Observations 5087 5087 5087 5087
  • 55. Interpretation: Do agents matter?• Agents have an effect on knowledge: Households living in a village with an agent score better on the knowledge test than people living in a village with no agent.• The increased knowledge is reflected in a .055 point improvement in the average knowledge score. This improvement corresponds to moving half the households from the wrong answer to the correct answer on one question in the test.• There is no significant effect on awareness (having heard of the programme), enrolment or utilisation – but note that utilisation was hardly possible at all
  • 56. Effect disaggregated by agent contract type (1) (2) (3) (4) Heard of RSBY Have enrolled Knowledge Have utilisedFlat-pay agent in -0.0171 -0.0344 0.0415 -0.00000142village (0.0375) (0.0591) (0.0295) (0.00224)Knowledge-pay agent 0.0473** 0.0683 0.0816*** 0.000115in village (0.0226) (0.0442) (0.0213) (0.00221)Utilisation-pay agent in -0.0243 -0.0000599 0.0358 0.0000566village (0.0330) (0.0505) (0.0224) (0.00206)Bangalore Rural 0.0103 -0.0197 0.00645 0.0000481 (0.0238) (0.0345) (0.0173) (0.00141)Second survey 0.0536*** 0.0442*** -0.0127 0.00256* (0.0128) (0.0162) (0.0144) (0.00134)Constant 0.834*** 0.669*** 0.307*** 0.000825 (0.0223) (0.0411) (0.0179) (0.00188)Observations 5087 5087 5087 5087
  • 57. Interpretation: Does the agent’s contract type matter?• The effect on knowledge is driven primarily by agents who are paid according to the villagers’ results on the knowledge test.• These agents also have an effect on general awareness of the scheme• These agents may also have an effect on enrolment, but this result is not statistically significant• The other contract types (flat pay, utilisation pay) are not associated with significant improvements in any outcome variable
  • 58. Social matching versus incentives (1) (2) (3) Knowledge Knowledge KnowledgeKnowledge-pay agent in village 0.0548** 0.0523** 0.0585* (0.0253) (0.0253) (0.0337)Agent is SC/ST -0.00870 -0.00879 -0.00827 (0.0297) (0.0293) (0.0294)Household is SC/ST -0.0156 0.00537 0.00466 (0.0229) (0.0258) (0.0255)Bangalore Rural -0.0172 -0.0199 -0.0203 (0.0262) (0.0257) (0.0258)Second survey 0.0363 0.0351 0.0352 (0.0241) (0.0238) (0.0238)Households SC/ST status matches that of agent 0.0527** 0.0559* (0.0255) (0.0320)Households SC/ST status matches that of agent x Knowledge-pay -0.00973agent (0.0430)Constant 0.379*** 0.341*** 0.338*** (0.0292) (0.0349) (0.0365)Observations 746 746 746
  • 59. Interpretation: Incentives versus identity matching• SC/ST agents are no better or worse than non-SC/ST agents, holding other variables fixed• SC/ST households do no better or worse on the test than non- SC/ST households• But matching matters. If both agent and household are SC/ST, or neither, then the knowledge score is greater than if they do not match• The effect is of the same magnitude as, and more significant than, the effect of the incentive contract
  • 60. Conclusion• The demand side is under-studied in public service delivery – The take-up of important welfare programmes is low due to a lack of awareness in the target population• Recruiting local agents to spread information can make a difference to people’s knowledge about a scheme• Agents with monetary incentives do better• But social identity also matters. Agents seem to communicate better with households who are similar to themselves in terms of caste (and religion).
  • 61. Inclusion & Growth in India: Some Facts, Some Conclusions Surjit S Bhalla Prepared for International Growth Centre Growth Week London, Sept 21, 2011 SeptSurjit Bhalla 1 Inclusion & Growth in India: Some Facts, Some Conclusions 2011
  • 62. World Bank Poverty Lines – A changing goalpost •Surjit Bhalla Sept 2011 2 Inclusion & Growth in India: Some Facts, Some Conclusions
  • 63. Inclusive Growth In India  Characteristics of Inclusive Growth  NSS Surveys – consumption NSS Surveys – employment and wage income  Poverty Decline – large by any definition, but major problems with the data Inequality Change  Education: Girls Catch up  What is happening to female employment?  The Importance of government redistributive programs SeptSurjit Bhalla 3 Inclusion & Growth in India: Some Facts, Some Conclusions 2011
  • 64. Growth - Yes Indian Growth Performance, 1980-2009 Average(5 years) Average (20 years) Year Growth Rank Growth Rank 1980 3.2 56 3.7 60 1985 5.4 19 4.1 35 1990 6.0 12 4.3 27 1995 5.2 28 4.9 17 2000 6.3 11 5.7 11 2005 7.0 7 6.1 6 2009 8.5 4 6.5 4 Source: World Bank , World Development Indicators SeptSurjit Bhalla 4 Inclusion & Growth in India: Some Facts, Some Conclusions 2011
  • 65. Regional Distribution of Growth Acceleration more rapid in formerly slower growing states Chattisgarh Jharkhand 5 Rajasthan Orissa 4 Uttaranchal Gujarat Bihar Haryana 3 Assam Delhi Maharashtra Tamil Nadu Madhya Pradesh Punjab & Kashmir Jammu 2 Uttar Pradesh Kerala Andhra Pradesh Karnataka 1 Himachal Pradesh West Bengal 0 .1 .2 .3 .4 .5 iyup accygdpku Fitted values Notes: X axis represents per capita growth during the period 1993-2002; the Y axis is the acceleration in per capita growth 1992-2009 i.e. growth 2003-2009 minus growth 1993-2002. SeptSurjit Bhalla 5 Inclusion & Growth in India: Some Facts, Some Conclusions 2011
  • 66. Problems in Measurements – Sharp Decline in NSS Estimates Survey to National Accounts Ratio in India Year Survey National Accounts Survey/NA Ratio 1983 123.4 152.9 80.7 1993/94 333.5 539.6 61.8 1999/00 586.9 1057.5 55.5 2004/05 728.8 1472.3 49.5 2007/08 976.6 2068.7 47.2 2009/10 1240 2701 45.9 Notes: The survey and national accounts estimates are in current rupees per capita per month; the NA estimate is for the base year prevailing at the time of the survey. SeptSurjit Bhalla 6 Inclusion & Growth in India: Some Facts, Some Conclusions 2011
  • 67. Consumption Inequality – An increase, after 2004/5 NSS Consumption Inequality (Gini) in India 1983-2009/10 Year 1983 1993/94 1999/00 2004/05 2007-08 2009-10 Measure,Nominal Uniform Recall (30 days) 32.6 32.7 32.3 36.8 Mixed Recall (30/365 days) 30.4 30.3 32.3 35.1 34.8 36.4 Modified Mixed Recall (7/30/365) 35.4 Adjusted to National Accts 36 37.8 36.5 43.4 42.4 46.6 Measure,Real Uniform Recall (30 days) 31.9 30.4 29 32.8 Mixed Recall (30/365 days) 29.5 27.8 29 30.8 30.7 32.8 Modified Mixed Recall (7/30/365) 32.0 Adjusted to National Accts 35.4 35.5 33.2 39.8 37.8 42.8 SeptSurjit Bhalla 7 Inclusion & Growth in India: Some Facts, Some Conclusions 2011
  • 68. Sharp Decline in Education Inequality Education Inequality in India - 1983-2009 Year India Rural Urban Female Male 1983 0.71 0.76 0.56 0.79 0.63 1993/94 0.66 0.69 0.53 0.73 0.59 2004/05 0.58 0.62 0.47 0.64 0.52 2007/08 0.52 0.54 0.42 0.58 0.46 2009/10 0.49 0.52 0.41 0.55 0.43 % change 1983/09 -31 -31.6 -26.8 -30.4 -31.7 Source: NSSO employment-unemploy ment data, different years SeptSurjit Bhalla 8 Inclusion & Growth in India: Some Facts, Some Conclusions 2011
  • 69. Education – Girl Catch-up Progress of Youth Education in India, 1983 - 2009/10 Years of Education (ages 8-24) Literacy (% of population of age 8-24) Female/ State 1983 2009 % change Male 1983 2009 % change Female/Male Andhra Pradesh 3 7.1 136 90 51 91 78 94 Bihar 2.6 5.1 96 78 43 80 86 85 HP 4.7 7.8 66 104 78 99 27 99 Madhya Pradesh 2.9 6.2 114 89 52 89 71 91 Maharashtra 4.6 7.8 69 97 73 97 33 98 Orissa 3 6.7 123 93 54 92 70 94 Rajasthan 2.6 6.1 135 78 45 97 115 86 Tamil Nadu 4.4 8.1 84 101 73 99 36 99 Uttar Pradesh 3.1 6 93 92 51 87 70 91 West Bengal 3.7 6.3 70 97 63 93 48 97 All India 3.6 6.7 86 93 60 91 52 94 Bimaru states 2.9 5.9 103 87 49 87 78 90 Small states 5.1 7.4 45 98 77 98 27 98 North East 4.5 7 55 100 78 99 15 99 Notes: Bimaru states refers to the aggregate of the poor states - Bihar, Madhya Pradesh, Rajasthan and UP. Literacy is defined as greater than or equal to two years of education SeptSurjit Bhalla 9 Inclusion & Growth in India: Some Facts, Some Conclusions 2011
  • 70. Education – the Poor Catch-up Youth Educational Attainment, 1983 - 2009/10 Social category Average years of schooling Relative female/male education (in %) 1993/ 2004/ 1983 94 05 2007/08 2009/10 1983 1993/94 2004/05 2007/08 2009/10 Dis-privileged 2.5 3.4 5.4 5.5 6.0 51.9 64.7 82.8 88.1 90.3 - SC 2.5 3.4 5.5 5.7 6.1 46.5 60.4 80.8 88.3 89 - ST 2 3 4.9 5.3 5.8 43.6 57.5 79 80.8 84.1 - SCST 2.3 3.3 5.3 5.6 6.0 45.4 59.4 80.2 86 88.9 - Muslims 2.9 3.7 5.4 5.4 5.9 64.4 75.8 88.9 92.2 91.8 Privileged 4.3 5.2 6.9 6.8 7.2 66.8 77.2 87.6 92.7 94.6 All groups 3.6 4.5 6.3 6.3 6.7 62.8 73.4 85.8 90.8 92.7 Notes: Youth defined as those between 8 and 24 years. SeptSurjit Bhalla 10 Inclusion & Growth in India: Some Facts, Some Conclusions 2011
  • 71. Income Inequality Income Inequality in India - 1983-2009 Wage Income Per Wage Income Per Year Person Household Nominal Real Nominal Real 1983 0.53 0.53 0.50 0.50 1993 0.51 0.49 0.48 0.46 1999 0.55 0.53 0.51 0.49 2004 0.56 0.53 0.53 0.50 2007 0.54 0.50 0.52 0.49 2009 0.53 0.50 0.52 0.49 % change 1983/09 0 -5.6 4 -2 Source: NSSO employment-unemploy ment data, different years SeptSurjit Bhalla 11 Inclusion & Growth in India: Some Facts, Some Conclusions 2011
  • 72. Real Wage Real Wage per day per person Overall Regular Salaried Casual Labor Total Male Female Total Male Female Total Male Female 1983 46 54 26 77 80 55 28 32 20 1993/94 58 67 36 103 108 77 36 41 27 1999/00 76 87 48 141 147 114 44 50 32 2004/05 83 94 53 138 146 101 49 55 35 2007/08 86 97 53 155 163 117 46 52 32 2009/10 104 114 73 177 185 140 63 69 48 Growth 1983-1993 26% 24% 38% 34% 35% 40% 28% 28% 35% Growth 1993-2009 79% 70% 102% 72% 71% 82% 75% 68% 77% *Wage was deflated using rural price index of 2004/05 as deflatorSurjit Bhalla Sept 2011 12 Inclusion & Growth in India: Some Facts, Some Conclusions
  • 73. Wage Income Vs Consumption Real Monthly Per Capita Real Monthly Per Capita Wage Consumption of those reporting Overall Real Monthly Per Capita Income wage income Consumption Total Rural Urban Total Rural Urban Total Rural Urban 1983 1221 876 2166 503 414 746 486 442 642 1993/94 1561 1159 2563 554 474 757 546 497 698 1999/00 2075 1476 3466 554 465 761 540 482 693 2004/05 2261 1625 3547 719 602 958 703 618 911 2007/08 2571 1826 4075 659 539 902 636 554 831 2009/10 2927 2080 4517 724 585 985 700 602 910 Growth 1983-1993 28 32 18 10 14 1 12 12 9 Growth 1993-2009 87 79 76 30 23 30 28 21 30 *Wage was deflated using rural price index of 2004/05 as deflatorSurjit Bhalla Sept 2011 13 Inclusion & Growth in India: Some Facts, Some Conclusions
  • 74. Real Consumption Growth, by percentiles, 1983-2004/5 60.0 50.0 zp0483k 40.0 30.0 20.0 0 20 40 60 80 100 ptile SeptSurjit Bhalla 14 Inclusion & Growth in India: Some Facts, Some Conclusions 2011
  • 75. Real Consumption Growth, by percentiles, 1983-2009/10 70.0 60.0zp0983k 50.0 40.0 30.0 0 20 40 60 80 100 ptile SeptSurjit Bhalla 15 Inclusion & Growth in India: Some Facts, Some Conclusions 2011
  • 76. Real Wage Income Per Person Growth, by percentiles, 1983-2004/05 100.0 80.0 zp0483pp 60.0 40.0 0 20 40 60 80 100 ptile SeptSurjit Bhalla 16 Inclusion & Growth in India: Some Facts, Some Conclusions 2011
  • 77. Real Wage Income Per Person Growth, by percentiles, 1983-2009/10 140.0 120.0zp0983pp 100.0 80.0 60.0 0 20 40 60 80 100 ptile SeptSurjit Bhalla 17 Inclusion & Growth in India: Some Facts, Some Conclusions 2011
  • 78. Real Wage Income Per Household Growth, by percentiles, 1983-2004/05 100.0 80.0 zp0483phh 60.0 40.0 20.0 0 20 40 60 80 100 ptile SeptSurjit Bhalla 18 Inclusion & Growth in India: Some Facts, Some Conclusions 2011
  • 79. Real Wage Income Per Household Growth, Difference percentiles, 1983-2004/05 80.0 60.0 zd0483phh 40.0 20.0 0.0 0 10 20 30 40 50 ptile Notes: Each percentile represents the difference in growth rates of the poor and rich percentile e.g. the first percentile represents the difference in growth of the 1st and 100th percentile; second the difference in growth of the 2nd and 99th etc. SeptSurjit Bhalla 19 Inclusion & Growth in India: Some Facts, Some Conclusions 2011
  • 80. Workdays: Casual Vs Total No NREGA Effect? - Proportion of Casual Workdays in Rural Areas same in 1999/00 and 2009/10 Casual Worker workdays in a week Total Workdays in a week Ratio (a/b) (Mn) (Mn) Rural Total (a) Rural Urban Total (b) Rural Urban 1983 390 344 46 1546 1255 291 0.25 1993/94 587 507 80 2150 1648 502 0.27 1999/00 645 555 90 2253 1693 560 0.29 2004/05 645 558 87 2751 2046 705 0.23 2007/08 944 802 142 3000 2176 824 0.31 2009/10 812 684 128 2757 1923 834 0.29Surjit Bhalla Sept 2011 20 Inclusion & Growth in India: Some Facts, Some Conclusions
  • 81. Workdays: Casual Public Works Vs Total Casual works Less than a third of Public works and only 2 percent of all casual workers – and yet causing wage increases and NREGA – inflation? Casual Worker workdays in Public All Casual Worker Works in a week NREGA workdays in a week workdays in a week (Mn) (Mn) (Mn) 2004/05 5.1 NA 645 2007/08 24.7 12.7 944 2009/10 40.1 14.3 812Surjit Bhalla Sept 2011 21 Inclusion & Growth in India: Some Facts, Some Conclusions
  • 82. NREGA 2009: NSSO survey Vs MoRD Ministry of Rural NSSO Survey Development No. of Household having NREGA job card 61.5 Mn 116 Mn No. of Households sought work in NREGA 76.9 Mn 45.5 Mn No. of Households reported working in NREGA 42.8 Mn 45 Mn Daily Status 2.7 Mn NANo. of people reported working in NREGA by daily status Weekly Status 2.4 Mn NA Total No. of days worked in NREGA in one year by household level 1.6 Bn 1.8 Bn 14.3 Mn (Equiv. 0.74 Bn Total No. of days worked in NREGA in one week by daily status in one year) NASurjit Bhalla Sept 2011 22 Inclusion & Growth in India: Some Facts, Some Conclusions
  • 83. Employment Trends 2004-2009  LFPR for age group 15-59 declined from 62.1% to 56.6% However if we take school/college going into account, LFPR(adj) decline from 71.2% to 68.9% Thus some decline can be explained by movement from labor force into education Most of the decline in LFPR is contributed by females in age 25-59 (43.6% to 34.4%), specially for rural females (50.7% to 39.9%) Sharp decline in rural women of age 25-59 self-employed in agriculture (27.2% to 18%) The decline in above category has been across the consumption quantile range No explanation till now, concerns about correctness of survey dataSurjit Bhalla Sept 2011 23 Inclusion & Growth in India: Some Facts, Some Conclusions
  • 84. Labor Force Participation Rate LFPR – a tale of 2 changes: 15-24 (education) and 25-59 (why the decline?) 15-24 25-59 15-59 Total Male Female Total Male Female Total Male Female 1983 50.3 72.2 27.7 66.0 94.3 37.0 60.7 86.8 33.8 1993/94 48.4 66 29 68.7 95.1 41.6 62.1 85.4 37.6 1999/00 44.6 62.2 25.6 67.5 94.6 39.8 60.2 84 35.3 2004/05 46.0 63.2 27.2 69.5 95.3 43.3 62.1 84.9 38.4 2007/08 40.8 59.6 20.4 66.4 95.8 37.0 58.5 84.3 32.0 2009/10 36.3 51.8 18.8 65.5 96.3 34.4 56.6 82.2 29.8Surjit Bhalla Sept 2011 24 Inclusion & Growth in India: Some Facts, Some Conclusions
  • 85. Adjusted Labor Force Participation Rate LFPR Adjusted for education (still a decline) 15-24 25-59 15-59 Total Male Female Total Male Female Total Male Female 1983 66.5 95.2 36.7 66.2 94.5 37.1 66.3 94.8 36.9 1993/94 71.1 95.0 45.0 68.9 95.4 41.7 69.6 95.2 42.8 1999/00 70.3 93.3 45.5 67.8 94.9 40.0 68.6 94.4 41.7 2004/05 74.1 95.8 50.5 69.8 95.6 43.5 71.2 95.7 45.7 2007/08 73.4 96.1 48.6 66.7 96.1 37.2 68.8 96.1 40.7 2009/10 75.7 96.2 52.6 66.0 96.8 34.7 68.9 96.6 40.0 *Adjusted labor force includes persons reporting to attend educational institutionSurjit Bhalla Sept 2011 25 Inclusion & Growth in India: Some Facts, Some Conclusions
  • 86. HARDIK SHAH MEMBER SECRETARYGUJARAT POLLUTION CONTROL BOARD Growth Week 2011 International Growth Centre London School of Economics 21-09-2011 1
  • 87.  In 2001-02, the Hon’ble Supreme Court had identified sixteen cities of India including Ahmedabad as highly polluted Directed the MoEF to have the action plans prepared GoG prepared an action plan and submitted to the MOEF in 2002 The Environment Pollution (Prevention & Control) Authority (EPCA) constituted under directions of Hon’ble Supreme Court of India by the MoEF, GoI under the Chairmanship of Shri Bhure Lal GPCB updated the Air Pollution Control Action Plan for Ahmedabad city in 2004 and submitted this plan to EPCA GoG constituted Task Force headed by Chief Secretary to review the progress of implementation of this action plan 2
  • 88.  Through the implementation of the Air Pollution Control Action Plan, it has been possible to bring down air pollution in the city of Ahmedabad significantly in terms of RSPM (Respirable Suspended Particulate Matter) As per year 2001 data, Ahmedabad was 4th most polluted city in India as identified by Hon’ble Supreme Court Ranking of Ahmedabad improved to 13th in year 2005, 43rd in year 2006 and 66th in year 2009 3
  • 89.  To strengthen the air quality monitoring network To identify the potential sources – Vehicular, Industrial and others To augment public transport To introduce cleaner fuel in vehicles – conversion of vehicles and setting up of fueling stations Setting up of gas grid Cleaner fuel in industries 4
  • 90.  To augment the infrastructure – avoid traffic at strategic locations – underpasses and over bridges Regular cleaning / sweeping of roads Stoppage of burning of garbage – MSW Management To strengthen the APCMs in Industries Public awareness Plantation and greening in city and also in industrial areas 5
  • 91.  GAIL and ONGC helpless to supply CNG to Ahmedabad Non-existence of gas-grid / network for PNG Non-existence of legal instrument for compulsory conversion of vehicles to cleaner fuel Chicken or Egg story : Conversion First OR Gas Network First? Resistance of Auto-rickshaw owners : socio-economic aspects Paucity of funds in Municipal Corporation for introduction of New Buses running on cleaner fuel Resistance from Industries for adoption of stringent APCMs How to check fuel adulteration? Efficient and effective public transportation 6
  • 92.  GSPC took lead to bring gas Set up of city gas supply network Gas filling stations : both by private company and PSU Exercise of powers under Environment (Protection) Act, 1986 Series of consultative meetings with Auto-rickshaw Association : tie up with the banks for easy loans & no reduction in rickshaw fare Mass transit system : BRTS and improved AMTS services Stringent APCMs in Industries using solid fuels (coal / lignite) Efficient and vigorous monitoring of air quality (source & ambient ) 7
  • 93. As On June– 2011 Total vehicles on CNG 107024 CNG Auto Rickshaws 72937 AMC/AMTS  CNG Buses on road 557  Ordered- feeder buses 650  low floor Euro-III buses 50 GSRTC - CNG buses 155 (Entire fleet on Ahmedabad- Gandhinagar route is on CNG buses) CNG stations - Operated by Adani & HPCL 66 8
  • 94. Data about CNG/ PNG vehicles Vehicle Numbers CNG Rickshaw 72937 LPG Rickshaw 26 CNG LMV Car 29117 LPG LMV Car 32613CNG Delivery Van 4258LPG Delivery Van 259 LPG Motorbike 253 CNG Bus 712 9
  • 95.  AMC and AUDA have undertaken 20 projects of construction of flyover, over bridges, underpass, River bridges, widening of road etc. AMC completed 40 KM corridor from RTO to Naroda as a part of BRTS Phase-I. Public Transport increased up to 16 % Under Vehicle Inspection Program, 112 new PUC Centres as per revised system are registered 10
  • 96. • Parking space near BRT bus shelters – autos, bicycles, two- wheelers Inner• Ticketing integration for BRT, city AMTS and BRT feeder• Multi-storied parking plots: 3 2 1 4 5 Kalupur1. Municipal plot located behind 6 Rly. Stn. 7 Navrangpura bus station2. Navrangpura Municipal Market Plot3. Vastrapur lakefront4. Kalupur octroi office5. Kalupur Railway station6. Sarangpur Bus terminal7. Sarangpur Anand market 13
  • 97.  Identified industries having major boilers have upgraded APCM in form of ESP, Bag Filter and MCS. Out of 129 total industrial unit 70 units installed ESP or Bag Filters Remaining have modified APCM by providing MCS, Wet scrubber etc. 571 units switched over to Natural Gas as Fuel 175 Foundry units(Cupola furnace) carried out technological up gradation in APCM 14
  • 98. Up gradation of APCM to achieve revised AAQM norms L.D. College of Engineering, Ahmedabad had carried out study of 87 industrial units of Narol Industrial area. The report is under finalization stage Based on suggestions industrial units will Upgrade existing APCM 15
  • 99.  AAQM stations at different locations 13  AAQM stations are operated by GEMI 11  GPCB 01  Torrent Power 01 From June, 2011 PM 2.5 is being measured AAQM stations are operated as per CPCB guidelines ie 104 samples per year (Twice in a week). Three more AAQM stations are provided in GIDC areas for monitoring of VOC only. One Continuous AAQM station is operated at Maninagar, result are planned to be displayed online on website of GPCB as well as CPCB, New Delhi 16
  • 100. Sr. Station location Type of Zone Operated byNo.1. Above Police Chokey, Naroda GIDC Industrial GEMI2. Cadila Laboratory, Narol Industrial GEMI3. L.D. Engineering College, Residential GEMI Navrangpura4. Shardaben Hospital, Saraspur Residential + Commercial GEMI5. R.C. Technical School Industrial GEMI6. Referral Hospital, Behrampura Residential + Commercial GEMI7. Mukesh Industries, Narol Industrial GEMI8. S.P. Ring Road, Naroda Residential + Commercial GEMI9. Nava Vadaj Urban Health Centre, Nava Residential GPCB Vadaj10. Chinmay Seva Trust, Satelite Residential GEMI11. Vatva- Odhav S.P.Ring Road Residential + Commercial GEMI12. Above Police Chokey, Nehru Bride Commercial GEMI 17
  • 101. Sr. Station location Type of Zone Operated byNo.1. Vatva Industrial Association, GIDC Industrial GEMI Vatva2. Odhav Industrial Association, GIDC Industrial GEMI Odhav3. Udhyognagar Police Chowky, GIDC Industrial GEMI Naroda 18
  • 102. AAQM ANNUAL AVERAGE 2005 - 2011 NARODA GIDC AHMEDABAD (NAMP) 450 400 350CONC. μg/m3 300 250 200 150 100 50 0 2005 2006 2007 2008 2009 2010 2011 RSPM μg/m3 150.31 143.86 150.6 125.92 128.37 150.62 108.57 SPM μg/m3 358.9 330.8 350.79 331.43 286 382.13 249.47 SO2 μg/m3 14.94 13.42 16.9 13 17.32 19.9 19.96 NOx μg/m3 29.5 26.88 30.69 21.41 22.63 26.64 34.96 19
  • 103. 400 AAQM ANNUAL AVERAGE 2005 - 2011 350 CADILA BRIDGE NAROL AHMEDABAD (NAMP) 300CONC. μg/m3 250 200 150 100 50 0 2005 2006 2007 2008 2009 2010 2011 RSPM μg/m3 143.03 121.17 102.2 83.24 90 85.87 77 SPM μg/m3 339.66 269.9 235.92 205.95 199.93 189.3 176.6 SO2 μg/m3 14.45 12.1 14.45 12.58 18.71 16.02 13.19 NOx μg/m3 28.14 24.68 25.92 20.59 23.56 21.15 22.1 20
  • 104. AAQM ANNUAL AVERAGE 2005-2011 L.D.ENG.COLLAGE AHMEDABAD ( NAMP ) 250CONC. μg/m3 200 150 100 50 0 2005 2006 2007 2008 2009 2010 2011 RSPM μg/m3 99.59 73.66 61.23 72.015 81.95 69.44 60.17 SPM μg/m3 233.13 163.3 137 178.49 184.56 147.34 132.47 SO2 μg/m3 11.58 9.02 8.56 12.13 13.33 12.06 10.73 NOx μg/m3 22.37 18.78 14.36 18.21 18.13 17.11 14.79 21
  • 105. AAQM ANNUAL AVERAGE 2005-2011 SARDABEN HOSPITAL SARASPUR AHMEDABAD ( NAMP ) 250 200CONC. μg/m3 150 100 50 0 2005 2006 2007 2008 2009 2010 2011 RSPM μg/m3 81.65 91.76 85.34 80.09 87.86 80.09 65.83 SPM μg/m3 196.3 205.3 196.05 205.77 195.07 181.55 151.27 SO2 μg/m3 11.94 10.44 11.61 12.34 14.41 14.16 11.67 NOx μg/m3 25.11 21.63 19.91 19.02 19.85 19.13 17.47 22
  • 106. AAQM ANNUAL AVERAGE 2005-2011 R.C. TECHNICAL AHMEDABAD ( NAMP ) 250 200CONC. μg/m3 150 100 50 0 2005 2006 2007 2008 2009 2010 2011 RSPM μg/m3 84.24 100.66 85.11 81.53 88.3 93.68 67.2 SPM μg/m3 199.83 227.76 195.51 198.61 195.88 189.38 154.2 SO2 μg/m3 11.75 10.4 11.05 11.88 21.26 15.25 12.38 NOx μg/m3 25.13 22.16 19.2 19.82 19.48 20.07 17.36 23
  • 107. AAQM ANNUAL AVERAGE 2005-2011 BEHRAMPURA REFRAL HOSPITAL AHMEDABAD ( NAMP ) 250 200CONC. μg/m3 150 100 50 0 2005 2006 2007 2008 2009 2010 2011 RSPM μg/m3 83.01 91.03 85.79 82.18 85.95 86.56 64.33 SPM μg/m3 198.94 203.86 197.16 193.75 192.9 183.28 147.63 SO2 μg/m3 11.67 10.08 11.14 12.3 16.45 15.38 12.17 NOx μg/m3 24.52 21.48 19.28 19.57 20.83 20.12 19.11 24
  • 108. AAQM ANNUAL AVERAGE 2005-2011 MUKESH INDUSTRIES NAROL AHMEDABAD( SAMP ) 700 600CONC. μg/m3 500 400 300 200 100 0 2005 2006 2007 2008 2009 2010 2011 RSPM μg/m3 219.46 184.34 214.16 174.1 172.98 188.43 163.37 SPM μg/m3 609.03 545.57 537.5 499.7 403.08 462.21 391.93 SO2 μg/m3 21.15 23.15 19.63 15.2 20.14 21.06 20.44 NOx μg/m3 36.36 35.46 33.92 23.4 24.76 27.71 39.36 25
  • 109. AAQM ANNUAL AVERAGE 2005-2011 S.P.RING ROAD NARODA AHMEDABAD ( SAMP ) 250 200CONC. μg/m3 150 100 50 0 2007 2008 2009 2010 2011 RSPM μg/m3 71.95 73.32 84.99 85.7 74.6 SPM μg/m3 163.66 175.22 190.92 180.32 179.07 SO2 μg/m3 9.32 11.69 13.57 13.61 12.54 NOx μg/m3 14.74 17.81 19.4 18.02 20.52 26
  • 110. AAQM ANNUAL AVERAGE 2005-2011 NAVA VADAJ URBEN HEALTH AHMEDABAD ( SAMP ) 250 200CONC. μg/m3 150 100 50 0 2007 2008 2009 2010 2011 RSPM μg/m3 74.86 78.9 86.26 88.64 70.67 SPM μg/m3 172.28 191.5 195.9 189.44 162.23 SO2 μg/m3 9.8 12.3 14.51 14.62 11.99 NOx μg/m3 15.81 18.5 19.9 18.82 17.86 27
  • 111. AAQM ANNUAL AVERAGE 2005-2011 SATELLITE AREA AHMEDABAD ( SAMP ) 250 200CONC. μg/m3 150 100 50 0 2007 2008 2009 2010 2011 RSPM μg/m3 76.09 80.56 87.87 82.25 70.3 SPM μg/m3 170.62 192.54 196.96 85.49 154.83 SO2 μg/m3 9.27 12.28 14.91 14.13 14.41 NOx μg/m3 14.96 19 20.5 18.74 22.03 28
  • 112. AAQM ANNUAL AVERAGE 2005-2011 VATVA - ODHAV S.P.RING ROAD AHMEDABAD ( SAMP ) 200 180 160CONC. μg/m3 140 120 100 80 60 40 20 0 2007 2008 2009 2010 2011 RSPM μg/m3 79.12 78.57 83.83 85.49 69.85 SPM μg/m3 181.74 188.94 189.56 180 167.37 SO2 μg/m3 10.3 12.27 14.28 14.13 12.56 NOx μg/m3 15.9 18.97 19.58 18.74 21.45 29
  • 113. AAQM ANNUAL AVERAGE 2005-2010 TORRENT POWER, SABARMAT AHMEDABA( SAMP ) 300 250 200 150 100CONC. μg/m3 50 0 2005 2006 2007 2008 2009 2010 RSPM μg/m3 100.14 89.7 95.2 94.09 77.49 71.84 SPM μg/m3 279.2 260.2 270.54 255.75 170.07 158.36 SO2 μg/m3 32.08 33.29 28.42 29.31 27.29 33.27 NOx μg/m3 19.99 24.19 25.7 26.13 24.73 24.27 30
  • 114. OTHER ACTIONS In day time (9 AM to 8 PM) big private vehicles (Luxuries) are not allowed on the arterial roads. Wall to Wall carpeting of the road is now inbuilt design component on BRTS route to prevent secondary emission of Particulates Matter (PM) In large construction projects, barricading around the site is enforced to contain fugitive emission Scientific Land Fill Site for the disposal of the MSW is now functional in AMC- Open burning of the MSW is stopped through better collection system Massive tree plantation in the urban area by the AMC in open plots and along the BRTS corridor Public Information and Awareness-Announcement of traffic situation during peak hours on local FM radio stations, Display Boards at Traffic Junctions etc. 31
  • 115. A WAY FOREWARD Metro Rail Project connecting APMC-Vasna to Gandhinagar – A MRTS project-Survey work initiated Sabarmati River Front Project- In advance stage of its construction-will relieve the traffic pressure on Arterial roads in addition to give new mode of transportation-ferry boats Integration of Metro Rail Project & BRTS will convert more people to the common transport modes Increase in public awareness through various campaigns- involvement of specialized institutes like MICA for preparing programs Use of PNG and other cleaner fuel in non-compliant industries Strengthening of the ambient air quality monitoring network 32
  • 116. Thank you