Growth Week 2011: Country Session 10 - India-Central

Foreign Investors under stress

Ajay Shah Ila Patnaik Nirvikar Singh

September 15, 2011

Shah,Patnaik,Singh () Foreign Investors under stress September 15, 2011 1 / 32

Part I

Questions


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.


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?”


These questions are a little different from those
emphasised 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 theoretical
interpretations.


Part II

Measurement strategy


Three key ideas

1 How to focus on tail events? Analogous to a tail beta: Focus on the
extreme days.


Three key ideas

extreme days.
2 How to identify impacts? Treat a tail event as a shock, and use the
event study methodology to trace out impacts.


Three key ideas

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).


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.


Part III

Data


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 Daily
Renormalisation Express FII flows as percent to the overall domestic
market capitalisation.


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


Summary statistics: Event on S&P 500

Shocks 1.5% 2.5% 5%



Summary statistics: Event on FII

Shocks 1.5% 2.5% 5%



Summary statistics: Event on VIX

Shocks 1.5% 2.5% 5%



Summary statistics: By year

5% 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


Part IV

Methodology


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.


Part V

Results


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).


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

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)


Very good days on Nifty are associated with buying by FIIs both
before and after the event date.
Very good days on Nifty are generally positive news days: so FIIs
could be responding either to the news or to Nifty.
Relatively modest eﬀects: Total buying of 0.06 basis points of Indian
market capitalisation.
Asymmetry: Such evidence is not found on bad days.


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.


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



Very good / bad days for FII investment are likely to be associated
with news.
It is hence not surprising to see Nifty being higher or lower on event
date.
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.


Q3: What happens to foreign investors on an extreme day
for 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?


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

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



FIIs seem to buy more Nifty when there is good news on the S&P 500
But no such eﬀects in the other direction.


Part VI

Summary and conclusions


Concerns of developing country policy makers




5 “Is the behaviour of foreign investors asymmetric, where bad days are
punished but great positive news is not?”


Innovations of our approach

Treat extreme events (uncontaminated) as pure shocks and watch
what happens
Event study methodology


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.


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.


Thank you.


Motivating Agents to Spread
Information: 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)

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

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?

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)

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

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

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

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

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

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

Effect of awareness-spreading agents

(1) (2) (3) (4)
Heard of Have enrolled Knowledge Have utilised
RSBY
Agent 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

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

Effect disaggregated by agent contract type
(1) (2) (3) (4)
Heard of RSBY Have enrolled Knowledge Have utilised
Flat-pay agent in -0.0171 -0.0344 0.0415 -0.00000142
village (0.0375) (0.0591) (0.0295) (0.00224)

Knowledge-pay agent 0.0473** 0.0683 0.0816*** 0.000115
in village (0.0226) (0.0442) (0.0213) (0.00221)

Utilisation-pay agent in -0.0243 -0.0000599 0.0358 0.0000566
village (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

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

Social matching versus incentives
(1) (2) (3)
Knowledge Knowledge Knowledge
Knowledge-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)

Household's SC/ST status matches that of agent 0.0527** 0.0559*
(0.0255) (0.0320)

Household's SC/ST status matches that of agent x Knowledge-pay -0.00973
agent (0.0430)

Constant 0.379*** 0.341*** 0.338***
(0.0292) (0.0349) (0.0365)
Observations 746 746 746

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

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).

Inclusion & Growth in India:
Some Facts, Some Conclusions

Surjit S Bhalla
Prepared for International Growth Centre
Growth Week
London,
Sept 21, 2011

Sept
Surjit Bhalla 1 Inclusion & Growth in India: Some Facts, Some Conclusions
2011

World Bank Poverty Lines – A changing goalpost
•

Surjit Bhalla Sept 2011 2 Inclusion & Growth in India: Some Facts, Some Conclusions

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

Sept
2011

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

Sept
2011

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.

Sept
2011

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.

Sept
2011

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

Sept
2011

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

Sept
2011

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

Sept
2011

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.

Sept
2011

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
Sept
2011

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 deflator


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 deflator


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

Sept
2011

Real Consumption Growth, by percentiles, 1983-2009/10
70.0
60.0
zp0983k
50.0
40.0
30.0

0 20 40 60 80 100
ptile

Sept
2011

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

Sept
2011

Real Wage Income Per Person Growth, by percentiles, 1983-2009/10
140.0
120.0
zp0983pp
100.0
80.0
60.0

0 20 40 60 80 100
ptile

Sept
2011

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

Sept
2011

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.

Sept
2011

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 0.29


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 14.3 812


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 NA
No. 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) NA


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 data


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


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 29.8


Adjusted Labor Force Participation Rate

LFPR Adjusted for education (still a decline)
15-24 25-59 15-59


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 institution


HARDIK SHAH
MEMBER SECRETARY
GUJARAT POLLUTION CONTROL BOARD

Growth Week 2011
International Growth Centre
London School of Economics
21-09-2011 1

 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

 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

 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

 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

 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

 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

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

Data about CNG/ PNG vehicles
Vehicle Numbers
CNG Rickshaw 72937
LPG Rickshaw 26
CNG LMV Car 29117
LPG LMV Car 32613
CNG Delivery Van 4258
LPG Delivery Van 259
LPG Motorbike 253
CNG Bus 712
9

 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

• 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 Kalupur
1. Municipal plot located behind 6 Rly. Stn.
7
Navrangpura bus station
2. Navrangpura Municipal Market Plot
3. Vastrapur lakefront
4. Kalupur octroi office
5. Kalupur Railway station
6. Sarangpur Bus terminal
7. Sarangpur Anand market
13

 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

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

 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

Sr. Station location Type of Zone Operated by
No.
1. Above Police Chokey, Naroda GIDC Industrial GEMI
2. Cadila Laboratory, Narol Industrial GEMI
3. L.D. Engineering College, Residential GEMI
Navrangpura
4. Shardaben Hospital, Saraspur Residential + Commercial GEMI
5. R.C. Technical School Industrial GEMI
6. Referral Hospital, Behrampura Residential + Commercial GEMI
7. Mukesh Industries, Narol Industrial GEMI
8. S.P. Ring Road, Naroda Residential + Commercial GEMI
9. Nava Vadaj Urban Health Centre, Nava Residential GPCB
Vadaj
10. Chinmay Seva Trust, Satelite Residential GEMI
11. Vatva- Odhav S.P.Ring Road Residential + Commercial GEMI
12. Above Police Chokey, Nehru Bride Commercial GEMI
17

Sr. Station location Type of Zone Operated by
No.
1. Vatva Industrial Association, GIDC Industrial GEMI
Vatva
2. Odhav Industrial Association, GIDC Industrial GEMI
Odhav
3. Udhyognagar Police Chowky, GIDC Industrial GEMI
Naroda

18

AAQM ANNUAL AVERAGE 2005 - 2011
NARODA GIDC AHMEDABAD (NAMP)
450
400
350
CONC. 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

400
AAQM ANNUAL AVERAGE 2005 - 2011
350 CADILA BRIDGE NAROL AHMEDABAD (NAMP)
300
CONC. 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

AAQM ANNUAL AVERAGE 2005-2011
L.D.ENG.COLLAGE AHMEDABAD ( NAMP )

250
CONC. 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

SARDABEN HOSPITAL SARASPUR AHMEDABAD ( NAMP )
250

200
CONC. 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

R.C. TECHNICAL AHMEDABAD ( NAMP )
250
200
CONC. 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

BEHRAMPURA REFRAL HOSPITAL AHMEDABAD ( NAMP )
250

200
CONC. 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

MUKESH INDUSTRIES NAROL AHMEDABAD( SAMP )
700
600
CONC. 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

S.P.RING ROAD NARODA AHMEDABAD ( SAMP )
250

200
CONC. 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

NAVA VADAJ URBEN HEALTH AHMEDABAD ( SAMP )
250

200
CONC. 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

SATELLITE AREA AHMEDABAD ( SAMP )
250

200
CONC. 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

VATVA - ODHAV S.P.RING ROAD AHMEDABAD ( SAMP )
200
180
160
CONC. 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

TORRENT POWER, SABARMAT AHMEDABA( SAMP )
300
250
200
150
100
CONC. 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

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

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

Growth Week 2011: Country Session 10 - India-Central

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Growth Week 2011: Country Session 10 - India-Central