Millenium_Development_Goals.pps

7/28/2019 Millenium_Development_Goals.pps

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Attaining the Millennium

Development Goals in India:How Likely & What Will It

Take?


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Millennium Development

Goals (MDGs)

As you all know, the MDGs are a set ofnumerical and time-bound targets tomeasure achievements in human and social

development.


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Five MDGs analyzed in this

Report

Child and infant mortality reduction

Reduction in child malnutrition

Universal primary enrollment

Elimination of gender disparity in school

enrollment Reduction of hunger-poverty (calorie deficiency)


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Analysis has been at a highly aggregate level

typically the level of the country. This is

meaningless in a large and heterogeneous

country like India.

The likelihood of attaining the MDGs hasnt

been usefully linked to the factors that

influence MD indicators. This is necessary toaddress the question: what will it take to

attain the MDGs?

Limitations of much of the MDG

discussion so far


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MDG Attainment in the Poor

States of India

The poorest states in India (e.g., Uttar Pradesh, Bihar,Rajasthan, Orissa, and Madhya Pradesh):

are among the most populous in the country, and

have among the worst MD indicators.

Owing to more rapid population growth, these states willaccount for an even larger share of Indias population in2015.

Therefore, Indias attainment of MDGs will largely dependon the performance of these states.


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Tremendous spatial variation in

levels of & changes in MD indicators

There are very large inter-state and intra-statevariations in all MD indicators in India. Forinstance, the IMR for the country is 66 infant

deaths per 1,000 live births. But it varies from afigure of 11 in Kerala to 90 in Orissa.

Intra-state variations in infant mortality and inprimary school enrollment rates are even greater,as seen in the following map.


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IMR (Regions)per 100 live births

100 to 130 (3)90 to 100 (10)80 to 90 (6)70 to 80 (15)

60 to 70 (9)50 to 60 (8)20 to 50 (5)0 to 20 (2)

missing (21)

Infant Mortality Rate, 1997-99


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Net primary enrollment rates also vary

a great deal across regions


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And there is a great deal intrastate variation in

IMR decline as well, with some regions showing


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as in changes in net primary enrollments.


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Geographic Concentration of MD

indicators The wide disparity in MD indicators results

in the geographical distribution of these

indicators being heavily concentrated.

This indicates the need for targeting MDG-

related interventions to poorly-performing

states, districts, and perhaps even villages(if these could be identified).


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Case of infant mortality

Four states

Uttar Pradesh

Madhya Pradesh

Bihar

Rajasthan

Account for more than 50% of infant mortality in

India Four more states account for another 21%, or a

cumulative 72%


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Contribution of the 21 larger states to national infant deaths, 200097

969389

83

76

67

43

57

25

6 5 5 5 4 4 3 3 3 2 2 2 1 0 0 0 0

9 89

0

10

20

30

40

50

60

70

80

90

100

UttarPrad

esh

MadhyaPrad

esh

Bihar

Rajasthan

AndhraPrad

esh

Maharashtra

Orissa

WestBen

gal

Gujarat

Karnataka

TamilNadu

Assam

Jharkhand

Chhatisg

arh

Haryana

Pun

jab

Jammu&Kash

mir

Delhi

Uttaranc

hal

HimachalPrad

esh

Kerala

Cumulativecontribution(%

)

Cumulative share in total number of infant deaths nationally

Share in total number of infant deaths nationally

51% 21%


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Infant deaths are even more

concentrated at the district and the

village levels.


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Only one-fifth of the districts and villages in the country

account for one-half of all infant deaths

Cumulative distribution of infant deaths in India across districts and

villages, 1994-98

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

Cumulative % of districts or villages (ranked by infant deaths)

Cumula

tive%o

fnationalinfan

tdeaths

Villages

Districts


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and more than half of all underweight children are

found in only a quarter of all villages and districts in the

country.

Cumulative distribution of all underweight 0-35 month old children inIndia across villages and districts, 1998-99

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

Cumulative % of villages or districts (ranked by number of underweight children)

Cumulative%

ofallunderweightch

ildreninthe

country Districts Villages


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Out-of-school children are even more concentrated. Nearly

three-quarters of all out-of-school children in the country

are found in a mere 20% of villages (and 50% of districts).

Cumulative distribution of all out-of-school 6-11 year olds in India across

villages and districts, 1999-2000

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

Cumulative % of villages or districts (ranked by number of out-of-school 6-11 year olds)

Cumulative%o

fallout-of-school6-11y

earoldsin

thecountry Districts Villages


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Identification of villages with poor

MD indicators Unfortunately, currently-available data cannot

allow identification of specific villages that

account for most of the infant deaths,underweight children, or out-of-school children

in the country, because most sample surveys are

not large or representative enough at the village

level.

But new, emerging methodologies are available

to do this.


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Most Deprived Regions in India

But we can identify the most-deprived regions in

the country.

There are two regions in the country that are themost deprived in terms of all the 5 MDG

indicators we have analyzed (Southwestern M.P.

and Southern Rajasthan).

There are another 6 regions that are most deprived

in terms of 4 of the 5 indicators we have analyzed.


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MDG attainment

Clearly, attaining the MDGs will require

action in the poorest states, districts and

villages.

How can it be done? What will it take?


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Estimation of household,

behavioral models of MD indicators Using household survey data from various

sources, we have attempted to quantify the factors

associated with the reduction of infant mortality,child malnutrition, schooling enrollment, gender

disparity, and hunger-poverty.

These models are used to project changes in MD

indicators in the poor states by 2015 under certain

intervention scenarios.


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We have considered:

General InterventionsEconomic growth

Expanded adult male and female

schooling

Increased access to water & sanitation

Improved electricity coverage

Increased access topucca roads


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Sectoral Interventions

Increased government spending on health andfamily welfare, nutrition, and elementary education

Various sector-specific interventions, such as

More professionally-assisted deliveriesAntenatal care coverage and tetanus toxoidimmunization for pregnant women

Increased number of primary schools per childaged 6-11

Reduction in the pupil-teacher ratioGreater irrigation coverage

Increased foodgrain production per capita.


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Results of the Simulations

Large improvements in all the MD

indicators are possible with concerted action

in many areas.

Both general and sector-specific

interventions will be important in attainingthe MDGs.


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Infant mortality could decline by 50% if the poor states were

to be brought up to the level of the non-poor states

Projected decline in the infant mortality rate in the poor states by 2015 under

different intervention scenarios (Base IMR=76 in 2000)

757474

7173

67

71

62

68

51

67

46

65

43

64

39

35

45

55

65

75

National average Average of the non-poor states

Poor states are brought up to the:

Sanitation coverage

Electricity coverage

Regular electricity coverage

Adult female schooling

Government expenditure per capita on health and family welfare

Pucca road coverage

Tetanus toxoid immunization coverage

Antenatal care coverage

Intervention


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Any single intervention wont go very far in

attaining the MDGs.

What is needed is a package of

interventions.


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The child underweight rate could decline by 40% if the poor

states were to be brought up to the level of the non-poor states

Projected decline in the in the child underweight rate in the poor states by 2015

under different intervention scenarios (Base rate=51 in 2000)

504950

4849

4748

43

47

40

44

34

43

31

43

30

25

30

35

40

45

50



Sanitation coverage

Electricity coverage


Adult female schooling

Improved living standards (consumption expenditure per capita)

Government expenditure on nutrition programs per child aged 0-6 years

Pucca road coverageMedical attention at birth

Intervention


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Projected increase in the net primary attendance rate for 6-11 year olds in the poor

states by 2015 under different intervention scenarios (Base rate=50% in 2000)

505150

51

54

63

54

64

54

64

54

64

56

68

56

69

56

69

45

50

55

60

65

70



Adult male schoolingAdult female schoolingImproved living standards (consumption expenditure per capita)Government expenditure on elementary education per child 6-15 yearsCrime against women and girlsPucca road coverageElectricity coverage

Number of primary schools per 1,000 children aged 6-11Pupil teacher ratio in primary schools

Intervention

The net primary enrollment rate in the poor states could

increase from 50% to 69% if the poor states were to be brought

up to the level of the non-poor states


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Trajectory of Selected MD

Indicators to 2015We have also made some assumptions

about how the various policy

interventions might change over time,and

then traced out the path of the MDindicators to 2015.


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Assumptions about policy

interventions to 2015Assumptions about various interventions to reduce the infant mortality rate in the poor states, 1998-99 to 2015

Intervention Starting value Assumed change per year Ending value in 2015

Population with no access to toilets (%) 76.5 -2% points 42.5

Population coverage of regular electricity

supply 27.7 1% point 44.7

% villages having access topucca roads 59.5 1% point 76.5

Consumption expenditure per capita 422 3% 698

Adult male schooling years 4.5 0.25 8.5

Adult female schooling years 2.0 0.3 6.8

Government expenditure on health and family

welfare per capita 95 4% 185

Government expenditure on nutrition

programs (ICDS) per child 0-6 years 51 4% 98

Government expenditure on elementary

education per child 6-14 years 955 4% 1,789


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Assumptions about various interventions to reduce the infant mortality rate in the poor states, 1998-99 to 2015

Intervention Starting value Assumed change per year Ending value in 2015

Coverage of antenatal care 55.5 1% point 72.5

% of pregnant women obtaining tetanus

toxoid immunization 70 1% points 87

% of professionally-attended deliveries 32.3 1.5% points 57.8

Crime against women (number of female

kidnappings and rapes per 100,000

population) 1.65 -0.05 0.85

Crime against women (number of female

kidnappings and rapes per 100,000

population) 1.65 -0.05 0.85

Number of primary schools per 1,000 children

aged 6-11 years 5.1 .2 8.3

Pupil-teacher ratio in primary schools 91 -1 75

Share of secondary education in total

government expenditure on education 36 1% 52

% of area irrigated 29.2 1% point 45.2

Food grain production per capita in districts 186 2% 255


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The simulations suggest that attaining the infant mortality

MDG in the poor states will be challenging but not impossible

with a package of interventions

Projected infant mortality rate in the poor states to 2015, under different

intervention scenarios(graph shows cumulative effect of each additional intervention)

20

30

40

50

60

70

80

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

20

30

40

50

60

70

80

Tetanus toxoid immunization

Real gov't health exp. per capita

Access to sanitation


Mean schooling years of adult females

Village access to pucca roads

Access to antenatal care

Intervention

MDG for poor states


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Likewise, it would be possible to reach the child malnutrition

MDG in the poor states with a package of interventions

Projected % of children 0-3 who are underweight in the poor states to 2015,under different intervention scenarios

(graph shows cumulative effect of each additional intervention)

20

25

30

35

40

45

50

55

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

20

25

30

35

40

45

50

55

Medical attention at birthReal gov't exp. on nutrition per childAccess to sanitationReal income growthRegular electricity coverageMean schooling years of adult femalesVillage access to pucca roads

Intervention

MDG for poor states


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but attaining the 100% net primary enrollment goal by

2015 will be problematic in the poor states

Projected net primary enrollment rate in the poor states to 2015,under different intervention scenarios


45

50

55

60

65

70

75

80

85

90

95

100

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

45

50

55

60

65

70

75

80

85

90

95

100

Reduction in the primary pupil teacher ratio

Increased number of primary schools per 1,000 children aged 6-11

Reduction in crime against womenReal income growth

Increase in the mean schooling years of adult females

Increase in the mean schooling years of adult males

Increased electricity access

Greater gov't exp on elementary schooling per child 6-14

InterventionMDG


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Likewise, it will be very difficult for the poor states to attain

the 100% primary completion goal by 2015

Projected primary completion rate (%) in the poor states to 2015, under

different intervention scenarios(graph shows cumulative effect of each additional intervention)

45

50

55

60

65

70

75

80

85

90

95

100

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

45

50

55

60

65

70

75

80

85

90

95

100

Reduction in the primary pupil teacher ratioReduction in crime against womenImproved road accessReal income growth

Increase in mean schooling years of adult femalesIncrease in mean schooling years of adult malesGreater gov't exp on elementary schooling per child 6-14Increased electricity access

Intervention

MDG


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Note that increasing the net primary enrollment

rate to 100% (the MD goal) is different from

getting all children aged 6-11 in school.

The simulations suggest that getting all children

aged 6-11 in school is attainable with the same set

of interventions discussed earlier.


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Projected % of children aged 6-11 attending school in the poor states to

2015, under different intervention scenarios(graph shows cumulative effect of each additional intervention)

50

55

60

65

70

75

80

85

90

95

100

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

50

55

60

65

70

75

80

85

90

95

100

Increased electricity coverageIncrease in mean schooling years of adult malesIncrease in mean schooling years of adult femalesReal income growthReduction in crime against womenReduction in the primary pupil teacher ratioExpansion of number of primary schools per child 6-11

Intervention


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Other MDGs

What about:

Gender disparity in schooling, and

Hunger poverty?


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Complete elimination of the gender disparity in primary and

secondary school enrollment also appears difficult in the poor

states.

Projected male-female difference (in percentage points) in school attendance rate of

children aged 6-18 in the poor states to 2015, under different intervention scenarios


0

5

10

15

20

25

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

0

5

10

15

20

25

Real income growthExpanded road accessIncrease in share of secondary educ. in total gov't exp. on educ.Increase in mean schooling years of adult femalesIncrease in mean schooling years of adult malesReduction in crime against womenExpanded electricity access

Intervention

MD goal


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But elimination of hunger-poverty in the poor states is very

likely with a package of interventions, especially since hunger-

poverty appears to be very responsive to economic growth.

Projected incidence of hunger-poverty (calorie deficiency) (%) in the

poor states to 2015, under different intervention scenarios(graph shows cumulative effect of each additional intervention)

20

25

30

35

40

45

50

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

20

25

30

35

40

45

50

Increased access to safe water

Improved road accessIncrease in mean schooling years of adult males

Increase in mean schooling years of adult females

Increased foodgrain production per capita

Increased irrigation coverage

Real income growth

Intervention

MDG Target in 2015


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Summing Up

Meeting the MDGs will be challenging, especially forthe poor states in India.

A number of interventions, including

economic growth

improved infrastructure (especially water and sanitation,electricity, and road access)

expansion of female schooling, and

scaling up of public spending on the social sectors

will be needed in order to attain the MDGs.


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Also important will be a number of sectoral interventions, suchas

improved access to antenatal care

Immunization

nutritional supplementation

home-based neonatal services

increasing the density of schools lowering the pupil-teacher ratio

raising agricultural production.

Targeting interventions, public spending, and economic growth

opportunities to the poor states and, within those, to the poordistricts and villages will be critical.


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Finally, the importance of

systematically monitoring MD outcomes atdisaggregated levels and

evaluating the impact of public programs

cannot be overemphasized.

Currently, there is no system for monitoringprogress toward attainment of the MDGs atthe sub-national level.


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In addition, most public interventions, such asthe Integrated Child Development Services

and the District Primary Education Program,have not been subjected to rigorous,independent evaluation.

In order to choose the right set ofinterventions with which to attain the MDGs,it is critical to know which programs have

been successful in improving MD indicators

and which have not.


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Caveats

Estimations and simulations subject to usual

problems of measurement error, estimation

bias, etc. Therefore, projections are indicative and

should be used in rough-order planning.


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Simulations focus on quantitative variables and

not on qualitative variables, such as governance.Does not mean that governance is not important,

just that it is difficult to take that into account in

the simulations.

The simulations assume business as usual. Anyimprovements in governance will result in

speedier attainment of MDGs.

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