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India’s Approach to MDG Data Gaps S. Chakrabarti Director Central Statistical Organisation India

India’s Approach to MDG Data Gaps

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India’s Approach to MDG Data Gaps. S. Chakrabarti Director Central Statistical Organisation India. NDP Framework. FYP - India’s development framework Derives strategies from track record Sets goals and targets Identifies intervention mechanism Defines approach to programmes - PowerPoint PPT Presentation

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Page 1: India’s Approach to MDG Data Gaps

India’s Approach to MDG Data Gaps

S. Chakrabarti

Director

Central Statistical Organisation

India

Page 2: India’s Approach to MDG Data Gaps

NDP Framework

FYP - India’s development framework Derives strategies from track record Sets goals and targets Identifies intervention mechanism Defines approach to programmes Allocates resources

National Policies and Action Plans Political Agenda

Page 3: India’s Approach to MDG Data Gaps

MDGs- another framework

Poses new challenges Compatibility with NDP framework Reformation of existing Statistical objectives Relevance of the indicators – How many?

Optimality of choice Transformation burden Degrees of freedom

Page 4: India’s Approach to MDG Data Gaps

India’s Approach

Recognizing the potentiality of existing processes

Minimum conflict with NDP framework –discretion

lowkey troubleshooting Reliance on alternatives Allowance for invisibility

Page 5: India’s Approach to MDG Data Gaps

Actionable Indicators

40 out of 48 are relevant 35 out of 40 for tracking – straightway 34 of 35 are visible in MDGR 6 out of 40 are missing – in the lab

Page 6: India’s Approach to MDG Data Gaps

Classification

Identical

Similar

Alternative

Invisible

having exact conformity with the

standard definitions

definitionally modified as per data

availability or for contextual reasons

different indicator in the absence of

quality data for the prescribed

left out either for reasons of contextual

irrelevancy or for complete lack of data

Page 7: India’s Approach to MDG Data Gaps

Size of the classes

No. of Indicators in each class

Identical 13

Similar 18

Alternative 3

Invisible 6

Total 40

Page 8: India’s Approach to MDG Data Gaps

Minimum loss

1/3rd of actionable set are identical – matching globally

50% of actionable set are of altered kind – includes 7.5% of real proxy type

15% are missing -

Page 9: India’s Approach to MDG Data Gaps

ALTERED VS. STANDARD

Issue: ‘Altered’ Indicators

Page 10: India’s Approach to MDG Data Gaps

Poverty Headcount Ratio

State specific poverty lines for rural and urban areas – sub-national

National poverty lines for rural and urban areas differ from States’ poverty lines

All-India implicit poverty line for the urban areas is nearly 57% higher than that for rural areas at 2004-05 prices

1.1A

Page 11: India’s Approach to MDG Data Gaps

PHR (contd.)

Poverty line (hence PHR) is based on distribution of persons by household per capita monthly consumption expenditure corresponding to the consumption basket associated with the given calorie norm (2400 kcal in rural areas and 2100 kcal in urban areas) and meeting a minimum of non-food requirements such as clothing, shelter, transport, etc.

Percentage of population below the national poverty line [= PHR] is weighted average of States’ PHRs

Relative price differentials in different states get reflected

1.1A

Page 12: India’s Approach to MDG Data Gaps

Youth Literacy

For age group (15-24 yrs) there are 2 MDG indicators

Literacy Rate of 15-24 year olds (2.3) Ratio of Literate Women to Men, 15-24 years old (3.2)

India reports in MDGR Adult literacy rate for the age group (15yrs+) for 2.3 Literacy gender parity index for (7yrs+) for 3.2

2.3 + 3.2

Page 13: India’s Approach to MDG Data Gaps

Youth Literacy (contd.)

India’s reporting in keeping with NLM objectives and programme determeined

Data available from decennial census and NSS can be tailored to get both measures for age group (15-24 yrs)

Shift to the right age bracket on cards

2.3 + 3.2

Page 14: India’s Approach to MDG Data Gaps

Underweight Children

Prevalence rate has reference age group of 0-59 months prescribed

India’s data is based of National Family Health Survey (NFHS) conducted for the years 1992-93, 1998-99 and 2005-06

Last two surveys has reference age 0-35 months while the first one has 0-47 months

Results of last 2 NFHSs are not comparable with first NFHS results

1.4

Page 15: India’s Approach to MDG Data Gaps

Underweight: < (- 2 SD) from median weight of reference age group (MDG prescribed)

Underweight : < (- 2 SD) from mean as per WHO’s standard for the age of the child (India’s criterion)

global comparability is vitiated, while reporting for health statistical

frame work is kept in view

Underweight Children (contd.)1.4

Page 16: India’s Approach to MDG Data Gaps

INCIDENCE BIAS

Issue: coverage

Page 17: India’s Approach to MDG Data Gaps

HIV related

Two indicators: HIV prevalence among pregnant women aged 15-24

years and Condom use percentage at high-risk age.

Data on these are collected through Annual round of HIV sentinel surveillance at identified

sentinel sites (clinics) conducted during 12 weeks from 1st August to 31st October every year.

Behavioural Sentinel Surveillance Survey’ (BSS) conducted once in three years among general population and high-risk groups.

6.3 + 6.4

Page 18: India’s Approach to MDG Data Gaps

HIV related

The estimates are too specific to high-risk zones, both at state-level and national level.

Whereas MDG prescription stresses to high-risk sex in general

The findings of the two for high-risk groups differ as the latter survey is conducted by an independent organisation.

6.3 + 6.4

Page 19: India’s Approach to MDG Data Gaps

Malaria Prevalence

Limitation of these rates is that they grossly underestimate the incidence in tribal, hilly, difficult and inaccessible areas, which cover 20% of population but 80% of malaria cases.

6.5 + 6.6

Page 20: India’s Approach to MDG Data Gaps

TB Prevalence

Death rate due to TB as per notified cases under DOTS or captured through ARTI is grossly underestimated

It is Important as India’s TB burden is of great concern.

6.7 + 6.8

Page 21: India’s Approach to MDG Data Gaps

Whose is more important?

Issue: compatibility with international bodies

Page 22: India’s Approach to MDG Data Gaps

CO2 Emission

National reporting to the United Nations Framework Convention on Climate Change, which follows the Intergovernmental Panel on Climate Change guidelines, is based on national emission inventories and covers all sources of anthropogenic carbon dioxide emissions as well as carbon sinks (such as forests).

7.4

Page 23: India’s Approach to MDG Data Gaps

In the global CO2 emission estimate of the

Carbon Dioxide Information Analysis Centre of OAK Ridge National Laboratory, USA, the calculated country estimates of emissions include emission from consumption of solid, liquid and gas fuels, cement production and gas flaring.

Convergence of international obligations is a far cry

CO2 Emission7.4

Page 24: India’s Approach to MDG Data Gaps

How Good are they?

Issue: Use of Proxy indicators

Page 25: India’s Approach to MDG Data Gaps

Primary Enrolment

Net Enrolment Ratio (NER) in primary education is defined as the ratio of the number of children of official school age who are enrolled in primary school to the total population of children of official school age. (MDG)

Gross Enrolment Ratio (GER) which is defined as the number of pupils enrolled in a given level of education, regardless of age, expressed as a percentage of the population in the normative age group for the same level of education, is calculated for Class I-V and age 6-11 years.(India)

The limitation of this indicator is that, in some cases, the figure is more than 100% due to enrolment of children beyond the age group 6-11 years.

2.1 +2.2

Page 26: India’s Approach to MDG Data Gaps

‘Survival rate to Grade 5’ is defined as the percentage of a cohort of pupils enrolled in Grade 1 of the primary level of education in a given school-year who are expected to reach Grade 5 (MDG)

Apparent survival rate based on the share of enrolment in Grade II and subsequent primary grades in relation to enrolment in Grade I in a year is worked out (India)

Primary Enrolment2.1 +2.2

Page 27: India’s Approach to MDG Data Gaps

Are they really missing?

Issue: missing data

Page 28: India’s Approach to MDG Data Gaps

Missing Indicators

Proportion of the population below minimum level of dietary energy consumption.( Indicator 5: MDG1)

Contraceptive Prevalence Rate. (Indicator 19C: MDG6)

Ratio of School Attendance of Orphans to School Attendance of non-orphans aged 10-14 years. (Indicator 20: MDG6)

Page 29: India’s Approach to MDG Data Gaps

Missing Indicators(contd.)

Proportion of households with access to secure tenure. (Indicator 32: MDG7)

Unemployment Rate of Young People Aged 15-24 years, Each Sex and Total. (Indicator 45: MDG8)

Proportion of population with Access to Affordable Essential Drugs on a Sustainable Basis. (Indicator 46: MDG8)

Page 30: India’s Approach to MDG Data Gaps

Why Missing ?

Some are under serious examination Those which can be approximately

estimated by re-tabulation of survey data, required enterprise is missing

Proxy indicators are not in sight

Page 31: India’s Approach to MDG Data Gaps

Issues in focus

Need to use altered indicators is predominant and economic

Linkage with programme initiatives should be supported

Proxy indicators can be justifiable Non-availability may be probed in

statistical labs.

Page 32: India’s Approach to MDG Data Gaps

The race to reach the indicators may out run the

race to the Goals

Thanks