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Cost of Inaction in Family Planning in India |
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ON FOUNDATIO
Cost of Inaction in
Family Planning in India
An Analysis of Health and
Economic Implications
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Cost of Inaction in
Family Planning in India
An Analysis of Health and Economic Implications
A Research Study Commissioned by
Population Foundation Of India
New Delhi
October 2018
Conducted by
Institute of Economic Growth
Institute Of Economic Growth
Delhi University Enclave
Delhi 110007
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Suggested Citation:
Population Foundation of India. (2018). Cost of Inaction in Family Planning in India: An Analysis of Health and
Economic Implications. New Delhi, India
Published by:
POPULATION FOUNDATION OF INDIA
B - 28, Qutab Institutional Area
New Delhi – 110016
www.populationfoundation.in
October 2018
Study team:
William Joe
Amarnath Tripathi
A.A. Jayachandran
This study has been supported by the Bill & Melinda Gates Foundation (BMGF).The findings expressed in
the study are those of the contributors and do not necessarily represent the opinion of the BMGF.The study
may be quoted, in part or full, by individuals or organisations for academic and advocacy purposes, with due
acknowledgements to the source. Prior permission is required from Population Foundation of India for other
uses and distribution.
iv Cost of Inaction in Family Planning in India
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Contents
List of Tables
List of Figures
Acronyms and Abbreviations
Acknowledgement
Executive Summary
1 Cost of Family Planning Inaction in India
1.1. Family Planning: A Renewed Emphasis under SDGs
1.2. Family Planning in India:An Overview
1.3. Family Planning: Policies and Expectations
1.4. Need and Relevance of the Study
1.5. Scope and Objectives of the Study
1.6. Cost of Family Planning Inaction: A Framework
1.7. Report Outline
2 Demographic Consequences of Inaction
2.1. Motivation
2.2. Data and Methods
2.3. Results
2.4. Role of Fertility Decline in Reducing Maternal and Infant Deaths
2.5. Conclusions
3 Economic Gains with Family Planning Investments
3.1. Motivation
3.2. Fertility Reduction and Economic Growth: Pathways
3.3. Data and Methods
3.4. Results
3.5. Conclusion
4 Budgetary Savings with Family Planning Investments
4.1. Background
4.2. Data and Methods
4.3. Results
4.4. Conclusion
5 Impact on Out-of-Pocket Healthcare Expenditure
5.1. Background
5.2. Data and Methods
5.3. Results
5.4. Conclusion
6 Conclusion and Recommendations
6.1. Summary of Key Findings
6.2. Recommended Actions
References
Annexures
vii
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Contents v
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vi Cost of Inaction in Family Planning in India
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List of Tables
1.1 Key Demographic and Family Planning Indicators, India
13
2.1 Calibrations to Match Total Populations for SELECTED States and India
22
2.2 Projected Populations for India by Different Agencies for the Year 2025
22
2.3 Life Expectancy at Birth for Males and Females for Different Scenarios
23
2.4 Projected Total Population (in millions), India and Selected States
25
2.5 Projected Growth Rate of Total Population, India and Selected States
26
2.6 Projected Child Population (in millions), India and Selected States
27
2.7 Projected Total Fertility Rate, India and Selected States
28
2.8 Projected Total Pregnancies and Births (in millions), India and Selected States
30
2.9 Projected Risk Adjusted Infant Mortality Rate, India and Selected States
31
2.10 Projected Averted Maternal Deaths (in 000’s), India and Selected States
31
2.11 Cumulative Maternal Deaths and Unsafe Abortions Averted per 100000 Live
35
Births between 2001 and 2031, India and Selected States
3.1 Key Assumptions for the Economic Growth Simulation Analysis
43
3.2 GDP and Per Capita GDP of India and Selected States, 2001-15
44
3.3 Growth Rate of GDP and PCGDP of India and Selected States, 2001-15
44
3.4 Social and Economic Outlay of India and Selected States, 2001-15
45
3.5 Growth Rate of Social and Economic Outlay of India and Selected States, 2001-15 (%)
45
3.6 Social and Economic Outlay as % of GDP and GSDP, India and Selected States, 2001-15
46
3.7 Mean Years of Schooling, LFPR and Dependency Ratio, India and Selected States
46
for Selected Years
3.8 GDP (in Rs. billion at 2004-05 prices) under Current Trend and Policy Scenario based on
47
Coale and Hoover Model, India and Selected States 2016-31
3.9 Per Capita GDP (at 2004-05 prices) under Current Trend and Policy Scenario
47
based on Coale and Hoover Model, India and Selected States 2016-31
3.10 GDP Growth Rate under Current Trend and Policy Scenario based on Coale
48
and Hoover Model, India and Selected States 2016-31
3.11 Per Capita GDP Growth Rate under Current Trend and Policy Scenario
48
based on Coale and Hoover Model, India and Selected States 2016-31
3.12 GDP (Rs. billion at 2004-05 prices) under Current Trend and Policy Scenario
50
based on Ashraf et al Model, India and Selected States 2016-31
3.13 Per Capita GDP (at 2004-05 prices) under Current Trend and Policy Scenario
50
based on Ashraf et al Model, India and Selected States 2016-31
Contents vii
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3.14 GDP Growth Rate under Current Trend and Policy Scenario based on
50
Ashraf et al Model, India and Selected States 2016-31
3.15 Per Capita GDP Growth Rate under Current Trend and Policy Scenario
50
based on Ashraf et al Model, India and Selected States 2016-31
4.1 NHM Components with Savings Potential due to Family Planning Policies
56
4.2 NHM Budget Statement for Different Activities, India 2016-17
58
4.3 NHM Budget with Fertility Reduction for the Period 2017-31, India
63
and States
4.4 NHM Budget Savings Potential for the Period 2017-31, India and Selected States
63
5.1 OOP Expenditure Averted on Child Hospitalisation,All India, 2004 and 2014
68
5.2 Estimated Savings on Total Exp. on Child Hospitalisation (0 to 5 years),
69
All India, 2020, 2025 and 2030
5.3 OOPE Expenditure Averted on Child Hospitalisation, Selected States, 2004 and 2014
70
5.4 Estimated Savings on Total Exp. on Child Hospitalisation (0 to 5 years),
71
Selected States, 2020, 2025 and 2030
5.5 Total Exp.Averted on Child Outpatient Care (0 to 5 years),All India, 2004 and 2014
73
5.6 Estimated Savings on Total Exp. on Child Outpatient Care (0 to 5 years),
74
All India, 2020, 2025 and 2030
5.7 Total Exp.Averted on Child Outpatient Care (0 to 5 years), Selected States, 2004 and 2014
74
5.8 Total (Medical and Non-Medical) Exp.Averted on Childbirth (0 to 5 years),
76
All India, 2004 and 2014
5.9 Projected Total (Medical and Non-Medical) Exp.Averted on Childbirth,
76
All India, 2020, 2025 and 2030
5.10 Total (Medical and Non-Medical) Exp.Averted on Childbirth (0 to 5 years),
77
Bihar and Rajasthan, 2004 and 2014
5.11 Total (Medical and Non-Medical) Exp.Averted on Childbirth (0 to 5 years),
78
Madhya Pradesh, 2004 and 2014
5.12 Projected Total (Medical and Non-Medical) Exp.Averted on Childbirth,
79
Bihar and Rajasthan, 2020, 2025 and 2030
5.13 Projected Total (Medical and Non-Medical) Exp.Averted on Childbirth,
80
Madhya Pradesh and Uttar Pradesh, 2020, 2025 and 2030
5.14 Percentage of Households Incurring Catastrophic Exp. on Child Hospitalisation
82
(and Childbirth) above 100 Per cent of Annual Household Per Capita Consumption
Exp. by Wealth Quintiles, All India, NSS, 2004 and 2014
6.1 Demographic and Health Consequences (in million)
85
6.2 NHM Budget Savings Potential (in million) for the period 2017-31, India and States
87
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List of Figures
1.1 The 5 SDG Themes of People, Planet, Prosperity, Peace and Partnership
8
1.2 Levels of Maternal Mortality Ratio (MMR), India 2001-13
10
1.3 Milestones in Family Planning Programme in India
11
1.4 Conceptual Framework to Analyse Cost of Family Planning Inaction
18
1.5 Benefits of Timely Implementation of Family Planning Policies
19
2.1 Projected Child Population under Current and Policy Scenario (in millions)
27
2.2 Projected TFR under Current and Policy Scenario, Selected States
29
2.3 Contribution of MMR and Fertility Reductions on the Potential Number
33
of Maternal Deaths Averted in 2011, India and Selected States
2.4 Contribution of Decrease in Live Births on the Potential Number of
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Maternal Lives Saved in 2011, India and Selected States
2.5 Contribution of IMR and Fertility Reductions on the Potential Number
34
of Infant Deaths Averted in 2011, India and Selected States
2.6 Contribution of Decrease in Live Births on the Potential Number of
34
Infant Lives Saved in 2011, India and Selected States
3.1 Per Capita GDP (in Rs. at 2004-05 prices) under Current Trend and
47
Policy Scenario based on Coale and Hoover Model, India 2016-31
3.2 Per Capita SDP (in Rs. at 2004-05 prices) under Current Trend and Policy Scenario
48
based on Coale and Hoover Model, States 2016-31
3.3 Per Capita SDP Growth (at 2004-05 Prices) under Current Trend
49
and Policy Scenario based on Coale and Hoover Model, States 2016-31
4.1 Details of Expenditure, National Health Mission, India 2012-13 to 2015-16
59
4.2 Percentage Distribution of Expenditure, NHM India 2012-13 to 2015-16
59
4.3 Breakup of RCH Flexible Pool Expenditure, NHM India 2012-13 to 2015-16
60
6.1 Additional Per Capita Income and Growth with Effective Policy Scenario
86
List of Figures ix
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Acronyms and Abbreviations
FP
SDGs
TFR
PFI
NHM
GDP
NSDP
GSDP
LFPR
RBSK
NIDDCP
NPP
NHP
MMR
IMR
UHC
ICPD
MDGs
EAG
RCH
NRHM
ASHAs
mCPR
NFHS
SRS
ANS
WPP
UNPD
ASFR
UN
CBR
JSY
ICOR
GFCF
JSSK
OOPE
NSSO
PAP
Family Planning
Sustainable Development Goals
Total Fertility Rate
Population Foundation of India
National Health Mission
Gross Domestic Product
Net State Domestic Product
Gross State Domestic Product
Labour Force Participation Rate
Rashtriya Bal Swasthya Karyakram
National Iodine Deficiency Disorders Control Programme
National Population Policy
National Health Policy
Maternal Mortality Ratio
Infant Mortality Rate
Universal Health Coverage
International Conference on Population and Development
Millennium Development Goals
Empowered Action Group
Reproductive and Child Health
National Rural Health Mission
Accredited Social Health Activists
Modern Contraceptive Prevalence Rate
National Family Health Survey
Sample Registration System
Age Not Stated
World Population Prospects
United Nations Population Division
Age Specific Fertility Rate
United Nations
Crude Birth Rate
Janani Suraksha Yojana
Incremental Capital Output Ratio
Gross Fixed Capital Formation
Janani Shishu Suraksha Karyakram
Out-of-Pocket Expenditure
National Sample Survey Office
Proportion of Ailing Persons
x Cost of Inaction in Family Planning in India
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Acknowledgment
This is a report of the research project on ‘Cost of Inaction on Family Planning in India’ commissioned by
the Population Foundation of India, New Delhi. It provides estimated human and monetary costs incurred
by inaction on family planning in both demographic and economic terms. It charts the budgetary and health
expenditure consequences and its implications for India and for the four selected high focus states of Bihar,
Madhya Pradesh, Rajasthan and Uttar Pradesh.The study has vital implications for policymaking and increasing
investment in family planning, especially improving the quality of care, expanding contraceptive choices enabling
access and strengthening the family planning programme in the country. In particular, we highlight that with
timely action, India can avert a large number of maternal and infant deaths.The report also demonstrates how
India’s economy can benefit through a higher growth rate realised from a favourable population age structure
and human capital accumulation. Besides, both governments and households can save considerable resources
presently being allocated towards maternal and child healthcare.
Two young health economists: Abhishek Kumar and Sunil Rajpal have worked with us on this project and made
significant contributions to data collection, estimation and analysis. We express our gratitude to them.
We have received valuable comments from project advisors, Professors K S James and Arvind Pandey on
estimation approach and earlier drafts of this report for which we express our appreciation.We are grateful
to Poonam Muttreja, Executive Director, PFI and Alok Vajpeyi, Head, Knowledge Management and
Core Grants, PFI and J. Pratheeba, Health Economist, PFI for their support and feedback. We are also thankful
to Richa Shankar (BMGF),Y.P. Gupta (Avenir Health), Sarang Deo (ISB), Gautam Chakraborty (USAID), Prerana
(Accountability Initiative), Gautam Narayanan (9 dot 9), Amitabh Kundu (PFI), Francesca Barolo Shergill (PFI),
Ritesh Laddha (PFI), Sanghamitra Singh (PFI), Sweta Das (PFI), Nitin Bajpai (PFI) and, Purnima Khandelwal
(PFI) for their participation and useful comments and suggestions made during the discussion of the report
organised by the Population Foundation of India in December 2017.
We express our thanks to the administrative and support staff of the Institute of Economic Growth for their
full-hearted co-operation without which this work would have not been completed within the timeframe
planned.
William Joe
Amarnath Tripathi
A. A. Jayachandran
Acknowledgment xi
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xii Cost of Inaction in Family Planning in India
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Executive Summary
I. Cost of Inaction in Family Planning
Since the launch of Family Planning Programme in
1952 India has had varied success in achieving the
envisaged goals and objectives, particularly those of
population stabilisation and addressing the unmet
need for family planning. Currently, India’s population
of 1.3 billion accounts for a 17 per cent share in the
total global population of 7.6 billion. By 2022, India
is set to become the most populated country in the
world.
With population growth as a prominent
developmental concern, India adopted a revised
National Population Policy (NPP) in 2000 that
derives its basic philosophy from the International
Conference on Population and Development (ICPD
1994) Plan of Action.The NPP 2000 takes cognisance
of the concerns raised by women’s organisations
in the country and considers the changing global
understanding on population, reproductive health,
equity and rights.
The NPP calls for a comprehensive approach
to population stabilisation and recommends the
addressing of the social determinants of health,
promoting women’s empowerment and education,
adopting a target-free approach, encouraging
community participation and ensuring a convergence
of service delivery at the community level.
Effective family planning policies can have a
discernible influence on all the 17 Sustainable
Development Goals (SDGs). Investments on family
planning have also proven to be effective in terms
of returns. However, inaction in implementation of
family planning policies may directly and indirectly
delay the progress towards the SDGs. It can lead to
slow improvements in social, economic, demographic
and health outcomes.
The cost of inaction in family planning can be
understood as the loss of potential benefits to
individuals, households, economy and society due
to specific programme or policy inaction. Family
planning inaction can have an adverse impact on
the social and economic development of India,
particularly in the demographically backward states.
Many of these implications are apparent in the form
of poor economic and health development in these
states.
This study, aims to examine the cost of family
planning inaction on: a) Demographic and health
parameters; b) economic growth and per capita
income; d) National Health Mission budgetary
allocations; and, e) household out-of-pocket
expenditure.
The broad objectives of the study are as follows:
1) To provide an estimate of the cost of inaction in
family planning that results in the loss of health and
economic well-being for India and the four selected
states of Bihar, Madhya Pradesh, Rajasthan and Uttar
Pradesh. 2) To inform advocacy efforts with study
findings and evidence to strengthen and give priority
to family planning within the country’s socio-political
and developmental agenda.
Executive Summary 1
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The cost of family planning inaction is calculated
by comparing the projected estimates under
two scenarios, namely: a) Current Scenario
and b) Policy Scenario. The Current Scenario
is defined as the ‘business-as-usual’ scenario
whereby the Union and the State governments
continue the existing strategies and approach
towards family planning. In contrast, the Policy
Scenario approach is an active strategic stance
that gets reflected in improved demographic and
family planning parameters. In our case, we have
a set of targets envisioned by the national and
respective state population policies to construct
the policy scenario. All policy documents have
laid out different strategies to achieve those set
goals. However, these strategies are not properly
monitored for their successful implementation,
envisaged outcomes and goals. Implementing
agencies were not briefed about corrective
measures in a timely manner. This leads to
differences in demographic and family planning
outcomes across the two scenarios.
II. Demographic and Health Costs of
Inaction
The following would be the demographic and health
costs of inaction if appropriate investments in family
planning are not made over the next 15 years:
India will add an extra population of 149
million by 2031 with Bihar (24 million), Madhya
Pradesh (14 million), Rajasthan (5 million) and Uttar
Pradesh (31 million) accounting for one-half of this
number.
India will have an increased child population (0-4
years) of 22.7 million by 2031 with Bihar (3.3
million), Madhya Pradesh (2.3 million), Rajasthan (1.1
million) and Uttar Pradesh (4.1 million) accounting
for about one-half of it.
India will have to meet the cost of 69 million
additional births during 2016-31. Bihar (13 million),
Madhya Pradesh (9 million), Rajasthan (3 million) and
Uttar Pradesh (18 million) will have to incur major
costs as they jointly account for over 60 per cent of
these births.
India will witness 2.9 million additional infant deaths
with the bulk of these deaths occurring in Bihar (0.6
million), Madhya Pradesh (0.5 million), Rajasthan (0.2
million) and Uttar Pradesh (1.2 million).
1.2 million maternal deaths can be prevented in India
in this period with half of it in Bihar (0.2 million),
Madhya Pradesh (0.1 million), Rajasthan (0.1 million)
and Uttar Pradesh (0.3 million).
Table I: Demographic and Health Consequences (in million)
Indicators
Additional Population 2031
Additional Child Population 2031
Additional Births 2016-31
Additional Infant Deaths 2016-31
Maternal Deaths Averted 2016-31
Unsafe Abortions Averted 2016-31
Bihar
24
3.3
13
0.6
0.2
22.3
MP Rajasthan UP
14
05
31
2.3
1.1
4.1
09
03
18
0.5
0.2
1.2
0.1
0.1
0.3
16.0
14.3
33.8
India
149
22.7
69
2.9
1.2
205.8
2 Cost of Inaction in Family Planning in India
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India can potentially avoid 206 million unsafe
abortions with significant benefits for the four states,
particularly Bihar (22 million) and Uttar Pradesh
(34 million).
More than one third of the potential number of
maternal lives saved across the country between
2001 and 2011 can be attributed to a decrease in
the number of live births. For the populous states
like Bihar and Uttar Pradesh, the effect of fertility
decline on the potential number of maternal lives
saved is estimated to be 62 per cent and 57 per
cent, respectively.
III. Impact on Per Capita Income and
Economic Growth
The following would be the economic gains if
appropriate investments in family planning are made
over the next 15 years:
an additional per capita income of 13 per cent
during 2026-31.This implies that the Per Capita
GDP (PCGDP in 2004-05 prices) for India could be
Rs. 153,368 under the Policy Scenario compared to
Rs. 135,924 under the Current Scenario.
India would also benefit from an additional 0.4
percentage point increase in per capita GDP growth
rate during 2026-31.
Significant benefits for all the four states are noted
but the largest gain could be experienced by Madhya
Pradesh with an additional per capita income of 18
per cent during 2026-31. Madhya Pradesh could
also benefit from an additional 0.5 percentage point
increase in per capita GDP growth rate the same
period.
IV. National Health Mission (NHM)
Budgetary Savings Potential
With active family planning policies, India will enjoy
Figure I: Additional Per Capita Income (in %) and
Growth with an Effective Policy Scenario
20
18
15
14
15
13
10
8
5
0
Bihar
Madhya Rajasthan
Uttar India
Pradesh
Pradesh
.6
.4
0.4
.2
0.5
0.3
8
0.4
0.3
.0
Bihar
Madhya Rajasthan
Uttar
India
Pradesh
Pradesh
Substantial financial savings under the National
Health Mission (NHM) Programme Components
could accrue over the next 15 years if appropriate
family planning measures are implemented.The
following would be the potential NHM budgetary
savings if appropriate investments in family planning
are made over the next 15 years:
Cumulative savings of Rs. 270000 million in total
budgetary allocations for health.
Cumulative savings of around Rs. 60000 million in
the maternal health programme.
Cumulative savings of Rs. 3000 million from lower
delivery costs on account of the reduced number of
births.
Cumulative savings of Rs. 5500 million in the RBSK
programme and cumulative savings of Rs. 790 million
in the adolescent programme.
Cumulative savings of Rs. 13000 million under
immunisation coverage on account of the reduced
number of births.
Executive Summary 3
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V. Reduction in Household Out-of-
Pocket Expenditure
With an effective implementation of the NPP 2000,
Indian households could achieve about a one-
fifth reduction in total out-of-pocket expenditure
on delivery care and child hospitalisation.The
magnitude of savings in OOPE could be much larger
for households in Madhya Pradesh (35 per cent) and
Uttar Pradesh (30 per cent).
Over the period 2014-30, Indian households would
have cumulatively saved Rs. 715320 million on
account of reductions in household OOPE toward
delivery care. Significant cumulative savings would
arise from Uttar Pradesh (Rs. 11,2300 million) and
Bihar (Rs. 62320 million).
Similarly, during 2014-30, Indian households would
have cumulatively saved Rs. 6,0780 million on
account of reduced household OOPE toward child
hospitalisation. About one-fifth of such cumulative
savings would come from Uttar Pradesh (Rs. 6900
million) and Bihar (Rs. 5880 million).
Currently, Indian households experience high level
of financial hardships while seeking hospitalisation
and delivery care. In 2014, about 14 per cent cases
of delivery care and about 20 per cent cases of child
hospitalisation experienced catastrophic out-of-
pocket expenditures.
Recommended Actions
In the last three years, several new family planning
programmes have been introduced and these
include:
• A bigger basket of choice:Three new methods
have been introduced in the National
Family Planning programme: (i) Injectable
Contraceptive DMPA (Antara) (ii) Centchroman
pill (Chhaya) (iii) Progesterone only pill (POP).
• GoI has launched Mission Parivar Vikas
for substantially increasing the access to
contraceptives and family planning services in
the 145 high fertility districts of seven High
Focus States (HFS) with a TFR of 3 and above.
These are the states of: Uttar Pradesh, Bihar,
Rajasthan, Madhya Pradesh, Chhattisgarh,
Jharkhand and Assam.
• The launch of a Logistics Management
Information System (FP-LMIS) by the
Government of India (GoI).This is a new
software designed to provide robust
information on the demand and distribution
of contraceptives to health facilities and the
ASHAs.
Table II: NHM Budget Savings Potential (in millions) for the Period 2017-31, India and States
Component
Maternal Health
Child Health
Adolescent
RBSK
Training
NRHM Additionalities
Procurement
Immunisation
NIDDCP
Bihar
7310
180
40
140
280
12970
4130
1330
40
MP
5690
330
40
490
210
9490
1840
650
10
Rajasthan
2950
170
10
160
190
8940
2020
420
10
UP
7060
130
20
490
140
22490
2060
2520
10
4 Cost of Inaction in Family Planning in India
India
59930
3070
790
5470
4240
139720
42500
13260
160
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However, each of these programmes requires a
well-planned roll out strategy and goals which at
the moment is not clear. Moreover, India has also
pledged to provide universal access to reproductive
health services including contraceptives by 2030
as part of its commitment to the Sustainable
Development Goals (SDGs).
Some key recommendations to strengthen the
family planning programme are:
Specific strategies to address reproductive
health needs of adolescents and youth: While
it is well recognized that adolescents and youth
have distinctive needs, access to reproductive
health services by adolescents and youth is mired in
challenges of access to services; attitudinal barriers
among providers and restrictive social norms.
Greater investments and early interventions in
their education, health including reproductive and
sexual health needs and skill development activities
will enhance their contribution to economic
output and growth.To meet India’s commitments
to the SDGs and FP2020 and considering the huge
demographic dividend, specific health strategies
especially for adolescents and youth that address
their health needs and priorities is critical.This
strategy should underscore a voluntary, rights and
choice-based approach for addressing their sexual
and reproductive health concerns. Specific focus on
increasing access to information and reproductive
health services, delaying their age of marriage, first
pregnancy and empowering them to take informed
decisions on spacing between children is the only
way to address population momentum.
Increased allocations for family planning:
Planning and prioritisation of family planning
budgets should adequately address the gaps in use
of spacing contraceptives. Budget proposals should
emphasise on making available at scale voluntary
spacing methods that ensure effective reproductive
health solutions for both the mother and the child.
Availability of a greater resource envelope for
family planning in the national and states’ health
budgets and accelerating its spending will contribute
to higher economic output, greater savings and
investments as a result of reductions in fertility in
the country, specifically across high TFR states such
as Bihar and Uttar Pradesh.The budget allocations
should factor in the growing need for contraceptive
requirements of 53% of India’s population in the
reproductive age group. Further, the allocations and
programmes should be synchronised to reflect the
shift in focus from limiting to spacing methods and
activities that drive demand and cater to unmet
need.
Multi-sectoral response and community
engagements: Family planning approaches are
complex and are influenced by social, cultural,
economic and environmental factors. It entails a
huge component of influencing knowledge and
behavior change in the population, which requires
collective efforts from different sectors and the
community.While there has been emphasis on the
supply side aspects of the health system, it is equally
important to address the demand side factors
through greater community engagement and multi-
sectoral response that address the critical gaps in
implementation and scaling up of family planning
programmes. Engagement with different stakeholders
across different sectors will enable a leverage of the
expertise, knowledge, skills, resources and reach for
improving family planning outcomes. Best practices
from Social and Behaviour Change Communication
(SBCC) initiatives and convergence models such as
state and district level working groups need to be
scaled up.
Quality family planning services under
Universal Health Coverage: Existing policies
ensure free delivery of care services as well as
postnatal care in public health facilities; however
there are issues with quality and access to
services, especially in remote and underserved
areas. Increasing the availability and access to
reproductive health services and addressing
the unmet need for contraceptives should be a
priority among other aspects that aim to achieve
Universal Health Coverage (UHC).This will enable
better reproductive maternal and child health care
Executive Summary 5
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outcomes.The study also reveals that households
incur high and catastrophic healthcare payments for
child birth as well as inpatient care for children. Such
a high cost of treatment often acts as a deterrent
for seeking quality healthcare.With provision of
quality FP services and increasing its reach under the
UHC, households will have fewer children and can
save huge out of pocket expenditures on child birth
and child hospitalization.
Promote female education and labour force
participation: The study observes that inaction
in family planning can adversely affect per capita
income and output of the economy. Reducing
the fertility rates along with increasing women’s
education, delaying their marriage age and increasing
opportunities for them in the labour market will
enable increased economic output and permit
resources for alternative investments. Simulation
analysis reveals that economic gains can be much
higher when female education and labour force
participation are promoted and enabled.At present,
there are significant gender differentials in the
average years of schooling across the four high focus
study states. Besides, the huge gender gap in labour
market participation reflects a lack of employment
opportunities for females and is indicative of a
gendered nature of economic activities in India.
Development policies and initiatives in the country
should actively promote avenues for economic
empowerment of women by supporting their
education and employment in skill-based industries
and services.
6 Cost of Inaction in Family Planning in India
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1 Cost of Family Planning
Inaction in India
Introduction and Objectives
1.1. Family Planning: A Renewed
Emphasis under SDGs
Family Planning is an important area for research,
advocacy and policymaking in India. Since
independence, the Union and State
Governments have accorded high priority on family
planning under various developmental policies
and programmes.The intrinsic and instrumental
relevance of family planning is widely acknowledged
by the national and international community even
as the latter assumes greater salience in policy
discourse and communication.
India adopted the National Population Policy
(NPP) in 2000.This takes its basic outline from
the Programme of Action that emerged from
the International Conference on Population and
Development (ICPD 1994) and from the concerns
of women’s organisations in the country thereby
taking into consideration the changing understanding
on population, reproductive health, equity and rights.
The policy calls for a comprehensive approach to
population stabilisation and recommends addressing
the social determinants of health, promoting
women’s empowerment and education, adopting
a target-free approach, encouraging community
participation and ensuring a convergence of service
delivery at the community level. Socio-cultural
factors such as marriage age, age at first birth and
education of girls for maternal and infant well-being
find a prominent place in the policy along with
promoting a basket of contraceptive choices.
The Sustainable Development Goals (SDGs) of
the 2030 Agenda reinforce the rights perspective,
whereby all Member Nations reaffirm their
commitment to “ensure universal access to sexual
and reproductive health and reproductive rights as
agreed in accordance with the Programme of Action
of the International Conference on Population
and Development and the Beijing Platform for
Action and the outcome documents of their review
conferences”.
To achieve this important goal, Member Nations
are mandated to devise gender sensitive laws and
regulations that guarantee women access to sexual
and reproductive health care, information and
education.They are authorised to systematically
monitor the progress through periodic assessments
of their autonomy in and information on decision-
making with regard to sexual relations, contraceptive
use and reproductive health care.
The SDGs Agenda 2030 reiterates the intrinsic value
of family planning and unambiguously outlines its
relevance for achieving the broader objective of gender
equality and empowerment of women and girls. It
further identifies the need for devising effective policies
to achieve greater and equitable improvements in
gender-related outcomes with a specific focus on the
marginalised sections of the society.
Further, it is important to draw attention towards
the direct as well as the implicit associations
between Family Planning and the 17 SDGs.
In this regard, following Starbird et al (2016),
Figure 1 shows that voluntary family planning is
invariably linked to all the 17 SDGs and can render
considerable impacts on all the five underlying
themes of People, Planet, Prosperity, Peace, and
Partnership.
The authors specifically outline that family planning
can be instrumental in accelerating the progress
Cost of Family Planning Inaction in India Introduction and Objectives 7
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Figure 1.1. The 5 SDG themes of People, Planet, Prosperity, Peace and Partnership
POEPLE
SDG 1. No Poverty
SDG 2. Zero Hunger
SDG 3. Good Health
SDG 4. Quality Education
SDG 5. Gender Equality
PARTNERSHIP
SDG 17. Partnerships for the Goals
PLANET
SDG 6 Clean Water and Sanitation
SDG 7. Affordable and Clean Energy
SDG 9. Innovation and Infrastructure
SDG 11. Sustainable Cities and Communities
SDG 12. Responsible Consumption
Family Planning
Impacts
SDG 13. Climate Action
SDG 14. Life Below Water
SDG 15. Life on Land
PEACE
SDG 10. Reduce Inequalities
SDG 16. Peace and Justice
PROSPERITY
SDG 8. Decent Work and
Economic Growth
Source: Starbird et al (2016)
across the five different themes underlying the
SDGs Agenda. Also, better performance in the
domain of family planning has direct as well as spill-
over effects on several other goals and indicators
which can further escalate the advancement of
the post-2015 development agenda. Investments in
family planning have a direct bearing on household
poverty and the customary standard of living.
This effect may occur through various direct or
indirect ways. For instance, the household savings
potential in terms of reduced healthcare costs
is an elementary pathway, whereas an enhanced
scope for human capital investments among
children as well as improved female labour market
participation are more dynamic pathways.
Similarly, at the macroeconomic level, fertility
decline opens a window of opportunity to harness
the demographic dividend associated with a higher
share of a working age population with a reduced
dependency ratio. Besides, the environmental
benefits of population stabilisation are also apparent
in the form of mitigated pressure on natural
resources including land and water.
Importantly, it is cautioned that in the absence
of universal access to family planning and
reproductive health services, the impact and
effectiveness of other interventions will be less,
will cost more, and will take longer to achieve.
In particular, it is critical for the governments
and the developmental community to ensure
adequate investments in family planning with a
focus on promoting knowledge and awareness
to encourage informed discussions on access,
choices and voluntary uptake.
Such unprecedented relevance of family planning
in terms of global health and sustainable
development invariably elevates population
8 Cost of Inaction in Family Planning in India
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policy as a prime objective in the development
agenda of governments, national and international
organisations as well as civil society. In particular, the
issue has considerable bearing on states and regions
with poor maternal and child health indicators and a
disconcerting status of reproductive rights.
1.2. Family Planning in India: An
Overview
India’s population of 1.3 billion accounts for a 17
per cent share in the total global population of 7.6
billion. By 2022, India is projected to overtake China
to become the most populated nation on the planet.
However, unlike China, India’s population is yet to
achieve significant progress in terms of demographic,
economic and health outcomes.These inter-country
disparities in development progress have widened
over the years.
For instance, during the 1950s, the TFR of China
(6.1) was slightly higher than that of India (5.9) but
since the 1970s, China’s TFR declined at a faster
rate than India’s to provide an early demographic
advantage.This steep decline in the fertility rates
of China is majorly attributed to the adoption of
the one-child policy (Aird 1978, Bongaarts and
Greenhalgh 1985) whereas, India’s fertility decline
has been relatively slow (Bloom 2011, Bhat undated).
Prior to Independence, population growth in India
was essentially viewed in a Malthusian framework
that postulated disastrous consequences for
economic growth and development. Since then,
there has been greater consensus on reducing
population growth through both positive and
negative checks. In fact, India is the first nation
to have formulated a national family planning
programme in 1952 with explicit policy efforts
and provisions under subsequent five-year plans.
The programme was run through the Health
Department with a strategy that was based on
incentives, targets and female sterilisation.
However, during 1976-77, Family Planning in India
encountered its most turbulent phase on account
of a coercive policy approach towards population
control.This had wide socio-political ramifications
that rendered a long-lasting shock on family planning
in India. In particular, family planning had to undergo
a major strategic reinvention and recovery.The term
family planning was replaced with family welfare and
accompanied with an explicit policy assurance to
dissuade various forms of compulsion associated
with it, including female sterilisation. However,
because of the severe backlash of the erstwhile
coercive approach, family planning in India showed
minimal progress during the 1980s and 1990s.
In particular, it may be noted that throughout the
1970s, 80s and 90s, India’s population grew
at the rate of about 2.5 per cent per annum. Such a
high population growth rate implied an accelerated
doubling of the population from the 1975 level of
about 650 million. Further, at the current population
growth rate of 1.2 per cent, it is projected that
India’s population will reach 1.5 billion by 2030 and
1.7 billion in 2050.
Family planning in India also displayed considerable
regional as well as socioeconomic heterogeneity.
The South Indian states were among the first
to experience lower fertility rates and achieve a
relatively stable population with favourable age
composition. Similarly, the rich and the educated also
benefited from family planning choices even as these
lacked a gender perspective. On the other hand, the
bulk of the population across the vast central, north
and eastern region continued to sustain high fertility
rates that prevented India to achieve replacement
level fertility rates of 2.1 even after almost seven
decades of family planning.
The absence of an effective approach towards
voluntary family planning resulted in major health
costs, particularly for women and children. For
instance, the Maternal Mortality Ratio (MMR) in
India was estimated to be more than 800 during
the 1970s, 500 during the 1980s and 400 during
the 1990s (Joe et al 2015). As such, India accounts
for about one-fifth of the global figure of maternal
deaths. Post-2000, the MMR reduction decelerated
Cost of Family Planning Inaction in India Introduction and Objectives 9
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Figure 1.2: Levels of Maternal Mortality Ratio (MMR), India 2001-13
450
438
400
350
301
300
250
199
200
150 173
100
50
2001-03
India
375
308
254
257
212
174
178
149
127
149
127
105
2004-06
EAG & Assam
2007-09
South India
2010-12
Others
246
167
115
93
2011-13
Source: SRS Bulletin, Office of the Registrar General, India
with the result that India was unable to meet
its targets in maternal health in the Millennium
Development Goals (MDGs). Moreover, the MMR
across the 8 Empowered Action Group (EAG) states
and Assam continues to be much higher than the
national average (Figure 1.2).
The shift in thinking in India’s policies, approaches
and strategies has been shaped by the International
Conference on Population and Development (ICPD)
held in Cairo in 1994, which argued for a paradigm
shift from the earlier emphasis on population
control to that of a rights-based approach and
sustainable development. Being a signatory, India
attempted to integrate population policies within
the broader perspective of sexual and reproductive
rights, gender and sustainable development. Family
planning based on voluntary choice mechanisms was
emphasised whereby health promotion through IEC
and motivation activities was envisaged as the key
instrument.The policy approach post-1995 gradually
aimed at providing comprehensive Reproductive and
Child Health (RCH) services.
With the launch of the National Rural Health
Mission (NRHM) in 2005, the RCH approach
was expanded to include the Accredited Social
Health Activists (ASHAs) in outreach activities.
These community level female health workers are
expected to work as an interface between the
community and the public health system and engage
in effective communication at the individual level.
They are supported through financial incentives
for their efforts and achievements.The RCH
component under the NRHM continues to evolve
in scope and coverage and has since developed
into the Reproductive, Maternal, Newborn, Child
and Adolescent Health (RMNCH+A) approach,
which seeks to renew India’s commitment towards
improving maternal health and child survival in the
country.
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In recent years, three major national and
international policy declarations (SDGs Agenda
2030, the FP 2020 and the National Health
Policy 2017) have influenced India’s approach
towards family planning. All these documents have
stressed the importance of decentralisation in
policy planning, community involvement in health
planning, integration of healthcare services and the
convergence of institutional efforts to achieve family
planning objectives. However, in India, these aspects
continue to be the Achilles heel in the policymaking
on family planning.
1.3. Family Planning: Policies and
Expectations
Policies on family planning in India have essentially
aimed at achieving a stable population size
commensurate with the level of resources and
opportunities available (Figure 1.3). For this purpose,
the achievement of replacement level TFR of 2.1
continues to be an important milestone for various
national population policies and programmes
(Srinivasan 2017).
Over the years, there have been two fundamental
shifts in approach in family planning in India. First,
the government has scaled back from excessive
reference to the Malthusian theory on population
growth and has acted positively on the heavily
gender biased and target oriented approach;
and second, there is an increased recognition of
voluntary family planning based on community
engagement and the provision of information and
choices. In this regard, it is worthwhile to briefly
review the major policy expectations from family
planning in India.
The National Population Policy (NPP 1976)
undermined the role of education and development
in family planning and encouraged coercive means
to reduce population growth that was deemed
Figure 1.3: Milestones in the Family Planning Programme in India
1952
National Family Planning Programme
2017
Third National Health Policy
1976
First National Population Policy
2015
Sustainable Development Goals
1983
First National Health Policy
2012
Family Planning 2020 Summit
1994
India at ICPD Cairo
1996
Target Free Approach
2005
National Rural Health Mission
2002
Second National Health Policy
1997
Reproductive and Child Health Programme
2000
Second National Population Policy
Source: Based on Family Planning Division, Government of India (2014)
Cost of Family Planning Inaction in India Introduction and Objectives 11
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inimical to economic growth.The NPP 1976 aimed
at reducing birth rates from 35 per 1000 in 1975
to 25 per 1000 in 1984.This was expected to slow
down the population growth rate to 1.4 per cent
per annum in 1984 (Singh 1976).
While the NPP 1976 also highlighted the
importance of female education, this could hardly
be implemented in an environment, which was
experiencing a severe backlash to a coercive policy
stance on female sterilisation.The National Health
Policy (NHP1983) also refrained from making an
exclusive reference to family planning though it
advocated in favour of a new NPP for achieving the
goal of a stable population.
The long-awaited National Population Policy 2000
was instrumental in reorienting the strategic
approach towards family planning in India. In its
policy statement, the NPP affirms its commitment
towards a voluntary approach and informed
choice and consent of citizens while availing of
reproductive health care services; and continuation
of the target-free approach in administering family
planning services.
The NPP 2000 aimed to address the unmet needs
for contraception, healthcare infrastructure, and
health personnel, and to provide integrated service
delivery for basic reproductive and child healthcare.
The policy further intended to achieve replacement
level TFR by 2010 and a stable population by
2045.The NHP 2002 further endorsed the policy
approach and underscored the importance of
population stabilisation in order to maximise
socioeconomic well-being. However, it is clear
that the ensuing policy efforts were inadequate to
achieve these envisaged objectives.
In 2005, India launched the flagship scheme of the
National Rural Health Mission (NRHM 2005) which
had a major influence on family planning and health
indicators. NRHM is much appreciated for boosting
the supply-side through adequate provisions of
technical, financial and managerial inputs and
for devising incentive mechanisms to achieve
certain desirable objectives. Subsequently, various
programme activities were brought under the
umbrella of the National Health Mission (NHM) with
specific components planned for rural and urban
areas and implemented through the NRHM and the
NUHM, respectively. However, evidence suggests
that the total (central and state release) expenditure
on family planning has stagnated at the same level
since 2011. The total outlay on family planning was
Rs. 4020 million in 2011-12, Rs 4200 million in 2012-
13, which decreased to Rs. 3960 million in 2013-
2014. Further, the estimated total expenditure in
2015-16 is Rs. 7420 million.
In 2012, India became a signatory to the Family
Planning 2020 (FP 2020) goals, an outcome of
the London Summit on Family Planning the
same year.This helped rejuvenate the family
planning programme in the country as it involved
commitment towards enhanced financial allocations
as well as strategic reforms to promote innovations
and outreach activities on family planning and related
sectors. Considerable emphasis is now placed on
adolescent health, teenage pregnancies and other
sociocultural barriers to health and family welfare.
The RMNCH+A approach launched in 2013 can
be instrumental in promoting choices in the use of
various modern methods of contraception.
The FP 2020 commitments of India aim to ensure
a modern Contraceptive Prevalence Rate (mCPR)
of 65.9 per cent to achieve its FP 2020 targets
and to reach an additional 48 million users.The
Government has emphasised that “Vision FP 2020
for India is not just about providing contraceptive
services to an additional 48 million users but avoid
23.9 million births, 1 million infants deaths and over
42000 maternal deaths by 2020” (Government of
India 2014).
The SDGs Agenda 2030 as well as the National
Health Policy 2017 endorse the FP 2020 strategic
approach that outlines the need for gender sensitive
and rights-based family planning with adequate public
investments and community involvement.
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1.4. Need and Relevance of the Study
In the last seven decades, India has launched various
policies and programmes to promote voluntary
and choice-based family planning to achieve a stable
population size that is commensurate with the
available resources and opportunities. However,
despite considerable policy engagements, gaps
remain in meeting the family planning requirements
of the population. Such policy challenges are delaying
the prospects of achieving the replacement level
TFR.
The current demographic scenario of India varies
with considerable heterogeneity between major
Indian states (Table 1.1). In particular, Bihar, Madhya
Pradesh, Rajasthan and Uttar Pradesh require
concerted policy focus to improve upon their
demographic, health and family planning situation.
Clearly, accelerated progress in these states is
necessary for India’s demographic progress as well
as giving a boost to the economic and social well-
being of the country.
Table 1.1 presents the key demographic, health and
family planning indicators for India and the four
aforementioned major states based on information
from the Sample Registration System (SRS) Bulletin
2015 and the most recent round of the National
Family Health Survey (NFHS 2015-16).As per
Census 2011, the four selected states of Bihar,
Madhya Pradesh, Rajasthan and Uttar Pradesh have
a total population of about 440 million and account
for 37 per cent of the total population of the
country.They continue to have a decadal growth
of over 20 per cent which is much higher than the
targets envisaged under the three NPPs. In fact, in
2015, the birth rates in these states aries found
to be higher than the target of 25 per thousand
specified under the first NPP in 1976.
The TFR of these states is much higher than the
replacement level fertility. In particular, Bihar and
Uttar Pradesh require specific efforts to reduce TFR
levels. Such increased exposure and probability of
childbirth elevates the risk of maternal mortality.
In fact, with a vulnerable health system and
Table 1.1: Key Demographic and Family Planning Indicators, India
Indicators
Population* (in million)
Adolescent Pop. ** (in million)
Youth Pop.*** (in million)
Women Pop.* (in million)
Decadal Growth* (%)
Child Sex Ratio*
Child Population* (million)
Birth Rate 2015#
Death Rate 2015#
IMR 2015#
MMR 2011-13#
Total Fertility Rate^
mCPR^ (%)
Female Sterilisation^ (%)
Total Unmet Need^ (%)
Bihar
104
23
18
49
25.1
935
19
26.3
6.2
42
208
3.4
23.3
20.7
21.2
MP Rajasthan
73
69
15
15
16
15
38
33
20.3
21.4
918
888
11
11
25.5
24.8
7.5
6.3
50
43
221
244
2.3
2.4
49.6
53.5
42.2
40.7
12.1
12.3
UP
India
199
1210
44
238
46
237
91
587
20.1
17.6
902
919
30
159
26.7
20.8
7.2
6.5
46
37
285
167
2.7
2.2
31.7
47.8
17.3
36.0
18.1
12.9
Note: Figures and estimates based on: *Census of India, 2011; #Sample Registration System; and, ^National Family Health Survey 2015-16.
Population figures are rounded off to the nearest decimal.
Cost of Family Planning Inaction in India Introduction and Objectives 13
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developmental profile, these states display high
maternal mortality ratios which may hamper the
overall progress towards the SDGs 3 and 5 on
health and gender equality respectively.
The uptake of modern methods of contraception
needs both strategic and policy focus in Bihar
and Uttar Pradesh.While Rajasthan and Madhya
Pradesh have a relatively higher mCPR, the bulk
of contraception options are in the form of
limiting techniques singularly dominated by female
sterilisation. Importantly, all these states report high
levels of unmet need for family planning.
Nationally representative household surveys such
as the NFHS and the District Level Household
and Facility Survey (DLHS) have confirmed the
socioeconomic gradient and spatial differentials
in unmet need for family planning.The unmet
need is particularly high among the marginalised
socioeconomic groups including the poor (18 per
cent total unmet need) and the Muslims (19 per
cent total unmet need). Similarly, married women
aged 15-19 years also report extremely high
levels of unmet need (25 per cent unmet need
for spacing). A comparison of NFHS 2005-06 and
2015-16 reveals that the overall level of the total
unmet need and unmet need for spacing in India is
almost invariant. A similar non-response in levels of
unmet need is noted for Bihar and Madhya Pradesh
whereas only marginal reductions are apparent in
Rajasthan and Uttar Pradesh.
Furthermore, a skewed pattern of method-mix
confirms that family planning in India is highly
gendered and is almost synonymous with female
sterilisation. NFHS 2015-16 confirms that about
75 per cent of the mCPR in India is in the form
of female sterilisation whereas all other modern
methods account for less than one-fourth share in
the overall use of modern contraception. In fact, the
uptake of male sterilisation has declined from 1.0
per cent in 2005-06 to 0.3 per cent in 2015-16. It is
totally negligible across the four selected states and
is almost zero per cent in Bihar and Uttar Pradesh.
The issue of informed and choice-based method-mix
has hitherto remained a neglected aspect of family
planning policies in India. In the past, the government
has essentially focused on the promotion and
provision of permanent methods, especially female
sterilisation. The choice basket available to Indian
men and women is also found to be more restrictive
and until 2015 had not included options such as
injectables which are available in countries such as
Bangladesh, Bhutan, Nepal and Indonesia. Increasing
the availability of the choice of contraceptives has
the potential to drive demand. For instance, a
study concluded that the addition of one method
available to half of the population is associated with
a 4 to 8 per cent increase in the use of modern
contraceptives (Ross and Stover 2013).
Contraceptive use has been conventionally
approached as an effective means to curb population
growth. Cleland et al (2012) estimate that since the
1950s contraception use accounts for about 75 per
cent of fertility decline observed across developing
countries. However, since the 1980s there has been
increased attention towards the intrinsic value of
family planning and focus on its impact on maternal
and child health and its relevance for ascertaining
sexual and reproductive health rights.The research
and development community is systematically
engaged in advocating both the instrumental and
intrinsic benefits of family planning. For instance,
some of the earlier studies, such as Coale and
Hoover (1958), have approached fertility reduction
from an economic growth perspective whereas
recent efforts such as the Lancet Series on Family
Planning (Cleland et al 2012, Cottingham et al 2012)
effectively highlight the intrinsic gains as well as its
inter-linkages with gender equality and well-being
framed within a rights perspective.
Cleland et al (2012) further estimate that if
all unmet need is addressed through effective
contraception, developing countries can achieve
almost a one-third reduction in the number of
maternal deaths.Women in developing countries
are experiencing such risks is undeniably a major
cause of concern and it unambiguously reflects
the high global cost of family planning inaction. It
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is worth noting that researchers and policymakers
are usually preoccupied with the gains or benefits
of a specific policy action but have, by and large,
neglected the huge costs associated with policy
inaction. Such inaction gets reflected in the slow
pace of improvement in individual health and family
well-being, and through various channels of savings,
human capital accumulation and labour productivity,
which affects the macroeconomic growth and
sustainable development prospects of the country.
A systematic analysis and understanding of the
cost of inaction on family planning has considerable
relevance for research, advocacy and policymaking.
The cost of inaction in family planning affects men
and women in terms of not only the ability to plan
their families, but also their overall well-being.This
includes their ability to continue the education of
children and participate in the workforce, their
overall earnings, use of health services and ensuring
a sustainable environment as there are well-
documented complementarities among different
actions (Starbird et al 2016). However, there is a
dearth of research and analysis to comprehend
the cost of family planning inaction in India.This
present endeavour of the Population Foundation of
India (PFI) is motivated by this elementary concern
and aims to fill this gap by estimating the cost
of inaction in the area of family planning and to
highlight the high opportunity cost paid by society.
It is expected that the study findings will support
ongoing advocacy efforts of repositioning family
planning and placing it as a key priority on the
political, social and economic agenda of the country.
PFI, in commissioning this study, believes that it will
lead to an informed discourse and consensus among
the policymakers, champions, the media and civil
society on the need to take actions that will help
the country to accomplish the SDGs and other
development goals by 2030.
1.5. Scope and Objectives of the Study
The foregoing section outlines that the cost of
family planning inaction can have significant influence
over a range of developmental outcomes. In fact,
the progress towards each of the 17 SDGs and
national goals is likely to be influenced by our policy
approach and commitment towards family planning.
However, because of time and resource constrains, a
comprehensive analysis of the cost of family planning
inaction is beyond the scope of the present effort.
This endeavour aims to examine the influence
of family planning inaction on the following five
selected dimensions: a) population growth; b)
maternal and infant deaths; c) macroeconomic
growth and per capita income; d) budgetary
implications for NHM; and, e) potential household
savings in terms of averted out-of-pocket
expenditure on maternal care and child healthcare
utilisation.
For analytical purposes, we describe the estimates
for two situations: a) Current Trend and b) Policy
Trend and present the projections for the period
2016-31. Current trend is defined as the ‘business
as usual” scenario whereby the union and the
state governments continue the past strategies
and approach towards family planning. In contrast,
the policy trend attempts to describe a scenario
where the union and the state governments
undertake greater efforts in the implementation
of the respective population policies. In our case,
we have a set of targets envisioned by national and
respective state population policies to construct
the policy scenario. All policy documents laid down
different strategies to achieve those set goals.We
do not perceive any lack in those. However, these
strategies are not properly monitored for their
successful implementation, envisaged outcomes and
goals. Implementing agencies were not briefed about
corrective measures in a timely manner.This leads
to differences in demographic and family planning
outcomes across the two scenarios.
Population growth can be examined through various
dimensions such as the overall level, growth rates,
age-sex distribution, sex-ratios, spatial patterns and
social and religious composition. However, the
present analysis essentially approaches the projection
exercise to obtain the national and state-level total
population and child population figures as a critical
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input while analysing the other dimensions of the
study.The exercise also facilitates a direct comparison
of population differentials under current trend and
policy trend based on the achievement of proactive
replacement level TFR strategies under the national
and state-level population policies that were adopted.
Maternal and child health is examined through
several critical indicators and outcomes which
directly or indirectly affect household well-being and
socioeconomic development. All these outcomes are
affected differently by the family planning approach
and policies pursued by the government. For
analytical purposes, this study focuses on estimating
the total maternal and infant deaths in the country
and the selected states under both current and policy
scenarios.The study also estimates the total number
of pregnancies avoided and the total number of
unsafe abortions averted. In addition, a decomposition
analysis is presented to ascertain the contribution of
fertility decline towards the overall reductions in the
IMR and the MMR during the last decade.
Economic growth is a complex phenomenon and
is an outcome influenced by a range of local and
global factors, inputs and policies. Demographic,
health and related factors are inextricably linked
to macroeconomic growth and can influence the
pace of growth through a variety of channels. It is
well established that population growth and age
structure have a direct impact on per capita income
via channels such as savings, capital accumulation
and labour productivity.This study projects the
economic growth and per capita income changes
expected under the two different scenarios.The
policy scenario is based on projected changes in
population growth and the associated age structure.
In addition, assumptions regarding the educational
level are included to capture the favourable impact
of reduced population growth on human capital
accumulation.
The study also presents the potential budgetary
savings under the National Health Mission that can
be attributable to a reduced number of pregnancies
and childbirth nationally and for the four selected
states. It may be noted that a reduced population
growth offers considerable scope for similar
budgetary savings across a range of development
programmes (such as ICDS and the Mid-Day Meal
Scheme). But a comprehensive assessment of the
fiscal dimension is presently beyond the scope of
the study. Nevertheless, these figures can serve as a
benchmark for comprehending the potential savings
under various women and child oriented welfare
programmes.
Finally, the study presents the potential aggregate
household savings for the economy in terms of
averted out-of-pocket expenditure on account
of maternity care and child healthcare. For the
latter subgroup, the analysis covers both inpatient
and outpatient care but is restricted to child
population aged 0 to 5 years only. It is worth noting
that a similar nature of household savings can be
realised via reduced requirements for consumption
expenditure which can then provide greater
resources to households for alternative purposes,
including possibilities of higher investments for
women and child well-being.
Overall and Specific Objectives
The broad objectives of the study are as follows:
• To provide an estimate of the costs of inaction
in family planning that result in a loss of health
and economic well-being for India and for the
four selected states of Bihar, Madhya Pradesh,
Rajasthan and Uttar Pradesh.
• To inform the advocacy efforts with study
findings and evidence to strengthen and give
priority to family planning within the country’s
socio-political and developmental agenda.
The specific objectives of the study are as follows:
• To present the projections for the total population
and child population and compare the current
trends in vital parameters with those articulated in
the national and state population policies.
• To estimate the cost of family planning inaction on
measurable health and demographic parameters of
India and selected states with a focus on maternal
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and infant deaths, unwanted pregnancies avoided
and unsafe abortions averted.
• To quantify the loss to the GDP and per capita
income of India and the four selected states
based on a comparison of current change and
policy trend in demographic and family planning
indicators.
• To compare the potential budgetary savings
under the National Health Mission for India and
the selected states based on the policy trend
derived from the population policy oriented
family planning scenario.
• To discuss the potential household savings in
terms of total out-of-pocket expenditure averted
on account of reduced numbers of pregnancies
and child birth and an associated equivalent
reduction in the total health expenditure for
maternity care and child healthcare.
1.6. Cost of Family Planning Inaction:
A Framework
The cost of inaction in family planning can be
understood as the loss of potential benefits to
individuals, households, economy and society due
to specific programme or policy inaction. Family
planning inaction can have an adverse impact on
the social and economic development of India,
particularly the demographically backward states.
Many of these implications are apparent in the
form of high levels of maternal and child mortality
across these states. However, a comprehensive
assessment of the consequences of inaction needs
to be guided by an analytical framework that outlines
the nature of inaction and its implications.This
critical concern is receiving increasing attention from
researchers and policymakers and can facilitate an
impactful understanding of the magnitude and varied
dimensions of adversities associated with policy
inaction.
Anand et al (2012), among others, attempted to
develop a framework to guide such assessments
across regions and contexts.This describes the
various pathways and channels through which
inaction can render a negative impact on individuals,
households, the economy and the society.The
quantitative assessment of monetary and non-
monetary implications of such inaction is ascertained
through realistic assumptions derived from field
observations or empirical evidence across similar
contexts. Importantly, the cost of inaction approach
needs to be distinguished from the conventional
cost-benefit analysis that often underperforms
in assessing diverse benefits and also fails to fully
account for foregone benefits (opportunity costs).
In the case of family planning inaction, a number of
such benefits in the form of demographic and health
outcomes, women empowerment and its impact on
economic outcomes, are very clearly discernible.
These benefits associated with policy action can be
described as constitutive and consequential benefits.
The former accrues more directly whereas the
latter is not necessarily fully conceivable and can be
apparent as unintentional but favourable change.
An important distinction between examining costs
and cost-benefit ratios is the relative merit and
perspective of comparing benefits across a range of
policy options or investment alternatives. It must be
cautioned that the selection of a policy action based
on expected costs to overcome inaction cannot
justify the implicit subjectivity in decision-making.
For instance, a decision to invest in action A or
action B cannot be arrived at in isolation without
the perspective of all those who are affected by both
these actions.While cost-benefit assessments may
have merit in exploring issues related to the quality
of life, they certainly cannot capture the enormity
of benefits that may accrue because of lives saved
or deaths averted. By extension, any cost-benefit
analysis therefore is of little relevance wherever
investments are meant for life-saving policy action.
A complete assessment of costs and benefits of life
saved and its relevance for the individual and the
affected groups is therefore necessary to arrive at
an overall judgment regarding the cost of inaction.
Clearly, in this overall exercise it is important to
recognise that a monetary valuation of benefits is
not a straightforward task if individual preferences,
particularly fundamental rights, including the right to
life, are socially valued.
Cost of Family Planning Inaction in India Introduction and Objectives 17
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Figure 1.4: Cost of Inaction in Family Planning:The Conceptual Framework
Family Planning
Inaction
Demographic
Higher population growth,
TFR, MMR, IMR, unintended
pregnancies, unsafe
abortions
Health
Poor reproductive and child
health, nutrition, health
status, life expectancy,
longevity
Social
Low per capita
investments, slow
change in
sociocultural
outlook (towards
marriage, births &
pregnancies)
Economic
High dependency
ratio; slow
improvements in
work participation;
investments;
consumption;
GDP
Social
Weak progress in
gender rights &
preferences;
political
participation; status
of women &
marginalized
groups
Delayed progress towards SDGs 2030
Economic
Slow
improvements in
capital formation;
education, skill
development;
labor productivity;
wage equity
Effective family planning policies can have a
discernible influence on all the 17 SDGs.The
investments on family planning are proven to be
effective in terms of returns. For instance, in the
cost-benefit analysis by the Copenhagen Consensus,
family planning was recognised as the second most
effective investment in terms of returns. It will be
most appropriate for a developing economy like
India to further its investments in family planning
and lay the ground for improving the socioeconomic
fabric of the nation. Further, the high rate of returns
on family planning investments can also accelerate
the economic growth of the nation. But as described
in the previous section, this analysis restricts the
focus of the cost of inaction analysis to five selected
dimensions: a) population growth; b) maternal and
infant deaths; c) macroeconomic growth and per
capita income; d) budgetary implications for NHM;
and, e) potential household savings in terms of
averted out-of-pocket expenditure on maternity
care and child healthcare utilisation.
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Figure 1.4 presents a conceptual framework to align
the analysis with the study objectives. Overall, the
net benefits from an active family planning scenario
vis-à-vis the current trend is denoted as the net
impact expected from policy action.This impact can
be expected in terms of both direct and indirect
channels and will be mediated through demographic
factors, which in turn will influence the social
and economic determinants underlying broader
developmental outcomes.
Family planning policies can directly influence
maternal and child health outcomes, particularly
morbidity and mortality, thereby immediately
contributing towards a reduced population
growth.These improvements can lead to changes
in economic factors, such as work participation,
savings and investments in the economy to render
a favourable impact on GDP growth. Further, it
is expected that these demographic changes can
be instrumental in bringing about social changes.
In sum, an active policy environment to promote
voluntary and choice-based family planning can
make significant contributions towards bettering the
society. Moreover, these are intricately linked to the
SDGs, global peace, prosperity, gender equality, social
justice and women empowerment.
Figure 1.5 also presents a graphical assessment of
potential gains associated with timely policy action
to promote voluntary and choice-based family
planning. Panel (A) shows that in the absence of
effective policies the economy tends to forego
additional economic growth. In fact, the magnitude
of such losses can significantly increase with
time. However, from a public health investment
perspective, the government will be required to
initially incur an additional expenditure towards
family planning policies though the magnitude of
such expenses will gradually decline (panel B).
Whereas in the absence of such investments,
governments will be required to sustain high levels
of expense for a long period of time. Finally, from
panel (C) it is apparent that timely action can lead
to faster reductions in maternal and infant mortality
than what is feasible under the business as usual
scenario.
1.7. Report Outline
The report is presented in six sections: Following
the introduction, Section 2 presents the methods,
results and limitations of the analysis on the
demographic consequences of inaction. Section
3 analyses the economic impact of alternative
population growth trajectories. Section 4 outlines
the budgetary implications of family planning inaction
on the NHM. Section 5 describes the out-of-pocket
expenditure incurred by households and potential
savings attributable to a reduced fertility rate.
Section 6 concludes with policy recommendations.
Figure 1.5: Benefits of Timely Implementation of Family Planning Policies
Additional Growth
Action
Additional Investment
Future Savings
Inaction
Additional Reductions
Decision Point
Time
Decision Point
Time
Decision Point
Time
Cost of Family Planning Inaction in India Introduction and Objectives 19
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2 Demographic Consequences
of Inaction
Inferences based on projection analysis
2.1. Motivation
Recognising the importance of maternal health,
the United Nations member countries adopted
the reduction of the Maternal Mortality Ratio
(MMR) to below 70 and universal access to
quality reproductive health services by 2030 as
a Sustainable Development Goal. The relevance
of reducing maternal mortality stems from the
fact that the vicious circles of high fertility, early
marriages, low income and illiteracy along with
prevailing patriarchal and conventional sociocultural
perceptions and ideologies have prevented married
couples, particularly women, from voluntarily
utilising quality family planning services for sexual
and reproductive healthcare.A poor status of family
planning can have significant adverse consequences
on other aspects of development. Generally,
women with poor access to and knowledge of
family planning services have a higher proportion of
unwanted pregnancies and an elevated risk of unsafe
abortions.
The first pregnancy at an early age and inadequate
birth spacing often poses risks to both maternal
and child health and can affect the physical and
cognitive outcomes among children.With inadequate
provisions and access to family planning services,
women constantly live under the shadows of health
uncertainty and are exposed to a higher risk of
various kinds of sexually transmitted infections
and diseases. Consequently, both mother and child
fail to realise their full potential and capabilities. In
this case, the provision of voluntary family planning
services can be instrumental in improving the pace
of empowering women and making the realisation of
other SDGs, such as those related to the eradication
of poverty, achieving universal education, reducing
child mortality and promoting gender equality, a
distinct possibility.
From a policy perspective, though the implications
are crystal clear, there are certain regions and
communities that have been left behind particularly
those from economically backward states.Therefore,
the emancipation, empowerment and development
of the deprived people remain a question of social
justice and equity. Against this backdrop, this
section applies the cohort-component method1 to
outline the potential demographic consequences
of family planning inaction across the high focus
and economically backward states of Bihar,
Madhya Pradesh, Rajasthan and Uttar Pradesh.The
analysis particularly highlights the impact on key
demographic indicators such as population growth,
maternal and infant deaths, unwanted pregnancies
and unsafe abortions.This section concludes by
presenting a decomposition analysis to highlight the
potential contribution of fertility reduction in the
overall decline in the MMR and the IMR.
2.2. Data and Methods
Population Projections
This study features population projections carried
out for the whole of India and the four high priority
states of Bihar, Madhya Pradesh, Rajasthan and Uttar
Pradesh for two scenarios.These represent two
fertility trends which are constructed by taking
1 When the cohort component method is used as a projection tool, it assumes the components of demographic change, mortality, fertility,
and migration, will remain constant throughout the projection period.
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the normal course of fertility trajectories and
fertility decline if the state or the country follows
its respective population policy. For the purpose of
projections, the DemProj and FamPlan modules of
the Spectrum suite of tools (version 5.571) are used.
The Spectrum software mainly uses the “component
method” of projection for making population
forecasts.The rationale of the component method
rests on the undisputable fact that population
growth is determined by fertility, mortality and
migration rates.
Inputs and Assumptions
The task of projection primarily requires fixing of
the base year and duration of the projection period.
For the present analysis, 2001 is being considered as
the base year as most of the input data are available
for this year and the final year of the projection
is being fixed at 2031.The time horizon for the
projection is fixed at 30 years in view of the fact
that most of the projections used to be medium
range (25-30 years) without many hazards in using
various assumptions to make projections. Further, it
may be noted that any long-term (beyond 30 years)
projections can induce biases in the assumptions of
fertility, mortality and migration. In addition to this,
the timeline for the SDGs is also till 2030. At this
point, it is important to mention that information
on the other two components (mortality and
migration) are to be taken as it is from the
respective data sources. Fixing the base year at 2001
also allows the fixing of fertility measures to match
the projected population for the year 2011 with the
corresponding population from the 2011 Census.
Base Year Population
The population projection firstly requires
information on population distribution and size by
age and sex for the base year. For both males and
females, the population is then divided into five-year
age groups from 0-4 years to 75-79 years.The final
age group comprises those people who are aged
80 years and above.The base year populations for
India and the states have been sourced from the
Census of India.The base year five-year age group
population by sex was taken from the Census, 2001.
It is worth mentioning here that,‘Age Not Stated’
(ANS) counts are equally distributed across all the
age groups.
Total Fertility Rate
The total fertility rate (TFR) is the average number
of children that would be born alive to a woman
(or a group of women) during her lifetime assuming
she will pass through all her childbearing years
conforming to the age-specific fertility rates of a
given year. Also, the year-wise TFR is to be taken into
the Spectrum model for the projection period (2001
to 2031).
The TFRs are sourced from the Sample Registration
System (SRS) as one of the inputs for population
projection.Time series data for TFRs for all-India and
the states are available from the SRS. Additionally,
model constants are required to further the
projections for which the TFRs are fitted with the
Gompertz model.With the help of this, the TFRs are
further projected for the projection period beyond
2015.The Gompertz model for TFRs has been fitted
separately for the states and the country using Stata
software.The equation used is the following double
exponential expression:
Where, b0, b1, b2 are model parameters obtained
from the known values of Y and X for the period
2001 through 2015. The Gompertz curve describes
the changes in fertility well and was used by Bhat
(undated) and the RGI’s2 expert group projection
in 2006.The projections curve was fitted with the
differential forms of TFRs using the formula:
Where, a, is the minimum value of TFR (lower
asymptote), U is its maximum value (upper
asymptote) during the time period considered. Bhat
2 Registrar General of India
Demographic Consequences of Inaction 21
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(undated) has assumed 1.7 as the lower asymptote
for India and the states for projecting populations till
2050. However, for the present analysis, we assumed
1.8 as the lower asymptote since the projection
ends in 2031. As previously mentioned, the TFRs
are calibrated in order to match total populations
between the projected and the actual census count
for the year 2011.While using the actual TFRs from
the SRSs and other inputs, it was observed that the
projected population totals are much lower than
the Census population for the year 2011 for all the
four states and India.Therefore,Table 2.1 provides
the level of calibrations carried out to match total
populations for different states and India.
It is important to understand that population
projections require a maximum level of precision
about the current levels of fertility and mortality.
Therefore, any underestimations regarding fertility
and mortality information may lead to significant
underassessment of future possible scenarios
and growth (Bhat, undated). Several studies have
supported the evidences of underestimation of
fertility rates provided by the SRS: like 7 per cent
during 1981-91 by Bhat (2002); two rounds of
NFHS (Retherford and Mishra, 2001) data using
the own-children method showed that the level
of the general fertility rate was higher than the
corresponding SRS estimate by 9.6 per cent during
Table 2.1: Calibrations to Match Total Populations for SELECTED States and India
State
TFR Calibrated
from SRS^
times
Population Projected,
2011
million
Population Census,
2011
million
Bihar
1.16*
n.c.
n.c.
Madhya Pradesh
1.04
72.74
72.63
Rajasthan
1.02
68.63
68.65
Uttar Pradesh
No adjustments
199.80
199.58
INDIA
1.10
1,210.85
1,210.33
^:TFR calibrations carried out on SRS estimates for the period 2001 to 2010 to match total populations for the year 2011.
*: For Bihar, the TFR calibration took an unprecedented 1.16 times.The Expert Group felt that 2001 should not be considered as the base
year for the state. Therefore,2011 was considered as the base year for the Bihar state population projections.
n.c.: Not calculated
Table 2.2: Projected Populations for India by Different Agencies for the Year 2025
Sl. No.
1
2
3
4
5
6
Author/Organisation
World Bank, 1994
United Nations, 1998
Visaria and Bhat, 1999
Population Foundation of India, 1999
Dyson and Hanchate, 2000
Bhat, 2000
7
RGI’s Expert Group, 2006
8
PFI & PRB3, 2007
9
WPP4, 2017 Revision (UNPD5)
10
PFI (current study), 2017
Population in 2025 (Millions)
1370
1330
1393
1400
1381
1380 (Optimistic Scenario)
1403(Realistic Scenario)
1389
1449 (Scenario B in 2026)
1464 (Scenario A in 2026)
1451 (Medium variant)
1419.8 (Current Scenario)
3 Population Reference Bureau
4 World Population Prospects
5 Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat
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1978-92 and 6.8 per cent during 1984-99.The
input of TFR is required for all the years during the
projection period (2001 through 2031).
Age Distribution of Fertility (%)
The age specific fertility rate (ASFR) has been
accessed from NFHS 1998-99 and 2005-06 for India
and the states.While projecting beyond 2005/06, it
was assumed that the proportion of births is likely
to reduce for the age groups 15-19 and 35 and
beyond in view of an increase in the age of marriage
and more and more women using contraception
after completing their desired family size.
Sex Ratio at Birth
The sex ratio at birth is the number of male births
per 100 female births.The input for this indicator
is the SRS. India’s sex ratio at birth is 111 as per
SRS 2015.The sex ratio at birth for India in 2031
was assumed to be 107 and the values for the
intermediate years between 2015 and 2031 were
interpolated based on the current and assumed
values. To make projections for the four states, the
value of sex ratio at birth for India for the year 2015
was taken into account assuming that these states
will achieve a sex ratio at birth of 111by 2031.
Life Expectancy
The SRS provides five-year abridged life tables for
India and the states and required inputs have been
accessed from these. Further, the values for male and
female were considered from the table below under
the “Normal Improvement” scenario to project for
future years.These constants are provided by the
UN by considering different country scenarios that
have experienced demographic transitions.
Model Life Tables
This is a one-time input that is required throughout
the projections. A life table6 is a table of values
based on a series of related functions having to do
with survivorship over intervals of time. Spectrum
suite allows the selection of the appropriate life
table from a list of nine in-built life tables – Coale-
Demeny (West, East, North and South) and the
United Nations (General, Latin America, Chile, South
Asia, and East Asia). However, the selection of an
appropriate life table depends on the current levels
of IMR matched with the projected IMRs. Thus, for
India this study adopts the Coale-Demeny (North),
the UN model (Chile) for Bihar, Coale-Demeny
(North) for Madhya Pradesh, Coale-Demeny (East)
for Rajasthan and Coale-Demeny (North) for Uttar
Pradesh.
Migration
In general, migration refers to the number of
migrants moving into (positive numbers) or out
(negative numbers) of the area for which the
population projection is being prepared. If the
Table 2.3: Life Expectancy at Birth for Males and Females for Different Scenarios
Initial Level
(e0 in years)
60 -62.5
62.5 -65
65 -67.5
67.5 -70
70 -72.5
72.5 -75
75 -77.5
Fast Improvement
Male
Female
0.50
0.50
0.46
0.50
0.40
0.50
0.30
0.46
0.24
0.40
0.20
0.30
0.16
0.24
Normal Improvement
Male
Female
0.46
0.50
0.40
0.50
0.30
0.46
0.24
0.40
0.20
0.30
0.16
0.24
0.10
0.20
Source: http://statsmauritius.govmu.org/English/Pages/2000/volumeIII/popu.htm
Slow Improvement
Male
Female
0.40
0.40
0.40
0.40
0.30
0.40
0.20
0.30
0.16
0.24
0.10
0.20
0.06
0.16
6 Life tables are tables of data on survivorship and fecundity of individuals within a population
Demographic Consequences of Inaction 23
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projections are done at national level, it is said to be
international migration. However, if the projection
area is a region or a city, it is said to be interregional
migration. More specifically, the input under this
“tab” is that of total net migrants per year and their
age distribution, which has been taken from Census
2001.The total number of net migrants (1991 to
2001) was assumed to be steady throughout the
projection period for the states and India.
2.2.2. FamPlan Module
This model determines the family planning
parameters required to meet specific fertility goals.
It is a helpful tool to determine the number of family
planning users, new acceptors, and commodities
required by method as well as sources to achieve
a total fertility rate (TFR) goal and given estimates
of changes in the other proximate determinants
of fertility (i.e., the proportion of women of
reproductive age in union, and postpartum
infecundability). Given the scope of the current
work, inputs and assumptions related to the FamPlan
module are to be viewed with limited importance
than the inputs and assumptions related to the
DemProj module.
Inputs for FamPlan Module
Method Mix for Projection Period
A method mix is the percentage of all users who
are using a particular method of contraception and
therefore these figures should sum to 100 per cent.
The source for these inputs is the NFHS. While the
method mix for the past years was considered from
NFHS-2 to NFHS-3, for future years, the distribution
of spacing methods has been increased in view of
the government’s efforts to increase their uptake
and the introduction of new spacing methods in the
basket.
Proximate Determinants for Projection
Period
Proximate determinants are a set of variables
which directly impinge on fertility outcomes;
these include the proportion of women in sexual
union, the duration of the period of inability to
conceive following a birth, and the level and quality
of contraceptive practice; and to a lesser degree,
the underlying capability to conceive, the levels of
induced abortion, and the prevalence of pathological
sterility. All values have been taken from the NFHS
for the states and India, keeping the same value for
the entire projection period.
Child Survival – Onetime Values
Infant and under-five mortalities are correlated with
the prevalence of risky births. Risky births are those
that are too closely timed or occur in older women
who have had many births.Apart from IMR and the
Under-Five Mortality Rates (U5MR), the other four
indicators are taken as default values. SRS based
values have been considered for IMR and U5MR.
Impact Rates, Method Attributes and
Effectiveness
Under these three “tabs”, most of the values are
kept as default values except for the female/male age
at sterilisations, which are taken from the NFHS and
(one-time) value for MMR from the SRS.
Scenario Building
To estimate the cost of inaction, the study
considered the following two scenarios to project
the population – the current scenario and the
policy scenario. Following this, the cost of inaction
can be estimated by taking into account the
outcomes that result as a lack of timely action, i.e.,
not achieving the policy goals and the population
growing exponentially.Thus, the difference between
the two population sizes can provide an insight into
the cost of inaction. Under the normal scenario,
fertility is allowed to move along the SRS values
with an appropriate calibration to match the 2011
Census population for the states and India.The
calibrated TFR values (from 2001 to 2015) have
been fitted with the Gompertz model to estimate
the model parameters.The future TFR values
(2016 and beyond) have been estimated using this
model. However, under the policy scenario, fertility
trajectories were extracted from the state or India
specific policy documents.
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To elaborate, the NPP 2000 document had
projected the CBR (from 27.2 in 1997 to 21.0 in
2010), IMR (from 71 in 1997 to 30 in 2010) and TFR
(from 3.3 in 1997 to 2.1 in 2010) if the policy was
fully implemented.The Uttar Pradesh Population
Policy 2000 had projected the following: CBR from
28.2 in 2001 to 18.8 in 2016, IMR from 79.7 in
2001 to 60.8 in 2016, and TFR from 4.0 in 2001 to
2.1 in 2016.The Madhya Pradesh Population Policy
2000 had projected the following: CBR from 31.5 in
1997 to 21.1 in 2011, IMR from 97 in 1997 to 62 in
2011, and TFR from 4.0 in 1997 to 2.1 in 2011.The
Rajasthan Population Policy 1999 had projected the
following: CBR from 32.1 in 1997 to 18.4 in 2016,
IMR from 85 in 1997 to 57 in 2016, and TFR from
4.1 in 1997 to 2.1 in 2016.
For Bihar, we consider the UP Population Policy
scenario since the state is yet to adopt any
population policy.The TFRs under the policy
scenario fitted with the Gompertz curve were
calculated by assuming a lower asymptote of 1.6
since fertility dropped much faster than the current
scenario. Model parameters have been estimated
and further TFRs projected for the period being
considered.Thus, we have two sets of scenarios
where TFRs are allowed to drop rapidly under the
policy scenario, whereas under the current scenario,
the TFRs are following the natural course of decline.
All other inputs were kept the same under the two
scenarios.
2.3. Results
2.3.1.Total Population
Table 2.4 presents the projection for the total
(male and female) population (all ages) for India and
the selected states from 2006 to 2031. Overall, at
the country level, the total population under the
current trend is projected to be 1368 million and
1486 million in 2021 and 2031 respectively. On
the other hand, the projections under the policy
scenario show the total population at 1259 million
in 2021 and 1337 million in 2031. The absolute
difference between the two scenarios is estimated
to be 110 million and 149 million in 2021 and 2031
respectively.
In Bihar, the total population is projected to be
125 million and 138.9 million in 2021 and 2031
respectively, under the current trend. However,
under the policy trend, it is projected to be
107.6 million and 114.9 million in 2021 and 2031,
respectively.The difference between the projected
populations under the two trends in Bihar is
Table 2.4: Projected Total Population (in million), India and Selected States
Year
India
Bihar
Rajasthan
Madhya
Pradesh
Uttar
Pradesh
Scenarios
Current Trend
Policy Trend
Difference
Current Trend
Policy Trend
Difference
Current Trend
Policy Trend
Difference
Current Trend
Policy Trend
Difference
Current Trend
Policy Trend
Difference
2006
1122
1093
29
93.3
90.5
2.8
62.5
62.3
0.2
66.4
65.0
1.4
182
179.0
3.4
2011
1210
1150
61
104.0
97.0
7.0
68.6
67.9
0.7
72.7
69.0
3.8
199.8
190.9
8.9
2016
1294
1205
89
115.4
102.0
12.6
74.8
72.8
2.0
79.3
72.5
6.8
216.8
200.9
15.9
2021
1368
1259
110
125.0
107.6
17.4
80.3
77.2
3.1
85.3
75.7
9.7
231.1
208.5
22.6
2026
1432
1304
127
132.3
111.6
20.8
84.9
80.9
4.0
90.4
78.4
12.0
242.5
215.1
27.5
2031
1486
1337
149
138.9
114.9
24.0
88.8
83.8
5.0
95.0
80.8
14.2
252.0
220.7
31.4
Demographic Consequences of Inaction 25
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Table 2.5: Projected Growth Rate of Total Population, India and Selected States (in %)
Year
India
Bihar
Rajasthan
Madhya
Pradesh
Uttar
Pradesh
Scenarios
Current Trend
Policy Trend
Difference
Current Trend
Policy Trend
Difference
Current Trend
Policy Trend
Difference
Current Trend
Policy Trend
Difference
Current Trend
Policy Trend
Difference
2006
1.81
1.24
0.57
2.48
1.81
0.67
1.81
1.24
0.57
2.01
1.53
0.47
2.00
1.59
0.41
2011
1.58
1.05
0.54
2.30
1.45
0.85
1.58
1.05
0.54
1.91
1.23
0.68
1.86
1.28
0.58
2016
1.39
0.97
0.42
2.21
1.20
1.00
1.39
0.97
0.42
1.81
1.02
0.79
1.70
1.04
0.66
2021
1.15
0.89
0.26
1.65
0.93
0.72
1.15
0.89
0.26
1.52
0.88
0.63
1.32
0.76
0.56
2026
0.92
0.72
0.20
1.18
0.73
0.45
0.92
0.72
0.20
1.19
0.73
0.46
0.99
0.63
0.36
2031
0.76
0.50
0.26
0.99
0.61
0.38
0.76
0.50
0.26
1.00
0.59
0.41
0.78
0.52
0.26
estimated to be 17.4 million in 2021, 20.8 million in
2026 and 24.0 million in 2031.
Similarly, in Rajasthan, the total population projected
under the current trend exceeds projections under
the policy trend by 3.1 million, 4.0 million and 5.0
million for 2021, 2026 and 2031 respectively.The
projected difference between populations under the
two trends for Madhya Pradesh is 9.7 million for
2021, 12 million for 2026 and 14.2 million for 2031.
In Uttar Pradesh, the projected population under
the current trend is 231.1 million in 2021 and 252.0
million in 2031, which is 22.6 million and 31.4 million
higher than projections under the policy trend.
The total population in India under the current
scenario is projected to grow at 0.92 and 0.76 per
cent per annum between 2021 to 2026 and 2026 to
2031, respectively (Table 2.5). Whereas, under the
policy scenario, the projected annual growth rate of
population is 0.72 per cent between 2021 and 2026
and 0.50 per cent between 2026 and 2031.
The annual growth rate of the population in Bihar
between 2026 and 2031 is projected to be 0.99
and 0.61 per cent under the current and policy
scenarios, respectively. In Rajasthan, the annual
growth rate of the population under the current
trend is estimated to be 0.92 and 0.76 per cent for
2021-2026 and 2026-31, respectively. It is projected
to be 0.72 and 0.50 per cent respectively, under the
policy scenario.
Similarly, in Madhya Pradesh, the population under
the current trend is projected to grow at 1.15, 0.92
and 0.76 per cent annually for 2016-2021, 2021-
2026 and 2026-2031, respectively. However, under
the policy trend, it is projected to grow at 0.88 per
cent for 2021-2026, 0.73 per cent for 2021-2026
and 0.59 per cent for 2026-2031. In Uttar Pradesh,
the projections show 0.99 and 0.78 per cent annual
growth in the total population under the current
trend and 0.63 and 0.52 per cent under the policy
trend for 2021-2026 and 2026-31, respectively.
2.3.2. Child Population (0-4 years)
Table 2.6 presents child populations estimated under
the current and policy trends from 2001 to 2031
for India and the selected states. The total child
population in India is projected to be 117.6 million,
109.5 million and 104.8 million under the current
trend in 2021, 2026 and 2031, respectively.The
projected child population under the policy trend
is 96.5 million in 2021, 91.2 million in 2026 and 82.1
million in 2031.
26 Cost of Inaction in Family Planning in India
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Table 2.6: Projected Child Population (in million), India and Selected States
Year
India
Bihar
Rajasthan
Madhya
Pradesh
Uttar
Pradesh
Scenarios
Current Trend
Policy Trend
Difference
Current Trend
Policy Trend
Difference
Current Trend
Policy Trend
Difference
Current Trend
Policy Trend
Difference
Current Trend
Policy Trend
Difference
2006
128.4
99.3
29.1
13.9
11.1
2.8
7.8
7.7
0.2
8.5
7.1
1.4
24.1
20.7
3.4
2011
128.0
95.9
32.1
14.3
10.1
4.2
8.1
7.5
0.6
8.7
6.4
2.3
24.7
19.2
5.6
2016
125.4
96.5
28.9
15.3
9.5
5.7
8.2
7.0
1.3
8.9
5.9
3.1
25.0
17.8
7.2
2021
117.6
96.5
21.1
13.5
8.7
4.8
7.7
6.6
1.1
8.4
5.6
2.8
22.6
15.7
6.9
2026
109.5
91.2
18.3
11.6
8.0
3.5
6.9
6.0
0.9
7.6
5.2
2.4
19.9
14.8
5.0
2031
104.8
82.1
22.7
11.0
7.7
3.3
6.5
5.5
1.0
7.3
5.0
2.3
18.4
14.3
4.0
Figure 2.1: Projected Child Population under Current and Policy Scenario (in million)
16
14
12
10
8
2000
(a) Bihar
2010
Year
Current
2020
Policy
2030
9
8
7
6
5
2000
(b) Madhya Pradesh
2010
2020
Year
Current
Policy
8.0
7.5
7.0
6.5
6.0
5.5
2000
(c) Rajasthan
2010
Year
Current
2020
Policy
2030
31
27
23
19
15
2000
(d) Uttar Pradesh
2010
Year
Current
2020
Policy
2030
2030
Demographic Consequences of Inaction 27
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In Bihar, the total child population under the current
trend is projected to be higher than the population
under the policy trend by 4.8 million in 2021, 3.5
million in 2026 and 3.3 million in 2031. Further, the
child population in Rajasthan is estimated to be 6.9
million and 6.5 million under the current scenario,
and 6.0 million and 5.5 million under the policy
scenario for 2021 and 2031, respectively.
The projected child population for Madhya Pradesh
under the current and normal trend is 7.3 million
and 5.0 million in 2031.The difference between the
projected populations under these two trends is 2.4
million and 2.3 million for 2026 and 2031,
respectively.The projected child population in
Uttar Pradesh under the current trend exceeds the
population under the policy trend by 6.9 million in
2021, 5.0 million in 2026 and 4.0 million in 2031.The
child population for Uttar Pradesh in 2031 is
projected to be 18.4 million and 14.3 milllion under
the current and policy trends, respectively.
The trends of projected child population for the
four states are shown in Figure 2.1. It is clear that
the projected child population under the policy
scenario is significantly lower than the population
under the current scenario. However, as the fertility
rates reduced in the future, the difference between
the projections made under the two trends are
narrowed from 2021 to 2031. In all four states,
the projections under the current scenario are
higher than estimations made under the policy
scenario. It can be observed that the trend of
the child population projected under the current
scenario shows an upward trend till 2018 and
then downward up to 2030, whereas under the
policy scenario, a smooth downward trend can be
observed.
Similar observations are apparent from projections
for Madhya Pradesh, but the difference between the
estimations under the current and policy trends
is much higher in this case. Further, in Rajasthan,
the gap between the projections under the two
scenarios is widening after 2025. In Uttar Pradesh,
the projections under the policy trend are much
lower than those under the current trend.
2.3.3.Total Fertility Rate
The projections for the total fertility rate for India
and the selected states are presented in Table 2.7.
Estimates show that the TFR projected under the
current trend in India is projected to be 2.1 in 2021,
1.9 in 2026 and 1.8 in 2031. On the other hand, the
TFR under the policy trend is estimated to be 1.8,
Table 2.7: Projected Total Fertility Rate, India and Selected States
Year
India
Bihar
Rajasthan
Madhya
Pradesh
Uttar
Pradesh
Scenarios
Current Trend
Policy Trend
Difference
Current Trend
Policy Trend
Difference
Current Trend
Policy Trend
Difference
Current Trend
Policy Trend
Difference
Current Trend
Policy Trend
Difference
2006
3.1
2.3
0.8
4.8
3.7
1.2
3.6
3.4
0.2
3.7
2.9
0.8
4.2
3.5
0.7
2011
2.7
2.0
0.7
4.2
2.8
1.4
3.1
2.8
0.3
3.2
2.2
1.0
3.5
2.6
0.9
2016
2.4
1.9
0.5
3.4
2.2
1.3
2.6
2.1
0.5
2.8
1.8
1.0
2.9
2.1
0.8
2021
2.1
1.8
0.3
2.6
1.7
0.9
2.2
2.0
0.2
2.4
1.7
0.7
2.3
1.7
0.7
2026
1.9
1.8
0.2
2.2
1.6
0.6
2.0
1.7
0.3
2.1
1.6
0.5
2.0
1.6
0.4
2031
1.8
1.7
0.1
1.9
1.6
0.3
1.9
1.6
0.3
2.0
1.6
0.4
1.8
1.6
0.2
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Figure 2.2: Projected TFR under Current and Policy Scenarios, Selected States
5
4
3
2
2000
4
3.5
3
2.5
2
1.5
2000
(a) Bihar
2010
2020
Year
Current
Policy
(c) Rajasthan
2030
2010
2020
Year
Current
Policy
2030
4
3.5
3
2.5
2
1.5
2000
5
4
3
2
1
2000
(b) Madhya Pradesh
2010
2020
Year
Current
Policy
(d) Uttar Pradesh
2030
2010
2020
Year
Current
Policy
2030
1.8 and 1.7 for 2021, 2026 and 2031, respectively.
Thus, the TFR projected under the current trend
is projected to be higher than the TFR estimated
under the policy scenario by 0.2 and 0.1 per woman
for 2026 and 2031, respectively.
For 2031, the TFR in Bihar under the current
and policy trends is projected to be 1.9 and 1.6,
respectively. The difference between the TFR
projected under the current and policy trends is
0.9, 0.6 and 0.3 per woman for 2021, 2026 and 2031
respectively. In Rajasthan, the TFR projected under
the current trend is 2.2 in 2021, 2.0 in 2026 and 1.9
in 2031. However, under the policy scenario, it is
projected to be 2.0, 1.7 and 1.6 for 2021, 2026 and
2031, respectively.
Furthermore, the TFR in Madhya Pradesh under the
current trend is projected to be 2.4 in 2021, 2.1 in
2026 and 2.0 in 2031. Under the policy trend, it is
estimated to be 1.7 in 2021, 1.6 in 2026 and 2031.
Similarly, in Uttar Pradesh, the difference between
the TFR projected under the current and policy
trends is 0.7, 0.4 and 0.2 per woman in 2021, 2026
and 2031, respectively.
Figure 2.2 presents the trends in the total fertility
rate projected from 2001 to 2031 under the current
and policy trends for India and the four states
respectively.These show a noticeable difference
between the TFR estimated under the two scenarios
with the projected decrease being higher under the
current scenario.
2.3.4.Total Pregnancies
Estimates regarding total pregnancies projected
under the current and policy trends for India and
the four states are depicted in Table 2.8. Overall,
the total number of pregnancies under the current
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Table 2.8: Projected Total Pregnancies and Births (in millions), India and Selected States
States Scenarios
2001
2011
P*
B*
P*
B*
Current Trend 39.4 27.4 40.5 26.6
India
Policy Trend
33.5 22.4 33.0 20.1
Difference
5.9
5.0
7.5
6.5
Current Trend
3.4
3.0
3.6
3.1
Bihar Policy Trend
3.4
3.0
2.4
2.1
Difference
0.0
0.0
1.2
1.1
Current Trend
2.0
1.7
2.0
1.7
Rajasthan Policy Trend
1.9
1.7
1.8
1.6
Difference
0.0
0.0
0.2
0.1
Current Trend
2.2
1.9
2.2
1.9
Madhya
Pradesh
Policy Trend
1.9
1.7
1.5
1.3
Difference
0.2
0.2
0.7
0.6
Current Trend
5.9
5.2
6.1
5.3
Uttar
Pradesh
Policy Trend
5.9
5.1
4.6
3.9
Difference
0.1
0.1
104
1.4
Note: P* Number of Pregnancies and B* refers to Number of Births.
2021
P*
B*
37.1 23.5
32.1 19.8
5.0
3.7
3.0
2.6
2.0
1.7
1.0
0.9
1.7
1.5
1.6
1.4
0.1
0.2
1.9
1.7
1.3
1.1
0.6
0.5
5.1
4.5
5.4
3.2
-0.3
1.3
2031
P*
B*
34.7 21.2
27.2 16.2
7.5
5.0
2.5
2.2
1.8
1.6
0.7
0.6
1.5
1.3
1.3
1.1
0.2
0.2
1.7
1.5
1.2
1.0
0.6
0.5
5.2
3.8
4.8
2.9
0.3
0.8
scenario are estimated to be 37.1 million and 34.7
million for 2021 and 2031, respectively. However,
under the policy trend, they are projected to be 32.1
million in 2021 and 27.2 million in 2031.
2.3.5.Total Births
The estimates regarding total childbirths for India
and the four states are presented in Table 2.8.
Under the current trend, the total births in India
are projected to be 23.5 million and 21.2 million
for 2021 and 2031, respectively. However, the births
under the policy trend are estimated be 19.8 million
in 2021 and 16.2 million in 2031. For Bihar, the total
births are projected to decrease from 2.6 to 2.2
million under the current scenario and from 1.7 to
1.6 million under the policy scenario for 2021 and
2031, respectively.
Further, the number of births in Rajasthan are
estimated to be 1.5 million in 2021 and 1.3 million
in 2031 under the current scenario, whereas under
the policy trend, they are projected to be 1.4 and
1.1 million in 2021 and 2031, respectively.The
difference between the total births projected under
the two trends in Madhya Pradesh is 0.5 million.
for 2021 and 2031. Similarly, in Uttar Pradesh, the
number of births projected under the current trend
is 1.3 million and 0.8 million higher than the births
projected under the policy trend for 2021 and 2031,
respectively.
2.3.6. Infant Mortality Rate
The risk adjusted infant mortality rate projected
under the current scenario for India is 41 and
35.8 per thousand live births for 2021 and 2031,
respectively (Table 2.9). Under the policy scenario,
it is projected to decrease from 52 per thousand
in 2006 to 40 per thousand in 2021 and 37.8 per
thousand in 2031.
In Bihar, the IMR is projected to decrease from 36
per thousand in 2016 to 22 per thousand in 2021
and further still to 12 per thousand in 2031 under
the current scenario. The projected IMR under
the policy scenario is 7 per thousand for 2021 and
8 per thousand for 2031. Similarly, in Rajasthan,
the projected IMR under the current trend is 34.8
and 29.3 per thousand against 31.1 and 24.7 per
thousand under the policy scenario for 2021 and
2031, respectively.
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Table 2.9: Projected Risk Adjusted Infant Mortality Rate (per 1000 live births), India and Selected States
Year
India
Bihar
Rajasthan
Madhya
Pra-desh
Uttar
Pradesh
Scenarios
Current Trend
Policy Trend
Difference
Current Trend
Policy Trend
Difference
Current Trend
Policy Trend
Difference
Current Trend
Policy Trend
Difference
Current Trend
Policy Trend
Difference
2006
57.0
52.0
5.0
57.0
37.0
20.0
56.9
54.9
2.0
73.7
47.1
26.6
61.0
47.0
14.0
2011
50.6
45.9
4.7
47.0
23.0
24.0
49.4
45.5
3.9
60.8
40.1
20.7
46.0
27.0
19.0
2016
45.2
41.9
03.3
36.0
14.0
22.0
41.7
32.9
8.8
49.8
34.1
15.7
41.0
24.0
17.0
2021
41.0
40.0
1.0
22.0
07.0
15.0
34.8
31.1
3.7
42
29.8
12.2
31.0
15.0
16.0
2026
37.7
38.6
-0.9
16.0
07.0
9.0
31.0
26.2
4.8
36.1
26.4
9.7
24.0
15.0
9.0
2031
35.8
37.8
-2.00
12.0
08.0
4.0
29.3
24.7
4.6
30.8
23.4
7.4
17.0
12.0
5.0
Table 2.10: Projected Averted Maternal Deaths (in 000’s), India and Selected States
Year
India
Bihar
Rajasthan
Madhya
Pradesh
Uttar
Pradesh
Scenarios
Current Trend
Policy Trend
Difference
Current Trend
Policy Trend
Difference
Current Trend
Policy Trend
Difference
Current Trend
Policy Trend
Difference
Current Trend
Policy Trend
Difference
2006
55.99
55.27
0.72
4.15
5.81
1.66
4.68
4.83
0.16
4.95
5.40
0.44
7.07
8.91
1.84
2011
66.63
65.56
1.07
5.57
7.88
2.31
5.93
6.28
0.35
5.87
6.78
0.92
10.52
13.34
2.82
2016
75.76
73.50
2.26
7.27
9.53
2.27
7.17
8.01
0.84
6.90
7.87
0.97
12.35
15.32
2.96
2021
84.01
76.82
7.19
9.63
10.93
1.30
8.40
8.75
0.35
8.00
8.46
0.45
16.19
18.66
2.48
2026
91.23
78.43
12.8
11.45
11.51
0.06
9.39
9.76
0.37
9.07
8.81
-0.26
19.81
20.39
0.58
2031
96.51
79.19
17.32
13.25
11.76
-1.49
10.13
10.24
0.12
9.96
8.92
-1.04
24.07
22.45
-1.62
In Madhya Pradesh, the projected IMR under the
current scenario is 12.2 per thousand and 7.4 per
thousand higher than estimates under the policy
scenario. In Uttar Pradesh, the IMR under the
current scenario is projected to decrease from
61 per thousand in 2006 to 31 in 2021 and 17
per thousand in 2031. However, under the policy
situation, the IMR is projected to decline from 47
per thousand to 15 per thousand in 2021 and 12 per
thousand in 2031.
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2.3.6. Maternal and Infant Deaths Averted
The risk adjusted infant mortality rate projected
under the current scenario for India is 41 and
35.8 per thousand live births for 2021 and 2031,
respectively (Table 2.10). Under the policy scenario,
it is projected to decrease from 52 per thousand
in 2006 to 40 per thousand in 2021 and 37.8 per
thousand in 2031. In Bihar, the IMR is projected to
decrease from 36 per thousand in 2016 to 22 per
thousand in 2021 and further to 12 per thousand in
2031 under the current scenario. The projected IMR
under the policy scenario in Bihar is 7 per thousand
for 2021 and 8 per thousand for 2031. Similarly,
in Rajasthan, the projected IMR under the current
trend is 34.8 and 29.3 per thousand against 31.1
and 24.7 per thousand under the policy scenario
for 2021 and 2031,respectively. In Madhya Pradesh,
the projected IMR under the current scenario is
12.2 per thousand and 7.4 per thousand higher
than estimates under the policy scenario. In Uttar
Pradesh, the IMR under the current scenario is
projected to decrease from 61 per thousand in
2006 to 31 in 2021 and 17 per thousand in 2031.
However, under the policy situation, the IMR is
projected to decline from 47 per thousand to 15 per
thousand in 2021 and 12 per thousand in 2031.
2.4. Role of Fertility Decline in
Reducing Maternal and Infant Deaths
This analysis is based on data from the Sample
Registration System (SRS), Census of India and uses
the decomposition technique suggested by Jain
(2011). To estimate the number of maternal and
infant deaths that were averted in the index year
(2011), we first estimated the actual incidence of the
total number of maternal deaths using the observed
level of fertility and the MMR (Maternal Mortality
Ratio) in 2011, factoring in the change observed in
both fertility and MMR between 2001 and 2011.
We have used the Crude Birth Rate (CBR) as an
indicator of fertility. Further, total live births were
calculated as the product of CBR and population
size.The potential number of maternal deaths
between 2001 and 2011 were estimated under
three counterfactual scenarios related to changes
in fertility and MMR: (1) No change in fertility and
MMR, (2) decline in fertility and no change in MMR,
and (3) decline in MMR and no change in fertility.
Similarly, the potential number of infant deaths was
estimated using this approach under the following
three scenarios: (1) No change in fertility and IMR,
(2) decline in fertility and no change in IMR, and (3)
decline in IMR and no change in fertility.
The gross effect of a change in fertility and MMR
on the potential number of maternal and infant
lives saved in 2011 has been calculated by taking
the difference between the actual maternal deaths
observed in 2011 and the potential number of
maternal deaths estimated for 2011under the
different scenarios.The difference between the
potential number of maternal deaths reflects the
gross effect of MMR on the potential number of
lives saved. Similarly, the gross effect of a decline in
fertility on the potential number of maternal lives
saved in 2011 has been estimated by subtracting
the potential number of maternal deaths estimated
in Scenario 3 from the number of deaths estimated
under Scenario 1. To estimate the net effect
of fertility and MMR decline on the number of
maternal lives saved, the joint effect (overlap) of a
decline in both was then calculated. Therefore, the
net effect of these components on the number of
maternal lives saved in 2011 can be segregated as
those:Attributable to fertility decline, attributable to
MMR decline, and attributable to both fertility and
MMR decline jointly. A similar approach was adopted
to decompose the potential number of infant lives
saved by replacing the MMR with the IMR.
The combined effect of both fertility and MMR
reductions on the potential number of maternal lives
saved can be decomposed by approaching any of the
following three pathways: Fertility declines cause a
decline in MMR, MMR declines reduce fertility, or
a combination of the two. An increase in income
and educational level, a better standard of living,
and a spread of health services and awareness can
simultaneously lead to both a fertility decline and
a decrease in MMR. Although there is no empirical
evidence to support the suggestion that a fertility
decline can cause a reduction in the MMR, it is a
commonly accepted idea that because of associated
changes in age-parity and composition of births,
fertility decline can lead to an MMR reduction, even
if the age and parity specific MMR remains constant
(Jain 2011). However, there is no known mechanism
that shows whether an MMR decline has any effect
32 Cost of Inaction in Family Planning in India
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on fertility reduction.The overlap effect is likely to
elicit the share of effect of fertility reduction on
the potential number of lives saved that is realised
through changes in the composition of births.
Finally, the net effect of an MMR decline reflects
that proportion of potential maternal lives saved,
a result of safe motherhood initiatives like the
Janani Suraksha Yojana (JSY), which indicate
institutional deliveries, skilled medical staff and
improved obstetric care services. On the other
hand, the net effect of fertility decline reflects the
share of maternal lives saved, which is the result
of a decrease in the annual number of live births.
A similar approach has been adopted to estimate
the net effect of fertility and IMR decline on the
potential number of infant lives saved.
2.4.1. Potential Number of Maternal
Lives Saved
The potential number of maternal lives saved as
the result of a decline in fertility and MMR between
2001 and 2011 for India is depicted in Table 2.11.
It is worth mentioning here the crude birth rate
(CBR) is used as an indicator for fertility changes. In
India, the estimates from the first scenario with no
changes in fertility and MMR show approximately
138461 maternal deaths for India during 2001 to
2011. On the other hand, about 44087 maternal
deaths actually occurred with a decline in fertility
and MMR.
Therefore, observed declines in both fertility
and MMR potentially saved 94373 maternal
lives. Further, about 37.5 per cent (35376) of
maternal lives saved can be attributed to safe
motherhood programmes and the remaining 62.5
per cent (32733) to fertility decline; out of which,
approximately 35 per cent are the result of a decline
in the number of births and 27 per cent to age-
parity composition of births.
In Bihar, the contribution of fertility and MMR
reduction on the potential number of maternal
lives saved is about 38 per cent and 62 per cent
respectively (Table 2.12). The contribution of
fertility reduction on the potential number of
maternal deaths averted is relatively lower (41.6
per cent) and the contribution of safe motherhood
initiatives, higher (58.4 per cent) in Rajasthan (Table
2.13).
Figure 2.3: Contribution of MMR and Fertility
Reductions on the Potential Number of Maternal
Deaths Averted in 2011, India and Selected States
80
60 58.3
57.2
59.1
61.9
62.5
40
41.7 42.8
40.9
38.1
37.5
20
0
Rajasthan Uttar
Madhya
Bihar
India
Pradesh Pradesh
MMR Fertility
Figure 2.4: Contribution of Decrease in Live Births
on the Potential Number of Maternal Lives Saved in
2011, India and Selected States
40
34.7
29.6
26.9
24.2
20
20.0
0
Rajasthan Uttar
Madhya
Bihar
India
Pradesh Pradesh
Similarly, 59.1 per cent of maternal deaths averted
in Madhya Pradesh can be attributed to fertility
reduction, out of which, 30 per cent is ascribed to a
reduction in live births (Table 2.14). In Uttar Pradesh,
both fertility and MMR reduction respectively
contribute to 57.2 and 42.8 per cent of potential
maternal deaths averted (Table 2.15).
Further it is evident from Figure 2.3, that except for
Rajasthan, the effect of fertility reduction is higher
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than the effect of a decline in MMR on the potential
number of maternal lives saved in all the states as
well as India. Across selected states, the effect of
fertility reduction on potential maternal lives saved
is highest, whereas the effect of safe motherhood
is highest in Rajasthan. Figure 2.4 shows the
percentage contribution of a decline in the number
of live births on the potential number of maternal
lives saved. It can be observed that Rajasthan reflects
the highest contribution with about 35 per cent of
maternal lives saved, which can be attributed to a
decrease in live births.
2.4.2. Potential Number of Infant Lives Saved
Table S2.6 (Annexure A) presents estimates
regarding the contribution of fertility and IMR
decline on the potential number of infant lives saved
between 2001 and 2011 for India. The estimated
number of infant deaths in the first scenario with no
change in the CBR and IMR is about 30360, whereas
the actual number of infant deaths observed is
11615. Altogether, about 18000 infant lives were
potentially saved during this period. Overall, about
69 per cent of infant lives saved are attributable to
fertility reduction and the remaining 31 per cent can
be ascribed to improved conditions for childbirth as
a result of several initiatives on maternal and child
healthcare.
In Bihar, the contribution of fertility reduction
on the potential number of infant lives saved is
estimated to be about 77 per cent, out of which, 54
per cent was due to a decrease in live births (Table
S2.7). The remaining 23 per cent of infant deaths
averted is attributed to a decline in IMR through
the provision of improved health facilities. Similarly,
51 per cent of the infant lives saved in Rajasthan is
attributed to fertility reduction and 49 per cent to
an improved health environment and facilities which
lead to a decline in IMR (Table S2.8). In Madhya
Pradesh, almost 70 per cent of infant deaths averted
are the result of fertility reduction and 30 per cent,
the result of a decline in IMR (Table S2.9). In Uttar
Pradesh, the net effect of fertility reduction and a
decline in IMR on the potential infant deaths averted
are 69 and 31 per cent respectively (Table S2.10).
The effect of a decline in IMR and fertility on the
potential number of infant lives saved is depicted
in Figure 2.3. The effect of fertility reduction on
the potential number of maternal deaths averted is
higher as compared to the effect of a decline in IMR
across all selected states and India as well. Further,
the effect of fertility reduction on the number of
maternal lives saved is highest in Bihar.
Evidently, the effect of a decline in live births on
the potential number of maternal lives saved is
estimated to be highest in Rajasthan and lowest in
Madhya Pradesh (Figure 2.5). Figure 2.6 shows the
Figure 2.5: Contribution of IMR and Fertility
Reductions on the Potential Number of Infant Deaths
Averted in 2011, India and Selected States
80
69.3
70
60
48.5 51.5
40
30.7
30
20
77.3
22.7
69
31
0
Rajasthan Uttar
Madhya
Bihar
India
Pradesh Pradesh
MMR Fertility
Figure 2.6: Contribution of a Decrease in Live Births
on the Potential Number of Infant Lives Saved in 2011,
India and Selected States
60
54.8
46.0
47.8
47.6
40
33.4
20
0
Rajasthan Uttar
Madhya
Bihar
India
Pradesh Pradesh
effect of a decrease in live births on the potential
number of infant deaths averted in India and
selected states. Clearly, the effect of a decrease
in live births is estimated to be highest in Uttar
Pradesh and lowest in Madhya Pradesh.
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Table 2.11: Cumulative Maternal Deaths and Unsafe Abortions Averted per 100,000 Live Births between 2001
and 2031, India and Selected States
States
Bihar**
Rajasthan
Madhya Pradesh
Uttar Pradesh
India
Maternal Deaths Averted
Current
Policy
455
321
447
525
401
584
280
412
292
343
**The estimates for Bihar are from 2011 to 2031.
Unsafe Abortions Averted
Current
Policy
58589
58762
45407
53296
47190
68631
30707
45192
49277
58266
2.4.3. Cumulative Maternal Deaths and
Unsafe Abortions Averted
Table 2.11 depicts the cumulative sum of maternal
deaths averted per 100,000 live births under the
current and policy trends between 2001 and 2031.
Overall, the projected total number of maternal
deaths averted estimated under the current trend
is 292 per 100,000 births, whereas under the
policy trend, it is projected to be 343 per 100,000
births. Similarly, 525 deaths per 100,000 births are
projected to be saved in Rajasthan under the policy
trend against 447 per 100,000 births estimated
under the current scenario. Interestingly, the
estimated cumulative number of maternal deaths
averted under the policy trend is almost double of
those estimated under the current trend for Uttar
Pradesh.
Information regarding the total number of unsafe
abortions averted per 100,000 births is presented in
Table 2.11. At the national level, cumulatively, 49277
unsafe abortions per 100,000 births are projected
to be saved under the current scenario. However,
under the policy scenario, it is projected to be
higher at 58266 per 100,000 births. In Rajasthan,
the total number of infant deaths averted between
2001 and 2031 is projected to be 45407 and 53296
per 100,000 births under the current and policy
scenarios, respectively. Similarly, in Uttar Pradesh,
the difference between the cumulative infant deaths
averted under the current and policy trends is
almost 14485 per 100,000 births.
2.5. Conclusion
Overall, at the national level, the total population
under the current trend is projected to be
1368 million and 1486 million in 2021 and 2031,
respectively. On the other hand, the projections
under the policy scenario show the total population
at 1259 million in 2021 and 1337 million in 2031.
The absolute difference between the two scenarios
is estimated to be 110 million and 149 million in
2021 and 2031,
respectively.
The total population in India under the current
scenario is projected to grow at 0.92 per cent and
0.76 per cent per annum between 2021 to 2026 and
2026 to 2031 respectively (Table 2.5). Under the
policy scenario, the projected annual growth rate of
the population is 0.72 per cent between 2021 and
2026 and 0.50 per cent between 2026 and 2031.
The total child population in India is projected to
be 117.6 million, 109.5 million and 104.8 million
under the current trend in 2021, 2026 and 2031,
respectively. Under the policy trend, this is projected
to be 96.5 million in 2021, 91.2 million in 2026 and
82.1 million in 2031.
The projections for the total fertility rate for India
and selected states are presented in Table 2.7. The
estimates show that the TFR projected under the
current trend in India is shown to be 2.1 in 2021, 1.9
in 2026 and 1.8 in 2031. On the other hand, the TFR
under the policy trend is estimated to be 1.8, 1.8
and 1.7 for 2021, 2026 and 2031, respectively.
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Overall, the total number of pregnancies under
the current scenario in India are estimated to be
37.1 millions and 34.7 million for 2021 and 2031,
respectively. However, under the policy trend, they
are projected to be 32.1 million in 2021 and 27.2
million in 2031.
Under the current trend, total births in India are
projected to be 23.5 million and 21.2 million for
2021 and 2031, respectively. However, births under
the policy trend are estimated be 19.8 million in
2021 and 16.2 million in 2031.
Overall, the projected total number of maternal
deaths averted estimated under the current trend
stands at 292 per 100,000 births, whereas under the
policy trend, it is 343 per 100,000 births.
At the national level, cumulatively, 49277 unsafe
abortions per 100,000 births are projected to be
averted under the current scenario. However, under
the policy scenario these are projected to be higher
at 58266 per 100,000 births.
The most common indicators of maternal mortality
are:The maternal mortality rate, the average number
of times that a woman has faced risk of death
due to pregnancy-related causes in her lifetime,
and the total number of maternal deaths. It is to
be noted that all these indicators change with
change in fertility decline. However, in general, safe
motherhood programmes like JSY are the focal point
of studies for understanding the decline in maternal
mortality (Lim et al 2010; Paul 2010).Against this
backdrop, Jain (2011) in his study, proposed a simple
decomposition method for estimating the effect of
a decline in MMR and fertility on the decline in a
potential number of maternal deaths. The present
study extends this analysis to show the effect of a
decrease in the number of live births on maternal
deaths averted between 2001 and 2011 for all India
and selected states.
Estimates from the decomposition analysis clearly
show that against common perception, fertility
decline has made substantial contributions towards
a reduction in the potential number of maternal and
infant deaths. It is globally understood that achieving
low levels of fertility and MMR are priority goals,
as also reflected in SDG 3.1.1.The fact that fertility
decline plays an important role in reducing MMR,
is also corroborated by this analysis. At this point,
it is important to note that the magnitude of the
contribution of fertility decline depends directly on
the pace at which fertility levels decline takes place
in a particular region/state.
The decomposition estimates clearly reflect a
substantial contribution of fertility decline in
reducing maternal deaths from 2001 and 2011 for
India as well as the selected states. The effect of
total fertility reduction on the potential number of
maternal and infant lives saved in 2011 is significantly
higher as compared to the effect of a decline in
MMR and IMR respectively, in India as well as the
selected states, except for Rajasthan.
At the national level, almost 35 per cent of the
potential numbers of maternal lives saved are
attributable to a decrease in the number of live
births resulting from fertility decline. In Bihar, the
effect of a decline in MMR and live births on the
potential numbers of maternal lives saved is 38.1
per cent and 24.2 per cent, respectively. In Rajasthan,
almost 58 per cent and 20 per cent of maternal lives
saved can be attributed to a decline in MMR and a
decrease in the number of live births respectively. In
Madhya Pradesh, the effect of safe motherhood and
a decrease in live births on the number of maternal
deaths averted is estimated to be 40 per cent and
29 per cent, respectively. In Uttar Pradesh, 42 per
cent and 27 per cent of the potential number of
maternal lives saved can be attributed to a decline in
the MMR and the number of live births, respectively.
The potential number of maternal and infant
deaths, which can be attributed to fertility decline
is highest in Bihar and lowest in Madhya Pradesh.
The contribution of a decrease in live births on the
potential number of maternal lives saved is highest
in Rajasthan and lowest in Madhya Pradesh.The
potential number of infant deaths averted in 2011,
attributed to a decrease in live births, is estimated
to be highest in Uttar Pradesh and lowest in Madhya
Pradesh.
It is worth recalling that promoting safe
motherhood requires undertaking several
simultaneous efforts, such as making improvements
in emergency obstetric services, ensuring availability
36 Cost of Inaction in Family Planning in India
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of midwives in rural communities, enhancingmedical
infrastructure and strengthening of referral systems
between the rural communities and health providers.
However, coinciding improvements in all these
services, especially in rural areas, are yet to reach
a level of adequacy. In this regard, the literature
that is available, do not establish any significant role
played by safe motherhood initiatives such as JSY
in maternal mortality decline (Lim et al 2010). The
lack of association between the rise in numbers of
institutional deliveries under initiatives like the JSY
and a decrease in maternal mortality calls for facility-
based studies to compile data on the proportion
of women with complications among institutional
deliveries, and the prevailing case fatality rate (CFR)
among these. (Jain 2010).
However, the estimates regarding the decomposition
analysis are limited by the fact that fertility is used
as an independent variable in the statistical models
used for estimating IMR.Therefore, it is important
to understand that the potential contribution
of fertility decline can differ in cases of other
independent estimates for MMR, fertility and other
population indicators.
In conclusion, it may be noted that we do not place
an emphasis on an analysis of the mCPR.While this
has an instrumental relevance in improving family
planning, it may not necessarily represent a direct
cost of inaction. Besides, the CPR is only one of the
many factors influencing fertility change. Our focus
is to provide quality family planning methods to
those who need it, focus on adolescents and early
parity groups to make use of appropriate family
planning methods in high focus states.The gains in
implementing a quality family planning programme
are analysed here – some of which are listed above.
With NFHS-4 revealing that declining fertility is
not necessarily associated with mCPR declines,
an exclusive study on the impact of mCPR on
population parameters can offer valuable insights for
shaping family planning policies.
Demographic Consequences of Inaction 37
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3 Economic Gains with Family
Planning Investments
Impact on growth and per capita income
3.1. Motivation
The association between population growth
and economic growth is an important area of
research and policy analysis (James 2011).Three
kinds of views are apparent: a) The pessimist
view (Malthusian legacy), that population growth
negatively affects economic growth; b) the optimist
view, that population growth is beneficial for
economic growth and; c) the neutralist view, that
population is unrelated to economic performance
(Bloom et al 2003). While there are different
perspectives about the effect of population growth
on economic growth (Sinding 2009; Das Gupta,
Bongaarts, and Cleland 2011), it is important to
note that the causality between the two could run
from either side. In other words, these could be
endogenously determined within the system.
Population, if not productively employed can exert
a negative influence on economic growth and
development.Therefore, in large and populous
countries such as China and India, policies have
specifically aimed at achieving fertility decline and
reductions in the population growth rate. However,
achieving a faster decline in fertility rates is not a
straightforward task as it is an outcome influenced
by multiple economic and social factors such as
institutions and culture which are marked by inertia.
It is difficult to quantify the effect of the reduction
in fertility on economic growth as this is mediated
by a range of factors and the impact is scattered
over time with considerable lags. In this regard,
it is worthwhile to engage with elementary
growth economics to outline certain fundamental
determinants of economic growth. Since the 18th
century, the question of growth and its determinants
have attracted the attention of classical economists
and policymakers alike. Nevertheless, in the last
century more fundamental growth elements
and mechanisms were outlined through various
influential models including the Harrod-Domar and
Solow models (Barro and Sala-i-Martin 2003).
The Harrod-Domar model is influenced by the
Keynesian theory and assumes that the rate of
growth is affected by the level of savings and the
productivity of capital investment (capital-output
ratio). Growth could be boosted either by increasing
the level of savings or reducing the capital-output
ratio.The Solow model (1957) is an improvement
over the Harrod-Domar model and endogenises the
capital output ratio.
During this period Coale and Hoover (1958)
came up with a model which accentuated that
rapid population growth can hamper economic
growth, particularly in low-income countries. If
the population growth rate was constant then the
results of both these models would be the same.
Coale and Hoover assume that in cases where
people have more children, there will be greater
amount of consumption, leading to the saving of a
small fraction of the income, and resulting in lower
growth. Similarly, in cases where the investment
is higher in housing, education and medicine, less
would be available for more productive investment
such as infrastructure.The resultant rise in
incremental capital output ratio (ICOR) will reduce
growth. Other studies which have tried to prove
the correlation between population and economic
growth have tried to improve upon the Solow
model.These include the growth models of Barro
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(1991), Mankiw Romer and Weil (1992) and Bloom
and Mahal (1997).
The implications of population growth are of grave
concern to Indian policymakers, both because of its
sheer size as well as features. India is undergoing
a demographic transition but the fertility rates in
some states are still very high and can lead to an
ever-higher population in any country. As such, with
a reduced dependency burden on a working age
population it is possible to attain higher economic
growth, but policies will need to singularly focus
on developing the skills and productivity of the
workforce. For instance, jobless growth and high
levels of undernutrition are important concerns
that call for immediate policy attention.There
are plenty of other concerns such as high rural-
to-urban migration, haphazard urban planning
and proliferations of urban slums, disregard for
regulations and environmental degradation, among
many others.
All these factors restrict governmental capacity for
effective public investments and also lead to a poor
quality of life in both rural and urban areas.The
other issue could be the future needs, particularly
greater policy attention may be required for the
increasing proportion of an elderly population post
2040. In the absence of social security and safety
nets, elderly welfare can be a major concern for
households and the governments. Clearly, India
must do well to address the intrinsic population
momentum* and should design effective family
planning policies to ensure and enhance the welfare
of the people. An obvious impact of such effective
policymaking can be realised in the form of higher
economic growth and per capita incomes.With
this as the central point, this section presents
growth-theory informed projections to understand
the possible impact of the cost of family planning
inaction on economic growth.
3.2. Fertility Reduction and Economic
Growth: Pathways
The Malthusian view adhered to the basic reasoning
that the more the number of people sharing the
available goods and services, less will be available for
each person. However, as observed through the lens
of growth theories, the effect of the population size
on future growth results from higher consumption
today and improving human capital stock in an
economy. In case the saving levels are lower today,
the pace of capital formation will decline, which in
turn will lead to a lower output in production in
the future.Therefore, the capacity of the nation to
produce more goods and services in future will be
affected by the fertility decisions of the households
today.
To analyse the overall effect of fertility on economic
growth, the focus should be to first identify the
possible channels through which fertility influences
the path of growth. Clearly, there are resource
constraints when the population growth rate is
high. In the model of Coale and Hoover (1958)
resources are diverted from saving towards current
consumption due to a high demand from an ever
increasing population.To elaborate, in case there
are a larger number of children in the household it
implies greater consumption and an inability to save
a higher proportion of the income. A larger share
of children in the population also implies that there
is a greater demand for health and education, and
governments, particularly in developing countries,
have to incur higher public expenditure to meet
these demands.Therefore, the investment in physical
capital is diverted for other public purposes.
This pathway is effectively outlined in the neo-
classical growth models. In particular, the Solow
model (1956) indicates that while an increase in the
population growth rate raises the growth rate of the
aggregate output, it has no permanent effect on the
* Due to a large proportion of population in the reproductive age group (53 per cent), India’s population will continue to grow in terms of
absolute numbers even after replacement level of fertility is achieved.
Economic Gains with Family Planning Investments 39
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growth rate of the per capita output. Moreover, an
increase in the population growth rate lowers the
steady-state level of per capita output.
Ashraf et al. (2013) provides a comprehensive
explanation of all the mechanisms through which
the decline in fertility levels might affect economic
growth.The first effect which they discuss is
the Malthus effect. In his original model Malthus
theorised that all life forms have a propensity for
exponential population growth when resources
are abundant but that actual growth is limited by
available resources. In the present context, the
effect of population on reduced per capita growth
is on account of overcrowding of the fixed factor.To
elaborate, suppose the amount of land is fixed but
the population is increasing; therefore the utilisation
and returns to land in production diminishes relative
to the growth of the population, assuming that
the output is produced using the Cobb-Douglas
production function.
The Solow effect, also known as the phenomenon
of capital shallowing, states that a rapid population
growth lowers the ratio of capital to labour.The
workforce thus works with less capital, consequent
to which there is a poor rate of savings, which then
reduces the productivity of labour. Also, the age
structure of the population, which itself is a function
of past fertility and mortality behaviours, is an
important source of economic growth.
Four channels have been identified through which
age structure affects economic growth: Dependency
effect, life cycle saving effect, labour supply effect and
experience effect. In this context, the phenomenon
of the demographic dividend and its effect on
growth is widely cited in literature. It occurs when
due to a decline in fertility, the proportion of
working people relative to dependents in the total
population is high and indicates that more people
have the potential to be productive and contribute
to the growth of the economy.
The dependency effect is realised when the income
per capita rises given the income per worker due
to an increase in the proportion of the working age
population. Also, due to an increase in the share of
the working age population and a lower dependency
burden, the saving rate gets an impetus, which leads
to a higher capital formation and a higher output
known as the life cycle saving effect.
The experience effect arises on account of increase
in the average age of the working age population
which could boost productivity. Also, in case the
proportion of the elderly in the workforce increase
relative to the new workers, the labour supply can
increase manifold.This effect is called the labour
supply effect.These channels are interrelated.To
elaborate, the increase in the proportion of the
dependents implies a reduced labour supply. But in
case the old age dependency increases then we can
assume that there is a substantial experience effect
provided the elderly are allowed to work. Similarly,
the higher the proportion of working age people
relative to the children and the elderly means that
there is greater magnitude of a lifesaving effect.
Child care effect refers to the availability of more
productive time for adults due to a reduction in the
fertility rate and the associated child rearing time.
There will be a greater impact of this effect on the
females as presently they spend a large amount
of time in child care and related domestic unpaid
activities.Therefore, it is expected that as the child
rearing time is reduced females can use their freed-
up time to engage themselves in the workforce and
the female labour participation rate could improve
substantially.This effect has important implications
from the perspective of policymaking in India as the
female Labour Force Participation Rate (LFPR) is
very low as compared to other countries.
A reduction in the number of siblings is often
associated with an increase in parental investment
in education per child. In case a couple have fewer
children they are able to allocate their spending
and investments more effectively on them. It can
be assumed that human capital formation is higher
in case there is higher investment per child.This is
called the child-quality effect.
40 Cost of Inaction in Family Planning in India
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Finally, an increase in the size of the population
may raise productivity directly, by allowing for
economies of scale, or it may induce technological
or institutional change that raises income per
capita.This is called the Boserup effect. In this study
since we are projecting the population till 2031
and looking at the effect on economic growth for
achieving immediate fertility policy goals, only the
dependency effect has been considered.
The realisation of other effects such as the Solow
and Malthus effect are expected to be visible only
after a period of 25-30 years; therefore, we do not
consider those effects here.
3.3. Data and Methods
3.3.1. Simulation Approach
There are three widely used approaches for
estimating the effect of fertility on prospects
for economic growth: macroeconomic analysis,
microeconomic analysis and simulation exercises. In
macroeconomic analysis, the historical relationship
between population and per capita income is
observed. In this field, the contributions of Kuznet
(1967) and Kelly (1978) are important benchmarks.
More recent methods use regression techniques
in a growth accounting framework to establish a
relationship between per capita income growth and
demographic variables such as a dependency ratio
and a population growth rate (Barro 1991 Mankiw,
Romer and Weil 1992, Bloom et al 2010). However,
the results using these approaches are questionable
once we consider endogeneity and reverse causality.
On the other hand, microeconomic analysis focuses
on households as the main entity which influence
the fertility levels and hence the standard of living
by affecting saving rates and investment. However,
the limitation of these models is their inability to
explain the macroeconomic effects of a reduction in
fertility.The change in a household’s decisions and
its effect on the economy through various channels
might happen with a considerable lag. Also, it is not
possible to exactly pinpoint the channel through
which the major change might have resulted.To
consolidate the limited microeconomic evidence
with macroeconomic findings is problematic given
the inconsistency of microeconomic estimates along
different studies.
Simulation models are based on the relationship
between economic and demographic variables.
The Coale and Hoover model is treated as the
cornerstone of the modern economic–demographic
simulation models.The advantage of these is that
they can be extended to include multiple sectors
such as agriculture and industry.Also, fertility
and savings could be made endogenous in these
models.The saving decisions of households have
important implications for capital formation and
economic growth.Therefore, simulation models
are useful in case the future effects of population
on economic growth are to be considered. The
recent development of the overlapping generations
model (OLG) and the stress on saving and human
capital has further increased the popularity of these
models.Therefore, the elements of both macro and
micro analysis are included and the results are more
reliable than those through other approaches.The
only problem with these models is the accuracy
of assumptions regarding the households and the
economy. Depending upon the objectives, the
complexity can be quite high which could be difficult
to capture. But from the viewpoint of policymakers,
they are favoured in obtaining a meaningful estimate
of the potential effect of demographic changes.
We follow the Coale and Hoover model and
construct an economic–demographic simulation
model in which fertility can be exogenously varied.
The paths of per capita GDP are compared under
the two scenarios starting from 2016 at an interval
of 5 years: 2021, 2026 and 2031. In India’s policy
framework there are clear goals for fertility levels
to be achieved by the next decade.Assuming that
these targets will be met we create two scenarios:
a “current trend” in which fertility is following
the current trend pattern and will keep falling at
the observed rate, and an “policy trend” in which
fertility is lower and will decline at a faster pace
Economic Gains with Family Planning Investments 41
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given the policy objectives and due to investment in
family planning.The population forecasts based on
these policy scenarios have been used to compare
per capita income for the forecasted years.
The rate of output is assumed to be dependent on
the resources that can be devoted for productive
facilities and certain other developmental outlays.
Total outlay (F) could be categorised as meant for
direct growth (D) and welfare (W).
F = D +W
The amount of outlay will depend upon the base
year output (Y0), the current output (Y), the number
of consumers or population in the base year (C0)
and current year (C). Also, the savings propensity,
denoted by a, plays an important role. Based on
these factors the outlay available for growth and
welfare investments is estimated as follows:
[ ( F =
C
F0
C0
+
a
Y
C
-
F0
C0
The welfare outlays (W) could be further
categorised as those required for the current needs
of the population (WC) and those required for
additional people (Wi).
W = Wc + Wi
The outlay allocation for the purpose of welfare
is dependent upon the population growth rate.
Following Coale and Hoover, it is assumed that the
welfare outlay for additional people is 10 times the
outlay for the current needs i.e.,
Wi = 10pWc
Where, p is the population growth rate. From here
we can derive a relationship between W and Wc,
W = Wc + 10pWc
The above equation is rearranged to estimate the
value of Wc for a given level of expenditure W.
Further, the productive effect of the outlays of
various types is given by the following equation. It is
being assumed that the effect of a certain portion of
welfare outlays on output is felt after 10 years.
G = D + (ecWc + eiWi)L + (ecWc + eiWi) t-10 (1-Lt-10)
Finally, the output projection is approximated by the
following equation:
Yt+5 = Yt + 5(G/R)
Where R is the ratio between outlays and increase
in income.Therefore, the growth outlay has been
estimated first and added to the output for that
particular year to arrive at the figure for the output
after 5 years.The capital output ratio has been
assumed to be around 3 for the simulation exercise.
Following Ashraf et al. (2013), we construct another
economic–demographic simulation model in which
fertility can be exogenously varied and the human
capital formation is given significant weight.Their
methodology is an improvement over the existing
model of Coale and Hoover (1958) and many
others.We use a limited version of their work as we
consider only the dependency effect, as the policy
targets of fertility for different states are yet to be
achieved.The realisation of other effects such as the
Solow and Malthus effect is expected to materialise
only after 25-30 years.To initialise the simulation
exercise, we consider an aggregate production given
by a standard Cobb–Douglas production function in
a neo-classical growth framework.The factor inputs
are physical capital, and effective labour, so that the
aggregate output in period t is Yt.The original model
of Ashraf (2013) uses land as a factor of production
and assumes congestion of the fixed factor in
accordance with the Malthusian predictions. But
there is no clarity on whether land could be used
productively as well or whether the returns could
be positive. Furthermore, the type of land could also
have different implications for exhaustion of the
total product.To avoid all these complications, we
consider only Labour and Capital.
Mathematically, the production function assumes the
following form:
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Where K is capital, L is labour, and A is total factor
productivity (its growth rate is assumed to be
zero).The physical capital has been assumed to be a
constant proportion of the output.
Stock of effective labour is given by,
Ht =
hsi,t X hei,t X LFPRit X Nit
Where, Nit is the number of individuals of age
i in the population in period t. LFPR is the labour
force
participation
rate.
hs
i,t
and
he
i,t
are
levels
of
human capital from schooling and experience.The
years of schooling are aggregated into human capital
from schooling using a log-linear specification. θ is
assumed to be 10 per cent. Si,t is the mean year of
schooling.
Human capital from on-the-job experience for a
worker of age i in period t is given by,
Following Ashraf et al. (2013), the value of Φ has
been assumed to be .052 and Ψ as -.0009875. Using
regression analysis, the values of α and β have been
estimated for each cross section: India and the four
states.The estimated values of the parameters have
been used in the production function to simulate
the value of GDP, given the assumptions about the
factors of production based on previous trends.
3.3.2. Data Sources
We use the data for India and the four states:
Rajasthan, Bihar, Madhya Pradesh and Uttar
Pradesh for the period 1981-2016.The trends of
GDP and state public spending (economic and
social expenditure) as well as Gross Fixed Capital
Formation (GFCF) over this period have been
analysed to make specific assumptions about
the parameters for the simulation exercise.The
entire analysis has been done at 2004-05 prices.
The data for GDP, NSDP1 and public expenditure
has been taken from Handbook of Economics,
RBI for different years.The public expenditure for
the India economy is categorised as capital and
revenue expenditure.And, within these categories
the expenditure could be incurred either on social
services or economic services. Economic and
social expenditure have been taken as proxy for
development and welfare outlays in the Coale and
Hoover model. It has been assumed that the total
outlays are approximately 23 per cent of the GDP. A
lag of 10 years has been assumed for the realisation
of the effect of welfare outlays on the growth outlay.
The ratio of outlays required for additional people
(Wi) to those for current needs of the population
(Wc) has been assumed to be 10 times the rate
Table 3.1: Key Assumptions for the Economic Growth Simulation Analysis
Parameter
Return to an additional year of mean schooling (θ)
Return to education (Φ)
Return to education (Ψ)
Capital to GDP ratio
Rate of growth of capital
Ratio of outlay for future needs to current needs
Lag effect of welfare outlays for future needs on output
Total outlay as a fraction of GDP
Assumption
10%
0.052
-0.0009875
1.77
6-7%
10
10 years
20-23%
7 Net State Domestic Product
Economic Gains with Family Planning Investments 43
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of growth of the population.The ratio of capital to
GDP has been assumed to be 1.8 times and given
the past trend, capital has been assumed to grow
at 7 per cent per annum. Projections of GDP have
been made from year 2016 onwards at an interval of
5 years.
The key assumptions for the economic growth
simulation exercise are summarised as follows:
The major rounds of Employment and
Unemployment Surveys of the National Sample
Survey conducted during these years are the 50th
Round (1993-94), 55th Round (1999-00), 61st Round
(2004-05) and the 68th Round (2011-12).We have
used these rounds to calculate and extrapolate the
Labour Force Participation Rate (LFPR) and mean
years of education for both males and females.The
parameter depicting return to schooling has been
assumed to be 10 per cent (Ashraf et al. 2013).The
population projections presented in the previous
chapter have been used to calculate the mean age of
males and females in the labour force and the share
of working age population. Data for divided states
have been considered in the simulation exercise to
maintain consistency in analysis.The weights used in
simulating growth rates are based on a panel of 80
observations for four Indian states and India. Each
cross section containing 16 observations was used
to generate weights for the respective entity using a
Cobb-Douglas production function model with two
factors: Labour and Capital.
3.4. Results
3.4.1. Current Trends in Economic Growth
Table 3.2 presents the figures for the average GDP
and per capita GDP for India and the four states
for the period 2001-05, 2006-10 and 2011-15.The
average GDP and the per capita GDP of India over
the period 2011-15 is Rs. 59000 billion and
Rs. 41,757 respectively. Across the states, the GDP
of Uttar Pradesh (Rs. 4000 billion) during this period
is relatively higher followed by Rajasthan (Rs. 2200
billion). But a higher GDP does not reflect the
inequality, which is prevalent and is a poor measure
Table 3.2: GDP and Per Capita GDP of India and Selected States, 2001-15
States
Bihar
Madhya Pradesh
Rajasthan
Uttar Pradesh
India
2001- 05
650
960
1100
2200
28000
GDP (in billion rupees)
2006 -10
2011-15
2001- 05
960
1400
7487
1300
1900
15120
1500
2200
17955
3000
4000
12635
42000
59000
23062
PCGDP
2006 -10
2011-15
10170
13847
19354
25760
23685
31546
15721
19186
32051
41757
Table 3.3: Growth Rate of GDP and PCGDP of India and Selected States, 2001-15
States
Bihar
Madhya Pradesh
Rajasthan
Uttar Pradesh
India
GDP Growth Rate (%)
2001-05
2006-10
2.13
11.56
4.30
8.24
6.84
9.12
4.24
7.23
6.75
8.62
2011-15
5.39
6.52
5.88
4.61
6.67
PCGDP Growth Rate (%)
2001-05
2006-10
2011-15
0.20
9.89
4.24
2.31
6.43
5.42
4.94
7.25
4.49
2.18
5.28
3.62
5.21
6.84
5.21
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Table 3.4: Social and Economic Outlay of India and Selected States, 2001-15 (in millions)
States
Bihar
Madhya Pradesh
Rajasthan
Uttar Pradesh
India
Social Outlay
2001- 05
2006 -10
570
980
630
970
870
1300
1300
2500
1700000
3000000
2011-15
240
2200
2200
4700
4400000
Economic Outlay
2001- 05
2006 -10
360
910
810
1000
530
850
1500
2300
2500000
4100000
2011-15
2200
2300
1900
3700
6000000
Table 3.5: Growth Rate of Social and Economic Outlay of India and Selected States, 2001-15 (%)
States
Bihar
Madhya Pradesh
Rajasthan
Uttar Pradesh
India
Social Outlay
2001- 05
2006 -10
1.42
9.95
0.62
13.32
2.91
9.05
8.46
14.18
6.11
13.24
2011-15
30.94
11.06
17.41
18.68
10.07
Economic Outlay
2001- 05
2006 -10
2.18
26.71
17.82
3.77
13.01
7.47
30.25
9.32
7.48
12.27
2011-15
29.73
20.87
25.87
26.94
9.67
of the standard of living.Therefore, we consider per
capita GDP, which is the highest for Rajasthan (Rs.
31,546) and Madhya Pradesh (Rs. 25,760).
A glance at the average growth of GDP and per
capita reveals that these growth rates for India have
moderated over the period 2011-15 (Table 3.3).
The growth rate of GDP is down by 2 per cent and
per capita GDP by 1.6 per cent over the period
2011-15.The global financial crisis and sluggishness
in investment are two of the major factors for this
behaviour.The year-to-year variation in the observed
Net State Domestic Product (NSDP) is quite high.
The biggest impact was felt by Bihar where the
average GDP growth rate came down to 5.39 per
cent over 2011-15 from 11.56 per cent over the
period 2006 -10. A reduction of 2 to 3 per cent
in the growth rate is observable for other states.
Similarly, the highest decline in the per capita growth
rate was observed in the case of Bihar (from 9.89
per cent over 2006-10 to 4.24 per cent over 2011-
15).
Table 3.4 presents the figures for the social and
economic outlays. Social outlays are the expenditure
of the government to enhance the welfare of the
people and economic outlays are the expenditure
incurred for augmenting the productive capacity
of the economy.The figures for India include the
expenditure by both the Centre and the states.
At the India level, the expenditure on economic
activities is higher than the social expenditure but
no such pattern is apparent across states.
The growth rates of social and economic outlays
in Table 3.5 show that the average expenditure for
India during the period 2006-10 has been higher as
compared to other periods.The explanation behind
this behaviour could be the financial crisis and the
resulting subdued demand. Comparing the trends at
the state level we observe that the rate of growth
of social outlays has been dramatically higher in
the recent period (from 9.95 per cent in 2006-10
to 30.94 in 2011-15). Similarly, the growth rate of
the economic expenditure in the case of Madhya
Pradesh, Rajasthan and Uttar Pradesh has been quite
favourable. But these figures need to be considered
carefully as the year-on-year variation in these
expenditures is high.
Economic Gains with Family Planning Investments 45
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Table 3.6 presents the share of social and economic
outlay in the total GDP for India and the NSDP for
the states. Over the period 2006 -10, the share of
social expenditure to the GDP for India increased
from 5.98 to 6.96 per cent and to 7.36 per cent
over 2011-15.The share of economic expenditure
in the recent period seems to be increasing
gradually at the national level, and at the state level,
there appears to be an equal distribution of public
expenditure.To elaborate, in the case of Madhya
Pradesh, both types of expenditure are hovering
at around 11 per cent for the period 2011-15 and,
around 15 and 10 per cent in the case of Bihar and
Uttar Pradesh, respectively.
One of the main determinants of economic growth
is the availability of human capital and the number of
effective workers, which is determined by the level
of education and the labour force participation rate.
A favourable age structure can result in a higher
proportion of workers which can boost growth
prospects.Table 3.7 presents the mean year of
schooling, labour force participation rate and the
dependency ratio.The mean years of schooling are
higher across males as compared to females. In the
case of India, the mean year of schooling increased
from 5.7 to 6.8 per cent in the case of males and
from 3.6 to 4.8 per cent for females over the period
2004 -11.The figures for the states show a similar
trend with the mean year of schooling being higher
among females in Madhya Pradesh (4.2 per cent
in 2011) and 6.3 per cent in the case of Rajasthan
and Uttar Pradesh.The LFPR is also lower among
females. As of 2011, this stood at 421 for females
and 839 for males with it being higher among
females in Rajasthan (542) and highest in the case of
Bihar (856).The Dependency Ratio is defined as the
proportion of the child and elderly population to the
total population. In the case of India, this declined
from 42 per cent in 2001 to 38 per cent in 2011
with a dramatic drop to around 40 per cent across
the states, except in Bihar, where the dependency
burden was relatively higher (48 per cent).
Table 3.6: Social and Economic Outlay as % of GDP and GSDP, India and Selected States, 2001-15
States
Bihar
Madhya Pradesh
Rajasthan
Uttar Pradesh
India
Social Outlay as a % of GDP
2001-05
2006-10
2011-15
0.087
0.102
0.169
0.065
0.071
0.113
0.082
0.082
0.096
0.058
0.082
0.116
5.984
6.968
7.360
Economic Outlay as a % of GDP
2001-05
2006-10
2011-15
0.055
0.095
0.154
0.083
0.078
0.120
0.050
0.055
0.083
0.068
0.075
0.092
8.894
9.657
10.022
Table 3.7: Mean Years of Schooling, Labour Force Participation Rate and Dependency Ratio, India and Selected
States for Selected Years
States
Bihar
Madhya Pradesh
Rajasthan
Uttar Pradesh
India
Mean Year of Schooling
2004
2011
M
F
M
F
4.7 1.8 5.7 2.9
4.9 2.6 6.1 4.0
4.9 2.1 6.3 3.2
5.4 2.7 6.3 3.9
5.7 3.6 6.8 4.8
LFPR (per 1000)
2004
2011
M
F
M
F
784 130 856 269
813 373 841 451
762 428 829 542
802 251 840 354
798 312 839 421
Dependency Ratio
2001 2011
All
All
0.49 0.48
0.46 0.40
0.47 0.40
0.48 0.41
0.42 0.39
Note: M - Males; F - Females
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3.4.2. Economic Growth Projections: Coale-
Hoover Model
The GDP for India and the NSDP for the states
under the current trend and policy scenario is
presented in Table 3.8. As expected, the GDP
in absolute terms for India will be higher under
the policy scenario with an active family planning
environment. States like Bihar and Madhya Pradesh
are on a lower base as compared to Rajasthan and
Uttar Pradesh. Overall, Bihar and Rajasthan seem
to be the largest beneficiaries of the investment in
family planning given the favourable demographic
dynamics.
It is expected that under the policy scenario the
average per capita GDP of India will be Rs. 1,42,363
over 2026 - 2031 as compared to Rs.1,25,922 under
current trends (Table 3.9). Across the states, the
per capita GDP of Rajasthan over 2026-31 will be
comparatively higher under both the current (Rs.
74214) and policy (Rs. 81793) scenarios followed by
Table 3.8: GDP (in Rs. billion at 2004-05 prices) under Current Trend and Policy Scenario based on Coale and
Hoover Model, India and Selected States 2016-31
States
Bihar
Madhya Pradesh
Rajasthan
Uttar Pradesh
India
Current Trend
2016-21
2021-26
2302
3172
3263
4452
3386
4718
6098
8081
98144
135368
2026-31
4382
6093
6589
10752
187123
Policy Scenario
2016-21
2021-26
2359
3331
3268
4465
3433
4846
6221
8406
98847
137040
2026-31
4711
6118
6854
11394
190312
Table 3.9: Per Capita GDP (in Rs. at 2004-05 prices) under Current Trend and Policy Scenario based on Coale
and Hoover Model, India and Selected States 2016-31
States
Bihar
Madhya Pradesh
Rajasthan
Uttar Pradesh
India
Current Trend
2016-21
2021-26
19318
25269
38241
49243
42155
55606
26384
33323
71718
94561
2026-31
33270
64163
74214
42661
125922
Policy Scenario
2016-21
2021-26
21793
29621
43185
56926
44451
59909
29838
39089
78515
105066
2026-31
40575
75748
81793
51633
142363
Figure 3.1: Per Capita GDP (in Rs. at 2004-05 prices) under Current Trend and Policy Scenario based on Coale
and Hoover Model, India 2016-31
150000
100000
Per Capita GDP India
6797
10505
16441
50000 71718 71718
94561 94561
125922 125922
Secular Alternate Secular Alternate Secular Alternate
2016-21
2021-26
2026-31
Note: Projection of Per capita GDP using fertility rates under secular and
Alternate scenario
Per Capita GDP growth rate India
8
0.39
0.47
0.5
6
4
2
5.64 5.64
6.37 6.37
6.63 6.63
0
Secular Alternate Secular Alternate Secular Alternate
2016-21
2021-26
2026-31
Note: Projection of Per capita GDP using fertility rates under secular and
Alternate scenario
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Figure 3.2: Per Capita SDP (in Rs. at 2004-05 prices) under Current Trend and Policy Scenario based on Coale
and Hoover Model, States 2016-31
Per Capita SDP Bihar
7305
Per Capita SDP MP
7305
2475
19318 19318
4352
25269 25269
33270 33270
2475
38241 38241
4352
25269 25269
33270 33270
Secular Alternate Secular Alternate Secular Alternate
2016-21
2021-26
2026-31
Secular Alternate Secular Alternate Secular Alternate
2016-21
2021-26
2026-31
Per Capita SDP Rajasthan
7579
2296
42155 42155
4303
55606 55606
74214 74214
Per Capita SDP UP
3454
5766
8972
26384 26384
33323 33323
42661 42661
Secular Alternate Secular Alternate Secular Alternate
2016-21
2021-26
2026-31
Secular Alternate Secular Alternate Secular Alternate
2016-21
2021-26
2026-31
Note: Projection of Per capita SDP using fertility rates under Secular and Alternate scenario
Table 3.10: GDP Growth Rate (%) under Current Trend and Policy Scenario based on Coale and Hoover Model,
India and Selected States 2016-31
States
Bihar
Madhya Pradesh
Rajasthan
Uttar Pradesh
India
Current Trend
2016-21
2021-26
7.48
7.56
7.20
7.29
7.49
7.87
6.40
6.51
7.11
7.59
2026-31
7.63
7.37
7.93
6.61
7.65
Policy Scenario
2016-21
2021-26
8.17
8.23
7.24
7.33
7.86
8.23
6.93
7.02
7.31
7.73
2026-31
8.28
7.40
8.28
7.11
7.77
Table 3.11: Per Capita GDP Growth Rate (%) under Current Trend and Policy Scenario based on Coale and
Hoover Model, India and Selected States 2016-31
States
Bihar
Madhya Pradesh
Rajasthan
Uttar Pradesh
India
Current Trend
2016-21
2021-26
5.90
6.16
5.28
5.75
5.60
6.38
4.76
5.26
5.64
6.37
2026-31
6.33
6.06
6.69
5.60
6.63
Policy Scenario
2016-21
2021-26
6.86
7.18
6.09
6.36
6.27
6.96
5.94
6.20
6.14
6.76
2026-31
7.40
6.61
7.31
6.42
7.10
48 Cost of Inaction in Family Planning in India
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Figure 3.3: Per Capita SDP (in at 2004-05 prices) under Current Trend and Policy Scenario based on Coale and
Hoover Model, States 2016-31
Per Capita SDP Growth rate Bihar %
1.02
1.07
0.96
Per Capita SDP Growth rate MP %
0.81
0.61
0.55
5.9 5.9
6.16 6.16
6.33 6.33
5.28 5.28
5.75 5.75
6.06 6.06
Secular Alternate
2016-21
Secular Alternate
2021-26
Secular Alternate
2026-31
Per Capita SDP Growth rate Rajasthan %
0.62
0.58
0.67
5.6 5.6
6.38 6.38
6.69 6.69
Secular Alternate
2016-21
Secular Alternate
2021-26
Secular Alternate
2026-31
Per Capita SDP Growth rate UP %
0.82
0.94
1.18
4.76 4.76
5.26 5.26
5.6 5.6
Secular Alternate
2016-21
Secular Alternate
2021-26
Secular Alternate
2026-31
Note: Projection of Per capita SDP growth rate using fertility rates under Secular and Alternate scenario
Secular Alternate
2016-21
Secular Alternate
2021-26
Secular Alternate
2026-31
Madhya Pradesh (Rs. 64163 in the current scenario
and Rs. 75748 in policy scenario).
Table 3.10 shows that the long-term GDP growth
rate of India under the policy scenario is 7.77 and
7.65 per cent under the current or business-as-
usual scenario. Bihar and Rajasthan are expected
to grow at a higher rate of 8.28 per cent under the
policy scenario as compared to 7.63 and 7.93 under
the business-as-usual scenario.There is an average
difference of 0.5 per cent in the growth rate between
the two scenarios. Uttar Pradesh could lag behind
because of a lower allocation for the growth outlay.
As shown in Table 3.11, the per capita GDP growth
rate is expected to be higher for Rajasthan and Bihar
under both the current (6.69 and 6.33 per cent) and
policy scenarios (7.31 and 7.40 per cent).The sheer
size of the population of Uttar Pradesh puts it at a
disadvantage; the per capita GDP growth could be
lower at 5.6 per cent and 6.42 when family planning
measures are adopted. Similarly, Madhya Pradesh is
expected to lag behind (6.06 and 6.61 per cent).
3.4.3. Economic Growth Projections: Ashraf
et al Model
In Table 3.12, the GDP for India and the NSDP for
the states is presented under the current and policy
scenarios, using a more dynamic simulation model
with a major focus on human capital formation.The
GDP in absolute terms for India will be higher under
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Table 3.12: GDP (Rs. billion at 2004-05 prices) under Current Trend and Policy Scenario based on Ashraf et al
Model, India and Selected States 2016-31
States
Bihar
Madhya Pradesh
Rajasthan
Uttar Pradesh
India
Current Trend
2016-21
2021-26
2314
3285
3351
4774
2862
4038
6376
9235
100316
141665
2026-31
4731
6898
5781
13599
201986
Policy Scenario
2016-21
2021-26
2311
3293
3353
4782
2876
4081
6414
9318
100617
142891
2026-31
4763
6907
5866
13745
205022
Table 3.13: Per Capita GDP (in Rs. at 2004-05 prices) under Current Trend and Policy Scenario based on Ashraf
et al Model, India and Selected States 2016-31
States
Bihar
Madhya Pradesh
Rajasthan
Uttar Pradesh
India
Current Trend
2016-21
2021-26
19415
26175
39272
52806
35632
47589
27590
38086
73304
98960
2026-31
35920
72649
65116
53964
135924
Policy Scenario
2016-21
2021-26
21339
29270
44307
60961
37240
50447
30764
43322
79921
109552
2026-31
41030
85520
70002
62282
153368
Table 3.14: GDP Growth Rate (%) under Current Trend and Policy Scenario based on Ashraf et al Model, India
and Selected States 2016-31
States
Bihar
Madhya Pradesh
Rajasthan
Uttar Pradesh
India
2016-21
7.20
7.23
7.06
7.58
7.10
Current Trend
2021-26
7.43
7.51
7.29
7.88
7.31
2026-31
7.65
7.64
7.52
8.14
7.52
2016-21
7.22
7.31
7.20
7.70
7.21
Policy Scenario
2021-26
7.51
7.52
7.41
7.95
7.43
2026-31
7.75
7.71
7.60
8.18
7.65
Table 3.15: Per Capita GDP Growth Rate (%) under Current Trend and Policy Scenario based on Ashraf et al
Model, India and Selected States 2016-31
States
Bihar
Madhya Pradesh
Rajasthan
Uttar Pradesh
India
2016-21
5.94
5.72
5.59
6.27
5.93
Current Trend
2021-26
6.40
6.30
6.15
6.90
6.36
2026-31
6.67
6.61
6.57
7.32
6.73
2016-21
6.23
6.40
5.97
6.92
6.30
Policy Scenario
2021-26
6.65
6.77
6.44
7.24
6.69
2026-31
7.10
7.09
6.86
7.64
7.13
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the policy scenario. States like Bihar and Rajasthan
are on a lower base as compared to Madhya Pradesh
and Uttar Pradesh. Overall, Bihar and Rajasthan
seem to be the largest beneficiaries as human
capital investment in these states could contribute
immensely to growth.
The PCGDP for India is expected to be 153368
rupees under the policy scenario and 135924 rupees
under the business-as-usual case.This could be an
underestimation because of the modest benefits
which we are assuming. In fact, with better policies
the mean years of schooling and LFPR could be
higher which could result in higher growth.
GDP growth rate for India is expected to be 7.65
per cent under the policy scenario and 7.52 per cent
under the normal scenario.The projected NSDP
growth rate using this model is higher for Uttar
Pradesh under both the scenarios (8.14 and 8.18
per cent respectively) followed by Bihar and Madhya
Pradesh.The per capita GDP growth rate for India
is expected to be around 7.13 per cent under the
policy scenario and 6.73 per cent under current
trends.The growth rate for Bihar and Madhya
Pradesh is expected to be higher.
3.5. Conclusion
The following would be the economic gains if
appropriate investments in family planning are made
over the next 15 years:
• With active family planning policies, India will
enjoy an additional per capita income of 13 per
cent in 2026-31.This implies that the Per Capita
GDP (PCGDP in 2004-05 prices) for India
could be Rs. 153,368 under the policy scenario
compared to Rs. 135,924 under the current
scenario.
• India would also benefit from an additional 0.4
percentage point increase in the per capita GDP
growth rate during 2026-31.
• Significant benefits for all the four states are also
noted but the largest gain could be experienced
by Madhya Pradesh with an additional per capita
income of 18 per cent in 2026-31. Madhya
Pradesh could also benefit from an additional 0.5
percentage point increase in the per capita GDP
growth rate during 2026-31.
The Lucas formula shows that if the GDP is growing
at a stable rate then it takes 70/g years for the GDP
to double provided that the rate is sustained.We are
assuming that in the long run, the GDP growth rate
will be around 7.5 per cent. But if this is sustained
then the per capita GDP will get doubled within the
next 10 years (70/7.5).The per capita GDP which
is around 55,000 will be around 111,000 around
2026.This is approximately equal to the estimates
which we are getting from the simulation models.
In the Coale and Hoover model, the per capita
GDP is Rs. 94,560 for 2026 while in Ashraf et.al.
model the other model it is Rs. 98,960. Also, based
on the given parameters, the growth rate of GDP
will be 0.12 points higher than the growth rate
observed under current trends.The per capita GDP
growth using this model is 6.63 per cent under
the business-as-usual scenario and 7.10 under the
policy scenario with effective family planning. For a
better understanding, the second simulation model
also incorporates human capital into the model.
Therefore, the per capita GDP growth using this
simulation model is slightly higher under both the
current (6.73 per cent) and policy scenarios (7.13
per cent).
At the state level, Uttar Pradesh is reporting the
highest GDP followed by Rajasthan. It seems by 2031
the net increase in absolute GDP will be higher for
Uttar Pradesh and Rajasthan as compared to the
other states.The figure for absolute GDP estimated
from the second model could be at a variance as
the role of human capital plays an important role in
this model. It may also be noted that in this model
the net addition to GDP for Rajasthan is particularly
lower as it fares badly on literacy indicators. If
both the scenarios are considered under the Coale
and Hoover model, then Uttar Pradesh seems to be
the biggest beneficiary in terms of net addition to
GDP.
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Using the Caole and Hoover model we observe that
the per capita GDP growth rate will be higher for
Bihar and Rajasthan and above 7 per cent if family
planning measures are adopted. On the other hand,
using the Ashraf.et.al model we observe that the
per capita GDP growth rate will be higher for Uttar
Pradesh and Bihar.
Reduction in fertility rates translates into better
education and employment opportunities.The mean
year of schooling is lower for females across all the
states reflecting the prevalent gender discrimination.
The situation seems to be worse for Bihar where
the mean year of schooling among females is only
2.9. In case, the number of households with more
number of children declines then women have a
better chance to complete their education.This
in turn can have a favourable effect in the sense
that educated women are more aware and better
informed which translates into better care of the
children.Time saved in child rearing due to fewer
number of children further leads to higher efficiency
in raising the children with an effective use of
resources.The net impact of child caring could be
derived in terms of higher human capital formation
and a slight contribution towards the GDP growth
rate.The entire process is further related to greater
women empowerment.
It is not unexpected that there is a negative
correlation between an education gap and the LFPR.
In India, women with low education levels are usually
employed as casual labourers, predominantly in
agricultural activities. Better education attainments
could open up a plethora of opportunities for
them in non-agricultural settings.Time saved from
not rearing children could be used to develop
skills.We can expect higher female labour force
participation when women are able to invest more
time in improving their skills for better paid work.
The additional income accruing to the household
in this fashion could lead to a higher investment
in the education of children. In the case of India,
women are at a bigger disadvantage if the LFPR are
observed. For Bihar, the female LFPR is as low as 269
as compared to 829 for males. The child care quality
effect arising from parental investment in children
is boosted when females are also contributing
monetarily.The major obstacles in the path are
the patriarchal societal and institutional norms and
practices, which hamper women’s equal participation
in all walks of life, including the home and the
workplace.
The fertility rate of India today is around 2.4 and
as per the National Population Policy of 2000, the
target of 2.1 should have already been achieved.
Similarly, the target of 2.1 for the states is expected
to be met by 2025.The high fertility rates have
grave implications for these states, which already
lag behind on the growth front and have a large
population dragging them further back. States such
as Bihar, Rajasthan, Madhya Pradesh and Uttar
Pradesh have huge economic potential because
most of the resources are unutilised. But due to high
birth rates and unwanted children, the resources,
which could have been used for human capital
development more effectively are getting wasted.
It is expected that the per capita investment on
education and health will be higher if family planning
practices are adopted today.The costs and benefits
we estimate are on the lower side as there are huge
externalities involved, which cannot be measured in
monetary terms.
3.5.1. Limitations
A major limitation of our approach is that we
consider the unidirectional nature of fertility. It
has been observed over the last decade that with
increasing income per capita, the fertility rates have
declined. Although it is hard to prove the causation,
there is a strong correlation between the two. As
the per capita capital stock gradually improves in the
economy, the proportion of skilled labour is higher
and higher, which leads to the gradual reduction of
fertility. Given the lack of substantial evidence about
the reduction in fertility due to higher income, we
focus only on the effect of fertility reduction on
economic growth.Also, because we are observing a
modest increase in the growth rate of the per capita
income, we assume the reduction in fertility will also
be moderate.
52 Cost of Inaction in Family Planning in India
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In the simulation-based models the sensitivity of
the results with respect to the parameterisation
of the underlying economic relations is critical.To
elaborate, we are assuming the growth outlay to be
at 23 per cent of the GDP and the experience effect
to be at 10 per cent. Furthermore, the mean year of
schooling and the Labour Force Participation Rate
are extrapolated at a constant rate. In the immediate
future, due to a change in policies regarding
education or employment generation, the value of
these parameters can change rapidly.Therefore, the
results could change depending upon the behaviour
of these parameters.
The simulation based model is definitely an
improvement over the Coale and Hoover approach
as it considers the effect of human capital on
economic growth. But this can be made more
dynamic by modelling the household decision-
making process.This will allow us to understand in
greater detail the process of human and physical
capital formation.The saving and investment
potential of the Indian economy is higher and the
growth could easily cross double digit figures. So the
simulated per capita income values might be taken
with a pinch of salt as we are expecting a better
standard of living over the next decade.
We have taken into account only the dependency
effect. However there are a number of other effects
of the reduction in fertility, such as the Solow effect,
the Malthus effect, and the schooling and child care
effect. But given the fact that we are projecting the
population till 2031, these effects have not been
considered. It will take a substantial amount of time
(approximately 25 to 30 years) for these channels
to provide an impetus to economic growth. It is
expected that with an improvement in technology,
the marginal productivity of the factors might
improve.Also, there is limited evidence regarding the
schooling effect and the child care effect in the case
of developing countries. Accounting for these effects
and studying each mechanism in greater detail
could provide more clarity about the true effects of
fertility reduction.
Another limitation of this model is that we have
considered only the role of education and the
favourable dynamics related to increasing the
effectiveness of labour.There are other factors at
play, like increased health expenditure, which could
boost the productivity of labour.To elaborate, with
a reduction in population and an investment in
health, it is possible that the health infrastructure
will be better and the accessibility per person will
improve.The improvement in health in this manner
could lead to an augmentation of human capital and
higher growth. But due to the unavailability of data
and vagueness about the potential impact on growth
through this channel we have ignored the effect
that an increased health expenditure would have on
labour productivity.
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4 Budgetary Savings with
Family Planning Investments
Case of National Health Mission
4.1. Background
NFHS 4 reveals that over one-fifth of Indian women
and over one-fourth of Indian men get married
before the ages of 18 and 21 years, respectively.
Early marriage has a vital impact on sexual and
reproductive health and is associated with a
higher number of child births. In particular, a high
proportion of unmet need for family planning and
sizable incidences of unwanted pregnancies have a
significant impact on the household and the society.
In India, the bulk of maternal and child health
services are provided through the public health
system. A large adult population along with
high fertility rates in selected regions tends to
increase the cost of health service provisioning.
The budgetary consequences of such patterns are
observed in the form of poor quality healthcare
services and a thin spread of resources across those
regions. As the government is resource constrained,
the high economic and human cost of inaction in
family planning crowds out the limited resources for
less productive purposes and results in substantial
economic losses.
There are three aspects of population growth which
should be considered in analysing the cost - size of
the population, its growth rate and age distribution.
The cost due to high fertility largely falls on the
households and the government.There could be
two types of cost associated with inaction in family
planning, the burden of which is borne by both
- direct and indirect costs. Direct costs could be
categorised as programme related costs and costs
borne by households as indirect costs. At present,
the National Health Mission (NHM) is the main
programme, which focuses on improving maternal
and child health using different nutrition and care-
based intervention policies.
By reducing fertility and pregnancy related
complications, the public expenditure related
to maternal and child health will be reduced.
Households incur expenditure on drugs, provision of
food, cleaning materials, transportation and service
tips during pregnancy. It is being assumed that when
the effectiveness of policy programmes improves,
costs borne by the households will substantially
reduce.The direct cost could be further categorised
as a fixed and variable cost. It is imperative for the
government to invest in family planning programmes
to bring down both the social and economic
costs, which could be freed up for investment in
development projects thereby augmenting the
productive capacity of the economy.
India was the first country in the world to adopt an
official family planning programme way back in 1952.
Since then, a number of programmes focussing on
controlling the population have been undertaken
in the country. Initially, the focus was to improve
the health of the people rather than control the
growing population. It was only after 1971 when the
Census revealed an alarming growth in population
levels that the family planning strategies started
receiving attention. Since then, the policy discourse
has moved in various trajectories and now focuses
on population stabilisation as opposed to the former
agenda of population control.
During these periods the policies started setting
definite demographic goals in terms of birth rates.
The idea was to curtail the number of births
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by using family planning methods. But when the
National Rural Health Mission (NRHM) was
launched in 2005, the health rationale received so
much attention that the demographic rationale was
subsumed within it.The NRHM particularly aimed to
address the health needs of High Focus States which
were found to be reporting poor maternal and child
health indicators.
Today, family planning efforts are just one of the
many activities under the reproductive and child
health component of the National Health Mission.
Keeping in view the success of the NRHM, the focus
on covering rural areas and the rural population
will continue along with up scaling to include
non-communicable diseases and expanding health
coverage to urban areas.There were a number of
new initiatives, taken with the launch of the NRHM,
which include the creation of a community level
workforce of ASHAs.They are the trained female
frontline workers who are instrumental in creating
the demand for health services acting as an interface
between the community and the public health
system.They build awareness about maternal and
child health programmes and have been successful
in increasing the utilisation of outpatient services,
diagnostic facilities, institutional deliveries and
inpatient care.
The NRHM was also instrumental in providing
healthcare workers to underserved areas and
involved in the capacity building of nursing staff
and auxiliary workers such as the Auxiliary
Nurse Midwifes (ANMs). Schemes such as the
Janani Suraksha Yojana (JSY) and the Janani Shishu
Suraksha Karyakram (JSSK) have focussed on
reducing maternal and child mortality by providing
cash transfers, free drugs, free diagnostics and
free transport.The Rashtriya Bal Swasthya
Karyakram (RBSK) was launched in 2013 with the
aim of screening diseases specific to childhood,
developmental delays, disabilities, birth defects and
deficiencies. Free drugs and diagnostic services are
provided and immunisation of children undertaken
to reduce the out-of-pocket expenditure and
improve their survival rates.
One of the important initiatives under the NRHM
is the National Iron +Initiative, which has significant
implications in improving and promoting the status
of both maternal and child health. Given the high
prevalence of anaemia all the beneficiaries receive
iron and folic acid supplementation irrespective
of their Iron/Hb status.Today the coverage of the
NRHM has increased manifold and the benefits can
be seen in terms of a reduction in maternal and
child deaths and a decline in fertility.
The issue of an informed and choice-based method-
mix has hitherto remained a neglected aspect of
family planning policies in India. In the past, the
government essentially focused on the promotion
and provision of permanent methods, particularly
female sterilisation. Though the choice basket
available to Indian men and women has been found
to be limited, it now includes options such as
injectables. This addition to the available choices of
contraceptive methods is encouraging. For instance,
a study concluded that the addition of one method
available to half of the population is associated with
a 4 to 8 per cent increase in the use of modern
contraceptives (Ross and Stover 20138).
Although the government resources are constrained,
it is critical to make greater investments, at least
in the short-to-medium run, in order to achieve
significant reductions in fertility levels.To elaborate,
evidence suggests that the total (central and
state release) expenditure on family planning has
stagnated at the same level since 2011. For instance,
the total outlay on family planning was Rs. 4020
million in 2011-12, Rs 4200 million in 2012-13, which
decreased to Rs. 3960 million in 2013-2014. Further,
the estimated total expenditure in 2015-16 is Rs.
7420 million.
It is anticipated that with effective voluntary family
planning policies the fertility levels would not
8 Global Health: Science and Practice August 2013
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only be lower but can also translate into sizeable
monetary savings for other priority programmes.
This section presents the savings potential that can
be achieved, if India and the states were successful
in following the National Population Policy goals
and objectives related to the expansion of family
planning services and TFR reduction.
4.2. Data and Methods
The data for the purpose of analysis have been
taken from the website of the National Health
Mission, which is available under the NHM finance
head (http://www.nhm.gov.in/nrhm-components/
nhm-finance.html).The data for India has been taken
for the financial year 2016-17 only because there
are inconsistencies in the data layout, which have
been used for the previous years. It is difficult to
consolidate data for the same heads.The budget also
has a huge variation for certain components.Within
these, there are particular items, for which the
expenditure has been incurred in recent years.
Therefore, to maintain uniformity we have just
considered data for the last available year. On
the other hand, the data for the states have been
obtained from the state PIPs.The budget sheets
for the latest four years (2013-14 to 2016-17) have
been considered as the data layout is similar across
these years.The cost, due to high fertility, largely
falls on the government and households.There
could be two types of cost associated with inaction
in family planning, the burden of which is borne by
both: direct and indirect costs. We focus on the
investment by government for the purpose of family
planning here.There are government interventions
for providing access to health facilities to reduce
maternal and child mortality. By reducing fertility and
pregnancy related complications, it is expected that
the public expenditure related to maternal, and child
Table 4.1: NHM Components with Savings Potential due to Family Planning Policies
Component
Maternal
Health
Child Health
(newborn)
Adolescent
Health
Child Health
(0-18 years)
Costs
There are a number of schemes,
such as JSY9 and JSSK10, to improve
maternal health and promote safe
deliveries.We also consider the costs
incurred on integrated outreach
schemes and maternal death
reviews/audits to track programme
improvements.
Costs incurred for providing
newborn care units, care of sick and
severely malnourished children.
Estimation Logic
Cost = variable cost due to change
in home and institutional births +
fixed cost
Cost = fixed cost assumed here for
sick infants up to 1 year
RKSK11 focuses on adolescent health.
The programme provides preventive,
promotive, curative and counselling
services, and routine check-ups at
primary, secondary and tertiary
levels to adolescents, married and
unmarried, girls and boys.
RBSK12 aims at identification and early
intervention for children from birth
to 18 years to cover defects at birth,
deficiencies, diseases, development
delays including disability.
Cost = fixed cost assumed here for
facility based and community level
services.
Cost = fixed operational cost of the
RBSK programmes considered here.
Data and Assumptions
The number of pregnant women will
be projected for future years and the
cost will be discounted by a rate of
3 per cent to arrive at the current
cost estimate.We consider policy
scenarios where fertility rates are
both high and low (based on policy
target).
The cost will be discounted at 3 per
cent to arrive at the current cost
estimate. Reduction of 10 per cent
assumed under policy scenario due
to lesser demand.
The cost will be discounted by a rate
of 3 per cent to arrive at the current
cost estimate. Reduction of 10 per
cent assumed under policy scenario
due to lesser demand.
The cost will be discounted by a rate
of 3 per cent to arrive at the current
cost estimate. Reduction of 10 per
cent assumed under policy scenario
due to lesser demand.
9 Janani Suraksha Yojana (JSY) is a safe motherhood intervention run by the Government of India’s National Rural Health Mission (NRHM)
10 Janani–Shishu Suraksha Karyakram (JSSK), a national initiative to make available better health facilities for women and child.
11 Rashtriya Kishor Swasthya Karyakram (National Adolescent Health Programme)
12 Rashtriya Bal Swasthya Karyakram (RBSK)
13 Accredited Social Health Activist
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Component
Training
Additionalities
Procurement
Immunisation
National Iodine
Deficiency
Disorders
Control
Programme
Costs
Estimation Logic
Cost of Training Institutes & Skill Lab,
development of training packages,
Maternal Health Training, IMEP
Training, Child Health Training, Family
Planning Training, Adolescent Health
Trainings / RKSK Training, Programme
Management Training etc.
Cost = fixed cost of providing
training to personnel under different
interventions is considered.
Costs incurred for selection and
training of ASHAs13 , including
procurement of drug kit, incentive,
award and capacity building. Also
costs incurred for the strengthening
of BCC/IEC Bureaus (state and
district levels), state BCC/IEC strategy.
Cost = Fixed cost for selection,
training and incentives for ASHAs
and strengthening the BCC/IEC
strategy.
Costs incurred for the procurement
of equipment; equipment for RKSK &
RBSK; drugs; and consumables such as
Zinc,Vitamin A,Vitamin K1, ORS, etc.
Cost = Fixed cost for procurement
+ variable costs computed for drugs
& consumables.
Costs incurred on vaccination of
children.Vaccines protect children
against diseases like measles, mumps,
rubella, hepatitis B, polio, tetanus and
diphtheria.
Cost for future years = per unit cost
per newborn based on existing data
and numbers of newborn.
Costs related to: promotion &
production of iodised salt, its
monitoring, distribution & quality
control at the production level
through laboratories, could be used.
Cost: Fixed cost of the programme
considered here.
Data and Assumptions
The cost will be discounted by a rate
of 3 per cent to arrive at the current
cost estimate. Reduction of 10 per
cent assumed under policy scenario
due to lesser demand.
The cost will be discounted by a rate
of 3 per cent to arrive at the current
cost estimate. Reduction of 10 per
cent assumed under policy scenario
due to lesser demand.
The expenditure on supplements and
therefore the variable cost on drugs
will decline due to a reduction in the
population under the policy scenario.
For future years, the birth rate could
be forecasted to estimate the target
population.The resulting cost has
been discounted at 3 per cent rate.
This cost is incurred for providing
services to the newly born children.
The cost will be discounted by a rate
of 3 per cent to arrive at the current
cost estimate. Reduction of 10 per
cent assumed under policy scenario
due to lesser demand.
health will be significantly reduced. Furthermore,
expenditure for the vaccination of children could
also be minimised.
The expenditure of government is based on different
components of the NRHM which have been
implemented. Also, the main assumption underlying
the analysis is that the costs have been kept constant
for future years. It is however, expected to change
on account of inflation or economies of scale
achieved due to better management or organisation
in implementing the schemes. Under the policy
population growth scenario, it is expected that the
fixed cost will decline but at a slower pace. For
future years, we are assuming a deduction of 10 per
cent in fixed cost.
A component-wise analysis gives us a better
understanding about the pattern of the
expenditure. In the following Table 4.1, we present
the components which have been considered
for the purpose of analysis, the main ones being:
Maternal Health, Child Health, Adolescent Health,
Rashtriya Bal Swasthya Karyakram RBSK,Training,
Additionalities under NRHM (Mission Flexible
Pool), Procurement, Immunisation and National
Iodine Deficiency Disorders Control Programme
(NIDDCP).
To compute the variable cost associated with the
components the target population was calculated
using the population projections under both the
normal and policy scenarios.The relevant variables
which have been used are - the number of total
pregnancies, women in the reproductive age group,
population in age group 0 to 5 years, 5 to 10 years,
10 to 19 years, 20 to 49 years, home deliveries and
deliveries in the public health facilities. .The per
unit cost for each intervention was obtained for
the available years by dividing the total cost in the
budget by the relevant population associated with
that particular budget head.The estimation of cost
for future years was calculated by applying these
unit costs to the population figures for the normal
Budgetary Savings with Family Planning Investments 57
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7.10 Page 70 |
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and policy scenarios beginning from 2017 to 2031.
The present value of these costs was computed at a
discount rate of 3 per cent.
4.3. Results
Table 4.2 presents the budget allocated for different
activities and strategies within the National Health
Mission in the financial year 2016-17.The major
share of the budget has been allocated for RCH
Flexible Pool (38.1 per cent) and additionalities
under the Mission Flexible Pool (51.6 per cent).The
components under RCH include maternal health,
child health, family planning, RBSK, RKSK, human
resources and management cost.A sizeable portion
of the RCH flexible pool budget (13 per cent)
is allotted for improving maternal health related
activities, such as the Janani Suraksha Yojana and the
Janani Shishu Suraksha Karyakram to promote safe
delivery.Also, expenditure is incurred on integrated
outreach schemes and maternal death reviews/
audits, which are conducted to track improvement
in the maternal health programmes.
51 per cent of the budget is for timeline activities.
Here the cost is incurred for the selection and
training of ASHAs, the procurement of drug kits,
incentives and awards to ASHAs as well as capacity
building of the ASHA Resource Centre.Additionally,
costs are also incurred for the strengthening of the
BCC/IEC Bureaus (state and district levels) and
development of the state BCC/IEC strategy.The
Table 4.2: NHM Budget Statement for Different Activities, India 2016-17
S. No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
NRHM-RCH Flexible Pool
RCH - TECHNICAL STRATEGIES & ACTIVITIES
(RCH Flexible Pool)
MATERNAL HEALTH
CHILD HEALTH
FAMILY PLANNING
RASHTRIYA KISHOR SWASTHYA KARYAKRAM
RBSK
TRIBAL RCH
PNDT Activities
HUMAN RESOURCES
TRAINING
PROGRAMME / NRHM MANAGEMENT COST
VULNERABLE GROUPS
TIME LINE ACTIVITIES - Additionalities under NRHM
(Mission Flexible Pool)
IMMUNISATION
NIDDCP
IDSP
NVBDCP
NLEP
RNTCP
Grand Total
14 Integrated Disease Surveillance Project
15 National Vector Borne Disease Control Programme
16 National Leprosy Eradication Programme
Rs. Millions
128281
44600
3819
8953
987
6800
175
166
44259
5743
12681
949
173644
14251
196
1223
5877
1539
11396
336408
58 Cost of Inaction in Family Planning in India
% Share
38.1
13.3
1.1
2.7
0.3
2.0
0.1
0.0
13.2
1.7
3.8
0.0
51.6
4.2
0.1
0.4
1.7
0.5
3.4
100
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share for immunisation related activities is around 4
per cent of the total budget.
Figure 4.1 presents the details of expenditure
of different components such as Reproductive
and Child Health (RCH), infrastructure, and the
communicable and non-communicable disease
programme under the National Health Mission for
the period 2012-13 to 2015-16. Clearly, the
expenditure on RCH increased steadily over the
period from Rs. 124190 million in 2012-13 to Rs.
171500 million in 2015-16. Surprisingly, till 2014-15,
the expenditure incurred on infrastructure declined
Figure 4.1: Details of Expenditure, National Health Mission, India 2012-13 to 2015-16
Budget for activities and schemes within National Health Mission India
124190
140600
158230
171500
64010
62910
57570
69830
5710 2160
2012-13
RCH
5380 2390
2013-14
INFRA
7190 3020
2014-15
CD
9140 4060
2015-16
NCD
Note: RCH - Reproductive Child Health, IF- Infrastructure, CD - Communicable Disease, NCD - Non-Communicable Disease
Figure 4.2: Percentage Distribution of Expenditure, NHM India 2012-13 to 2015-16
100%
80%
60%
40%
20%
India
Flexible Pool for Non Communicable
Disease Programmes
Flexible Pool for Communicable
Disease Control Programmes
Infrastructure Maintenance
NRHM-RCH Flexible Pool
0%
2012-13
2013-14
2014-15
2015-16
Budgetary Savings with Family Planning Investments 59
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to Rs. 57570 million. But in 2015-16, it again increased
to Rs. 69830 million.As of 2015-16, the expenditure on
communicable and non-communicable diseases was Rs.
9140 million and Rs. 4060 million respectively.
Figure 4.2 shows the distribution of expenditure
on different components of the NHM for the
period 2012-13 to 2015-16. A consistent trend
could be observed for the entire period with the
expenditure on RCH being the biggest component.
This share increased steadily from around 63 per
cent in 2012-13 to 67 per cent in 2015-16.There
is a slight substitution of expenditure from RCH
with infrastructure maintenancefor the years
Figure 4.3: Breakup of RCH Flexible Pool Expenditure, NHM India 2012-13 to 2015-16
Budget share for activities and schemes within National Health Mission India
2012-13
NIDD
0.0%
PPI
3.9%
MF
46.8%
RCH
40.4%
2013-14
NIDD
PPI 0.0%
2.8%
MF
45.6%
RCH
48.4%
RI
RI
3.1%
2.9%
2014-15
NIDD
0.0%
PPI
2.2%
MF
48.4%
RCH
46.6%
2015-16
NIDD
0.0%
PPI
2.0%
MF
48.7%
RCH
46.2%
RI
RI
2.7%
3.1%
Note: RCH - RCH Flexible Pool, MF - Mission Flexible Pool, RI - Routine Immunization, PPI - Pulse Polio Immunization and
NIDD - National I.D.D Control Programme
60 Cost of Inaction in Family Planning in India
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2013-14 and 2014-15.The expenditure incurred
on communicable disease and non-communicable
disease remained stable over the period.
Figure 4.3 shows the break-up of the NRHM-RCH
Flexible Pool expenditure.The main items within the
RCH are: RCH Flexible Pool, Mission Flexible Pool,
Routine Immunisation, Pulse Polio Immunisation, and
the National Iodine Deficiency Disorders Control
Programme.The expenditure on different items
remained stable. In 2015-16, 46 per cent of the total
budget was spent on RCH Flexible Pool, 48.7 per
cent on Mission Flexible Pool and around 5 per cent
on Routine Immunisation, Pulse Polio Immunisation
and the National Iodine Deficiency Disorders
Control Programme.The share of allocation for the
Mission Flexipool increased slightly over the last
couple of years.
The details of expenditure on different components
under the National Health Mission for Bihar for the
period 2012-13 to 2015-16 is presented in Annexure
Fig. S1.We see that the expenditure on
RCH increased steadily over the period from Rs.
9890 million in 2012-13 to Rs. 11740 million in
2015-16.The expenditure incurred on infrastructure
declined to Rs. 2450 million in 2014-15, but again
increased to Rs. 3630 million in 2015-2016. As of
2015-16, the expenditure on communicable and
non-communicable diseases was Rs. 690 million and
Rs. 110 million respectively.The flexible pool for
noncommunicable disease increased substantially
from Rs. 40 million in 2012-13 to Rs. 110 million in
2015-16
The distribution of expenditure on different
components of the NHM for Bihar for the period
2012-13 to 2015-16 shows that the share of
expenditure incurred on RCH was the biggest
component followed by infrastructure (Annexure
Fig. S2).The share of expenditure on RCH steadily
increased from around 74 per cent in 2012-13 to
79.5 per cent in 2014-15 and then declined to 72.6
per cent in 2015-16.The expenditure for RCH was
reduced and the expenditure on infrastructure and
communicable diseases increased for 2014-15.The
expenditure incurred on non-communicable disease
remained stable over the period.
The break-up of the NRHM-RCH Flexible Pool
expenditure for Bihar shows that the expenditure
on different items has remained stable. In 2015-
16, 63.5 per cent of the total budget was spent
on the RCH Flexible Pool, 27.8 per cent on the
Mission Flexible Pool and around 8 per cent on
routine immunisation, pulse polio immunisation and
NIDDCP (Annexure Fig. S3).The share of allocation
for the Mission Flexible Pool shows a slight increase
over the last couple of years and a decline in the
pulse polio immunisation expenditure from 5 per
cent in 2014-15 to 3.6 per cent in 2015-16.
The details of expenditure for the period 2012-13
to 2015-16 for Madhya Pradesh shows that the
expenditure on RCH increased drastically over
the period, rising from Rs. 8270 million in 2012-
13 to Rs. 15540 million in 2015-16 (Annexure Fig.
S4).The expenditure incurred on infrastructure
remained stable at around Rs. 4000 million. But
the expenditure on the non-communicable disease
programme shows tremendous growth from 2013-
14 onwards, reaching Rs. 260 million in 2015-16.
As of 2015-16, the expenditure on communicable
diseases was Rs. 500 million.
In Madhya Pradesh, the share of expenditure on
RCH is the biggest component and has increased
from around 67.6 per cent in 2012-13 to 77.2
per cent in 2015-16 (Annexre Fig. S5).A certain
proportion of the expenditure for infrastructure
has been reduced and substituted with expenditure
on RCH for 2015-16.The expenditure incurred
on communicable disease and non-communicable
disease has remained stable over the period at
around 2.6 per cent and 1.3 per cent, respectively.
Further, in 2015-16, 48.9 per cent of the total
budget was spent on RCH Flexible Pool, 47.3 on
Mission Flexible Pool and around 4 per cent on
routine immunisation, pulse polio immunisation and
NIDDCP (Annexure Fig. S6).The share of allocation
for the Mission Flexible Pool has shown a slight
increase over the last couple of years.We see a
decline from 5.3 per cent in 2012-13 to 2.8 per cent
in 2015-16 in the share of expenditure on routine
immunisation.
Budgetary Savings with Family Planning Investments 61
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In the case of Rajasthan, the expenditure on RCH
has stagnated at around Rs. 12000 million for the
last two years (Annexure Fig. S7).The expenditure
incurred on infrastructure declined by Rs. 100
million in 2015-16 from 2014-15. As of 2015-16,
the expenditure on communicable and
noncommunicable diseases was Rs. 290 million and
Rs. 410 million respectively. Rajasthan seems to be
focussing on increasing the expenditure for the
flexible pool for the non-communicable diseases
programme, which has increased by Rs. 300 million
years.
However, a consistent trend could be observed for
the entire period with the share of expenditure on
RCH being the biggest component. It has remained
steady at around 69 per cent from 2013-14
onwards (Annexure Fig. S8). Interestingly, the share
of expenditure on the non-communicable diseases
programmes increased to 2.8 per cent in 2015-16
from 0.8 per cent in 2012-13.
In Rajasthan, the expenditure on the RCH Flexible
Pool declined from 58 per cent in 2012-13 to 40.9
per cent in 2015-16 (Annexure Fig. S9). On the
other hand, the expenditure on the Mission Flexible
Pool increased significantly from 36.6 per cent in
2012-13 to 56.6 per cent in 2015-16.The share of
allocation for Mission immunisation and pulse polio
immunisation has declined over the last couple of
years.
In Uttar Pradesh, the expenditure on RCH increased
from Rs. 21340 million in 2012-13 to Rs. 26660
million in 2015-16 (Annexure Fig. S10).The
expenditure incurred on infrastructure increased to
Rs. 14810 million in 2015-16 from Rs. 13530 million
in 2014-15. As of 2015-16, the expenditure on
communicable and non-communicable diseases was
Rs. 1330 million and Rs. 350 million respectively.The
expenditure on both these components has shown a
big jump in the last few years.
A consistent trend could be observed for the entire
period with the share of expenditure on RCH being
the biggest component. It has steadily increased
from around 42.3 per cent in 2012-13 to 61.8 per
cent in 2015-16 (Annexure Fig. S11). It seems there
has been a substitution of expenditure for RCH
with expenditure from infrastructure, which shows
a steady decline from 56.6 per cent in 2012-13
to 34.3 in 2015-16.The expenditure incurred on
communicable and non-communicable diseases has
remained stable over the period.
In 2015-16, 38.3 per cent of the total budget was
spent on RCH Flexible Pool, 54.3 on Mission
Flexible Pool and around 7 per cent on routine
immunisation, pulse polio immunisation and
NIDDCP in Uttar Pradesh (Annexure Fig. S12).The
share of allocation for the Mission Flexible Pool
increased from 36.2 per cent in 2012-13 to 54.3 per
cent in 2015-16.
Tables 4.3 and 4.4 present the results of the analysis
of NHM components.The cost for the period 2017-
2031 for each component has been estimated under
both current patterns and policy scenario.The net
present discounted value has been calculated using a
discount rate of 3 per cent. It is estimated that
over the next 15 years, India can save around Rs.
59930 million in the maternal health programme.
The savings in fixed cost (Rs. 910 million) might be
lesser then the variable cost (Rs. 59020 million).
At the state level, the highest amount of savings
could be realised by Bihar (Rs. 7310 million)
followed by Uttar Pradesh (Rs. 7060 million).The
cost of child health is assumed to be fixed in nature.
We have considered here the expenditure incurred
for newborn care units, care of sick children up to
one year and severely malnourished children.We
can save up to Rs. 3070 million for this component
as the number of births are expected to be lower
under the policy scenario as compared to the
normal scenario. Also, there will be a reduction
in the number of cases of malnourished and sick
children.
There will be a fallout of the reduction in the
number of additional children entering adolescence.
The government incurs sizeable expenditure
62 Cost of Inaction in Family Planning in India
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Table 4.3: NHM Budget with Fertility Reduction for the Period 2017-31, India and States
Component
(in million)
TC
Maternal
Health
FC
VC
Child Health TC
Adolescent TC
RBSK
TC
Training
TC
NRHM
Additionalities
TC
TC
Procurement FC
VC
TC
Immunisation FC
VC
NIDDCP
TC
Rajasthan
MP
UP
Bihar
India
C*
23730
1300
22440
1900
150
1740
2110
97300
20680
16430
4250
4220
3180
1040
50
P*
20780
1180
19600
1730
140
1580
1920
C*
19530
120
19400
3580
410
5300
2370
88360 103260
18660
14920
3740
3800
2890
910
40
21370
19470
1900
4750
3670
1080
80
P*
13840
110
13730
3250
370
4810
2160
C*
47250
2060
45200
1390
150
5370
1490
P*
40190
1870
38320
1260
130
4880
1350
C*
36250
1230
35020
1970
350
1600
3060
P*
28940
1120
27820
1790
310
1460
2780
C*
334400
9740
324660
32920
8510
58630
45440
P*
274470
8830
265640
29850
7720
53160
41200
93770 244770 222280 141240 128270 1496940 1357220
19530
18130
1400
4100
3340
760
70
42390
21580
20820
19790
15150
4610
40
40330
20370
19960
17270
13750
3510
30
31430
17980
13440
10770
8020
2730
380
27300
16330
10980
9440
7280
2160
340
355730
231860
123870
118860
96530
22330
1690
313230
210220
103010
105600
87520
18090
1530
Note: TC- Total Cost; FC – Fixed Cost;VC – Variable Cost; S* - Current Trend,A* - Alternative Trend
Table 4.4: NHM Budget Savings Potential for the Period 2017-31, India and States
Component
(in Million)
Maternal Health
Child Health
Adolescent
RBSK
Training
NRHM
Additionalities
Procurement
Immunisation
NIDDCP
Total Cost
Fixed Cost
Variable Cost
Total Cost
Total Cost
Total Cost
Total Cost
Total Cost
Total Cost
Fixed Cost
Variable Cost
Total Cost
Fixed Cost
Variable Cost
Total Cost
Rajasthan
2950
120
2840
170
10
160
190
8940
2020
1510
510
420
290
130
10
MP
5690
10
5670
330
40
490
210
9490
1840
1340
500
650
330
320
10
UP
7060
190
6880
130
20
490
140
22490
2060
1210
860
2520
1400
1100
10
Bihar
7310
110
7200
180
40
140
280
12970
4130
1650
2460
1330
740
570
40
India
59930
910
59020
3070
790
5470
4240
139720
42500
21640
20860
13260
9010
4240
160
on the health of adolescents through two main
programmes on which the major part of the
expenditure is apportioned: Rashtriya Kishor
Swasthya Karyakram (RKSK) and Rashtriya Bal
Swasthya Karyakram (RBSK).The focus of the
RKSK scheme is on reorganising the existing public
health system in order to meet the service needs
of adolescents. A core package under this includes
Budgetary Savings with Family Planning Investments 63
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preventive, promotive, curative and counselling
services, a regular provision of routine check-ups
at primary, secondary and tertiary levels of care for
adolescents, married and unmarried, girls and boys
during the clinic sessions.The RBSK is an important
initiative aimed at an early identification and early
intervention for children from birth to 18 years
to cover defects at birth, deficiencies, diseases and
development delays including disability. As much as
Rs. 5470 million can be saved in this programme and
Rs. 790 million in the adolescent programme
through the adoption of family planning methods. At
the state level, the main beneficiaries will be Madhya
Pradesh and Uttar Pradesh in the case of RBSK.
Additionalities is another major component of the
NHM programme. Here the expenditure is incurred
for training the ASHAs, buying drug kits for them
and providing them different types of incentives.
Approximately, Rs. 140000 million could be saved as
the demand for ASHAs will decline substantially with
a reduction in the target population.The fixed cost
of procurement is due to the purchase of equipment
for RKSK and RBSK and the variable cost through
the buying of drugs and supplements for mother
and children.The cost saving is approximately equal
under both these heads. Bihar stands to benefit
immensely through investment in family planning.
The budget for immunisation includes expenditure
on cold chains18, review meetings to strengthen
the project, pulse polio operating costs and other
activities. A net saving of Rs. 13260 million could be
realised by investing in family planning and a major
reduction would be observed in the case of fixed
costs.Among the states, the major beneficiary would
be Uttar Pradesh (Rs. 2520 million).
4.4. Conclusion
Family planning programmes have been unable to
achieve the required targets. Given the historical
background and the fact that India was the first
country to officially adopt a family planning
programme, it seems that the desired outcomes
have not been realised and that there continues
to be an urgent need for accelerated action.While
the goal of the family planning programme has
been to reduce fertility and birth rates ever since it
began, the results have been disappointing and are
perhaps largely due to inadequate investments in
key aspects of family planning.These include making
improvements in the quality of care, building the
capacity of service providers and increasing the
variety of contraceptive choices.
It can be safely affirmed that although the required
targets with respect to the demographic and
health goals have not been achieved, the NRHM
has on the other hand been successful in achieving
other significant outcomes. Public health services
which had fallen into a state of abeyance and were
dysfunctional for a period of time, have now been
revived.The achievement of the programme has
been clearing blockages that hindered the timely
and adequate provision of drugs at primary health
care units and the deployment of key health workers
to meet the needs of specific age groups and
beneficiaries such as lactating and pregnant mothers,
children aged 0-5 years and adolescents. Still huge
gaps exist and these pertain mainly to the allocation
of an abysmal budget and low utilisation of the funds
available.
As of 2015-16, the fertility rates of Uttar Pradesh
and Madhya Pradesh are higher at 3.1 and 2.91
respectively.These states will not be able to reach
the replacement levels of fertility before 2026.
Under the policy scenario they should have obtained
the target by 2015.The inability to control the
fertility and birth rates will have repercussions in
terms of a higher population growth, which will add
to the population momentum.
Under the National Health Mission, over the
period 2012-13 to 2015-16, there seems to be
a shift in the pattern of expenditure on different
18 The purpose of the vaccine “cold chain” is to maintain product quality from the time of manufacture until the point of administration by
ensuring that vaccines are stored and transported within WHO-recommended temperature ranges.
64 Cost of Inaction in Family Planning in India
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components such as reproductive and child health
(RCH), infrastructure, and the communicable and
non-communicable disease programme. It was
observed that during this time the expenditure on
RCH increased steadily and that on infrastructure
declined, particularly in the years 2013-14 and
2014-15. Interestingly, in the case of Bihar, Uttar
Pradesh and Rajasthan the expenditure on non-
communicable diseases rose substantially.
The share of expenditure on different components
also gives us an idea about the significance of the
components.A consistent trend could be observed
across all the states for the entire period with the
expenditure on RCH being the biggest component
followed by infrastructure.The expenditure
incurred on communicable and non-communicable
disease remained stable over the period except for
Rajasthan where the share increased to 2.8 per cent
in 2015-16 from 0.8 per cent in 2012-13.
Within the NRHM-RCH Flexible Pool, the main
programmes on which expenditure was incurred
were the RCH Flexible Pool, the Mission Flexible
Pool, routine immunisation, pulse polio immunisation
and the NIDDCP.The expenditure on different items
remained stable and the share of allocation for the
Mission Flexible Pool increased slightly over the last
couple of years. Surprisingly, all the states recorded a
decline in expenditure on pulse polio immunisation
over the study period.
According to UN population projections, inaction
in family planning and policy failure will lead to
an increase in India’s population till 2065.The
effects of an uncontrolled population explosion
have been present in almost all policy discussions.
It is the government, which suffers the most as
given its responsibility to enhance the welfare of
the population its task is to incur expenditure for
the purpose of development. But there is a huge
opportunity cost of the resources, which can be
fruitfully used to augment the productive capacity of
the economy. As per our estimates, India can save
Rs. 270 billion over the next 15 years if family
planning measures are implemented effectively.The
greatest beneficiary will be Uttar Pradesh which is
the most populous of all the considered states.
There are related benefits on the quality of life
and the well-being of people.The public discourse
usually treats family planning policies as different
from development policies, focusing only on birth
and fertility when we talk about them.When
the National Health Mission was launched the
focus shifted towards health from demography.
Therefore, in order to achieve the desired
results, it is imperative that when family planning
strategies are implemented they should be viewed
in the larger social and development context, and
the interlinkages of demographic factors with
these processes understood within the various
geographical areas.
19 The 2017 Revision of World Population Prospects is the twenty-fifth round of official United Nations population estimates and
projections prepared by the Population Division of the Department of Economic and Social Affairs of the UN Secretariat
Budgetary Savings with Family Planning Investments 65
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5 Out-of-Pocket Healthcare
Expenditure
Insights from NSS Health Surveys
5.1. Background
Out-of-pocket (OOP) expenditure is the
predominant mode of health care financing in India
and accounts for 70 per cent of the total health
care expenditure in the country (NHSRC 2016).
Such a high share of OOP expenditure is a major
policy concern and has catastrophic implications
on household living standards and well-being
(Sauerborn et al 1996; Kruk et al. 2009).
It is estimated that in 1999-2000, OOP expenditure
pushed about 32.5 million persons below the
poverty line (Garg and Karan 2008). Similarly, in
2004, this again drove 11.9 million households
(63.2 million persons) into poverty (Berman et al
2010). Also, in the same year, 60 per cent of rural
households and 40 per cent of urban households
had to use distress means, such as borrowings or
sale of household assets to finance inpatient care
services (Joe 2014). Given such intricacies and
amidst persistently low health insurance coverage
(about 15 per cent, NSSO 2015), the issue of
financial protection in health assumes high policy
relevance for India and similar other countries.
In India, public spending on health as a proportion of
GDP is lower in comparison to other neighbouring
countries such as Sri Lanka and Bangladesh, which is
worrying.The public health spending in India is 1.4
per cent of the GDP as against 2.5 per cent envisaged
in the National Health Policy 2017. Such is the state
of affairs that certain vulnerable groups have no
option but to delay or put off decisions related to
their health.These social groups rank the lowest on
the socio-economic ladder and are geographically
isolated and socially excluded from the mainstream
population.They have low income and lack health
care financing sources.There is no denying the fact
that in reality, the principle of health equity does not
hold and inequalities continue to foster inequities,
despite the Government of India’s persistent efforts
to promote inclusive growth.
Not surprisingly, the higher burden of maternal
and child healthcare costs among low-income
households, particularly in backward states, is a
prominent concern in healthcare financing. These
expenditures are regressive and can be avoided
with basic health care financing mechanisms such
as public provisioning and universal health coverage
for maternal and child healthcare. For instance, it is
estimated that a fully functioning, maternal and child
healthcare package cannot only save households
from incurring catastrophic expenditure but also has
the cumulative potential of averting maternal deaths
and disability in developing countries.
The Sustainable Development Goals, therefore,
strongly encourage the international development
community to design and implement effective health
financing policies to achieve universal coverage
while providing reproductive and child healthcare
services.This section presents insights regarding the
potential OOP expenditure reductions that can be
attributable to reduced fertility rates across India
and four selected high focus states.
5.2. Data and Methods
5.2.1. National Sample Survey Data
The analysis is based on data from two consecutive
waves of nationally representative surveys, namely
66 Cost of Inaction in Family Planning in India
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the Morbidity and Healthcare Survey (60th round,
2004) and Social Consumption: Health Survey (71st
round, 2014) conducted by the National Sample
Survey Office (NSSO), Ministry of Statistics and
Programme Implementation (MOSPI), Government
of India.
These surveys collect comparable data from sample
households selected randomly from a multistage
stratified survey design. The first strata (First Stage
Units) include census villages as rural areas and
urban blocks as urban areas across India; whereas
the second strata (Second Stage Units) are sample
households.
Data on aspects of morbidity, treatment-seeking
and financing of hospitalisation (inpatient)
and ambulatory (outpatient) care services for
the reference period of 365 days and 15 days,
respectively are available from these surveys.
The ailments for which such medical care
is sought, the extent of use of Government
hospitals, and the expenditure incurred on
treatment received from the public and private
sectors, is also available. In addition to this, these
surveys also provide comparable information
on aspects of maternity and child healthcare,
including the financing of hospitalisation services
(public and private) for the reference period of
365 days.
Additionally, the survey also provides household
level information on demographics and access
to services and utilities as well as individual level
data on age, sex, education, monthly per capita
expenditure and primary occupation of households.
Italso elicits information on the first and second
major sources of financing the expenditure on
inpatient and outpatient healthcare.
The present analysis is based on a total pooled
sample of 73,868 households (47,302 rural + 26,566
urban) in 2004 and 65,932 households (36,480
rural + 29,452 urban) in 2014. Altogether, data on
385,055 and 335,499 individuals is available from
2004 and 2014, respectively.
5.2.2. Statistical Analyses and Outcomes
Child Inpatient Care
The estimates for average and total out-of-pocket
(OOP) medical and non-medical expenditure on
child (0 to 5 years) inpatient care under the normal
and policy scenario are reported for 2004 and 2014.
The reference period for data on child inpatient care
is 365 days.The medical expenditure mainly includes
information on doctor’s/surgeon’s fee, expenditure
on medicines, diagnostic tests, bed charges and other
miscellaneous expenses (like attendant charges,
physiotherapy charges, personal medical appliances,
blood and oxygen). The total expenditure is the sum
of medical expenditure, transportation charges for
the patient, food and other miscellaneous costs.
The projections for the total OOP expenditure on
child inpatient care are also presented for 2020,
2025 and 2030 for all India and selected states. The
projections are simple assuming constant increase in
hospitalisation rates as observed between 2004 and
2014. Further, the average total OOP expenditure
per hospitalised case is assumed constant as
observed in 2014 for state level projections.
Child Outpatient Care
The estimates for average and total OOP
expenditure on child outpatient care for all India
and selected states are reported through cross
tables. These are presented separately for the
current and policy scenarios for both the years
(2004 and 2014). The reference period for data
on outpatient care is 15 days. The proportion
of ailing persons (PAP) per thousand is reported
along with total expenditure estimates. Because
of data specific limitations, only the total (medical
and non-medical) expenditure is presented
for child outpatient treatment. Moreover, it is
important to understand that the PAP include
only those persons who have received outpatient
care at least once, hereby excluding those who
haven’t got treatment on any medical advice.
Out-of-Pocket Healthcare Expenditure 67
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The estimates for the total OOP expenditure are
also projected for 2020, 2025 and 2030 by using
total birth projections under the current and policy
trends. The projections are made assuming constant
increase/decrease in PAPs as observed between
2004 and 2014. However, because of data specific
limitations, the average total expenditure on per
ailing person is assumed constant for projections.
Expenditure on Childbirth
The estimates for average and total out-of-pocket
expenditure on institutional and home-based birth
under the current and policy scenario are presented
through cross tables. These are presented
separately for 2004 and 2014. Further, the average
total expenditure is the sum of medical and non-
medical expenditure. In this regard, the medical
expenditure covers information on the doctor’s/
surgeon’s fee, spending on medicines, diagnostic
tests, bed charges and other miscellaneous expenses
(like attendant charges, physiotherapy charges,
personal medical appliances, blood and oxygen).The
non-medical expenditure captures information on
transport charges for the patient, food, transport of
others, costs incurred on escorts and their lodgings.
The projections for the total OOP expenditure on
births under the current and policy scenarios are
also estimated. To enable these, it is assumed that
100 per cent births by 2030 will be institutional.
Additionally, the increase in the unit cost of
institutional births is assumed to be constant as
observed between 2004 and 2014 for all India as
well as state level projections. However due to
data specific limitations, the unit cost of home-
based births is assumed to be constant as estimated
in 2014 for state level projections. It is worth
mentioning here that these assumptions will not
affect the relative difference between estimates
under the current and policy trends of total births.
5.3. Results
5.3.1. Out-of-Pocket Expenditure: Child
Inpatient Care
Table 5.1 shows estimates regarding OOP
expenditure on child hospitalisation (0-5 years)
under the current and policy scenarios and the
difference between these for 2004 and 2014. The
observed hospitalisation rate among children is 22
and 31 per 1000 for 2004 and 2014 respectively.
Table 5.1: OOP Expenditure Averted on Child Hospitalisation,All India, 2004 and 2014
Variables
Number per 1000 persons
hospitalised*
Total number of births (in millions)
Total number of hospitalised cases
Average medical expenditure per
hospitalised child*++ (Rs. in millions)
Total medical expenditure on child
hospitalisation (Rs. in millions)
Average total expenditure per
hospitalised child*++ (Rs. in millions)
Total expenditure on child
hospitalisation (Rs. in millions)
Total expenditure on child
hospitalisation averted (%)
C*
22
27.2
599901
74000
4439
76930
4620
++ Estimates adjusted for inflation
C* Current Trend; P* Policy Trend; Net* Expenditure Averted
2004
P*
22
21.0
463891
74000
3432
76930
3570
Net*
22
6.1
136010
74000
1006
76930
1050
22.7
C*
31
26.2
814322
106850
8701
122530
9980
2014
P*
31
20.1
623311
106850
6660
122530
7640
Net*
31
6.1
191011
106850
2041
122530
2340
23.5
68 Cost of Inaction in Family Planning in India
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Following this, the difference between total
hospitalised cases between both scenarios is 136010
in 2004 and 191011 in 2014. The estimated total
OOP expenditure (medical and non-medical) in the
current and policy situation is Rs. 4615 million and
Rs. 3568 million respectively, for 2004 and Rs. 9977
million and Rs. 7637 million, respectively in 2014.
The estimated relative difference between the two
scenarios is about 22.67 per cent and 23.45 per cent
for 2004 and 2014, respectively.
Similarly, the information regarding the projected
OOP expenditure on child hospitalisation under the
two different scenarios for 2020, 2025 and 2030 is
depicted in Table 5.2. It is worth mentioning that an
increase in the hospitalisation rate, and the average
medical and non-medical expenditure is assumed to
be constant as observed between 2004 and 2014.
The projected total (medical and non-medical)
OOP expenditure under the current scenario is Rs.
14116 million, Rs. 20741.4 million and Rs. 30967
million for 2020, 2025 and 2030, respectively.
Similarly,the projected OOP expenditure under the
policy scenario is Rs. 11789 million, Rs. 16992 million
and Rs.23956 million for 2020, 2025 and 2030,
respectively.The estimates clearly show a relative
difference of 16.49 per cent for 2020, 18.09 per cent
for 2025 and 22.64 per cent for 2030 between the
current and policy trends.
Table 5.3 presents estimates for OOP expenditure
on child hospitalisation in selected states for 2004
and 2014. In Bihar, the total OOP expenditure
on child hospitalisation under the current trend is
almost 7.11 per cent and 18.48 per cent higher than
the expenditure estimated under the policy scenario
for 2004 and 2014, respectively. Similarly, for 2004
and 2014, the total OOP expenditure under the
current scenario is 0.90 per cent and 16.84 per
cent higher than the policy scenario in Rajasthan. In
Madhya Pradesh, the relative difference between the
expenditure estimates under the current and policy
trends is 16.21 per cent and 34.71 per cent for 2004
and 2014, respectively. Finally, in Uttar Pradesh, the
total OOP expenditure estimated under the current
scenario is 13.63 per cent and 30 per cent higher
than estimates under the policy scenario for 2004
and 2014, respectively.
Table 5.2. Estimated Savings on Total Expenditure on Child Hospitalisation (0 to 5 years), All India, 2020, 2025
and 2030
Variables
C*
Hospitalised per 1000 persons
37.3
Total number of births (in millions)
23.8
Total hospital cases (in millions)
0.88
Average. medical expenditure per
hospitalisation ++ (Rs. in millions)
128700
Total medical expenditure child
hospitalisation (Rs. in millions)
11440
Average total expenditure per
hospitalised child*++ (Rs. in millions)
158840
Total child hospitalisation
expenditure (Rs. in millions)
14120
Total child hospitalisation
expenditure averted (%)
2020
P*
37.3
19.9
0.74
128700
9550
158840
11790
Net*
37.3
3.9
0.14
128700
1890
158840
2330
16.49
C*
45.0
22.4
1.00
155020
15620
205900
20740
2025
P*
45.0
18.3
0.82
155020
12790
205900
16990
Net*
45.0
4.1
0.18
155020
2830
205900
3750
18.09
C*
54.2
21.4
1.16
186720
21660
266910
30970
2030
P*
54.2
16.6
0.89
186720
16760
266910
23960
Net*
54.2
23.8
0.26
186720
4900
266910
7010
22.64
++ Estimates adjusted for inflation
Note: Trend in average medical and total exp. is projected by taking an observed increase of 20.19 % and 29.63 % per five years respectively
between 2004 and 2014 (assuming constant increase). Increase in hospitalisation rate is assumed constant at observed 20.45% per five years.
C* Current Trend; P* Policy Trend; Net* Expenditure Averted
Out-of-Pocket Healthcare Expenditure 69
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Table 5.3: OOP Expenditure Averted on Child Hospitalisation, Selected States, 2004 and 2014
Uttar Pradesh
Number per 1000 persons
hospitalised*
Total number of births (in millions)
Total number of hospitalised cases
Average medical expenditure per
hospitalised child*++ (Rs. in millions)
Total medical expenditure on child
hospitalisation (Rs. in millions)
Average total expenditure per
hospitalised child*++ (Rs. in millions)
Total expenditure on child
hospitalisation (Rs. in millions)
Total expenditure on child
hospitalisation averted (%)
Madhya Pradesh
Number per 1000 persons
hospitalised*
Total number of births (in millions)
Total number of hospitalised cases
Average medical expenditure per
hospitalised child*++ (Rs. in millions)
Total medical expenditure on child
hospitalisation (Rs. in millions)
Average total expenditure per
hospitalised child*++ (Rs. in millions)
Total expenditure on child
hospitalisation (Rs. in millions)
Total expenditure on child
hospitalisation averted (%)
Rajasthan
Number per 1000 persons
hospitalised*
Total number of births (in millions)
Total number of hospitalised cases
Average medical expenditure per
hospitalised child*++ (Rs. in millions)
Total medical expenditure on child
hospitalisation (Rs. in millions)
Average total expenditure per
hospitalised child*++ (Rs. in millions)
Total expenditure on child
hospitalisation (Rs. in millions)
Total expenditure on child
hospitalisation averted (%)
C*
12
5.2
62,578
109950
688.0
111710
699.0
13
1.82
23,782
81910
194.7
83620
198.8
15
1.65
24,892
74820
186.2
86610
215.5
2004
P*
12
4.5
54,045
109950
594.2
111710
603.7
13
1.53
19,925
81910
163.2
83620
166.6
15
1.64
24,668
74820
184.5
86610
213.6
Avt*
12
0.7
8,533
109950
C*
17
5.3
89690.9
137120
2014
P*
17
3.7
62783.6
137120
93 1229.8 860.8
111710 152350 152350
95.3
1366 956.5
13.63
13
0.29
3,857
81910
31.58
83620
32.2
16.21
21
1.85
38986
52200
21
1.21
25452.3
52200
203.5 132.8
66700 66700
260.0 169.71
15
20
0.14
1.74
224 34881.74
74820 59070
1.6
206.0
86610 72150
1.9
251.6
0.90
20
1.45
29007
59070
171.3
72150
209.2
Avt*
17
0.15
26,907
137120
368.9
152350
409.9
29.99
21
0.64
13534
52200
70.6
66700
90.2
34.71
20
0.29
5875
59070
34.7
72150
42.3
16.84
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Bihar
Number per 1000 persons
hospitalised*
Total number of births (in millions)
Total number of hospitalised cases
Average medical expenditure per
hospitalised child*++ (Rs. in millions)
Total medical expenditure on child
hospitalisation (Rs. in millions)
Average total expenditure per
hospitalised child*++ (Rs. in millions)
Total expenditure on child
hospitalisation (Rs. in millions)
Total expenditure on child
hospitalisation averted (%)
5
2.49
12469
89740
111.8
90030
112.2
* Estimated using National Sample Survey 2004 and 2014
++ Estimates adjusted for inflation
C* Current Trend; P* Policy Trend;Avt * Expenditure Averted
2004
5
2.31
11582
89740
103.9
90030
104.2
2014
5
0.18
887
89740
16
2.42
38781
108340
16
1.97
31611
108340
16
4.4
7167
108340
7.9
420.1
342.5
77.6
90030 124190 124190 124190
8.0
481.6
392.6
89.0
7.11
18.48
Table 5.4: Estimated Savings on Total Expenditure on Child Hospitalisation (0 to 5 years) Selected States, 2020,
2025 and 2030
Variables
Bihar
C*
Number per 1000 persons
hospitalised*
33.6
Total number of births (in
millions)
2.2
Total number of hospitalised cases
(in millions)
0.07
Average medical expenditure per
hospitalised child*++
(Rs. in millions)
108340
Total medical expenditure on
child hospitalisation (Rs. in
805
millions)
Average expenditure per
hospitalised child*++
(Rs. in millions)
124190
Total expenditure on child
hospitalisation (Rs. in millions)
923
Total expenditure on child
hospitalisation averted (%)
Rajasthan
Number per 1000 persons
hospitalised*
23.3
Total number of births
(in millions)
1.6
Total number of hospitalised cases
(in millions)
0.036
2020
P*
33.6
1.8
0.06
108340
649
124190
743
23.3
1.4
0.032
Net*
33.6
0.4
0.01
108340
156
124190
179
19.42
23.3
0.2
0.005
C*
70.56
2.2
0.15
108340
1663
124190
1906
27.2
1.4
0.038
2025
P*
70.56
1.7
0.12
108340
1283
124190
1470
27.2
1.2
0.033
Net*
70.56
0.5
0.03
108340
380
124190
435
22.84
27.2
0.2
0.005
C*
109
2.2
0.24
108340
2617
124190
3000
31.7
1.3
0.042
2030
P*
109
1.6
0.17
108340
1910
124190
2189
31.7
1.1
0.035
Net*
109
0.6
0.06
108340
707
124190
811
27.02
31.7
0.2
0.007
Out-of-Pocket Healthcare Expenditure 71
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Variables
2020
2025
2030
Average medical expenditure per
hospitalised child*++
(Rs. in millions)
59070
59070
59070
59070
59070
59070
59070
59070
59070
Total medical expenditure on
child hospitalisation (Rs. in
millions)
216
188
27
224
194
30
249
209
40
Average expenditure per
hospitalised child*++
(Rs. in millions)
72150 72150 72150 72150 72150 72150 72150 72150 72150
Total expenditure on child
hospitalisation (Rs. in millions)
263
230
34
273
237
36
304
255
49
Total expenditure on child
hospitalisation averted (%)
12.75
13.32
16.05
Madhya Pradesh
Number per 1000 persons
hospitalised*
27.4
27.4
27.4
35.9
35.9
35.9
46.9
46.9
46.9
Total number of births
(in millions)
1.7
1.2
0.6
1.6
1.1
0.5
1.5
1.0
0.5
Total number of hospitalised cases
(in millions)
0.047
0.032
0.015
0.056
0.039
0.017
0.071
0.048
0.023
Average medical expenditure per
hospitalised child*++
(Rs. in millions)
52200
52200
52200
52200
52200
52200
52200
52200
52200
Total medical expenditure on
child hospitalisation
(Rs. in millions)
2470
1660
810
2910
2020
890
3690
2490
1190
Average expenditure per
hospitalised child*++
(Rs. in millions)
66700 66700 66700 66700 66700 66700 66700 66700 66700
Total expenditure on child
hospitalisation (Rs. in millions)
315
212
103
372
258
113
471
319
152
Total expenditure on child
hospitalisation averted (%)
32.64
30.46
32.35
Uttar Pradesh
Number per 1000 persons
hospitalised*
20.5
20.5
20.5
24.8
24.8
24.8
30.0
30.0
30.0
Total number of births
(in millions)
4.6
3.2
1.4
4.0
3.0
1.0
3.8
2.9
0.9
Total number of hospitalised cases
(in millions)
0.095
0.066
0.029
0.100
0.076
0.024
0.114
0.088
0.026
Average medical expenditure per
hospitalised child*++
(Rs. in millions)
137120
137120
137120
137120
137120
137120
137120
137120
137120
Total medical expenditure on
child hospitalisation
(Rs. in millions)
1302
900
402
1367
1038
329
1565
1211
354
Average expenditure per
hospitalised child*++
(Rs. in millions)
152350 152350 152350 152350 152350 152350 152350 152350 152350
Total expenditure on child
hospitalisation (Rs. in millions)
1447
1000
447
1519
1153
366
1739
1346
394
Total expenditure on child
hospitalisation averted (%)
30.88
24.09
22.64
* Estimated using National Sample Survey 2004 and 2014
++ Estimates adjusted for inflation
C* Current Trend; P* Policy Trend;Avt* Expenditure Averted
Note:Average medical and total expenditure is assumed to be constant as observed in 2014. The increase in hospitalisation rate is assumed
to be constant as observed between 2004 and 2014.
72 Cost of Inaction in Family Planning in India
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The projected estimates for OOP expenditure on
child inpatient care in selected states for 2020, 2025
and 2030 are presented in Table 5.4. At this point,
it is important to note that despite an increase in
hospitalisation rate, the average medical and non-
medical expenditure is assumed to be constant as
observed between 2004 and 2014.
In Bihar, the projected expenditure under the
current scenario is about 19.42 per cent higher than
the policy scenario for 2020, 22.84 per cent higher
for 2025 and 27.02 per cent higher for 2030. In
Rajasthan, the OOP expenditure on inpatient care
under the current scenario is projected to be 12.75
per cent more than that projected under the policy
trend for 2020. Similarly, these estimates are 13.32
per cent higher in 2025 and 16.05 per cent higher in
2030. For Madhya Pradesh, the difference between
the OOP expenditure estimates under the current
and policy situations is about 32.64 per cent in
2020, 30.46 per cent in 2025 and 32.35 per cent in
2030. Similarly, the projected expenditure on child
inpatient care in Uttar Pradesh is 30.88 per cent
higher under the current scenario compared to the
policy trend for 2020. Furthermore, these estimates
are 24.09 per cent higher for 2025 and 22.64 per
cent higher for 2030.
5.3.2. Out-of-Pocket Expenditure: Child
Outpatient Care
Table 5.5 shows information on the OOP
expenditure on child (0 to 5 years) outpatient care
in India for 2004 and 2014 taking into consideration
the total number of births under the current and
policy scenarios.The observed proportion of
ailing persons (PAP) in 2004 and 2014 are 77 per
thousand and 89 per thousand, respectively. Under
the current scenario, the total expenditure
on outpatient care is Rs. 1474 million in 2004 and
Rs. 1634 million in 2014.The total expenditure
under the policy scenario is Rs. 1139 million and
Rs. 1251 million in 2004 and 2014, respectively.
Similarly,Table 5.6 presents the total expenditure
projected on child outpatient care for 2020, 2025
and 2030. An increase in the proportion of ailing
persons is assumed to be constant as observed
between 2004 and 2014. Estimates show that
the total medical and non-medical expenditure
under the current scenario exceeds that of the
policy scenario by Rs 263 million in 2020, Rs. 293
million in 2025 and Rs. 378 million in 2030.
Table 5.5: Total Expenditure Averted on Child Outpatient Care (0 to 5 years) All India, 2004 and 2014
PAP (per 1000 persons)
Total number of births (in millions)
Total number of ailing persons
(in millions)
Average total expenditure per ailing child*++
(Rs. in millions)
Total medical and non-medical expenditure
(Rs. in millions)
Total expenditure on child hospitalisation
averted (%)
C*
77
27.2
2.1
7020
1474
* Estimated using National Sample Survey 2004 and 2014
++ Estimates adjusted for inflation
C* Current Trend; P* Policy Trend;Avt* Expenditure Averted
2004
P*
77
21.0
1.62
7020
1139
Avt*
77
6.1
0.48
7020
334.2
22.67
C*
89
26.2
2.3
6990
1634
2014
P*
89
20.1
1.8
6990
1251
Avt*
89
6.1
0.55
6990
383.3
23.46
Out-of-Pocket Healthcare Expenditure 73
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Table 5.6: Estimated Savings on Total Expenditure on Child Outpatient Care (0 to 5 years) All India, 2020, 2025
and 2030
PAPs (per 1000 persons)
Total number of births (in millions)
Total number of ailing persons
(in millions)
Average total expenditure per ailing
child*++ (Rs. in millions)
Total medical And non-medical
expenditure (Rs. in millions)
Total expenditure on child hospitalisation
averted (%)
2020
C*
95.9
23.8
2.3
6990
1596
2020
2025
P*
95.9
19.9
1.9
6990
1333
2030
Avt*.
95.9
3.9
0.4
C*
C*
103.4
22.4
2.3
6990 6990
263 1619
16.49
2025
P*
P*
103.4
18.3
1.9
Net*
Avt*.
103.4
4.1
0.4
C*
C*
111.5
21.4
2.4
6990 6990 6990
1326
293 1669
18.09
2030
P*
P*
111.5
16.6
1.8
Net*
Avt*.
111.5
4.8
0.5
6990 6990
1291
378
22.64
* Estimated using National Sample Survey 2004 and 2014
++ Estimates adjusted for inflation
Note:An increase in PAPs is assumed to be constant at the observed figure of 7.79% per five years.
C* Current Trend; P* Policy Trend;Avt* Expenditure Averted
Table 5.7: Total Expenditure Averted on Child Outpatient Care (0 to 5 years) Selected States, 2004 and 2014
Bihar
PAP (per 1000 persons)
Total number of births (in millions)
Total number of ailing persons (in millions)
Average total expenditure per ailing
child*++ (Rs. in millions)
Total medical and non-medical expenditure
(Rs. in millions)
Total expenditure on child hospitalisation
averted (%)
Rajasthan
PAP (per 1000 persons)
Total number of births (in millions)
Total number of ailing persons (in millions)
Average total expenditure per ailing
child*++ (Rs. in millions)
Total medical and non-medical expenditure
(Rs. in millions)
Total expenditure on child hospitalisation
averted (%)
Madhya Pradesh
PAP (per 1000 persons)
Total number of births (in millions)
C*
44
2.5
0.11
7380
2004
P*
44
2.3
0.10
7380
810
752
Avt*.
44
0.2
0.01
7380
5.8
7.11
54
1.7
0.09
13260
54
1.6
0.09
13260
54
0.01
0.001
13260
118.8 117.8
1.1
0.90
56
56
56
1.8
1.5
0.3
C*
55
2.4
0.13
8720
2014
P*
55
2.0
0.11
8720
116.2
94.8
56
1.7
0.10
7870
76.9
56
1.5
0.08
7870
63.9
52
52
1.9
1.2
Avt*.
55
0.4
0.02
8720
21.5
18.48
56
0.3
0.02
7870
12.9
16.84
52
0.6
74 Cost of Inaction in Family Planning in India
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9.7 Page 87 |
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Total number of ailing persons (in millions)
Average total expenditure per ailing
child*++ (Rs. in millions)
Total medical and non-medical expenditure
(Rs. in millions)
Total expenditure on child hospitalisation
averted (%)
Uttar Pradesh
PAP (per 1000 persons)
Total number of births (in millions)
Total number of ailing persons (in millions)
Average total expenditure per ailing
child*++ (Rs. in millions)
Total medical and non-medical expenditure
(Rs. in millions)
Total expenditure on child hospitalisation
averted (%)
0.10
6220
63.7
81
5.2
0.42
7320
309
* Estimated using National Sample Survey 2004 and 2014
++ Estimates adjusted for inflation
C* Current Trend; P* Policy Trend;Avt* Expenditure Averted
2004
0.09
6220
53.4
81
4.5
0.36
7320
267
0.02
6220
10.3
16.2
81
0.7
0.06
7320
42
13.6
0.10
9240
2014
0.06
9240
89.2
58.2
67
5.3
0.35
9210
326
67
3.7
0.25
9210
228
0.03
9240
31.0
34.7
67
1.6
0.11
9210
98
30.0
The information on medical and non-medical
expenditure incurred on child outpatient care in
selected states for 2004 and 2014 is depicted in
Table 5.7. The estimated PAP below five years in
Bihar is 44 per thousand and 55 per thousand in
2004 and 2014, respectively. The total expenditure
averted on child hospitalisation is estimated at
7.11 per cent in 2004 and 18.48 per cent in 2014.
Similarly, in Rajasthan, the PAPs is estimated at 54
and 56 per thousand for 2004 and 2014, respectively.
The total expenditure averted on child
hospitalisation estimated for 2004 and 2014 is 1
per cent and 16.48 per cent respectively. The PAPs
below five years in Madhya Pradesh decreases from
56 to 52 per 1000 persons between 2004 and 2014.
The estimated total expenditure is 16.2 per cent and
34.7 per cent higher in 2004 and 2014, respectively,
under the current trend as opposed to the policy
trend. In Uttar Pradesh, the total expenditure under
the current situation is estimated to be Rs. 42
million higher than the policy situation for 2004 and
Rs. 98 million higher for 2014.
5.3.3. Out-of-Pocket Expenditure: Childbirth
Table 5.8 presents information on the total OOP
expenditure on institutional as well as home-based
births in India for 2004 and 2014. The estimated
proportion of institutional and home-based births is
43.3 per cent and 56.7 per cent respectively in 2004
and 82.5 and 17.5 per cent respectively in 2014. The
absolute difference between the total expenditure
(institutional and home-based) on births under the
current and policy scenarios is Rs 22469 million in
2004 and Rs. 45948 million in 2014. The total OOP
expenditure (medical and non-medical) estimated
under the current trend is 22.6 per cent and 23.4
per cent higher compared to estimates under the
policy scenario for 2004 and 2014, respectively.
Similarly, simple projections of total expenditure
on births for 2020, 2025 and 2030 are portrayed in
Table 5.9. It has been assumed that all the deliveries
will be institutional by 2030. Further, assuming a
constant increase in the proportion of institutional
deliveries and unit cost of births, the total
expenditure, taking total births under the current
scenario, is Rs. 33788 million higher in 2020, Rs.
40330 million in 2025 and Rs. 55684 million in 2030.
Out-of-Pocket Healthcare Expenditure 75
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9.8 Page 88 |
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Table 5.8: Total (Medical and Non-Medical) Expenditure Averted on Childbirth (0 to 5 years) All India, 2004
and 2014
Total number of births (in millions)
Proportion of institutional births (%)
Proportion of home-based births (%)
Total institutional births (in millions)
Total home-based births (in millions)
Average total expenditure per institutional
birth++ (Rs. in millions)
Average total expenditure per home birthα++
(Rs. in millions)
Total institutional birth expenditure
(Rs. in millions) (a)
Total home-based birth expenditure
(Rs. in millions) (b)
Total expenditure on birth
(Rs. in millions) (a + b)
Total expenditure on birth averted (%)
C*
27.2
43.3
56.7
11.8
15.5
70200
10490
82886
16218
99104
2004
P*
21
43.3
56.7
9.1
12.0
70200
10490
64094
12541
76635
Avt*
6.1
43.3
56.7
2.7
3.5
70200
10490
18791
3677
22469
22.7
C*
26.2
82.5
17.5
21.7
4.6
85070
25080
184359
11529
195888
2014
P*
20.1
82.5
17.5
16.6
3.5
85070
25080
141115
8824
149940
Avt*
6.1
82.5
17.5
5.1
1.1
85070
25080
43244
2704
45948
23.4
α - Exp. on Home-based Childbirth are assumed to be constant
++ Estimates adjusted for inflation
C* Current Trend; P* Policy Trend;Avt* Expenditure Averted
Table 5.9: Projected Total (Medical and Non-Medical) Expenditure Averted on Childbirth All India, 2020, 2025
and 2030
C*
Total births (in millions)
23.8
Proportion of institutional births (%)
88
Proportion of home-based births (%)
12
Total institutional births (in millions)
20.9
Total home-based births (in millions)
2.9
Average expenditure per
institutional birth*++ (Rs. in millions)
94070
Average expenditure per home-
based birth*++ (Rs. in millions)
27710
Total expenditure on institutional
births (Rs. in millions) (a)
197030
Total expenditure on home-based
births (Rs. in millions) (b)
7910
Total expenditure on births
(Rs. in millions) (a + b)
204950
Total expenditure on births averted
(%)
2020
P*
19.8
88
12
17.5
2.4
94070
27710
164550
6610
171160
2025
2030
Avt*
C*
P*
Avt*
C*
P*
Avt*
3.9
22.4
18.3
4.0
21.4
16.5
4.8
88
94
94
94
100
100
100
12
6
6
6
0
0
0
3.5
21.1
17.2
3.8
21.4
16.6
4.8
0.5
1.3
1.1
0.2
0.0
0.0
0.0
94070 103940 103940 103940 114850 114850 114850
27710 30620 30620 30620 33830 33830 33830
32480 218860 179280 39590 245970 190290 55680
1300 4120 3370
740
0
0
0
33790 222980 182650 40330 245970 190290 55680
16.49
18.09
22.64
*Estimated using National Sample Survey 2004 and 2014
++ Estimates adjusted for inflation
Note:Trend in unit cost of childbirth (both institutional and home-based) has been projected by taking an observed average increase of 10.5 per
cent per five years between 2004 and 2014 (assuming constant increase). It has been assumed that all childbirths will be institutional by 2030.
C* Current Trend; P* Policy Trend;Avt* Expenditure Averted
76 Cost of Inaction in Family Planning in India
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9.9 Page 89 |
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Table 5.10: Total (Medical and Non-Medical) Expenditure Averted on Childbirth (0 to 5 years) Bihar and Rajasthan,
2004 and 2014
Bihar
Total number of births (in millions)
Proportion of institutional births (%)
Proportion of home-based births (%)
Total number of institutional births
(in millions)
Total number of home-based births
(in millions)
Average total expenditure per institutional child
birth*++ (Rs. in millions)
Average total expenditure per home-based
childbirth α*++ (Rs. in millions)
Total expenditure on institutional childbirth
(Rs. in millions) (a)
Total expenditure on home-based childbirth
(Rs. in millions) (b)
Total expenditure on childbirth
(Rs. in millions) (a + b)
Total (Medical and Non-Medical) Expenditure on
Childbirths Averted (%)
Rajasthan
Total number of births (in millions)
Proportion of institutional births (%)
Proportion of home-based births (%)
Total number of institutional births
(in millions)
Total number of home-based births
(in millions)
Average total expenditure per institutional
childbirth*++ (Rs. in millions)
Average total expenditure per home-based
childbirth a*++ (Rs. in millions)
Total expenditure on institutional childbirth
(Rs. in millions) (a)
Total expenditure on home-based childbirth
(Rs. in millions) (b)
Total expenditure on childbirth
(Rs. in millions) (a + b)
Total (Medical and Non-Medical) Expenditure on
Childbirths Averted (%)
C*
2.4
18.45
81.55
0.46
2.03
48880
23060
2249
4690
6939
P*
2.3
18.45
81.55
0.42
1.89
48880
23060
2089
4356
6445
2004
Avt*
0.17
18.45
81.55
0.03
0.14
48880
23060
160
333.7
493
7.11
C*
2.4
71
29
1.72
0.70
68700
23060
11822
1621
13443
P*
1.9
71
29
1.40
0.57
68700
23060
9637
1321
10959
1.65
32.75
67.25
0.543
1.116
72680
27200
3950
3035
6985
1.64
32.75
67.25
0.539
1.106
72680
27200
3914
3008
6922
0.01
32.75
67.25
0.005
0.010
72680
27200
35.6
27.3
62.9
0.90
1.74
85
15
1.482
0.262
45030
27200
6675
711
7387
1.45
85
15
1.233
0.218
45030
27200
5551
591
6143
2014
Avt*
0.4
71
29
0.32
0.13
68700
23060
2185
299.6
2484
18.48
2.9
85
15
0.250
0.044
45030
27200
1124
119
1244
16.84
α - Exp. on home-based childbirth is assumed to be constant
* Estimated using National Sample Survey 2004 and 2014
++ Estimates adjusted for inflation
C* Current Trend; P* Policy Trend;Avt* Expenditure Averted
Out-of-Pocket Healthcare Expenditure 77
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9.10 Page 90 |
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Table 5.11: Total (Medical and Non-Medical) Expenditure Averted on Childbirth (0 to 5 years) Madhya Pradesh,
2004 and 2014
Madhya Pradesh
Total number of births
Proportion of institutional births (%)
Proportion of home-based births (%)
Total number of institutional births
(in millions)
Total number of home-based births
(in millions)
Average total expenditure per institutional
childbirth*++ (Rs. in millions)
Average total expenditure per home-based
childbirth α*++ (Rs. in millions)
Total expenditure on institutional childbirth
(Rs. in millions) (a)
Total expenditure on home-based childbirth
(Rs. in millions) (b)
Total expenditure on childbirth (Rs. in millions)
(a + b)
Total (Medical and Non-Medical) Expenditure on
Childbirths Averted (%)
Uttar Pradesh
Total number of births (in millions)
Proportion of institutional births (%)
Proportion of home-based births (%)
Total number of institutional births
(in millions)
Total Number of home-based births
(in millions)
Average total expenditure per institutional
childbirth*++ (Rs. in millions)
Average total expenditure per home-based
childbirth α*++ (Rs. in millions)
Total expenditure on institutional childbirth
(Rs. in millions) (a)
Total expenditure on home-based childbirth
(Rs. in millions) (b)
Total expenditure on childbirth
(Rs. in millions) (a + b)
Total (Medical and Non-Medical) Expenditure on
Childbirths Averted (%)
C*
0.182
42
58
0.77
1.05
75910
25020
5849
2649.2
8498.4
5.21
16.57
83.43
0.86
4.35
86440
30350
7469.3
13204.6
20674
P*
0.153
42
58
0.64
0.88
75910
25020
4900
2219.6
7120.3
4.50
16.57
83.43
0.74
3.75
86440
30350
6450.8
11404
17854.7
2004
Avt*
0.029
42
58
0.12
0.17
75910
25020
948.5
429.6
1378.1
16.22
C*
0.185
83
17
1.54
0.31
41170
25020
6343
789.6
7133.4
P*
0.121
83
17
0.100
0.20
41170
25020
4141
515.5
4657.1
0.71
16.57
83.43
0.11
0.59
86440
30350
1018.5
1800.6
2819.2
13.64
5.27
71
29
3.74
1.53
67900
30350
25434.8
4643.6
30078.4
3.69
71
29
2.62
1.07
67900
30350
17804.3
3250.5
21055
2014
Avt*
0.064
83
17
0.053
0.10
41170
25020
2202
274.1
2476.3
34.71
1.58
71
29
1.12
0.45
67900
30350
7630.4
1393.1
9023
30.00
α Expenditure on home-based childbirth is assumed to be constant
* Estimated using National Sample Survey 2004 and 2014
++ Estimates adjusted for inflation
C* Current Trend; P* Policy Trend;Avt* Expenditure Averted
78 Cost of Inaction in Family Planning in India
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10 Pages 91-100 |
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10.1 Page 91 |
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Table 5.12.: Projected Total (Medical and Non-Medical) Expenditure Averted on Childbirth Bihar and Rajasthan,
2020, 2025 and 2030
Bihar
Total births (in millions)
Institutional births (%)
Home-based births (%)
Total institutional births (in millions)
Total home-based births (in millions)
Average expenditure per
institutional birth*++ (Rs. in
millions)
Average expenditure per home-
based birth*++ (Rs. in millions)
Total expenditure on institutional
births (Rs. in millions) (a)
Total expenditure on home-based
births (Rs. in millions) (b)
Total expenditure on births
(Rs. in millions) (a + b)
Total Expenditure on Births
Averted (%)
Rajasthan
Total births (in millions)
Institutional births (%)
Home-based births (%)
Total institutional births (in millions)
Total home-based births (in
millions)
Average expenditure per
institutional birth*++ (Rs. in
millions)
Average expenditure per home-
based birth*++ (Rs. in millions)
Total expenditure institutional
births (Rs. in millions) (a)
Total expenditure home-based
births (Rs. in millions) (b)
Total expenditure on births (Rs. in
millions) (a + b)
Total Expenditure on Births
Averted (%)
C*
2.2
81
19
1.8
0.4
82630
23060
14796
968.6
15764.5
1.56
90
10
1.4
0.2
36465.3
27200
5135.8
425.7
5561.5
2020
P*
1.7
81
19
1.4
0.3
82630
23060
11923
780.5
12703.5
1.36
90
10
1.2
0.1
36465.3
27200
4480.9
371.4
4852.2
Avt*
0.4
81
19
0.3
0.1
82630
23060
2873
188.1
3061
19.42
0.19
90
10
0.2
0.0
36465.3
27200
655
5403
709.2
12.75
C*
2.1
91
9
2.0
0.2
99370
23060
19668
451.4
20119.6
1.39
95
5
1.3
0.1
29529.6
27200
3910.5
189.6
4100.1
2025
P*
1.6
91
9
1.5
0.2
99370
23060
15175
348.3
15523.4
1.20
95
5
1.1
0.1
29529.6
27200
3389.7
164.3
3554.0
Avt*
0.4
91
9
0.5
0.0
99370
23060
4493
103.1
45960.3
22.84
1.85
95
5
0.2
0.0
29529.6
27200
520.8
25.2
546.1
13.32
C*
2.2
100
0
2.2
0.0
119520
23060
26486
0.0
26486.4
1.32
100
0
1.3
0.0
23913.1
27200
3173
0.0
3173
2030
P*
1.6
100
0
1.6
0.0
119520
23060
19330
0.0
19330
1.11
100
0
1.1
0.0
23913.1
27200
2663.6
0.0
2663.6
Avt*
0.5
100
0
0.6
0.0
119520
23060
7156
0.0
7156.4
27.02
0.21
100
0
0.2
0.0
23913.1
27200
509.4
0.0
509.4
16.05
++ Estimates adjusted for inflation
Note: Trend in unit cost of institutional childbirth is projected by taking an observed average decrease between 2004 and 2014 and unit cost
of home-based births is assumed to be constant. It has been posited that all births will be institutional by 2030.
C* Current Trend; P* Policy Trend;Avt* Expenditure Averted
Out-of-Pocket Healthcare Expenditure 79
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10.2 Page 92 |
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Table 5.13.: Projected Total (Medical and Non-Medical) Expenditure Averted on Childbirth Madhya Pradesh and
Uttar Pradesh, 2020, 2025 and 2030
Madhya Pradesh
Total births (in millions)
Institutional births (%)
Home-based births (%)
Total institutional births (in millions)
Total home-based births (in millions)
Average expenditure per
institutional birth*++ (Rs. in millions)
Average expenditure per home-
based birth*++ (Rs. in millions)
Total expenditure institutional
births (Rs. in millions) (a)
Total expenditure home-based
births (Rs. in millions) (b)
Total expenditure on births
(Rs. in millions) (a + b)
Total Expenditure on Births
Averted (%)
Uttar Pradesh
Total births (in millions)
Institutional births (%)
Home-based births (%)
Total institutional births (in millions)
Total home-based births (in millions)
Average expenditure per
institutional birth*++ (Rs. in millions)
Average expenditure per home-
based birth*++ (Rs. in millions)
Total expenditure institutional
births (Rs. in millions) (a)
Total expenditure home-based
births (Rs. millions) (b)
Total expenditure on births (Rs. in
millions) (a + b)
Total Expenditure on Births
Averted (%)
C*
1.7
89
11
1.5
0.19
31750
25020
4870
470
5340
4.6
81
19
3.7
0.9
60620
30350
22710
2670
25370
2020
P*
1.2
89
11
1.0
0.13
31750
25020
3280
320
3600
3.1
81
19
2.6
0.6
60620
30350
15690
1840
17540
Avt*
0.56
89
11
0.5
0.06
31750
25020
1590
150
1740
32.64
0.15
81
19
1.2
0.3
60620
30350
7010
820
7830
30.88
C*
1.5
95
5
1.5
0.08
24490
25020
3610
190
3810
4.0
91
9
3.7
0.4
54120
30350
19790
1100
20880
2025
P*
1.18
95
5
1.0
0.05
24490
25020
2510
140
2650
0.30
91
9
2.8
0.3
54120
30350
15020
830
15850
Avt*
0.47
95
5
0.4
0.02
24490
25020
1100
60
1160
30.46
0.9
91
9
0.9
0.1
54120
30350
4770
260
5030
24.09
C*
1.5
100
0
1.5
0.0
18880
25020
2840
0
2840
3.8
100
0
3.8
0
48320
30350
18400
0
18400
2030
P*
1.0
100
0
1.0
0.0
18880
25020
1920
0
1920
2.9
100
0
2.9
0
48320
30350
14230
0
14230
Avt*
4.87
100
0
0.5
0.0
18880
25020
920
0
920
32.35
0.9
100
0
0.9
0
48320
30350
4170
0
4170
22.64
++ Estimates adjusted for inflation
Note:Trend in unit cost of institutional childbirth is projected by taking an observed average decrease between 2004 and 2014 and unit cost
of home=based births is assumed to be constant. It has been posited that all births will be institutional by 2030.
C* Current Trend; P* Policy Trend;Avt* Expenditure Averted
80 Cost of Inaction in Family Planning in India
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The estimated OOP expenditure on institutional
and home-based births for Bihar and Rajasthan are
depicted in Table 5.10. In Bihar, the proportion of
institutional births was estimated at 81.55 per cent
and home-based births is estimated at18.45 per cent
in 2004, which increased to 71 per cent institutional
births and 29 per cent home-based birthsin 2014.
The OOP expenditure in births in Bihar is Rs. 493.6
million (7.11 per cent) higher under the current
scenario than the policy scenario for 2004 and Rs.
2484.6 million (18.48 per cent) higher for 2014.
Similarly, in Rajasthan, 32.75 per cent of total births
are estimated to be institutional and 67.25 per cent
births to be home-based for 2004. This proportion
increased to 85 per cent and reduced to15 per cent,
respectively in 2014. The difference between OOP
expenditure estimated under the current and policy
scenarios is Rs. 63 million (0.90 per cent) and Rs.
1244 million (16.84 per cent) for 2004 and 2014
respectively.
Table 5.11 presents estimates of OOP expenditure
on total births in Madhya Pradesh and Uttar Pradesh
for 2004 and 2014, respectively. In 2004, 42 per cent
and 58 per cent of total births in Madhya Pradesh
are estimated to be institutional and home-based,
respectively. However, in 2014, the proportion of
institutional births increased to 83 per cent and
home-based births decreased to 17 per cent.The
total expenditure on births in Madhya Pradesh
under the policy scenario is Rs. 7120.3 million in
2004 and Rs. 4657.1 million in 2014. On the other
hand, the total OOP expenditure estimated under
the current scenario is Rs 8498.4 million (16.22 per
cent higher) and Rs 7133.4 million (34.71 per cent
higher) in 2004 and 2014, respectively.
Similarly, in Uttar Pradesh, the proportion of
institutional births increased from 16.57 per cent to
71 per cent between 2004 and 2014, whereas the
proportion of home-based births decreased from
83.43 per cent in 2004 to 29 per cent in 2014. In
Uttar Pradesh, the estimated total OOP expenditure
on births under the current scenario is Rs. 2819.2
million (13.64 per cent) higher than the policy
scenario in 2004 and Rs. 9023.5 million (30 per cent)
higher for 2014.
Information regarding the total OOP expenditure
in selected states for 2020, 2025 and 2030 is
presented in Tables 5.12 and 5.13. An increase
in the proportion of institutional deliveries is
assumed to be constant as observed between
2004 and 2014. Also, all births are assumed to
be institutional by 2030. The total expenditure
on births in Bihar under the current scenario is
projected to be 19.42 per cent higher than the
policy scenario in 2020, 22.84 per cent higher in
2025 and 27.02 per cent higher in 2030. Similarly,
in Rajasthan, the projected total expenditure
under the current scenario exceeds the policy
scenario by 12.75 per cent in 2020, 13.32 per
cent in 2025 and 16.05 per cent in 2030. In
Madhya Pradesh, the expenditure estimates under
the current births trend is projected to be higher
than the policy trend estimates by Rs. 1743.4 million
(32.64 per cent) in 2020, Rs. 1159.6 million (30.46
per cent) in 2025, and Rs. 920 million (32.33 per
cent) in 2030.These estimates for Uttar Pradesh
are projected to be 30.88 per cent higher under the
current scenario in 2020, 24.09 per cent in 2025 and
22.6 per cent higher in 2030.
5.3.4. Catastrophic Expenditure on Inpatient
Care and Institutional Births
We also present an alternative approach
to estimate the incidence of catastrophic
expenditure by accounting for variations in the
household size in the estimation process. Under
the conventional approach, a household can be
classified as incurring catastrophic expenditure
if THE/HCE > Ca (where Ca= 0.1).Where,THE
stands for total health expenditure and HCE
for household consumption expenditure and
Ca denotes the threshold used for identifying
catastrophic expenditure where threshold values
can be set at various levels such as 10%, 20% and
so on.
However, we propose a slight modification in
approach, one that retains the NHP formulation
but replaces the denominator with per capita
annual household consumption expenditure.
Out-of-Pocket Healthcare Expenditure 81
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Therefore, health expenditure can be considered
catastrophic if THE/PHCE > Ca (where Ca= 1). It
may be noted that varying levels of thresholds can
be used to understand the patterns and intensity
of such payments. Besides, it is straightforward to
extend this approach in the context of food-based
expenditure.
In this study, we employ expenditure thresholds
of 100 per cent of annual household per
capita consumption expenditure to discern
the magnitude and socio-economic patterns of
such catastrophic expenditure related to child
hospitalisation and institutional births in India and
selected states. The estimates for the percentage
of households incurring such expenditures for
2004 and 2014 are reported.
Table 5.14 displays information on the proportion
of households incurring catastrophic expenditure
on child inpatient care for 2004 and 2014. Overall,
the estimated proportion of households spending
more than one member’s per capita expenditure
on child inpatient care increased from 5.3 per
cent to 20.1 per cent between 2004 and 2014.
Further, 6.9 per cent households in 2014 against
2.9 per cent in 2004, were estimated to incur
catastrophic expenditure on child hospitalisation in
public facilities. On the other hand, 26.0 per cent
households incurred catastrophic expenditure in
private hospitals in 2014 against 6.6 per cent in
2004. In 2004, 9.8 per cent households among the
lowest quintile and 2.8 per cent among the highest
quintile are estimated to be spending more than one
member’s consumption expenditure. Similarly, 31.1
per cent of the poorest households and 10.6 per
cent of the richest households incurred catastrophic
expenditure on child hospitalisation in 2014.
The percentage of households incurring catastrophic
expenditure on institutional births for 2004 and
2014 are presented in Table 5.14. Overall, 8.5 per
cent households in 2004 and 14.3 per cent in 2014,
incurred catastrophic expenditure on institutional
Table 5.14: Percentage of Households Incurring Catastrophic Expenditure on Child Hospitalisation (and
Childbirth) above 100 Per cent of Annual Household Per Capita Consumption Expenditure by Wealth Quintiles
All India, NSS, 2004 and 2014
Wealth Quintiles
Lowest Quintile
Second Quintile
Third Quintile
Fourth Quintile
Highest Quintile
All
Lowest Quintile
Second Quintile
Third Quintile
Fourth Quintile
Highest Quintile
All
Public (%)
4.6
2.4
3.3
0.5
2.3
2.9
2.7
2.8
1.6
0.2
0.9
1.8
2004
Private (%) All (%)
Child Hospitalisation
16.0
9.8
8.0
5.9
6.5
5.4
2.6
2.1
2.8
2.8
6.6
5.3
Institutional Deliveries
23.5
10.2
21.3
9.6
11.7
6.9
19.0
10.7
6.6
5.5
14.7
8.5
Public (%)
12.1
7.7
5.9
1.7
1.0
6.9
3.6
1.7
1.4
0.9
0.6
2.1
2014
Private (%)
48.6
30.5
24.9
20.5
12.6
26.0
58.4
45.2
36.3
28.8
20.0
35.8
All (%)
31.1
22.5
19.0
15.9
10.6
20.1
13.7
14.0
14.8
14.6
14.5
14.3
82 Cost of Inaction in Family Planning in India
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deliveries. Further, 1.8 per cent in 2004 and 2.1 per
cent of households in 2014 were estimated to spend
more than one member’s consumption expenditure
on deliveries in public facilities. Furthermore, 2.7
per cent of households from the poorest quintile
and 0.9 per cent of households from the richest
quintile incurred catastrophic expenditure on public
hospital births in 2004. Similarly, in 2014, 0.6 per
cent of the richest 20 per cent households and 3.6
per cent of the poorest 20 per cent households
spent above their per capita annual household
expenditure on public hospital deliveries.
5.4. Conclusion
Overall, the total OOP expenditure on child
healthcare (inpatient and outpatient) estimated
under the current scenario is 22.67 per cent and
23.45 per cent higher than the expenditure gauged
under the policy scenario for 2004 and 2014,
respectively. The projected difference between
the total OOP expenditure on child healthcare
(inpatient and outpatient) estimated under the
current and policy scenarios is 16.49 per cent in
2020, 18.09 per cent in 2025 and 22.64 per cent in
2030 for all India.
With the effective implementation of the National
Population Policy 2000, Indian households could
achieve about a one-fifth reduction in the total out-
of-pocket expenditure on delivery care and child
hospitalisation.The magnitude of savings in OOPE
could be much larger for households in Madhya
Pradesh (35 per cent) and Uttar Pradesh (30 per
cent).
During 2014-30, Indian households would have
cumulatively saved Rs. 715320 million on account of
household OOPE toward delivery care. Significant
cumulative savings arise from Uttar Pradesh (Rs.
112300 million) and Bihar (Rs. 62320 million).
Similarly, during 2014-30, Indian households would
have cumulatively saved Rs. 60780 million on account
of household OOPE toward child hospitalisation.
About one-fifth of such cumulative savings arise
from Uttar Pradesh (Rs. 6900 million) and Bihar (Rs.
5880 million)..
Currently, Indian households experience high
financial hardships while seeking hospitalisation and
delivery care. In 2014, about 14 per cent cases of
delivery care and about 20 per cent cases of child
hospitalisation experienced catastrophic out-of-
pocket-expenditures.
In Bihar, the total expenditure on child healthcare
and births estimated under the current situation
exceeds the estimates under the policy situation by
7.11 per cent in 2004 and 18.48 per cent in 2014.
Further, it is projected to exceed by 19.42 per cent
in 2020, 22.84 per cent in 2025 and 27.02 per cent
in 2030.
In Rajasthan, the total expenditure on child
healthcare and births estimated under the current
situation exceeds the estimates under the policy
situation by 0.90 per cent in 2004 and 16.84 per
cent in 2014. Further, it is projected to exceed by
12.75 per cent in 2020, 13.32 per cent in 2025 and
16.05 per cent in 2030.
In Madhya Pradesh, the total expenditure on child
healthcare and births estimated under the current
situation exceeds the estimates under the policy
situation by 16.21 per cent in 2004 and 34.71 per
cent in 2014. Further, it is projected to exceed by
32.64 per cent in 2020, 30.46 per cent in 2025 and
32.33 per cent in 2030.
In Uttar Pradesh, the total expenditure on child
healthcare and births estimated under the current
situation exceeds the estimates under the policy
situation by 13.63 per cent in 2004 and 29.99 per
cent in 2014. Further, it is projected to exceed by
30.88 per cent in 2020, 24.09 per cent in 2025 and
22.64 per cent in 2030.
Overall, 5.3 per cent and 20.1 per cent of
households are estimated to incur catastrophic
expenditure on child hospitalisation in 2004 and
2014, respectively.The incidence of catastrophic
Out-of-Pocket Healthcare Expenditure 83
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expenditure on child inpatient care in public
hospitals increased from 2.9 per cent in 2004 to 6.9
per cent in 2014. Further, for those seeking care
in private hospitals, the proportion of households
incurring catastrophic expenditure increased
almost four times from 6.6 per cent to 26 per cent
between 2004 and 2014.
The incidence of catastrophic expenditure on child
hospitalisation is significantly higher among poor
households compared to richer households, both
in the public and private facilities.The increase in
the proportion of households with catastrophic
spending is higher among those seeking care in
private hospitals than in public hospitals.
The proportion of households with catastrophic
spending on childbirth in public hospitals increased
from 1.8 per cent in 2004 to 2.1 per cent in 2014.
The proportion of households with catastrophic
spending on childbirth in private hospitals increased
from 14.7 per cent in 2004 to 35.8 per cent in 2014.
The results elicit a clear socio-economic gradient in
the incidence of catastrophic spending on childbirth.
Therefore, catastrophic expenditure on deliveries
is significantly higher among poor households as
compared to richer households, in both public and
private hospitals.
The increase in the proportion of households
incurring catastrophic expenditure on childbirth
is significantly higher for those seeking delivery in
private facilities than those in public facilities.
5.5. Limitations
The increase in hospitalisation rate among children
is assumed to be constant for expenditure
projections.The unit cost of child inpatient and
outpatient care and institutional births is also
assumed to be constant for state level projections.
The total expenditure on child healthcare and births
are not estimated separately for public and private
hospitals because of data specific limitations.
The sample size for both NSS rounds (60th
and 71st) is different.The present analysis does
not cover information on disease-centric OOP
expenditure. Further, the present estimates do not
capture the information on SES22 distribution of
total OOP expenditure.
22 Socioeconomic status
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6 Conclusions and
Recommendations
6.1. Summary of Key Findings
6.1.1. Demographic and Health Costs
The study finds the following demographic and
health costs if appropriate investments in family
planning are not made over the next 15 years:
India will have an additional population of 149
million by 2031 with Bihar (24 million), Madhya
Pradesh (14 million), Rajasthan (5 million) and Uttar
Pradesh (31 million) accounting for one-half of this
additional population.
There will be an additional child population (0-4
years) of 22.7 million by 2031 with Bihar (3.3
million), Madhya Pradesh (2.3 million), Rajasthan (1.1
million) and Uttar Pradesh (4.1 million) accounting
for about one-half of this additional child population.
India would have to meet the costs of 69 million
additional births during 2016-31. Bihar (13 million),
Madhya Pradesh (9 million), Rajasthan (3 million) and
Uttar Pradesh (18 million) will have to incur major
costs as they jointly account for over 60 per cent of
these births.
India would witness 2.9 million additional infant
deaths with the bulk of these occurring in Bihar (0.6
million), Madhya Pradesh (0.5 million), Rajasthan (0.2
million) and Uttar Pradesh (1.2 million).
India could prevent 1.2 million maternal deaths with
about half of these being averted across Bihar (0.2
million), Madhya Pradesh (0.1 million), Rajasthan (0.1
million) and Uttar Pradesh (0.3 million).
India could avert 206 million unsafe abortions with
significant benefits for the four states, particularly
Bihar (22 million) and Uttar Pradesh (34 million).
More than one third of the potential number of
maternal lives saved across the country between
2001 to 2011 can be attributed to a decrease in
the number of live births. For the populous states
like Bihar and Uttar Pradesh, the effect of fertility
Table 6.1: Demographic and Health Consequences (in million)
Indicators
Additional Population 2031
Additional Child Population 2031
Additional Births 2016-31
Additional Infant Deaths 2016-31
Maternal Deaths Averted 2016-31
Unsafe Abortions Averted 2016-31
Bihar
MP
24
14
3.3
2.3
13
09
0.6
0.5
0.2
0.1
22.3
16.0
Rajasthan
05
1.1
03
0.2
0.1
14.3
UP
India
31
149
4.1
22.7
18
69
1.2
2.9
0.3
1.2
33.8
205.8
Conclusions and Recommendations 85
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declines on the potential number of maternity lives
saved is estimated to be 62 per cent and 57 per
cent, respectively.
6.1.2. Impact on Per Capita Income and
Economic Growth
The following would be the economic benefits if
appropriate investments in family planning are made
over the next 15 years:
With active family planning policies, India will enjoy
an additional per capita income of 13 per cent
in 2026-31.This implies that the Per Capita GDP
(PCGDP in 2004-05 prices) for India could be
Rs. 153,368 under the policy scenario compared to
Rs. 135,924 under the current scenario.
India would also benefit from an additional 0.4
percentage point increase in per capita GDP growth
rate during 2026-31.
Significant benefits for all the four states are noted
but the largest gain could be experienced by
Madhya Pradesh with an additional per capita
income of 18 per cent in 2026-31. Madhya Pradesh
could also benefit from an additional 0.5 percentage
point increase in per capita GDP growth rate during
2026-31.
6.1.3. National Health Mission (NHM)
Budgetary Savings Potential
Substantial financial savings under the National
Health Mission (NHM) Programme Components
could accrue over the next 15 years if appropriate
family planning measures are implemented.The
following would be the potential NHM budgetary
savings if appropriate actions in family planning are
made over the next 15 years:
• Cumulative savings of Rs. 27,0000 million in total
budgetary allocations for health
• Cumulative savings of around Rs. 6,0000 million
in the maternal health programme
• Cumulative savings of Rs. 3000 million from
lower delivery costs on account of the reduced
number of births
• Cumulative savings of Rs. 5500 million in the
RBSK programme
• Cumulative savings of Rs. 790 million in the
adolescent programme
• Cumulative savings of Rs. 13000 million under
immunisation coverage on account of the
reduced number of births
Figure 6.1: Additional Per Capita Income and Growth with Effective Policy Scenario
18
14
15
13
8
0.5
0.4
0.4
0.3
0.3
Bihar Madhya Pradesh Rajasthan Uttar Pradesh India
86 Cost of Inaction in Family Planning in India
Bihar Madhya Pradesh Rajasthan Uttar Pradesh India
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Table 6.2. NHM Budget Savings Potential (in million) for the period 2017-31, India and States
Component
Maternal Health
Child Health
Adolescent
RBSK
Training
NRHM Additionalities
Procurement
Immunisation
NIDDCP
Bihar
7310
180
40
140
280
12970
4130
1330
40
MP
5690
330
40
490
210
9490
1840
650
10
Rajasthan
2950
170
10
160
190
8940
2020
420
10
UP
7060
130
20
490
140
22490
2060
2520
10
India
59930
3070
790
5470
4240
139720
42500
13260
160
6.1.4. Reduction in Household Out-of-Pocket
Expenditure
With the effective implementation of the NPP 2000,
Indian households could achieve about a one-
fifth reduction in total out-of-pocket expenditure
on delivery care and child hospitalisation.The
magnitude of savings in OOPE could be much larger
for households in Madhya Pradesh (35 per cent) and
Uttar Pradesh (30 per cent).
During 2014-30, Indian households would have
cumulatively saved Rs. 71,5320 million on account of
household OOPE toward delivery care. Significant
cumulative savings arise from Uttar Pradesh (Rs.
11,2300 million) and Bihar (Rs. 62320 million).
Similarly, during 2014-30, Indian households
would have cumulatively saved Rs. 6,0780 million
on account of household OOPE toward child
hospitalisation. About one-fifth of such cumulative
savings would come from Uttar Pradesh (Rs. 6900
million) and Bihar (Rs. 5880 million).
Currently, Indian households experience high level
of financial hardships while seeking hospitalisation
and delivery care. In 2014, about 14 per cent cases
of delivery care and about 20 per cent cases of child
hospitalisation experienced catastrophic OOPE.
6.2. Recommended Actions
In the last three years, several new family planning
programmes have been introduced and these
include:
• An bigger basket of choice:Three new
methods have been introduced in the
National Family Planning programme: (i)
Injectable Contraceptive DMPA (Antara) (ii)
Centchroman pill (Chhaya) (iii) Progesterone
only pill (POP).
• GoI has launched Mission Parivar Vikas
for substantially increasing the access to
contraceptives and family planning services in
the 145 high fertility districts of seven High
Focus States (HFS) with a TFR of 3 and above.
These are the states of: Uttar Pradesh, Bihar,
Rajasthan, Madhya Pradesh, Chhattisgarh,
Jharkhand and Assam.
• The launch of a Logistics Management
Information System (FP-LMIS) by the
Government of India (GoI).This is a new
software designed to provide robust
information on the demand and distribution
of contraceptives to health facilities and the
ASHAs.
Conclusions and Recommendations 87
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However, each of these programmes requires a
well-planned roll out strategy and goals which at the
moment is not clear. Moreover, India
has also pledged to provide universal access
to reproductive health services including
contraceptives by 2030 as part of its commitment to
the Sustainable Development Goals (SDGs).
Some key recommendations to strengthen the family
planning programme are:
Specific strategies to address reproductive
health needs of adolescents and youth: While
it is well recognized that adolescents and youth
have distinctive needs, access to reproductive
health services by adolescents and youth is mired in
challenges of access to services; attitudinal barriers
among providers and restrictive social norms.
Greater investments and early interventions in
their education, health including reproductive and
sexual health needs and skill development activities
will enhance their contribution to economic
output and growth.To meet India’s commitments
to the SDGs and FP2020 and considering the huge
demographic dividend, specific health strategies
especially for adolescents and youth that address
their health needs and priorities is critical.This
strategy should underscore a voluntary, rights and
choice-based approach for addressing their sexual
and reproductive health concerns. Specific focus on
increasing access to information and reproductive
health services, delaying their age at marriage, first
pregnancy and empowering them to take informed
decisions on spacing between children is the only
way to address population momentum which
contributes to 70 per cent of the population growth.
Increased allocations for family planning:
Planning and prioritisation of family planning
budgets should adequately address the gaps in use
of spacing contraceptives. Budget proposals should
emphasise on making available at scale voluntary
spacing methods that ensure effective reproductive
health solutions for both the mother and the child.
Availability of a greater resource envelope for
family planning in the national and states’ health
budgets and accelerating its spending will contribute
to higher economic output, greater savings and
investments as a result of reductions in fertility
in the country, specifically across high TFR states
such as Bihar and Uttar Pradesh. Budget allocations
should factor in the growing need for contraceptive
requirements of 53% of India’s population in the
reproductive age group. Further, the allocations and
programmes should be synchronised to reflect the
shift in focus from limiting to spacing methods and
activities that drive demand and cater to unmet
need.
Multi-sectoral response and community
engagements: Family planning approaches are
complex and are influenced by social, cultural,
economic and environmental factors. It entails a
huge component of influencing knowledge and
behavior change in the population, which requires
collective efforts from different sectors and the
community.While there has been emphasis on the
supply side aspects of the health system, it is equally
important to address the demand side factors
through greater community engagement and multi-
sectoral response that address the critical gaps in
implementation and scaling up of family planning
programmes. Engagement with different stakeholders
across different sectors will enable a leverage of the
expertise, knowledge, skills, resources and reach for
improving family planning outcomes. Best practices
from Social and Behaviour Change Communication
(SBCC) initiatives and convergence models such as
state and district level working groups need to be
scaled up.
Quality family planning services under
Universal Health Coverage: Existing policies
ensure free provisioning of delivery care services
as well as postnatal care in public health facilities;
however there are issues with quality and access
to services, especially in remote and underserved
areas. Increasing the availability and access to
reproductive health services and addressing
the unmet need for contraceptives should be a
priority among other aspects that aim to achieve
Universal Health Coverage (UHC).This will enable
88 Cost of Inaction in Family Planning in India
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better reproductive maternal and child health care
outcomes.The study also reveals that households
incur high and catastrophic healthcare payments for
child birth as well as inpatient care for children. Such
a high cost of treatment often acts as a deterrent
for seeking quality healthcare.With provision of
quality FP services and increasing its reach under the
UHC, households will have fewer children and can
save huge out of pocket expenditures on child birth
and child hospitalization.
Promote female education and labour force
participation: The study observes that inaction in
family planning can have a slowing down effect on
per capita income and output of the economy for
the nation and the states. Reducing fertility rates
along with increasing women’s education, delaying
their marriage age and increasing opportunities for
them in the labour market will enable increased
economic output and permit resources for
alternative investments. Simulation analysis reveals
that economic gains can be much higher when
female education and labour force participation
are promoted and enabled. At present, there are
significant gender differentials in the average years
of schooling across the four high focus study
states. Besides, the huge gender gap in labour
market participation reflects a lack of employment
opportunities for females and is indicative of a
gendered nature of economic activities in India.
Development policies and initiatives in the country
should actively promote avenues for economic
empowerment of women by supporting their
education and employment in skill-based industries
and services.
Conclusions and Recommendations 89
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Cleland, J., Conde-Agudelo, A., Peterson, H., Ross,
J., & Tsui,A. (2012). Contraception and Health.The
Lancet, 380 (9837), 149-156.
Coale,A. J. and Edgar M. Hoover. 1958. Population
Growth and Economic Development in Low-
Income Countries: A Case Study of India’s Prospects.
Princeton University Press.
Cottingham, J., Germain, A., & Hunt, P. (2012). Use of
Human Rights to Meet the Unmet Need for Family
Planning.The Lancet, 380 (9837), 172-180.
Das Gupta, M., Bongaarts, J., & Cleland, J. (2011).
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Review of the Evidence.
Garg, C. C., & Karan,A. K. (2008). Reducing Out-
of-Pocket Expenditures to Reduce Poverty:A
Disaggregated Analysis at Rural-Urban and State
Level in India. Health Policy and Planning, 24(2), 116-
128.
Government of India. (2014). India’s Vision FP 2020,
Family Planning Division, Ministry of Health and
Family Welfare, Government of India.
90 Cost of Inaction in Family Planning in India
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Jain,A. K. (2011). Measuring the Effect of Fertility
Decline on the Maternal Mortality Ratio. Studies in
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James, K. S. (2011). India’s Demographic Change:
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Joe,W., S Sharma, J Sharma,Y M Shanta, M
Ramanathan, U S Mishra and B Subha Sri. (2015).
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References 91
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11.4 Page 104 |
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Annexure A
Table S2.1: Effect of Declines in MMR and Fertility on Maternal Deaths Averted in 2011, India
Decomposition Inputs
Population (Million): 2001
Symbol Source/
Estimation
P1
Census
Population (Million): 2011
P2
Census
Annual Population Growth Rate: 2001
r1
SRS
Annual Population Growth Rate: 2011
r2
SRS
Crude Birth Rate (births per 1000 population): 2001
CBR1 SRS
Crude Birth Rate (births per 1000 population): 2011
CBR2 SRS
MMR (maternal deaths per 100000 births): 2001
M1
SRS
MMR (maternal deaths per 100000 births): 2011
M2
SRS
Population and Births
Estimated population in 2016 assuming constant annual growth EP1
since 2001
Population in 2011
P2
P1*r2
P2
Projected births in 2016 assuming constant fertility
EB2
EP1*CBR1*1000
Actual births in 2011
B2
P2*CBR2*1000
Estimated Number of Maternal Deaths
No change in CBR and no change in MMR
D1
EB2*M1/100000
MMR declined but CBR not declined
D2
EB2*M2/100000
CBR declined but MMR not declined
D3
B2*M1/100000
Both CBR and MMR declined
D4
B2*M2/100000
Potential Number of Maternal Lives Saved in 2016 from:
Total effect of decline in MMR
Y
D1-D2
Total effect of fertility decline
X
D1-D3
Total effect of declines in both fertility and MMR
Z
D1-D4
Overlap between the effect of declines in fertility and MMR
XZ
Y+X-Z
Net effect of decline in MMR
Y-XZ
Net effect of fertility decline
X-XZ
Per cent Distribution of the Potential Number of Lives Saved in 2016
Effect of safe motherhood
[(Y-XZ)/Z] *100
Effect of decrease in births
[(X-XZ)/Z]*100
Effect of fertility reduction realised through its effect on MMR
reduction
(XZ/Z)*100
Estimates
1,029
1,211
2.11
1.76
25.4
21.8
301
167
1811
1211
46000416
26399800
138461.25
76820.69
79463.4
44087.67
61640.56
58997.85
94373.59
26264.83
35376
32733
37.5
34.7
27.8
92 Cost of Inaction in Family Planning in India
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11.5 Page 105 |
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Table S2.2: Effect of Declines in MMR and Fertility on Maternal Deaths Averted in 2011, Bihar
Decomposition Inputs
Population (Million): 2001
Symbol Source/
Estimation
P1
Census
Population (Million): 2011
P2
Census
Annual Population Growth Rate: 2001
r1
SRS
Annual Population Growth Rate: 2011
r2
SRS
Crude Birth Rate (births per 1000 population): 2001
CBR1 SRS
Crude Birth Rate (births per 1000 population): 2011
CBR2 SRS
MMR (maternal deaths per 100000 births): 2001
M1
SRS
MMR (maternal deaths per 100000 births): 2011
M2
SRS
Population and Births
Estimated population in 2016 assuming constant annual growth EP1
since 2001
Population in 2011
P2
P1*r2
P2
Projected births in 2016 assuming constant fertility
EB2
EP1*CBR1*1000
Actual births in 2011
B2
P2*CBR2*1000
Estimated Number of Maternal Deaths
No change in CBR and no change in MMR
D1
EB2*M1/100000
MMR declined but CBR not declined
D2
EB2*M2/100000
CBR declined but MMR not declined
D3
B2*M1/100000
Both CBR and MMR declined
D4
B2*M2/100000
Potential Number of Maternal Lives Saved in 2016 from:
Total effect of decline in MMR
Y
D1-D2
Total effect of fertility decline
X
D1-D3
Total effect of declines in both fertility and MMR
Z
D1-D4
Overlap between the effect of declines in fertility and MMR
XZ
Y+X-Z
Net effect of decline in MMR
Y-XZ
Net effect of fertility decline
X-XZ
Per cent Distribution of the Potential Number of Lives Saved in 2016
Effect of safe motherhood
[(Y-XZ)/Z] *100
Effect of decrease in births
[(X-XZ)/Z]*100
Effect of fertility reduction realised through its effect on MMR
reduction
(XZ/Z)*100
Estimates
83
104
2.48
2.21
31.2
27.7
531
208
183
104
5723016
2880800
30389.21
11903.87
15297.05
5992.06
18485.34
15092.17
24397.15
9180.36
9305
5912
38.1
24.2
37.6
Annexure A 93
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Table S2.3: Effect of Declines in MMR and Fertility on Maternal Deaths Averted in 2011, Rajasthan
Decomposition Inputs
Population (Million): 2001
Symbol Source/
Estimation
P1
Census
Population (Million): 2011
P2
Census
Annual Population Growth Rate: 2001
r1
SRS
Annual Population Growth Rate: 2011
r2
SRS
Crude Birth Rate (births per 1000 population): 2001
CBR1 SRS
Crude Birth Rate (births per 1000 population): 2011
CBR2 SRS
MMR (maternal deaths per 100000 births): 2001
M1
SRS
MMR (maternal deaths per 100000 births): 2011
M2
SRS
Population and Births
Estimated population in 2016 assuming constant annual growth EP1
since 2001
Population in 2011
P2
P1*r2
P2
Projected births in 2016 assuming constant fertility
EB2
EP1*CBR1*1000
Actual births in 2011
B2
P2*CBR2*1000
Estimated Number of Maternal Deaths
No change in CBR and no change in MMR
D1
EB2*M1/100000
MMR declined but CBR not declined
D2
EB2*M2/100000
CBR declined but MMR not declined
D3
B2*M1/100000
Both CBR and MMR declined
D4
B2*M2/100000
Potential Number of Maternal Lives Saved in 2016 from:
Total effect of decline in MMR
Y
D1-D2
Total effect of fertility decline
X
D1-D3
Total effect of declines in both fertility and MMR
Z
D1-D4
Overlap between the effect of declines in fertility and MMR
XZ
Y+X-Z
Net effect of decline in MMR
Y-XZ
Net effect of fertility decline
X-XZ
Per cent Distribution of the Potential Number of Lives Saved in 2016
Effect of safe motherhood
[(Y-XZ)/Z] *100
Effect of decrease in births
[(X-XZ)/Z]*100
Effect of fertility reduction realised through its effect on MMR
reduction
(XZ/Z)*100
Estimates
57
69
1.81
1.39
31.1
26.2
508
244
79
69
2464053
1797320
12517.39
6012.29
9130.39
4385.46
6505.1
3387
8131.93
1760.18
4745
1627
58.3
20
21.6
94 Cost of Inaction in Family Planning in India
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11.7 Page 107 |
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Table S2.4: Effect of Declines in MMR and Fertility on Maternal Deaths Averted in 2011, Madhya Pradesh
Decomposition Inputs
Population (Million): 2001
Symbol Source/
Estimation
P1
Census
Population (Million): 2011
P2
Census
Annual Population Growth Rate: 2001
r1
SRS
Annual Population Growth Rate: 2011
r2
SRS
Crude Birth Rate (births per 1000 population): 2001
CBR1 SRS
Crude Birth Rate (births per 1000 population): 2011
CBR2 SRS
MMR (maternal deaths per 100000 births): 2001
M1
SRS
MMR (maternal deaths per 100000 births): 2011
M2
SRS
Population and Births
Estimated population in 2016 assuming constant annual growth EP1
since 2001
Population in 2011
P2
P1*r2
P2
Projected births in 2016 assuming constant fertility
EB2
EP1*CBR1*1000
Actual births in 2011
B2
P2*CBR2*1000
Estimated Number of Maternal Deaths
No change in CBR and no change in MMR
D1
EB2*M1/100000
MMR declined but CBR not declined
D2
EB2*M2/100000
CBR declined but MMR not declined
D3
B2*M1/100000
Both CBR and MMR declined
D4
B2*M2/100000
Potential Number of Maternal Lives Saved in 2016 from:
Total effect of decline in MMR
Y
D1-D2
Total effect of fertility decline
X
D1-D3
Total effect of declines in both fertility and MMR
Z
D1-D4
Overlap between the effect of declines in fertility and MMR
XZ
Y+X-Z
Net effect of decline in MMR
Y-XZ
Net effect of fertility decline
X-XZ
Per cent Distribution of the Potential Number of Lives Saved in 2016
Effect of safe motherhood
[(Y-XZ)/Z] *100
Effect of decrease in births
[(X-XZ)/Z]*100
Effect of fertility reduction realised through its effect on MMR
reduction
(XZ/Z)*100
Estimates
60
73
2.01
1.81
31
26.9
441
221
109
73
3366600
1955630
14846.71
7440.19
8624.33
4321.94
7406.52
6222.38
10524.76
3104.13
4302
3118
40.9
29.6
29.5
Annexure A 95
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Table S2.5: Effect of Declines in MMR and Fertility on Maternal Deaths Averted in 2011, Uttar Pradesh
Decomposition Inputs
Population (Million): 2001
Symbol Source/
Estimation
P1
Census
Population (Million): 2011
P2
Census
Annual Population Growth Rate: 2001
r1
SRS
Annual Population Growth Rate: 2011
r2
SRS
Crude Birth Rate (births per 1000 population): 2001
CBR1 SRS
Crude Birth Rate (births per 1000 population): 2011
CBR2 SRS
MMR (maternal deaths per 100000 births): 2001
M1
SRS
MMR (maternal deaths per 100000 births): 2011
M2
SRS
Population and Births
Estimated population in 2016 assuming constant annual growth EP1
since 2001
Population in 2011
P2
P1*r2
P2
Projected births in 2016 assuming constant fertility
EB2
EP1*CBR1*1000
Actual births in 2011
B2
P2*CBR2*1000
Estimated Number of Maternal Deaths
No change in CBR and no change in MMR
D1
EB2*M1/100000
MMR declined but CBR not declined
D2
EB2*M2/100000
CBR declined but MMR not declined
D3
B2*M1/100000
Both CBR and MMR declined
D4
B2*M2/100000
Potential Number of Maternal Lives Saved in 2016 from:
Total effect of decline in MMR
Y
D1-D2
Total effect of fertility decline
X
D1-D3
Total effect of declines in both fertility and MMR
Z
D1-D4
Overlap between the effect of declines in fertility and MMR
XZ
Y+X-Z
Net effect of decline in MMR
Y-XZ
Net effect of fertility decline
X-XZ
Per cent Distribution of the Potential Number of Lives Saved in 2016
Effect of safe motherhood
[(Y-XZ)/Z] *100
Effect of decrease in births
[(X-XZ)/Z]*100
Effect of fertility reduction realised through its effect on MMR
reduction
(XZ/Z)*100
Estimates
166
191
2
1.7
32.1
27.8
606
285
282
191
9058620
5307020
54895.24
25817.07
32160.54
15125.01
29078.17
22734.7
39770.23
12042.64
17036
10692
42.8
26.9
30.3
96 Cost of Inaction in Family Planning in India
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11.9 Page 109 |
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Table S2.6: Effect of Declines in IMR and Fertility on Maternal Deaths Averted in 2011, India
Decomposition Inputs
Population (Million): 2001
Symbol Source/
Estimation
P1
Census
Population (Million): 2011
P2
Census
Annual Population Growth Rate: 2001
r1
SRS
Annual Population Growth Rate: 2011
r2
SRS
Crude Birth Rate (births per 1000 population): 2001
CBR1 SRS
Crude Birth Rate (births per 1000 population): 2011
CBR2 SRS
IMR (infant deaths per 1000 births): 2001
M1
SRS
IMR (infant deaths per 1000 births): 2011
M2
SRS
Population and Births
Estimated population in 2016 assuming constant annual growth EP1
since 2001
Population in 2011
P2
P1*r2
P2
Projected births in 2016 assuming constant fertility
EB2
EP1*CBR1*1000
Actual births in 2011
B2
P2*CBR2*1000
Estimated Number of Maternal Deaths
No change in CBR and no change in IMR
D1
EB2*M1/1000
IMR declined but CBR not declined
D2
EB2*M2/1000
CBR declined but IMR not declined
D3
B2*M1/1000
Both CBR and IMR declined
D4
B2*M2/1000
Potential Number of Maternal Lives Saved in 2016 from:
Total effect of decline in IMR
Y
D1-D2
Total effect of fertility decline
X
D1-D3
Total effect of declines in both fertility and IMR
Z
D1-D4
Overlap between the effect of declines in fertility and IMR
XZ
Y+X-Z
Net effect of decline in IMR
Y-XZ
Net effect of fertility decline
X-XZ
Per cent Distribution of the Potential Number of Lives Saved in 2016
Effect of decline in IMR
[(Y-XZ)/Z] *100
Effect of decrease in births
[(X-XZ)/Z]*100
Effect of fertility reduction realised through its effect on IMR
reduction
(XZ/Z)*100
Estimates
1,029
1,211
2.11
1.76
25.4
21.8
66
44
1811
1211
46000416
26399800
30360.27
20240.18
17423.87
11615.91
10120.09
12936.41
18744.36
4312.14
5808
8624
31
46
23
Annexure A 97
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Table S2.7: Effect of Declines in IMR and Fertility on Maternal Deaths Averted in 2011, Bihar
Decomposition Inputs
Population (Million): 2001
Symbol Source/
Estimation
P1
Census
Population (Million): 2011
P2
Census
Annual Population Growth Rate: 2001
r1
SRS
Annual Population Growth Rate: 2011
r2
SRS
Crude Birth Rate (births per 1000 population): 2001
CBR1 SRS
Crude Birth Rate (births per 1000 population): 2011
CBR2 SRS
IMR (infant deaths per 1000 births): 2001
M1
SRS
IMR (infant deaths per 1000 births): 2011
M2
SRS
Population and Births
Estimated population in 2016 assuming constant annual growth EP1
since 2001
Population in 2011
P2
P1*r2
P2
Projected births in 2016 assuming constant fertility
EB2
EP1*CBR1*1000
Actual births in 2011
B2
P2*CBR2*1000
Estimated Number of Maternal Deaths
No change in CBR and no change in IMR
D1
EB2*M1/1000
IMR declined but CBR not declined
D2
EB2*M2/1000
CBR declined but IMR not declined
D3
B2*M1/1000
Both CBR and IMR declined
D4
B2*M2/1000
Potential Number of Maternal Lives Saved in 2016 from:
Total effect of decline in IMR
Y
D1-D2
Total effect of fertility decline
X
D1-D3
Total effect of declines in both fertility and IMR
Z
D1-D4
Overlap between the effect of declines in fertility and IMR
XZ
Y+X-Z
Net effect of decline in IMR
Y-XZ
Net effect of fertility decline
X-XZ
Per cent Distribution of the Potential Number of Lives Saved in 2016
Effect of decline in IMR
[(Y-XZ)/Z] *100
Effect of decrease in births
[(X-XZ)/Z]*100
Effect of fertility reduction realised through its effect on IMR
reduction
(XZ/Z)*100
Estimates
83
104
2.48
2.21
31.2
27.7
62
44
183
104
5723016
2880800
3548.27
2518.13
1786.1
1267.55
1030.14
1762.17
2280.72
511.6
519
1251
22.7
54.8
22.4
98 Cost of Inaction in Family Planning in India
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12.1 Page 111 |
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Table S2.8: Effect of Declines in IMR and Fertility on Maternal Deaths Averted in 2011, Rajasthan
Decomposition Inputs
Population (Million): 2001
Symbol Source/
Estimation
P1
Census
Population (Million): 2011
P2
Census
Annual Population Growth Rate: 2001
r1
SRS
Annual Population Growth Rate: 2011
r2
SRS
Crude Birth Rate (births per 1000 population): 2001
CBR1 SRS
Crude Birth Rate (births per 1000 population): 2011
CBR2 SRS
IMR (infant deaths per 1000 births): 2001
M1
SRS
IMR (infant deaths per 1000 births): 2011
M2
SRS
Population and Births
Estimated population in 2016 assuming constant annual growth EP1
since 2001
Population in 2011
P2
P1*r2
P2
Projected births in 2016 assuming constant fertility
EB2
EP1*CBR1*1000
Actual births in 2011
B2
P2*CBR2*1000
Estimated Number of Maternal Deaths
No change in CBR and no change in IMR
D1
EB2*M1/1000
IMR declined but CBR not declined
D2
EB2*M2/1000
CBR declined but IMR not declined
D3
B2*M1/1000
Both CBR and IMR declined
D4
B2*M2/1000
Potential Number of Maternal Lives Saved in 2016 from:
Total effect of decline in IMR
Y
D1-D2
Total effect of fertility decline
X
D1-D3
Total effect of declines in both fertility and IMR
Z
D1-D4
Overlap between the effect of declines in fertility and IMR
XZ
Y+X-Z
Net effect of decline in IMR
Y-XZ
Net effect of fertility decline
X-XZ
Per cent Distribution of the Potential Number of Lives Saved in 2016
Effect of decline in IMR
[(Y-XZ)/Z] *100
Effect of decrease in births
[(X-XZ)/Z]*100
Effect of fertility reduction realised through its effect on IMR
reduction
(XZ/Z)*100
Estimates
57
69
1.81
1.39
31.1
26.2
80
52
79
69
2464053
1797320
1971.24
1281.31
1437.86
934.61
689.93
533.39
1036.64
186.69
503
347
48.5
33.4
18
Annexure A 99
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Table S2.9: Effect of Declines in IMR and Fertility on Maternal Deaths Averted in 2011, Madhya Pradesh
Decomposition Inputs
Population (Million): 2001
Symbol Source/
Estimation
P1
Census
Population (Million): 2011
P2
Census
Annual Population Growth Rate: 2001
r1
SRS
Annual Population Growth Rate: 2011
r2
SRS
Crude Birth Rate (births per 1000 population): 2001
CBR1 SRS
Crude Birth Rate (births per 1000 population): 2011
CBR2 SRS
IMR (infant deaths per 1000 births): 2001
M1
SRS
IMR (infant deaths per 1000 births): 2011
M2
SRS
Population and Births
Estimated population in 2016 assuming constant annual growth EP1
since 2001
Population in 2011
P2
P1*r2
P2
Projected births in 2016 assuming constant fertility
EB2
EP1*CBR1*1000
Actual births in 2011
B2
P2*CBR2*1000
Estimated Number of Maternal Deaths
No change in CBR and no change in IMR
D1
EB2*M1/1000
IMR declined but CBR not declined
D2
EB2*M2/1000
CBR declined but IMR not declined
D3
B2*M1/1000
Both CBR and IMR declined
D4
B2*M2/1000
Potential Number of Maternal Lives Saved in 2016 from:
Total effect of decline in IMR
Y
D1-D2
Total effect of fertility decline
X
D1-D3
Total effect of declines in both fertility and IMR
Z
D1-D4
Overlap between the effect of declines in fertility and IMR
XZ
Y+X-Z
Net effect of decline in IMR
Y-XZ
Net effect of fertility decline
X-XZ
Per cent Distribution of the Potential Number of Lives Saved in 2016
Effect of decline in IMR
[(Y-XZ)/Z] *100
Effect of decrease in births
[(X-XZ)/Z]*100
Effect of fertility reduction realised through its effect on IMR
reduction
(XZ/Z)*100
Estimates
60
73
2.01
1.81
31
26.9
86
59
109
73
3366600
1955630
2895.28
1986.29
1681.84
1153.82
908.98
1213.43
1741.45
380.96
528
832
30.3
47.8
21.9
100 Cost of Inaction in Family Planning in India
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Table S2.10: Effect of Declines in IMR and Fertility on Maternal Deaths Averted in 2011, Uttar Pradesh
Decomposition Inputs
Population (Million): 2001
Symbol Source/
Estimation
P1
Census
Population (Million): 2011
P2
Census
Annual Population Growth Rate: 2001
r1
SRS
Annual Population Growth Rate: 2011
r2
SRS
Crude Birth Rate (births per 1000 population): 2001
CBR1 SRS
Crude Birth Rate (births per 1000 population): 2011
CBR2 SRS
IMR (infant deaths per 1000 births): 2001
M1
SRS
IMR (infant deaths per 1000 births): 2011
M2
SRS
Population and Births
Estimated population in 2016 assuming constant annual growth EP1
since 2001
Population in 2011
P2
P1*r2
P2
Projected births in 2016 assuming constant fertility
EB2
EP1*CBR1*1000
Actual births in 2011
B2
P2*CBR2*1000
Estimated Number of Maternal Deaths
No change in CBR and no change in IMR
D1
EB2*M1/1000
IMR declined but CBR not declined
D2
EB2*M2/1000
CBR declined but IMR not declined
D3
B2*M1/1000
Both CBR and IMR declined
D4
B2*M2/1000
Potential Number of Maternal Lives Saved in 2016 from:
Total effect of decline in IMR
Y
D1-D2
Total effect of fertility decline
X
D1-D3
Total effect of declines in both fertility and IMR
Z
D1-D4
Overlap between the effect of declines in fertility and IMR
XZ
Y+X-Z
Net effect of decline in IMR
Y-XZ
Net effect of fertility decline
X-XZ
Per cent Distribution of the Potential Number of Lives Saved in 2016
Effect of decline in IMR
[(Y-XZ)/Z] *100
Effect of decrease in births
[(X-XZ)/Z]*100
Effect of fertility reduction realised through its effect on IMR
reduction
(XZ/Z)*100
Estimates
166
191
2
1.7
32.1
27.8
83
57
282
191
9058620
5307020
7518.65
5163.41
4404.83
3025
2355.24
3113.83
4493.65
975.42
1380
2138
30.7
47.6
21.7
Annexure A 101
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Figure S1. Details of Budget Expenditure, NHM Bihar 2012-13 to 2015-16
Budget for activities and schemes within National Health Mission Bihar
9,890
11,130
11,340
11,740
3,180
3,370
2,450
3,630
240 40
2012-13
250 50
2013-14
RCH
INFRA
480
0
2014-15
CD
690
110
2015-16
NCD
Note: RCH - Reproductive Child Health, IF - Infrastructure, CD - Communicable Disease, NCD - Non - Communicable Disease
Figure S2. Distribution of Expenditure, NHM Bihar 2012-13 to 2015-16
100%
80%
60%
40%
20%
0%
2012-13
2013-14
2014-15
2015-16
Bihar
Flexible Pool for Non
Communicable Disease
Programmes
Flexible Pool for
Communicable Disease
Control Programmes
Infrastructure Maintenance
NRHM-RCH Flexible Pool
102 Cost of Inaction in Family Planning in India
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Figure S3. Breakup of RCH Flexi-Pool Expenditure, NHM Bihar 2012-13 to 2015-16
Budget share for activities and schemes within National Health Mission Bihar
2012-13
2013-14
MF
29.4%
PPI
5. 6%
PPI
4.2%
MF
26.0%
RI 2.8%
RCH
62.2%
RI 4.3%
RCH
65.5%
2014-15
PPI
5.0%
MF
27.5%
RI 5.2%
RCH
62.3%
2015-16
MF
27.8%
NIDD
0.0%
PPI
3.6%
RI 5.1%
RCH
63.5%
Note: RCH - RCH Flexible Pool, MF - Mission Flexible Pool, RI - Routine Immunization, PPI - Pulse Polio Immunization and
NIDD - National I.D.D Control Programme
Annexure A 103
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Figure S4: Details of Budget Expenditure, NHM Madhya Pradesh 2012-13 to 2015-16
Budget for activities and schemes within National Health Mission Madhya Pradesh
15540
11520
12450
8270
3470
3890
402
3810
280 220
2012-13
370 60
2013-14
540
60
2014-15
500 260
2015-16
RCH
INFRA
CD
NCD
Note: RCH - Reproductive Child Health, IF - Infrastructure, CD - Communicable Disease, NCD - Non - Communicable Disease
Figure S5: Distribution of Expenditure, NHM Madhya Pradesh 2012-13 to 2015-16
100%
80%
60%
40%
20%
0%
2012-13
2013-14
2014-15
Madhya Pradesh
Flexible Pool for Non
Communicable Disease Programmes
Flexible Pool for Communicable
Disease Control Programmes
Infrastructure Maintenance
2015-16
NRHM-RCH Flexible Pool
104 Cost of Inaction in Family Planning in India
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Figure S6: RCH Flexi-Pool Expenditure, NHM Madhya Pradesh 2012-13 to 2015-16
Budget share for activities and schemes within National Health Mission Madhya Pradesh
2012-13
2013-14
PPI
2.6%
MF
35.8%
RI 5.3%
RCH
56.3%
PPI
1.2%
MF
42.8%
RCH
52.2%
RI 3.8%
MF
41.8%
2014-15
NIDD
0.0%
PPI
0.9%
RCH
54.3%
RI 3.0%
2015-16
NIDD
0.0%
PPI
0.9%
MF
47.3%
RCH
48.9%
RI 2.8%
Note: RCH - RCH Flexible Pool, MF - Mission Flexible Pool, RI - Routine Immunization, PPI - Pulse Polio Immunization and
NIDD - National I.D.D Control Programme
Annexure A 105
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Figure S7: Details of Budget Expenditure, NHM Rajasthan 2012-13 to 2015-16
Budget for activities and schemes within National Health Mission Rajasthan
7620
10170
12090
12030
3710
4050
4650
4550
390 100
2012-13
RCH
240 110
2013-14
INFRA
260 120
2014-15
CD
290 410
2015-16
NCD
Note: RCH - Reproductive Child Health, IF - Infrastructure, CD - Communicable Disease, NCD - Non - Communicable Disease
Figure S8: Distribution of Expenditure, NHM Rajasthan 2012-13 to 2015-16
100%
80%
60%
40%
20%
0%
2012-13
2013-14
2014-15
Rajasthan
Flexible Pool for Non
Communicable Disease Programmes
Flexible Pool for Communicable
Disease Control Programmes
Infrastructure Maintenance
2015-16
NRHM-RCH Flexible Pool
106 Cost of Inaction in Family Planning in India
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12.9 Page 119 |
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Figure S9: Breakup of RCH Flexi-Pool Expenditure, NHM Rajasthan 2012-13 to 2015-16
Budget share for activities and schemes within National Health Mission Rajasthan
2012-13
2013-14
PPI
2.8%
MF
36.6%
NIDD
0.0%
PPI
1.3%
RI 2.6%
RCH
58.0%
MF
51.1%
RCH
45.2%
RI 2.3%
MF
53.1%
2014-15
NIDD
0.0%
PPI
1.2%
RCH
43.4%
RI 2.3%
2015-16
NIDD
0.0%
PPI
0.5%
MF
56.6%
RCH
40.9%
RI 2.0%
Note: RCH - RCH Flexible Pool, MF - Mission Flexible Pool, RI - Routine Immunization, PPI - Pulse Polio Immunization and
NIDD - National I.D.D Control Programme
Annexure A 107
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Figure S10: Details of Budget Expenditure, NHM Uttar Pradesh 2012-13 to 2015-16
Budget for activities and schemes within National Health Mission Uttar Pradesh
26660
18480
13800
16240
12690
21340
13530
14810
190 160
310 0
1040 460
1330 350
2012-13
2013-14
2014-15
2015-16
RCH
INFRA
CD
NCD
Note: RCH - Reproductive Child Health, IF - Infrastructure, CD - Communicable Disease, NCD - Non - Communicable Disease
Figure S11: Distribution of Expenditure, NHM Uttar Pradesh 2012-13 to 2015-16
100%
80%
60%
40%
20%
0%
2012-13
2013-14
2014-15
Uttar Pradesh
Flexible Pool for Non
Communicable Disease Programmes
Flexible Pool for Communicable
Disease Control Programmes
Infrastructure Maintenance
2015-16
NRHM-RCH Flexible Pool
108 Cost of Inaction in Family Planning in India
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13.1 Page 121 |
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Figure S12: Breakup of RCH Flexi-Pool Expenditure, NHM Uttar Pradesh 2012-13 to 2015-16
Budget share for activities and schemes within National Health Mission Uttar Pradesh
2012-13
2013-14
PPI
9.2%
MF
36.2%
PPI
7.7%
MF
26.5%
RI 5.7%
RCH
48.9%
RI 6.9%
RCH
58.9%
MF
43.8%
2014-15
NIDD
0.0%
PPI
4.4%
RCH
47.4%
2015-16
MF
54.2%
NIDD
0.0%
PPI
3.9%
RCH
38.3%
RI 4.4%
RI 3.6%
Note: RCH - RCH Flexible Pool, MF - Mission Flexible Pool, RI - Routine Immunization, PPI - Pulse Polio Immunization and
NIDD - National I.D.D Control Programme
Annexure A 109
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Notes
110 Cost of Inaction in Family Planning in India
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13.3 Page 123 |
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Cost of Inaction in Family Planning in India
About PFI
Population Foundation of India is a national NGO which promotes and advocates
for the effective formulation and implementation of gender sensitive population,
health and development strategies & policies. The organisation was founded in
1970 by a group of socially committed industrialists under the leadership of the
late JRD Tata and Dr Bharat Ram. PFI addresses population issues within the
larger discourse of empowering women and men, so that they are able to take
informed decisions related to their fertility, health and well-being. It works with
the government, both at the national and state levels and with NGOs in the areas
of community action for health, urban health, scaling up of successful pilots and
social & behaviour change communication. PFI is guided by an eminent governing
board and advisory council comprising distinguished persons from civil society,
the government and the private sector.
Annexure A
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13.4 Page 124 |
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Population Foundation of India
Head Office: B-28, Qutab Institutional Area, New Delhi - 110016
T: +91-1 1- 43894100, F: +91-1 1- 43894199 I www.populationfoundation.in
PopFoundIndia
PFI3
popfoundind
112 Cost of Inaction in Family Planning in India