Abstract
This article tries to evaluate the feasibility of attaining the sustainable development goals (SDG) with respect to health and well-being in India at the sub-national level based on health indicators of Millennium Development Goals (MDGs). Using different rounds of National Family Health Survey (NFHS), the present paper finds wide range of -state variations of health indicators set for MDG in India; the national level performance of MDG target for health indicators is not so disappointing, but our sub-national level performance is not so encouraging. We evaluate the health status of the states over time by creating a health deprivation index (DIH) which is based on power mean formula from a set of health deprivation parameters. Results suggest that all the states have been experiencing a decline of health deprivation, but the pace of decline is not uniform; consequently, some major states are not performing well compared to national average. Panel data regression gives us that state-specific character does matter towards variations of DIH. General economic development measured by rising income along with human capital investment like expenditure on health and education plays a significant role in reducing DIH. The female literacy rate is found to be a profound effect in reducing DIH in all the regression models. Infrastructure stock index (II) appears to be in expected direction in the regression result, but it does not appear statistically significant; this may be due to exclusion of quality aspect of infrastructure. Low level public expenditure on health and education is a major constraint of our health progress; lifestyle diseases have been a major threat towards achieving SDG with respect to health and well-being in India.
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Notes
- 1.
The United Nations Development Group (UNDG) in 2003 has provided a framework of 18 targets and 53 indicators for measuring the progress towards individual targets. A revised indicator framework drawn up by the Inter-agency and Expert Group (IAEG) on MDGs came into effect in 2008 which is comprised of 8 goals, 21 targets and 60 indicators. However, India has not accepted this revised framework of MDG.
- 2.
Human poverty index (HPI) was developed by Sen and Anand (1997), and it was used by UNDP (1997). HPI has been considered a good index for capturing human deprivation, and P(α) does satisfy the following important properties. Here, Pi stands for deprivation parameter (i = 1, 2 and 3).
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1.
$$ \min \left\{{P}_1,{P}_2,{P}_3\right\}\le P\left(\alpha \right)\le \max \left\{{P}_1,{P}_2,{P}_3\right\} $$
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2.
As α → ∞, P(α) →  max {P1, P2, P3}
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3.
P(α) is homogeneous of degree 1 in {P1, P2, P3}
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4.
For each i = 1, 2, 3; \( \frac{\partial P\left(\alpha \right)}{\partial {P}_i}>0 \)
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5.
P(α) is convex with respect to Pi. For each i = 1, 2, 3; \( \frac{\partial^2P\left(\alpha \right)}{\partial {P}_i^2}>0 \)
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6.
For any i, \( \frac{\partial P\left(\alpha \right)}{\partial {W}_i}\ge 0 \)as Pi ≥ P(α), similarly,\( \frac{\partial P\left(\alpha \right)}{\partial {W}_i}\le 0 \) as Pi ≤ P(α)
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7.
For given P1, P2 and P3 that are not equal, if α > γ > 0, then P(α) > P(γ)
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8.
The HPI is not sub-group decomposable. For α ≥ 1, \( \sum \limits_{j=1}^m\frac{n_j}{n}{P}_j\left(\alpha \right)\ge P\left(\alpha \right) \) where nj be the population in the jth group, \( n=\sum \limits_{j=1}^m{n}_j \), Pj(α)be the HPI of jth group.
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9.
The elasticity of substitution (σ) between any two poverty sub-indices of P(α), that is, between any two of P1, P2 and P3, is constant and equal to \( \frac{1}{\alpha -1} \).
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1.
References
Anand S, Sen A (1997) Concepts of Human Development and Poverty: A Multidimensional Perspective, Background Papers for Human Development Report. New York, UNDP, New York
Bhattacharya G, Haldar SK (2015): Does demographic dividend yield economic dividend? India, a case study’’, Economics Bulletin, 2015, 35(2), 1274–1291
Goli S, Arokiasamy P (2013) Trends in health and health inequalities among major states of India: assessing progress through convergence models. Health Econ Policy Law 9(2):143–168
Hembram S, Haldar SK (2020): Is India experiencing health convergence? An empirical. Economic Change and Restructuring, https://doi.org/10.1007/s10644-020-09269-6analysis
Johnson RA, Wichern DW (2007) Applied multivariate statistical analysis, 6th edn. Pearson Prentice Hall, Upper Saddle River
National Health Policy (2017) Ministry of Health and Family Welfare. New Delhi, Government of India
Nunnally JC, Bernstein IH (1994) The assessment of reliability. Psychom Theory 3:248–292
Raychaudhuri, A. & S.K. Haldar (2009). An investigation into the inter-district disparity in West Bengal, 1991–2005. Economic and Political Weekly, 44(26 and 27), 258–63
The Ministry of Statistical & Program Implementation (2015) Government of India, New Delhi
United Nations Development Programme (1997) Human Development Report. Oxford University Press, New York
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Appendices
Appendix 1
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The six infrastructure variables are as follows: number of PHC, number of school (both primary and upper primary school), number of ANMs at PHC, number of doctors at PHC, road length and rail route.
The first PC captures the maximum variability of the data, and it is written as follows:
The second PC is written as follows:
The third PC is written as follows:
We run a paired sample t test between PC1 and the respective factor loadings of PC1 in order to find the significant factor loadings, and we consider only the first PC since it captures the maximum variance of data.
Here, the first PC captures more than 50% of variation; so there is no need to consider the second and third PC. Using those significant factors, loading one can estimate the infrastructure index. The first PC can be written as follows:
Appendix 2: Variables, Definition and Data Sources
Definitions | Abbreviation | Sources |
|---|---|---|
Per capita net state domestic products at constant price 2004–2005 | PCNSDP | RBI |
Per capita education expenditure | PCEE | RBI, NITI Aayog |
Per capita health expenditure | PCHE | RBI, NITI Aayog |
Female literacy rate | FLR | National Family Health Survey |
Number of primary health centre per 150,000 population | PHC | NITI Aayog, Rural Health Statistics 2014–2015 |
Number of primary school per 30,000 children age between 6 and 14 (primary and upper primary school) | School | DISE State Report |
Number of ANMs at PHC per 20,000 population | ANM | NITI Aayog, Rural Health Statistics 2014–2015 |
Number of doctors (working at PHC) per 20,000 population | Doctor | NITI Aayog, Rural Health Statistics 2014–2015 |
Road length per 1000 km2 | RL | RBI |
Rail route per 1000 km2 | RR | RBI |
Appendix 3
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Haldar, S.K., Hembram, S. (2020). Health Progress in India with Respect to Millennium Development Goals: Are Health Targets of SDGs Achievable? An Empirical Study at Sub-National Level. In: Hazra, S., Bhukta, A. (eds) Sustainable Development Goals. Sustainable Development Goals Series. Springer, Cham. https://doi.org/10.1007/978-3-030-42488-6_3
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