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Estimation and uncertainty quantification for the output from quantum simulators
Levels and trends in the sex ratio at birth and missing female births for 29 states and union territories in India 1990–2016: A Bayesian modeling study
1. | Institute of Policy Studies, Lee Kuan Yew School of Public Policy, National University of Singapore, 1C Cluny Road, House 5259599, Singapore |
2. | International Institute for Population Sciences, Govandi Station Road, Mumbai, Maharashtra 400088, India |
The sex ratio at birth (SRB) has risen in India and reaches well beyond the levels under normal circumstances since the 1970s. The lasting imbalanced SRB has resulted in much more males than females in India. A population with severely distorted sex ratio is more likely to have prolonged struggle for stability and sustainability. It is crucial to estimate SRB and its imbalance for India on state level and assess the uncertainty around estimates. We develop a Bayesian model to estimate SRB in India from 1990 to 2016 for 29 states and union territories. Our analyses are based on a comprehensive database on state-level SRB with data from the sample registration system, census and Demographic and Health Surveys. The SRB varies greatly across Indian states and union territories in 2016: ranging from 1.026 (95% uncertainty interval [0.971; 1.087]) in Mizoram to 1.181 [1.143; 1.128] in Haryana. We identify 18 states and union territories with imbalanced SRB during 1990–2016, resulting in 14.9 [13.2; 16.5] million of missing female births in India. Uttar Pradesh has the largest share of the missing female births among all states and union territories, taking up to 32.8% [29.5%; 36.3%] of the total number.
References:
[1] |
L. Alkema, F. Chao, D. You, P. Jon and C. C. Sawyer,
Sawyer, National, regional, and global sex ratios of infant, child, and under-5 mortality and identification of countries with outlying ratios: a systematic assessment, The Lancet Global Health, 2 (2014), e521-e530.
doi: 10.1016/S2214-109X(14)70280-3. |
[2] |
L. Alkema and J. New,
Global estimation of child mortality using a Bayesian B-spline bias-reduction model, The Annals of Applied Statistics, 8 (2014), 2122-2149.
doi: 10.1214/14-AOAS768. |
[3] |
G. N. Allahbadia, The 50 million missing women, Journal of Assisted Reproduction and Genetics, 19 (2002), 411-416. Google Scholar |
[4] |
G. Aravamudan, Disappearing Daughters: The Tragedy of Female Foeticide, Penguin Books, New Delhi, 2007. Google Scholar |
[5] |
I. Attané and C. Z. Guilmoto, Watering the Neighbour's Garden: The Growing Demographic Female Deficit in Asia, Committee for International Cooperation in National Research in Demography, Paris, 2007. Google Scholar |
[6] |
J. Banister,
Shortage of girls in China today, Journal of Population Research, 21 (2004), 19-45.
doi: 10.1007/BF03032209. |
[7] |
S. Basten and G. Verropoulou,
"Maternity migration" and the increased sex ratio at birth in Hong Kong SAR, Population Studies, 67 (2013), 323-334.
doi: 10.1080/00324728.2013.826372. |
[8] |
J. Bongaarts,
The implementation of preferences for male offspring, Population and Development Review, 39 (2013), 185-208.
doi: 10.1111/j.1728-4457.2013.00588.x. |
[9] |
J. Bongaarts and C. Z. Guilmoto,
How many more missing women? Excess female mortality and prenatal sex selection, 1970–2050, Population and Development Review, 41 (2015), 241-269.
doi: 10.1111/j.1728-4457.2015.00046.x. |
[10] |
Y. Cai and W. Lavely, China's missing girls: Numerical estimates and effects on population growth, China Review, 3 (2003), 13-29. Google Scholar |
[11] |
A. Chahnazarian,
Determinants of the sex ratio at birth: Review of recent literature, Biodemography and Social Biology, 35 (1988), 214-235.
doi: 10.1080/19485565.1988.9988703. |
[12] |
F. Chao, P. Gerland, A. R. Cook and L. Alkema,
Systematic assessment of the sex ratio at birth for all countries and estimation of national imbalances and regional reference levels, Proceedings of the National Academy of Sciences, 116 (2019), 9303-9311.
doi: 10.1073/pnas.1812593116. |
[13] |
F. Chao, D. You, P. Jon, L. Hug and L. Alkema,
National and regional under-5 mortality rate by economic status for low-income and middle-income countries: a systematic assessment, The Lancet Global Health, 6 (2018), e535-e547.
doi: 10.1016/S2214-109X(18)30059-7. |
[14] |
M. Das Gupta, Z. Jiang, B. Li, Z. Xie, W. Chung and H. Bae,
Why is son preference so persistent in East and South Asia? A cross-country study of China, India and the Republic of Korea, The Journal of Development Studies, 40 (2003), 153-187.
doi: 10.1596/1813-9450-2942. |
[15] |
G. Duthé, F. Meslé and J. Vallin,
High sex ratios at birth in the Caucasus: Modern technology to satisfy old desires, Population and Development Review, 38 (2012), 487-501.
doi: 10.1111/j.1728-4457.2012.00513.x. |
[16] |
B. Efron, The Jackknife, the Bootstrap, and other Resampling Plans, Society for Industrial and Applied Mathematics, Philadelphia, PA, 1982. |
[17] |
M. D. Frost, M. Puri and P. R. A. Hinde,
Falling sex ratios and emerging evidence of sex-selective abortion in Nepal: evidence from nationally representative survey data, BMJ open, 3 (2013), e002612.
doi: 10.1136/bmjopen-2013-002612. |
[18] |
A. Gelman and D. B. Rubin,
Inference from iterative simulation using multiple sequences, Statistical Science, 7 (1992), 457-472.
doi: 10.1214/ss/1177011136. |
[19] |
S. M. George,
Millions of missing girls: From fetal sexing to high technology sex selection in India, Prenatal Diagnosis, 26 (2006), 604-609.
doi: 10.1002/pd.1475. |
[20] |
S. M. George,
Sex selection/determination in India: Contemporary developments, Reproductive Health Matters, 10 (2002), 190-192.
doi: 10.1016/S0968-8080(02)00034-4. |
[21] |
D. Goodkind, Sex-selective Abortion, Reproductive Rights, and the Greater Locus of Gender Discrimination in Family Formation: Cairo's Unresolved Questions, University of Michigan, Population Studies Center, 1997. Google Scholar |
[22] |
D. Goodkind,
Child underreporting, fertility, and sex ratio imbalance in China, Demography, 48 (2011), 291-316.
doi: 10.1007/s13524-010-0007-y. |
[23] |
C. Z. Guilmoto, Sex Imbalances at Birth: Current Trends, Consequences and Policy Implications, UNFPA Asia and Pacific Regional Office, Bangkok, Thailand, 2012. Google Scholar |
[24] |
C. Z. Guilmoto,
The sex ratio transition in Asia, Population and Development Review, 35 (2009), 519-549.
doi: 10.1111/j.1728-4457.2009.00295.x. |
[25] |
C. Z. Guilmoto,
Skewed sex ratios at birth and future marriage squeeze in China and India, 2005–2100, Demography, 49 (2012), 77-100.
doi: 10.1007/s13524-011-0083-7. |
[26] |
C. Z. Guilmoto,
Son preference, sex selection, and kinship in Vietnam, Population and Development Review, 38 (2012), 31-54.
doi: 10.1111/j.1728-4457.2012.00471.x. |
[27] |
C. Z. Guilmoto, X. Hoàng and T. N. Van,
Recent increase in sex ratio at birth in Viet Nam, PLoS One, 4 (2009), e4624.
doi: 10.1371/journal.pone.0004624. |
[28] |
C. Z. Guilmoto and Q. Ren,
Socio-economic differentials in Birth Masculinity in China, Development and Change, 42 (2011), 1269-1296.
doi: 10.1111/j.1467-7660.2011.01733.x. |
[29] |
T. Hesketh and J. Min,
The effects of artificial gender imbalance: Science & Society Series on Sex and Science, EMBO reports, 13 (2012), 487-492.
doi: 10.1038/embor.2012.62. |
[30] | V. M. Hudson and A. M. Den Boer, Bare Branches: The Security Implications of Asia's Surplus Male Population, MIT Press, Cambridge, Mass, 2004. Google Scholar |
[31] |
P. Jha, M. A. Kesler, R. Kumar, F. Ram, U. Ram, L. Aleksandrowicz, D. G. Bassani, S. Chandra and J. K. Banthia,
Trends in selective abortions of girls in India: analysis of nationally representative birth histories from 1990 to 2005 and census data from 1991 to 2011, The Lancet, 377 (2011), 1921-1928.
doi: 10.1016/S0140-6736(11)60649-1. |
[32] |
P. Jha, R. Kumar, P. Vasa, N. Dhingra, D. Thiruchelvam and R. Moineddin,
Low male-to-female sex ratio of children born in India: national survey of 1$\cdot$1 million households, The Lancet, 367 (2006), 211-218.
doi: 10.1016/S0140-6736(06)67930-0. |
[33] |
R. Kaur, S. S. Bhalla, M. K. Agarwal and P. Ramakrishnan, Sex Ratio at Birth – The Role of Gender, Class and Education, United Nations Population Fund, New Delhi, 2017. Google Scholar |
[34] |
Lancet India Correspondent, Misuse of amniocentesis, The Lancet, 321 (1983), 812-813. Google Scholar |
[35] |
S. Li, Imbalanced sex ratio at birth and comprehensive intervention in China, in 4th Asia Pacific Conference on Reproductive and Sexual Health and Rights (Hyderabad, India, Oct 29-31, 2007), United Nations Population Fund, 2007. Google Scholar |
[36] |
T. Lin,
The decline of son preference and rise of gender indifference in Taiwan since 1990, Demographic Research, 20 (2009), 377-402.
doi: 10.4054/DemRes.2009.20.16. |
[37] |
K. Madan and M. H. Breuning,
Impact of prenatal technologies on the sex ratio in India: An overview, Genetics in Medicine, 16 (2014), 425-432.
doi: 10.1038/gim.2013.172. |
[38] |
F. Meslé, J. Vallin and I. Badurashvili, A sharp increase in sex ratio at birth in the Caucasus. Why? How?, in Watering the neighbour's garden: The growing demographic female deficit in Asia (eds. I. Attané and C. Z. Guilmoto), Committee for International Cooperation in National Research in Demography, (2007), 73–88. Google Scholar |
[39] |
N. Oomman and B. R. Ganatra,
Sex selection: The systematic elimination of girls, Reproductive Health Matters, 10 (2002), 184-188.
doi: 10.1016/S0968-8080(02)00029-0. |
[40] |
C. B. Park and N. H. Cho,
Consequences of son preference in a low-fertility society: Imbalance of the sex ratio at birth in Korea, Population and Development Review, 21 (1995), 59-84.
doi: 10.2307/2137413. |
[41] |
M. Plummer, Rjags: Bayesian Graphical Models Using MCMC, 2011. Available from: http://CRAN.R-project.org/package=rjags. Google Scholar |
[42] |
M. Plummer, JAGS: A program for analysis of bayesian graphical models using gibbs sampling, in Proceedings of the 3rd International Workshop on Distributed Statistical Computing, Vienna, Austria, (2003)Available from: http://mcmc-jags.sourceforge.net/. Google Scholar |
[43] |
M. Plummer, N. Best, K. Cowles and K. Vines, CODA: Convergence Diagnosis and Output Analysis for MCMC, R News, 6 (2006), 7-11. Available from: https://cran.r-project.org/package=coda. Google Scholar |
[44] |
R. Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2018. Available from: https://www.R-project.org/. Google Scholar |
[45] |
T. K. Roy and A. Chattopadhyay,
Daughter discrimination and future sex ratio at birth in India, Asian Population Studies, 8 (2012), 281-299.
doi: 10.1080/17441730.2012.714669. |
[46] |
K. C. Samir, M. Wurzer, M. Speringer and W. Lutz,
Future population and human capital in heterogeneous India, Proceedings of the National Academy of Sciences, 115 (2018), 8328-8333.
doi: 10.1073/pnas.1722359115. |
[47] |
A. Sen,
Missing women, British Medical Journal, 304 (1992), 587-588.
doi: 10.1136/bmj.304.6827.587. |
[48] |
B. R. Sharma, N. Gupta and N. Relhan,
Misuse of prenatal diagnostic technology for sex-selected abortions and its consequences in India, Public Health, 121 (2007), 854-860.
doi: 10.1016/j.puhe.2007.03.004. |
[49] |
O. P. Sharma and C. Haub, Sex ratio at birth begins to improve in India, Population Reference Bureau, 2008. Available from: http://www.prb.org/Publications/Articles/2008/indiasexratio.aspx. Google Scholar |
[50] |
Y. Su and M. Yajima, R2jags: A Package for Running Jags from R, 2015. Available from: http://CRAN.R-project.org/package=R2jags. Google Scholar |
[51] |
S. L. Tandon and R. Sharma, Female foeticide and infanticide in India: an analysis of crimes against girl children, International Journal of Criminal Justice Sciences, 1 (2006), 1-10. Google Scholar |
show all references
References:
[1] |
L. Alkema, F. Chao, D. You, P. Jon and C. C. Sawyer,
Sawyer, National, regional, and global sex ratios of infant, child, and under-5 mortality and identification of countries with outlying ratios: a systematic assessment, The Lancet Global Health, 2 (2014), e521-e530.
doi: 10.1016/S2214-109X(14)70280-3. |
[2] |
L. Alkema and J. New,
Global estimation of child mortality using a Bayesian B-spline bias-reduction model, The Annals of Applied Statistics, 8 (2014), 2122-2149.
doi: 10.1214/14-AOAS768. |
[3] |
G. N. Allahbadia, The 50 million missing women, Journal of Assisted Reproduction and Genetics, 19 (2002), 411-416. Google Scholar |
[4] |
G. Aravamudan, Disappearing Daughters: The Tragedy of Female Foeticide, Penguin Books, New Delhi, 2007. Google Scholar |
[5] |
I. Attané and C. Z. Guilmoto, Watering the Neighbour's Garden: The Growing Demographic Female Deficit in Asia, Committee for International Cooperation in National Research in Demography, Paris, 2007. Google Scholar |
[6] |
J. Banister,
Shortage of girls in China today, Journal of Population Research, 21 (2004), 19-45.
doi: 10.1007/BF03032209. |
[7] |
S. Basten and G. Verropoulou,
"Maternity migration" and the increased sex ratio at birth in Hong Kong SAR, Population Studies, 67 (2013), 323-334.
doi: 10.1080/00324728.2013.826372. |
[8] |
J. Bongaarts,
The implementation of preferences for male offspring, Population and Development Review, 39 (2013), 185-208.
doi: 10.1111/j.1728-4457.2013.00588.x. |
[9] |
J. Bongaarts and C. Z. Guilmoto,
How many more missing women? Excess female mortality and prenatal sex selection, 1970–2050, Population and Development Review, 41 (2015), 241-269.
doi: 10.1111/j.1728-4457.2015.00046.x. |
[10] |
Y. Cai and W. Lavely, China's missing girls: Numerical estimates and effects on population growth, China Review, 3 (2003), 13-29. Google Scholar |
[11] |
A. Chahnazarian,
Determinants of the sex ratio at birth: Review of recent literature, Biodemography and Social Biology, 35 (1988), 214-235.
doi: 10.1080/19485565.1988.9988703. |
[12] |
F. Chao, P. Gerland, A. R. Cook and L. Alkema,
Systematic assessment of the sex ratio at birth for all countries and estimation of national imbalances and regional reference levels, Proceedings of the National Academy of Sciences, 116 (2019), 9303-9311.
doi: 10.1073/pnas.1812593116. |
[13] |
F. Chao, D. You, P. Jon, L. Hug and L. Alkema,
National and regional under-5 mortality rate by economic status for low-income and middle-income countries: a systematic assessment, The Lancet Global Health, 6 (2018), e535-e547.
doi: 10.1016/S2214-109X(18)30059-7. |
[14] |
M. Das Gupta, Z. Jiang, B. Li, Z. Xie, W. Chung and H. Bae,
Why is son preference so persistent in East and South Asia? A cross-country study of China, India and the Republic of Korea, The Journal of Development Studies, 40 (2003), 153-187.
doi: 10.1596/1813-9450-2942. |
[15] |
G. Duthé, F. Meslé and J. Vallin,
High sex ratios at birth in the Caucasus: Modern technology to satisfy old desires, Population and Development Review, 38 (2012), 487-501.
doi: 10.1111/j.1728-4457.2012.00513.x. |
[16] |
B. Efron, The Jackknife, the Bootstrap, and other Resampling Plans, Society for Industrial and Applied Mathematics, Philadelphia, PA, 1982. |
[17] |
M. D. Frost, M. Puri and P. R. A. Hinde,
Falling sex ratios and emerging evidence of sex-selective abortion in Nepal: evidence from nationally representative survey data, BMJ open, 3 (2013), e002612.
doi: 10.1136/bmjopen-2013-002612. |
[18] |
A. Gelman and D. B. Rubin,
Inference from iterative simulation using multiple sequences, Statistical Science, 7 (1992), 457-472.
doi: 10.1214/ss/1177011136. |
[19] |
S. M. George,
Millions of missing girls: From fetal sexing to high technology sex selection in India, Prenatal Diagnosis, 26 (2006), 604-609.
doi: 10.1002/pd.1475. |
[20] |
S. M. George,
Sex selection/determination in India: Contemporary developments, Reproductive Health Matters, 10 (2002), 190-192.
doi: 10.1016/S0968-8080(02)00034-4. |
[21] |
D. Goodkind, Sex-selective Abortion, Reproductive Rights, and the Greater Locus of Gender Discrimination in Family Formation: Cairo's Unresolved Questions, University of Michigan, Population Studies Center, 1997. Google Scholar |
[22] |
D. Goodkind,
Child underreporting, fertility, and sex ratio imbalance in China, Demography, 48 (2011), 291-316.
doi: 10.1007/s13524-010-0007-y. |
[23] |
C. Z. Guilmoto, Sex Imbalances at Birth: Current Trends, Consequences and Policy Implications, UNFPA Asia and Pacific Regional Office, Bangkok, Thailand, 2012. Google Scholar |
[24] |
C. Z. Guilmoto,
The sex ratio transition in Asia, Population and Development Review, 35 (2009), 519-549.
doi: 10.1111/j.1728-4457.2009.00295.x. |
[25] |
C. Z. Guilmoto,
Skewed sex ratios at birth and future marriage squeeze in China and India, 2005–2100, Demography, 49 (2012), 77-100.
doi: 10.1007/s13524-011-0083-7. |
[26] |
C. Z. Guilmoto,
Son preference, sex selection, and kinship in Vietnam, Population and Development Review, 38 (2012), 31-54.
doi: 10.1111/j.1728-4457.2012.00471.x. |
[27] |
C. Z. Guilmoto, X. Hoàng and T. N. Van,
Recent increase in sex ratio at birth in Viet Nam, PLoS One, 4 (2009), e4624.
doi: 10.1371/journal.pone.0004624. |
[28] |
C. Z. Guilmoto and Q. Ren,
Socio-economic differentials in Birth Masculinity in China, Development and Change, 42 (2011), 1269-1296.
doi: 10.1111/j.1467-7660.2011.01733.x. |
[29] |
T. Hesketh and J. Min,
The effects of artificial gender imbalance: Science & Society Series on Sex and Science, EMBO reports, 13 (2012), 487-492.
doi: 10.1038/embor.2012.62. |
[30] | V. M. Hudson and A. M. Den Boer, Bare Branches: The Security Implications of Asia's Surplus Male Population, MIT Press, Cambridge, Mass, 2004. Google Scholar |
[31] |
P. Jha, M. A. Kesler, R. Kumar, F. Ram, U. Ram, L. Aleksandrowicz, D. G. Bassani, S. Chandra and J. K. Banthia,
Trends in selective abortions of girls in India: analysis of nationally representative birth histories from 1990 to 2005 and census data from 1991 to 2011, The Lancet, 377 (2011), 1921-1928.
doi: 10.1016/S0140-6736(11)60649-1. |
[32] |
P. Jha, R. Kumar, P. Vasa, N. Dhingra, D. Thiruchelvam and R. Moineddin,
Low male-to-female sex ratio of children born in India: national survey of 1$\cdot$1 million households, The Lancet, 367 (2006), 211-218.
doi: 10.1016/S0140-6736(06)67930-0. |
[33] |
R. Kaur, S. S. Bhalla, M. K. Agarwal and P. Ramakrishnan, Sex Ratio at Birth – The Role of Gender, Class and Education, United Nations Population Fund, New Delhi, 2017. Google Scholar |
[34] |
Lancet India Correspondent, Misuse of amniocentesis, The Lancet, 321 (1983), 812-813. Google Scholar |
[35] |
S. Li, Imbalanced sex ratio at birth and comprehensive intervention in China, in 4th Asia Pacific Conference on Reproductive and Sexual Health and Rights (Hyderabad, India, Oct 29-31, 2007), United Nations Population Fund, 2007. Google Scholar |
[36] |
T. Lin,
The decline of son preference and rise of gender indifference in Taiwan since 1990, Demographic Research, 20 (2009), 377-402.
doi: 10.4054/DemRes.2009.20.16. |
[37] |
K. Madan and M. H. Breuning,
Impact of prenatal technologies on the sex ratio in India: An overview, Genetics in Medicine, 16 (2014), 425-432.
doi: 10.1038/gim.2013.172. |
[38] |
F. Meslé, J. Vallin and I. Badurashvili, A sharp increase in sex ratio at birth in the Caucasus. Why? How?, in Watering the neighbour's garden: The growing demographic female deficit in Asia (eds. I. Attané and C. Z. Guilmoto), Committee for International Cooperation in National Research in Demography, (2007), 73–88. Google Scholar |
[39] |
N. Oomman and B. R. Ganatra,
Sex selection: The systematic elimination of girls, Reproductive Health Matters, 10 (2002), 184-188.
doi: 10.1016/S0968-8080(02)00029-0. |
[40] |
C. B. Park and N. H. Cho,
Consequences of son preference in a low-fertility society: Imbalance of the sex ratio at birth in Korea, Population and Development Review, 21 (1995), 59-84.
doi: 10.2307/2137413. |
[41] |
M. Plummer, Rjags: Bayesian Graphical Models Using MCMC, 2011. Available from: http://CRAN.R-project.org/package=rjags. Google Scholar |
[42] |
M. Plummer, JAGS: A program for analysis of bayesian graphical models using gibbs sampling, in Proceedings of the 3rd International Workshop on Distributed Statistical Computing, Vienna, Austria, (2003)Available from: http://mcmc-jags.sourceforge.net/. Google Scholar |
[43] |
M. Plummer, N. Best, K. Cowles and K. Vines, CODA: Convergence Diagnosis and Output Analysis for MCMC, R News, 6 (2006), 7-11. Available from: https://cran.r-project.org/package=coda. Google Scholar |
[44] |
R. Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2018. Available from: https://www.R-project.org/. Google Scholar |
[45] |
T. K. Roy and A. Chattopadhyay,
Daughter discrimination and future sex ratio at birth in India, Asian Population Studies, 8 (2012), 281-299.
doi: 10.1080/17441730.2012.714669. |
[46] |
K. C. Samir, M. Wurzer, M. Speringer and W. Lutz,
Future population and human capital in heterogeneous India, Proceedings of the National Academy of Sciences, 115 (2018), 8328-8333.
doi: 10.1073/pnas.1722359115. |
[47] |
A. Sen,
Missing women, British Medical Journal, 304 (1992), 587-588.
doi: 10.1136/bmj.304.6827.587. |
[48] |
B. R. Sharma, N. Gupta and N. Relhan,
Misuse of prenatal diagnostic technology for sex-selected abortions and its consequences in India, Public Health, 121 (2007), 854-860.
doi: 10.1016/j.puhe.2007.03.004. |
[49] |
O. P. Sharma and C. Haub, Sex ratio at birth begins to improve in India, Population Reference Bureau, 2008. Available from: http://www.prb.org/Publications/Articles/2008/indiasexratio.aspx. Google Scholar |
[50] |
Y. Su and M. Yajima, R2jags: A Package for Running Jags from R, 2015. Available from: http://CRAN.R-project.org/package=R2jags. Google Scholar |
[51] |
S. L. Tandon and R. Sharma, Female foeticide and infanticide in India: an analysis of crimes against girl children, International Journal of Criminal Justice Sciences, 1 (2006), 1-10. Google Scholar |


Data Source Type | Series Name (Series Period) | Total # Obs. (Range # Obs. per state/union territory) |
Census | Census (2011) | 29 (1–1) |
DHS | 598 (4–55) | |
DHS (1992–1993) | 101 (1–11) | |
DHS (1998–1999) | 107 (1–12) | |
DHS (2005–2006) | 100 (1–11) | |
DHS (2015–2016) | 290 (1–21) | |
SRS | SRS | 310 (3–17) |
Total | 937 (5–73) |
Data Source Type | Series Name (Series Period) | Total # Obs. (Range # Obs. per state/union territory) |
Census | Census (2011) | 29 (1–1) |
DHS | 598 (4–55) | |
DHS (1992–1993) | 101 (1–11) | |
DHS (1998–1999) | 107 (1–12) | |
DHS (2005–2006) | 100 (1–11) | |
DHS (2015–2016) | 290 (1–21) | |
SRS | SRS | 310 (3–17) |
Total | 937 (5–73) |
State/Union Territory | SRB | Maximum SRB | |||
1990 | 2016 | change 1990–2016 | Year | Value | |
Andhra Pradesh§ | 1.054[1.013; 1.095] | 1.089[1.056; 1.123] | 0.035[-0.018; 0.087] | 2015 | 1.089[1.061; 1.117] |
Arunachal Pradesh | 1.058[0.987; 1.131] | 1.047[0.991; 1.107] | -0.010[-0.087; 0.063] | ||
Assam§ | 1.063[1.018; 1.107] | 1.089[1.057; 1.124] | 0.027[-0.029; 0.083] | 2015 | 1.090[1.061; 1.121] |
Bihar§ | 1.074[1.036; 1.112] | 1.098[1.068; 1.128] | 0.023[-0.024; 0.072] | 2002 | 1.134[1.116; 1.153] |
Chhattisgarh | 1.038[0.985; 1.094] | 1.035[1.001; 1.070] | -0.003[-0.067; 0.060] | ||
Delhi¶§ | 1.148[1.084; 1.215] | 1.142[1.101; 1.184] | -0.006[-0.082; 0.067] | 2003 | 1.160[1.125; 1.197] |
Goa | 1.065[0.993; 1.139] | 1.068[1.008; 1.130] | 0.003[-0.075; 0.077] | ||
Gujarat¶§ | 1.114[1.068; 1.159] | 1.140[1.108; 1.173] | 0.026[-0.029; 0.083] | 2000 | 1.161[1.136; 1.186] |
Haryana¶§ | 1.181[1.130; 1.233] | 1.181[1.143; 1.217] | -0.000[-0.064; 0.063] | 2000 | 1.226[1.199; 1.255] |
Himachal Pradesh¶ | 1.116[1.062; 1.173] | 1.087[1.048; 1.128] | -0.029[-0.097; 0.037] | 2001 | 1.137[1.104; 1.172] |
Jammu and Kashmir¶§ | 1.150[1.087; 1.215] | 1.128[1.091; 1.166] | -0.021[-0.096; 0.049] | 2003 | 1.171[1.137; 1.207] |
Jharkhand§ | 1.094[1.037; 1.152] | 1.092[1.057; 1.127] | -0.002[-0.070; 0.063] | 1999 | 1.101[1.061; 1.142] |
Karnataka | 1.059[1.020; 1.101] | 1.061[1.033; 1.091] | 0.002[-0.049; 0.052] | 2004 | 1.075[1.056; 1.095] |
Kerala | 1.062[1.018; 1.106] | 1.038[1.006; 1.069] | -0.025[-0.080; 0.029] | 2002 | 1.085[1.063; 1.109] |
Madhya Pradesh§ | 1.086[1.047; 1.127] | 1.086[1.056; 1.115] | -0.001[-0.051; 0.047] | 1999 | 1.092[1.071; 1.115] |
Maharashtra | 1.079[1.037; 1.122] | 1.103[1.053; 1.154] | 0.024[-0.040; 0.087] | 2011 | 1.111[1.073; 1.150] |
Manipur | 1.056[0.989; 1.132] | 1.062[1.006; 1.121] | 0.005[-0.070; 0.080] | ||
Meghalaya | 1.053[0.986; 1.123] | 1.043[0.988; 1.100] | -0.009[-0.084; 0.063] | ||
Mizoram | 1.032[0.963; 1.107] | 1.026[0.971; 1.087] | -0.005[-0.080; 0.067] | ||
Nagaland | 1.054[0.986; 1.125] | 1.051[0.994; 1.110] | -0.003[-0.080; 0.070] | ||
Orissa | 1.072[1.031; 1.115] | 1.058[1.028; 1.089] | -0.014[-0.066; 0.037] | ||
Punjab¶§ | 1.210[1.157; 1.264] | 1.156[1.115; 1.194] | -0.054[-0.119; 0.008] | 2000 | 1.250[1.218; 1.282] |
Rajasthan¶§ | 1.133[1.092; 1.175] | 1.140[1.108; 1.173] | 0.007[-0.044; 0.058] | 2004 | 1.161[1.141; 1.182] |
Sikkim | 1.050[0.971; 1.132] | 1.048[0.984; 1.117] | -0.001[-0.079; 0.072] | ||
Tamil Nadu | 1.056[1.014; 1.096] | 1.083[1.052; 1.114] | 0.027[-0.024; 0.080] | 2015 | 1.083[1.058; 1.109] |
Tripura | 1.053[0.987; 1.122] | 1.046[0.991; 1.105] | -0.007[-0.081; 0.068] | ||
Uttar Pradesh¶§ | 1.105[1.073; 1.138] | 1.131[1.104; 1.160] | 0.026[-0.016; 0.069] | 2002 | 1.152[1.136; 1.169] |
Uttarakhand§ | 1.118[1.047; 1.190] | 1.152[1.116; 1.189] | 0.034[-0.043; 0.109] | 2015 | 1.154[1.123; 1.186] |
West Bengal | 1.052[1.013; 1.091] | 1.059[1.030; 1.088] | 0.007[-0.042; 0.056] |
State/Union Territory | SRB | Maximum SRB | |||
1990 | 2016 | change 1990–2016 | Year | Value | |
Andhra Pradesh§ | 1.054[1.013; 1.095] | 1.089[1.056; 1.123] | 0.035[-0.018; 0.087] | 2015 | 1.089[1.061; 1.117] |
Arunachal Pradesh | 1.058[0.987; 1.131] | 1.047[0.991; 1.107] | -0.010[-0.087; 0.063] | ||
Assam§ | 1.063[1.018; 1.107] | 1.089[1.057; 1.124] | 0.027[-0.029; 0.083] | 2015 | 1.090[1.061; 1.121] |
Bihar§ | 1.074[1.036; 1.112] | 1.098[1.068; 1.128] | 0.023[-0.024; 0.072] | 2002 | 1.134[1.116; 1.153] |
Chhattisgarh | 1.038[0.985; 1.094] | 1.035[1.001; 1.070] | -0.003[-0.067; 0.060] | ||
Delhi¶§ | 1.148[1.084; 1.215] | 1.142[1.101; 1.184] | -0.006[-0.082; 0.067] | 2003 | 1.160[1.125; 1.197] |
Goa | 1.065[0.993; 1.139] | 1.068[1.008; 1.130] | 0.003[-0.075; 0.077] | ||
Gujarat¶§ | 1.114[1.068; 1.159] | 1.140[1.108; 1.173] | 0.026[-0.029; 0.083] | 2000 | 1.161[1.136; 1.186] |
Haryana¶§ | 1.181[1.130; 1.233] | 1.181[1.143; 1.217] | -0.000[-0.064; 0.063] | 2000 | 1.226[1.199; 1.255] |
Himachal Pradesh¶ | 1.116[1.062; 1.173] | 1.087[1.048; 1.128] | -0.029[-0.097; 0.037] | 2001 | 1.137[1.104; 1.172] |
Jammu and Kashmir¶§ | 1.150[1.087; 1.215] | 1.128[1.091; 1.166] | -0.021[-0.096; 0.049] | 2003 | 1.171[1.137; 1.207] |
Jharkhand§ | 1.094[1.037; 1.152] | 1.092[1.057; 1.127] | -0.002[-0.070; 0.063] | 1999 | 1.101[1.061; 1.142] |
Karnataka | 1.059[1.020; 1.101] | 1.061[1.033; 1.091] | 0.002[-0.049; 0.052] | 2004 | 1.075[1.056; 1.095] |
Kerala | 1.062[1.018; 1.106] | 1.038[1.006; 1.069] | -0.025[-0.080; 0.029] | 2002 | 1.085[1.063; 1.109] |
Madhya Pradesh§ | 1.086[1.047; 1.127] | 1.086[1.056; 1.115] | -0.001[-0.051; 0.047] | 1999 | 1.092[1.071; 1.115] |
Maharashtra | 1.079[1.037; 1.122] | 1.103[1.053; 1.154] | 0.024[-0.040; 0.087] | 2011 | 1.111[1.073; 1.150] |
Manipur | 1.056[0.989; 1.132] | 1.062[1.006; 1.121] | 0.005[-0.070; 0.080] | ||
Meghalaya | 1.053[0.986; 1.123] | 1.043[0.988; 1.100] | -0.009[-0.084; 0.063] | ||
Mizoram | 1.032[0.963; 1.107] | 1.026[0.971; 1.087] | -0.005[-0.080; 0.067] | ||
Nagaland | 1.054[0.986; 1.125] | 1.051[0.994; 1.110] | -0.003[-0.080; 0.070] | ||
Orissa | 1.072[1.031; 1.115] | 1.058[1.028; 1.089] | -0.014[-0.066; 0.037] | ||
Punjab¶§ | 1.210[1.157; 1.264] | 1.156[1.115; 1.194] | -0.054[-0.119; 0.008] | 2000 | 1.250[1.218; 1.282] |
Rajasthan¶§ | 1.133[1.092; 1.175] | 1.140[1.108; 1.173] | 0.007[-0.044; 0.058] | 2004 | 1.161[1.141; 1.182] |
Sikkim | 1.050[0.971; 1.132] | 1.048[0.984; 1.117] | -0.001[-0.079; 0.072] | ||
Tamil Nadu | 1.056[1.014; 1.096] | 1.083[1.052; 1.114] | 0.027[-0.024; 0.080] | 2015 | 1.083[1.058; 1.109] |
Tripura | 1.053[0.987; 1.122] | 1.046[0.991; 1.105] | -0.007[-0.081; 0.068] | ||
Uttar Pradesh¶§ | 1.105[1.073; 1.138] | 1.131[1.104; 1.160] | 0.026[-0.016; 0.069] | 2002 | 1.152[1.136; 1.169] |
Uttarakhand§ | 1.118[1.047; 1.190] | 1.152[1.116; 1.189] | 0.034[-0.043; 0.109] | 2015 | 1.154[1.123; 1.186] |
West Bengal | 1.052[1.013; 1.091] | 1.059[1.030; 1.088] | 0.007[-0.042; 0.056] |
India & State/Union Territory | Average AMFB (, 000) | CMFB (, 000) | Proportion of National CMFB (%) | |||
1990–2000 | 2001–2016 | 1990–2016 | 1990–2000 | 2001–2016 | 1990–2016 | |
India | 461[378; 544] | 612[551; 672] | 14,861[13,239; 16,465] | 100 | 100 | 100 |
Andhra Pradesh | 2[-16; 22] | 21[12; 29] | 359[85; 632] | 0.6[0.0; 4.5] | 3.4[2.0; 4.7] | 2.4[0.6; 4.1] |
Assam | 2[-7; 11] | 8[3; 13] | 146[14; 279] | 0.4[0.0; 2.2] | 1.3[0.5; 2.0] | 1.0[0.1; 1.9] |
Bihar | 45[21; 68] | 67[54; 80] | 1,567[1,202; 1,918] | 9.8[4.8; 14.3] | 10.9[9.0; 12.9] | 10.6[8.4; 12.6] |
Delhi | 10[5; 15] | 12[9; 14] | 295[213; 379] | 2.1[1.1; 3.3] | 1.9[1.5; 2.4] | 2.0[1.4; 2.6] |
Gujarat | 41[26; 56] | 46[39; 53] | 1,187[974; 1,402] | 9.0[5.8; 12.3] | 7.5[6.4; 8.7] | 8.0[6.6; 9.4] |
Haryana | 32[25; 38] | 33[30; 37] | 885[778; 990] | 6.9[5.3; 9.0] | 5.4[4.8; 6.2] | 6.0[5.2; 6.8] |
Himachal Pradesh | 4[2; 6] | 3[2; 4] | 88[52; 125] | 0.9[0.4; 1.4] | 0.4[0.3; 0.6] | 0.6[0.3; 0.8] |
Jammu and Kashmir | 8[4; 11] | 9[7; 11] | 234[176; 292] | 1.7[0.9; 2.6] | 1.5[1.2; 1.8] | 1.6[1.2; 2.0] |
Jharkhand | 12[0; 24] | 14[7; 20] | 352[156; 550] | 2.6[0.0; 5.2] | 2.2[1.2; 3.3] | 2.4[1.1; 3.6] |
Karnataka | 5[-8; 18] | 6[0; 12] | 145[-41; 334] | 1.1[0.0; 3.9] | 0.9[0.0; 1.9] | 1.0[0.0; 2.2] |
Kerala | 5[-3; 12] | 0[-3; 3] | 52[-56; 156] | 1.0[0.0; 2.6] | 0.0[0.0; 0.5] | 0.3[0.0; 1.0] |
Madhya Pradesh | 27[8; 46] | 28[18; 38] | 742[470; 1,031] | 5.9[1.9; 9.8] | 4.6[3.1; 6.0] | 5.0[3.3; 6.7] |
Maharashtra | 26[-2; 54] | 43[15; 71] | 975[318; 1,613] | 5.6[0.0; 11.2] | 7.0[2.6; 11.1] | 6.5[2.3; 10.4] |
Punjab | 35[29; 42] | 29[25; 32] | 846[746; 949] | 7.7[6.0; 9.8] | 4.7[4.1; 5.3] | 5.7[5.0; 6.6] |
Rajasthan | 56[39; 73] | 71[62; 80] | 1,755[1,495; 2,004] | 12.1[8.5; 16.1] | 11.6[10.2; 13.2] | 11.8[10.2; 13.6] |
Tamil Nadu | 4[-10; 18] | 9[3; 15] | 191[-13; 382] | 0.9[0.0; 3.7] | 1.5[0.5; 2.5] | 1.3[0.0; 2.5] |
Uttar Pradesh | 142[101; 184] | 207[185; 229] | 4,873[4,262; 5,498] | 30.9[23.6; 38.1] | 33.8[30.9; 37.0] | 32.8[29.5; 36.3] |
Uttarakhand | 5[1; 8] | 7[5; 9] | 160[86; 234] | 1.0[0.1; 1.8] | 1.1[0.8; 1.5] | 1.1[0.6; 1.6] |
India & State/Union Territory | Average AMFB (, 000) | CMFB (, 000) | Proportion of National CMFB (%) | |||
1990–2000 | 2001–2016 | 1990–2016 | 1990–2000 | 2001–2016 | 1990–2016 | |
India | 461[378; 544] | 612[551; 672] | 14,861[13,239; 16,465] | 100 | 100 | 100 |
Andhra Pradesh | 2[-16; 22] | 21[12; 29] | 359[85; 632] | 0.6[0.0; 4.5] | 3.4[2.0; 4.7] | 2.4[0.6; 4.1] |
Assam | 2[-7; 11] | 8[3; 13] | 146[14; 279] | 0.4[0.0; 2.2] | 1.3[0.5; 2.0] | 1.0[0.1; 1.9] |
Bihar | 45[21; 68] | 67[54; 80] | 1,567[1,202; 1,918] | 9.8[4.8; 14.3] | 10.9[9.0; 12.9] | 10.6[8.4; 12.6] |
Delhi | 10[5; 15] | 12[9; 14] | 295[213; 379] | 2.1[1.1; 3.3] | 1.9[1.5; 2.4] | 2.0[1.4; 2.6] |
Gujarat | 41[26; 56] | 46[39; 53] | 1,187[974; 1,402] | 9.0[5.8; 12.3] | 7.5[6.4; 8.7] | 8.0[6.6; 9.4] |
Haryana | 32[25; 38] | 33[30; 37] | 885[778; 990] | 6.9[5.3; 9.0] | 5.4[4.8; 6.2] | 6.0[5.2; 6.8] |
Himachal Pradesh | 4[2; 6] | 3[2; 4] | 88[52; 125] | 0.9[0.4; 1.4] | 0.4[0.3; 0.6] | 0.6[0.3; 0.8] |
Jammu and Kashmir | 8[4; 11] | 9[7; 11] | 234[176; 292] | 1.7[0.9; 2.6] | 1.5[1.2; 1.8] | 1.6[1.2; 2.0] |
Jharkhand | 12[0; 24] | 14[7; 20] | 352[156; 550] | 2.6[0.0; 5.2] | 2.2[1.2; 3.3] | 2.4[1.1; 3.6] |
Karnataka | 5[-8; 18] | 6[0; 12] | 145[-41; 334] | 1.1[0.0; 3.9] | 0.9[0.0; 1.9] | 1.0[0.0; 2.2] |
Kerala | 5[-3; 12] | 0[-3; 3] | 52[-56; 156] | 1.0[0.0; 2.6] | 0.0[0.0; 0.5] | 0.3[0.0; 1.0] |
Madhya Pradesh | 27[8; 46] | 28[18; 38] | 742[470; 1,031] | 5.9[1.9; 9.8] | 4.6[3.1; 6.0] | 5.0[3.3; 6.7] |
Maharashtra | 26[-2; 54] | 43[15; 71] | 975[318; 1,613] | 5.6[0.0; 11.2] | 7.0[2.6; 11.1] | 6.5[2.3; 10.4] |
Punjab | 35[29; 42] | 29[25; 32] | 846[746; 949] | 7.7[6.0; 9.8] | 4.7[4.1; 5.3] | 5.7[5.0; 6.6] |
Rajasthan | 56[39; 73] | 71[62; 80] | 1,755[1,495; 2,004] | 12.1[8.5; 16.1] | 11.6[10.2; 13.2] | 11.8[10.2; 13.6] |
Tamil Nadu | 4[-10; 18] | 9[3; 15] | 191[-13; 382] | 0.9[0.0; 3.7] | 1.5[0.5; 2.5] | 1.3[0.0; 2.5] |
Uttar Pradesh | 142[101; 184] | 207[185; 229] | 4,873[4,262; 5,498] | 30.9[23.6; 38.1] | 33.8[30.9; 37.0] | 32.8[29.5; 36.3] |
Uttarakhand | 5[1; 8] | 7[5; 9] | 160[86; 234] | 1.0[0.1; 1.8] | 1.1[0.8; 1.5] | 1.1[0.6; 1.6] |
Median error | 0.005 |
Median absolute error | 0.035 |
Below 95% prediction interval (%) | 0.4 |
Above 95% prediction interval (%) | 0.3 |
Expected (%) | 2.5 |
Below 80% prediction interval (%) | 3.0 |
Above 80% prediction interval (%) | 2.8 |
Expected (%) | 10 |
Median error | 0.005 |
Median absolute error | 0.035 |
Below 95% prediction interval (%) | 0.4 |
Above 95% prediction interval (%) | 0.3 |
Expected (%) | 2.5 |
Below 80% prediction interval (%) | 3.0 |
Above 80% prediction interval (%) | 2.8 |
Expected (%) | 10 |
1995 | 2005 | 2015 | |
Median error | 0.003 | -0.001 | 0.003 |
Median absolute error | 0.003 | 0.002 | 0.003 |
Below 95% uncertainty interval (%) | 0 | 0 | 0 |
Above 95% uncertainty interval (%) | 0 | 0 | 0 |
Expected proportions (%) | ≤2.5 | ≤2.5 | ≤2.5 |
Below 80% uncertainty interval (%) | 0 | 0 | 0 |
Above 80% uncertainty interval (%) | 0 | 0 | 0 |
Expected proportions (%) | ≤10 | ≤10 | ≤10 |
1995 | 2005 | 2015 | |
Median error | 0.003 | -0.001 | 0.003 |
Median absolute error | 0.003 | 0.002 | 0.003 |
Below 95% uncertainty interval (%) | 0 | 0 | 0 |
Above 95% uncertainty interval (%) | 0 | 0 | 0 |
Expected proportions (%) | ≤2.5 | ≤2.5 | ≤2.5 |
Below 80% uncertainty interval (%) | 0 | 0 | 0 |
Above 80% uncertainty interval (%) | 0 | 0 | 0 |
Expected proportions (%) | ≤10 | ≤10 | ≤10 |
Symbol | Description |
Indicator for observation, |
|
Indicator for year, |
|
Indicator for Indian state/union territory, |
|
Indicator for data source type, |
|
The |
|
The |
|
The |
|
The non-sampling variance parameters with non-SRS data source type for |
|
The model fitting for the true SRB for state/union territory |
|
The difference between |
|
The baseline level parameter of SRB for the whole India on the log-scale. | |
Autoregressive parameter in AR(1) time series model for |
|
Variance of distortion parameter in AR(1) time series model for |
|
The state-specific level parameters for |
|
The variance parameter for |
Symbol | Description |
Indicator for observation, |
|
Indicator for year, |
|
Indicator for Indian state/union territory, |
|
Indicator for data source type, |
|
The |
|
The |
|
The |
|
The non-sampling variance parameters with non-SRS data source type for |
|
The model fitting for the true SRB for state/union territory |
|
The difference between |
|
The baseline level parameter of SRB for the whole India on the log-scale. | |
Autoregressive parameter in AR(1) time series model for |
|
Variance of distortion parameter in AR(1) time series model for |
|
The state-specific level parameters for |
|
The variance parameter for |
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