Correction

SOCIAL SCIENCES Correction for “Systematic assessment of the at birth for all countries and estimation of national imbalances and re- gional reference levels,” by Fengqing Chao, Patrick Gerland, Alex R. Cook, and Leontine Alkema, which was first published April 15, 2019; 10.1073/pnas.1812593116 (Proc. Natl. Acad. Sci. U.S.A. 116, 9303–9311). The authors wish to note the following: “We made an error in the calculation of the number of missing female births. We used the total number of births as the starting point of our calculation, when instead we should have used the number of male births. The results regarding the sex ratio at birth estimates are not affected, but the reporting on the number of missing female births is affected by about a factor of 2. Changes to the manu- script include: Updates of the annual and cumulative missing births reported in Table 3, Fig. 3, and associated numbers in text; updates of the inflation probabilities (SI Appendix, Table S9) and reference in text (however, the selection of the countries with strong statistical evidence of an inflation was not affected by this update); additional explanation of how missing female births were calculated in the main text; and updated equations in the SI Appendix. Additionally, we thank Christophe Z. Guilmoto for pointing out the error in the calculation of the missing female births. We apologize for the errors in our work.” An updated version of the article with changes to address these errors is included with this Correction. The online version of the article remains as originally published. Dataset S4 and the SI Appendix have been corrected online. In the SI Appendix, the following material has been corrected: On page 11, the equation in the last line on the page; on page 12, the full text of the bullet point that begins “δc < 95%”; on page 19, Table S9; on page 21, Table S12; and, also on page 21, the full text of the two paragraphs beginning “For reference year 1987” and “For India reference year 1997.”

Published under the PNAS license. Published online June 24, 2019.

www.pnas.org/cgi/doi/10.1073/pnas.1908359116

13700 | PNAS | July 2, 2019 | vol. 116 | no. 27 www.pnas.org Downloaded by guest on September 28, 2021 Downloaded by guest on September 28, 2021 orce , Corrected 2019 26 June www.pnas.org/cgi/doi/10.1073/pnas.1812593116 Chao Fengqing levels and reference imbalances all regional national for of birth estimation at and ratio countries sex the of assessment Systematic rmbt h ouainadtemdcletbihet avail- establishment, medical the and population the abortion sex- induced both having for of from tolerance still occurrence large a while the include families abortions for selective large conditions avoid Necessary offspring. to male means abor- a by sex-selective provides composition Consequently, tion sex (22). ideal selection sex the to and attaining resorting families effect”: small “squeezing desired a the in both resulted that levels world low the to (31– fallen around has to available fertility Thirdly, access method. increasingly the and providing become 35), diagnosis have sex abortion prenatal sex-selective 1970s, motivation. the the since provides Secondly, which abortion have preference, combi- inflation sex-selective son SRB a strong to abnormal Europe persisting with to lead societies eastern most that due Firstly, in factors 24). is (22, main and SRB three countries in of imbalance Asian nation have increasing of SRBs The number decades, (14–30). a recent ethnic over in among However, risen within variations range. remained SRB known natural history, human few that of a most For only (1–13). groups with 1.05, around “baseline abortion. sex-selective term to assessing henceforth due on imbalance we SRB focus (which and a level”) levels with reference countries, all SRB for the births) live female to W preference son birth at ratio and sex million, 30.7] million. [16.5; 26.6] 23.1 [15.5; 20.7 with with China, India, in in miss- are those births of 54.8] female majority [36.4; ing The 45.0 globally. births in in female resulting 1.077] missing 1970–2017, million [1.058; during evidence imbalance 1.067 statistical strong SRB and with of countries Asia, 12 eastern identify We in Oceania. Asia, southeastern 1.072] in [1.054; 1.072] 1.036]) [1.055; 1.063 [1.027; 1.063 interval reference to Africa uncertainty regional sub-Saharan (95% estimated in 1.031 The from refer- inflation. range and the national levels regional around and SRB levels, fluctuation the ence the model We levels, 2017. for reference to estimation national 1950 SRB from for countries methods 202 all Bayesian from develop information We of countries. country-years 10,835 16,602 with surveys and and database observations, extensive systems, an registration compile vital inflation We from SRB abortion. assess sex-selective con- to to to and due uncertainty, SRB methods with with reproducible estimates SRB associated for struct uncertainty needs are the refer- There SRB of observations. unknown because of and because levels complicated ence degree is the imbalance of SRB Estimation of decline. a fertility is and prenatal of determination, decades births) coex- sex available few live the readily past preference, by female son driven the of to abortion, istence over male sex-selective world of of the consequence ratio direct of (SRB; parts 11, birth in March Raftery at imbalance E. Adrian ratio Member sex Board Editorial The by accepted 2018) and 23, Kingdom, July United review Southampton, for Southampton, (received of 01003-9304 2019 University MA Bijak, and Amherst, Jakub 117549; Massachusetts, by Singapore of 10017; Edited System, University NY Health Sciences, York, University Health New National and Nations, and Health United Singapore Affairs, Public of Social of University and School Epidemiology, National Economic Health, of Public Department of Division, School Population Nations United Section, Projections a

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SOCIAL SCIENCES implement two Bayesian hierarchical models to estimate SRBs in two types of country-years: (i) those that are not affected by sex-selective abortion and (ii) those that may be affected by sex-selective abortion that leads to SRB imbalances. In the model for country-years not affected by sex-selective abortion, the SRB is given by the product of a baseline value and a country-year-specific multiplier that accounts for natural fluctuation around the baseline value. We allow baseline values to differ across countries within a region, and across regions, to incorporate SRB differences due to ethnic origin (1–13). Hence, for this purpose, regions refer to groupings of countries based on their dominant ethnic group (SI Appendix, Table S17). For exam- ple, we group countries in Europe, North America, Australia, and New Zealand to refer to the regional grouping of countries with a majority of Caucasians. Within each country and region, we assume that the baseline value is constant over time. southeastern The model for natural fluctuations in the SRB is fitted to the global database after excluding data from country-years that may have been affected by masculinization of the SRB. We use inclusive criteria to identify such country-years, based on a com- bination of qualitative and quantitative approaches. We select countries with at least one of the following manifestations of son preference: (i) a high level of desired sex ratio at birth (DSRB), (ii) a high level of sex ratio at last birth (SRLB), or (iii) strong Fig. 1. Global and regional SRB estimates in 1990 and 2017, and regional son preference or inflated SRB suggested by a literature review. baseline values of SRB. Dots indicate median estimates, and horizontal lines The earliest start for the sex ratio inflation is set to 1970, which refer to 95% uncertainty intervals. Regional baseline values are in dark is when sex-selective abortions first became available. green, where the vertical line segments refer to median estimates, and We parametrize SRB inflation during a sex ratio transition green shaded areas are 95% uncertainty intervals. using a trapezoid to allow for consecutive phases of increase, stagnation, and a decrease back to zero. We incorporate the fertility squeeze effect by using the total fertility rate [TFR, 0.013]. During 1990–2000, the increases on regional SRB are obtained from the UN WPP 2017 (37)] into the model to inform significantly above zero in eastern Asia at 0.042 [0.009; 0.083], the start year of SRB inflation. Parameters are estimated with southern Asia at 0.014 [0.005; 0.022], and Caucasus and cen- a Bayesian hierarchical model (42) to share information across tral Asia at 0.012 [0.005; 0.020]. Between 2000 and 2017, the countries about the inflation start year, the maximum inflation, changes of SRB are not significant for any regions. However, and the length of inflation period during the three phases. on a global level, the decrease of SRB during 2000–2017 is To quantify the effect of SRB imbalance due to sex-selective significantly below zero at −0.010 [−0.018; −0.002]. abortion, we calculate the annual number of missing female The regional SRB baseline values range from 1.031 [1.027; births (AMFB) and the cumulative number of missing female 1.036] in sub-Saharan Africa to 1.063 [1.055; 1.072] in southeast- births (CMFB) over time. AMFB is defined as the difference ern Asia, 1.063 [1.054; 1.072] in eastern Asia, and 1.067 [1.058; between the number of female live births based on SRB without 1.077] in Oceania (Table 1 and Fig. 1). When comparing to the inflation and the number of female live births based on SRB with conventional value of 1.05 for SRB baseline adopted by the UN inflation. The number of female live births based on SRB with- WPP (37), the regional baseline values differ significantly from out inflation were obtained from the estimated number of male 1.05 for 6 out of 10 regions: significantly above 1.05 for “ENAN” births, and the baseline SRB for the respective country-year (16). (the combination of countries in Europe, North America, Aus- CMFB for a certain period is the sum of AMFB over the period. tralia, and New Zealand), southeastern Asia, eastern Asia, and We define countries with strong evidence of SRB inflation to be Oceania and significantly below 1.05 for sub-Saharan Africa and those countries with at least 1 y with at least 95% probability of Latin America and the Caribbean. In 2017, the aggregated SRB a positive number of missing female births (AMFB > 0). in three regions (southern Asia, Caucasus and central Asia, and eastern Asia) are significantly above their corresponding regional Results baseline median estimates. In 1990, the aggregated regional-level The compiled database, annual estimates for national, regional, SRB in southern Asia and eastern Asia are significantly above and global SRB during 1950–2017, and national AMFB during their regional baseline median estimates. 1970–2017 are available in Datasets S1–S4. The SRB estimates for selected years by country are in SI Appendix, Table S20. National SRB Estimates Case Studies. We illustrate SRB estimates for 12 countries which are identified to have strong statistical evi- Global and Regional SRB Estimates. The global and regional SRB dence of SRB inflation. The SRB median estimates and 95% median estimates and 95% uncertainty intervals in 1990 and 2017 uncertainty intervals for the 12 countries are shown in Table 2 are presented in Fig. 1 and Table 1. Globally, the SRB in 2017 and Fig. 2. TFR estimates are overlaid onto SRB estimates in is 1.068 (95% uncertainty interval, [1.059; 1.077]). Levels and Fig. 2, to illustrate the relationship between the start year of trends vary across regions. In 2017, the regional-level estimated SRB inflation period and fertility decline, as incorporated into SRBs range from 1.032 [1.026; 1.039] in sub-Saharan Africa to the model to estimate the start year of inflation period. 1.133 [1.076; 1.187] in eastern Asia. Among the 12 countries, 9 are from Asian regions (Caucasus Between 1990 and 2017, the change in the global SRB is not and central Asia, eastern Asia, southeastern Asia, and south- statistically significant. For the same period, none of the regional ern Asia). TFR values at the start of sex ratio transitions vary estimated SRBs have significant reductions, while Caucasus and across countries. As shown in Fig. 2, India is a country with a central Asia have an increase at 0.010 [0.001; 0.019]. Between high TFR value of 5.2 at the start of its inflation period in 1975, 1990 and 2000, the increase in global SRB is at 0.005 [−0.001; while SRB inflation is estimated to start in Vietnam in 2001 when

9304 | www.pnas.org/cgi/doi/10.1073/pnas.1812593116 Chao et al. Downloaded by guest on September 28, 2021 Downloaded by guest on September 28, 2021 awn rvneo hn atr Asia eastern China of Province Taiwan, Korea of Republic Oceania ogKn,SRo hn atr Asia eastern China of SAR Kong, Hong Asia eastern Asia southeastern acssadcnrlAi 1.062 Asia central and Caucasus China Vietnam Georgia etr Asia western Azerbaijan Armenia Tunisia ai mrc and America Latin Africa sub-Saharan Montenegro Albania otenAfrica northern otemxmmSBatriflto trs er“a”rfr oteya nwihtemxmmSBi fe nainstarts. inflation after is SRB maximum the al. et which Chao in year the to refers “Max” Year starts. inflation after SRB maximum the to India inflation Country SRB of evidence statistical strong with countries for results SRB 2. Repub- Table after in maximum occurred maximum its earliest reached The SRBs Dur- SRB countries. inflation, countries. 9 transitions, the 12 in in ratio of 2000 all China sex start for of the the 2017 SAR Since ing before Kong, 1.0. maximum Hong at their in TFR reached and a 2.0 with to 2004 declined TFR its value. baseline ENAN Asia southern World region World, 2017 and 2000, 1990, in SRB regional and Global 1. Table h Caribbean the onre r rsne yrgo.Mda siae r ubr eoebakt.Nmesi rcesae9%ucranyitras R Mx refers “Max” SRB intervals. uncertainty 95% are brackets in Numbers brackets. before numbers are estimates Median region. by presented are Countries global a estimate not does model the —, intervals. uncertainty 95% are brackets the in Numbers brackets. the before numbers are estimates Median aeievle1990 value Baseline .1;0.017] [−0.019; 0.016] [−0.017; 0.014] [ [−0.014; 1.089] [1.045; 0.083] [0.009; 1.089] [1.046; 1.187] 1.088] [1.076; [1.048; 1.189] [1.128; 1.077] [1.058; 1.147] [1.080; 1.072] [1.054; .0;0.018] [ −0.007; 0.017] [−0.007; 0.010] [−0.008; 1.086] [1.061; 1.077] [1.060; 1.076] [1.059; 1.072] [1.055; 100 .7][.5;104 100 .8][.6;104 005 .2][ 0.020] [0.005; 1.084] [1.065; 1.085] [1.070; 1.074] [1.056; 1.075] [1.050; .1;0.008] [−0.011; 0.006] [−0.011; 0.009] [−0.006; 1.063] [1.044; 1.064] [1.048; 1.064] [1.045; 1.056] [1.044; .1;0.001] [−0.010; 0.003] [ −0.007; 0.019] [−0.015; 0.002] [−0.007; 0.020] [ −0.013; 1.039] [1.026; 0.006] 1.041] [−0.009; [1.029; 1.079] 1.043] [1.037; [1.031; 1.070] [1.042; 1.036] [1.027; 1.072] [1.043; 1.064] [1.036; .0;0.003] [−0.004; 0.002] [ −0.005; 0.002] [−0.001; 1.057] [1.051; 1.056] [1.054; 1.056] [1.054; 1.061] [1.055; 100 .6][.6;112 109 .1][.6;117 005 .2][ 0.022] [0.005; 1.107] [1.066; 1.116] [1.079; 1.102] [1.066; 1.063] [1.040; .0;0.008] [−0.009; 0.008] [−0.009; 0.007] [−0.007; 1.053] [1.036; 1.053] [1.036; 1.053] [1.037; 1.045] [1.037; atr Asia eastern atr Asia 1.063 eastern Asia southeastern Asia central and Caucasus acssadcnrlAi 1.068 1.059 Asia central and Caucasus Asia central and Caucasus Africa northern ENAN ENAN otenAsia southern 1.067 1.063 1.063 1.050 1.041 1.031 1.050 1.058 1.052 — Region .0;003 [−0.018; 0.013] [−0.001; 1.077] [1.059; 1.085] [1.071; 1.081] [1.064; .6 .6 1.067 1.133 1.068 1.157 1.068 1.115 .6 .6 1.073 1.068 1.067 .6 .7 1.075 1.077 1.065 .5 .5 1.053 1.056 1.055 .4 .4 1.044 1.032 1.045 1.056 1.035 1.045 1.056 1.037 1.057 .5 .5 1.054 1.055 1.055 .8 .9 1.086 1.068 1.097 1.078 1.084 1.073 SRB 100 .7][.9;110 105 .8][92 1987] [1972; 1.087] [1.065; 1.110] [1.090; 1.079] [1.060; 106 .9][.4;114 109 .9][02 2005] [2002; 1.098] [1.059; 1.174] [1.140; 1.098] [1.066; 102 .9][.3;111 104 .7][98 1984] [1978; 1.078] [1.034; 1.171] [1.131; 1.092] [1.052; 107 .0][.4;121 109 .0][92 1989] [1972; 1.205] [1.079; 1.221] [1.141; 1.103] [1.047; 102 .9][.8;111 100 .8][91 2005] [1991; 1.186] [1.070; 1.171] [1.089; 1.099] [1.052; 101 .9][.4;117 107 .6][98 1994] [1988; 1.168] [1.097; 1.197] [1.145; 1.095] [1.041; 104 .9][.9;111 109 .9][97 1994] [1977; 1.092] [1.039; 1.141] [1.090; 1.098] [1.034; 101 .8][.6;110 105 .0][91 1991] [1971; 1.100] [1.045; 1.130] [1.067; 1.084] [1.031; 104 .8][.5;123 107 .4][90 1993] [1990; 1989] [1976; 1.149] [1.087; 1.081] [1.028; 1.203] [1.150; 1.108] [1.061; 1.084] [1.034; 1.080] [1.034; 108 .0][.0;110 104 .1][93 1997] [1973; 1981] [1970; 1.113] [1.054; 1.124] [1.071; 1.150] [1.104; 1.137] [1.090; 1.101] [1.058; 1.087] [1.035; tr year Start 2000 1.069 1.082 1.072 1.073 1.076 1.056 1.057 1.079 1.061 .9]i 03,Hn og A fCia(.5 110 1.174] [1.140; (1.157 China of SAR Kong, 2005), Hong in [1.145; 2003), (1.171 1.221] Azerbaijan in [1.141; 2000), 1.197] (1.179 in 1.203] China [1.150; in (1.176 are Armenia inflation 2012. since of in SRB Vietnam start maximum in the in-country occurred of latest estimates median the highest and The 1990, in Korea of lic 0719–0020–071990–2017 2000–2017 1990–2000 2017 1.100 1.151 1.157 1.179 1.126 1.171 1.176 1.085 1.115 1.099 1.127 1.113 Max SRB −0.002 −0.002 0.000 0.042 0.001 0.012 0.000 0.002 0.001 0.014 0.005 PNAS | 1.076 1.078 1.056 1.143 1.122 1.065 1.072 1.134 1.117 1.054 1.083 1.098 07Satiflto Max inflation Start 2017 a ,2019 7, May hneo SRB of Change .4;0.079] [ −0.043; 0.029] −0.081; .1;006 001 0.019] [0.001; 0.006] −0.011; .1;0.017] [−0.011; 0.003] −0.024; −0.024 −0.003 −0.003 −0.001 −0.002 −0.001 −0.011 −0.001 −0.010 0.000 0.005 | .1;0.005] [−0.014; −0.002] o.116 vol. 922004 1982 042011 2004 921990 1982 012012 2001 912005 1981 912003 2000 1991 2000 1992 1982 922003 1992 901997 1980 982006 1988 951995 1975 | Year o 19 no. −0.001 −0.001 −0.001 −0.005 −0.002 −0.001 −0.005 0.018 0.010 0.002 0.006 | 9305

SOCIAL SCIENCES Albania Armenia Azerbaijan 1988 1992 5.0 1991 6 1.3 (TFR=3.1) 1.3 (TFR=2.4) 1.3 (TFR=3) 6 4.5 5 4.0 1.2 5 1.2 1.2 3.5 4 4 1.1 1.1 3.0 1.1

3 2.5 3

1.0 1.0 2.0 1.0 2 2 1.5 1950 1970 1990 2010 1950 1970 1990 2010 1950 1970 1990 2010 China Georgia Hong Kong, SAR of China 1981 1992 2016 2004 2013 5 1.3 (TFR=2.6) 1.3 (TFR=2.1) 1.3 (TFR=1) 6 2.8

2.6 4 1.2 5 1.2 1.2 2.4

4 3 2.2 1.1 1.1 1.1 3 2.0 2

1.0 1.0 1.8 1.0 2 1.6 1 1950 1970 1990 2010 1950 1970 1990 2010 1950 1970 1990 2010 India Republic of Korea Montenegro 1975 6 1982 2007 1980 4.5 1.3 (TFR=5.2) 1.3 (TFR=2.3) 6 1.3 (TFR=2.2)

4.0 5 5 1.2 1.2 1.2 3.5 4 4 3.0 1.1 1.1 1.1 3 2.5 3 2 1.0 1.0 1.0 2.0

2 1 1950 1970 1990 2010 1950 1970 1990 2010 1950 1970 1990 2010 Taiwan, Province of China Tunisia Vietnam 1982 7 1982 7 2001 1.3 (TFR=2.3) 1.3 (TFR=4.9) 1.3 (TFR=2) 6 6 6 1.2 1.2 5 1.2 5 5 4 1.1 4 1.1 4 1.1 3 3 1.0 3 2 1.0 1.0 2 1 0.9 2 1950 1970 1990 2010 1950 1970 1990 2010 1950 1970 1990 2010

Fig. 2. SRB estimates during 1950–2017 for countries with strong statistical evidence of SRB inflation. The scale on the left y axis refers to SRB, and the scale on the right y axis refers to TFR. Red lines and shaded areas are country-specific SRB median estimates and their 95% uncertainty intervals. Dark green horizontal lines are median estimates for regional SRB baselines. Light green horizontal lines are median estimates for national SRB baselines. Observations from different data series are differentiated by colors, where VR data are black solid dots. The blue square dots are the UN WPP 2017 TFR estimates. Blue vertical lines indicate median estimates for start and end years (if before 2017) of SRB inflation period. TFR values in the start years of SRB inflation periods are shown.

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Table during mother socioe- for size, condition family order, environment weight, birth maternal conditions, child, conception, conomic preceding at the age of paternal sex or maternal including data. without or information limited neighboring with and country-years country-years data-rich between sharing information allows which of and sys- estimation, a for using censuses, VR model synthesized hierarchical history, are Bayesian national These birth reports. from and full surveys data on national-level surveys available international all SRB tems, extensive include an compiled to that have base- countries database We assesses all imbalances. and and 2017 for levels to SRB line 1950 of from analysis estimates annual systematic produces a is study Our Discussion [36.04%; CMFB. 46.05% total and and the China of 61.34%] in respectively, [41.05%; 2017 56.13%], 51.30% and up 1970 made [15.5; between India 20.7 CMFB with in India, The concentrated in million. and are 26.6] million, 2017 30.7] [16.5; and 23.1 of The 1970 with 3). China, evidence between Fig. and CMFB statistical 3 (Table of strong million majority 54.8] with [36.4; 45.0 countries is inflation 12 SRB the for Estimates. CMFB Births Female Missing high- 1.205] the [1.079; while 1.143 and Korea, Azerbaijan China. in of in 1.168] Republic [1.097; lowest Tunisia 1.134 in the are in 1.078] est 1.081] 2017, Kong [1.028; [1.034; By Hong 1.054 1.056 Georgia. are for and countries for 2013 12 2016 the in in among fluctu- Korea, SRBs and natural of China), of Republic of range for 1990). (SAR the in 2007 to 1.171] in back [1.131; ations converged (1.151 have Korea SRBs of The Republic and 2011), in (ENAN) Montenegro (ENAN) Tunisia (N.A.) Armenia Georgia (C.C.A.) Azerbaijan (C.C.A.) (C.C.A.) ogKn,SR0 SAR Kong, Hong (E.A.) China (S.E.A.) Vietnam eulco oe 97 Korea of Republic (E.A.) awn rvne17 Province Taiwan, (E.A.) (E.A.) fChina of fChina of onre r rsne yrgo.Mda siae r h ubr eoetebakt.Nmesisd h rcesaete9%ucranyintervals. uncertainty 95% the are brackets the inside Numbers brackets. the before numbers the are estimates Median region. by presented are Countries factors individual-level to related is value baseline SRB The 203 ,0][,9;853 674 302 1,8;2,3][89;9.9 3.0 87][14;5.2 3.4 56.13] [36.04; 53.32] [31.46; 58.73] [36.40; 94.09] [28.94; 26,638] [15,480; 13,042] [6,744; 8,553] [4,692; 7,303] [2,043; 18 ,8][,9;933 854 736 1,5;3,1][.0 94][88;6.6 4.0 53][10;61.34] [41.05; 65.33] [43.00; 61.46] [38.81; 69.41] [3.10; 30,714] [16,457; 17,366] [8,544; 9,343] [4,697; 7,581] [158; 6;10 10 1][0 1 27 0][.8 .5 09;17][.4 .7 04;0.95] [0.49; 0.27] [0.04; 1.74] [0.93; 3.05] [0.68; 402] [237; 61] [10; 218] [140; 160] [69; 0 4 1 4 9 8 1;4][.0 .0 00;01][.4 .8 00;0.09] [0.03; 0.08] [0.04; 0.11] [0.01; 0.20] [0.00; 42] [12; 18] [9; 14] [1; 14] [0; 0 6 1;4][7 8 3;11 00;04][.8 .0 00;02][.8 0.26] [0.08; 0.26] [0.07; 0.30] [0.08; 0.44] [0.01; 111] [39; 58] [17; 40] [12; 26] [0; 0 0 7 6 7 9 1;3][.0 .3 00;01][.3 .9 00;0.09] [0.03; 0.09] [0.03; 0.12] [0.05; 0.13] [0.00; 38] [15; 19] [7; 16] [7; 10] [0; 3 8 1;6][3 6 4;13 00;07][.3 .5 00;02][.0 0.33] [0.10; 0.25] [0.06; 0.45] [0.13; 0.74] [0.04; 143] [45; 56] [13; 60] [18; 48] [3; ,6 ,7 9,865 6,576 4,260 ,3 ,2 2902,6 16 05 48 51.30 54.84 50.52 41.66 23,064 12,910 7,023 3,131 0 ][;3] [1; 3] [0; 0 ][9 3 9;18 14 6][.0 .3 01;03][.5 .3 02;0.40] [0.24; 0.11] [0.06; 0.63] [0.35; 0.15] [0.08; 0.33] [0.13; 0.11] [0.06; 0.03] [0.00; 0.00] [0.00; 166] [114; 45] [32; 128] [91; 32] [22; 43] [19; 14] [9; 2] [0; 0] [0; 0 ][;9][7;78 26 9][.0 .0 00;06][.3 .1 06;1.82] [0.60; 3.51] [1.13; 0.64] [0.00; 0.00] [0.00; 797] [276; 778] [271; 91] [0; 0] [0; 0 ][;0 1;1][0 7 00;00][.0 .0 00;00][.2 0.04] [0.02; 0.08] [0.04; 0.00] [0.00; 0.00] [0.00; 17] [10; 17] [10; 0] [0; 0] [0; 10 0 1 0 0 0 0 rm17 o21,tetotal the 2017, to 1970 From 176 26 11 11 29 40 7 2 0 0 MB(thousands) CMFB 1 3] [1; 109 492 13 36 26 12 13 28 34 2 0715.84.04.146.05 41.91 47.30 56.68 20,701 2 ][.0 .5 00;00][.0 .1 00;0.02] [0.01; 0.01] [0.00; 0.02] [0.00; 0.05] [0.00; 8] [2; 139 492 301 21 72 38 23 13 90 af(8oto 1)o h siae onr-ee baseline country-level estimated the of 212) almost of addition, In out study. we (88 regions the from half of significantly majority differ the regions. for values within 1.05 baseline countries regional across and estimated regions The across SRB in ence series baseline time around a fluctuations SRB in the add temporal and model values. capture time we to over Hence, constant component 52–54). as 46, values 12, ethnic baseline in (2, changes values explained in than were SRB baseline rather changes in circumstances that trends country-specific found time by consistent countries of are industrial analyses results our Previous several that studies. ethnicity find those by and with differences 6) estimates SRB section baseline on Appendix, model-based (SI studies the from compare information We with unani- study. of aggregations larger this absence for in opt the we in groupings, ancestry Caribbean regional However, agreed African the mously countries. of and Caribbean majority America the the Latin of in divide because to subregions is two or the into there 9), within levels 4, since SRB (3, regions to biological region the example, smaller in for into heterogeneity possible, additional Africa are Fur- sub-Saharan grouping colonization. the European divide or of to regions refinements due which similar origins ther origin, from ethnic ethnic countries similar to grouping with regional due by the SRB approximate in at we the differences capita the between per estimate relation do GDP consistent and (see a TFR (GDP) level find product and not domestic SRB gross do observed and We parity) capita. of per proxy are a exceptions Two (as interest. TFR of is country-years ethnic information all for aforementioned and available the not hypothesis, of Most Trivers–Willard 43–51). (1–13, the origin is, that pregnancy, 5 ehglgttencsiyt cnweg h aeiediffer- baseline the acknowledge to necessity the highlight We eto 4 section Appendix, SI 0.00 0.01 0.13 0.00 0.00 0.00 0.00 0.00 1.29 0.22 PNAS rprino h oa MB % CMFB, total the of Proportion | 0.05 0.01 0.19 0.21 0.08 0.08 0.00 0.00 1.27 0.29 a ,2019 7, May o ealdaaye) We analyses). detailed for | o.116 vol. 0.06 0.01 0.15 0.46 0.11 0.05 2.09 0.06 0.12 0.14 | o 19 no. 0.05 0.01 0.16 0.31 0.08 0.05 1.09 0.03 0.20 0.67 | 9307

SOCIAL SCIENCES Sex ratio at birth in 2017 1.10 and above 1.07 to 1.10 1.05 to 1.07 1.03 to 1.05 Less than 1.03 No data Cumulative number of missing female births (in millions) 1970−2017 6 23

Fig. 3. SRB in 2017 and the CMFB during 1970–2017, by country. Countries are colored by the levels of their SRB median estimates. Radii of circles are proportional to CMFB for countries. For high-resolution plot of Fig. 3, see SI Appendix, section 11.

values, accounting for ethnic difference across countries within became possible. Consequently, for such countries, the national a region, are significantly different from 1.05. The resulting base- SRB baselines are mostly informed by region, albeit with uncer- line values provide a better reflection of observed heterogeneity tainty that captures the cross-country variability in national than the widely adopted value 1.05 which is typically used in pop- baselines. ulation estimation (37, 55–59). Based on the estimate of 38.2 We introduce the concept of being at risk for SRB inflation, million total births in sub-Saharan Africa in 2017 (37), an esti- which refers to populations where masculinization of the SRB mated SRB of 1.03 would result in 183,400 more female births may be (but is not necessarily) present. We identify countries than 1.05 would. When using 1.05 instead of the regional baseline at risk for SRB inflation using a combination of qualitative and value as a reference in sub-Saharan Africa, potential deviations quantitative measures. For the quantitative selection criteria, we that would signify the early stage of SRB inflation would be more identify countries with high SRLB/DSRB using universal cutoff likely to be misjudged as natural fluctuations, and the severity of values suggested by Bongaarts (15), regardless of ideal family size prenatal sex discrimination would be more probable to be under- (62). Out of the 212 countries considered in this study, we obtain estimated. Note, however, that this is a hypothetical statement; information on son preference in the form of SRLB/DSRB we find no significant evidence of SRB inflation in sub-Saharan and/or the literature on 90 countries. We also identify an addi- African countries to date. tional 65 countries with VR coverage for the period 1970–2017 We find that, during 1970–2017, missing female births for which no SRB inflation has been suggested. Fifty-seven coun- occurred mostly in China and India, resulting in CMFB of 23.1 tries have no information available to establish whether they are [16.5; 30.7] million and 20.7 [15.5; 26.6] million, respectively. at risk for SRB inflation (given in SI Appendix, Table S18). For Details on data preprocessing for China and India are in SI these countries, we assume that they are not at risk for SRB Appendix, section 1. Our estimates of AMFB for China and India inflation. This is a study limitation, as a sex ratio bias may go are similar, albeit slightly lower than those by other studies on unnoticed. The limitation is mainly for the affected countries missing female births (60, 61); see SI Appendix, section 7. only; the countries without information contribute only 3.2% to There are limitations to our modeling of the SRB imbal- the total number of births globally in 1970–2017. ance. Firstly, in the SRB inflation model, we incorporate fertility To identify countries with strong statistical evidence of SRB decline as a covariate. The other necessary conditions for sex- inflation among countries at risk for SRB inflation, we assess the selective abortion, for example, the intensity of son preference, uncertainty of AMFB and select countries with inflation proba- the tolerance for induced abortion, the accessibility of the tech- bility of at least 95%. Twelve countries are identified, including nology of early sex detection, and legality of medical abortion one country (Tunisia) for which SRB inflation has not been (36), are not included due to the lack of information for all reported in the articles obtained in the literature review. A lower country-years of interest. An analysis of the relation between son cutoff of the inflation probability would have resulted in a larger preference—as measured through the DSRB—and the sever- set of countries being identified as having inflation. In particu- ity of the SRB imbalance does not suggest that son preference lar, Singapore, Morocco, Nepal, and Turkey have probabilities is predictive of the intensity (SI Appendix, section 4). Secondly, higher than 80% of having inflation. We present the uncertainty the functional form used for the inflation is a simple trape- regarding the SRB inflation for these countries (see SI Appendix, zoid one. Attempts to fit more-flexible models were unsuccessful section 2). We do not highlight the measured SRB inflation due to identifiability issues between natural fluctuations and the in countries with greater uncertainty, because there is a higher actual inflation. If more detailed information regarding predic- probability that the measured inflation is just a natural fluctua- tors and/or sex-selective abortion becomes available, the model tion. In addition, we find that, for countries with greater uncer- can be expanded to incorporate it and provide more detailed tainty about possible inflation, the estimation of the start year of insights into SRB inflation. Thirdly, some countries may have inflation is sensitive to model assumptions (see SI Appendix, sec- limited data before 1970, the year when sex-selective abortion tion 5 for a sensitivity analysis). In-depth analyses are needed to

9308 | www.pnas.org/cgi/doi/10.1073/pnas.1812593116 Chao et al. Downloaded by guest on September 28, 2021 Downloaded by guest on September 28, 2021 trs o R nainuigtetreslcinciei.W assume We al. criteria. et and Chao selection DSRB/SRLB on three countries information the 29 without countries using identify 122 we inflation DSRB/SRLB remaining and SRB on the countries, that information for total, 90 is risk society. In there for at patrimonial review. considered, literature a countries literature and/or be 212 the criteria that to the by populations or of with identified preference out countries son are also as a countries empiri- We well have Twenty-three with SRLB. as to countries inflation, high considered identify SRB with are to of countries review evidence 13 literature identify cal and We systematic SRLB. DSRB a and high the conduct DSRB follow with compute We to countries countries. (15) 11 83 Bongaarts from in SRLB Surveys described the Health steps generate and and Demographic countries 283 Inflation. 73 in from SRB Surveys for Health Risk and Demographic at to Countries 1950 of from Selection estimates annual the use we (37); version 2017. 2017 WPP UN the (65). criteria IGME in is UN sources in data S19. given the on Table is information India Detailed using and 1. China section identified for dis- Appendix, processing are exclude data natural on We and crises information 1 ). Additional conflict The section national-level occur. , Appendix where (SI asters country-periods a inclusion from before use 1 data data section we vary- VR Appendix , with files, for observations (SI checks microdata for periods available errors reference sampling with ing calculate Arab data to Pan data method survey survey and jackknife national-level For Health, and Family reports. for and from Development), Project Child Health Arab for and Pan Indi- Project interna- Multiple (Demographic Surveys, Survey, reports, Cluster reports Health Reproductive cator or annual Surveys, microdata from Fertility World from Bangladesh Surveys, data and survey Pakistan, tional India, system registration for sampling Database, data Mortality Human the data. and Yearbook with countries of 202 the country-years of 82.2 each average, for available On are information. corresponding data and SRB, of availability national-level country-years on data 16,602 points The to to countries. data Due 202 10,835 from 2017. data includes with database of database a as construct we 90,000 criteria, inclusion than greater size ulation in Inputs. provided Model are model and section. this database in summarized the on Details to needed Methods and data Materials in gap the as assessments. over well precise variation more as natural provide countries, its and across birth and at time ratio sex the of assessment the in against discriminated most period. are prenatal under- births better female fluctuation. to where natural divisions stand subnational a assess from should distinguishable national work be Future the not on may imbalance and significant sex not level statistically imbalanced may a imbalances as with subgroup-specific flagged birth the be enough, of small proportion is the ratio of If (67). proportion affected. Indonesia the on births for depends documented imbalance SRB been national-level The have geographical been SRB and has (66), in Nepal abortion differences in sub- sex-selective births for higher-order example, for imbalances For reported SRB births. or of level groups subnational the at disparities from disasters. data natural exclude and conflicts to national (65) with country-periods (IGME) for com- Estimation of Group these are Inter-agency scope Mortality UN crises how the Child the during from beyond Analyzing procedures issues is adopt We quality 64). SRB mon. data of 63, Moreover, trend study. 51, and tran- our level and (49, the rapid SRB affect in crises in and result disaster, changes to natural reported sient famine, are war, depression like economic factors Other abortion. for inflation SRB the of nonexistence countries. such or existence the confirm siae fTRadnme fbrh o l onre r bandfrom obtained are countries all for births of number and TFR of Estimates Demographic UN the from data VR compile we database, SRB the In the of importance the underscore study our of findings The SRB mask may which SRB, national-level analyzes study Our sex-selective to due imbalance SRB the assessing on focus We epoueSBetmtsfr22cutiswt oa pop- total with countries 212 for estimates SRB produce We .W odc aaquality data conduct We ). egnrt SBi 220 in DSRB generate We IAppendix SI IAppendix, SI and SI h eann 7cutiswtotifraincvrol .%o all of 3.2% only present. cover were information preference 1970–2017. without son in globally or countries births imbalance 57 SRB remaining identified if been The search have literature VR would with the assume countries we in 65 includes which This 1970–2017, inflation. during SRB coverage for risk at not are literature osrce nteya 05bsdo vial aaa httm.W also We time. been that at have data available would on median based that obtain 2005 intervals year and the the uncertainty database, in without constructed training and model the remaining projections, fit correspond- the We 2004, estimates, database. to after reduced collected factor global are the inflation that of 20% data to all ing out leave we inflation, performance. model assess to simulation a and exercises, Validation. Model in are details with The observations variance. more error uncer- with smaller and country-periods for errors, narrower estimates smaller are with model-based ranges point observations and tainty data Resulting by data, a weighted decreases. to strongly VR variance assigned more with weight are error the associated its as errors fitting, as model than increases the larger in reflected be is with to this variance—associated model tend error the the data hence within non-VR non- Errors—and reflect estimated type. and to are source sampling computed data errors of are by Nonsampling sum errors design. Sampling the sampling is data. and the non-VR data variance for for VR error account errors for where We sampling errors observations, censuses. stochastic across and by variance surveys, given error systems, VR in from differences quality data ing 95% Model. least Quality at Data is zero 1970–2017. than during greater y having AMFB 1 as least of identified at probability is for These country the 2). a if Table AMFB: inflation in the listed SRB on (as based inflation selected SRB are of countries evidence statistical strong Appendix, with and (SI data year levels VR start fertility high-quality the between with for relation countries mean 2). in the section The observed of inflation. as of analysis SRB start exception an the of the by to year with determined priors start distributions, is the vague truncated of assign these We mean of zero. the SD at and trape- truncations mean the of lower the parameters with other inflation the function, SRB for zoid used of are year distributions Normal start levels. the truncated intro- for the a in distributions those follow described for hierarchical is onward The factor 1970 inflation duction. from of parametrization modeled inflation The is countries. SRB factor without inflation country-years SRB of (i year–specific (ii model parts: and two the above, of described by sum as given the level, as SRB inflation SRB free for risk at Inflation. countries SRB Potential with 29 Country-Years of the Model for 1970 1970 available before widely from not 1970. year year before was reference affordable reference technology or with with abortion sex-selected data inflation since SRB the countries, from for keep data excluding risk We by at database onward. reduced countries a 29 to model the the fit we values, national line corresponding from deviations their levels natural to) different these suggest close capture estimated data multipliers baselines. the (or the the that by where trends, such countries or given 1, For toward) are baselines. shrunk a national inflation information, (or within limited without to very 1 equal with SRBs order is or is of data multiplier multiplier process any the each country-year–specific series without to countries time The For autoregressive priors dif- country. values. same an uniform ethnic baseline the with the independent modeled toward regional capture assign pooled the to We are are of regions. baselines baselines across national regional national same a ference The on The the origin baseline. within region. ethnic regional countries a to time. across due in differ over differences level SRB (ii to value incorporate values and baseline to baseline respective time, region, of for its over fluctuation allow around constant natural We SRB the be (i country-specific captures to components: the that assumed two multiplier of is country-year–specific product which a value, as baseline inflation Inflation. without SRB Without years Country-Years of Model detail. ntefis u-fsml aiaineecs o onre ihu SRB without countries for exercise validation out-of-sample first the In countries 12 identify we inflation, SRB for risk at countries 29 the of Out base- estimate to and inflation, without country-years for SRB estimate To euetooto-apeadi-apevalidation in-sample and out-of-sample two use We t ecntutadt ult oe oacutfrvary- for account to model quality data a construct We itiuint atr tr er ihotyn TFR outlying with years start capture to distribution PNAS ongtv R nainfco.Tecountry- The factor. inflation SRB nonnegative a ) eto 2 section Appendix, SI | a ,2019 7, May eto 2. section Appendix, SI | emdlSBi country- in SRB model We o.116 vol. xlisteslcinin selection the explains emdlSBi h 29 the in SRB model We | h inflation- the ) o 19 no. national a ) | 9309 a )

SOCIAL SCIENCES conduct an in-sample validation to test the performance of the model with- observation is defined as the difference between the left-out observation out the inflation factor. We randomly leave out 20% of the global reduced and the posterior median of the predictive distribution based on the train- database and fit the model with the training database. We repeat this ing database. Coverage refers to the percentage of left-out data points process 30 times. falling above or below their corresponding 95% or 80% prediction inter- In the second out-of-sample exercise, we focus on the 29 countries at vals. For the 30 rounds of in-sample validations, we compute the averages risk for SRB inflation and leave out all data in these countries collected of these measures. The model validation results suggest that the models are after 2009, corresponding to approximately 20% of the data for these coun- reasonably well calibrated (SI Appendix, section 3). tries. We fit the model with the inflation factor to the training set to obtain median estimates and projections for the SRB and inflation. We also assess ACKNOWLEDGMENTS. We thank Christophe Z. Guilmoto for helpful com- ments and discussions, Vladimira Kantorova for guidance on data sources, the performance of the inflation model by simulating the SRB for each coun- and Danan Gu for guidance on Chinese data. We thank the reviewers and try after 1970 based on the median estimates of the global parameters of the editors for their insightful comments and suggestions. This work is the inflation model (and not the country-specific data). supported by a research grant from the National University of Singapore We calculate various validation measures to assess model performance, (R-608-000-125-646). The study described is solely the responsibility of the including prediction errors and coverage. The error for each left-out authors and does not necessarily represent the official views of the UN.

1. Chahnazarian A (1988) Determinants of the sex ratio at birth: Review of recent 34. Oomman N, Ganatra BR (2002) : The systematic elimination of . literature. Soc Biol 35:214–235. Reprod Health Matters 10:184–188. 2. Dubuc S, Coleman D (2007) An increase in the sex ratio of births to India-born mothers 35. Tandon SL, Sharma R (2006) Female foeticide and in India: An analysis of in England and Wales: Evidence for sex-selective abortion. Popul Dev Rev 33:383–400. crimes against children. Int J Crim Justice Sci 1:1. 3. Garenne M (2002) Sex ratios at birth in African populations: A review of survey data. 36. Garenne M, Hohmann S (2014) Gender saturation in the Southern Caucasus: Family Hum Biol 74:889–900. composition and sex-selective abortion. J Biosoc Sci 46:786–796. 4. Garenne M (2008) Poisson variations of the sex ratio at birth in African demographic 37. United Nations (2017) prospects: The 2017 revision. Available at surveys. Hum Biol 80:473–482. esa.un.org/unpd/wpp/Download/Standard/Population/. Accessed June 28, 2017. 5. Graffelman J, Hoekstra RF (2000) A statistical analysis of the effect of warfare on the 38. Kashyap R, Villavicencio F (2016) The dynamics of son preference, technology dif- human secondary sex ratio. Hum Biol 80:433–445. fusion, and fertility decline underlying distorted sex ratios at birth: A simulation 6. James WH (1984) The sex ratios of black births. Ann Hum Biol 11:39–44. approach. 53:1261–1281. 7. James WH (1985) The sex ratio of oriental births. Ann Hum Biol 12:485–487. 39. Kashyap R, Villavicencio F (2017) An agent-based model of sex ratio at birth dis- 8. James WH (1987) The human sex ratio. Part 1: A review of the literature. Hum Biol tortions. Agent-Based Modelling in Population Studies (Springer, New York), pp 59:721–752. 343–367. 9. Kaba AJ (2008) Sex ratio at birth and racial differences: Why do black women give 40. Dubuc S, Sivia DS (2018) Is sex ratio at birth an appropriate measure of prenatal birth to more females than non-black women? Afr J Reprod Health 12:139–150. sex selection? Findings of a theoretical model and its application to India. BMJ Glob 10. Ruder A (1985) Paternal-age and birth-order effect on the human secondary sex ratio. Health 3:e000675. Am J Hum Genet 37:362–372. 41. Murray CJ, et al. (2018) Population and fertility by age and sex for 195 countries and 11. Marcus M, et al. (1998) Changing sex ratio in the United States, 1969–1995. Fertil territories, 1950–2017: A systematic analysis for the Global Burden of Disease Study Steril 70:270–273. 2017. Lancet 392:1995–2051. 12. Mathews T, Hamilton BE (2005) Trend analysis of the sex ratio at birth in the United 42. Lindley DV, Smith AF (1972) Bayes estimates for the linear model. J R Stat Soc Series States. Natl Vital Stat Rep 53:1–17. B Stat Methodol 34:1–18. 13. Visaria PM (1967) Sex ratio at birth in territories with a relatively complete 43. Biggar RJ, Wohlfahrt J, Westergaard T, Melbye M (1999) Sex ratios, family size, and registration. Eugen Q 14:132–142. birth order. Am J Epidemiol 150:957–962. 14. Basten S, Verropoulou G (2013) Maternity migration and the increased sex ratio at 44. Catalano RA (2003) Sex ratios in the two Germanies: A test of the economic stress birth in Hong Kong SAR. Popul Stud 67:323–334. hypothesis. Hum Reprod 18:1972–1975. 15. Bongaarts J (2013) The implementation of preferences for male offspring. Popul Dev 45. Catalano RA, Bruckner T (2005) Economic antecedents of the Swedish sex ratio. Soc Rev 39:185–208. Sci Med 60:537–543. 16. Bongaarts J, Guilmoto CZ (2015) How many more ? Excess female 46. Davis DL, Gottlieb MB, Stampnitzky JR (1998) Reduced ratio of male to female births mortality and prenatal sex selection, 1970–2050. Popul Dev Rev 41:241–269. in several industrial countries: A sentinel health indicator? JAMA 279:1018–1023. 17. Das Gupta M, et al. (2003) Why is Son preference so persistent in East and South 47. Jacobsen R, Møller H, Mouritsen A (1999) Natural variation in the human sex ratio. Asia? A cross-country study of China, India and the Republic of Korea. J Dev Stud Hum Reprod 14:3120–3125. 40:153–187. 48. Maconochie N, Roman E (1997) Sex ratios: Are there natural variations within the 18. Duthe´ G, Mesle´ F, Vallin J, Badurashvili I, Kuyumjyan K (2012) High sex ratios at birth human population? Br J Obstet Gynaecol 104:1050–1053. in the Caucasus: Modern technology to satisfy old desires. Popul Dev Rev 38:487–501. 49. Song S (2012) Does famine influence sex ratio at birth? Evidence from the 1959–1961 19. Goodkind D (2011) Child underreporting, fertility, and sex ratio imbalance in China. Great Leap Forward Famine in China. Proc Biol Sci 279:2883–2890. Demography 48:291–316. 50. Trivers RL, Willard DE (1973) Natural selection of parental ability to vary the sex ratio 20. Attane´ I, Guilmoto CZ (2007) Watering the Neighbour’s Garden: The Growing of offspring. Science 179:90–92. Demographic Female Deficit in Asia (Comm Int Coop Natl Res Demography, Paris). 51. Venero Fernandez´ SJ, Medina RS, Britton J, Fogarty AW (2011) The association 21. Guilmoto CZ, Hoang` X, Van TN (2009) Recent increase in sex ratio at birth in Vietnam. between living through a prolonged economic depression and the male:female birth PLoS One 4:e4624. ratio–A longitudinal study from Cuba, 1960–2008. Am J Epidemiol 174:1327–1331. 22. Guilmoto CZ (2009) The sex ratio transition in Asia. Popul Dev Rev 35:519–549. 52. Grech V, Vassallo-Agius P, Savona-Ventura C (2003) Secular trends in sex ratios at birth 23. Guilmoto CZ, Ren Q (2011) Socio-economic differentials in birth masculinity in China. in North America and Europe over the second half of the 20th century. J Epidemiol Dev Change 42:1269–1296. Community Health 57:612–615. 24. Guilmoto CZ (2012) Sex Imbalances at Birth: Trends, Consequences and Pol- 53. Branum AM, Parker JD, Schoendorf KC (2009) Trends in US sex ratio by plurality, icy Implications (UN Population Fund Asia Pacific Regional Office, Bangkok City, gestational age and race/ethnicity. Hum Reprod 24:2936–2944. Thailand). 54. James WH (1998) Was the widespread decline in sex ratios at birth caused by 25. Guilmoto CZ (2012) Skewed sex ratios at birth and future marriage squeeze in China reproductive hazards? Hum Reprod 13:1083–1084. and India, 2005–2100. Demography 49:77–100. 55. Andersen ML, Taylor HF (2007) Sociology: Understanding a Diverse Society (Thomson- 26. Guilmoto CZ (2012) Son preference, sex selection, and kinship in Vietnam. Popul Dev Wadsworth, Belmont, CA), 4th edition. Rev 38:31–54. 56. Carmichael GA (2016) Fundamentals of Demographic Analysis: Concepts, Measures 27. Hudson VM, Den Boer A (2004) Bare Branches: The Security Implications of Asia’s and Methods (Springer, Cham, Switzerland). Surplus Male Population (MIT Press, Cambridge, MA). 57. Caselli G, Vallin J, Wunsch G (2006) Demography: Analysis and Synthesis: A Treatise in 28. Lin Tc (2009) The decline of son preference and rise of gender indifference in Taiwan Population Studies (Elsevier, New York). since 1990. Demogr Res 20:377–402. 58. Preston S, Heuveline P, Guillot M (2001) Demography: Measuring and Modeling 29. Mesle´ F, Vallin J, Badurashvili I (2007) A sharp increase in sex ratio at birth in the Population Processes (Blackwell, Oxford). Caucasus. Why? How? Watering the Neighbour’s Garden: The Growing Demographic 59. United Nations (1983) Manual X: Indirect Techniques for Demographic Estimation Female Deficit in Asia (Comm Int Coop Natl Res Demography, Paris), pp. 73–88. (United Nations, New York). 30. Park CB, Cho NH (1995) Consequences of son preference in a low-fertility society: 60. Hull TH (1990) Recent trends in sex ratios at birth in China. Popul Dev Rev 16:63–83. Imbalance of the sex ratio at birth in Korea. Popul Dev Rev 21:59–84. 61. Jha P, et al. (2006) Low male-to-female sex ratio of children born in India: National 31. Allahbadia GN (2002) The 50 million missing women. J Assist Reprod Genet 19: survey of 1·1 million households. Lancet 367:211–218. 411–416. 62. Retherford RD, Roy T (2003) Factors affecting sex-selective abortion in India and 17 32. George SM (2002) Sex selection/determination in India: Contemporary developments. major states (Int Inst Population Sci, Mumbai, India), National Family Health Survey Reprod Health Matters 10:190–192. Subject Reports No. 21. 33. Goodkind D (1997) Sex-selective abortion, reproductive rights, and the greater locus 63. Catalano R, Bruckner T, Marks AR, Eskenazi B (2006) Exogenous shocks to the human of gender discrimination in family formation: Cairo’s unresolved questions (Univ sex ratio: The case of September 11, 2001 in New York City. Hum Reprod 21:3127– Michigan Population Stud Cent, Ann Arbor), Report 97-383. 3131.

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