Demography(2012)49:77-100 D0I10.1007/s13524-011-0083-7 Skewed Sex Ratios at Birth and Future Marriage Squeeze in China and India,2005-2100 Christophe Z.Guilmoto Published online:17 December 2011 C Population Association of America 2011 Abstract I examine the potential impact of the anticipated future marriage squeeze on nuptiality patterns in China and India during the twenty-first century.I use population projections from 2005 to 2100 based on three different scenarios for the sex ratio at birth (SRB).To counteract the limitations of cross-sectional methods commonly used to assess the severity of marriage squeezes,I use a two-sex cohort-based procedure to simulate marriage pattems over the twenty-first century based on the female dominance model.I also examine two more-flexible marriage functions to illustrate the potential impact of changes in marriage schedules as a response to the marriage squeeze. Longitudinal indicators of marriage squeeze indicate that the number of prospective grooms in both countries will exceed that of prospective brides by more 50%for three decades in the most favorable scenario.Rates of male bachelorhood will not peak before 2050,and the squeeze conditions will be felt several decades thereafter,even among cohorts unaffected by adverse SRB.Ifthe SRB is allowed to return to normalcy by 2020, the proportion of men unmarried at age 50 is expected to rise to 15%in China by 2055 and to 10%in India by 2065.India suffers from the additional impact of a delayed fertility transition on its age structures. Keywords China.India.Sex ratio at birth.Marriage simulation.Marriage squeeze Introduction The proportion of male birth cohorts has reached unusually high levels over the last 20 years in several Asian countries.'In many countries,the sex ratio at birth The literature on the sex ratio issues in Asia is now abundant and describes in particular determinants of gender discrimination.On the diversity of situations across Asia,see Croll(2000),Miller(2001),Attane and Guilmoto (2007),and UNFPA-sponsored case studies (UNFPA 2007). C.Z.Guilmoto(☒ Institut de recherche pour le developpement/CEPED,19 rue Jacob,75006 Paris,France e-mail:christophe.guilmoto @ird.fr ②Springer
Skewed Sex Ratios at Birth and Future Marriage Squeeze in China and India, 2005–2100 Christophe Z. Guilmoto Published online: 17 December 2011 # Population Association of America 2011 Abstract I examine the potential impact of the anticipated future marriage squeeze on nuptiality patterns in China and India during the twenty-first century. I use population projections from 2005 to 2100 based on three different scenarios for the sex ratio at birth (SRB). To counteract the limitations of cross-sectional methods commonly used to assess the severity of marriage squeezes, I use a two-sex cohort-based procedure to simulate marriage patterns over the twenty-first century based on the female dominance model. I also examine two more-flexible marriage functions to illustrate the potential impact of changes in marriage schedules as a response to the marriage squeeze. Longitudinal indicators of marriage squeeze indicate that the number of prospective grooms in both countries will exceed that of prospective brides by more 50% for three decades in the most favorable scenario. Rates of male bachelorhood will not peak before 2050, and the squeeze conditions will be felt several decades thereafter, even among cohorts unaffected by adverse SRB. If the SRB is allowed to return to normalcy by 2020, the proportion of men unmarried at age 50 is expected to rise to 15% in China by 2055 and to 10% in India by 2065. India suffers from the additional impact of a delayed fertility transition on its age structures. Keywords China . India . Sex ratio at birth . Marriage simulation . Marriage squeeze Introduction The proportion of male birth cohorts has reached unusually high levels over the last 20 years in several Asian countries.1 In many countries, the sex ratio at birth Demography (2012) 49:77–100 DOI 10.1007/s13524-011-0083-7 1 The literature on the sex ratio issues in Asia is now abundant and describes in particular determinants of gender discrimination. On the diversity of situations across Asia, see Croll (2000), Miller (2001), Attané and Guilmoto (2007), and UNFPA-sponsored case studies (UNFPA 2007). C. Z. Guilmoto (*) Institut de recherche pour le développement/CEPED, 19 rue Jacob, 75006 Paris, France e-mail: christophe.guilmoto@ird.fr
78 C.Z.Guilmoto (hereafter SRB)has increased above the standard range of 104-106 male births per 100 female births,reaching values above 110 or even 120.This process of demographic masculinization stems mostly from the increasing frequency of sex- selective abortions across Asia,from the Caucasus to South and East Asia.While discrimination against unborn girls today is a dismal reflection of the status of women,sex imbalances may also lead tomorrow to the potential disruption of marriage systems set off by the unavoidable shortage in prospective brides. In this article,I aim to evaluate the potential severity of the marriage crisis and to explore the potential responses of nuptiality systems to future changes in China and India.I selected these two countries because of their demographic weight in the world and their early rise in SRB over the last two decades.For this article, compared with previous studies,demographic parameters have been updated and the study period extended from the conventional year 2050 to 2100 in view of the especially long-term impact of SRB imbalances on marriage patterns.But the most important difference from previous research is the use of a longitudinal simulation procedure to simulate future male and female marriages rather than relying on cross- sectional indicators of sex ratio imbalances. The article starts with a presentation of the data and models used to simulate future marriage patterns.Taking 2005 as baseline year,my simulations rely on various population projections based on three scenarios of change in SRB over the coming decades.I also describe the different marriage models used in the simulations.Next,I present results from the simulations,starting with new estimates of the extent of the marriage squeeze and the analysis of the respective contribution to it of changes in age structures and in birth masculinity.Two additional simulations illustrate the extent to which mere changes in marriage timing could reduce the intensity of the marriage squeeze.The article concludes with a synthesis of the results and a review of some of the implications of my findings. Data and Models for Marriage Simulations The simulation of marriage pattems in China and India requires first a set of population projections based on different SRB scenarios for the future.Since other long-term trends in age structures may also affect the marriage-sex ratio,I also develop a set of projections without rise in SRB levels.I examine two dimensions of population change in the first sections and describe the parameters used in the population projections.I then discuss the limitations of the cross-sectional sex ratio indicators of marriage squeeze and present a more realistic indicator of marriage squeeze based on longitudinal marriage simulations.These simulations are based on specific parameters for projecting female marriage patterns in the future.At the end of this section,I also explore what other responses of male nuptiality to the increasing marriage squeeze conditions could be by presenting two alternative marriage functions. Impact of Population Structures on Sex Imbalances Since men usually marry younger women,the birth cohorts of future husbands tend to be older (Esteve and Cabre 2005;McDonald 1995).But the size of these birth ②Springer
(hereafter SRB) has increased above the standard range of 104–106 male births per 100 female births, reaching values above 110 or even 120. This process of demographic masculinization stems mostly from the increasing frequency of sexselective abortions across Asia, from the Caucasus to South and East Asia. While discrimination against unborn girls today is a dismal reflection of the status of women, sex imbalances may also lead tomorrow to the potential disruption of marriage systems set off by the unavoidable shortage in prospective brides. In this article, I aim to evaluate the potential severity of the marriage crisis and to explore the potential responses of nuptiality systems to future changes in China and India. I selected these two countries because of their demographic weight in the world and their early rise in SRB over the last two decades. For this article, compared with previous studies, demographic parameters have been updated and the study period extended from the conventional year 2050 to 2100 in view of the especially long-term impact of SRB imbalances on marriage patterns. But the most important difference from previous research is the use of a longitudinal simulation procedure to simulate future male and female marriages rather than relying on crosssectional indicators of sex ratio imbalances. The article starts with a presentation of the data and models used to simulate future marriage patterns. Taking 2005 as baseline year, my simulations rely on various population projections based on three scenarios of change in SRB over the coming decades. I also describe the different marriage models used in the simulations. Next, I present results from the simulations, starting with new estimates of the extent of the marriage squeeze and the analysis of the respective contribution to it of changes in age structures and in birth masculinity. Two additional simulations illustrate the extent to which mere changes in marriage timing could reduce the intensity of the marriage squeeze. The article concludes with a synthesis of the results and a review of some of the implications of my findings. Data and Models for Marriage Simulations The simulation of marriage patterns in China and India requires first a set of population projections based on different SRB scenarios for the future. Since other long-term trends in age structures may also affect the marriage-sex ratio, I also develop a set of projections without rise in SRB levels. I examine two dimensions of population change in the first sections and describe the parameters used in the population projections. I then discuss the limitations of the cross-sectional sex ratio indicators of marriage squeeze and present a more realistic indicator of marriage squeeze based on longitudinal marriage simulations. These simulations are based on specific parameters for projecting female marriage patterns in the future. At the end of this section, I also explore what other responses of male nuptiality to the increasing marriage squeeze conditions could be by presenting two alternative marriage functions. Impact of Population Structures on Sex Imbalances Since men usually marry younger women, the birth cohorts of future husbands tend to be older (Esteve and Cabré 2005; McDonald 1995). But the size of these birth 78 C.Z. Guilmoto
Skewed Sex Ratios at Birth and Future Marriage Squeeze 79 cohorts stems also from long-term trends:the number of annual births tends to increase during the first phase of the demographic transition but decreases later after prolonged fertility decline.India's case is probably emblematic of this situation because the number of births recorded a regular increment until 1990.For instance, the annual increase in the birth cohort size reached 1.5%during the 1970s.This means that there were,on average,7.7%more prospective wives born during this decade than husbands born five years earlier (five years being the current age difference at marriage),and this imbalance affected the marriage market 20 years later.Incidentally,this disequilibrium in the past is often associated with the concomitant rise in dowry observed in India after independence (Mari Bhat and Halli 1999).But with decreases in fertility and further changes in age structures,the number of births started declining in the 1990s,and,according to my population projections,this reduction in the size of birth cohorts is expected to accelerate in the future.For instance,by 2025,an average birth cohort in a given year would be 7% larger than the cohort born five years later.Without any rise in the SRB,male adults would therefore become more numerous than their prospective brides. China presents an undoubtedly more complicated picture because of the irregular size of its birth cohorts since the 1950s.While the number of births has,on the whole,decreased since the 1980s,this decline is less rapid than in India and also is disturbed by the regular ups and downs that are a legacy of China's volatile demographic past.Short-term fluctuations therefore have a marked effect on age and sex distributions in China and will directly influence the sex ratio of adults-a point highlighted by Goodkind(2006)and Rallu(2006).But the decline in the number of births will also be pronounced in China,especially during 2020-2035.3 As a result, the impact of skewed SRBs in China and India on adult sex ratios is likely to be compounded by future age-structural transformations.I therefore insert a separate projection set designed to assess the potential influences of changes in age structures on marriage imbalances. Birth Imbalances in the Future My demographic projections for China and India will start from 2005 and extend to 2100.They are based on the most recent demographic estimates as well as on assumptions that are different from previous attempts.Parameters for these projections have mostly been borrowed from the 2006 prospects by the United Nations Population Division,but several adjustments and corrections have been made (see Appendix A). SRB levels for the future decades are also essential to my projections.SRB started to increase above normal values 20 years ago in China and India (for China, Data used in this section are based on United Nations estimates for 1950-2005 complemented by projection results for the period beyond 2005. According to my projections,the overall yearly decline in birth cohort size during the 2005-2100 period is 0.25%in China and 0.4%in India (rapid transition scenario). No projection exists for India.Forecasts of China's future sex imbalances (Attane 2006;Tuljapurkar et al.1995)are based on 1990 or 2000 census data and on fixed fertility and mortality assumptions. Estimates provided by Jiang et al.(2007)follow a more realistic demographic scenario.An alternative method based on nuptiality tables has also been proposed by Jiang (2011). ②Springer
cohorts stems also from long-term trends: the number of annual births tends to increase during the first phase of the demographic transition but decreases later after prolonged fertility decline. India’s case is probably emblematic of this situation because the number of births recorded a regular increment until 1990. For instance, the annual increase in the birth cohort size reached 1.5% during the 1970s. This means that there were, on average, 7.7% more prospective wives born during this decade than husbands born five years earlier (five years being the current age difference at marriage), and this imbalance affected the marriage market 20 years later.2 Incidentally, this disequilibrium in the past is often associated with the concomitant rise in dowry observed in India after independence (Mari Bhat and Halli 1999). But with decreases in fertility and further changes in age structures, the number of births started declining in the 1990s, and, according to my population projections, this reduction in the size of birth cohorts is expected to accelerate in the future. For instance, by 2025, an average birth cohort in a given year would be 7% larger than the cohort born five years later. Without any rise in the SRB, male adults would therefore become more numerous than their prospective brides. China presents an undoubtedly more complicated picture because of the irregular size of its birth cohorts since the 1950s. While the number of births has, on the whole, decreased since the 1980s, this decline is less rapid than in India and also is disturbed by the regular ups and downs that are a legacy of China’s volatile demographic past. Short-term fluctuations therefore have a marked effect on age and sex distributions in China and will directly influence the sex ratio of adults—a point highlighted by Goodkind (2006) and Rallu (2006). But the decline in the number of births will also be pronounced in China, especially during 2020–2035.3 As a result, the impact of skewed SRBs in China and India on adult sex ratios is likely to be compounded by future age-structural transformations. I therefore insert a separate projection set designed to assess the potential influences of changes in age structures on marriage imbalances. Birth Imbalances in the Future My demographic projections for China and India will start from 2005 and extend to 2100. They are based on the most recent demographic estimates as well as on assumptions that are different from previous attempts.4 Parameters for these projections have mostly been borrowed from the 2006 prospects by the United Nations Population Division, but several adjustments and corrections have been made (see Appendix A). SRB levels for the future decades are also essential to my projections. SRB started to increase above normal values 20 years ago in China and India (for China, 2 Data used in this section are based on United Nations estimates for 1950–2005 complemented by projection results for the period beyond 2005. 3 According to my projections, the overall yearly decline in birth cohort size during the 2005–2100 period is 0.25% in China and 0.4% in India (rapid transition scenario). 4 No projection exists for India. Forecasts of China’s future sex imbalances (Attané 2006; Tuljapurkar et al. 1995) are based on 1990 or 2000 census data and on fixed fertility and mortality assumptions. Estimates provided by Jiang et al. (2007) follow a more realistic demographic scenario. An alternative method based on nuptiality tables has also been proposed by Jiang (2011). Skewed Sex Ratios at Birth and Future Marriage Squeeze 79
80 C.Z.Guilmoto see,e.g.,Banister(2004),Li et al.(2007),and Zeng et al.(1993);for India,see Mari Bhat (2002a,b)and Patel (2006)).As available data indicate,SRB has risen to a level of above 115 in many Asian countries,from Armenia to China,and seems to have leveled off since 2000.There are,in fact,reasons to believe that SRB levels will not increase indefinitely and may ultimately decline.Both China and India during the last decade have introduced or strengthened comprehensive programmes to tackle sex-selective abortions (Joseph and Center for Youth Development and Activities(CYDA)2007;Li et al.2007).Moreover,recent trends indicate that in several areas,the SRB may be about to level off or to decline.For instance,data for China based on the 2000 census (long form)and on the 2005 1%sample survey reflects a near stagnation of the national average,from 119.9 in 2000 to 120.5 in 2005,which has already been interpreted as the beginning of a turnaround,with a significant decline in birth masculinity being observed in southeastem provinces such as Guangdong or Guangxi.There has also been a perceptible shift in SRB since 2002 in several states of northwest India,such as Haryana,Delhi,and Punjab.In addition to these concordant traces of moderate decrease,the remarkable experience of South Korea-where SRB first rose to 116 in 1990 and then gradually declined to 106 in 2008-suggests that SRB may follow typical transitional patterns,with an initial rise followed by a later decline (Guilmoto 2009). In order to explore the possible consequences of future gender imbalances,it seems crucial to consider several different SRB scenarios.First,according to a no- transition scenario,SRB will remain at its current level until 2100;the SRB would therefore stay at 120 in China and at 113 in India during the entire twenty-first century.Second,according to a rapid-transition scenario,the SRB starts decreasing immediately after 2005 and declines to a normal 105 level in 15 years-at a pace slightly faster than that observed in South Korea after 1990.This is admittedly a rather optimistic transitional scenario in which birth imbalances would have vanished by 2020.Both scenarios are deliberately extreme.The first,"business-as- usual"scenario implies,for instance,that a high SRB would remain sustainable,in demographic and social terms,during the entire century,a proposition that seems rather implausible in view of the implications of abnormal sex ratios in the long run. In contrast,the second,transitional scenario would require a complete change in gender attitudes in 15 years,something that government interventions or spontaneous social change may not be able to achieve.But taken together,these first two scenarios may reasonably be seen as the upper and lower limits for simulating sex-transitional change in China and India. I have also included an entirely different scenario based on the hypothesis of the absence of any sex ratio imbalance since 1980.This third baseline scenario of normal SRB posits a constant SRB of 105 and will serve to highlight the specific impact on marriage imbalances of age-structural changes caused in particular by the process of fertility decline in China and India (see Appendix A for details). 5 Kulkarni(2010)analyzed the recent SRB downturn in India;see also Sharma and Haub(2008).Chinese trends are described in Das Gupta et al.(2009)and Guilmoto and Ren(2011).Preliminary data from the Chinese 2010 census put the sex ratio at birth at 118,confirming the slight downturn.Original data are found in the reports of the 2000 census and of the 2005 1%sample survey for China,while Indian data are from the annual reports of the Sample Registration System. ②Springer
see, e.g., Banister (2004), Li et al. (2007), and Zeng et al. (1993); for India, see Mari Bhat (2002a, b) and Patel (2006)). As available data indicate, SRB has risen to a level of above 115 in many Asian countries, from Armenia to China, and seems to have leveled off since 2000. There are, in fact, reasons to believe that SRB levels will not increase indefinitely and may ultimately decline. Both China and India during the last decade have introduced or strengthened comprehensive programmes to tackle sex-selective abortions (Joseph and Center for Youth Development and Activities (CYDA) 2007; Li et al. 2007). Moreover, recent trends indicate that in several areas, the SRB may be about to level off or to decline. For instance, data for China based on the 2000 census (long form) and on the 2005 1% sample survey reflects a near stagnation of the national average, from 119.9 in 2000 to 120.5 in 2005, which has already been interpreted as the beginning of a turnaround, with a significant decline in birth masculinity being observed in southeastern provinces such as Guangdong or Guangxi. There has also been a perceptible shift in SRB since 2002 in several states of northwest India, such as Haryana, Delhi, and Punjab.5 In addition to these concordant traces of moderate decrease, the remarkable experience of South Korea—where SRB first rose to 116 in 1990 and then gradually declined to 106 in 2008—suggests that SRB may follow typical transitional patterns, with an initial rise followed by a later decline (Guilmoto 2009). In order to explore the possible consequences of future gender imbalances, it seems crucial to consider several different SRB scenarios. First, according to a notransition scenario, SRB will remain at its current level until 2100; the SRB would therefore stay at 120 in China and at 113 in India during the entire twenty-first century. Second, according to a rapid-transition scenario, the SRB starts decreasing immediately after 2005 and declines to a normal 105 level in 15 years—at a pace slightly faster than that observed in South Korea after 1990. This is admittedly a rather optimistic transitional scenario in which birth imbalances would have vanished by 2020. Both scenarios are deliberately extreme. The first, “business-asusual” scenario implies, for instance, that a high SRB would remain sustainable, in demographic and social terms, during the entire century, a proposition that seems rather implausible in view of the implications of abnormal sex ratios in the long run. In contrast, the second, transitional scenario would require a complete change in gender attitudes in 15 years, something that government interventions or spontaneous social change may not be able to achieve. But taken together, these first two scenarios may reasonably be seen as the upper and lower limits for simulating sex-transitional change in China and India. I have also included an entirely different scenario based on the hypothesis of the absence of any sex ratio imbalance since 1980. This third baseline scenario of normal SRB posits a constant SRB of 105 and will serve to highlight the specific impact on marriage imbalances of age-structural changes caused in particular by the process of fertility decline in China and India (see Appendix A for details). 5 Kulkarni (2010) analyzed the recent SRB downturn in India; see also Sharma and Haub (2008). Chinese trends are described in Das Gupta et al. (2009) and Guilmoto and Ren (2011). Preliminary data from the Chinese 2010 census put the sex ratio at birth at 118, confirming the slight downturn. Original data are found in the reports of the 2000 census and of the 2005 1% sample survey for China, while Indian data are from the annual reports of the Sample Registration System. 80 C.Z. Guilmoto
Skewed Sex Ratios at Birth and Future Marriage Squeeze 81 Measuring Marriage Squeeze Adult sex ratios weighted by marriage rates provide the usual index to assess the intensity of demographic disequilibria in the marriage market.This indicator allows the incorporation ofboth the size of specific cohorts and the effects ofage-specific nuptiality rates in my computations.However,it presents serious limitations for the appraisal ofthe actual impact of sustained sex imbalances.The major issue related to strictly synchronic indicators,such as weighted sex ratios,is that they do not take the potential effects ofthe past nuptiality experience of each cohort into consideration.When surplus male bachelors fail to marry in a given year,they will unavoidably inflate the pool of potential grooms in the following year,and if the sex disequilibrium does not reduce rapidly, unmarried bachelors will accumulate in the marriage market and further aggravate the squeeze conditions.This is a direct application of a basic law in queuing theory according to which the number of people in a system (here the marriage market)is a function not only of arrival rates(cohort size)but also of the queuing time(number of years unmarried).But usual cross-sectional sex ratio indicators fail to reflect the cumulative impact of the marriage squeeze in the previous periods. A more appropriate solution to this conundrum is the two-sex cohort-based simulation of marriages.In this approach,I compute the number of first unions by using the estimated number of single men and women during each five-year period starting from 2005.In so doing,I deduce the size of the unmarried population at the end of each period and use it to simulate marriages taking place during the next period.This approach is longitudinal as we follow individual cohorts and their nuptiality over the years.It also makes it possible to estimate the mean age at marriage and the proportion of people unmarried at age 50.To assess the intensity of the future marriage squeeze,I use a cohort-based ratio of expected first male marriages to expected first female marriages,which I refer to here as the marriage squeeze indicator (MSD).In addition to the specific effect of cohort sizes (also captured by the weighted sex ratio),the MSI is influenced by the population who did not marry during the previous periods.This simulation technique is based on first- marriage tables by age,sex,and period.But while weighted sex ratios are computed from first-marriage rates applied to the projected population,the cohort-based method uses marriage probabilities(ratio of marriages to single population by age and sex)and is therefore affected by any backlog of unmarried men or women. A key component in the simulations is the adjustment function used to quantify the number of marriages occurring in case of marriage squeeze,when the expected numbers of male and female marriages differ.The main marriage function used in the simulations is a modified female dominance (FD)model,which is applicable when there is a deficit of women.In the original FD model,the number of marriages is determined only by female marriage rates.'Since not all men are able to marry, their nuptiality rates must be adjusted downward.The FD model presupposes that female marriage rates will follow a fixed trajectory and that they will not be affected by variations in the number of unmarried men as long as there is a male surplus.I Little's law states that the average number in a given stable system is equal to the rate of new arrivals in the system multiplied by their average time in the system(Tijms 2003:50-52). 7 See Keyfitz and Caswell (2005)and Iannelli et al.(2005)for a broader discussion of marriage models. ②Springer
Measuring Marriage Squeeze Adult sex ratios weighted by marriage rates provide the usual index to assess the intensity of demographic disequilibria in the marriage market. This indicator allows the incorporation of both the size of specific cohorts and the effects of age-specific nuptiality rates in my computations. However, it presents serious limitations for the appraisal of the actual impact of sustained sex imbalances. The major issue related to strictly synchronic indicators, such as weighted sex ratios, is that they do not take the potential effects of the past nuptiality experience of each cohort into consideration. When surplus male bachelors fail to marry in a given year, they will unavoidably inflate the pool of potential grooms in the following year, and if the sex disequilibrium does not reduce rapidly, unmarried bachelors will accumulate in the marriage market and further aggravate the squeeze conditions. This is a direct application of a basic law in queuing theory according to which the number of people in a system (here the marriage market) is a function not only of arrival rates (cohort size) but also of the queuing time (number of years unmarried).6 But usual cross-sectional sex ratio indicators fail to reflect the cumulative impact of the marriage squeeze in the previous periods. A more appropriate solution to this conundrum is the two-sex cohort-based simulation of marriages. In this approach, I compute the number of first unions by using the estimated number of single men and women during each five-year period starting from 2005. In so doing, I deduce the size of the unmarried population at the end of each period and use it to simulate marriages taking place during the next period. This approach is longitudinal as we follow individual cohorts and their nuptiality over the years. It also makes it possible to estimate the mean age at marriage and the proportion of people unmarried at age 50. To assess the intensity of the future marriage squeeze, I use a cohort-based ratio of expected first male marriages to expected first female marriages, which I refer to here as the marriage squeeze indicator (MSI). In addition to the specific effect of cohort sizes (also captured by the weighted sex ratio), the MSI is influenced by the population who did not marry during the previous periods. This simulation technique is based on firstmarriage tables by age, sex, and period. But while weighted sex ratios are computed from first-marriage rates applied to the projected population, the cohort-based method uses marriage probabilities (ratio of marriages to single population by age and sex) and is therefore affected by any backlog of unmarried men or women. A key component in the simulations is the adjustment function used to quantify the number of marriages occurring in case of marriage squeeze, when the expected numbers of male and female marriages differ. The main marriage function used in the simulations is a modified female dominance (FD) model, which is applicable when there is a deficit of women. In the original FD model, the number of marriages is determined only by female marriage rates.7 Since not all men are able to marry, their nuptiality rates must be adjusted downward. The FD model presupposes that female marriage rates will follow a fixed trajectory and that they will not be affected by variations in the number of unmarried men as long as there is a male surplus. I 6 Little’s law states that the average number in a given stable system is equal to the rate of new arrivals in the system multiplied by their average time in the system (Tijms 2003:50–52). 7 See Keyfitz and Caswell (2005) and Iannelli et al. (2005) for a broader discussion of marriage models. Skewed Sex Ratios at Birth and Future Marriage Squeeze 81
82 C.Z.Guilmoto have,however,added a constraint to this model:the age difference at marriage between men and women is assumed to remain the same.In other words,women are not only able to marry at the chosen age but are also able to select husbands with a constant age difference.This model offers surplus men no flexibility,such as delaying marriage.Because this model depends almost exclusively on female nuptiality behavior,it is crucial to delineate appropriately the most likely course of female marriage patterns over the coming decades. Simulating Female Nuptiality in Asia It would be rather unrealistic to assume that current female nuptiality patterns will remain unchanged in China and India until 2100.On the contrary,Asian marriage systems are today characterized by rapid and deep transformations:under the impact of various factors such as prolonged education,urbanization,access to formal employment,and increasing social autonomy,women have delayed their marriages in many East Asian countries and metropolitan areas.New phenomena,such as the end of universal marriage or,to a lesser extent,cohabitation,have even emerged over the last 15 years. In India,women still marry rather early-19.8 years is the latest estimated average age at first marriage(see Appendix B).Such a low figure may appear at first sight to reflect the permanence of traditional matrimonial arrangements privileging early female union soon after menarche.Until the 1930s,the country was indeed characterized by very early marriages,with a large proportion of women betrothed before reaching physiological maturity.But the pace of change observed in India has been remarkable,and age at first marriage has regularly increased ever since:it increased from 13 to 15 years in 1951,reaching 18.3 in 1981 and 20.2 in 2001 (census-based estimates).The progress in female age at first marriage-one additional year per decade since 1931-has,in fact,been strictly linear in India. Moreover,recent NFHS data (IIPS 2007)indicate that age at marriage in today's India is also closely correlated to education levels as well as to urban residence and socioeconomic status.With such covariates of late female marriage,current trends in rapidly modernizing India suggest further gains in mean age at marriage in the future.A plausible hypothesis for India thus consists in positing a gradual rise in female age at first marriage in India,reaching 23.5 years at the middle of the century, at a level similar to what is observed today in China.I have also posited a gradual leveling of remarriage rates between men and women in 2005-2050.10 In 2005,Chinese women married on average at the age of 23.5 years,a value significantly above the legal age at union (20 years),but with only 2%of women still unmarried in the 30-to 34-year age group.The proportion single at 50 was as low as 0.2%.Female age at first marriage was 22.4 years at the time of the 1982 Jones(2007)provides the most recent comprehensive synthesis of nuptiality in East Asia.Detailed statistics and case studies are also available in Jones and Ramdas(2004)and Xenos et al.(2006).as well as in United Nations (1990)for trends prior to the 1990s.See also Retherford et al.(2001)on Japan,and Kwon (2007)on South Korea. This figure is slightly above that of Kerala women today but still distinctly below that of Sri Lankan women(Caldwell 2005). 10 Higher remarriage rates among men than women represent a rather untenable hypothesis in view of the mounting surplus of unmarried men.On remarriage in India,see Mari Bhat and Halli(1999)and Chen(2000). ②Springer
have, however, added a constraint to this model: the age difference at marriage between men and women is assumed to remain the same. In other words, women are not only able to marry at the chosen age but are also able to select husbands with a constant age difference. This model offers surplus men no flexibility, such as delaying marriage. Because this model depends almost exclusively on female nuptiality behavior, it is crucial to delineate appropriately the most likely course of female marriage patterns over the coming decades. Simulating Female Nuptiality in Asia It would be rather unrealistic to assume that current female nuptiality patterns will remain unchanged in China and India until 2100. On the contrary, Asian marriage systems are today characterized by rapid and deep transformations: under the impact of various factors such as prolonged education, urbanization, access to formal employment, and increasing social autonomy, women have delayed their marriages in many East Asian countries and metropolitan areas.8 New phenomena, such as the end of universal marriage or, to a lesser extent, cohabitation, have even emerged over the last 15 years. In India, women still marry rather early—19.8 years is the latest estimated average age at first marriage (see Appendix B). Such a low figure may appear at first sight to reflect the permanence of traditional matrimonial arrangements privileging early female union soon after menarche. Until the 1930s, the country was indeed characterized by very early marriages, with a large proportion of women betrothed before reaching physiological maturity. But the pace of change observed in India has been remarkable, and age at first marriage has regularly increased ever since: it increased from 13 to 15 years in 1951, reaching 18.3 in 1981 and 20.2 in 2001 (census-based estimates). The progress in female age at first marriage—one additional year per decade since 1931—has, in fact, been strictly linear in India. Moreover, recent NFHS data (IIPS 2007) indicate that age at marriage in today’s India is also closely correlated to education levels as well as to urban residence and socioeconomic status. With such covariates of late female marriage, current trends in rapidly modernizing India suggest further gains in mean age at marriage in the future. A plausible hypothesis for India thus consists in positing a gradual rise in female age at first marriage in India, reaching 23.5 years at the middle of the century, at a level similar to what is observed today in China.9 I have also posited a gradual leveling of remarriage rates between men and women in 2005–2050.10 In 2005, Chinese women married on average at the age of 23.5 years, a value significantly above the legal age at union (20 years), but with only 2% of women still unmarried in the 30- to 34-year age group. The proportion single at 50 was as low as 0.2%. Female age at first marriage was 22.4 years at the time of the 1982 8 Jones (2007) provides the most recent comprehensive synthesis of nuptiality in East Asia. Detailed statistics and case studies are also available in Jones and Ramdas (2004) and Xenos et al. (2006), as well as in United Nations (1990) for trends prior to the 1990s. See also Retherford et al. (2001) on Japan, and Kwon (2007) on South Korea. 9 This figure is slightly above that of Kerala women today but still distinctly below that of Sri Lankan women (Caldwell 2005). 10 Higher remarriage rates among men than women represent a rather untenable hypothesis in view of the mounting surplus of unmarried men. On remarriage in India, see Mari Bhat and Halli (1999) and Chen (2000). 82 C.Z. Guilmoto
Skewed Sex Ratios at Birth and Future Marriage Squeeze 83 census,and it has only marginally increased since then.Jones (2007:466)attributed these features to both institutional and structural factors.Among men,the average age at first marriage was 25.1 years and has remained constant over the last two decades.However,because the experience of neighboring countries,such as South Korea and Japan,suggests that female age at marriage may rise in the future,I have also postulated a gradual increase in age at marriage among Chinese women up to 26.5 years in 2050,a pace of change comparable to that hypothesized for India.It may be observed that the projected female age at first marriage for China in 2050 remains significantly below the current figures for Japan and South Korea,where women today marry,on average,at age 29. Alterative Marriage Models The modified FD model corresponds to a reasonable scenario of future marriages based on both nuptiality changes among Asian women and a strictly parallel rise in male nuptiality.Yet,this system allows for almost no flexibility in marriage patterns since both female and male marriage schedules are fixed.In this section,I relax some of these assumptions and explore two alternative ways in which the marriage market may adjust to gender imbalances,mostly through a gradual increase in the age difference between spouses caused either by earlier female marriages or by delayed male marriages(methods and parameters are in Appendix B). One possible change corresponds to symmetrical changes in male and female marriage schedules.This is the harmonic mean(HM)model,which is probably the most commonly used marriage matching function(Schoen 1981;see also Okun 2001;Qian and Preston 1993;and Raymo and Iwasawa 2005).This method provides the basis for a self-regulatory marriage system in the case of imbalances,such that the surplus sex is assumed to temporarily defer marriage while the deficit sex is expected to marry earlier.At the same time,the average age at first marriage of the combined male and female cohorts remains the same.The HM model implies that the deficit sex takes advantage of the relative surplus of the opposite sex by marrying earlier because its pool of prospective spouses has momentarily expanded.In other words,union is regarded as partly constrained by the number of suitable partners,and marriage probabilities are expected to rise when the relative size of the unmarried population of the opposite sex increases.Because union has long been nearly universal among women in China and India,there is no pool of available unmarried women,and the application of the HM model entails a reduction in female age at marriage.Such an adjustment may be conceivable if current constraints on marriage-such as intense dowry negotiations in India or prohibition of early marriage in China-were relaxed. Similarly,the abundance of marriageable men could also improve the probability of women finding suitable partners by shortening the search period.2 I also use a different model that combines features from the FD model (rising female age at marriage)and the HM model(rising age difference).In this hybrid model based on delayed male marriage (DMM),I posit a regular increase in female I am grateful to the suggestion of an anonymous reviewer on this point. Better marriage opportunities for women and lower dowry costs in high sex-ratio societies are among the hypotheses put forward by Guttentag and Secord(1983). ②Springer
census, and it has only marginally increased since then. Jones (2007:466) attributed these features to both institutional and structural factors. Among men, the average age at first marriage was 25.1 years and has remained constant over the last two decades. However, because the experience of neighboring countries, such as South Korea and Japan, suggests that female age at marriage may rise in the future, I have also postulated a gradual increase in age at marriage among Chinese women up to 26.5 years in 2050, a pace of change comparable to that hypothesized for India.11 It may be observed that the projected female age at first marriage for China in 2050 remains significantly below the current figures for Japan and South Korea, where women today marry, on average, at age 29. Alternative Marriage Models The modified FD model corresponds to a reasonable scenario of future marriages based on both nuptiality changes among Asian women and a strictly parallel rise in male nuptiality. Yet, this system allows for almost no flexibility in marriage patterns since both female and male marriage schedules are fixed. In this section, I relax some of these assumptions and explore two alternative ways in which the marriage market may adjust to gender imbalances, mostly through a gradual increase in the age difference between spouses caused either by earlier female marriages or by delayed male marriages (methods and parameters are in Appendix B). One possible change corresponds to symmetrical changes in male and female marriage schedules. This is the harmonic mean (HM) model, which is probably the most commonly used marriage matching function (Schoen 1981; see also Okun 2001; Qian and Preston 1993; and Raymo and Iwasawa 2005). This method provides the basis for a self-regulatory marriage system in the case of imbalances, such that the surplus sex is assumed to temporarily defer marriage while the deficit sex is expected to marry earlier. At the same time, the average age at first marriage of the combined male and female cohorts remains the same. The HM model implies that the deficit sex takes advantage of the relative surplus of the opposite sex by marrying earlier because its pool of prospective spouses has momentarily expanded. In other words, union is regarded as partly constrained by the number of suitable partners, and marriage probabilities are expected to rise when the relative size of the unmarried population of the opposite sex increases. Because union has long been nearly universal among women in China and India, there is no pool of available unmarried women, and the application of the HM model entails a reduction in female age at marriage. Such an adjustment may be conceivable if current constraints on marriage—such as intense dowry negotiations in India or prohibition of early marriage in China—were relaxed. Similarly, the abundance of marriageable men could also improve the probability of women finding suitable partners by shortening the search period.12 I also use a different model that combines features from the FD model (rising female age at marriage) and the HM model (rising age difference). In this hybrid model based on delayed male marriage (DMM), I posit a regular increase in female 11 I am grateful to the suggestion of an anonymous reviewer on this point. 12 Better marriage opportunities for women and lower dowry costs in high sex-ratio societies are among the hypotheses put forward by Guttentag and Secord (1983). Skewed Sex Ratios at Birth and Future Marriage Squeeze 83
84 C.Z.Guilmoto age at marriage as in the FD model,but also a two-year increase in the age gap between men and women.This DMM model presupposes that women would accept marriage to older men more than they did in the past,suggesting that social status or accumulated assets of older men compensate the growing age difference.This could also be interpreted as a lengthened search period among unmarried men caused by the diminishing number of prospective brides. Results My simulations rely on three different population projections based on SRB parameters -namely,the no-transition scenario,the rapid-transition scenario,and the normal-SRB scenario.Based on these projected populations,I examine in the first section the extent of the marriage squeezes as measured by cross-sectional and longitudinal indicators. Since the SRB may be overestimated in China,I include the results of a sensitivity analysis based on lower SRB levels for this country (Appendix C). The results of marriage simulations presented in the following section are based first on the FD model,which assumes a nuptiality regime determined by the future course of female nuptiality.The outcomes of these simulations are given in terms of marriage tempo (age at marriage)and intensity (unmarried men at age 50).Finally,I use the two additional alternative marriage functions(HM and DMM models)to illustrate different ways in which male and female nuptiality may adjust to the marriage crunch. A New Look at the Marriage Squeeze I start with the usual index of marriage squeeze computed as weighted sex ratios.As Fig.I indicates,the rise in the weighted adult ratio in China is rather abrupt after 2010 in both SRB scenarios,and the sex ratio reaches 122 in 2025.Results based on the normal SRB scenario show that the imbalance was bound to increase to 108 in 2020 because of past fluctuations in birth cohort size in China(Goodkind 2006).Following the transitional scenario,the adult sex ratio will record an equally rapid fall after 2025 and will oscillate around 104 during the second part of the century.As expected,the difference between the normal and rapid-transition scenarios declines gradually,and both series are similar in 2050,when the impact of surplus male births before 2020 disappears.In contrast,the no-transition scenario suggests that the weighted adult sex ratio in China would not retumn to normal levels and would instead oscillate after 2025 around a high level of 119.My results agree with findings already found in the literature on China suggesting that the marriage squeeze will peak in 2030 (Jiang et al.2007). When measured through marriage simulations,the marriage squeeze in the future appears rather different from the preceding picture(Fig.1 and Table 1).As previously explained,the MSI is computed as the sex ratio of expected first marriages based on the single populations estimated during the previous period,while the weighted sex ratio is based on projected age and sex distributions.Both indicators would be similar if the observed proportions unmarried in each period were identical to that of the corresponding marriage tables.But the gradual accumulation of unmarried men in several cohorts tends to enlarge the number of expected marriages,leading to MSI levels far higher than projected weighted sex ratios. ②Springer
age at marriage as in the FD model, but also a two-year increase in the age gap between men and women. This DMM model presupposes that women would accept marriage to older men more than they did in the past, suggesting that social status or accumulated assets of older men compensate the growing age difference. This could also be interpreted as a lengthened search period among unmarried men caused by the diminishing number of prospective brides. Results My simulations rely on three different population projections based on SRB parameters —namely, the no-transition scenario, the rapid-transition scenario, and the normal-SRB scenario. Based on these projected populations, I examine in the first section the extent of the marriage squeezes as measured by cross-sectional and longitudinal indicators. Since the SRB may be overestimated in China, I include the results of a sensitivity analysis based on lower SRB levels for this country (Appendix C). The results of marriage simulations presented in the following section are based first on the FD model, which assumes a nuptiality regime determined by the future course of female nuptiality. The outcomes of these simulations are given in terms of marriage tempo (age at marriage) and intensity (unmarried men at age 50). Finally, I use the two additional alternative marriage functions (HM and DMM models) to illustrate different ways in which male and female nuptiality may adjust to the marriage crunch. A New Look at the Marriage Squeeze I start with the usual index of marriage squeeze computed as weighted sex ratios. As Fig. 1 indicates, the rise in the weighted adult ratio in China is rather abrupt after 2010 in both SRB scenarios, and the sex ratio reaches 122 in 2025. Results based on the normal SRB scenario show that the imbalance was bound to increase to 108 in 2020 because of past fluctuations in birth cohort size in China (Goodkind 2006). Following the transitional scenario, the adult sex ratio will record an equally rapid fall after 2025 and will oscillate around 104 during the second part of the century. As expected, the difference between the normal and rapid-transition scenarios declines gradually, and both series are similar in 2050, when the impact of surplus male births before 2020 disappears. In contrast, the no-transition scenario suggests that the weighted adult sex ratio in China would not return to normal levels and would instead oscillate after 2025 around a high level of 119. My results agree with findings already found in the literature on China suggesting that the marriage squeeze will peak in 2030 (Jiang et al. 2007). When measured through marriage simulations, the marriage squeeze in the future appears rather different from the preceding picture (Fig. 1 and Table 1). As previously explained, the MSI is computed as the sex ratio of expected first marriages based on the single populations estimated during the previous period, while the weighted sex ratio is based on projected age and sex distributions. Both indicators would be similar if the observed proportions unmarried in each period were identical to that of the corresponding marriage tables. But the gradual accumulation of unmarried men in several cohorts tends to enlarge the number of expected marriages, leading to MSI levels far higher than projected weighted sex ratios. 84 C.Z. Guilmoto
Skewed Sex Ratios at Birth and Future Marriage Squeeze 85 Weighted sex ratio 150 140 ..No transition .Rapid transition -SRB=105 xes 130 100 90 2000 2010202020302040205020602070208020902100 Marriage squeeze indicator 210 200 .No transition Rapid transition o-SRB=105 ezeenbs 160 150 140 130 120 110 100 90 20002010202020302040205020602070208020902100 Fig.1 Two indicators of marriage squeeze according to three SRB scenarios,China:2005-2100.See the text for details on SRB scenarios and the indicators used In the more favorable rapid-transition scenario,marriage squeeze peaks at 160 in 2035 and does not decline substantially before 2060.A level of 160 means that in 2030,60%more single men than single women are expected to marry within the next five years according to the corresponding nuptiality table.A large part of this 60%comes from the lower-than-expected marriage rates observed during the previous 20 years and the associated backlog of unmarried men.The indicator stagnates at a high level after 2035 as new cohorts born in 2020-with normal SRB levels-enter the marriage market.But the real decline takes place after 2055,when the cohorts most affected by birth imbalances reach 50 years. If the sex ratio were,on the contrary,to remain stable at 120 (the no-transition scenario), the MSI would grow to 190 and stabilize in later decades to 170.But it is interesting to observe the sizable impact of mere changes in age structures,with an MSI reaching 130 in 2055 according to the normal-SRB scenario.In other words,the rapid shrinking of age structures appears to have a sizable independent effect on the future marriage squeeze. Thanks to this set of estimates,the MSI for each SRB scenario may be broken into two components:the age-structural component,summarized in the baseline 105 curve,and the additional effect of past SRB disturbances.In China,the age-structural effects appear to account for more than half of the marriage squeeze after 2050. India's weighted sex ratios(Fig.2 and Table 1)have a smoother profile because of the country's far more regular age distributions and do not reach 110 in the transitional scenario.However,the analysis is more accurate when it is based on the longitudinal For simplicity,the effects of birth imbalances and age-structural change on the mariage squezeare assumed to be additive. ②Springer
In the more favorable rapid-transition scenario, marriage squeeze peaks at 160 in 2035 and does not decline substantially before 2060. A level of 160 means that in 2030, 60% more single men than single women are expected to marry within the next five years according to the corresponding nuptiality table. A large part of this 60% comes from the lower-than-expected marriage rates observed during the previous 20 years and the associated backlog of unmarried men. The indicator stagnates at a high level after 2035 as new cohorts born in 2020—with normal SRB levels—enter the marriage market. But the real decline takes place after 2055, when the cohorts most affected by birth imbalances reach 50 years. If the sex ratio were, on the contrary, to remain stable at 120 (the no-transition scenario), the MSI would grow to 190 and stabilize in later decades to 170. But it is interesting to observe the sizable impact of mere changes in age structures, with an MSI reaching 130 in 2055 according to the normal-SRB scenario. In other words, the rapid shrinking of age structures appears to have a sizable independent effect on the future marriage squeeze. Thanks to this set of estimates, the MSI for each SRB scenario may be broken into two components: the age-structural component, summarized in the baseline 105 curve, and the additional effect of past SRB disturbances.13 In China, the age-structural effects appear to account for more than half of the marriage squeeze after 2050. India’s weighted sex ratios (Fig. 2 and Table 1) have a smoother profile because of the country’s far more regular age distributions and do not reach 110 in the transitional scenario. However, the analysis is more accurate when it is based on the longitudinal 90 100 110 120 130 140 150 Weighted Sex Ratio 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 Weighted sex ratio No transition Rapid transition SRB = 105 90 100 110 120 130 140 150 160 170 180 190 200 210 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 Marriage Squeeze Marriage squeeze indicator No transition Rapid transition SRB = 105 Fig. 1 Two indicators of marriage squeeze according to three SRB scenarios, China: 2005–2100. See the text for details on SRB scenarios and the indicators used 13 For simplicity, the effects of birth imbalances and age-structural change on the marriage squeeze are assumed to be additive. Skewed Sex Ratios at Birth and Future Marriage Squeeze 85
86 C.Z.Guilmoto Table 1 Marriage indicators according to three SRB scenarios,China and India:2010-2100 No SRB Transition Rapid SRB Transition Normal SRB Mean Age Mean Age Mean Age at Marriage at Marriage Single Marriage at Marriage %Single Marriage Marriage %Single Squeeze Men at Squceze Men at Squceze Men at Indicator Male Female Age 50 Indicator Male Female Age 50 Indicator Male Female Age 50 China 201098.6 25.6 23.3 3.3 98.6 25.6 233 33 95.4 25.6 232 3.3 2020 125.7 26.0 242 3.3 124.1 26.0 242 32 1055 25.9 24.2 3.0 2030162.5 27.4 24.7 4.3 159.5 27.6 24.7 叉) 119.8 27.2 24.6 2.6 2040170.6 27.6 25.1 10.0 156.6 28.0 25.0 93 118.7 27.3 25.0 4.2 2050185.8 28.3 26.1 16.7 154.8 28.6 26.0 14.5 125.4 282 26.0 6.0 2060 184.5 28.7 26.5 9> 143.9 28.8 26.5 14.6 126.6 28.8 26.4 6.8 2070 172.7 285 26.5 21.4 126.8 28.5 26.4 12.8 1202 28.6 26.4 7.8 2080 172.7 28.7 26.7 121.0 28 26.7 1202 28.8 26.7 7.6 2090 170.6 28.7 26.6 115.4 28.8 26 118.3 28.8 26.5 7.1 2100 168.7 26.5 110.4 28.8 265 116.2 28.7 265 6.8 India 2010 104.0 24.3 19.5 104.0 24.3 19.5 1.1 1015 24.3 19.5 1.1 2020 126.9 25.0 20.2 125.1 25.0 202 13 1113 24.9 202 1.2 2030 149.9 26.1 21.2 144.9 26.2 21.1 2.4 118.2 26.0 21.1 l.6 2040 172.0 27.2 22.1 157.7 27.3 22.1 5.0 125.0 27.0 22.1 2.7 2050 189.0 28.1 23.1 163.6 28.1 23.0 8.0 130.8 28.0 23.0 4.1 2060191.4 28.7 23.5 12.9 156.8 28.6 235 10.0 132.0 28.7 23.5 5.6 2070183.4 28.7 23.6 14.9 1412 28.5 23.6 102 128.8 28.7 23.6 6.7 2080182.6 28.6 23.7 14.9 134.0 28.523.6 8.6 130.1 28.7 23.6 7.0 2090183.2 28.723.6 14.3 1292 28.623.6 72 1318 28.723.6 6.9 2100183.6 28.623.6 14.2 125.3 28.723.6 6.5 1333 28.623.6 7. Notes:For the marriage squeeze indicator,the sex ratio of expected first marriages is computed on simulated single populations (per 100:see the text for details).Mean age at marriage is computed on simulated marriages(female dominance model)during the five preceding years.SRB scenarios and the marriage simulation procedure are described in the text and in Appendices A and B. MSI.In the rapid-transition scenario,the index rises gradually to 165 in 2055,in spite of the sex ratio transition completed in 2020.This rise has obviously been aggravated by shrinking birth cohorts as the MSI in the normal-SRB scenario reaches 130 in 2050 and stays at this level until the end of the century.Changes in age structures account for more than half of the rise in weighted sex ratios from the 2030s,and the marriage squeeze in this scenario does not decrease during the second half of the century because of the lower fertility and larger spousal age difference in India.The more pessimistic scenario of high SRB results in a continuous rise in marriage imbalances until 2055,after which the indicator remains above 180 during the following decades. Measurements based on marriage simulations in China and India correct the lower and shorter trends suggested by the weighted sex ratios.In particular, because of the queuing effect,the marriage squeeze captured by the cohort- based simulations is likely to be more intense than usually predicted and might not decline before 2060.Even in the more optimistic SRB scenario,the number Springer
MSI. In the rapid-transition scenario, the index rises gradually to 165 in 2055, in spite of the sex ratio transition completed in 2020. This rise has obviously been aggravated by shrinking birth cohorts as the MSI in the normal-SRB scenario reaches 130 in 2050 and stays at this level until the end of the century. Changes in age structures account for more than half of the rise in weighted sex ratios from the 2030s, and the marriage squeeze in this scenario does not decrease during the second half of the century because of the lower fertility and larger spousal age difference in India. The more pessimistic scenario of high SRB results in a continuous rise in marriage imbalances until 2055, after which the indicator remains above 180 during the following decades. Measurements based on marriage simulations in China and India correct the lower and shorter trends suggested by the weighted sex ratios. In particular, because of the queuing effect, the marriage squeeze captured by the cohortbased simulations is likely to be more intense than usually predicted and might not decline before 2060. Even in the more optimistic SRB scenario, the number Table 1 Marriage indicators according to three SRB scenarios, China and India: 2010–2100 No SRB Transition Rapid SRB Transition Normal SRB Marriage Squeeze Indicator Mean Age at Marriage % Single Men at Age 50 Marriage Squeeze Indicator Mean Age at Marriage % Single Men at Age 50 Marriage Squeeze Indicator Mean Age at Marriage % Single Men at Male Female Male Female Male Female Age 50 China 2010 98.6 25.6 23.3 3.3 98.6 25.6 23.3 3.3 95.4 25.6 23.2 3.3 2020 125.7 26.0 24.2 3.3 124.1 26.0 24.2 3.2 105.5 25.9 24.2 3.0 2030 162.5 27.4 24.7 4.3 159.5 27.6 24.7 4.1 119.8 27.2 24.6 2.6 2040 170.6 27.6 25.1 10.0 156.6 28.0 25.0 9.3 118.7 27.3 25.0 4.2 2050 185.8 28.3 26.1 16.7 154.8 28.6 26.0 14.5 125.4 28.2 26.0 6.0 2060 184.5 28.7 26.5 19.7 143.9 28.8 26.5 14.6 126.6 28.8 26.4 6.8 2070 172.7 28.5 26.5 21.4 126.8 28.5 26.4 12.8 120.2 28.6 26.4 7.8 2080 172.7 28.7 26.7 20.4 121.0 28.7 26.7 9.6 120.2 28.8 26.7 7.6 2090 170.6 28.7 26.6 18.9 115.4 28.8 26.5 7.2 118.3 28.8 26.5 7.1 2100 168.7 28.7 26.5 18.5 110.4 28.8 26.5 6.0 116.2 28.7 26.5 6.8 India 2010 104.0 24.3 19.5 1.1 104.0 24.3 19.5 1.1 101.5 24.3 19.5 1.1 2020 126.9 25.0 20.2 1.3 125.1 25.0 20.2 1.3 111.3 24.9 20.2 1.2 2030 149.9 26.1 21.2 2.5 144.9 26.2 21.1 2.4 118.2 26.0 21.1 1.6 2040 172.0 27.2 22.1 5.4 157.7 27.3 22.1 5.0 125.0 27.0 22.1 2.7 2050 189.0 28.1 23.1 9.3 163.6 28.1 23.0 8.0 130.8 28.0 23.0 4.1 2060 191.4 28.7 23.5 12.9 156.8 28.6 23.5 10.0 132.0 28.7 23.5 5.6 2070 183.4 28.7 23.6 14.9 141.2 28.5 23.6 10.2 128.8 28.7 23.6 6.7 2080 182.6 28.6 23.7 14.9 134.0 28.5 23.6 8.6 130.1 28.7 23.6 7.0 2090 183.2 28.7 23.6 14.3 129.2 28.6 23.6 7.2 131.8 28.7 23.6 6.9 2100 183.6 28.6 23.6 14.2 125.3 28.7 23.6 6.5 133.3 28.6 23.6 7.1 Notes: For the marriage squeeze indicator, the sex ratio of expected first marriages is computed on simulated single populations (per 100; see the text for details). Mean age at marriage is computed on simulated marriages (female dominance model) during the five preceding years. SRB scenarios and the marriage simulation procedure are described in the text and in Appendices A and B. 86 C.Z. Guilmoto
