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. ②SpringerMeasuring 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 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.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