This paper examines how the number of siblings that parents have affects their fertility decisions in China. The population control policies in China affected individuals unequally across birth cohorts and regions. The exogenous variation in fertility is used to identify the effect of the number of siblings on the number of children for the next generation. The results show that a couple tends to have 0.034–0.068 more children (2.3–4.6% of the average number of children) and is 2.4–6.8 percentage points more likely to violate the One-Child Policy (9.3–27.1% of the violation rate) if the husband and the wife have one more sibling each. Moreover, the effect on fertility is stronger for couples in lower-income provinces where the fertility rate is higher and in rural areas where the One-Child Policy was enforced less strictly. Finally, I show that the ideal family size of the husband and wife is an important channel through which the number of siblings affects fertility. I also find that the effect of people's number of siblings has a larger effect on their ideal number of children than on their actual number of children, suggesting that they are constrained from achieving their fertility ideals.

This study explores the process by which the treatment of infertility, which has been in the hands of the private sector, has been taken over by the state as a matter of public health. It argues that this shift stems from the pro-natalist policies of the state to help increase the population. Infertility treatment, using assisted reproductive technologies and its legitimization by the Islamic jurists, is used as a lens through which to examine the state's body politic. The frequent reversals of policies, since the late nineteenth century to the present, are shown to be directly linked with the nation-building goals of the state, expecting the citizens to readjust their reproductive behavior to meet the state’s policies.

In mid-2016, the population of Sub-Saharan Africa (SSA) was almost 1 billion people. By 2050, the population of the region will probably reach 2.1 billion people [Population Reference Bureau (2016)]. In 2100, SSA's population could be almost 4 billion people [United Nations (2015)]. This rapid demographic increase would translate into a possible quadrupling of the current SSA population by the end of the century (unless fertility would decline sharply in the near future). Nonetheless, the region has embarked on its demographic transition, i.e., the shift from high to low crude birth rates and crude death rates, albeit this process has occurred in SSA at a slower pace than in the rest of the developing world. In particular, the decline of fertility has been slower in SSA than in the other regions of the world. The rapid population growth and the occurrence of a demographic transition in the region have generated discussions on the prospects for SSA to open a demographic window of opportunity and capture a first demographic dividend. However, two crucial dimensions, which have so far been rather neglected, need more attention. First, one will need to define with more accuracy the sub-populations of the working-age adults and their young and older dependents, therefore refining the calculation of the dependency ratio. In particular, one will need to assess the population of the young dependents as well as the population of adults who are actually working. Second, it will be also necessary to examine the conditions required to trigger a faster and significant fertility decline in the region. This is most important because the relationship between the active adults and their dependents is predicated by the fertility decline, which will bring the changes to the age structure.

Population aging is the population issue of the XXI century and many indices are used to measure its level and pace. In Science (2010), Sanderson and Scherbov suggested improvements to the measure of elderly dependency ratio. They identified several limitations to the use of chronological age as the main variable and proposed a new index, the Adult Disability Dependency Ratio, defined as the number of adults at least 20 years old with disabilities divided by the number of similarly aged adults without disabilities. They used the Sullivan prevalence-based method by multiplying derived disability rates to macro population projections. They showed results for several ECE and OECD countries; results for Canada (see online annex, available at https://www.sciencemag.org/content/329/5997/1287/suppl/DC1) were derived using coefficients of Italy. However, disability is a complex multidimensional process (see Carrière, Keefe, Légaré, Lin, & Rowe, 2007; Légaré and Décarie, 2011), and microsimulation can take into account its implied complexity. Our results for Canada, presented here, exceed those in Science to show how more-sophisticated projections of disabled older adults can improve the analysis. We used LifePaths, a Statistics Canada’s microsimulation model, to provide a perspective of the phenomena unobtainable with prevalence-based methods.