Flows of international migration are needed in the Asia-Pacific region to understand the patterns and corresponding effects on demographic, social, and economic change across sending and receiving countries. A major challenge to this understanding is that nearly all of the countries in this region do not gather or produce statistics on flows of international migration. The only information that are widely available represent immigrant population stocks measured at specific points in time—but these represent poor proxies for annual movements. In this paper, we present a methodology for indirectly estimating annual flows of international migration amongst 53 populations in the Asia-Pacific region and four macro world regions from 2000 to 2019 using a generation–distribution framework. The estimates suggest that 27–31 million persons from the Asia-Pacific region have changed their countries of usual residence during each year in the study. Southern Asia is estimated to have had the largest inflows and outflows, whilst intra-regional migration and return migration were highest in Eastern, Southern, and South-Eastern Asia. India, China, and Indonesia were estimated to have had the largest emigration flows and net migration losses. As a first attempt to estimate international migration flows in the Asia-Pacific region, this paper provides a basis for understanding the dynamics and complexity of the large-scale migration occurring in the region.

BACKGROUNDThe variable-r model provides demographers with a way to explore the contributions of demographic components (fertility, mortality, migration) to changes in populations' age structures. However, traditional variable-r methods require extremely long mortality series to explore growth at oldest-old ages. OBJECTIVEOur goal is to disentangle the old-age growth rate into two main components: the growth rate at some younger age, and reductions in mortality between the younger and older ages. METHODSWe focus on an adaptation of the variable-r model that can use shorter mortality series to explore population growth between two ages. RESULTSUsing data from the Human Mortality Database, we explore how these two components are driving the growth rate of 100-year-olds. Observed growth of those reaching age 100 results primarily from the high growth rates when those cohorts were 80-year-olds, and from time reductions in cohort mortality between ages 80 and 100. However, the latter component behaves differently across populations, with some countries experiencing recent slowdowns in cohort mortality declines or increases in mortality between ages 80 and 100. CONCLUSIONSWe find great diversity in the level of old-age mortality improvements across populations, and heterogeneity in the drivers of these improvements. Our findings highlight the need CONTRIBUTIONWe present illustrations of the use of the variable-r method to monitor demographic change in an online interactive application, estimated even when only short historical series of demographic data are available.

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The demographic balance equation relates the population growth rate with crude rates of fertility, mortality, and net migration. All these rates refer to changes occurring between two time points, say, t and t + h. However, this fundamental balance equation overlooks the contribution of historical fertility, mortality, and migration in explaining these population counts. Because of this, the balance equation only partially explains a change in growth rate between time t and t + h as it does not include the contribution of historical population trends in shaping the population at time t. The overall population growth rate can also be expressed as the weighted average of age-specific growth rates. In this article, we develop a method to decompose the historical drivers of current population growth by recursively employing the variable-r method on the population's average age-specific growth rates. We illustrate our method by identifying the unique contributions of survival progress, migration change, and fertility decline for current population growth in Denmark, England and Wales, France, and the United States. Our results show that survival progress is mainly having an effect on population growth at older ages, although accounting for indirect historical effects illuminates additional contributions at younger ages. Migration is particularly important in Denmark and England and Wales. Finally, we find that across all populations studied, historical fertility decline plays the largest role in shaping recent reductions in population growth rates.

Multistate modeling is a commonly used method to compute healthy life expectancy. However, there is currently no analytical method to decompose the components of change in summary measures calculated from multistate models. In this paper, we develop and describe a derivative-based method to decompose the difference in population-based health expectancies estimated via a multistate model into two main components: the proportion resulting from differences in initial health structure, and the proportion resulting from differences in health transitions. We illustrate the method using data on Activities of Daily Living disability from the US Health and Retirement Study to decompose the sex differential in disability-free life expectancy (HLE) among older Americans. Our results suggest that the sex gap in HLE results primarily from differences in transition rates between disability states, rather than from the initial health structure of male and female populations. The methods introduced in this paper enable researchers, including those working in fields other than health, to decompose the relative contribution of initial population structure and transition probabilities to differences in state-specific life expectancies from multistate models.

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