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Africa and Asia are the two most populous continents in the world and are projected to increase further in the near future and this puts the governments under great stress in terms of increased public expenditure and dealing with a low revenue generation. Thus, the purpose of this study is to assess the influence of population age structure on the size of government expenditure in Africa and Asia covering the period 1990–2018.

The study employed panel fully modified ordinary least squares (FMOLS) estimation in estimating the relevant relationship between the variables in the model.

The key findings revealed that the major population age structures that influence the size of government expenditure in Africa are population aged 0–14 years and population aged 15–64 years, while that of Asia are population aged 15–64 years and population aged 65 years and above. The findings provided strong support for the Population Reference Bureau report (2019) that countries in Africa are home to some of the world's youngest population, that is, those aged 15 years or below, while Asia is home to some of the world's oldest population, that is, those aged 65 years and above.

While generalized method of moments (GMM) estimation is beneficial in the presence of endogeneity, it is only designed for situations with a small time period (T) and a large number of cross sections (N). Hence, the estimation technique was limited only to FMOLS as the number of the cross sections or countries which is ten for Africa and ten for Asia is lower than the time period which is 29 years (1990–2018).

Empirical literature investigating the influence of population age structure on the size of government expenditure has focussed mainly on one aspect of the population age structure and government expenditure, which is the influence of ageing population on government expenditure on health. Hence, this study focussed on assessing the influence of population age structure on the size of government expenditure. The study is unique as it compared the two most populous continents in the world, which are Africa and Asia to determine which of the population age structures have the most significant influence on the size of government expenditure.

This paper explores the empirical relationship between population age structure and bilateral trade.

The author includes age structure in both log and Poisson pseudo-maximum likelihood (PPML) formulations of the gravity equation of trade. The author studies relative age effects, using differences in the demographic structure of each country-pair.

The author finds that a relatively larger share of population in working age increases bilateral exports. This is robust to various estimation models, as well as to changes in the method of specifying the demographic controls. Old-age shares have a negative, but less robustly estimated impact on trade. Estimating instead the balance of trade between trading partners produces similar results, with positive effects of age structure peaking later in working life.

Global populations are poised to undergo a massive transition. Trade a crucial way that the demographic deficits of one country may be offset by the dividends of another as comparative advantages shift along with the size and strength of their underlying workforce.

The author’s work is among the first to quantify the effect of relative age structure between two countries and their bilateral trade flows. Focusing on the aggregate flows, relative age shares and PPML estimates of the trade relationship, this paper provides the most comprehensive picture to date on how age structure affects trade.

This article examines the impact of population age structure on the real exchange rate. Data on a panel of 22 OECD (Organization of Economic Cooperation and Development) countries over 1980–2015 period are used to estimate the empirical model. Using fixed effect model the paper finds that different age cohorts have a significant influence on the real exchange rates in the sample countries. The results are mostly consistent with the theoretical framework discussed in the paper and also with the findings of previous studies in this area. These results have important policy implications given the fact that the population is ageing in almost all the OECD economies these days.

South Africa is in the last stage of the first demographic transition (FDT) – yet already depicts aspects of the second transition. The last stage of the FDT is characterized by lower levels of fertility closer to or at the replacement level of the average of 2.1 children per woman, and improvements in mortality displayed by declining infant and childhood mortality leading to increasing life expectancy at birth. The second demographic transition (SDT) is driven by lifestyle changes that are determinants of demographic patterns. Such lifestyle changes are declining marriage rates, increasing attention on human development, and thus changing family formation patterns. South Africa’s youth are at the centre of this transition. The population census of 2011 shows an age structure of South Africa that is characterized by a larger proportion of 20-35-year-olds. This resulted from a long period of declining fertility and to some extent improvements in mortality at all ages. This age structure, with adequate investments - is expected to yield a period of economic growth resulting from a reduced dependency ratio. However, improved health care, investments in human development, and higher employment opportunities are required to harness the benefit. This chapter aims to explore the national and provincial preparedness of South African youth to contribute to economic growth of the country. In particular, the chapter will focus on demographic factors such as sex ratio; youth mortality and morbidity; and youth fertility levels as these factors are highly correlated with human development.

Africa's population is the second largest and fastest growing in the world after Asia, and this puts African governments under great stress in terms of increased public expenditure and is faced with a low revenue generation. Hence, the need for this study. The purpose of this paper is to examine the socio-economic determinants of public expenditure in Africa by assessing the influence of population age structure using a sample of the top ten most populous countries in Africa covering period of 1989 to 2018.

The study employed panel fully modified ordinary least square (OLS) in estimating the relevant relationship between the variables in the model. The dynamic ordinary least square (DOLS) model was also used to check the robustness of the fully modified ordinary least square (FMOLS) results.

The findings revealed that the major population age structure that influences the growth of public expenditure in Africa are population ages (0–14) and population ages (15–64), but the former poses a stronger significant influence than the latter while population ages (65 and above) has a negative and insignificant influence. Also, in terms of other socio-economic factors, self-employment has a reducing and significant influence on public expenditure. GDP per capita has a negative and insignificant influence while foreign aid and unemployment rate has an increasing influence. Finally, inflation rate and control of corruption (CC) has a negative relationship with public expenditure.

The study argues that an increase in the young and working population will put enormous pressure on the government in the provision of more jobs and other public infrastructures such as health care and education. In the context of African economy with a low revenue generation, public expenditure will be low and the desperately poor masses will be denied of these public infrastructures.

Several studies (Jibir and Aluthge, 2019; Tayeh and Mustafa, 2011; Okafor and Eiya, 2011; Obeng and Sakyi, 2017; Ofori-Abebrese, 2012) have investigated the determinants of public expenditure using total population as a variable. However, this study is unique as it focused on the influence of population age structure on public expenditure in Africa. Also, the study incorporated other socio-economic determinants of public expenditure such as self-employment, standard of living, inflation rate, unemployment rate, foreign aid and corruption in its analytical model. To the best of our knowledge, some of these variables have not been employed in previous studies.

This paper's objective is to provide improvement plans that take into account problems in the housing sector, as well as low fertility and demographic problems caused by aging, by suggesting long‐term policy directions considering all future possible changes in fertility and housing policies.

This research analyzes age specific fertility rates by socioeconomic index, composition of three scenarios about the total fertility rate, the forecast of the future population by the cohort component method, and the forecast of housing demand according to the Mankiw‐Weil (M‐W) model.

If the fertility rate increases through improvements in socioeconomic conditions, then we see that housing demand also increases. If the fertility rate level is higher than that of scenario 3, then a housing demand decrease comes later. However, even if the low fertility rate issue is addressed, the problem with the housing market due to the aging is expected to continue for the time being.

If the population decrease cohort is accumulated due to the continuously low fertility, then it will cause an increase in the reduction effect of housing demand. Considering housing spaces, the elderly require a relatively large amount of available space in Korea where the aged population is rapidly increasing. This increase in seniors could counterbalance the reduced demand for housing.

This is a long‐term‐oriented paper about housing demand and the changing trends in Korea, which is undergoing demographic changes due to low fertility and ongoing aging. We need to monitor fertility rates and the structure of population changes to achieve a stable housing market, and we should also recognize that these structural changes by age will have diverse ripple effects on housing demand.

The purpose of this paper is to predict the population of Anhui province. The authors analyze the trend of the main demographic indicators.

On the basis of the main methods of statistics, this paper studies the tendency of the population of Anhui province. It mainly analyzes the sex structure and the age structure of the current population. Based on the GM(1,1) model, this paper forecasts the total population, the population sex structure, and the population age structure of Anhui province in the next ten years.

The results show that the total population was controlled well, but there have been many problems of the population structure, such as the aging population, high sex ratio, heavy social dependency burden, and the declining labor force.

This paper forecasts the main indexes of the population of Anhui province and provides policy recommendations for the government and the relevant departments.

This paper utilizes data analysis method and the grey forecasting model to study the tendency of the population problems in Anhui province.

Knowledge about current size and structure of a country's population is needed for the formulation and implementation of policies and programs in almost all sectors of public life. The purpose of this paper is to provide population projection for Oman, using population census and family health survey data.

Component method is used for projecting future population of Oman. Population Census data of 2003 by sex and by five‐year age groups were used. The base life expectancy of Oman is assumed to be 73 years and the base total fertility considered as 5.1 children per woman.

Depending upon the achievement of replacement fertility by the year 2025 or 2030 or 2035 the population of Oman in 2050 will vary from 4.5 million to 5.0 million. The different scenarios of population projection indicate that the population of Oman will not be stabilized before 2100.

Population projection depends on assumptions about mortality, fertility, base life expectancy and migration. If these assumptions change then the projections will also change.

Because of high fertility in the past, women in the reproductive ages will increase for up to several decades. Thus, population growth will continue because of momentum effect, even if Oman achieves replacement fertility say in 2030. The age at marriage will increase.

The rapid socio‐economic development and increased women empowerment will create a new outlook and ideas about lifestyles, leading to a decline in fertility. The decline in fertility is strongly related to social, health, education, employment opportunities of women and economic development, which through a variety of mechanisms, reduces the fertility desired and increases the fertility regulation through contraception, birth spacing and increased age at marriage of females. Because of increase in life expectancy and falling birth rate, the absolute number of the elderly population will have enormous impact on health care needs and hospitalization.

This paper deals with the population projection of Oman. Timely and accurate information about population trends is crucial for the socio‐economic development of a country. Knowledge about current size and structure of a country's population is needed for the formulation and implementation of policies and programs in almost all sectors of public life such as health, education and employment.

The last reformation in Iran’s population policy was announced inside “General Population Policy” (GPP) in 2013. One of the main objectives of the GPP is controlling population aging. The aim was to designing and evaluating different scenarios for achieving this objective.

For this purpose, a system dynamics model was built from cohort age groups. The model simulated Iran’s population structure from 2000 to 2050. The system dynamics model was validated in 2000 till 2011 period (R² = 94%). Data were extracted from the United Nations population division repository and represent a reducing trend in the fertility rate of Iran. This situation was named the “base” scenario. The simulation results for this scenario showed that Iran will face aging such that between 2000 and 2050 the median age will increase from 25 to 43 years. Based on these results, the base scenario could not achieve the GPP objective. So three alternative scenarios were designated: stabilization, increasing and hyper increasing.

The median age and the aging index are descending only in the hyper increasing scenario which means controlling aging. Therefore, the hyper increasing scenario is the only way to realize the GPP’s objective. To realize the hyper increasing scenario, it is essential to consider the total dependency ratio which shows the level of pressure on the workforce. Reducing this pressure increases the propensity to have more children (fertility index) and this is essential for maintaining high fertility rate.

The value of the research rests on a precise simulation model to forecast the population structure and aging. The research will serve as a guide for Iranian policymaker and support strong recommendations to bring the GPP along with supporting policies such as childbearing and child care assistance.