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The study aims to answer the following question: Does using different measures to assess population ageing lead to the same conclusions? The study evaluates the advancement of ageing in mainland China using selected conventional measures based on chronological age and the prospective age measures in which changes in life expectancy are considered. The conventional oldage dependency ratio and the prospective old-age dependency ratio, as well as the conventional median age and the prospective median age were calculated and compared in two versions. Conventional measures reveal a very unfavourable trends in China’s population ageing and indicate that a faster increase in life expectancy leads to a faster population ageing. Prospective measures show a more moderate increase resulting from longer life expectancy. The use of both, conventional and prospective measures provide a more accurate understanding of ageing, which is essential for effective policy and social planning.

Purpose: The purpose of this paper is to explore the relationship between housing prices, population age structure and savings rate by analyzing the variables of housing prices, population age structure and savings rate, which is of great theoretical and practical significance for the formulation of policies to alleviate the aging population and childlessness in China.   Theoretical framework: Most of the studies on the relationship among the consumer price of commercial housing, the old-age dependency ratio, the child dependency ratio, and the savings rate of the residents in China are based on the GMM model, while this study constructs a quadratic VAR model to empirically analyze and test the four variables of the consumer price of commercial housing, the old-age dependency ratio, the child dependency ratio, and the savings rate of the residents, and to study whether there is a long-term stable relationship among these four variables and other general patterns that exist among these four variables. other general laws that exist among these four variables.   Design/Methodology/Approach: This study decides to start from the perspective of residents' saving rates. It conducts descriptive statistical analysis and empirical analysis by using 33 macroeconomic data (samples) from 1990-2022, official data of the National Bureau of Statistics (NBS), and housing price data from Jurassic Data Network (JDN). The hypotheses are initially set up by descriptive analysis, and then the VAR function model is constructed to analyze and test the hypotheses empirically.   Findings: It is found that the sales price of commercial housing is positively correlated with the savings rate of residents; the child dependency ratio is positively correlated with the savings rate of residents; the old-age dependency ratio is insignificantly correlated with the savings rate of residents; and there is a long-term and stable correlation among the four variables of the sales price of commercial housing, the child dependency ratio, the old age dependency ratio and the savings rate of residents.   Research, Practical & Social implications: This study improves the relevant theoretical research of economics, demography, sociology and other interdisciplinary disciplines, complements the existing research paths and methods of empirical analysis, and obtains relevant empirical research conclusions. The significance of the study is to provide assistance to the formulation of national macroeconomic and monetary policies, and to contribute to the adjustment of population policies, the reform of the pension security system, and the revision of the medical security system. It also contributes to the adjustment of the population policy, the reform of the pension security system, and the revision of the medical security system.   Originality/Value: The study is able to enrich the research practices on the sales price of commercial properties, the dependency ratio of young children, the dependency ratio of the elderly and the savings rate of the population, and make a new contribution to the improvement of people's livelihood, the development of the real estate industry's transformation and upgrading, and the formulation of rational population and economic policies.

In this paper we compare several types of economic dependency ratios for a selection of European countries. These dependency ratios take into account not only the demographic structure of the population, but also the differences in agespecific economic behaviour such as labour market activity, income and consumption as well as age-specific public transfers. In selected simulations where we combine patterns of age-specific economic behaviour and transfers with population projections, we show that in all countries population ageing would lead to a pronounced increase in dependency ratios if present age-specific patterns were not to change. Our analysis of cross-country differences in economic dependency demonstrates that these differences are driven by both differences in age-specific economic behaviour and in the age composition of the populations. The choice of which dependency ratio to use in a specific policy context is determined by the nature of the question to be answered. The comparison of our various dependency ratios across countries gives insights into which strategies might be effective in mitigating the expected increase in economic dependency due to demographic change.

The paper examines the relationship between dependent variables (spending on social protection and spending on social protection benefits) and variables that capture the economic structure of the population and economic activity on the example of panel data of 26 European Union (EU) countries (except for Bulgaria and Croatia) over the years 2000-2017. In particular, the economic structure of the population is analysed by the age dependency ratio, while economic activity is expressed by the real GDP per capita growth rate and the unemployment rate. Regardless of the estimation technique for specifications with variables transformed to first differences, the results of the study indicate a statistically significant association between the dependent variables and the macroeconomic conditions of the EU countries. By contrast, the relationships between the dependency ratios and the dependent variables are positive but generally statistically insignificant, although EU countries are undergoing advanced processes of population ageing.

Purpose – Aging in Japan is advancing faster than in other major developed nations, and this is expected to have substantial effects on the country’s economic systems, including its social security system. What kind of effect will the falling birth rate, aging society and declining population have on the real estate market? Will the often mentioned real estate price asset meltdown really occur? The purpose of this paper is to address these questions by investigating how much demographic factors affected real estate prices in Japan and the USA. Design/methodology/approach – The authors use regional panel data for Japan and the USA real estate prices and estimate the effects of demographic factors, such as dependency ratio, i.e. the ratio of population aged 65+ to population aged 20-64. For Japan, as no region-by-region quality-adjusted housing price indexes covering the entire country exist, data are constructed by conducting quality adjustment using hedonic regression. Findings – Both in Japan and the USA, real estate prices in a region are inversely correlated with the old age dependency ratio in that region, and positively correlated with the total number of population in that region. The demographic factor had a greater impact on real estate prices in Japan than in the USA. For Japan, it was also found that demographic impact on land prices will be −2.4 per cent per year in 2012-2040, while it was −3.7 per cent per year in 1976-2010, suggesting that aging will continue to have downward pressure on land prices over the next 30 years, although the demographic impact will be slightly smaller than it was in 1976-2010, as the old age dependency ratio will not increase as much as it did before. Originality/value – Japan’s regional panel data are newly constructed based on a hedonic approach. Analyzing the effect of dependency ratio for Japan and the USA panel data is a new challenge. Forecasting future impact of demographic factor on Japan’s land prices based on the population forecast is a new challenge.

Japan is the oldest society in the world. It has the highest proportion of the population aged 65 and over, a demographic indicator that has been used by demographers for more than a century. One of the main objectives of this study is to apply a new indicator—the cognition-adjusted dependency ratio (CADR)—to remeasure the level of population aging from an innovative point of view. To compute this new index, we apply the mean age-group-specific immediate recall scores for Japan and four other Asian countries, and we compare the results with those derived from the United States and various developed nations in Europe. Our analysis shows that Japan’s pattern and level of age-related decline in cognitive functioning are highly comparable to those of many other developed nations, particularly in Continental Europe. Among the other Asian countries, Malaysia shows a pattern of change similar to countries in Southern Europe, although Malaysia has slightly lower scores than Southern Europe in all age groups. More importantly, these comparative results based on CADR are astonishingly different from the corresponding results obtained from conventional old-age dependency ratios. The Japanese case is the most salient example.

The article investigates the relationship between demographic burden and insurance market by employing panel vector autoregression models with six groups of endogenous variables to a dynamic panel data set of 25 economies for the period 1980–2016. Demographic burden is represented by dependency ratios measured in respect to the population younger than the age of 15 (young-age dependency ratio), population above the age of 64 (old-age dependency ratio) as well as males and females above the age of 64 being examined separately. As indicators of insurance market development, life insurance density, non-life insurance density, and total insurance density are used. The robustness of the results is verified across 10 subsamples of the main observation period. The conducted analyses show a heterogeneous impact of demographic burden on the insurance market. The impulse responses reveal that negative effects prevail in the long term, which may result from the negative impact of an increasing demographic burden on the economy. In the short term, growth in female and male old-age dependency ratios drives up life and non-life insurance density.

The conventional dependency ratio based on cohort-invariant cutoff points could overstate the true burden of population aging. Using optimal cohort-varying years of schooling and retirement age in a life-cycle model, we propose a modified definition of dependency ratio. We compare the proposed economic-demographic dependency ratio (EDDR) with the conventional definition and find that the conventional dependency ratio of the USA is projected to increase by 0.105 from 2010 to 2060, which is an over-projection of 86% when compared with the projected increase of 0.015 in the EDDR over the same period. Sensitivity analysis suggests that our finding is quite robust to reasonable changes in parameter values (except for one parameter), and the magnitude of over-projection ranges mainly from 0.079 to 0.102 (i.e., 75% to 97%). We follow the well-established Lee–Carter model to forecast stochastic mortality and employ the method of expanding duration to decompose the sources of over-projection.

In this paper, we have tried to record the existing conditions of the demographic dividend in India using state-wise analysis approach. The paper examines the dependency ratios using more refined measures other than the conventionally used. In the paper, we have tried to formulate few measures to calculate the dependency ratios which can give a clear idea of dependency ratio in India. In the analysis, we have tried to find that how far the change in per capita GDP can be explained by a change in labour productivity, labour participation rate and working age-to-total population ratio. The growth of per capita has been decomposed into three factors by using shapely decomposition approach: (1) growth of output per worker; (2) growth linked to change in employment; (3) growth of working age-to-total population ratio.

Although the matrix model for kinship networks includes many demographic processes, it is deterministic. It provides values of age-stage distributions of kin, but no information on (co)variances. Because kin populations are small, demographic stochasticity is expected to create appreciable inter-individual variation. To develop astochastick in ship model that includes demographics to chasticity and projects (co)variances of kin age distributions, and functions thereof. Kin populations are described by multitype branching processes. Means and covariances are projected using matrices that are generalizations of the deterministic model. The analysis requires only an age-specific mortality and fertility schedule. Both linear and nonlinear transformations of the kin age distribution are treated as outputs accompanying the state equations. The stochastic model follows the same mathematical framework as the deterministic model, modified to treat initial conditions as mixture distributions. Variances in numbers of most kin are compatible with Poisson distributions. Variances for parents and ancestors are compatible with binomial distributions. Prediction intervals are provided, as are probabilities of having at least one or two kin of each type. Prevalences of conditions are treated either as fixed or random proportions. Dependency ratios and their variances are calculated for any desired group of kin types. An example compares Japan under 1947 rates (high mortality, high fertility) and 2019 rates (low mortality, low fertility).