Research Article: Predicting population age structures of China, India, and Vietnam by 2030 based on compositional data

Date Published: April 11, 2019

Publisher: Public Library of Science

Author(s): Yigang Wei, Zhichao Wang, Huiwen Wang, Yan Li, Zhenyu Jiang, Alessandro Rizzo.


The changing population age structure has a significant influence on the economy, society, and numerous other aspects of a country. This paper has innovatively applied the method of compositional data forecasting for the prediction of population age changes of the young (aged 0–14), the middle-aged (aged 15–64), and the elderly (aged older than 65) in China, India, and Vietnam by 2030 based on data from 1960 to 2016. To select the best-suited forecasting model, an array of data transformation approaches and forecasting models have been extensively employed, and a large number of comparisons have been made between the aforementioned methods. The best-suited model for each country is identified considering the root mean squared error and mean absolute percent error values from the compositional data. As noted in this study, first and foremost, it is predicted that by the year 2030, China will witness the disappearance of population dividend and get mired in an aging problem far more severe than that of India or Vietnam. Second, Vietnam’s trend of change in population age structure resembles that of China, but the country will sustain its good health as a whole. Finally, the working population of India demonstrates a strong rising trend, indicating that the age structure of the Indian population still remains relatively “young”. Meanwhile, the continuous rise in the proportion of elderly population and the gradual leveling off growth of the young population have nevertheless become serious problems in the world. The present paper attempts to offer crucial insights into the Asian population size, labor market and urbanization, and, moreover, provides suggestions for a sustainable global demographic development.

Partial Text

Population age structure forecast is a research method that includes a comprehensive consideration of factors influencing development and a scientific calculation of population trend in the future [1]. Numerous researches have indicated that population age structure plays a decisive role in economic growth [2–10]. Since the supply of labor force and the saving rate continue to change over their life cycles, and given that their prolonged life span can contribute to the supply of labor force and saving rate, the change in population age structure can have a huge influence on a society’s economic performance. Fig 1 shows a description of the historical changes in population age structures from 1960 to 2016 in China, Vietnam, and India, in which it is shown clearly that the three countries differ significantly in this regard.

The size, structure and characteristics of population play a pivotal role in national economic policy-making. To study the errors of population prediction, statisticians and demographers has developed various methods of population prediction in the past two decades, such as ex-post evaluation, probabilistic Bayesian approach, Time Series Model [31–33] and Age Structure Prediction Model [34, 35]. An extensive literature review on population prediction and population age structure prediction is conducted to summarize the advantages and disadvantages of relevant theories and lay a solid theoretical foundation for this study.

The compositional data describe the intrinsic structural information of an integrated system [68], for instance, the population proportions of three countries discussed in this paper. As shown in Fig 5, the procedure for using forecasting models for compositional data contains three key processes. First, the observed compositional data are transformed to some new variables that have no constraints. Second, traditional forecasting models can be conducted on these transformed variables so as to forecast the intermediate values. Finally, these intermediate values are then retransformed back to the ideal forecasted values of the compositional structure.

Table 3 illustrates both the data transformation methods for compositional data and forecasting models. The test data (i.e., from 2008 to 2016) were used to measure the accuracy of all the alternative models. The forecasting performances of the family of NNETTS with different settings of parameters are summarized in Table 4, and the best ones related to three countries are then selected to compare with the other models in Table 5. Specifically, we chose NNETTS with D = 2 and m = 3 for China, D = 3 and m = 3 for India, and D = 2 and m = 2 for Vietnam in Table 5, respectively. To forecast the compositional data, two key processes are important. One is the transformation method for compositional data, and the other is the forecasting model. Rows and columns in Table 5 denote data transformation methods for compositional data and forecasting models, respectively, where the rows “Base” denote the corresponding conventional time series forecasting models and the sub-columns “RMSE” and “MAPE” denote the CoDa-RMSE and CoDa-MAPE values corresponding to the specified forecasting model for compositional data.

To explore the development of population structure and predict the changes in population size are of crucial importance for socioeconomic policy enactment. In recent years, China and the Southeast Asian countries will enter or have already entered the aging society. The increase of old people inevitably turns demographic dividend into a demographic burden. It is of urgent importance to not only China but every country around the world to fully utilize the potential contribution of population transformation. According to historical data, the present study has conducted quantitative analysis on the development pattern and future trend of the population structures of China, India, and Vietnam, and predicted the development trends of different age groups till the year 2030 for the three countries. In an attempt to propose strategic and practical suggestions to avoid the aging problem for planners of the global development blueprint, the findings of present study can be meaningful for the formulation and implementation of future demographic policies.




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