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Forecasting the Population Growth in China Based on the Welfare Efficiency and Demographic Statistics

Advances in Business and Management Forecasting

ISBN: 978-1-78635-534-8, eISBN: 978-1-78635-533-1

Publication date: 18 July 2016

Abstract

As an important carrier of sustainable economy and social development, population is the foundation of the whole society. Scientific predictions of future population growth will bring great reference to macro-economic and social planning. For China, as a country of the biggest population, the research on its population policy is worthwhile.

Previous literatures on population growth prediction are generally based on time-series analysis. However, the new two-child policy in China provides us an opportunity to predict the population growth from the perspective of the welfare efficiency, since each family is able to determine whether to have the second child on account of the family’s utility. The welfare efficiency is calculated through the database of newborn babies, disposable income per capita, living resource per capita, and health expenditure pre capital. These are the main factors by which each family decides whether to bear additional babies. In this chapter, we perform the micro-economic analysis on a new policy and propose a Data Envelopment Analysis (DEA) method to predict the population growth. Under the condition of policy adjustment, we successfully predict the population growth with this method. We also propose some suggestions concerning the implementation of the new policy.

Keywords

Acknowledgements

Acknowledgments

The financial supports from National Natural Science Foundation of China (Grant Nos. 71322101, 71301039 and 71271195) and USTC Foundation for Innovative Research Team (WK2040160008) are acknowledged.

Citation

Yang, F., Yang, Y. and Huang, Z. (2016), "Forecasting the Population Growth in China Based on the Welfare Efficiency and Demographic Statistics", Advances in Business and Management Forecasting (Advances in Business and Management Forecasting, Vol. 11), Emerald Group Publishing Limited, Leeds, pp. 169-183. https://doi.org/10.1108/S1477-407020160000011010

Publisher

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Emerald Group Publishing Limited

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