An empirical analysis of the relationship between economic development and population growth in China

Abstract

China has experienced a dramatic demographic transition since the latter half of the twentieth century, and thus, assessing the global economic implications is an important issue. This article uses time-series data on China to estimate the determinants of gross domestic product (GDP) per capita. According to the results of the presented co-integration analysis, population has a significantly negative impact on GDP per capita, while savings rate, total factor productivity and degree of industrialization have significantly positive impacts on GDP per capita. These results suggest that the share of the working-age population relative to the total population does not have a strong influence on GDP per capita. Therefore, the contribution of the working-age population to economic growth might not be as large as previously assumed. It is also possible that an increase in savings, remarkable industrialization and rapid technological progress have all stimulated economic growth in China greatly.

Keywords:

• economic development
• population growth; 
• population size
• VECM

JEL Classification:

• P23
• O33
• O53

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Erratum

Acknowledgements

We are grateful to two anonymous referees, and Professors Mitoshi Yamaguchi and Yukio Fukumoto for helpful comments and suggestions. This work was supported by JSPS KAKENHI Grant Number 24310031.

Notes

1. However, note that the findings of Meadows et al. (1972) have been criticized in several works, such as Kuznets (1966, 1973).

2. Income per capita is calculated as income divided by population; thus, an increase in population decreases income per capita if other conditions do not change. However, population can contribute to production through the labour force and can increase income per capita. Therefore, the size of the population can influence economic growth negatively, while the labour force can influence economic growth positively. The total effect of the size of the population and labour force is called the ‘direct effect of population.’

3. Population can induce technical change and increase economic growth. This effect is called the ‘indirect effect of population.’

4. National Bureau of Statistics of China, International Statistical Data 2009 (http://www.stats.gov.cn/tjsj/qtsj/gjsj/2009/index.htm).

5. The Solow residual is often used to measure technological progress. For China, however, the measurement would span a half century, over which it is conceivable that the production elasticities for each element of the production function would have changed greatly. The DEA approach developed by Coelli (1996) uses linear programming to measure annual production frontier shifts for every year from 1952 to 2008. Those shifts are used to represent TFP. The DEA approach was used in this article because it offers a way to overcome the problems associated with the Solow residual.

6. Data on capital stock and employment were required to measure technological progress. GDP, employment, and fixed capital formation data for individual provinces were obtained from the Department of Comprehensive Statistics of National Bureau of Statistics (2005) and National Bureau of Statistics of China (2001–2009). For his growth accounting analysis of the Chinese economy, Chow (2002) accurately measured capital stock for the years 1952–1998. This article uses Chow’s (2002) Chinese capital stock data for 1952 to measure province-level capital stock based on each province’s percentage of China’s GDP. Capital stock data for the years following 1952 were calculated considering Chow’s (2002) data and fixed capital formation and depreciation. We assume that depreciation rate is 10%.

7. This research focuses on a long-run relationship between population and GDP per capita, and accordingly, we discuss the short-run effects in the Appendix.

8. Considering that adjusted R-squared is the highest and the coefficients of all the variables are significant in model 6 in Table 4, model 6 might be the best specification. Therefore, we present the results of both models 5 and 6 in the Appendix Table. However, the results are not considerably different. Moreover, in terms of Schwarz Criteria, model 2 might be the best model, but the purpose here is to discuss the effects of as many explanatory variables as possible, so we focus on the result of model 5. Implication from the result of model 2 is quite similar to that from model 5.