A spatial approach to select pilot counties for programs to correct the biased sex ratio at birth in Shandong province, China

Abstract

The highly skewed sex ratio at birth (SRB) in China has stimulated numerous studies. However, the geographic distribution of SRB is seldom investigated, particularly at the county level. The need for an understanding at this level has increased since the Chinese government initiated its ‘Care for Girls’ campaign to improve the survival rate of females. This campaign has been initiated in a set of pilot counties. In this article we assess the effectiveness of the set of pilot counties in Shandong province and propose two alternate configurations. To do this, we first assess the spatial distribution of the SRB values by county in Shandong, expressed as a z-score (zSRB) after correcting for the biologically expected SRB value and population size of zero-aged children. A local Moran's I_i analysis of the zSRB values indicates a significant high–high cluster in the southwest of the province. The I_i , zSRB and female deficit (the difference of the observed from biologically expected number of zero-aged females) were then used to define two alternate configurations for the pilot counties. A comparison of the current and alternate configurations against a Monte Carlo randomisation analysis shows that the current configuration is significantly different from a random selection (p < 0.05) for the two criteria of maximising the aggregate female deficit and maximising the zSRB. Although this is a good result, both alternate configurations were more significant (p < 0.001), and therefore represent potentially better configurations for the campaign given the criteria used. The spatial analysis approach developed here could be used to improve the effectiveness of the Care-for-Girls campaign in Shandong province, and elsewhere in China.

Keywords:

• sex ratio at birth
• Shandong province
• ‘Care for Girls’ campaign
• LISA
• spatial analysis
• China

Acknowledgements

This work was supported by the National Natural Science Foundation of China (ID:40730526), and the open fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (ID:08I01).