China's Fertility Transition through Regional Space: Using GIS and Census Data for a Spatial Analysis of Historical Demography

Key features of reproductive behavior in China vary systematically through space and time. In this article we present an analysis of fertility change in regional space, using a 1% household sample from China’s 1990 population census. Elsewhere, we use the same data to analyze reproductive strategizing, but here we pursue the big picture with a straightforward analysis that takes reported births as an uncomplicated indicator of fertility. The article has three objectives: first, to introduce a novel, multilevel spatial model of regional structure constructed using a geographic information system (GIS); second, to demonstrate the potential for longitudinal data derived from onetime censuses to contribute to historical demography in conjunction with regional analysis; and third, to document the manner in which China’s fertility transition has unfolded in regional space. We argue that our spatial model, Hierarchical Regional Space (HRS), effectively captures the spatial structures of change in socioeconomic status, in family system norms, in the state’s birth-planning policy and enforcement, and in access to high-tech sex-selective abortion, which help to explain the observed patterns of demographic transition.

Our analysis begins with a data file consisting of over 12 million records comprising a 1% sample of China’s 1990 population census. Access to this data file has been made possible in collaboration with China’s State Statistical Bureau and the Beijing Institute of Information and Control. The 1% sample was selected from a master file listing some 1.2 billion returns by geographically ordered identification number. The full census returns of all persons in every 100th household are included in the sample. The returns offer a wealth of data on demographic status, education, and occupation. We manage this sample of census data, which we refer to as the ChinaS data file, with SAS statistical software (SAS Institute, Cary, North Carolina).

In constructing our spatial model, we turn to two additional data files containing data on China’s 2,800 county-level administrative units and over 12,000 cities and towns. These two files, dubbed ChinaA and ChinaT respectively, are linked to digital maps managed with ArcInfo GIS software (Environmental Systems Research Institute, Redlands, California). Additional GIS files describing China’s physiography, hydrography, and transportation network are also used in our spatial analysis (Henderson et al. 1999). The Australian Centre for the Asian Spatial Information and Analysis Network (ASIAN) at Griffith University in Brisbane had primary responsibility for the development of the spatial data files (Crissman 1997), which have been compiled at a nominal scale of 1:1,000,000 for an estimated spatial accuracy of +/−650 meters. By making use of a standardized (albeit undocumented) spatial coding system embedded in the census record identification numbers, we have been able to link ChinaS records to the county and settlement levels, corresponding to our ChinaA and ChinaT data files, and using this linkage we are able to position sampled households within the HRS model.

As we explain in this article, we conceive of China’s social and economic landscape in terms of regional hierarchies of cities and towns, each serving as the node of a regional or local territorial system. At the top level of these hierarchies, nine macroregional systems covering all of China Proper make up the study area of our research project. In this article we focus on just one of China’s macroregions, the Lower Yangzi. This regional economy-cum-society, centered on Shanghai, is one of the most advanced in China and had a population of 140 million as of 1990.

The Hierarchical Regional Space Model

Theoretical Foundations

HRS elaborates on some fundamental elements of modern geographical thought, including central place theory, location theory, regional systems theory, and diffusion theory. For agrarian societies...