Title: GCRF: Dynamics of Health & Environmental Inequalities in Hebei Province, China
Funder: Economic and Social Research Council

Abstract: Hebei province has some of the most polluted cities in the world. A key priority for the 74 million people who live there is to find ways of reducing air pollution and to address the associated social and health inequalities. It is crucial that the true cost of pollution is included in economic planning. The first step is to develop the necessary data infrastructure needed to provide the government a clear picture of how pollution, health and social deprivation are related and how they have changed over time. Our proposal offers an outstanding opportunity to make a step change in the quality and richness of evidence available to decision makers in Hebei Province. The potential for impact is exceptional due to: (a) the scale and importance of the problem we are seeking to address, and (b) access to senior policy makers made possible through collaboration with the Hebei Institute of Statistical Sciences (HISS). HISS plays a vital role in generating data and developing decision support tools for the Hebei government. Over the past year we have been forging a collaboration agreement with HISS. This is fortuitous in that it sets out a long-term vision for data access and research that will inform industrial restructuring and social policy in Hebei Province during a crucial phase in its development. The imperative for change has become all the more pressing since 2014 when the Premier of China declared "war" on air pollution. Because of Hebei's proximity to Beijing (Hebei essentially surrounds the capital), it has become a target for drastic measures to reduce air pollution. There is an urgent imperative, therefore, to provide an evidence base that will inform these decisions to make them as efficient and socially just as possible, maximising the benefits for the socially vulnerable. So, our proposed project is both timely, and highly relevant to the development challenges facing Hebei. The proposed programme of research will provide the Hebei government with robust estimates of the spatial dynamics of poverty, pollution and health. Ours will be the first attempt to construct deprivation indices for Hebei province and the first estimates anywhere in China of how the geography of deprivation has changed between the 2000 and 2010 Censuses. We will also provide a range of nuanced measures that capture how the spatial structure of poverty and segregation has evolved over time, revealing, for example, whether poverty has become less centralised in Hebei's key cities (an important trend in many Western conurbations but, as yet, an unexplored issue in China). We also want to help the Hebei government understand how pollution, health and deprivation are related by developing robust statistical models. This is vital if the true costs of pollution are to be included in economic and social policy decisions in a rational and systematic way. These research plans are made possible by the unprecedented opportunities for data access afforded through the collaboration between SMI and HISS. Together with the pressing policy issues noted above the newly available data also opens up an opportunity for world-leading methodological innovation. Our research team has pioneered statistical techniques for incorporating the effects of both spatial proximity and hierarchical structuring (e.g. individuals nested within neighbourhoods) in geographical data. The health variable we plan to model has particular features (bounded between 0 and 100). This motivates a novel extension of our methodology that will yield more reliable models of the relationship between health and pollution exposure. Our project will also establish a platform for exciting ambitious research opportunities in future, paving the way for further data linkage and potentially leading to a world-class longitudinal dataset that links individuals from multiple Censuses over time.


Research activity codes: 2.4 SURVEILLANCE AND DISTRIBUTION;2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: GENERIC HEALTH RELEVANCE