Social Capital and the Effectiveness of Land Use Policies: Evidence from Rural China, 2016

DOI

The dataset underpins a study on "Social Capital and the Effectiveness of Land Use Policies: Evidence from Rural China," drawing from the 17 Provinces Rural Land Survey by Renmin University of China. This research navigates the intricacies of land use policy effectiveness in rural China, underpinned by the significant reforms initiated by the 1986 Constitution allowing transactions of land use rights. These reforms enabled local governments to lease land use rights to the private sector, significantly contributing to fiscal revenues and fostering economic development and urban expansion at an impressive rate. However, this rapid transformation introduced several challenges, including legal, social, and environmental issues centered around land use policies. The study delves into the consequences of these reforms, such as the technical efficiency impacts on livestock grazing in Tibet versus the degradation of ecosystem services in Inner Mongolia, and the negative effects of full-scale land relocation practices on organic fertilizer usage. The complexity of redeveloping brownfields in rural areas and the crucial role of rural land tenure in investment, productivity, and participation in the land rental market are also highlighted. The effectiveness of land use policies has thus become a focal point for scholarly investigation, particularly regarding the impact on rural residents, who are critical stakeholders in the reform process. Central to this exploration is the concept of social capital, defined as the network of relationships among people who live and work in a particular society, enabling society to function effectively. Social capital, encompassing elements such as trust, social networks, and norms, plays a pivotal role in encouraging environmental restoration and climate change adaptation efforts. This has been observed not only in China but globally, suggesting a move towards behavioral land use policies that leverage social capital for cost-effective and sustainable outcomes. These policies aim to influence behaviors through intrinsic motivations rather than through monetary incentives or legal mandates, which often entail significant public expenditure and administrative costs. The data seeks to advance the discourse on land use policy by proposing a comprehensive analytical framework that includes various forms of social capital and measures policy outcomes both in the short and long term. Employing an innovative empirical strategy, the study addresses endogeneity issues and aims to provide a nuanced understanding of the relationship between social capital and land use policy outcomes. The findings suggest that social capital has a contextually dependent effect on policy effectiveness, varying across different policy objectives and stages of policy evaluation. This research underscores the importance of incorporating multiple dimensions of social capital into policy analysis and design, offering insights that could guide sustainable urbanization and rural development efforts.Although China has almost eliminated urban poverty, the total number of Chinese citizens in poverty remains at 82 million, most of which are rural residents. The development of rural finance is essential to preventing the country from undergoing further polarization because of the significant potential of such development to facilitate resource interflows between rural and urban markets and to support sustainable development in the agricultural sector. However, rural finance is the weakest point in China's financial systems. Rural households are more constrained than their urban counterparts in terms of financial product availability, consumer protection, and asset accumulation. The development of the rural financial system faces resistance from both the demand and the supply sides. The proposed project addresses this challenge by investigating the applications of a proven behavioural approach, namely, Libertarian Paternalism, in the development of rural financial systems in China. This approach promotes choice architectures to nudge people into optimal decisions without interfering with the freedom of choice. It has been rigorously tested and warmly received in the UK public policy domain. This approach also fits the political and cultural background in China, in which the central government needs to maintain a firm control over financial systems as the general public increasingly demands more freedom. Existing behavioural studies have been heavily reliant on laboratory experiments. Although the use of field studies has been increasing, empirical evidence from the developing world is limited. Meanwhile, the applications of behavioural insights in rural economic development in China remains an uncharted territory. Rural finance studies on the household level are limited; evidence on the role of psychological and social factors in rural households' financial decisions is scarce. The proposed project will bridge this gap in the literature. The overarching research question of this project is whether and how behavioural insights can be used to help rural residents in China make sound financial decisions, which will ultimately contribute to the sustainable economic development in China. The research will be conducted through field experiments in rural China. By relying on field evidences, the project team will develop policy tools and checklists for policy makers to help rural households make sound financial decisions. Two types of tools will be developed for policy makers, namely, "push" tools that aim to achieve short-term policy compliance among rural households so that they can break out of the persistent poverty cycle and "pull" tools that can reduce fraud, error, and debt among rural households to prevent them from falling back into poverty. Finally, the project team will also use the research activities and findings as vehicles to engage and educate rural residents, local governments, regulators, and financial institutions. Standard and good practice will be proposed to interested parties for the designs of good behavioural interventions; ethical guidelines will be provided to encourage good practice. This important step ensures that the findings of this project will benefit academia and practice, with long-lasting, positive impacts. The findings will benefit researchers in behavioural finance and economics, rural economics, development economics, political sciences, and psychology. The findings of and the engagement in this project will help policy makers to develop cost-effective behavioural change policies. Rural households will benefit by being nudged into sound financial decisions and healthy financial habits. The project will provide insights on how to leverage behavioural insights to overcome persistent poverty in the developing world. Therefore, the research will be of interest to communities in China and internationally.

We collected data by including a special module in the 17 Provinces Rural Land Survey administrated by Renmin University of China. This survey is a joint research project between Renmin University of China and the Rural Development Institute (RDI) in the US conducted since 1999. A total of seven rounds of surveys have been conducted since then, and we obtained our data from the latest round completed in 2016.

Identifier
DOI https://doi.org/10.5255/UKDA-SN-856277
Metadata Access https://datacatalogue.cessda.eu/oai-pmh/v0/oai?verb=GetRecord&metadataPrefix=oai_ddi25&identifier=d1c04308b6299d012793a41b173cce9061f7c74357fab1cee4b84c98fb63517d
Provenance
Creator Bao, H, University of Cambridge
Publisher UK Data Service
Publication Year 2024
Funding Reference Economic and Social Research Council
Rights Helen Bao, University of Cambridge; The Data Collection is available to any user without the requirement for registration for download/access.
OpenAccess true
Representation
Resource Type Numeric
Discipline Psychology; Social and Behavioural Sciences
Spatial Coverage 17 provinces in China: 1. Heilongjiang 2. Henan 3. Shandong 4. Jilin 5. Sichuan 6. Hebei 7. Yunnan 8. Anhui 9. Hubei 10. Jiangsu 11. Guizhou 12. Guangxi 13. Hunan 14. Shaanxi 15. Jiangxi 16. Zhejiang 17. Fujian; People's Republic of China