Disparities in Chinese economic development: approaches on different levels of aggregation
Section snippets
Introduction: zooming in on China’s regional development
Interregional disparities in China’s economic development have attracted considerable political and scholarly attention. Although in a country of such a vast size as China, large disparities seem to be an unavoidable fact of nature, there is the additional impact here of distorting policy interventions and spatially divergent institutional change, which renders the topic analytically very difficult and politically sensitive. Any kind of causal analysis will entail assignation of responsibility
The ICCE/GIS data base on Chinese prefectures and competing aggregation approaches
The ICCE/GIS data base contains some of the data categories most important for the comparison of trends and differences in regional development across China. There are virtually complete sets of data on per capita GDP, rural net income and urban disposable income per capita, as well as on the structural composition of GDP and employment. With regard to these categories, one of the most serious limitations are the gaps in data on population, because these are still based on the place of
Methodology: the general entropy measures (GEM) measure and approaches to regional decomposition
There are a number of approaches to the measurement of inequality and a wide discussion on the quality and meaning of different inequality indices.
Total inter-provincial and inter-prefectural inequalities compared
In order to convey a first impression of the possible insights to be gained from disaggregating below the provincial level, Table 1 shows the calculations of total national inequality for the four main indicators: GDPPC, RPCI, UPCI and TPCI on both prefectural and provincial level. With reference to the years 1993 and 1998, we compare the changes in the Gini index, the coefficient of variation (CV), the GEM and the max–min-indicator which is frequently applied in Chinese policy analyses.
Conclusion
Our study corroborates the familiar judgment that China is a vast and hugely varied country. At the same time, we have demonstrated that nationally homogenous policy interventions still have a strong impact in favor of the urban areas. Scrutiny of the GDPPC and the RPCI indicators shows a greater variety, whereas, the relative uniformity of the UPCI results and the stark differences between the urban and the rural sectors reveal the strength of institutional factors inherited from the socialist
Acknowledgements
Support of our research by the ‘Märkische Arbeitgeberverband’ is gratefully acknowledged. Additional support was generously provided by Volkswagen Foundation in the context of the “Sino-German Cooperation Programme in Empirical Economic Research”. Two anonymous referees helped us to sharpen our argument.
References (24)
- et al.
Spatial patterns of China’s rural industrial growth and prospects for the alleviation of regional income inequality
J. Comparative Econ.
(1999) Rural industrialization and increasing inequality: Emerging patterns in China’s reforming economy
J Comparative Econ.
(1994)Multidimensional generalizations of the relative and absolute inequality indices: the Atkinson–Kolm–Sen approach
J. Econ. Theory
(1995)Decomposition of China’s regional inequalities
J. Comparative Econ.
(1993)- Atkinson, A., 1983. The Economics of Inequality. Clarendon Press,...
- et al.
The new economic geography of Greater China
China Perspectives
(2000) - Chung, J.-Ho. (Ed.), 1999. Cities in China. Recipes for Economic Development in the Reform Era. Routledge,...
- Dernberger, R., 1996. Provincial yearbooks, CITAS county-level data files. Personal comments and evaluation,...
Of belts and ladders: state policy and uneven regional development in post-Mao China
Ann. Assoc. Am. Geographers
(1995)- Goodman, D.S.G. (Ed.), 1997. China’s Provinces in Reform: Class, Community and Political Culture. Routledge,...
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