Chinese regional productivity and urbanization: a county-level study in 2007-2010

Publication Date01 June 2015
AuthorFei Jin,Qi Zhang
SubjectEconomics,International economics
Chinese regional productivity
and urbanization: a county-level
study in 2007-2010
Fei Jin and Qi Zhang
School of Economics and Resource Management,
Beijing Normal University, Beijing, China
Purpose – This study aims to analyze the total factor productivity (TFP) performance of Chinese
counties and cities over the period from 2007 to 2010. Chinese regional and urban–rural TFP
performance are investigated by using county-level data, and the impact of the urbanization policy on
TFP is discussed.
Design/methodology/approach – The data envelopment analysis (DEA)-Malmquist technique and
Kumbhakar–Sun’s semi-parametric model are used for TFP change measurement and comparison. The
county-level TFP performances are summarized and studied by statistical methods. Their spatial
distribution is exhibited in a geographical thematic map.
Findings – The county-level analysis proves that China underwent a large-scale TFP decline over the
period from 2007 to 2010. Statistically speaking, cities’ TFP growth is more positive than counties’;
however, different provinces also have their regional characteristics. In addition, the Chinese Hukou
(household registration) institution divides Chinese urbanization into halves, which have the opposite
correlation on TFP growth.
Research limitations/implications – Because the collection of county-level data is enormous and
costly, this study only focuses on a very short period (2007-2010) with estimated data. This TFP change
analysis is limited to the short-term phenomenon around the 2008 international nancial crisis.
Practical implications – This study provides a visual spatial distribution for county-level TFP
change in China over the period 2007-2010. Results of the analysis demonstrate that the Chinese Hukou
system is among the policy factors that can inuence productivity in the course of urbanization.
Originality/value – The achievement of the rst nationwide county-level TFP change study for
economic growth in China is innovative. This study provides a unique perspective for understanding
productivity performance at the regional level over the period investigated, which provides invaluable
data for investigating the impact of urbanization and the rural–urban gap on TFP growth.
Keywords Chinese county-level economy, DEA-Malmquist method,
Kumbhakar-Sun’s semi-parametric model, Registered permanent residence, TFP, Urbanization
Paper type Research paper
1. Introduction
Proting from moves toward “reform and openness”, the Chinese economy has
experienced a rapid expansion since 1978, and urbanization has played a very important
role in this process. According to current ofcial indicator (the proportion of urban
population), the speed of Chinese urbanization was much more rapid than that of
comparable nations, such as the USA or the USSR during the corresponding time period.
In industrialized countries, urban areas represent the engines of output growth, and the
topic of economic efciency in Chinese urban areas has attracted the attention of a large
The current issue and full text archive of this journal is available on Emerald Insight at:
Journalof Chinese Economic and
ForeignTrade Studies
Vol.8 No. 2, 2015
©Emerald Group Publishing Limited
DOI 10.1108/JCEFTS-11-2014-0024
number of researchers. Although there is dispute regarding data convincingness,
Chinese urbanization provides a crucial case for studying the mechanisms of
Total factor productivity (TFP), a popular tool for the analysis of regional economic
growth, is a well-known technique for measuring input–output efciency. The modern
TFP concept based on economic growth theory was adapted from Solow (1957). TFP
change can be dened as a residual between output growth and input growth, which in
early theory was regarded as a generalized technical progress. Evidence from economic
history indicates that TFP growth will eventually become the fundamental source for
sustainable growth in industrialized countries. Continuous development and evolution
make contemporary TFP theory more diversied and complicated than earlier models,
which provide many derivative methodologies for empirical analysis.
Data envelopment analysis (DEA)-Malmquist is a frequently used methodology for
TFP change measurement with panel data. The Malmquist index was proposed by
Malmquist (1953), and Caves et al. (1982) introduced the index to productivity analysis.
Finally, Färe et al. (1989,1992,1994) instrumentalized the technique through DEA
technology. Since 2005, an increasing number of Chinese researchers have used the
DEA-Malmquist method for regional TFP change analysis, particularly at the province
level, such as Guo et al. (2005),Zhao et al. (2005),Zheng et al. (2006),Wang et al. (2006),
Zhang et al. (2008) and Lu et al. (2008). In recent years, new methodologies for TFP
change measurement have been developed through semi-parametric estimation. One
example of such a methodology is Kumbhakar and Sun’s (2012) semi-parametric smooth
coefcient model (abbreviated as KSM in this article). Yet, comparative studies on TFP
are often necessary because distinct theoretical principles may cause slightly different
The work of Li (1992) has been pioneering for Chinese macroeconomic TFP studies,
spurring many more TFP studies on China. Limited by objective factors, the Chinese
productivity level has been much lower than those of developed nations (Hall and Jones,
1999). Events such as the Great Famine (1959-1961) and the Cultural Revolution
(1966-1976), led China into a severe TFP recession, and some economists view Chinese
TFP growth since 1978 largely as a recovery (Krugman, 1994). Since 1990, China has
entered the stage of rapid capital accumulation, which is not only very similar to that
experienced by the “Asian Tigers” of the 1960s-1970s, but also very different from
post-war Western economies. Due to durable booming of capital, the growth
contribution by TFP hitherto was limited. Most studies assess that TFP growth in China
has slowed since 2000 (Tian and Yu, 2012), and since 2008, it has shrunk again as a
by-product of the international nancial crisis.
Though it is the world’s largest developing nation, China has an enormous
development gap between regions and urban-rural areas. Eastern China has entered the
post-industrial era, while western China has only just begun to industrialize. In
metropolises, gross domestic product (GDP) per capita has nearly caught up with the
developed countries; however, there is a rural population of about 100 million people still
living below the poverty line. Investigations of Chinese economy and productivity must
consider regional and urban–rural differences.
According to the existing system of gathering statistics, regional studies and urban–
rural studies on China are usually conducted in different spheres, rather than attempting
to integrate the two sub-disciplines. Current regional TFP analysis for China mainly

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