The social representation approach can produce a more robust
and flexible way of defining rurality, through accommodating the
effects of social and economic change in rural environments
(Woods, 2005: pp. 10–11). Usually, the effects of social and
economic change in rural environments can be primarily showed
by the change and output value of cultivated land, change and
employment of rural population, and the productivity of rural labor.
Accordingly, the assessment indicators of RDI were constructed
(Table 3). It seems that there is a strong correlationship between
the change of cultivated land and its output value. But the actual
situation in coastal China is that many farmlands are idle and even
the utilized farmlands are not in an intensive way because of the
low incentive for raising cultivated land-use intensity (Li & Wang,
2003). Similarly, there is not a strong correlationship between the
change of rural population and the percentage of employed population
in primary industry and the productivity of rural labor, for
existing lots of surplus rural labors in the statistic rural population
and most of them are rural–urban migrants (Fan, 2003; Long,
Heilig, et al., 2007). So, the five indices used to synthetically
measure the rurality degree in Table 3 are relatively independent,
and the variables also passed the tests for independence in the
software of SPSS 11.5 for Windows.
Methods
To classify the rural development types, we regard that, if
a certain indicator value of a county exceeds the sum of the indicator’s
mean and standard deviation, it can be determined that
rural development in this county is dominated by the factors
relative to the indicator. Accordingly, the indicator system classifying
rural development types was constructed (Table 4).
Because the socio-economic data for the various indicators in
Table 3 are in many different dimensions, it was impossible to