result, urban workers’ productivity and earnings exceeded those of their rural counterparts
(Louis Putterman, 1993; Yang and Zhou, 1999).”5
2. Data and methodology
The database for this study consists of a nationwide household survey conducted by
China’s Elderly Scientific Research Center in 1992, on rural and urban elderly. This
survey consists of two separate sets of responses, one for urban areas (9,889 respondents)
and the other for rural areas (10,194 respondents), and provides demographic,
socioeconomic, and health characteristics of the elderly.
Urban areas comprise small cities (<200,000 registered residents), medium cities
(200,000 to 500,000), big cities (500,000 to 1,000,000) and metropolitan cities (more than
1 million). Rural areas refer to all farming areas under the administration of counties.
Elderly are defined as 60 years old and over.
To measure income disparities we compute Gini coefficients and derive the
respective Lorenz curves. The Gini coefficient is a measure of dispersion of a distribution
and is mostly used to measure the distribution of income (it could also be applied to
consumption or wealth, for instance).6
In addition, we use cross-tabulation methods to illustrate the association between
income (dependent variable) and various socioeconomic characteristics of the elderly such
as age and type of family (or living arrangement). We rely on Chi-Square and Cramer’s V
tests to measure the significance of the data - Chi-square is used to test the significance of
the association between the independent and dependent variables, Cramer’s V test is used
to measure the magnitude of the association between the variables (Blalock, 1979).
3. Findings: Socioeconomic Disparities
We find that rural households are older, less educated, have more children, and are
somewhat larger than the urban counterparts (table 1). The average age of the head of the
household is slightly higher for the rural units than the urban ones, 69 and 68.4
respectively.
5 Quoted from Yang (1999, pp. 308).
6 See for instance Xu (2004) for a comprehensive literature review on the Gini index.