宁夏农村地区为例分析经济增长减少贫困了吗 第10页
Using the estimated poverty headcount rates, it is possible to estimate the poverty head counts for the sample provinces and for rural China. Poverty head count is the product of the head count rate by the rural population. Table 4 presents the poverty head counts for three large regions and for rural China.

The total number of poor in rural China ranged from 79.6 million to 196.8 million in 1995 and from 103.1 million to 187.0 million in 1998. Our estimates are slightly less than but not fundamentally different from those of other studies. For example, using a higher poverty line

However, even our lower estimate of rural poverty is much higher than the official figure of 42 million (People’s Daily, 2000). The large discrepancy between the estimates in this paper (and other studies) and the official figures may be due to sample biases, but it may also be due to some other reasons. The government may have used a very different approach to estimate poverty or it may have deliberately underestimated the incidence of poverty for political reasons.

Another difficult issue on rural poverty is related to the skewed distribution of income between regions. The west region accounts for about 25% of the total rural population but explains about 40% of the total poor. The east region accounts for over 39% of the rural population but explains less than 20% of the total poor. This spatial pattern implies that the west provinces are more susceptible to poverty than the rest of the country.

However, this does not mean that the other provinces have no serious poverty. In fact, even the most prosperous provinces, including Beijing, Tianjin, Shangdong, Guangdong, Zhejiang, and Jiangsu, have large numbers of poor people, and they are highly vulnerable to short-term fluctuations in production and incomes.

We use Eq. 2 in the previous section to estimate the poverty elasticities with respect to per capita income and the Gini coefficient for the rural population from the data for both 1995 and 1998. The regression results are presented in Table 5.

The estimated coefficients have the expected signs and are highly significant. The model explains 87% of the variations in poverty incidence, implying that these two variables are the dominant factors of rural poverty.

On the whole, the incidence of rural poverty is more sensitive to per capita income and inequality than the incidence of urban poverty. Based on the high poverty line, if per capita mean income rises by 10%, the poverty incidence will decline by almost 27%. On the other hand, if per capita mean income is held unchanged, a 10% rise in the Gini coefficient will lead to a 21% increase in poverty.
Table 3
Rural poverty headcount rates in 1995 and 1998

Table 4
Nomber of poor in rural China and regional distribution
 
Table 5
Effects of income and inequality on rural poverty
外文原稿之二
Growing inequality and poverty in China(2)
             Lucia HANMER
             Department for International Development (DFID),London, UK
             China Economic Review  15(2004)  145-163

4. Urban–rural income inequality

Urban–rural income inequality is an important dimension of income distribution in China. It also has an important implication on poverty reduction. There are many studies in the literature on this issue.

The extent of urban–rural income inequality can be manifested by the real per capita urban to rural consumption ratio as shown in Fig. 1 for the period 1952–1998.

Consumption is considered to be a better measure of living standards than income (Deaton, 1997), particularly for a long time series.4 In the prereform period, the ratio was consistently above 2.5, reaching the highest point of 3.7 in the late 1970s. Economic reforms introduced a series of policies to reduce urban–rural division. Such policies included increases in procurement prices for agricultural products, the adoption of household responsibility systems, and the relaxation of restrictions on labour mobility to nonagricultural activities in the rural areas and to employment in cities. As a result, the ratio declines to less than 2.5 for most of the 1980s.

However, economic reforms fail to create an environment for continuing reduction in urban–rural inequality. From the second half of the 1980s, state policies were reversed to the detriment of the rural population. Farm prices stopped rising. Prices of agricultural inputs rose. State investments were heavily geared towards the urban and industrial sector at the expense of the rural and agricultural sector.

Apart from industrial policy that is biased in favour of the formal urban sector, rural migrants to the cities have been barred from accessing all kinds of benefits and services (housing, education, and healthcare) provided to urban residents. They have to pay higher prices in parallel markets for such services. These institutional barriers introduce a wide urban–rural income gap with little possibility of adjustment via migration.Consequently, urban–rural per capita consumption ratio rose again from the late 1980s to the 1990s.

The high urban–rural consumption gap in China is contrasted by the same gap in other countries. Using data from thirty-six countries, Yang and Zhou (1996) suggest that urban  incomes are rarely more than twice the rural incomes. In most countries, urban–rural income ratios are below 1.5.

The high urban–rural income gap can be confirmed from our household survey data in 1998. The per capita urban–rural income ratios are measured at the provincial as well as the national level. It is important to note that the income ratios could be quite different from the consumption ratios. Since we do not have consumption data for 1998, we can only present the income data below .

The income ratios vary widely between provinces, implying that the extent of urban–rural income inequality differs spatially. The ratios range from 1.12 in Zhejiang to 3.3 in Gansu. The national ratio is 2.37 from the sample data and 2.51 from published official

5. Prediction of rural poverty up to 2015

Although we use much higher poverty lines for the urban sector than for the rural sector to measure poverty, the incidence of poverty in the rural sector is still substantially higher than in the urban sector. Hence, whether China is able to reduce poverty largely depends on its ability to reduce rural poverty. As a result, this section focuses on the prediction of rural poverty up to the year 2015. Since the high poverty line is more acceptable internationally, the prediction will focus on poverty measured by the high poverty line. Before we proceed our estimations, it is important to point out that there is no easy way to forecast the level of poverty in the future because poverty is affected by too many intertwined factors. The projection can be regarded as a heroic exercise given that China is undergoing a process of monumental transformation. At best, the projection can only provide a rough picture of what the future might look like. At worst, none of the scenarios presented can represent the true reality by the end of the forecast period.

Four alternative scenarios are estimated, with special attention to see whether China is able to halve its poverty number from 1990 to 2015 to meet the international poverty reduction target set out by the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (Hanmer & Naschold, 1999).

To do this, we need to estimate the incidence of poverty back to 1990 based on the information in this paper and additional information from official data. In the period 1990 to 1998, it is assumed that per capita income follows the trend of 1980 to 1998; that is, the annual growth of per capita rural income is 3.024%. In this period, the rural Gini coefficient increased rather rapidly, from .38 to .46. Using the poverty elasticity of income and the Gini coefficient, the incidence of rural poverty in 1990 by the high poverty line is estimated as 24.68%, the total number of poor is 223 million.

The poverty elasticities of income and the Gini coefficient are derived from Table 5 from the previous section. The prediction of poverty starts from the following formula,
  
                                                      (2)
where  、 、 denote the annual growth rates of poverty, the Gini coefficient, and per capita mean income, respectively. EPG and EPI denote the elasticities of poverty incidence with respect to the Gini coefficient and per capita mean income, respectively.

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