urban and rural areas is a greater problem in China where the National Bureau of Statistics (NBS) uses different survey instruments for urban and rural areas (whereas it is a unified survey instrument in India, as elsewhere). The two nationally representative annual surveys for China that we will draw on are the annual Rural Household Survey (RHS) and the annual Urban Household Survey (UHS).

For the RHS there are also comparability problems over time, as discussed in Ravallion and Chen (2006). One of the more serious problems is that there was a change in valuation methods for consumption of own-farm production in the RHS in 1990 when public procurement prices (held below market prices) were replaced by local selling prices.<sup>2</sup> For 1990 (the only year for which the two methods can be compared), Ravallion and Chen (2006) show that the new valuation method generates slightly lower inequality; for 1990 the aggregate Gini index for rural China drops from 31.5 percent to 29.9 percent; the rural headcount index of poverty drops substantially, from 37.6 percent to 29.9 percent. This reflects the high share of consumption from own-farm product among China’s poor.

Another problem in past work has been the failure to adjust for spatial cost-of-living differences. This can affect distributional comparisons over space and time. The extent of urban– rural disparities drops appreciably once one corrects for the fact that the urban cost of living is higher (Ravallion and Chen, 2006). Also the positive trend in urban–rural inequality since around 1980 (noted by many authors in the literature) vanishes once one allows for the fact that the rate of inflation has been higher in urban areas than rural areas, although a marked positive trend in urban–rural inequality since the mid-1990s is still evident.

In common with most countries, the bulk of the analysis of poverty and inequality in China (and India) has relied on repeated cross-sectional surveys, in which the samples at each date are treated as independent. Thus one does not track the living standards of the same households over time. We do not then know how much of the poverty at one date is persistent, and how much is transient (reflecting fluctuations in living standards, including movements in and out of poverty). (Some lessons from panel data studies will be reviewed later in the paper.) Lack of public access to the micro data for China has restricted the ability of researchers to try to address these data concerns. However, the micro data have been available for some selected provinces and time periods. Ravallion and Chen (1999) used the micro data for four provinces of southern China to correct for both the valuation methods for consumption of own product and the deflators. The corrections to the original survey data tend to entail lower measured inequality and they attenuate the rate of increase in inequality over time.

Not all the likely data problems mean a lower true level of inequality or a lower rate of increase over time. For example, if we could correct for selective compliance (whereby the

> 2 Until the mid-1990s, public procurement prices for grain were held below market prices. Using these prices to value own consumption over-estimates the true extent of both poverty and inequality. This practice was largely abandoned from 1990s onwards in favor of using local selling prices for valuation.

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