Adjusting for cost-of-living differences clouds these trends somewhat. For China, the urban rate of inflation has been higher than for rural areas and once one allows for this fact, one no longer finds a trend increase over time in the ratio of the urban mean to the rural mean (Ravallion and Chen, 2006).<sup>10</sup> However, there have been sub-periods, including the period from 1997 to the present, during which the relative urban-rural disparity has risen. Moreover, even allowing for cost-of-living differences, the absolute gap between rural and urban incomes has increased appreciably. This is also true of India.

# **...and that meant less poverty reduction than if growth had been more sectorally balanced**

The sectoral composition of growth mattered for poverty reduction in both countries. This can be seen from table 1, which provides regressions of the rate of change in poverty over time (that is, the difference in the log of the headcount rate of poverty) on both the overall rate of per-capita GDP growth (that is, the change in the log of GDP per-capita), as well as the shareweighted rates of growth of GDP in each of the three sectors. The sector-specific growth rates are share-weighted, to allow for the fact that sectors growing at the same rate are unlikely to have the same aggregate impacts when one accounts for a much smaller share of aggregate income than the other. When share-weighted, one obtains a straightforward testable hypothesis for whether the composition of growth matter, namely that the regression coefficients across growth components would be roughly equal (Ravallion and Datt, 1996). Note that these regressions are best viewed as decomposition tools rather than causal models of poverty reduction. Deeper explanations must endogenize growth rates and their composition; Ravallion and Chen (2006) provide models of poverty reduction in China that try to make some progress in that direction.

**_<u>Table 1: Poverty reduction and the sectoral composition of growth: China and India</u>_**

|||**China**||**In**|**dia**|
|---|---|---|---|---|---|
|Growth rate of GDP per capita|–2.60<br>(–2.16)|n.a.|n.a.|–0.99<br>(–3.38)|n.a.|
|Primary (share-weighted)|n.a.|–8.07|–7.85|n.a.|–1.16|
|||(–3.97)|(–4.09)||(–2.96)|
|Secondary (share-weighted)|n.a.|–1.75<br>(–1.21)|n.a.|n.a.|3.41<br>(1.84)|
|Tertiary (share-weighted)|n.a.|–3.08<br>(–1.24)|n.a.|n.a.|–3.42<br>(–2.74)|
|Secondary + tertiary<br>|n.a.|n.a.|–2.25<br>(–2.20)|n.a.|n.a.|
|<I>R<I><+><sup>2<+></sup>|0.21|0.43|0.42||0.75|

**_Source:_** Ravallion and Chen (2006) for China (1981–2001) and Ravallion and Datt (1996) for India (1951–1991). **_Note:_** t-ratios in parentheses.

10 There are other data problems with ambiguous implications for urban-rural disparities. The undercounting of rural migrants in China’s urban areas is likely to lead to an overestimation of the level and growth rate in the ratio of the urban mean to the rural mean. Against this effect, urban survey response rates tend to be lower than for rural areas and it be safely assumed that the rich tend to have lower response rates. Our discussions with the staff of China’s National Bureau of Statistics suggest that this problem is growing over time in China.

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