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POVERTY REDUCTION STRATEGY TN

influence of increased

influence of increased income inequality is relatively larger, but the influence of growth is predominant. Table 2.9 shows that inequality in urban incomes is much higher than that in rural incomes. To update the official poverty line used by the Planning Commission, the Consumer Price Index for Africultural Laborers (CPI-AL) and the Consumer Price Index for Industrial Workers (CPI-IW) are reweighted using national level consumption patterns of people around the poverty line in 1973-74. The basic price data are the same as for CPI-AL and CPI-IW, but the commodity level prices are weighted using the more recent and more poverty relevant weights. States Table 2.9: Growth and the Head Count Ratio: 1993-94 to 1999-00 HCR50 Derivative with Respect to Growth Six Years Growth Change in HCR55 Inequality Fixed Change in HCR55 Actual Rural Andhra Pradesh 29.2 -0.9 2.8 -2.5 -3 Karnataka 37.9 -0.91 9.5 -9 -7.2 Kerala 19.5 -0.62 19.6 -10.3 -9.5 Tamil Nadu 38.5 -0.9 15.7 -13.3 -14.1 All India 33 -0.88 8.7 -6.8 -6.7 Urban Andhra Pradesh 17.8 -0.62 18.5 -9 -6.9 Karnataka 21.4 -0.6 26.5 -12.9 -10.6 Kerala 13.9 -0.46 18.2 -7.1 -4.2 Tamil Nadu 20.8 -0.66 25.1 -12.9 -9.6 All India 17.8 -0.56 16.6 -7.4 -5.9 Source: Deaton and Dreze (2002). HCR50 and HCR55 - Head Count Ratio from 50 th Round and 55 th round, respectively. Bhalla (2002) uses the concept of ‘Shape of Distribution Elasticity’ (SDE), which indicates proportionate change in the HCR, following a one percent change in growth, assuming that there is no change in the distribution. He defines: dP = (g + i)* SDE where dP is the change in the head count ratio, g is the growth in average per capita consumption and i is the change in the share of consumption of the poor on or near the poverty line. Bhalla (2003) argues that the kind of elasticities estimated by Ravallion and 40

Dutt are not so relevant for predicting changes in the poverty HCR. One has to take into account the shape of the income distribution curve around the poverty line. The higher the SDE, the larger would be the impact of an increase in the growth rate in reducing the poverty. Bhalla observes “… if the impact of growth is assessed via the ‘mediation’ of SDE, then the correct growth-poverty elasticity is often 50 to 100 percent larger than one which is conventionally estimated. Table 2.10: Inequality Measures: Head Count Ratio Mean Relative Deviation Variance of Logs (MRD) States 50th Round 55th Round 55th Round Adjusted 50th Round 55th Round 55th Round Adjusted Andhra Pradesh 0.14 0.09 0.13 0.24 0.17 0.22 Karnataka 0.12 0.1 0.12 0.21 0.18 0.22 Kerala 0.15 0.14 0.16 0.26 0.24 0.27 Tamil Nadu 0.16 0.14 0.15 0.27 0.23 0.24 All India – Rural 0.14 0.11 0.14 0.23 0.21 0.24 Andhra Pradesh 0.17 0.16 0.17 0.3 0.29 0.33 Karnataka 0.16 0.18 0.17 0.31 0.32 0.34 Kerala 0.2 0.17 0.22 0.31 0.32 0.37 Tamil Nadu 0.21 0.27 0.2 0.39 0.34 0.35 All India – 0.19 0.2 0.21 0.34 0.34 0.37 Urban All India 0.17 0.18 0.19 0.29 0.29 0.32 Source: Deaton and Dreze (2002). Note: MRD is computed as eh difference between log arithmetic mean and log geometric mean. Bhalla’s (2003) estimates of SDE for rural and urban in selective states and all- India for 1983, and estimates of the HCR for 1999 using change in inequality and change in growth of per capita consumption over 1983 to 1999 are reproduced in Tables 2.11 and 2.12. 41

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