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POVERTY REDUCTION STRATEGY TN

Table 2.11: Growth

Table 2.11: Growth Inequality Poverty Connections: Rural India, 1983-1999 States Gini Change in Gini Change in Inequality Growth in Per Capita Consumption Total Growth 1983 1999 (1983-99) (1983-99) (1983-99) (1983-99) Andhra Pradesh 29.7 23.8 -22.1 14.6 9.0 23.6 Karnataka 30.9 24.4 -23.6 16.7 13.2 29.9 Kerala 31.9 28.9 -9.9 7.6 37.0 44.6 Tamil Nadu 36.6 28.4 -25.4 15.4 30.1 45.5 India 30.4 26.3 -14.5 7.8 18.5 26.3 SDE Change in HCR HCR HCR 1983 Predicted Annual (1983- 1983 1999 (1983-99) 99) Andhra Pradesh 0.8 -19.0 -16.2 27.3 11.0 Karnataka 0.8 -23.7 -19.1 36.3 17.2 Kerala 0.9 -39.3 -30.7 40.1 9.4 Tamil Nadu 0.8 -35.9 -34.1 54.4 20.4 India 0.9 -24.2 -20.9 48.2 27.3 Source: Bhalla (2003). Notes: 1. SDE is the 'shape of distribution elasticity' defined as the expected change in the poverty for each 1 percent of growth assuming that distribution of income remains unchanged. 2.Inequality change is the (log) change in the consumption share of the poor. This change is computed as the change in the share of the bottom 20 percent, if the HCR for the base year 1983, was below 25 percent, or of the bottom 40 percent if the HCR in 1983 was between 25 and 45 percent, etc. 3.Total growth in income is the sum of (log) growth in per capita consumption and log change in inequality. 4. Predicted change in head count ratio is given by the product of total growth and SDE. 5. Source of data: unit record NSS data for 1983 and 1999. Table 2.12: Growth Inequality Poverty Connections: Urban India, 1983-1999 Gini Change in Gini Change in Inequality Growth in Per Capita Consumption Total Growth States 1983 1999 (1983-99) (1983-99) (1983-99) (1983-99) Andhra Pradesh 33.1 31.5 -5.0 1.6 28.8 30.4 Karnataka 34.2 32.8 -4.2 4.3 26.4 30.7 Kerala 40.5 32.6 -21.7 13.4 24.2 37.6 Tamil Nadu 35.2 38.8 9.7 -5.5 41.2 35.7 India 33.9 34.7 2.3 -1.8 31.5 29.7 SDE Change in HCR HCR 1983 HCR 1999 1983 Predicted (1983-99) Annual (1983-99) Andhra Pradesh 0.7 -22.1 -23.7 51.2 27.4 Karnataka 0.7 -21.7 -19.1 44.2 25.1 Kerala 0.6 -24.2 -22.4 42.4 20.0 Tamil Nadu 0.7 -26.1 -25.7 48.5 22.8 India 0.8 -22.6 -21.7 45.1 23.4 Source: Bhalla (2003). Notes: As in Table 2.11. 42

The SDE estimates vary across the states for rural areas from a low of 0.48 for Punjab to a high of 1.3 for Assam with average for India being 0.92. For urban areas, the SDE estimates vary from a low of 0.56 for Punjab to a high of 1.08 for Assam with the average of 0.76 for India. For Tamil Nadu, it is 0.8 for rural and 0.7 for urban areas. 2.4 Inflation and the Incidence of Poverty Just like growth, price variations also have a considerable impact on the incidence of poverty. Deaton and Tarozzi (1999) examine the role of the price index in the estimation of poverty. Accuracy of price and poverty calculations is quite important at times when historically high rates of GDP growth do not seem to be resulting in sustained reduction in poverty. One of the tools used for these calculations is the measurement of inflation, which is important not just for establishing rates of inflation in urban and rural areas but comparing price levels between them and between different states. The two most important indexes in India are the Consumer Price Index for Industrial Workers (CPI-IW) and the Consumer Price Index for Agricultural Labourers (CPI-AL). Deaton and Tarozzi (1999) refer to problems with these indexes associated largely with the unusually long periods between revisions. They provide the sector wise inflation rates estimates over the six year period for India and for the 17 largest states. Separate indices are also provided for rural and urban sectors. They utilise information from the National Sample Survey (NSS) on prices themselves, providing a measure of unit value. Despite problems such as goods and services without defined units, or the difference between a unit value and an actual price, they show that the total expenditure elasticity of unit value is small. Deaton and Tarozzi find that the unit value data from NSS are useful for crosschecking other price indices and that there is a good agreement between the rate of increase of the official CPI-AL and CPI-IW indices and those reported in the NSS. They have also found that although there seems to be little bias in the CPI-IW, the CPI-AL might have been growing too quickly (corresponding with what Deaton and Tarozzi would expect from using an outdated Laspeyres index). Based on these results, Deaton and Tarozzi suggest that between 1987-88 and 1993-94, there was not a great difference in the rate of decline between urban and rural poverty (according to the headcount measure) and that rural poverty decline was understated in official poverty counts. Deaton and Tarozzi also take issue with some of the current poverty calculation procedures based on Expert Group Report of 1993 methods that result in urban prices 43

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