2 weeks ago


decomposition of poverty

decomposition of poverty is done for the year 1999-00. 9 Income level is proxied by mean consumption expenditure for both rural and urban areas. 10 Fifteen major States in India were included, which account for about 97 percent of the total population of the country. Due to differences in the price level, the data are adjusted for price fluctuations by the using official poverty line. The study shows that in the year 1999-00, performances of the states differed significantly in terms of their mean level of income and distribution of income. The mean consumption expenditure and the corresponding Gini coefficients are shown in Table 2.6 (for selected states) and Appendix Table 2.1. Having estimated mean income (proxied by consumption expenditure) and distribution reflected in their respective gini coefficients, spatial decomposition of poverty showed that the variations in the poverty between state and nation are largely explained by the differences in their mean income. However, there are certain exceptions in urban areas where low level of poverty is the result of not only of higher levels of income but also more equitable distribution of income. The study draws important policy implications, arguing for higher rates of growth of income at state level where the poverty levels are very high. Table 2.6: Per Capita Consumption Expenditure and their Gini Coefficients Mean Per Capita Gini Coefficients States Expenditure Rural Urban Rural Urban Andhra Pradesh 604.35 808.3 0.26 0.33 Karnataka 583.19 786.03 0.28 0.34 Kerala 711.91 913.45 0.32 0.34 Tamil Nadu 613.36 951.57 0.31 0.40 Source: Dhongde (2003). The main results of Dhongde’s (2003) study are summarised in Tables 2.7 and 2.8, and Appendix Tables 2.2 and 2.3. The poverty ratios are adjusted for differences in state prices. For the southern states, rural HCR is lower than the all-India rural ratio. The difference in all cases is explained largely by their higher mean income levels. In Keral and Tamil Nadu, income inequality (i.e., distribution component) increases rural poverty ratio. 9 10 For a detailed methodology on spatial decomposition of poverty see Dhongde (2003). The expenditure series is not only more stable than the income series but also the differences in the income and expenditure narrows down considerably when considered for the poor (Dhongde, 2003). 38

Table 2.7: Decomposition of the Head Count Ratio in 1999-00: Rural States Head Count Ratio (Percent) Total Difference with all India Ratio (Percentage Points) Mean Component (Percentage Points) Distribution Component (Percentage Points) Andhra Pradesh 11.76 13.06 9.29 3.77 Karnataka 16.38 8.44 7.5 0.95 Kerala 12.88 11.94 17.22 -5.28 Tamil Nadu 18.98 5.84 10.15 -4.30 Source: Dhongde (2003). However, the mean component offsets this negative effect and keeps the rural ratio of these states below the national average level. In the case of Andhra Pradesh and Karnataka, better distribution of income also has a positive effect on reducing the rural poverty ratio. In Tamil Nadu, urban HCR is slightly lower than all-India urban ratio. As in the case of rural HCR, mean income component has a positive effect on reducing urban ratio while distribution component has a negative effect. Table 2.8: Decomposition of the Head Count Ratio in 1999-00: Urban States Head Count Ratio (Percent) Total Difference with all India Ratio (Percentage Points) Mean Component (Percentage Points) Distribution Component (Percentage Points) Andhra Pradesh 26.35 -1.37 -1.95 0.58 Karnataka 27.2 -2.22 -3.15 0.93 Kerala 20.25 4.74 3.73 1.00 Tamil Nadu 23.81 1.17 2.92 -1.75 Source: Dhongde (2003). In another study, Deaton and Dreze (2002) provide a decomposition of the fall in the poverty head count ratio between 1993-94 and 1999-00 as being due to growth and change in inequality (Tables 2.9 and 2.10). Clearly, a very large portion of the decline is attributable to growth rather than any reduction in inequality. Growth implies an increase in average per capita expenditure (APCE). Column 2 in Table 2.9 shows Deaton and Dreze’s estimate of percentage point reduction in HCR associated with a distribution neutral one percent increase in APCE. This depends positively on the fraction of people living at or near the poverty line. The estimates show (column 4 in Table 2.9) that growth alone would have reduced the poverty HCR more than the actual, implying that the impact of increased inequality has been used to reduce the effect of growth. In the case of rural poverty, growth almost fully accounted for the reduction of poverty with a much adverse impact of worsened income distribution. In the case of urban poverty, the 39

World Comparative Economic And Social Data
Police Stations - Tamil Nadu Police
N u m b e r o f S c h o o l s - DISE
Census 2011 population of Latur district
PDF: 1.0MB - Population Reference Bureau