10 months ago


were all poverty

were all poverty reducing. The elasticity of poverty to non-farm growth differed significantly across states. The sectoral breakdown of growth was more significant to poverty reduction in states with lower standards of initial conditions. For a growing non-farm economy, human resource development and more equal land distribution seem to be strongly connected to poverty reduction, as is literacy for pro-poor growth. For example, more than half of the difference between the elasticity of the head count index of poverty to non-farm output for Bihar (the state with lowest elasticity) and Kerala (with highest) is attributable to the latter's substantially higher initial literacy rate (Ravallion and Datt, 1999). Ravallion and Datt used three sets of poverty indices as dependent variables, viz., the head count ratio (HCR), the poverty gap index (PGI) and the squared poverty gap index for 15 major states including Tamil Nadu. Two output variables were used. One, the real agricultural output per hectare of net sown area, and the other real nonagricultural output per person. Their results imply that higher output leads to a reduction in the poverty ratio. With respect to the HCR, one percentage point growth in real agricultural output per hectare of net sown area leads to a reduction of 0.11 percent. This effect of real agriculture output is the same for all states. In the case of non-agricultural output per person, the impact of growth differs from state to state. In the case of Tamil Nadu, its impact on both HCR and PGI is quite substantial. Every one percent increase in the real non-agricultural output leads to a reduction in the HCR of 0.28 percentage point. This impact is even larger on the povertygap ratio, amounting to nearly 0.4 percentage point. Thus, the results highlight that increase in non-agricultural income leads to reduction in the head count ratio and an even larger reduction in the depth of poverty. Increase in real per capita state development expenditure, which represents a fiscal variable, is also shown to have a negative impact on the poverty index. One percent increase in per capita development expenditure leads to a 0.14 percent fall in the HCR and 0.24 percent fall in PGI. The effect of state development expenditure is taken to be uniform across states in this exercise. The influence of the inflation rate is poverty increasing. One percent increase in the inflation rate leads to a 0.42 percent increase in the HCR and 0.59 percent increase in PGI. The results are summarised in Table 2.5. 36

In the context of interface between growth and poverty, it is useful to decompose the impact of income growth and income distribution on poverty. One policy concern in recent years has been whether wide differences in the poverty across regions in India are due to the differences in the mean income or the differences in the distribution of income (Dhongde, 2003). Several attempts have been made in the past to decompose the total change in poverty over a period of time (Kakwani and Subbarao, 1990; Datt and Ravallion, 1992; Shorrocks and Kollenikov, 2001; and Dongde, 2002). Table 2.5: Regressions for the State Poverty Measures Allowing for Inter-state Differences in Elasticities to Non-farm Output Head Count Ratio Poverty Gap Index Squared Poverty Gap Variables Index Coefficient t-ratio Coefficient t-ratio Coefficient t-ratio Real agricultural output per -0.11 -4.74 -0.201 -5.46 -0.271 -5.35 hectare of net sown area (current + lagged) (YLD) Real per capita state -0.14 -2.57 -0.241 -2.79 -0.338 -2.86 development expenditure lagged (GOV) Real non-agricultural output per person: current + lagged (NFP) Andhra Pradesh -0.291 -8.89 -0.425 -8.19 -0.524 -7.37 Assam -0.199 -5.05 -0.259 -4.13 -0.314 -3.65 Bihar -0.13 -2.59 -0.335 -4.21 -0.501 -4.58 Gujarat -0.285 -6.93 -0.444 -6.81 -0.55 -6.14 Karnataka -0.249 -7.06 -0.36 -6.42 -0.444 -5.77 Kerala -0.542 -14.8 -0.859 -14.79 -1.087 -13.64 Madhya Pradesh -0.184 -4.92 -0.318 -5.35 -0.421 -5.16 Maharashtra -0.191 -5.04 -0.248 -4.13 -0.27 -3.27 Orissa -0.33 -9.67 -0.531 -9.8 -0.7 -9.42 Punjab and Haryana -0.343 -10.09 -0.466 -8.65 -0.554 -7.49 Rajasthan -0.336 -7.39 -0.493 -6.84 -0.605 -6.11 Tamil Nadu -0.277 -7.97 -0.397 -7.2 -0.479 -6.33 Uttar Pradesh -0.253 -6.12 -0.359 -5.47 -0.444 -4.93 West Bengal -0.618 -11.57 -0.937 -11.06 -1.204 -10.35 Jammu and Kashmir -0.176 -5.12 -0.23 -4.21 -0.273 -3.65 Inflation rate (INF) 0.419 5.19 0.587 4.58 0.704 4.00 Time trend 0.017 6.46 0.027 6.51 0.036 6.21 Root mean square error 0.094 0.1491 0.2047 R Square 0.918 0.918 0.91 Source: Ravallion and Datt (2001). Note: All variables are measured in natural logarithms. A positive (negative) sign indicates that the variable contributes to an increase (decrease) in the poverty measure. The estimated model also included statespecific intercept effects, not reported in the Table. Total observations used in the estimation are 272. In a recent Study, Dhongde (2003) analysed how much of the total differences in the state and national level poverty can be explained by differences between state and national mean income, and differences in their income distribution. Based on NSS data, 37

World Comparative Economic And Social Data
Police Stations - Tamil Nadu Police
Nammakal - 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