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Engel curve Method for Measuring poverty

While the headcount index of poverty in India has fallen, per capita cereal consumption and calorie intake have also decreased. At the poverty line, the minimum calorie requirements used for defining this line in the base year are not being met in later years. While the official poverty line used the food requirement norm from nutrition science, this paper employs the food consumption requirement derived from the Engel curve as the norm to arrive at a measure of food consumption deprivation. It is shown that food deprivation levels do not reflect the same pattern that the traditional poverty indices depict, questioning the usefulness of traditional poverty indices for measuring food deprivation.

SPECIAL ARTICLEEconomic & Political Weekly EPW july 26, 2008115Engel Curve Method for Measuring PovertyT Krishna Kumar, Jayarama Holla, Puja GuhaWhile the headcount index of poverty in India has fallen, per capita cereal consumption and calorie intake have also decreased. At the poverty line, the minimum calorie requirements used for defining this line in the base year are not being met in later years. While the official poverty line used the food requirement norm from nutrition science, this paper employs the food consumption requirement derived from the Engel curve as the norm to arrive at a measure of food consumption deprivation. It is shown that food deprivation levels do not reflect the same pattern that the traditional poverty indices depict, questioning the usefulness of traditional poverty indices for measuring food deprivation. This paper is based on a P V Sukhatme Memorial Lecture by the first author at the Annual Conference of the Indian Society of Probability and Statistics, Nagpur, January 10, 2008.The work reported here is based on what the first author did with V Sitaramam and Anil Gore in the early 1990s. The authors thank N Krishnaji, Federico Perali and S Subramanian for discussions and comments on a working draft of this paper. They also thank the National Sample Survey Organisation for providing the data on CDs with excellent documentation.T Krishna Kumar (tkkumar@gmail.com) is guest faculty at the Indian Institute of Management, Bangalore. Jayarama Holla (jayarmh05@iimb.ernet.in) and Puja Guha (pujag05@iimb.ernet.in) are doctoral students at IIM, Bangalore. PV Sukhatme, as the statistical advisor of the Food and Agri-cultural Organisation of the United Nations, emphasised the importance of augmenting world food production and improving its distribution so as to reach the deprived sections of the human community across the world. He also brought that experience to address the hunger situation in India. It is in this connection that he made his pioneering contribution to the statis-tical measurement of the extent of hunger (or undernutrition) and malnutrition (or protein deficiency) [Sukhatme 1961, 1965, 1974]. He stressed the importance of examining the total food requirements on the one hand and its requirements at the indi-vidual level distinguished by age, sex and physical activity on the other. He went deeper into nutrition science to argue that calorie deficiency is much more important than protein deficiency, as the utilisation of proteins requires a minimum quantity of calories. His measure of incidence of hunger was based on the integration of the joint distribution of the requirements and availability over the set where the availability at the individual level is less than what is required. Parts of this method were borrowed, with due acknowledgements to Sukhatme, by Dandekar and Rath (1971) in providing a scientific basis for defining a poverty line. Indian literature on measurement of poverty has set the trends in poverty measurement elsewhere in the world. Thus, Sukhatme’s contributions to the estimation of hunger have a lasting impact on the measurement of poverty and its alleviation. Around 1991, Sitaramam (a biotechnologist) and Gore came up with an entirely new approach to the measurement of poverty without a poverty line. It was based on a cereal consumption dep-rivation curve, whose specification was like that of a saturation curve of catalysis used by biochemists. Kumar observed a simi-larity between the saturation curves used in catalysis and the Engel curves for necessities (or essential commodities) of economic analysis.1 This resulted in the first published paper on poverty measurement without a poverty line using Engel curves [Kumar, Gore and Sitaramam 1996].2 An empirical application of those ideas were provided by establishing a hierarchy of needs using National Sample Survey Organisation (NSSO) data and using cereals, the first in that hierarchy, as the commodity to measure the commodity-specific consumption deprivation [Sita-ramam, Kumar, Gore, Paranjpe, and Sastry 1996]. That study used grouped data of the NSSO for various earlier rounds (16th to 46th rounds covering the period 1960-90) and estimated saturating Engel curves for cereals using per capita monthly expenditure on cereals as a function of monthly per capita total expenditure.In recent years, many researchers studying poverty in India had access to ungrouped unit level data at the household level provided byNSSO. The earlier work of Sitaramam et al(1996)
0 2 4 6 8 10 12 14 16 1 2 3 4 5 6 7 8 9 10 11 12 Deprivation Saturation Consumption
SPECIAL ARTICLEEconomic & Political Weekly EPW july 26, 2008117he comments that the severity of poverty measured by a tradi-tional Foster Greer, Thorbecke (1984) type of measure lacks intu-itive economic meaning. Atkinson (1987) commented, nearly two decades ago, that there is a need to bring about a vertical integra-tion between poverty measurement and welfare economics based on consumption.Some of the essential goods and services such as food are pri-vate goods supplied mostly through the market mechanism, while some others such as primary health services, drinking water, and sanitation are public goods provided by the govern-ment. A few other essential goods and services such as education and non-primary health services are quasi-public goods provided by non-governmental organisations. The economic access to these basic goods and services is determined not only by the resources at the command of each individual or individual house-hold but also by the quantity and quality of public and quasi- public goods provided by the government and non-governmental organisations.5 Thus, personal income is not the major determi-nant of poverty. The traditional methods of poverty measure-ment based on income or total expenditure and its distribution thus seem to be only an indirect way to study poverty. Direct measures of poverty, based on consumption of essential com-modities and its distribution, will be much more useful.2 New Measure of Poverty Based on Engel CurveWe follow the route suggested by Atkinson (1987) and try to inte-grate poverty measurement with basic microeconomic analysis of demand for an essential commodity. The norm we choose for measuring consumption deprivation is a norm based on actual economic behaviour with its roots in the famous Engel law of consumption behaviour. Our approach can be explained easily by identifying two different ways of viewing poverty. One may view poverty at an individual level and then aggregate it over the set of all those who are poor in a given community. This requires asso-ciating poverty with the condition of the poor, thus necessitating the definition and identification of the poor.6 There is an alterna-tive view where one can consider an individual as a member of a community and it is the situation of the individual within the community that determines what norm the individuals set for themselves. It is this interaction of an individual with other members of her community that determines his priorities in consumption and what her requirements are. A saturating Engel curve for essential commodities provides the community-based objective measure of such consumption requirements of an essential com-modity. While any point on an Engel curve depicts the average consumption at a given income (or total expenditure) for that community, the saturation level depicts the average consumption on an essential commodity if the individual is not constrained by low income. We take this saturation level of consumption on an essential commodity as the requirement of that commo-dity. It is the cumulative shortfall of actual consumption of an essential commodity from this norm that we define as consumption deprivation. It is the consumption deprivation of an entire community with respect to all essential commodities that constitutes poverty of that community. This is the approach Kumar et al (1996) and Sitaramam et al (1996) took. In the latter work, a hierarchy of needs was established. Poverty was then defined as consumption deprivation of the most essential commodity, cereal. The Engel curve provides a wealth of information on a community’s con-sumption behaviour at various levels of total expenditure and for different family compositions. Dandekar and Rath (1971), Rao (1981) and Deaton and Tarozzi (2000) came very close to taking a full view of the Engel curve but confined to look at the poverty line portion of the Engel curve, only with the intention of deter-mining the total expenditure that supports minimum consump-tion of food. In determining the total expenditure at which the minimum calorie requirements are met, Dandekar and Rath used the observed empirical relation between food expenditure and total expenditure. Bhanoji Rao used the typical properties of an Engel curve of a necessity to suggest that the proportion of food expenditure increases, reaches a maximum and then declines. He suggested that the point where this proportion reaches a maximum could be taken as the threshold for acute poverty and suggested that one and half times that level can be taken as the poverty line. He left the Engel curve behind after deriving a pov-erty line from it. The Engel curve for a commodity is actually the demand function for that commodity, keeping the prices con-stant. It depicts the consumption behaviour of persons or house-holds with different levels of total expenditure. Deaton, who has made significant contributions to consumer behaviour and consumer demand functions, also examined the poverty issue Table 1: Adult Male Equivalent Scales for India Adult Male Adult Female Male Child Female Child1987-88 (43rd Round) Rural 1 0.7591317 0.5215729 0.47118906 Urban 1 0.871640840.3899016 0.441670621993-94 (50th Round) Rural 10.809067850.55370170.47533897 Urban 1 0.882981320.5211672 0.376581381999-2000 (55th Round) Rural 1 1.03733480.9721766 0.74000138 Urban 10.727468120.53903440.49041613Source: Based on NSSO data.Table 2: Total Expenditure Cut-off Points Obtained from Cubic Curve 43rd Round 50th Round 55th Round 1987-881993-941999-2000 Rural Urban Rural UrbanRuralUrbanAll India 848.25 1012.25 1053.22 784.72 1366.63 876.83Andhra Pradesh 1024.57 1261.24 1050.81 541.32 834.18 531.63Assam 741.311213.18842.48830.82801.481172.54Bihar 1104.13 1615.69 989.48 2591.46 1269.41 904.16Gujarat 842.731057.46823.091952.531278.401332.04Haryana 968.861015.591129.731164.23775.00834.94Karnataka 1857.09 1714.32 1552.44 1005.31 836.221894.39Kerala 1426.21637.271996.811680.282235.22836.80Maharashtra 556.801213.72 1490.09 918.101424.24 1037.81Madhya Pradesh 1152.72 685.79 971.88 2520.23 1371.99 505.94Orissa 471.39 936.44536.291328.20 810.931194.78Punjab 2178.69 635.32801.761155.16 1176.34 717.67Rajasthan 836.81690.681176.70502.72947.63680.86Tamil Nadu 1248.12 1000.72 968.68 873.32 1286.05 1138.14Uttar Pradesh 576.71 1478.93 1031.43 584.42 1206.41 1310.37West Bengal 562.50 1246.97 1062.78 908.18 1475.02 735.97Figures are in rupees per adult equivalent in 1993-94 prices.

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0 10 20 30 40 50 60 0 5 10 15 20 Food Deprivation Index Traditional measure of Poverty
0 10 20 30 40 50 60 0 5 10 15 20 SI* DD HCI

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SPECIAL ARTICLEjuly 26, 2008 EPW Economic & Political Weekly122Tables 4a (p 120) and 4b (p 121) present some of the poverty estimates and their correlations with our food deprivation index (SI*). From these correlations, it is evident that except for the base year 1993-94 for all other years the spatial correlation between the food deprivation index and traditional poverty indices HCI and PGI is very poor. If one considers food deprivation measure of poverty then the traditional poverty measures do not seem to measure it. We arranged the states in the increasing order of headcount index of poverty and plotted on one line graph the food consump-tion deprivation index and the headcount poverty index for all the states for rural and urban sectors separately. These graphs are provided in Figures 4a and 4b.5 ConcludingRemarksGiven the recent debate on the traditional poverty estimates in India we find it useful to revive a decade and a half old sugges-tion of Kumar, Gore, and Sitaramam to measure poverty using an Engel curve for essential commodities. We used household level data from three recent large consumer expenditure surveys of the NSSO and estimated food consumption deprivation. We showed that this index has very little spatial correlation with the traditional measures of poverty. NSSO data has some limitations but it seems to be the best avail-able data. The imputed values for home produce and meals eaten outside, the meals served to guests and domestic help, etc, leave sufficient room for subjective judgments and quite often the field investigator may be prompting the respondent on what prices to use for such imputations. There is a need to do alternate pilot sur-veys and studies to focus on individual questions of this sort.One may estimate the Engel curves for some other necessities such as health services, primary and secondary education, water, fuel and light, etc and estimate the associated consumption dep-rivations. The question then arises as to how to combine them into a single index of poverty. The study by Nathan, Mishra and Reddy (2008) provides a measure based on Euclidean distance between the target or norm and actual multidimensional deprivation. Human development indices and poverty indices can be brought into the manifold of microeconomics of consumer demand through Engel curve analysis of essential goods and services. Notes 1 Engel curve for a specific commodity is the relation between the consumption expenditure on that commodity as a function of income (total expenditure). It is sometimes also expressed as the proportion of income (or total expenditure) spent on a specific commodity against income (or total expenditure). 2 The first draft of the paper written by Sitaramam and Gore was privately circulated for comments in 1991. The revised paper by Kumar, Gore and Sitaramam was submitted for publication in February 1993 and presented at an international conference on poverty and income inequality in March 1994 held at Bangalore. 3 We thank an anonymous referee of theJournal of Development Studies who emphasised the need for such adjustment while reviewing our earlier work for that journal.4 Although P V Sukhatme was a member of the expert group on estimation of proportion and number of poor set up by the government in 1989, he expressed his dissent and reiterated his point of criticism by submitting a supplementary note as Annexure 1 to the ‘Report of the Expert Group’ (1993). Krishnaji (1981), however, was critical of the suggestion made by Sukhatme. He pointed out that the estimation of intra-individual varia-tion for the same age-sex-occupation specific group of households, required for Sukhatme’s procedure, cannot be derived from non-experimental data of the NSSO. 5 For a fuller discussion of some of these issues one may see Vaidyanathan (2001). 6 It is this aspect that gave the poverty line a central role in measurement of poverty. Once the poverty line is so defined, the attention was unwittingly shifted to income and income distribution rather than to consumption and consumption distri-bution and consumption deprivation. 7 They compared the new specification with some of the commonly used Engel curve specifications and found that the new specification was to be preferred on the basis of least error sum of squares and randomness and normality of the estimated errors [Sitaramam et al 1996]. 8 The traditional measures of poverty instead use the distribution of income or total expenditure below the norm of a poverty line. 9 After completing this work, we received data for the 61st round for the year 2004-05. We are at present extending the work by using this new data. There are other data sources that one can use for estimating the extent of poverty. The data collected by National Nutrition Monitoring Bureau is one. The panel data collected for a few villages by ICRISAT is another. One may see Vai-dyanathan (2001) for a discussion of relative mer-its of such different data sources.10 The procedure was iterative. When we first deter-mined the cut-off and estimated the cubic Engel curve once again, for the truncated sample, it remained convex at higher ranges of total expend-iture. Hence, we repeated this process until we obtained an Engel curve that is entirely concave up to the cut-off level of total expenditure. 11 This procedure is equivalent to our treating this cut-off point as a poverty line. This is analogous to but different from the suggestion of Rao (1981). Thus, while Kumar et al (1996) dispense with the focus axiom, we bring it back here, as the data do not conform to the theoretical specification. 12 Although we used adult equivalent scales, the adjusted household size (zi ) may still appear in the Engel curve as the adult equivalent scale only corrects for scale in food expenditure and the relation between these two variables (Engel curve) may still be subject to economies of scale [see Deaton and Paxton 1998]. In fact we did observe, in a majority of cases, a statistically sig-nificant negative coefficient for the adjusted household size.13 The results for urban sector and for other years of this table as well as other tables referred later can be obtained from the authors.ReferencesAtkinson, A B (1987): ‘On the Measurement of Poverty’,Econometrica, Vol 55, pp 749-64.Behreman, J and Anil Deolalikar (1987): ‘Will Devel-oping Country Nutrition Improve with Income? A Case Study for Rural South India’, Journal of Polit-ical Economy, Vol 95, pp 492-507.Dandekar, V M and Nilkanth Rath (1971): Poverty in India, Indian School of Political Economy.Deaton, A (2003): ‘Prices and Poverty in India: 1987-2000’, Economic & Political Weekly, Vol 38, pp 362-68.Deaton, A and Jean Dreze (2002): ‘Poverty and Inequality in India’,Economic & Political Weekly, Vol 37, pp 3729-48.Deaton, A and V Kozel (2005): ‘Data and Dogma: The Great Indian Poverty Debate’,World Bank Research Observer, Vol 20, No 2, pp 177-99.Deaton, A and C Paxton (1998): ‘Economies of Scale, Household Size, and the Demand for Food’, Journal of Political Economy, Vol 106, No 2, pp 897-930.Deaton, A and A Tarozzi (2000): ‘Prices and Poverty in India’, Princeton Research Programme in Deve-lopment Studies, available from http:www.wws.princeton.edu/~rpds. Foster, J, J Greer and E Thorbecke (1984): ‘A Class of Decomposable Poverty Indices’, Econometrica, Vol 42.Krishnaji, N (1981): ‘On Measuring Incidence of Undernutrition – What Is a Consumer Unit’, Economic & Political Weekly, Vol 16, pp 1509-11.Kumar, T K, A P Gore and V Sitaramam (1996): ‘Some Conceptual and Statistical Issues on Measurement of Poverty’,Journal of Statistical Planning and Inference, Vol 49, No 1, pp 53-71.Kumar, T K, S K Mallick and Jayarama Holla (2007): ‘Estimating Consumption Deprivation in India Using Survey Data: A State-Level Rural-Urban Analysis before and during Reform Period’, Work-ing Paper No 7, Centre for Globalisation Research, Queen Mary’s, University of London (forthcoming inJournal of Development Studies 2008). http://www.busman.qmul.ac.uk/cgr/CGR_Working_Papers/CGR-WP7.pdfLewbel, A (2006): ‘Engel Curves’ in New Palgrave Dictionary of Economics,Second Edition.Lipton, M (1997): ‘Editorial-Poverty: Are There Holes in the Consensus’, World Development,Vol 25, No 7, pp 1003-07.Meenakshi, J V and B Viswanathan (2003): ‘Calorie Deprivation in Rural India: 1983-1999-2000’, Economic & Political Weekly, Vol 38, pp 369-75.Nathan, Hippu Salk Kristle, Srijit Mishra and B Sud-hakar Reddy (2008): ‘An Alternative Approach to Measure HDI’, Working Paper 2008-01, Indira Gandhi Institute for Development Research, Mumbai, http//www.igidr.ac.in/pdf/publica-tion/WP-2008-001.pdfPerali, Federico (2003): The Behavioural and Welfare Analysis of Consumption Kluwer Academic Publishers.Pradhan, M and M Ravallion (2000): ‘Measuring Pov-erty Using Qualitative Perceptions of Consump-tion Adequacy’,Review of Economics and Statis-tics, Vol 82, No 3, pp 462-71.Rao, Bhanoji V V (1981): ‘Measurement of Deprivation and Poverty Based on the Proportion Spent on
SPECIAL ARTICLEEconomic & Political Weekly EPW july 26, 2008123Food: An Exploratory Exercise’,World Develop-ment, Vol 9, No 4, pp 337-53.Rath, Nilkanth (1996): ‘Poverty in India Revisited’, Indian Journal of Agricultural Economics, Vol 51, pp 76-108.Rath, Sharadini (2003): ‘Poverty by Price Indices’, Economic & Political Weekly, Vol 38, No 40, pp 4260-68.Sabatese, Ricardo, Brian W Gould and Hector Vellar-eal (2001): ‘Household Composition and Food Expenditure’,Food Policy, Vol 26, pp 571-86.Sen, Abhijit and Himanshu (2004): ‘Poverty and Inequality in India-I’,Economic & Political Weekly, Vol 39, pp 4247-63.Sen, Amartya and S Sengupta (1983): ‘Malnutrition of Rural Children and the Sex Bias’,Economic & Political Weekly, May, pp 855-64. Sen, Pronab (2005): ‘Of Calories and Things: Reflec-tions on Nutritional Norms, Poverty Lines, and Consumer Behaviour in India’,Economic & Politi-cal Weekly, Vol 40, pp 4611-18.Sitaramam, V, S A Paranjpe, T K Kumar, A P Gore and J G Sastry (1996): ‘Minimum Needs of Poor and Priorities Attached to Them’,Economic & Political Weekly, Vol 31, pp 2499-505.Sundaram and Tendulkar (2003): ‘Poverty Has Declined in 1990s: A Resolution of Comparability Problem in NSS Consumer Expenditure Data’, Economic & Political Weekly, Vol 38, No 4, pp 327-37. Sukhatme, P V (1961): ‘The World’s Hunger and the Future Needs of Food Supplies’, Journal of the Royal Statistical Society, Series A, Vol 124, pp 463-525. – (1965): Feeding India’s Growing Millions, Asia Publishing House, Bombay. – (1974): ‘The Protein Problem, Its Size and Nature’, Journal of Royal Statistical Society, Series A, Vol 137, Part 2, pp 166-99. Vaidyanathan, A (2001): ‘Poverty and Development Policy’, Kale Memorial Lecture, December 10, 2000, Gokhale Institute of Economics and Politics, Eco-nomic & Political Weekly, Vol 36, pp 1807-22.Table A1: 50th Round-R2s Associated with Alternate Engel Curves and the Percentage Shortfall in KGS Specification Filename R2-AB R2-WL R2-MWL R2-PH R2-BBL R2-KGS Max-R2 KGS-Shortfall % Shortfall50th Round-Rural All-India 0.9846020.9992880.9951710.9488320.9996830.9996600.9996830.0000240.002390Andhra Pradesh 0.978850 0.997279 0.997097 0.987867 0.997336 0.997230 0.997336 0.000106 0.010665Assam 0.9816070.9923530.9923630.9862810.9925420.9923460.9925420.0001950.019690Bihar 0.9791770.9963440.9956760.9739480.9961900.9962420.9963440.0001020.010226Gujarat 0.9515820.9692490.9665080.9592510.9826290.9683750.9826290.0142541.450629Haryana 0.951257 0.9876750.9859680.984058 0.9878900.9873790.9878900.0005110.051701Karnataka 0.9782420.9927330.9914850.9725790.9929640.9928690.9929640.0000950.009611Kerala 0.9788030.9854980.9882110.9449360.9901090.9723210.9901090.0177881.796567Maharashtra 0.9866130.9934010.9931510.9647780.9938300.9933010.9938300.0005300.053280Madhya Pradesh 0.982779 0.988892 0.988830 0.952696 0.988528 0.988599 0.988892 0.000294 0.029682Orissa 0.9589850.9860290.9833580.9762850.9875820.9858370.9875820.0017450.176666Punjab 0.9300850.9719310.9679760.9674880.9739590.9705670.9739590.0033920.348292Rajasthan 0.9863120.9965980.9963400.9916990.9965890.9965870.9965980.0000110.001139Tamil Nadu 0.979746 0.997315 0.992627 0.978496 0.997629 0.997505 0.997629 0.000124 0.012394Uttar Pradesh 0.991231 0.997987 0.997577 0.979881 0.998075 0.998058 0.998075 0.000017 0.001705West Bengal 0.980840 0.997420 0.996641 0.982433 0.997446 0.997394 0.997446 0.000052 0.0051855oth Round-Urban All-India 0.9727640.9988880.9965730.9870440.9991200.9989690.9991200.0001510.015102Andhra Pradesh 0.961153 0.996851 0.995802 0.988971 0.997289 0.996965 0.997289 0.000323 0.032427Assam 0.9727620.9965940.9954360.9913920.9967890.9966450.9967890.0001440.014482Bihar 0.9664300.9964680.9949230.9876990.9967990.9964280.9967990.0003710.037189Gujarat 0.9769720.9893270.9876860.9817100.9910380.9893610.9910380.0016760.169160Haryana 0.9608630.9851480.9847200.9788080.9858170.9852740.9858170.0005430.055077Karnataka 0.9583020.9700370.9712590.9583290.9761330.9707200.9761330.0054120.554479Kerala 0.9737750.9870840.9849610.9792690.987805 0.9865660.9878050.0012390.125416Maharashtra 0.9843210.9891650.9909060.9795940.9897200.9888740.9909060.0020310.205014Madhya Pradesh 0.962340 0.994102 0.994302 0.990255 0.994488 0.994227 0.994488 0.000261 0.026276Orissa 0.9871560.9963570.9963180.9903710.9967590.9963210.9967590.0004390.044017Punjab 0.9698150.9901760.9884960.9852510.9918700.9899900.9918700.0018800.189530Rajasthan 0.9851990.9946510.9943250.9775680.9945770.9945870.9946510.0000640.006447Tamil Nadu 0.956159 0.995265 0.993055 0.990436 0.995780 0.994918 0.995780 0.000862 0.086539Uttar Pradesh 0.975513 0.994793 0.994886 0.989702 0.994465 0.994748 0.994886 0.000137 0.013817West Bengal 0.974116 0.995272 0.995538 0.994313 0.994856 0.995068 0.995538 0.000470 0.047238Table A2: Results of Maximum Likelihood Estimation of Non-linear Engel Curve – All-India and 15 Major States: 1993-94 RuralFilename A SE(A) B B(SE) C C(SE) D D(SE) R2All-India 0.5945400.001059-0.0022690.0000001269.2403070.0031391125.1393290.0034570.998969Andhra Pradesh -70.445722 0.000415 0.001247 0.000000 1141.358441 0.000947 773.081585 0.001914 0.996965Assam -99.1009980.0011370.0077300.0000001181.0740820.004615768.7003860.0048800.996645Bihar -3.0347270.000477-0.0031620.0000001060.8429900.002599844.0386000.0040370.996428Gujarat 75.6478140.002488-0.0088990.0000001111.299119 0.006977900.1083020.0168640.989361Haryana -9.3980710.000314-0.0033990.0000001152.5344600.001369901.3285390.0026220.985274Karnataka -163.7327110.0005190.0085030.0000001379.3246480.001118936.1347890.0017600.970720Kerala 151.7808720.000360-0.0211520.0000001053.9642720.0007151126.8429090.0015310.986566Maharashtra 350.1761470.004797-0.0332410.000000706.7482310.0038841063.6040590.0210370.988874Madhya Pradesh 93.952264 0.000961 -0.015978 0.000000 843.394382 0.001777 648.402055 0.002952 0.994227Orissa -9.7455150.000590-0.0014710.000000976.7259330.008569758.9482580.0123830.996321Punjab -86.7825950.0018480.0017440.0000001118.9989130.004433738.2263160.0098800.989990Rajasthan -16.0739400.002382-0.0001750.0000001578.1135390.0058311448.9658100.0135050.994587Tamil Nadu -18.261492 0.000242 -0.004300 0.000000 991.082848 0.001281 667.640890 0.001984 0.994918Uttar Pradesh 157.021442 0.001212 -0.015997 0.000000 803.705785 0.002053 756.445189 0.005466 0.994748West Bengal 139.065866 0.001914 -0.014979 0.000000 777.939253 0.003910 659.298575 0.005896 0.995068SE stands for standard error.

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