Introduction Estimates of domestic product generated within the state boundaries are generally used for economic policy evaluations in the annual State Economic Surveys and policy formulations in the state annual budgets. Another occasion calling for such estimates is the quinquennial exercise for five-year plan policy formulations at the state level. Regional dimension of the development process has received considerable attention in recent years, particularly after the UNDP and Planning Commission (Government of India) efforts to bring out state-level Human Development and State Development reports. With the 73rd and 74th constitutional amendments providing for third tier of government in India, the relevance of statistical information at the regional level has increased all the more. |
Estimates of income and poverty at the regional, say, district level are important not only to understand the spatial dimension of the problem of underdevelopment but also to assess the underlying causes and relevant policy options. For instance, in India Maharashtra is the lead state in terms of per capita state domestic product and other measures of economic development like industrialisation and urbanisation. Yet, it lags behind many other states in terms of the pace of poverty reduction. Its levels of poverty, both in rural and urban sectors, were above the corresponding national average levels till 1993–94.1 Common perceptions would highlight the inter-regional disparities in natural resource endowments and economic growth rates as the contributory factor. Such information per se would not be sufficient to assess the real dimension of the problem. There is a need for verification of such perceptions and identification of the combination of discriminatory policies to achieve balanced development, both economic and human. For such an exercise, district-wise estimates for, say, 1998–99 indicate that Mumbai accounts for about 25 per cent of the state income, followed by Thane (10 per cent), Pune (nine per cent) and Nagpur (five per cent). In other words, these four urban centers account for about 50 per cent of the state income in Maharashtra.2 On the other hand, Amaravati division, consisting of the districts of Buldhana, Akola, Amravati and Yavatmal, contributes just seven per cent of the total state income. While the per capita annual domestic product was Rs 31922 in the district of Mumbai, it was only Rs 8990 in Amravati in 1998–99. A similar picture of pronounced disparity in growth experiences can also be observed across districts in Maharashtra. This kind of information, juxtaposed with input, process and impact indicators would enable a better perception of the problem of economic underdevelopment and the causes across districts. This is only to highlight the importance of statistical information at a disaggregated level in any attempt to focus attention and formulate policies at a regional level. |
States like Karnataka have begun monitoring poverty and human development across districts with reference to indicators like district domestic product (DDP). Estimates of DDP, of course, convey information about the income generated and are relevant indicators for policy formulation. They convey little about the distributional aspects. Hence, DDP estimates may be supplemented with estimates of inequality in consumption distribution and consumption-poverty, which could be used for poverty mapping, particularly when sample surveys of levels of living are infrequent. Such maps may be useful for policy formulations from a national perspective. Hence, it is important to ensure that the regional estimates of domestic product are comparable across states or regions. |
Poverty maps are generated by juxtaposing income/consumption based poverty indicators with information on related indicators from other sources. One widely used data source for estimates of consumption-based poverty is the National Sample Survey (NSS). NSS surveys are generally conducted at the national (based on central sample) and state (based on state sample) levels. To obtain reliable and representative estimates of consumption distribution and hence, poverty at the district level, the state and central samples are pooled. But, it is important to verify how representative are estimates of poverty lines? How far such poverty estimates are reliable and how far they are acceptable in terms of their plausibility. Accordingly, this paper addresses the following questions:
How are the district wise estimates of domestic product generated? How far are they comparable across states? What are the methodological options to generate representative estimates of poverty at the district level? How far are they reliable? What do the available empirical estimates indicate?
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This paper makes an attempt to examine these questions with particular reference to estimates of domestic product and poverty at the district level for the state of Maharashtra. The paper is orgnaised as follows: Section 2 deals with the concept of income that can be estimated and the methodology followed in the estimation of domestic product at the district level in the states of Karnataka, Maharashtra and Tamil Nadu, its implications and shortcomings. Section 3 deals with the possible methodological options on the generation of estimates of consumption distribution and poverty at the district level. Section 4 provides an empirical illustration with reference to district and region wise estimates of domestic product and poverty for Maharashtra. The final section concludes the paper. |
Top District Domestic Product (DDP) Concept For examining issues and policies relating to equity and social welfare, ideally the concept of income with reference to any given geographical unit/economic agent should be defined in terms of final accrual. However, for reasons like lack of information on factor and commodity flows across regions or among economic agents, estimation by such a method becomes impossible. Accordingly, Directorates of Economics & Statistics of States produce and publish estimates of domestic product generated within the state boundaries. They are reasonably reliable as estimates of income generation with respect to commodity producing sectors. For similar reasons, the concept of income at the district level is defined by the ‘income originating’ approach. That is, it is defined as the final bill of goods and services produced within the physical boundaries of a given district. In other words, the concept is restricted to a documentation of production activities. Of course, it is difficult to define the geographical contours of sectors like banking & insurance, central government administration, and transport & communication. Accounting for income generated from these sectors across regions would be a major problem. Another potential problem with district income estimates is that a district, rich in terms of endowment of natural resources like mineral and forest resources, could however be poor and backward in terms of actual income accruing to its residents. It may not be possible to take into account the net income flows to local residents from migrant relatives outside and vice versa. For reasons like these, estimates of district domestic product may not measure the real economic endowment levels and hence, welfare consequences of the growth process or different policy measures. They can at best only be used to measure the level of economic activity within a given region. Therefore, one option to examine equity and welfare related questions at the regional level could be to supplement the information on income generated within a district with estimates of the distribution of consumer expenditure across households. The following sub-sections explore such options with reference to estimates of district domestic product and estimates of poverty and inequality based on pooling Central and State sample results on Consumer Expenditure Surveys. Estimators: Representativeness and Comparability across States This section provides a brief profile of the methodology used in the estimation of district wise domestic products with special reference to Karnataka, Maharashtra and Tamil Nadu and their implications for studies on regional aspects of human development and poverty mapping.3 The methodological details are taken from Government of Maharashtra (2001), Palanidurai (2000) and Santappa (1996). District domestic products are estimated by methods similar to that used by the Central Statistical Organisation for the national accounts: that is, a combination of production and income approaches (and also expenditure approach in a few cases, for example, construction)). The state government departments have sought to generate estimates, to the extent possible, with reference to direct estimates available from the districts. For this purpose, output originating within district boundaries is classified into two categories: (a) Product, which can be estimated using direct information for the district; and (b) product that has to be estimated using indirect indicators. In spite of their best efforts, the states do not seem to have really succeeded in sticking to this approach. Instead, they seem to have resorted to different combinations of these two approaches depending upon data availability. For purposes of estimation, the entire district economy is classified into the following categories: (1) Primary sector covering agriculture, forestry, fisheries, mining & quarrying; (2) Secondary sector including Manufacturing (both registered & unregistered), electricity, gas & water supply; and (3) the Tertiary sector covering railways, transport & storage, communication, trade, hotels & restaurant, banking & insurance, real estate, ownership of dwelling & business services, public administration & other services. Production approach is used largely for the primary and the registered manufacturing sectors. Agriculture In Maharashtra, value added in agriculture in a given district is estimated on the basis of estimates of prices and output available for the district concerned. For crops for which such details are not available, but only estimates of area cultivated are available, estimates of output are made on the basis of pilot/ad hoc surveys or state-level estimates are allocated across districts as per the area estimates. Broadly a similar methodology is adopted in Karnataka and Tamil Nadu. However, estimates of value added are derived after deducting estimates of input costs interpolated from state aggregates. Given the information constraint, this is the best option. However, the estimates interpolated from state-aggregates disregard the heterogeneity across districts. Hence, they would suffer from the shortcoming that they do not consider inter-regional variations in yield and price levels. Livestock Estimates for Karnataka are based on district-wise estimates of output valued at state-level prices. However, for Maharashtra, state aggregates by type of livestock product are distributed across states with reference to the livestock population concerned. How about making allowances for differences in cattle type and yield? How about estimates of district-wise livestock population for different years? Are they adjusted for inter-district migration of cattle over years? It is not clear. Forestry & Logging Karnataka estimates are based on district-wise estimates of forest products (except fuel wood) valued at state prices; income from fuel wood is obtained from state level estimates with reference to census estimate of rural workforce. For Tamil Nadu, (i) estimates of value added in forestry are derived by allocating the state estimates with reference to district-wise area under forests; and (ii) income from fuel wood is worked out as a function of rural and urban population. However, the estimates in Maharashtra seem to be based largely on district-wise estimates of value of output available with the Forest Department and Forest Development Corporation. Value added is obtained by netting out ten per cent of the total value output. The estimates for Tamil Nadu may be inadequate to the extent they do not take into account the variations in forest coverage and type. Fishing Estimates for this sector are based on district-wise estimates of production and prices in Maharashtra and Tamil Nadu. Karnataka differed in that outputs are valued at state level prices. Karnataka approach would distort estimates, as prices of inland and marine fish would vary considerably. Mining and quarrying Estimates of output and prices are from districts. Input and cost estimates are from the state level. Interpolated cost estimates would distort the picture on income generated in a district. Manufacturing (registered) Value added estimates by districts are derived from the schedules of the Annual Survey of Industries. Manufacturing (unregistered) State-level estimates are allocated with reference to workforce numbers. This approach ignores inter-district differences in yield and price levels. Construction State estimates obtained by the expenditure approach are allocated with reference to workforce. There is a difference between workforce size by residence and that by place of work. DDP estimates for construction by district made with reference to workforce size by residence are likely to be affected by variations in the difference caused by daily, seasonal and even permanent migration of workforce. Electricity, gas and water supply Method varies among the three states. In Karnataka, sectoral income at the state level is distributed across districts as follows: as per the connected load (KWH) in the sub-sector electricity, as per no of bio-gas plants in the sub-sector gas and as per the data collected from municipalities/corporations and ASI schedules for water supply. Maharashtra, on the other hand, used workforce and value added in the private companies as indicators for electricity, no of biogas plants for gas and workforce for water supply. In Tamil Nadu the allocation is done with reference to workforce estimates Railways Karnataka allotted income from railways with reference to the length of railways while Maharashtra and Tamil Nadu considered workforce as the reference variable. Transport by other means and storage Karnataka obtained district-wise estimates of income from mechanised road transport in the public, private and unorganised sectors on the basis of district-wise workforce in the respective sub-sectors. A similar procedure is adopted for estimates of income from storage. Maharashtra too followed a similar procedure. Tamil Nadu used number of vehicles and Gross Trading income in addition to workforce estimates as reference variables for income from transport by other means and only workforce for income generated in the storage sector. Communication Karnataka used indicators like no of post and telegraph offices and no of radios in use while Tamil Nadu and Maharashtra allocated income across districts with reference to workforce estimates. Trade, hotels & restaurants: District-wise estimates are mad with reference to Gross Trading Income of commodity producing sectors in Karnataka, workforce in Maharashtra and both in Tamil Nadu. Real estate, ownership of dwellings and business services Karnataka has used census estimates of no of dwellings in each district as the relevant indicator. Maharashtra has made the allocation with reference to workforce estimates. Tamil Nadu has used workforce only for the sub-sectors real estate and business service. Estimated income dwelling is allotted on the basis of number of residential dwellings. Public administration The three states have used workforce estimates as the relevant indicator. Other Services Karnataka: For education, number of teachers, number of students enrolled etc. are used as the relevant variables. For medical & health, number of hospitals, PHCs & PHUs is the reference variable. For income generated in the sanitary service sub-sector, data from municipalities and corporations are used. Income from other sub-sectors is allocated with reference to workforce estimates. Both Maharashtra and Tamil Nadu have used workforce as the reference variable to for this major sub-sector. The preceding survey brings out that the methods adopted for estimation of district-wise domestic product are broadly similar across states. There are some differences for a couple of sectors. This would affect inter-state comparability of estimates at a point of time and changes therein over time. Similarly, valuation of district-wise quantities at state-level prices would convey a misleading picture about the real endowment levels across districts. Allocation of state aggregates with reference to workforce estimates or similar district specific indicator would not account for inter-regional heterogeneity and consequent income difference. These limitations may be overcome, to some extent, with reference to district-wise estimates of consumption, inequality and poverty, which, in a sense, are better measures since (i) consumption is a function of the final, net of all income flows, resource endowment of an individual; and (ii) NSS estimates of consumption are valued at local prices. Top District-wise consumption distribution and incidence of poverty An estimate of the vector of consumption distribution based on household surveys has some advantages. It provides information on distributional issues like incidence of poverty. In India, the National Sample Survey Organisation (NSSO) conducts periodic household surveys of domestic consumption and generates their distribution by monthly per capita expenditure classes by rural and urban sector, at the state and national levels separately. A major advantage of using estimates of consumption, instead of income, in any district-wise analysis in a semimonetised economy is that it is a better measure of resource endowment and hence entitlement of an individual. This is all the more so with reference to the National Sample Survey (NSS) data on consumer expenditure distribution, which takes into account consumption out of homegrown stocks, gifts and charities. Unlike the estimates of DDP, NSS estimates are direct survey based estimates and not interpolated ones; hence, are more representative than the DDP estimates. The estimates of consumer expenditure are based on local prices. They also permit an analysis of distributional issues at the district level. |
Such estimates could be of use in addressing equity-related issues in policy formulation. But how do we generate regional/district wise estimates of consumption distribution and hence poverty when the available survey estimates pertain to the rural and urban sectors in the state as a whole? This question is addressed in the following section. |
Data Source & Methodology This section examines the possible options with reference to estimates of consumer expenditure distribution at the district level. One possible and feasible option relates to estimates generated by pooling the NSS results obtained from the state and central samples. The state and central samples could be pooled following the methodology suggested by Minhas and Sardana (1990). Its feasibility is already demonstrated by the district wise estimates of consumer expenditure distribution and incidence of poverty for Karnataka (Government of Karnataka, 1999; Murgai, et al. 2003) and Tamil Nadu (Government of Tamil Nadu, 2003). Some of the methodological issues and their implications for estimates of poverty, which deserve attention in a study like this, are as follows: (i) Concept of poverty: In keeping with the approach generally adopted in India, the concept of absolute of poverty may be considered. In this context, the option could be with reference to the FGT class (Foster-Greer-Thorebecke, 1984) of poverty measures given by
where x is the household consumption expenditure, f (x) is its density, z denotes the poverty line, and a is a non-negative parameter. FGT measures for values of α = 0,1 and 2 measures of incidence, depth and severity of poverty respectively.4 Poverty line There can be two options to generate district wise estimates of poverty lines and hence, poverty:
Common State level poverty line for all districts: Estimates of poverty could be obtained with reference to the poverty line for the corresponding state aggregate recommended by the Expert Group (GoI, 1993). Using a common state-level poverty line for all districts has the advantage it permits comparison and aggregation hence, relative magnitude of the problem across districts; and District specific poverty lines: District-wise consumption patterns, supply conditions and hence, price levels do vary. A common state-poverty line for all the districts may not lead to representative estimates of deprivation. Ideally district-wise poverty estimates should be made with reference to poverty lines adjusted for inter-district variations in consumption patterns, prices and institutional parameters. Hence, one may consider examining the inter-district price dispersions for major staple food items and their implications for costs of the minimum consumption basket. A beginning may be made by estimating spatial price deflators in terms of unit values obtained as ratios of NSS estimates of consumer expenditures to quantities consumed of different cereals. Since cereals account for about 85 per cent of the calorie intake, these indices could be used to make corrections for inter-district price differences. A better option could be, after the fashion of the methodology suggested by the Expert Group (GoI, 1993) at the state level, as follows: For the base year, price adjustments for inter-regional price variations could be made with an estimated spatial price deflator. District-wise poverty lines for subsequent years may be attempted in terms of group-wise ALCPI price indices for different zones/regional centers corresponding to the given district.
Poverty measures can be derived from parameterised Lorenz curves using the following two functions:
where L is the share of the bottom p per cent of the population in aggregate consumption, p is a vector of (estimable) parameters of the Lorenz curve, P is a poverty measure expressed as a function of the average consumption μ to the poverty line z, and the parameters of the Lorenz curve.5 The estimates of the three different measure of poverty–headcount ratio, poverty-gap index and FGT index for the rural and urban sectors by states and all-India are provided in Tables 1 & 2 respectively. The estimates are made on the basis of the NSS data from the central sample for the years 1973–74, 1977–78, 1983, 1987–88, 1993–94 and 1999–2000 with reference to the poverty lines recommended by the Expert Group (GoI, 1993, 2000).
How do we define and estimate the minimum for cereal consumption, calorie intake and hence, poverty line particularly at a time when the country has undergone changes with respect to technology, health status, labour efficiency, etc. Measuring deprivation in such a context is not simple. For instance, estimates of per capita cereal consumption and calorie intake show a decline for rural all India. Incidence of calorie deficiency (unadjusted for age, size and composition of the population) has increased from about 65 per cent in 1972–73 to 75 per cent in 1993–94 (Suryanarayana, 2000). However, measures of impact, that is, indicators of health status exhibit continued improvement during the same period (Suryanarayana, 2001; Tables 6 & 7). This would cast doubts on simple statistical measures of incidence of poverty and undernutrition in a developing economy undergoing structural changes. Variables should be normalised with appropriate corrections for structural (demographic and economic factors) changes in the economy. This could be complemented with measures of health status in terms of anthropometric indicators or impact variables. Data collected from the National Family Health Surveys of 1992–93 and 1998–99 could be used for this purpose. Top Regional dimension of growth & deprivation: Maharashtra Income Generation An important characteristic feature of economic development is its balanced spread across regions. It would be worthwhile to examine how far economic development in Maharashtra is spread across districts/regions within the state. The fact Finding Committee on Regional Imbalances in Mahrashtra has gone into the question in considerable detail (GoM, 1984). Prabhu and Sarkar (1992) have attempted to classify the 29 districts of Maharashtra based on a comprehensive set of indicators for agriculture, industry, human resources and infrastructure. The districts are classified into three categories: (i) Highly developed (11 districts); (ii) Middle level of development (three districts); and (iii) underdeveloped (15 districts). All districts of western Maharashtra, excluding Dhule, belonged to the first two categories. All the districts of Marathwada, except Aurangabad, and six out of the nine districts of Vidarbha considered in the analysis, belonged to the category of underdeveloped districts. For this study, inter-district disparities in growth performance may be verified in terms of the simple final outcome measure, that is, district-wise estimates of income and its composition. The estimates for 1998–99 bring out the following features (Table 3):
Dhule is the poorest district in Maharashtra. It generates income, which on a per capita basis works out to be Rs 11,789. Its share in total Mahrashtra SDP is just 1.66 per cent. Mumbai, on the other hand, has a per capita domestic product of Rs 45,471, which is almost four times the level in Dhule. Mumbai is the richest district and accounts for a quarter of the SDP. The extent of unevenness in regional spread of economic development in Maharashtra could be appreciated better by a rank-order profile of the districts according to per capita district domestic product. The districts may be classified into four different ordinal groups, viz., the poorest, lower middle, upper middle and richest quarter with reference to the three quartiles, that is, lower quartile, middle quartile (median) and the upper quartile. The three quartiles for 1998–99 are 13827, 16700.5 and 20411 respectively. The poorest quarter in Mahrashtra consists of the districts of Dhule, Jalna, Osmanabad, Nanded, Yavatmal, Latur, Buldhana and Parbhani (Table 4). These eight districts account for just 11 per cent of the total income generated in the state. Five of these eight districts belong to the Aurangabad division.
The lower middle quarter consist of Ratnagiri, Bhandara, Ahmednagar, Beed, Satara, Akola, and Jalgaon. These districts, however, are spread across the six different geographical divisions of Maharahstra. Together they contribute just 14.3 per cent to the total SDP. The upper middle quarter consists of Wardha, Gadchiroli, Amaravati, Solapur, Chandrapur, Aurangabad, Sindhudurg, and Sangali. These districts are located in five divisions. Their SDP share at 14.8 per cent is marginally higher than tat of the lower middle quarter. The richest quarter consists of Nashik, Kolhapur, Pune, Nagpur, Raigad, Thane and Mumbai. The Konkan division dominates this group. Together they contribute about 60 per cent of the total SDP. Per capita domestic product in Mumbai district exceeds the upper quartile by more than three times the inter-quartile range (distance between the lower and upper quartiles). This is a statistical measure used to determine whether Mumbai is a far-outlier, that is, whether Mumbai is far removed from the remaining districts in terms of their general pattern. Mumbai, Thane and Raigad generate per capita income levels which exceed the upper quartile by more than one-and-half times the inter quartile range. Hence they constitute the outlier districts, which are different from the rest of state. In other words, Mumbai stands far above the rest of the state in terms of per capita income generated followed by Thane and Raigad. While Mumbai and Thane owe their preeminent position largely to the tertiary sector, which accounts for more than 50 per cent of the district domestic product, Raigad's status is based on the dominance of the secondary sector (62 per cent) (GoM, 2001b; p. 60). Thus, a striking character of economic development in Maharashtra is the wide disparity in income across districts. Mumbai accounted for about 25 per cent of the state income followed by Thane (10 per cent), Pune (nine per cent) and Nagpur (five per cent). In other words, these four urban districts alone accounted for about 50 per cent of the state income in Maharashtra (Table 3). On the other hand, Amaravati division, consisting of the districts of Buldhana, Akola, Amravati and Yavatmal, contributed just seven per cent of the total state income. It may be worthwhile to examine whether the extent of regional disparities in income levels has anything to do with policy administration or development strategies at the regional, say, division level? A comparison of income generation across divisions shows that the Konkan division, with a per capita DDP of Rs 37145, is the most prosperous division and accounts for nearly half (40%) of the total income generated in Maharashtra. Aurangabad, followed by Amaravati, are he poorest divisions. However, even Konkan division has Ratnagiri district, which has a per capita DDP of Rs 14354, less than the averages for Aurangabad and Amravati. Consumption Distribution: District wise Estimates Incidence of poverty is generally examined in terms of estimates of proportion of rural and urban population having a consumption level less than the normative minimum consumption expenditure to ensure sufficient energy (measured in calories) for leading an active and healthy life. This minimum is called the poverty line. Estimates of poverty are made on the basis of the published results of the National Sample Survey data on consumer expenditure distribution obtained from the central sample with reference to the poverty lines recommended by the Expert Group (GoI, 1993). These estimates for the different states of the Indian Union are presented in Tables 1 & 2. A comparative profile of rural and urban poverty vis a vis that of all-India is presented in Table 3. A disaggregated profile of average consumption, consumption inequality and poverty by rural and urban sectors at the district level in Maharashtra could be obtained only for a single year 1993–94 (Table 5).6 The findings are as follows:
Incidence of rural poverty increased between 1973–74 and 1977–78 and declined thereafter in rural Maharashtra. At the all-India level, it declined till 1987–88, increased by 1993–94 and declined thereafter. The percentage point reduction in incidence of rural poverty between 1973–74 and 1999–2000 was higher in Maharashtra (35.49) than at the all-India level (28.52). The number of poor has also declined between these two years. The decline was more pronounced in rural Maharashtra (42.53 per cent) than in rural India as a whole (24.47 per cent). As a result, incidence of rural poverty in Maharashtra, which was always above the national average, fell below it in 1999–2000 (Table 3). Urban poverty declined since 1973–74 in both Maharashtra and all-India with the difference that there was a marginal increase in the former between 1983 and 1987–88. Further, the reduction in urban poverty between 1973–74 and 1999–2000 was less in Maharashtra (16.32 percentage point) than in all-India (23.16 percentage point). However, the decline in poverty ratios was not sufficient to neutralise the growth in urban population; hence, the number of the poor has increased in urban Maharashtra and urban India as a whole. The increase in the number of urban poor was more in Maharashtra (37.98 per cent) than in all-India (17.42 per cent) (Table 2). Incidence of urban poverty in Maharashtra was less than the national average till the mid-80s. It has crossed the national average since then. Thanks to the pronounced decline in rural poverty and the predominant size of the rural sector, poverty in Maharashtra as a whole has declined since the mid-70s. Between 1973–74 and 1999–00, the proportion of poor population in Maharashtra declined by 28.90-percentage point as compared with 27.63 point at the all-India level. The percentage reduction in the number of poor was more in Maharashtra (21.42 per cent) than in India as a whole (16.80 per cent) (Table 3). It would be worthwhile to examine of incidence of poverty by sector across districts. Such estimates are made with reference to the poverty lines of Rs 194.94 for the rural and 328.56. for the urban sectors respectively. The database is obtained by pooling the central and state sample results for the year 1993–94 (Table 6). The estimates reveal significant inverse correlation between per capita consumer expenditure and incidence of poverty in both rural (−0.90) and urban (−0.93) areas. Inequality in rural consumption distribution is generally high in districts with higher levels of rural per capita consumption; correlation is 0.44.
Estimates of rural poverty at the district level for the year 1993–94 show that rural poverty has virtually been eliminated in the prosperous district of Raigad (4.94 per cent), followed by Kolhapur (6.97 per cent) and Thane (8.29 per cent) (Table 6). However, incidence of rural poverty was the maximum in Dhule (45.6 per cent), the district, which also ranks the lowest in terms of per capita district domestic product. Incidence of poverty in rural Maharashtra as a whole was 26.60 per cent. Incidence of urban poverty is the lowest in the district of Gadchiroli (6.11 per cent), followed by Mumbai (7.84 per cent). More than two-thirds of the urban population had consumer expenditure levels below the poverty line in the urban sectors of Ahmednagar (68.06 per cent), Akola (71.39 per cent) and Buldhana (74.09 per cent). About 31 per cent of the urban population in Mahrashtra lived below the poverty line in 1993–94. As regards incidence, depth and severity dimensions of poverty by sector across districts, there is positive and significant association in both rural and urban sectors (Table 7). The estimates of correlation between incidence & depth are numerically higher than those between incidence and severity in both the rural and urban sectors. This would highlight the need to for an independent focus on the severity dimension in addition to incidence of poverty from a policy perspective. Estimates of coefficient of variation show that percentage inter-district variations in severity is much higher than that in depth, which in turn is higher than incidence of poverty in both rural and urban sectors of Maharashtra.
Combined estimates for the district as a whole indicate that incidence of poverty was the least in Mumbai (7.84 per cent) and highest in Dhule (49 per cent), with the average for the state being 28.40 per cent. Inter-districts variation, as measured by the coefficient of variation, is much higher in incidence of poverty than in income generated, which in turn is higher than that in average consumption (Table 8).
There is significant negative correlation between the ranks of the districts in terms of per capita domestic product and incidence of poverty7 (Table 8). That is, incidence of poverty is generally higher in those districts, which are economically backward in terms of per capita domestic product. Thus, it would appear that lopsided regional spread of development is a major reason for poverty across districts in Maharashtra. There is an additional hypothesis that the observed disparities in income levels and incidence of poverty are due to excessive orientation towards indutrailisation and urbanization in Maharashtra. This hypothesis may be verified in terms association between incidence of poverty (rural-urban combined) and sectoral shares in income generated (Table 8). The estimates reveal significant inverse association between incidence of poverty and the share of the secondary sector in the district income generated. The correlation in (−) 0.50 and significant. Conversely, one may also observe that poverty is high in regions dominated by agriculture. The correlation between head count ratio and the share of the primary sector in the district domestic product is positive and 0.304. Needless to say, this would call for concerted efforts at balanced regional development of Maharashtra. As between sectors, the wide disparities in terms of poverty can be examined with reference to frequency distribution of districts by head-count ratio (Table 9). The range and extent of variability is less within the rural sector. The range is from five to 46 per cent. Twenty-two out of the thirty districts fall n the class intervals 10–19.99 and 20–29.99 per cent of poverty. Urban profile differs from the rural sector with respect to both range and variability. While the range is from 6 to 75 per cent and distribution is even across class intervals.
An ordering of the districts by sector wise estimates of head-count ratio brings out the following: The districts of Nanded, Parbhani, Solapur, Akola, Jalgaon, Buldhana and Dhule belong to the poorest quarter in terms of headcount ratio for the district as a whole. Among them, Dhule, Nanded, Buldhana and Parbhani belonged to the poorest quarter in term of per capita domestic product ranking also. Nashik, Aurangabad and Amravati division account for two each of these districts. A sector wise comparison shows that Buldhana, Dhule and Jalgaon belonged to the poorest quarter with reference to both rural and urban sector rankings. Nanded belonged to the poorest quarter with reference to the rural ranking while Akola, Parbhani and Solapur belonged to the poorest quarter category with reference to urban ranking only. At the opposite end, the districts of Mumbai, Raigad, Kolhapur, Thane, Pune, Sindhudurg, Osmanabad and Sangali belong to the richest quarter in terms of head-count ratio. Among them, Mumbai, Raigad, Thane, Pune and Kolhapur also belonged to the richest quarter in terms of per capita domestic product. As per the sector wise comparison, Raigad, Kolhapur, Thane and Pune belonged to the richest quarter in terms of both rural and urban rankings; Sangali, Osmanabad and Sindhudurg belonged to the richest quarter in terms of rural ranking. A profile (Table 11) of distribution of aggregate poverty across administrative districts/regions by rural/urban sectors separately and combined brings out the following:
Konkan is the only region whose percentage share in aggregate poverty in Maharashtra was proportionately less than that in population in both rural and urban sectors and hence, combined rural-urban too. Within Konkan, Sindhudurg is an exception, with its share in urban population exceeding that in urban poverty in Maharashtra. Pune is another division whose rural-urban combined share in total Mahrashtra population was more than its share in total poverty. However, the urban sector of the Pune division had a proportionately higher share in poverty than in population. Within the Pune division, share in population was more than that in poverty for the rural sector of Solapur and urban sectors of Satara, Sangali and Solapur. Amaravati and Nashik are the two regions, which had a proportionately higher share in poverty than in population at the aggregate as well as constituent district levels. However, within Amravati, the districts of Amravati and Yavatmal had their sectoral shares in rural poverty less than the corresponding shares in total population. At the aggregate level, Aurangabad and Nagpur divisions too had a lower share in population than in poverty. But, within Aurangabad division, the districts of Osmanabad and Latur had their rural sectoral shares in poverty less than that in population. As regards the Nagpur division, the districts of Nagpur and Bhandara had their rural poverty shares less than the corresponding population shares; Gadchiroli's urban population share exceeded its poverty share by 20 times. It is obvious that the district wise estimates of income generated and consumption distribution have immense policy relevance in terms of throwing light on policy issues, causes and solutions. However, a pertinent question that would arise is how far are such estimates of poverty and consumption distribution reliable? Officials at the state Directorates of Economics and Statistics express their own reservations. One option could be to examine how far such estimates are acceptable in terms of empirical correlates? This may be illustrated with reference to the Karnataka Human Development Report 1999. The report contains district wise comparison, which shows that the incidence of poverty in the districts of Bijapur and Raichur is lower than that of Bellary and Gulbarga even though all the four districts are similar with respect to many other indicators of deprivation. By this criterion, as the preceding discussion shows, the estimates generated for Maharashtra seem reasonably acceptable. One could also examine reliability in terms of standard errors of estimates of poverty. For instance, available estimates for Karnataka for the year 1999–2000 show that standard errors for district poverty estimates are large, more than 5–6 percentage points for some districts (Suryanarayana and Zaidi, 2002). For the district of Dakshina Kannada, the 95 per cent confidence for the headcount measure is (1.6 per cent, 13.8 percent). Hence, definite conclusions about variations in poverty across districts cannot be made. We do not have access to such unit record data for Maharashtra to carry out the verification in terms of standard errors of poverty estimates for districts. Therefore, following the findings for Karnataka, we make an attempt to estimate estimates of poverty for Maharashtra at the NSS regional level. Region wise Estimates This section reports on region wise estimates of poverty and calorie deficiency across six NSS regions, by sectors, of Maharashtra, namely, (i) Coastal (Greater Bombay, Thane, Raigad (Kulaba), Ratnagiri and Sindhudurg); (ii) Inland Western (Ahmednagar, Pune, Satara, Sangali, Solapur and Kolhapur); (iii) Inland Northern (Nashik, Dhule and Jalgaon); (iv) Inland Central (Aurangabad, Parbhani, Beed, Latur, Nanded, Osmanabad and Jalna); (v) Inland Eastern (Buldana, Akola, Amravati, Yavatmal, Wardha and Nagpur); and (vi) Eastern (Bhandara, Chandrapur and Gadchiroli) regions. Such estimates are made for two different years: (i) 1972/73 based on the published results by regions of NSS central sample; and (ii) 1999/2000 based on pooling the state and central samples. The estimates of poverty are made by estimating FGT class of measures with reference to the following poverty lines: (i) Rs 44.02 and Rs194.94 for 1972/73 and 1993/94 respectively for rural Maharashtra; and (ii) Rs 50.70 and Rs 328.56. respectively for urban Maharashtra. Estimates of nutrition insecurity (calorie deficiency) correspond to calorie norms of 2400 and 2100 calories for the rural and urban sectors respectively. The important features are: In 1972/73, within the rural sector, inland central region had the maximum average monthly per capita total consumer expenditure of Rs. 47.89 while the coastal region had the minimum at Rs. 40.63 (Table 12).8 By 1993/94, the profile changed: the coastal region had the maximum average monthly per capita total consumer expenditure Rs. 376.08 while the inland north has the minimum of Rs. 248.84 (Table 13). This seems to be the result of diverse growth experiences of different regions. Between the two years, price-adjusted consumption increased by more than 109 per cent in the coastal region; the increase was minimum in the inland north (34.40 per cent) (Table 14).
As regards the urban sector, the coastal region consisting of districts like Greater Bombay and Thane, as could expected, had the maximum average consumption of Rs. 103.55 and the Eastern region, with industrially backward districts, had the minimum at Rs. 45.45 in 1972/73 (Table 15). The coastal region continued to retain its lead in terms of average consumption in 1993/94. Average consumption was maximum in the Coastal region (Rs. 690.51). However, thanks to excellent growth in consumption, the Eastern region lost its status as the poorest region to Inland North, which had an average consumption of Rs. 377.29 (Table 17). Growth in consumption was 46 per cent in the Eastern region; coastal region has retained its lead in spite of the lowest increase in consumption (2.9 per cent) (Table 17).
Inequality in rural consumption distribution was the lowest in the Inland West and the highest in the Inland Central 1972/73 (Table 12). The scenario had changed since then. In 1993/94, the Inland North was the region with lowest degree of inequality while the Inland Central continues to have the highest extent of inequality (Table 13). As regards urban sector, inequality in 1972/73 was the lowest in relatively homogeneous and highly developed Inland North and the highest in the Coastal urban region consisting of districts from the entire spectrum – low to high-levels of development (Table 15). By 1993/94, extent of consumption inequality turned out to be the highest in the Inland Central and the lowest in the Inland North (Table 16). Thus, Inland north had remained as the region with the lowest extent of inequality in consumption in both the rural and urban sectors (Tables 12, 13, 15 & 16). The extent of deprivation in a given region depends upon both prosperity as reflected in the average level consumption and its distribution. Largely in keeping with the findings (i) and (ii), rural incidence of poverty was the lowest in the Inland West and highest in the Eastern region in 1972/73. Thanks to growth in consumption, incidence of poverty declined to the largest extent (by 77.67 per cent) in the Coastal region (Table 14). As a result, by 1993–94 the Coastal region improved its ranking from the equity perspective. In this year the incidence of rural poverty was the lowest in the Coastal region. Extent of poverty reduction between 1972/73 and 1993/94 was the lowest in the Inland North, which had the highest percentage of people below the poverty line in 1993/94 (Table 13). Urban poverty incidence had been the lowest in the Coastal belt (Tables 15 & 16). Percentage number of people in poverty was the highest in the Eastern region in 1972/73. Thanks to growth in consumption, which was the highest across regions in the urban sector, the Eastern region enjoyed poverty reduction to the highest extent (Table 17). When it comes to rural nutritional insecurity in 1972/73, the Inland Central was the least adversely affected with the highest calorie intake and lowest calorie deprivation in terms of all calorie deprivation measures (Table 12). Coastal region was the worst affected with the lowest amount of calorie intake per capita per diem, highest inequality in calorie intake distribution, calorie intake gap (P1) and severity of calorie shortfall (FGT2) and lowest protein consumption (Table 10). The extent of nutritional improvement as reflected in calorie intake varied across regions in the rural sector. It increased to the highest extent in the Inland West (15.28 per cent) and virtually stagnated in the Inland North (Table 14). In 1993/94, calorie intake was the lowest in the Inland North, incidence of calorie deficiency was the highest in the Inland East and severity of calorie deprivation was the highest in the Inland North (Table 14). Calorie intake was the highest in the Inland Central. Incidence of calorie deficiency was the lowest in the Inland Central (Table 14). Urban nutrition insecurity was the lowest in the Coastal belt in 1972/73; this region had the highest level of calorie intake and the lowest incidence of calorie deprivation (Table 15). Nutrition insecurity was the highest in the Inland East in terms of average calorie intake and incidence of calorie deprivation and in the Inland West in terms of depth and severity of calorie deprivation. Similar to the experience in the rural sector, extent of improvement in calorie intake between 1972/73 and 1993/94 varied across regions. Percentage increase in calorie intake was the highest in the East (19.1 per cent) and even negative in the Coastal belt (3.28 per cent) (Table 17). Consistent with these estimates, incidence of calorie deficiency declined to the highest extent in the Eastern region (75 per cent) and increased by about 12 per cent in the Coastal region (Table 17). In sum, urban nutrition insecurity was minimum in the Eastern region which had the highest average calorie intake (2271.70 calories); Coastal, Inland Western, Inland Eastern are the other regions which enjoyed calorie intake levels above the state average of 2085.75 calories. Average calorie intake was the lowest in Inland North (1973.30 calories); Inland Central is the other region, which had a calorie intake level less than the state average. Limitations and Issues In this brief section an attempt is made to examine the limitations of the regional analysis and comparisons presented in the preceding section. We present estimates of poverty based on the state and central sample respectively for the NSS survey years from 1973–74 up to 1999–2000 (Table 18). Of course, as can be expected, the estimates do not tally. The pattern and extent of divergence between the estimates based on the two sources vary over years and by sectors:
For instance, for rural Maharashtra, the estimates based on the state and central samples coincide for the year 1999–2000 but the central estimates exceeded that based on the state one by 20 percentage point for the year 1993–94, 10 percentage point for 1987–88, about five percentage point for 1977–78 and 1983; but the reverse is the case for the year 1973–74, when the estimate based on the state sample exceeded that based on the Central sample by about two percentage point. As regards the urban sector, the two estimates nearly matched for the years 1977–78 and 1999–2000; central sample estimates were lower than the state sample estimates for the year 1973–74 (by about six points), exceeded the state sample estimates by about seven points for 1983, by two pints for 1987–88 and by about eight points for 1993–94. The changing pattern and extent of divergence between the two estimates is really a factor, which would affect our comparisons based on state and central samples. Top Conclusion Statistical information on income generation and distribution at the regional level is quite important for economic policy formulation and evaluation. This paper (i) discusses methodological issues with reference to representativeness and comparability of estimates of income generation, consumption distribution and incidence of poverty at the regional level, district in particular, in India; and (ii) provides an empirical illustration for the state of Maharashtra. |
While some progress has been achieved with regard to estimation of district domestic products in different states, there is scope for generating (i) representative estimates based on direct district-specific information; and (ii) comparable estimates by uniform methods across states. Comparable estimates of domestic products across states are important for regular monitoring of poverty at the regional level and generating reasonable estimates of poverty through poverty mapping. States like Karnataka, Tamil Nadu and Maharashtra have generated district-wise estimates of consumption distribution by pooling central and state samples. How do we judge the extent of reliability of such estimates? There are doubts regarding acceptability of poverty estimates for a couple of districts in Karnataka as judged by their empirical correlates. Further, available estimates of standard errors of poverty estimates for poverty in Karnataka indicate wide confidence intervals and hence, little scope for unambiguous inter-district comparisons of poverty estimates. |
This paper provides an empirical illustration with reference to estimates of domestic product and poverty for the districts in Maharashtra. The district wise estimates SDP andpoverty for Maharashtra are broadly acceptable in terms of empirical correlates and common perception. A rigorous verification of reliability of estimates of poverty in terms of standard errors could not be carried out for lack of access to unit record data. Hence, this study makes estimates of poverty for Maharashtra at the NSS region level. Such estimates are made for two years, 1972–73 (based on the central sample) and 1993–94 (based on pooled state and central samples). The study shows inter-regional variations in growth experiences and poverty reduction between the two pints of time. |
An important issue that emerges is the extent of comparability of estimates based on the Central and State samples of the NSS. Available estimates of poverty based on the Central and State samples for the rural and urban sectors of Maharashtra for a few years indicate varying patterns and extent of divergence between the two sets of estimates. This would raise questions as to the reliability of any exercise based on comparisons of separate or combined estimates based state and central samples. |
Top Tables | Table 1: Estimates of Rural Poverty (%): States and All-India
| | State | 1973–74 | 1977–78 | 1983 | 1987–88 | 1993–94 | 1999–2000 |
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| | Head-count ratio (%) | Poverty gap index (PGI) (%) | Squared PGI (%) | Head-count ratio (%) | Poverty gap index (%) | Squared PGI (%) | Head-count ratio (%) | Poverty gap index (%) | Squared PGI (%) | Head-count ratio (%) | Poverty gap index (%) | Squared PGI (%) | Head-count ratio (%) | Poverty gap index (%) | Squared PGI (%) | Head-count ratio (%) | Poverty gap index (%) | Squared PGI (%) |
| | Andhra Pradesh | 46.94 | 12.80 | 4.93 | 38.10 | 9.90 | 3.79 | 27.05 | 5.88 | 1.76 | 21.76 | 4.22 | 1.24 | 16.66 | 2.93 | 0.87 | 11.37 | 1.81 | 0.53 | | Assam | 52.64 | 12.66 | 4.45 | 62.94 | 13.69 | 4.29 | 42.09 | 8.09 | 2.25 | 42.13 | 8.33 | 2.27 | 43.81 | 8.18 | 2.19 | 39.22 | 8.48 | 2.70 | | Bihar | 64.42 | 20.85 | 9.15 | 63.37 | 19.55 | 8.13 | 65.19 | 19.92 | 8.17 | 52.87 | 13.25 | 4.53 | 57.84 | 14.56 | 5.03 | 43.33 | 8.78 | 2.51 | | Gujarat | 45.67 | 11.00 | 3.75 | 41.24 | 11.09 | 4.16 | 27.07 | 5.00 | 1.39 | 28.22 | 4.85 | 1.15 | 22.41 | 3.96 | 1.12 | 12.77 | 2.09 | 0.59 | | Haryana | 34.78 | 8.49 | 2.81 | 27.94 | 6.27 | 2.14 | 19.21 | 3.63 | 1.07 | 18.32 | 3.43 | 1.05 | 26.55 | 5.64 | 1.79 | 8.04 | 1.31 | 0.39 | | Karnataka | 55.32 | 16.09 | 6.27 | 47.40 | 14.57 | 6.09 | 35.93 | 9.63 | 3.56 | 34.00 | 8.23 | 3.02 | 30.21 | 6.39 | 2.04 | 17.49 | 2.74 | 0.70 | | Kerala | 59.18 | 19.98 | 8.89 | 49.99 | 16.61 | 7.47 | 39.21 | 9.91 | 3.50 | 29.93 | 6.63 | 2.19 | 26.50 | 5.62 | 1.83 | 9.59 | 1.43 | 0.37 | | Madhya Pradesh | 63.84 | 20.57 | 8.63 | 63.74 | 20.85 | 9.00 | 48.96 | 13.61 | 5.12 | 40.50 | 9.90 | 3.32 | 40.51 | 9.67 | 3.31 | 36.42 | 7.71 | 2.28 | | Maharashtra | 58.96 | 17.55 | 7.07 | 63.33 | 21.07 | 8.97 | 45.28 | 11.35 | 3.85 | 42.57 | 10.34 | 3.40 | 37.61 | 9.30 | 3.73 | 23.47 | 4.47 | 1.30 | | Orissa | 69.07 | 23.18 | 10.09 | 72.83 | 26.50 | 12.44 | 67.53 | 21.96 | 9.71 | 59.14 | 16.91 | 6.48 | 49.90 | 11.92 | 4.00 | 48.03 | 11.74 | 3.93 | | Punjab | 28.84 | 6.37 | 1.93 | 15.26 | 3.78 | 1.73 | 14.31 | 2.42 | 0.65 | 12.91 | 1.96 | 0.49 | 13.95 | 1.92 | 0.42 | 6.16 | 0.74 | 0.16 | | Rajasthan | 43.63 | 11.66 | 4.25 | 36.88 | 10.36 | 3.93 | 34.20 | 9.15 | 3.42 | 29.38 | 7.97 | 3.64 | 26.94 | 5.21 | 1.53 | 13.43 | 1.92 | 0.48 | | Tamil Nadu | 57.67 | 17.04 | 6.76 | 57.63 | 17.98 | 7.45 | 53.60 | 17.02 | 7.23 | 46.00 | 12.97 | 4.90 | 32.99 | 7.40 | 2.52 | 20.72 | 3.81 | 1.11 | | Uttar Pradesh | 57.11 | 15.08 | 5.41 | 47.57 | 12.80 | 4.79 | 45.51 | 12.31 | 4.55 | 41.49 | 10.05 | 3.32 | 42.35 | 10.47 | 3.58 | 31.70 | 5.92 | 1.63 | | West Bengal | 72.96 | 27.24 | 13.10 | 68.84 | 23.63 | 10.59 | 62.24 | 20.59 | 9.23 | 48.63 | 11.64 | 4.03 | 41.32 | 8.31 | 2.45 | 32.01 | 6.46 | 1.88 | | All-India | 55.94 | 16.63 | 6.72 | 50.50 | 14.98 | 6.03 | 43.39 | 11.90 | 4.49 | 36.93 | 8.81 | 2.88 | 37.20 | 8.57 | 2.87 | 27.42 | 5.35 | 1.56 |
| | Source: Author's estimates based on the NSS central sample estimates corresponding to the poverty lines obtained as per the Expert Group (GoI, 1993) methodology using the Povcal software. |
| | | Table 2: Estimates of Urban Poverty (%): States and All-India
| | State | 1973–74 | 1977–78 | 1983 | 1987–88 | 1993–94 | 1999–2000 |
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| | Head-count ratio (%) | Poverty gap index (%) | Squared PGI (%) | Head-count ratio (%) | Poverty gap index (%) | Squared PGI (%) | Head-count ratio (%) | Poverty gap index (%) | Squared PGI (%) | Head-count ratio (%) | Poverty gap index (%) | Squared PGI (%) | Head-count ratio (%) | Poverty gap index (%) | Squared PGI (%) | Head-count ratio (%) | Poverty gap index (%) | Squared PGI (%) |
| | Andhra Pradesh | 51.38 | 13.91 | 5.04 | 42.83 | 11.92 | 4.75 | 36.74 | 9.78 | 3.72 | 43.57 | 12.20 | 4.65 | 37.94 | 9.47 | 3.14 | 27.52 | 5.76 | 1.61 | | Assam | 37.55 | 8.14 | 2.34 | 34.49 | 7.67 | 2.72 | 23.44 | 5.15 | 1.62 | 28.93 | 5.36 | 1.34 | 8.38 | 0.62 | 0.06 | 9.76 | 1.15 | 0.18 | | Bihar | 50.62 | 14.35 | 5.60 | 50.33 | 14.92 | 5.89 | 49.64 | 14.39 | 5.52 | 55.35 | 15.03 | 5.37 | 35.46 | 8.18 | 2.53 | 33.22 | 6.88 | 1.87 | | Gujarat | 51.27 | 13.57 | 5.02 | 40.91 | 9.81 | 3.29 | 37.75 | 8.27 | 2.54 | 37.74 | 8.91 | 2.87 | 28.91 | 6.25 | 1.86 | 15.87 | 2.68 | 0.70 | | Haryana | 40.15 | 9.58 | 3.00 | 33.76 | 7.74 | 2.41 | 19.62 | 3.98 | 1.24 | 20.54 | 3.79 | 0.96 | 17.58 | 3.26 | 0.91 | 10.20 | 2.20 | 0.84 | | Karnataka | 53.21 | 15.62 | 6.12 | 49.19 | 15.60 | 6.72 | 40.26 | 12.28 | 5.09 | 33.65 | 8.68 | 3.21 | 39.52 | 11.33 | 4.37 | 25.45 | 5.73 | 1.74 | | Kerala | 61.16 | 22.77 | 10.57 | 58.68 | 23.23 | 11.78 | 48.26 | 15.08 | 6.35 | 44.49 | 13.38 | 5.20 | 25.50 | 5.64 | 1.91 | 20.48 | 3.97 | 1.03 | | Madhya Pradesh | 60.68 | 18.27 | 7.35 | 59.07 | 19.82 | 8.60 | 51.42 | 14.24 | 5.22 | 46.17 | 13.42 | 5.16 | 48.31 | 13.47 | 5.11 | 37.45 | 9.50 | 3.27 | | Maharashtra | 43.84 | 13.93 | 5.28 | 38.75 | 12.32 | 5.36 | 37.55 | 11.32 | 4.64 | 38.12 | 11.47 | 4.73 | 34.88 | 10.35 | 4.11 | 27.52 | 6.89 | 2.44 | | Orissa | 57.55 | 18.85 | 8.01 | 51.76 | 17.05 | 7.55 | 48.79 | 14.14 | 5.53 | 45.05 | 13.08 | 5.07 | 41.22 | 11.47 | 4.27 | 42.50 | 11.01 | 3.85 | | Punjab | 28.12 | 4.80 | 1.09 | 25.92 | 6.15 | 2.37 | 22.60 | 4.85 | 1.51 | 15.51 | 2.14 | 0.40 | 11.82 | 1.76 | 0.41 | 5.68 | 0.66 | 0.13 | | Rajasthan | 56.58 | 15.80 | 5.75 | 44.28 | 11.84 | 4.25 | 38.31 | 10.12 | 3.78 | 42.96 | 11.06 | 3.93 | 30.95 | 7.09 | 2.21 | 21.26 | 3.51 | 0.79 | | Tamil Nadu | 52.17 | 15.17 | 5.83 | 49.00 | 15.36 | 6.55 | 44.56 | 13.51 | 5.64 | 42.64 | 12.23 | 4.68 | 38.92 | 10.55 | 4.05 | 21.82 | 4.64 | 1.53 | | Uttar Pradesh | 59.59 | 17.76 | 6.88 | 53.97 | 16.53 | 6.63 | 49.32 | 14.26 | 5.43 | 41.77 | 11.61 | 4.29 | 35.54 | 9.15 | 3.18 | 30.94 | 6.59 | 1.86 | | West Bengal | 35.23 | 10.15 | 4.02 | 36.01 | 9.99 | 3.76 | 30.71 | 7.75 | 2.77 | 33.47 | 7.98 | 2.52 | 23.31 | 4.67 | 1.25 | 16.48 | 2.83 | 0.72 | | All-India | 47.40 | 13.35 | 5.10 | 41.98 | 12.52 | 5.18 | 38.08 | 10.47 | 4.00 | 37.63 | 10.10 | 3.62 | 32.73 | 8.24 | 2.78 | 24.24 | 5.25 | 1.53 |
| | Source: author's estimates based on the NSS central sample estimates corresponding to poverty lines obtained as per the Expert Group (GoI, 1993) methodology using the Povcal software. |
| | | Table 3: Incidence of Poverty: Maharashtra & All-India
| | Maharashtra Year | Rural sector | Urban sector | Combined |
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| | Percentage Poor | Number of poor (million) | Percentage Poor | Number of poor (million) | Percentage Poor | Number of poor (million) |
| | 1973–74 | 58.96 | 21.54 | 43.84 | 7.65 | 54.07 | 29.19 | | 1977–78 | 63.33 | 24.73 | 38.75 | 7.75 | 55.01 | 32.47 | | 1983 | 45.28 | 19.40 | 37.55 | 9.06 | 42.49 | 28.46 | | 1987–88 | 42.57 | 19.51 | 38.12 | 10.48 | 40.90 | 30.00 | | 1993–94 | 37.61 | 19.17 | 34.88 | 11.10 | 36.56 | 30.27 | | 1999–'00 | 23.47 | 12.38 | 27.52 | 10.56 | 25.18 | 22.94 | | All-India |
| Rural sector | Urban sector | Combined |
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| | Percentage Poor | Number of poor (million) | Percentage Poor | Number of poor (million) | Percentage Poor | Number of poor (million) |
| | 1973–74 | 55.94 | 258.98 | 47.40 | 58.07 | 54.15 | 317.04 | | 1977–78 | 50.5 | 251.45 | 41.98 | 59.99 | 48.60 | 311.44 | | 1983 | 43.39 | 239.48 | 38.08 | 66.23 | 42.12 | 305.71 | | 1987–88 | 36.93 | 219.07 | 37.63 | 74.05 | 37.10 | 293.11 | | 1993–94 | 37.2 | 243.57 | 32.73 | 77.21 | 36.02 | 320.78 | | 1999–'00 | 27.42 | 195.60 | 24.24 | 68.19 | 26.52 | 263.79 |
| | Note: These estimates are based on estimates based on the central sample of the National Sample Survey for the corresponding rounds. | | Percentage poor = Head-count ratio (%). |
| | | Table 4: Estimates of District Domestic Product: Maharashtra (1998–99)
| | District | District Domestic Product (Rs) | Share in Total SDP (%) | Per capita District Domestic Product (s) |
| | Mumbai | 5115400 | 25.06 | 45471 | | Thane | 2001295 | 9.8 | 33200 | | Raigad | 628601 | 3.08 | 30364 | | Ratnagiri | 250346 | 1.23 | 14354 | | Sindhudurg | 188148 | 0.92 | 20016 | | Konkan Division | 8183790 | 40.09 | 37145 | | Nashik | 903770 | 4.43 | 20636 | | Dhule | 339249 | 1.66 | 11789 | | Jalgaon | 594605 | 2.91 | 16449 | | Ahmednagar | 583980 | 2.86 | 15251 | | Nashik Division | 2421604 | 11.86 | 16472 | | Pune | 1764358 | 8.64 | 28000 | | Satara | 432366 | 2.12 | 15563 | | Sangali | 511178 | 2.5 | 20411 | | Solapur | 663616 | 3.25 | 18097 | | Kolhapur | 708633 | 3.47 | 20925 | | Pune Division | 4080151 | 19.99 | 21892 | | Aurangabad | 490746 | 2.4 | 19365 | | Jalna | 188174 | 0.92 | 12047 | | Parbhani | 331160 | 1.62 | 13827 | | Beed | 316304 | 1.55 | 15303 | | Nanded | 346951 | 1.7 | 13068 | | Osmanabad | 187561 | 0.92 | 12905 | | Latur | 261136 | 1.28 | 13677 | | Aurangabad Div. | 2122030 | 10.4 | 14559 | | Buldhana | 296084 | 1.45 | 13823 | | Akola | 403398 | 1.98 | 16069 | | Amravati | 427676 | 2.1 | 17168 | | Yavatmal | 314921 | 1.54 | 13382 | | Amravati Div. | 1442079 | 7.06 | 16185 | | Wardha | 204615 | 1 | 16952 | | Nagpur | 1071256 | 5.25 | 28678 | | Bhandara | 344742 | 1.69 | 14467 | | Chandrapur | 388625 | 1.9 | 19325 | | Gadchiroli | 153139 | 0.75 | 17140 | | Nagpur Div. | 2162377 | 10.59 | 21138 | | MAHARASHTRA | 20412031 | 100 | 22763 |
| | Source: Government of Maharashtra (2001b; p. 7). |
| | | Table 5: Districts Ordered Based on Domestic Product (1998–99)
| | Rank in terms of Per Capita Domestic Product | District | Total District Product (Rs at current prices) | Share in total SDP (%) | Per capita Domestic product (Rs at current prices) | Division | Comment |
| | 1 | Dhule | 339249 | 1.66 | 11789 | Nashik | Poorest Quarter | | 2 | Jalna | 188174 | 0.92 | 12047 | Aurangabad | | | 3 | Osmanabad | 187561 | 0.92 | 12905 | Aurangabad | | | 4 | Nanded | 346951 | 1.7 | 13068 | Aurangabad | | | 5 | Yavatmal | 314921 | 1.54 | 13382 | Amravati | | | 6 | Latur | 261136 | 1.28 | 13677 | Aurangabad | | | 7 | Buldhana | 296084 | 1.45 | 13823 | Amravati | | | 8 | Parbhani | 331160 | 1.62 | 13827 | Aurangabad | Lower Middle | | 9 | Ratnagiri | 250346 | 1.23 | 14354 | Konkan | Quarter | | 10 | Bhandara | 344742 | 1.69 | 14467 | Nagpur | | | 11 | Ahmednagar | 583980 | 2.86 | 15251 | Nashik | | | 12 | Beed | 316304 | 1.55 | 15303 | Aurangabad | | | 13 | Satara | 432366 | 2.12 | 15563 | Pune | | | 14 | Akola | 403398 | 1.98 | 16069 | Amravati | | | 15 | Jalgaon | 594605 | 2.91 | 16449 | Nashik | Upper Middle | | 16 | Wardha | 204615 | 1 | 16952 | Nagpur | Quarter | | 17 | Gadchiroli | 153139 | 0.75 | 17140 | Nagpur | | | 18 | Amravati | 427676 | 2.1 | 17168 | Amravati | | | 19 | Solapur | 663616 | 3.25 | 18097 | Pune | | | 20 | Chandrapur | 388625 | 1.9 | 19325 | Nagpur | | | 21 | Aurangabad | 490746 | 2.4 | 19365 | Aurangabad | | | 22 | Sindhudurg | 188148 | 0.92 | 20016 | Konkan | | | 23 | Sangali | 511178 | 2.5 | 20411 | Pune | | | 24 | Nashik | 903770 | 4.43 | 20636 | Nashik | Richest Quarter | | 25 | Kolhapur | 708633 | 3.47 | 20925 | Pune | | | 26 | Pune | 1764358 | 8.64 | 28000 | Pune | | | 27 | Nagpur | 1071256 | 5.25 | 28678 | Nagpur | | | 28 | Raigad | 628601 | 3.08 | 30364 | Konkan | | | 29 | Thane | 2001295 | 9.8 | 33200 | Konkan | | | 30 | Mumbai | 5115400 | 25.06 | 45471 | Konkan | |
| | Source: Government of Maharashtra (2001b; p. 7) |
| | | Table 6: Average Monthly Consumption, Inequality And Poverty Across Districts: Maharashtra (1993/94)
| | District | Rural | Urban | Combined |
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|
| | Per Capita Consumption per month (Rs at current prices) | Head-count ratio (%) | Lorenz ratio (%) | Per Capita Consumption per month (Rs at current prices) | Head-count ratio (%) | Lorenz ratio (%) | Per Capita Consumption per month (Rs at current prices) | Head-count ratio (%) |
| | Ahmednagar | 269.18 | 33.37 | 26.8 | 316.39 | 68.06 | 30.6 | 276.65 | 38.86 | | Akola | 260.3 | 30.78 | 23.03 | 299.41 | 71.39 | 26.78 | 271.50 | 42.41 | | Amravati | 281.97 | 24.21 | 24.35 | 394.45 | 45.39 | 27.02 | 318.63 | 31.11 | | Aurangabad | 283.16 | 34.88 | 30.88 | 511.8 | 46 | 42.9 | 358.05 | 38.52 | | Beed | 251.78 | 34.23 | 24.31 | 344.48 | 57.82 | 30.84 | 268.41 | 38.46 | | Bhandara | 293.42 | 23.26 | 24.39 | 397.48 | 56.51 | 31.11 | 307.04 | 27.61 | | Buldhana | 245.16 | 37.76 | 24.13 | 283.33 | 74.09 | 26.49 | 253.02 | 45.24 | | Chandrapur | 284.73 | 32.04 | 27.77 | 430.07 | 35.55 | 25.9 | 325.48 | 33.02 | | Dhule | 232.31 | 45.57 | 23.58 | 329.25 | 62.32 | 25.65 | 252.19 | 49.00 | | Gadchiroli | 301.44 | 28.63 | 29.97 | 622.46 | 6.11 | 20.39 | 329.41 | 26.67 | | Jalgaon | 239.83 | 37.22 | 23.88 | 340.01 | 63.01 | 28.99 | 267.32 | 44.30 | | Jalna | 280.78 | 31.26 | 30.84 | 349.85 | 58.45 | 34.19 | 292.46 | 35.86 | | Kolhapur | 375.02 | 6.97 | 25.36 | 470.21 | 25.07 | 24.01 | 400.08 | 11.73 | | Latur | 343.59 | 24.66 | 32.54 | 404.23 | 42.35 | 27.76 | 355.95 | 28.27 | | Nagpur | 310.22 | 24.24 | 26.85 | 464.28 | 42.09 | 34.27 | 405.40 | 35.27 | | Nanded | 287.25 | 34.16 | 31.55 | 369.67 | 57.03 | 31.95 | 305.15 | 39.13 | | Nashik | 270.84 | 35.98 | 28.36 | 417.52 | 42.91 | 28.18 | 322.98 | 38.44 | | Osmanabad | 369.92 | 16.67 | 34.56 | 349.1 | 58.81 | 33.45 | 366.76 | 23.07 | | Parbhani | 301.82 | 28.55 | 31.97 | 323.57 | 60.1 | 27.81 | 309.46 | 39.63 | | Pune | 318.73 | 17.89 | 25.89 | 575.56 | 27.27 | 34.14 | 449.04 | 22.65 | | Raigad | 398.75 | 4.94 | 23.51 | 601.63 | 24.36 | 32.19 | 435.29 | 8.44 | | Ratnagiri | 311.83 | 24.69 | 29.98 | 445.59 | 29.31 | 25.13 | 323.79 | 25.10 | | Sangali | 401.56 | 14.43 | 33.54 | 386.59 | 53.26 | 24.21 | 398.16 | 23.26 | | Satara | 300.11 | 22.64 | 24.92 | 461.78 | 39.75 | 32.98 | 320.94 | 24.84 | | Sindhudurg | 286.76 | 20.55 | 21.54 | 389.07 | 53.31 | 27.24 | 294.52 | 23.04 | | Solapur | 278.25 | 31.16 | 26.86 | 331.4 | 62.92 | 26.69 | 293.54 | 40.30 | | Thane | 430.92 | 8.29 | 29.84 | 635.27 | 15.74 | 29.06 | 563.02 | 13.11 | | Wardha | 308.81 | 27.4 | 28.14 | 412.68 | 39.08 | 27 | 336.40 | 30.50 | | Yavatmal | 270.39 | 25.74 | 22.37 | 355.08 | 53.03 | 24.81 | 284.94 | 30.43 | | Mumbai | – | – | – | 721.59 | 7.84 | 28.39 | 721.59 | 7.84 | | Maharashtra | 302.9 | 26.6 | 28.76 | 537.19 | 31.24 | 33.77 | 393.57 | 28.40 |
| | Note: Estimates of poverty are with reference to the State poverty lines of Rs. 194.94 for the rural and Rs. 328.56 for the urban sector (GoI, 2000; p. 1.29) respectively. |
| | | Table 7: Poverty: Incidence, Depth And Severity By Sector Across Districts: Maharashtra (1993/94)
(Foster-Greer-Thorebecke Index)
| | District | Rural | Urban |
|
| | Head-count ratio (%) | Poverty gap index (PGI) (%) | Squared PGI (%) | Head-count ratio (%) | Poverty gap index (PGI) (%) | Squared PGI (%) |
| | Ahmednagar | 33.37 | 7.18 | 2.35 | 68.06 | 24.14 | 10.46 | | Akola | 30.78 | 6.83 | 2.31 | 71.39 | 23.27 | 9.73 | | Amravati | 24.21 | 5.45 | 1.81 | 45.39 | 12.88 | 5.08 | | Aurangabad | 34.88 | 10.89 | 4.68 | 46.00 | 13.30 | 5.39 | | Beed | 34.23 | 7.87 | 2.63 | 57.82 | 19.23 | 9.21 | | Bhandara | 23.26 | 4.14 | 1.03 | 56.51 | 13.06 | 3.90 | | Buldhana | 37.76 | 8.16 | 2.51 | 74.09 | 24.86 | 11.52 | | Chandrapur | 32.04 | 7.90 | 2.64 | 35.55 | 8.81 | 3.35 | | Dhule | 45.57 | 9.15 | 2.51 | 62.32 | 18.19 | 6.99 | | Gadchiroli | 28.63 | 6.60 | 2.12 | 6.11 | 0.72 | 0.12 | | Jalgaon | 37.22 | 8.76 | 3.10 | 63.01 | 18.89 | 7.45 | | Jalna | 31.26 | 11.81 | 7.48 | 58.45 | 23.25 | 12.03 | | Kolhapur | 6.97 | 1.07 | 0.32 | 25.07 | 5.36 | 1.96 | | Latur | 24.66 | 6.95 | 2.72 | 42.35 | 11.95 | 5.11 | | Nagpur | 24.24 | 4.54 | 1.22 | 42.09 | 11.89 | 4.48 | | Nanded | 34.16 | 10.62 | 4.71 | 57.03 | 18.20 | 7.52 | | Nashik | 35.98 | 6.879 | 1.81 | 42.91 | 11.44 | 4.12 | | Osmanabad | 16.67 | 2.98 | 0.75 | 58.81 | 20.37 | 10.12 | | Parbhani | 28.55 | 8.54 | 4.50 | 60.10 | 20.59 | 9.37 | | Pune | 17.89 | 3.42 | 1.14 | 27.27 | 6.51 | 2.23 | | Raigad | 4.94 | 0.63 | 0.15 | 24.36 | 4.24 | 0.97 | | Ratnagiri | 24.69 | 4.17 | 1.13 | 29.31 | 8.97 | 4.17 | | Sangali | 14.43 | 2.28 | 0.57 | 53.26 | 10.16 | 2.44 | | Satara | 22.64 | 3.75 | 0.93 | 39.75 | 11.05 | 4.20 | | Sindhudurg | 20.55 | 3.28 | 0.74 | 53.31 | 15.29 | 5.26 | | Solapur | 31.16 | 6.10 | 1.65 | 62.92 | 18.79 | 7.21 | | Thane | 8.29 | 1.46 | 0.46 | 15.74 | 2.77 | 0.67 | | Wardha | 27.40 | 5.29 | 1.37 | 39.08 | 9.07 | 3.04 | | Yavatmal | 25.74 | 4.79 | 1.47 | 53.03 | 14.42 | 5.38 | | Mumbai | – | – | – | 7.84 | 1.24 | 0.34 | | Maharashtra | 26.60 | 5.79 | 1.97 | 31.24 | 8.63 | 3.34 | | Standard deviation | 9.65 | 2.96 | 1.61 | 18.14 | 6.92 | 3.41 | | Coefficient of variation (%) | 36.26 | 51.16 | 81.79 | 58.06 | 80.17 | 102.00 |
| | | Table 8: Incidence of Poverty And Sectoral Profile of Income Generation: 1993/94
| | District | Consumption Indicators | DDP & Its Distribution across Sectors (%) |
|
| | Per Capita Consumption per month (Rs at current prices) | Head-count ratio (%) | Per Capita DDP (Rs at current prices) | Primary Sector | Secondary Sector | Tertiary Sector | Total |
| | Ahmednagar | 276.65 | 38.86 (8) | 7868 (22) | 29.62 | 25.60 | 44.78 | 100.00 | | Akola | 271.50 | 42.41 (4) | 8992 (14) | 33.09 | 13.64 | 53.27 | 100.00 | | Amravati | 318.63 | 31.11 (15) | 9610 (13) | 44.19 | 12.64 | 43.17 | 100.00 | | Aurangabad | 358.05 | 38.52 (9) | 10789 (11) | 21.81 | 40.45 | 37.74 | 100.00 | | Beed | 268.41 | 38.46 (10) | 7526 (24) | 43.27 | 13.73 | 43.00 | 100.00 | | Bhandara | 307.04 | 27.61 (19) | 7858 (23) | 40.81 | 24.46 | 34.72 | 100.00 | | Buldhana | 253.02 | 45.24 (2) | 7465 (25) | 37.29 | 12.02 | 50.69 | 100.00 | | Chandrapur | 325.48 | 33.02 (14) | 10912 (10) | 45.05 | 22.05 | 32.91 | 100.00 | | Dhule | 252.19 | 49.00 (1) | 6796 (30) | 33.98 | 16.81 | 49.21 | 100.00 | | Gadchiroli | 329.41 | 26.67 (20) | 11784 (7) | 73.28 | 6.82 | 19.90 | 100.00 | | Jalgaon | 267.32 | 44.30 (3) | 8605 (19) | 36.31 | 20.96 | 42.74 | 100.00 | | Jalna | 292.46 | 35.86 (12) | 7077 (28) | 46.85 | 12.86 | 40.28 | 100.00 | | Kolhapur | 400.08 | 11.73 (28) | 11567 (8) | 30.75 | 24.75 | 44.50 | 100.00 | | Latur | 355.95 | 28.27 (18) | 7376 (26) | 36.95 | 15.11 | 47.94 | 100.00 | | Nagpur | 405.40 | 35.27 (13) | 13504 (5) | 17.61 | 26.33 | 56.06 | 100.00 | | Nanded | 305.15 | 39.13 (7) | 7304 (27) | 33.90 | 17.56 | 48.55 | 100.00 | | Nashik | 322.98 | 38.44 (11) | 11050 (9) | 29.92 | 33.99 | 36.09 | 100.00 | | Osmanabad | 366.76 | 23.07 (24) | 7063 (29) | 49.01 | 13.22 | 37.77 | 100.00 | | Parbhani | 309.46 | 39.63 (6) | 8110 (20) | 46.13 | 12.90 | 40.97 | 100.00 | | Pune | 449.04 | 22.65 (26) | 15058 (4) | 13.45 | 42.02 | 44.53 | 100.00 | | Raigad | 435.29 | 8.44 (29) | 20245 (2) | 11.09 | 68.98 | 19.93 | 100.00 | | Ratnagiri | 323.79 | 25.10 (21) | 8888 (15) | 40.95 | 22.79 | 36.25 | 100.00 | | Sangali | 398.16 | 23.26 (23) | 10381 (12) | 35.71 | 18.47 | 45.82 | 100.00 | | Satara | 320.94 | 24.84 (22) | 8632 (17) | 35.46 | 22.29 | 42.25 | 100.00 | | Sindhudurg | 294.52 | 23.04 (25) | 13480 (6) | 61.46 | 10.36 | 28.18 | 100.00 | | Solapur | 293.54 | 40.30 (5) | 8628 (18) | 27.84 | 23.13 | 49.04 | 100.00 | | Thane | 563.02 | 13.11 (27) | 17521 (3) | 6.02 | 45.36 | 48.62 | 100.00 | | Wardha | 336.40 | 30.50 (16) | 8642 (16) | 36.44 | 17.74 | 45.81 | 100.00 | | Yavatmal | 284.94 | 30.43 (17) | 7957 (21) | 44.67 | 14.33 | 41.00 | 100.00 | | Mumbai | 721.59 | 7.84 (30) | 24382 (1) | 1.27 | 37.63 | 61.10 | 100.00 | | Maharashtra | 393.57 | 28.40 | 12326 | 21.29 | 31.29 | 47.42 | 100.00 | | Standard deviation | 97.61 | 10.88 | 4152.60 | – | – | – | – | | Coefficient of variation (%) | 24.80 | 38.31 | 33.69 | – | – | – | – |
| | Note: Figures in parentheses refer to the State ranking in terms of the respective indicator. | | Source: Estimates of DDP are from GoM (2001b, p.7). |
| | | Table 9: Frequency Distributions of Districts by Sector by Head-count ratio: Maharashtra (1993–94)
| | Head-count ratio (%) | Number of districts |
| | Rural | Urban | Combined |
| | 0–9.9 | 4 | 2 | 2 | | 10–19.99 | 3 | 1 | 2 | | 20–29.99 | 11 | 4 | 9 | | 30–39.99 | 11 | 3 | 12 | | 40–49.99 | 1 | 5 | 5 | | 50–59.99 | | 8 | | | 60–69.99 | | 5 | | | 70–79.99 | | 2 | |
| | Total | 30 | 30 | 30 |
| | | Table 10: District Ordering with reference to Head-count ratio (1993–94)
| | Rural | Urban | Combined | Comment |
|
|
| | District | Head-count ratio | District | Head-count ratio | District | Head-count ratio |
| | Mumbai | NA | Gadchiroli | 6.11 | Mumbai | 7.84 | Lower | | Raigad | 4.94 | Mumbai | 7384 | Raigad | 8.44 | Quarter | | Kolhapur | 6.97 | Thane | 15.74 | Kolhapur | 11.73 | | | Thane | 8.27 | Raigad | 24.36 | Thane | 13.11 | | | Sangali | 14.43 | Kolhapur | 25.07 | Pune | 22.65 | | | Osmanabad | 16.67 | Pune | 27.27 | Sindhudurg | 23.04 | | | Pune | 17.89 | Ratnagiri | 29.31 | Osmanabad | 23.07 | | | Sindhudurg | 20.55 | Chandrapur | 35.55 | Sangali | 23.26 | | | Satara | 22.64 | Wardha | 39.08 | Satara | 24.84 | Lower | | Bhandara | 23.26 | Satara | 39.75 | Ratnagiri | 25.10 | Middle | | Amravati | 24.21 | Nagpur | 42.09 | Gadchiroli | 26.67 | Quarter | | Nagpur | 24.24 | Latur | 42.35 | Bhandara | 27.61 | | | Latur | 24.66 | Nashik | 42.91 | Latur | 28.27 | | | Ratnagiri | 24.69 | Amravati | 45.39 | Yavatmal | 30.43 | | | Yavatmal | 25.74 | Aurangabad | 46.00 | Wardha | 30.50 | | | Wardha | 27.40 | Yavatmal | 53.03 | Amravati | 31.11 | Upper | | Parbhani | 28.55 | Sangali | 53.26 | Chandrapur | 33.02 | Middle | | Gadchiroli | 28.63 | Sindhudurg | 53.31 | Nagpur | 35.27 | Quarter | | Akola | 30.78 | Bhandara | 56.51 | Jalna | 35.86 | | | Solapur | 31.16 | Nanded | 57.03 | Nashik | 38.44 | | | Jalna | 31.26 | Beed | 57.82 | Beed | 38.46 | | | Chandrapur | 32.04 | Jalna | 58.45 | Aurangabad | 38.52 | | | Ahmednagar | 33.37 | Osmanabad | 58.81 | Ahmednagar | 38.86 | | | Nanded | 34.16 | Parbhani | 60.10 | Nanded | 39.13 | Upper | | Beed | 34.23 | Dhule | 62.32 | Parbhani | 39.63 | Quarter | | Aurangabad | 34.88 | Solapur | 62.92 | Solapur | 40.30 | | | Nashik | 35.98 | Jalgaon | 63.01 | Akola | 42.41 | | | Jalgaon | 37.22 | Ahmednagar | 68.06 | Jalgaon | 44.30 | | | Buldhana | 37.76 | Akola | 71.39 | Buldhana | 45.24 | | | Dhule | 45.57 | Buldhana | 74.09 | Dhule | 49.00 | | | Maharashtra | 26.60 | Maharashtra | 31.24 | Maharashtra | 28.40 | |
| | | Table 11: District wise Share (%) in Population and Poverty in Maharashtra by Sector: 1993:94
| | District | Rural | Urban | Combined |
|
|
| | Population | Poverty | ψ Factor | Population | Poverty | ψ Factor | Population | Poverty | ψ Factor |
| | Mumbai | NA | NA | | 32.50 | 8.38 | 0.26 | 12.58 | 3.55 | 0.28 | | Thane | 3.84 | 1.22 | 0.32 | 11.11 | 5.75 | 0.52 | 6.65 | 3.14 | 0.47 | | Raigad | 3.09 | 0.58 | 0.19 | 1.08 | 0.86 | 0.80 | 2.31 | 0.70 | 0.30 | | Ratnagiri | 2.91 | 2.75 | 0.95 | 0.45 | 0.44 | 0.96 | 1.96 | 1.77 | 0.90 | | Sindhudurg | 1.59 | 1.25 | 0.79 | 0.21 | 0.36 | 1.75 | 1.05 | 0.88 | 0.83 | | Konkan Div. | 11.42 | 5.8 | 0.51 | 45.34 | 15.80 | 0.35 | 24.55 | 10.04 | 0.41 | | Nashik | 5.13 | 7.07 | 1.38 | 4.48 | 6.33 | 1.41 | 4.88 | 6.76 | 1.39 | | Dhule | 4.17 | 7.27 | 1.74 | 1.70 | 3.49 | 2.05 | 3.21 | 5.67 | 1.77 | | Jalgaon | 4.78 | 6.81 | 1.42 | 2.86 | 5.94 | 2.07 | 4.04 | 6.45 | 1.60 | | Ahmednagar | 5.87 | 7.50 | 1.28 | 1.75 | 3.91 | 2.24 | 4.27 | 5.98 | 1.40 | | Nashik Div. | 19.95 | 28.64 | 1.44 | 10.8 | 19.67 | 1.82 | 16.41 | 24.86 | 1.51 | | Pune | 5.63 | 3.86 | 0.68 | 9.19 | 8.25 | 0.90 | 7.01 | 5.72 | 0.82 | | Satara | 4.41 | 3.83 | 0.87 | 1.03 | 1.35 | 1.31 | 3.11 | 2.78 | 0.90 | | Sangali | 3.53 | 1.95 | 0.55 | 1.65 | 2.88 | 1.75 | 2.80 | 2.35 | 0.84 | | Solapur | 4.76 | 5.67 | 1.19 | 3.04 | 6.30 | 2.07 | 4.09 | 5.94 | 1.45 | | Kolhapur | 4.55 | 1.21 | 0.27 | 2.58 | 2.13 | 0.82 | 3.79 | 1.60 | 0.42 | | Pune Div. | 22.89 | 16.52 | 0.72 | 17.49 | 20.91 | 1.20 | 20.80 | 18.39 | 0.88 | | Aurangabad | 3.08 | 4.11 | 1.34 | 2.37 | 3.59 | 1.51 | 2.81 | 3.89 | 1.39 | | Jalna | 2.34 | 2.80 | 1.20 | 0.76 | 1.45 | 1.92 | 1.73 | 2.23 | 1.29 | | Parbhani | 1.82 | 1.99 | 1.09 | 1.56 | 3.09 | 1.98 | 1.72 | 2.46 | 1.43 | | Beed | 3.09 | 4.05 | 1.31 | 1.07 | 2.04 | 1.90 | 2.31 | 3.20 | 1.39 | | Nanded | 3.77 | 4.93 | 1.31 | 1.66 | 3.11 | 1.88 | 2.95 | 4.16 | 1.41 | | Osmanabad | 2.24 | 1.43 | 0.64 | 0.63 | 1.23 | 1.94 | 1.62 | 1.34 | 0.83 | | Latur | 2.76 | 2.60 | 0.94 | 1.12 | 1.56 | 1.39 | 2.12 | 2.16 | 1.02 | | Aurangabad Div. | 19.1 | 21.91 | 1.15 | 9.17 | 16.07 | 1.75 | 15.26 | 19.45 | 1.27 | | Buldhana | 3.10 | 4.47 | 1.45 | 1.27 | 3.10 | 2.44 | 2.39 | 3.90 | 1.63 | | Akola | 3.27 | 3.85 | 1.18 | 2.08 | 4.88 | 2.35 | 2.81 | 4.29 | 1.53 | | Amravati | 3.07 | 2.84 | 0.93 | 2.35 | 3.51 | 1.49 | 2.79 | 3.12 | 1.12 | | Yavatmal | 3.56 | 3.50 | 0.99 | 1.17 | 2.04 | 1.74 | 2.63 | 2.89 | 1.10 | | Amravati Div. | 12.98 | 14.67 | 1.13 | 6.87 | 13.52 | 1.97 | 10.62 | 14.19 | 1.34 | | Wardha | 1.62 | 1.70 | 1.05 | 0.93 | 1.19 | 1.29 | 1.35 | 1.49 | 1.10 | | Nagpur | 2.60 | 2.41 | 0.93 | 6.65 | 9.21 | 1.38 | 4.17 | 5.29 | 1.27 | | Bhandara | 3.79 | 3.37 | 0.89 | 0.90 | 1.68 | 1.86 | 2.67 | 2.66 | 0.99 | | Chandrapur | 2.64 | 3.23 | 1.23 | 1.63 | 1.90 | 1.17 | 2.25 | 2.67 | 1.19 | | Gadchiroli | 1.49 | 1.63 | 1.10 | 0.22 | 0.05 | 0.20 | 1.00 | 0.96 | 0.96 | | Nagpur Div. | 12.12 | 12.34 | 1.02 | 10.33 | 14.03 | 1.36 | 11.43 | 13.06 | 1.14 | | Maharashtra | 100.00 | 100.00 | | 100.00 | 100.00 | | 100.00 | 100.00 | |
| | Note: ψ factor = (Share in Poverty/Share in Population) |
| | | Table 12: Select Regional Dimensions of Poverty: Rural Maharashtra (1972/73)
| | Region | Per capita Consumer Expenditure | Per capita calorie intake per diem |
|
| | Average | Lorenz ratio (%) | Headcount ratio (%) | Poverty gap index PGI (%) | Squared PGI (%) | Average (k. cal) | Pseudo-Lorenz ratio (%) | Headcount ratio (%) | Poverty gap index PGI (%) | Squared PGI (%) |
| | Coastal | 40.63 | 29.25 | 69.29 | 24.13 | 10.96 | 1783.00 | 16.72 | 88.52 | 27.10 | 11.35 | | Inland Western | 43.60 | 25.82 | 62.20 | 18.58 | 7.51 | 1793.00 | 12.45 | 93.03 | 26.42 | 9.10 | | Inland North | 41.81 | 27.36 | 66.19 | 21.36 | 9.52 | 1878.00 | 14.95 | 87.33 | 23.30 | 9.19 | | Inland Central | 47.89 | 38.25 | 66.62 | 22.52 | 10.21 | 2067.00 | 15.04 | 79.03 | 17.86 | 6.40 | | Inland East | 41.25 | 32.62 | 72.14 | 25.97 | 11.89 | 1960.00 | 16.72 | 83.88 | 22.60 | 8.02 | | Eastern | 42.56 | 31.07 | 72.54 | 23.39 | 9.61 | 1890.00 | 13.62 | 88.63 | 23.43 | 7.81 | | Maharashtra | 41.44 | 31.23 | 71.34 | 24.41 | 10.95 | 1895.00 | 14.55 | 86.63 | 23.13 | 8.22 |
| | Note: Estimates based on the central sample, NSSO (1983). Poverty estimates are with reference to the poverty line of Rs 44.02 corresponding to the Expert Group (GoI, 1979) poverty lines estimated by Minhas and Jain (1990) |
| | | Table 13: Select Regional Dimensions of Poverty: Rural Maharashtra (1993/94)
| | Region | Per capita Consumer Expenditure | Per capita calorie intake per diem |
|
| | Average | Lorenz ratio (%) | Headcount ratio (%) | Poverty gap index PGI (%) | Squared PGI (%) | Average (k. cal) | Pseudo-Lorenz ratio (%) | Headcount ratio (%) | Poverty gap index PGI (%) | Squared PGI (%) |
| | Coastal | 376.09 | 30.59 | 15.47 | 2.67 | 0.72 | 1997.81 | 11.97 | 83.11 | 18.64 | 5.64 | | Inland Western | 318.83 | 27.33 | 21.93 | 4.08 | 1.20 | 2066.91 | 10.56 | 83.12 | 16.15 | 4.14 | | Inland North | 248.84 | 25.68 | 40.08 | 8.10 | 2.46 | 1879.61 | 11.54 | 91.18 | 23.09 | 7.15 | | Inland Central | 301.32 | 31.45 | 29.05 | 8.67 | 3.72 | 2233.55 | 15.36 | 68.30 | 14.24 | 4.22 | | Inland East | 274.06 | 24.83 | 29.45 | 5.99 | 1.84 | 1999.03 | 7.78 | 93.20 | 17.56 | 4.14 | | Eastern | 291.91 | 26.62 | 27.82 | 5.89 | 1.81 | 2153.18 | 10.49 | 71.11 | 12.94 | 3.43 | | Maharashtra | 302.90 | 28.76 | 26.60 | 5.79 | 1.97 | 2059.38 | 11.34 | 82.80 | 16.83 | 4.49 |
| | Note: Estimates based on pooled state and central samples. Poverty lines refer to the poverty lines of Rs 194.94 as per the Expert Group methodology (GoI, 2000; p. 1.29) |
| | | Table 14: Growth & Poverty Reduction in Rural Maharashtra (1972/73–1993/94): The Regional Dimension
| | Region | Percentage change between 1972/73 & 1993/94 |
| | Average real per capita Consumption | Headcount ratio | Average per capita Calorie intake | Incidence of calorie Deficiency |
| | Coastal | 109.02 | (−)77.67 | 12.05 | (−)6.11 | | Inland Western | 65.13 | (−)64.74 | 15.28 | (−)10.65 | | Inland North | 34.40 | (−)39.45 | 0.09 | 4.41 | | Inland Central | 42.08 | (−)56.39 | 8.06 | (−)13.58 | | Inland East | 50.03 | (−)59.18 | 1.99 | 11.11 | | Eastern | 54.88 | (−)61.65 | 13.92 | (−)19.77 | | All Maharashtra | 65.65 | (−)62.71 | 8.67 | (−)14.42 |
| | Note: Percentage changes in real consumption are worked out with reference to the price changes implied in the poverty line estimates for the two years. |
| | | Table 15: Select Regional Dimensions of Poverty: Urban Maharashtra (1972/73)
| | Region | Per capita Consumer Expenditure | Per capita calorie intake per diem |
|
| | Average | Lorenz ratio (%) | Headcount ratio (%) | Poverty gap index PGI (%) | Squared PGI (%) | Average (k. cal) | Pseudo-Lorenz ratio (%) | Headcount ratio (%) | Poverty gap index PGI (%) | Squared PGI (%) |
| | Coastal | 103.55 | 33.74 | 17.29 | 4.08 | 1.57 | 2165.00 | 16.05 | 49.61 | 9.97 | 3.01 | | Inland Western | 66.31 | 33.23 | 43.96 | 13.93 | 6.32 | 1830.00 | 14.62 | 79.69 | 17.25 | 5.62 | | Inland North | 49.3 | 27.23 | 66.61 | 20.36 | 8.07 | 1823.00 | 11.52 | 78.31 | 15.71 | 4.43 | | Inland Central | 50.04 | 32.84 | 68.00 | 24.24 | 11.16 | 1898.00 | 13.48 | 81.46 | 14.92 | 3.91 | | Inland East | 48.84 | 29.77 | 66.47 | 22.89 | 10.23 | 1810.00 | 12.87 | 82.24 | 17.04 | 5.13 | | Eastern | 45.45 | 28.98 | 71.65 | 26.24 | 11.84 | 1906.00 | 15.76 | 70.61 | 16.27 | 4.80 | | Maharashtra | 74.89 | 36.97 | 40.84 | 13.31 | 5.95 | 1971.00 | 14.90 | 70.10 | 13.46 | 3.73 |
| Note:
Estimates based on the Central sample. Estimates are based on NSSO (1983). Poverty estimates are with reference to the poverty line of Rs 50.70 corresponding to the Expert Group (GoI, 1979) methodology estimated by Minhas, Kansal and Jain (1992).
|
| | | Table 16: Select Regional Dimensions of Poverty: Urban Maharashtra (1993/94)
| | Region | Per capita Consumer Expenditure | Per capita calorie intake per diem |
|
| | Average | Lorenz ratio (%) | Headcount ratio (%) | Poverty gap index PGI (%) | Squared PGI (%) | Average (k. cal) | Pseudo-Lorenz ratio (%) | Headcount ratio (%) | Poverty gap index PGI (%) | Squared PGI (%) |
| | Coastal | 690.51 | 29.11 | 10.99 | 1.77 | 0.46 | 2093.96 | 9.52 | 55.46 | 6.951 | 1.33 | | Inland Western | 486.51 | 33.01 | 37.84 | 9.66 | 3.29 | 2095.73 | 10.03 | 50.54 | 7.30 | 1.60 | | Inland North | 377.29 | 28.79 | 52.64 | 15.04 | 5.70 | 1973.30 | 10.38 | 71.04 | 10.15 | 2.58 | | Inland Central | 392.34 | 35.01 | 53.13 | 17.47 | 7.81 | 2055.77 | 11.20 | 51.45 | 8.82 | 2.44 | | Inland East | 399.61 | 32.03 | 50.34 | 15.05 | 6.02 | 2107.79 | 11.27 | 58.71 | 7.63 | 1.41 | | Eastern | 430.02 | 29.16 | 42.87 | 10.16 | 3.25 | 2271.70 | – | 17.60 | – | – | | Maharashtra | 537.19 | 33.77 | 31.24 | 8.63 | 3.34 | 2085.75 | 9.53 | 55.46 | 6.95 | 1.33 |
| | Note: Estimates based on pooled state and central samples Poverty lines refer to the poverty lines of Rs 194.94 as per the Expert Group Methodology (GoI, 2000) |
| | | Table 17: Growth & Poverty Reduction in Urban Maharashtra (1972/73–1993/94): The Regional Dimension
| | Region | Percentage change between 1972/73 & 1993/94 |
| | Average real per capita Consumption | Headcount ratio | Average per capita Calorie intake | Incidence of calorie Deficiency |
| | Coastal | 2.90 | (−)36.44 | (−)3.28 | 11.79 | | Inland Western | 13.22 | (−)13.92 | 14.52 | (−)36.58 | | Inland North | 18.09 | (−)20.97 | 8.24 | (−)9.28 | | Inland Central | 20.99 | (−)21.87 | 8.31 | (−)36.84 | | Inland East | 26.26 | (−)24.27 | 16.45 | (−)28.61 | | Eastern | 46.00 | (−)40.17 | 19.19 | (−)75.07 | | All Maharashtra | 10.69 | (−)23.51 | 5.82 | (−)20.88 |
| | Note: Percentage changes in real consumption are worked out with reference to the price changes implied in the poverty line estimates for the two years. |
| | | Table 18: Estimates of Poverty: State and Central Samples
| | Year | Maharashtra |
| | Rural sector | Urban sector | Combined |
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| | State sample | Central sample | State sample | Central sample | State sample | Central sample |
| | 1973–74 | 61.49 | 58.96 | 49.69 | 43.84 | 57.67 | 54.07 | | 1977–78 | 57.79 | 63.33 | 38.39 | 38.75 | 51.22 | 55.01 | | 1983 | 39.82 | 45.28 | 31.00 | 37.55 | 36.64 | 42.49 | | 1987–88 | 32.41 | 42.57 | 36.12 | 38.12 | 33.80 | 4090 | | (35.97)* | | (36.08)* | | (35.97)* | | | 1993–94 | 17.05 | 37.61 | 26.89 | 34.88 | 20.37 | 36.56 | | 1999–'00 | 24.26 | 23.47 | 26.46 | 27.52 | 25.19 | 25.18 |
| | Note: Estimates for state samples are based on data published in various annual Economic Survey reports of the GoM and those for central samples from various issues of Sarvekshana. Estimates refer to the poverty lines suggested by the Expert Group (GoI, 1993) | *Estimates based on disaggregate data in GoM (1996) |
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