ISSN (Print) - 0012-9976 | ISSN (Online) - 2349-8846

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A District-level Analysis

Disparities in Social Development in Maharashtra

This paper is based on the author’s MPhil thesis that was submitted to the Indira Gandhi Institute of Development Research, Mumbai under the supervision of M H Suryanarayana.

Regional imbalances within states have attracted the attention of researchers and policymakers alike. But the scarcity of district-level studies leaves much to be desired. Districts are the first stage of policy implementation, making it imperative to understand and analyse development at this third tier of administration. With some modification, this paper follows the methodology adopted by the Council for Social Development to construct a six-dimensional social development index for an analysis of development in the districts of Maharashtra. In addition to supporting existing evidences, this study finds interesting variations among the districts across all dimensions. Evidences of disparity across social dimensions even within pockets of prosperity are found in this study.

Researchers argue that social development is about “putting people at the centre of development” (Webbink 2011). Where elected governments are often judged by the outcomes of their policies, it becomes necessary for policymakers to choose policies that enable citizens to realise their full potential. However, social development is a function of multidimensional indicators. Its measure depends on the choice of parameters or indicators that a researcher believes to affect an individual’s array of choices. If that is the case, then it becomes imperative to understand how these different types of parameters or indicators aggregate to give a spatially comparable single index. In this stride, the objective of the current study is to construct an index based on a set of development parameters that we believe may affect social development of individuals in Maharashtra,1 and then compare the results across its various districts.

We rely on the choice of dimensions as suggested in the Social Development Report (CSD 2010, 2012, 2014). This report is brought out by the Council for Social Development (CSD) biennially since 2006. The social development report constructs the social development index (SDI) for major Indian states. The index is composed of the following dimensions: demography, health, education, basic amenities, economic deprivation, and social deprivation. The basic components have remained almost the same in all the reports but the methodology and database have changed over time.

Another approach to quantify social development is given by the International Institute of Social Studies in the Netherlands (Webbink 2011). This index lays stress on the institutional aspect of development. “Social development refers to the institutions of societies through which development is enhanced: the ‘soft’ dimensions of development, often invisible and difficult to measure” (Webbink 2011). The index is constructed by aggregating around 200 indicators (classified in six dimensions) using matching percentiles method. The dimensions of development used in the index are: civic activism, clubs and associations, intergroup cohesion, interpersonal safety and trust, gender equality and inclusion of minorities. The problem with this index is that it uses time periods that are averages for data from 1990 to 2010. Therefore, it is not possible to link the index to a specific year for a series of countries. The report itself states that the index has the limitation that it cannot be used for intra-country comparison.

The social progress index is another measure for social development. The index was developed in 2013 by a non-profit
organisation based in the United States (US) to have a broad understanding of development. The index is composed of around 50 indicators under three dimensions: basic human needs, opportunity, and foundations of well-being. Each of these dimensions has four components, which are measured using various indicators. The index is different from other measures of development as it uses only outcome indicators of social development and not economic development.

However, there is a dearth of study to measure social progress at the district level in India. To fill this void, this study constructs the SDI at the district level for Maharashtra. The index is used in this paper to analyse the disparities in development within the state. The social development approach is used because of the high social exclusion of various groups in India that is not captured by growth or human development approach.2 In SDI, we attempt to capture this social exclusion through the social deprivation dimension of the index.3

This paper contributes to the literature on regional disparity in two ways; first, constructing a more comprehensive index (six dimensions composed from 17 indicators) and second, studying at the level of disaggregation of districts. The latter are the fundamental units in the Indian administrative set-up. It is at this level that governmental planning meets administrative execution. With the focus on intra-state disparity, studies at this level become indispensable. In the past, studies at the district level have suffered due to the lack of reliable data at a disaggregated level. Availability of this cross-section data is bound to greatly facilitate our comparative developmental understanding (time-series data are still unthinkable).

The organisation of the paper is as follows: first, the paper gives a brief overview of regional imbalances in Maharashtra. Then, it details the methodology of the construction of SDI. Finally, it discusses the index values and ranks.

Regional Disparity in Maharashtra

Maharashtra is not only one of the wealthiest states of India4 but also one of the highly developed states in terms of the human development index (HDI). Even so, high intra-state variations are witnessed in Maharashtra. Vidarbha and Marathwada regions are widely perceived to be underdeveloped as compared to the rest of Maharashtra.5 The persistence of this stark disparity over the years in this otherwise wealthy state has attracted the attention of various researchers and government reports, some of which is presented in this section.

The report of the “Fact Finding Committee on Regional Imbalance in Maharashtra, 1983” under the chairpersonship of V M Dandekar pointed towards stark regional imbalance and a substantial backlog in the state.6 The committee also suggested measures for the removal of these backlogs, including long-term measures to avoid such regional imbalances in the future. This was followed by a special outlay from the annual plan 1985–86 to remove the backlog pointed out by the fact-finding committee. Subsequently, a study group was appointed in 1992 for the “identification of backward areas in Maharashtra state” under the chairpersonship of B A Kulkarni (GOm 1992). The study classified 17 districts in Maharashtra as “backward,” out of which 14 were from the Marathwada and Vidarbha regions. These studies were followed by the constitution of three statutory development boards (SDBs) by the President of India in August 1994.

The “Indicators and Backlog Committee” was appointed by the Government of Maharashtra to assess whether the expenditures made by the government after the Dandekar Committee had any impact on the “backlog” or not (GoM 1997). The total backlog estimated by this committee was found to be `14,000 crore (around four times the backlog estimated in 1984 by the Dandekar Committee). In real terms, the total backlog of the state had increased by 88% between 1984 and 1994. The share of the backlog of Vidarbha was estimated around 47% and of Marathwada as 29%.

Desarda (1996) pointed out that “Maharashtra’s backward regions, Vidarbha, Marathwada and parts of the Konkan have suffered not only because of sheer neglect, but also because the growth model of western Maharashtra is being foisted on a region with different socio-political and agro-climatic features.” The study showed that, for a long time, the state’s expenditure has been mainly on irrigation and power, as these two are supposed to be the drivers of growth. This led to the backlog in some or the other sector in all districts of Maharashtra.

The allocations for backlog removal have increased over time. From `500 crore in 1994, the allocation increased to `1,720 crore in 2001–02 (annual plan). Even after the increase in allocation for the removal of the backlog, its share has only increased in both Vidarbha and Marathwada regions over time. The problem is that the allocations for the removal of the backlog constitute a very small share of the annual plan. Overemphasis is laid on the fact that extra allocations are made for the removal of the backlog, which creates an illusion of the problem of these regions being addressed.

Thus, it can be seen that the issue of high regional imbalance in Maharashtra is a known fact to the people and the government for many years now. The actions and policies aimed at the removal of this imbalance do not seem to have shown any significant results. This paper intends to understand this disparity at the level of districts across six different dimensions of social development with the help of SDI.

The Social Development Index

The SDI has been used here to analyse the regional imbalances in the development of Maharashtra. The index is constructed using the following six dimensions:7


(i) Basic amenities: Clean fuel, drinking water, electricity and sanitation are the amenities covered by this dimension of SDI. These amenities are essential for the well-being of a person because they provide the physical and material comfort required for development.


(ii) Demography: It is the study of distribution of population and the changes in it with respect to births, deaths, migration, etc. Contraceptive prevalence rate (CPR) and total fertility rate (TFR) are the indices used to estimate this dimension.8 These indicators are important for measuring development due to the interlinkages they have with its other dimensions.

(iii) Economic deprivation: A person is economically deprived if they do not have the monetary resources to live life at the same standard as those around them. These monetary resources are required to afford the basic necessities of life. This paper has used the indicators of monthly per capita expenditure (mixed reference period [MPCEMRP]) and unemployment rate (UR) usual status to capture economic deprivation. MPCE is the household expenditure on consumption per member of the household. Here, the used mixed reference period (MRP) data for MPCE is used as it has fewer extreme values than the uniform reference period.


(iv) Education: Education is important for development as it increases the level of awareness of the people. It helps them in acquiring the skills required for employment. The indicators that this paper has used for estimation of education are literacy rate and pupil–teacher ratio.


(v) Health: A healthy person lives a longer life, is more productive in their work, and also invests more in human capital than an unhealthy person.9 This dimension has strong interlinkages with demography, employment, and education. The two indicators used for the estimation of health are percentages of undernourished children and institutional delivery.


(vi) Social deprivation: This dimension is of the utmost importance as it distinguishes the index from other indices of well-being. The dimension represents the idea of inclusion, which is of a great significance in a heterogeneous country like India, with disparities between social groups, religions, and genders. The indicators used to measure this dimension are: disparity ratio in the literacy rate between males and females, disparity ratio in employment between males and females (disparity in gender), disparity ratio in the literacy rate between Scheduled Castes (SCs), Scheduled Tribes (STs) and the total population (disparity in social groups), and the disparity in MPCE between Muslims and the total population (disparity between religions). In this paper, the disparity ratios are calculated by dividing the value of the indicator for a particular group (like Muslims or SCs) with the value of the indicator for the total population.

Methodology of Construction of SDI

Our choice of dimensions and indicators for the construction of the SDI relies on the Social Development Report published by the CSD. The first step in the construction of this index is to make all negative indicators positive. An indicator is negative if an increase in its value represents a decline in development (for example, unemployment rate, IMR). These indicators are made positive by taking their reciprocal value.

The second step is to normalise the data of all indicators using order-based statistics and make them scale-free. The order-based normalisation method used in this paper is adopted from Suryanarayana and Agrawal (2013). In the order-based normalisation, the goalpost for each indicator is defined in terms of its ordered distribution instead of extreme values. The upper and lower inner fences are used for computation of goalposts. The formula for normalisation is:



= Lower outer fence
= 1st quartile - (3×interquartile range)10

= Upper outer fence
= 3rd quartile + (3×interquartile range)

The above normalisation method has a major advantage, in that it gives better insights for skewed distributions than mean-based statistics.

The third step is to aggregate the indicators in order to estimate the dimensions of the index. The aggregation is done using geometric mean. For example, demography dimension is computed by taking the geometric mean of its indicator’s TFR and CPR.

The last step is to aggregate all dimensions using the geometric mean to get the final index value. The rationale for using the geometric mean is that it penalises for substitution between the components. The use of geometric mean ensures that all the dimensions are important and changes in any of them will not go unnoticed. Poor performance in even a single dimension will be directly reflected in the index value.


The index estimated in this paper has few limitations related to the data. The index is estimated for 2014 but data for the indicators are for different years. This is because recent district-level data for many indicators are not available. This is not a serious limitation of the paper as many indicators are medium term, that is, they do not change frequently. Indicators like fertility rates and undernourishment do not fluctuate widely every year. Thus, we can use data from different years to estimate social development.

Another data related limitation is that the sample of National Sample Survey Office (NSSO) survey may not be representative at the district level. The paper, therefore, uses the Ministry of Labour and Employment data (GoI 2012–13, 2013b) for unemployment instead of the NSSO data. But no alternative source was available for MPCE data at the district level and therefore the NSSO data has been used.

Results and Discussion

We have estimated the SDI for the districts of Maharashtra. Mumbai is the best performing district on the index (SDI
value: 0.605), followed by Wardha, Nagpur, and Bhandara (Figure 2). Nandurbar is the worst performer (SDI value: 0.038), followed by Ratnagiri and Dhule. The large gap in the index values (0.567) between the best and the worst performing districts of Maharashtra shows the high variation in social development across the state. Seventeen out of 35 districts in Maharashtra have a social development value less than the state’s average.11 Out of these districts, a major share is from Marathwada (seven districts) and Vidarbha region (four districts).

Table 2 shows the districts’ ranking according to the per capita net district domestic product (NDDP), HDI and SDI. The comparison between the rankings of per capita NDDP and SDI show that high economic growth does not necessarily translate into high levels of social development. Districts like Aurangabad, Nashik, Ratnagiri, and Thane have a high per capita NDDP rank but a low SDI rank. On the other hand, there are districts like Amravati, Beed, Gondia, Gadchiroli, and Yavatmal that have a low per capita NDDP rank but a high social development rank. Similarly, the correlation between the rankings of SDI and of HDI is not quite high. There are many districts that perform well on the HDI but fail to do so on the SDI (like Nashik, Thane, Aurangabad, and Jalgaon). These districts also have a high per capita NDDP, which is one of the reasons for their good performance on the HDI. Few districts like Bhandara, Gadchiroli, Latur, and Wardha have high SDI ranks but low rankings on HDI. Some of these districts (like Gadchiroli and Latur) perform badly in the health and education dimension (thus the low HDI) but excel in social deprivation and demography dimension.

The regional imbalance in Maharashtra can be clearly seen when we observe the social development ranks of the districts according to their region (Figure 3 shows the regions of Maharashtra). The districts have been divided into three groups based on their aggregate SDI ranking. Figure 5 shows the same classification, with green representing high SDI districts, yellow representing medium SDI, and red representing low SDI districts.12

(i) Khandesh (Nashik): All five districts in this region perform below average of Maharashtra. The entire region is in the lowest 10 ranking districts.


(ii) Vidarbha (Amravati): This region performs poorly on our index. All the districts, except Amravati, are below Maharashtra’s average (19th rank).

(iii) Marathwada region: Even this region performs quite poorly on our index. All the districts, except Latur, are below Maharashtra’s average.


(iv) Vidarbha (Nagpur): All the districts in this region (except Gondiya district) are in the top one-third districts. Even Gondiya (15th rank) ranks above Maharashtra’s average. Five out of the top 10 districts are from this region.


(v) Paschim Maharashtra: The region performs quite well on the SDI. While most of the districts are in the top one-third ranks, all the districts are better than average of Maharashtra.


(vi) Konkan: The region performs well on the index with all districts, except Ratnagiri, performing better than the average of Maharashtra. Even Ratnagiri’s low rank is a result of its poor performance in only two dimensions: demography and economic deprivation. In all other dimensions, it ranks in the top 10.

The above results of region-wise performance on SDI highlight the regional imbalance in Maharashtra. The three regions of Marathwada, Vidarbha, and Khandesh are quite underdeveloped (in terms of social development) as compared to the rest of Maharashtra. Figure 4 shows the performance of different regions of Maharashtra on the sDI. Vidarbha has no upper whisker because the third quartile is equal to the maximum of SDI value. It can be seen that compared to the rest of Maharashtra, both Vidarbha and Marathwada are not well-developed (in terms of SDI). The box plot shows that the second quartile for the rest of Maharashtra is higher than the maximum for Marathwada, Khandesh, and Vidarbha. In fact, the minimum for the rest of Maharashtra is higher than the maximum for Vidarbha and Khandesh regions.

Western regions of Maharashtra get one of the largest shares of the state’s funding (Khairnar and Pratishthan 2013). Their locational advantage leads to the region being abundant with industries (almost three-fourth industries of the state are located here). This concentration results in the migration of people from neighbouring backward districts in search of opportunities. Suryanarayana (2009) draws attention towards the regional disparity in the state by pointing to the fact that the four districts of Mumbai, Pune, Nashik, and Thane alone account for around half of the state’s net domestic product (NSDP).

Maharashtra is one of the most industrialised states in India but the major occupation still continues to be agriculture. Around 65% of the population is employed in agriculture and allied activities. Like the rest of India, irrigation for agriculture is still majorly monsoon-dependent. Fluctuation in monsoon rains leads to an adverse impact on the agricultural sector. Vidarbha, Marathwada, and Khandesh have been severely prone to droughts. Since 2009, the rainfall has been low in these regions, with 2012 and 2014–16 witnessing severe droughts.

Data from the National Crime Records Bureau (NCRB) reveal that Maharashtra recorded the highest number of suicides by distressed farmers in 2014. Around half of the total suicides were from Maharashtra.13 The main crop in Vidarbha, Marathwada, and Khandesh regions is cotton, whose price has witnessed a decline over time due to the import of cheap cotton post-liberalisation. The monopoly procurement scheme was also discontinued which led to a further fall in the prices. Eastern Maharashtra farmers continued to cultivate the cotton crop while most of the farmers of western Maharashtra (well-irrigated area) shifted from cotton to sugar cane. Sugar cane has the advantage that it gets heavy subsidies from the government and is thus an assured crop, compared to the risky cotton.

The intra-state disparity becomes more evident when the dimensions of this index for the districts of Maharashtra are analysed (Table 3).

(i) Demography: Among the districts of Maharashtra, Wardha ranks first in this dimension while Ratnagiri stands last. The two indicators of this dimension are TFR and CPR. Maharashtra has a low TFR of 2.2, but within Maharashtra we see huge variations in TFR. The highest TFR can be seen in Jalna (3.2) while the lowest can be seen in Mumbai and Sindhudurg (1.4).14 Maharashtra has a high CPR of 64%, and within Maharashtra the highest CPR is in Ratnagiri (38%) and the lowest in Bhandara (77%).17 Both indicators show almost the same variation within the state as the variations witnessed across India.


(ii) Education: Sindhudurg ranks first in this dimension while Nandurbar stands the last. The two indicators of this dimension are: literacy rate and pupil–teacher ratio. Maharashtra has a high literacy rate of 83.8%, but within Maharashtra we see huge variations in literacy rate (highest literacy rate: Nagpur [89.5%], lowest: Nandurbar [63%]). Maharashtra also has a good pupil–teacher ratio of 28, but within Maharashtra the highest (poorest) pupil–teacher ratio is in Mumbai Suburban (43) and lowest in Sindhudurg (10).


(iii) Health: Mumbai ranks first in this dimension, while Nandurbar stands last. The two indicators of this dimension are institutional deliveries and undernourished children. The highest institutional deliveries can be seen in Bhandara (99%) while the lowest can be seen in Nandurbar (55%). The percentage of undernourished children is low in Maharashtra (37%), but variations within Maharashtra are quite high (Nandurbar [55%] and Mumbai [23%]). Few districts like Chandrapur and Gadchiroli that perform well on the aggregate SDI do not perform well on this dimension.


(iv) Basic amenities: Mumbai ranks first in this dimension, while Nandurbar stands last. Chandrapur, which is one of the best in aggregate social development (7th rank), ranks lower than the state’s average in this dimension (24th rank). Similarly, Gadchiroli performs quite well on the aggregate index, performing poorly in this dimension (30th rank). On the other hand, Ratnagiri performs poorly on the aggregate index (35th rank) but ranks well in this dimension (10th rank).


(v) Economic deprivation: Nandurbar ranks first, while Sindhudurg stands last in this dimension. The two indicators of this dimension are unemployment rate and monthly per capita expenditure (MPCE).16 Maharashtra has a low unemployment rate of 16, but within Maharashtra there are districts like Sindhudurg that have a very high unemployment rate (151) and districts like Nandurbar and Gadchiroli with low unemployment rates (3 and 6, respectively). Nandurbar, which performs poorly on most of the indicators, has the lowest unemployment rate. Maharashtra has a high MPCE of 2,441, but within Maharashtra the highest MPCE is in Pune (3,342) and lowest in Gadchiroli (1,225). Sindhudurg district is one of the best in aggregate social development (6th rank) but ranks last in this dimension.


(vi) Social deprivation: Gadchiroli ranks first, while Jalgaon is last in this dimension. Ratnagiri performs poorly on the aggregate index (35th rank) but ranks quite well in this dimension (10th rank). Similarly, Yavatmal performs below the state’s average on the aggregate index (21st rank) but ranks quite well in social deprivation (5th rank).

This analysis of the dimensions of social development shows that there is a high disparity in each dimension within Maharashtra. Few districts that perform quite well on the aggregate dimension perform quite poorly on a few dimensions of the index. Therefore, to target the policies correctly, it is important to not only analyse the aggregate index but also the dimensions of the index.


This paper estimated an index to quantify social development for the districts of Maharashtra. This index is more comprehensive than the existing HDI for assessing the development scenario. It covers six dimensions of development: demography, education, health, basic amenities, economic deprivation, and social deprivation. The index is constructed by aggregating the indicators using geometric mean. A total of 17 indicators, ranging from literacy rate to employment, are used in the construction of this index. The index is constructed to examine the intra-state disparity in development for Maharashtra. The index also takes into account the marginalisation of some sections of society in the development process. The social deprivation dimension is incorporated in the construction of the index to account for this marginalisation.

The paper discusses the regional disparities in social development within Maharashtra. Studying intra-state disparities is important as large variations are seen across districts in the state. The analysis shows that, first, the correlation between SDI rankings and per capita NDDP is not quite high. There are many exceptions to the correlation in the state (for example, Nashik and Thane). Second, the comparison of SDI rankings with HDI rankings shows that the correlation between the two is not quite high. The districts that have a high HDI rank but a low SDI rank generally have a high NDDP rank too. Thus, SDI is important as it captures development in a more comprehensive manner than HDI. Third, the analysis of each dimension gives significant information about the development scenario of a region. In our analysis, we see that many districts that perform well on the aggregate SDI perform quite poorly on some dimensions of the index and vice versa. Last, the regions of Khandesh, Marathwada, and Vidarbha are quite underdeveloped as compared to the rest of Maharashtra. These regions have been underdeveloped for a long time. Over time, many committees have been constituted by the state government to deal with the issue of underdevelopment in these regions, but these areas continue to be quite underdeveloped. This highlights the fact that the government has not been able to target its policies correctly for these regions.


1 For more details on the concept of social development index (Figure 1, p 109).

2 Women, Dalits, Adivasis, Muslims and persons with disabilities are the groups that are most severely excluded from public goods in India (Centre for Equity Studies 2014).

3 Social deprivation consists of the following indicators: disparity ratio in literacy rate between the Scheduled Castes and the general population, between males and females, disparity in employment between males and females, disparity in MPCE between Muslims and the general population, child sex ratio.

4 Maharashtra contributes about a quarter of India’s industrial output as well as its GDP. Its per capita GDP is about 40% higher than the all-India average. Literacy rate for the state is over 82% (2011 census). The state is well endowed with natural resources.

5 Maharashtra is divided into six administrative divisions: Amravati, Aurangabad, Konkan, Nagpur, Nashik, and Pune. Amravati and Nagpur divisions are collectively called Vidarbha region. Aurangabad division is also referred to as Marathwada region. Remaining divisions (Konkan, Nashik, and Pune) are collectively called the “Rest of Maharashtra.”

6 Backlog was calculated by the fact-finding committee for nine sectors: roads, irrigation, water supply, village electrification, technical education, general education, land development and soil conservation, health services, and veterinary services. Backlog was defined by the committee as the difference between the district and state average for each of these sectors using appropriate indicators.

7 Data sources for these indicators are given in Table 1 (p 110).

8 Definitions of all the indicators are given in the Appendix.

9 Higher investments in human capital are through education (Bloom and Canning 2008). For details of the impact of the health of a child on their level of education, see Bleakley (2010).

10 The formulas have been modified from Minimum = 1st quartile-(1.5 × interquartile range) to remove negative normalised values. Any negative values were given the value of 0.000001 (negligible positive number).

11 Our study analyses 35 districts as data for Palghar district (created in 2014) was not available for many indicators.

12 Districts are divided in three groups. Top one-third SDI value districts are classified as high SDI, middle one-third as medium SDI, and lowest one-third as low SDI districts.

13 Farmer suicides of 2,658, out of 5,650, were from Maharashtra.

14 The highest TFR among all states was for Bihar (4.2) and the lowest was for Kerala and Tamil Nadu (1.6).

15 CPR is the lowest in Jharkhand (35%) and the highest in West Bengal (73%).

16 MPCE data is taken from NSS 68th round. The sample is not representative at district level. We use this data as no alternative source for MPCE is available for districts of the states.

17 Extracted from NSS 68th round unit-level data.


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Updated On : 17th Jul, 2022
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