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Social and Economic Inequalities: Contemporary Significance of Caste in India

Social and Economic Inequalities: Contemporary Significance of Caste in India

 Social and Economic Inequalities: Contemporary Significance of Caste in India Rajnish Kumar, Satendra Kumar, Arup Mitra In an attempt to revisit the caste issue in the Indian context this paper analyses a sample of households from the slums of four cities. Vulnerability conceptualised in terms of several socio-economic and demographic indicators exists among most of the social categories though the relative size of deprivation varies across social groups. In a binomial logit framework, based on the pooled sample, the extent of decline in the probability of experiencing well-being beyond a threshold limit is sharper for the socially backward classes than the others. However, in individual cities such a pattern is not so conspicuous implying that all the social categories are equally vulnerable. These findings have important policy implications, indicating that policy initiatives for deprived areas irrespective of caste factor are more important than the caste-based support measures.

SPECIAL ARTICLE

Social and Economic Inequalities: Contemporary Significance of Caste in India

Rajnish Kumar, Satendra Kumar, Arup Mitra

In an attempt to revisit the caste issue in the Indian context this paper analyses a sample of households from the slums of four cities. Vulnerability conceptualised in terms of several socio-economic and demographic indicators exists among most of the social categories though the relative size of deprivation varies across social groups. In a binomial logit framework, based on the pooled sample, the extent of decline in the probability of experiencing well-being beyond a threshold limit is sharper for the socially backward classes than the others. However, in individual cities such a pattern is not so conspicuous implying that all the social categories are equally vulnerable. These findings have important policy implications, indicating that policy initiatives for deprived areas irrespective of caste factor are more important than the caste-based support measures.

Rajnish Kumar, Satendra Kumar and Arup Mitra (arup@iegindia.org) are with the Institute of Economic Growth, Delhi.

M
any sociologists and social anthropologists have argued that economic and political development during the colonial rule and its aftermath led to the breakdown of the caste system, resulting in the breaching of the linkages between caste and traditional occupation (Ghurye 1961; Shah 1996, 2007; Desai 1984; Srinivas 2003). These scholars have also questioned the congruency between social and economic inequality and rejected the recommendations of the Mandal Commission for caste-based reservations (Desai 1984; Beteille 1965). Noted sociologist Ghurye (1961) shows that in a village in Maharashtra modern occupations in administration and other professional sectors have attracted people belonging to all castes. Beteille (1965), in an early study of a village in Tamil Nadu, showed that caste, class and power have now become differentiated axes of stratification and the caste status of an individual no longer determines her or his position in the class of power hierarchy. This implies that caste status no longer inhibits individuals from taking to new occupations. Beteille has gone further to show that the relevance of caste is decreasing among the upper and middle classes in Indian cities (Beteille 1991a,b). According to him, upper and middle classes in Indian cities, particularly among professionals, civil servants and managers not only have dissociated themselves from the caste occupation of their forefathers, but also they have increasingly departed from the lifestyle associated with their caste, making friends regardless of caste, accepting inter-caste marriages and securing appointments through merit rather than by approaching caste-fellows (Beteille 1991, 1992, see also 1996). Even those who still obeyed the moral obligations of their caste valued the legal prohibition against caste-related discrimination and adhered to forms of Hinduism that wished caste out of existence rather than justifying it. For the urban upper and middle classes, Béteille concluded, the significance of caste was diminishing in a number of social fields, with the notable exception of politics, where caste remained a powerful means of mobilisation. He emphasises the emergence of “caste-free” occupations in the countryside and cities, and advises that to reduce the current reproduction of inequality we must shift our focus away from caste.

Desai (1984) and Shah (1996) vehemently rejected the recommendations of the Mandal Commission for caste-based reservations or caste as a unit for identifying socially and educationally backward classes. Desai believes that if the state accepts caste as the basis for backwardness, it legitimises the caste system, which contradicts secular principles. He observes that the traditional caste system has broken down and contractual relationships

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between individuals have emerged. On a similar line, Shah (1996) counters the use of caste to define backwardness, saying “caste has become bigger and more heterogeneous in the last 60 or 70 years” and with such intra-caste heterogeneity how can one treat a whole caste as “backward”? Shah also asks how caste can be treated identically in both rural and urban areas.

Relationship between Caste and Occupation

The ahistorical analysis of Desai (1984) and Shah (1996) fails to acknowledge how some castes trace their roots outside the formal village and urban economies and the tradition of literacy. Centuries of socio-physical segregation and illiteracy compromise their position in today’s economy and society, including preventing them from taking advantage of the emerging so-called “caste neutral” occupations and India’s modern economy. The majority of individuals from hundreds of “lower” castes are concentrating in informal sectors outside the formal economy and in the slums of India’s cities.

Many studies show that despite the breakdown of the jajmani system and the dissociation between caste and traditional occupations, large sections of “lower” and artisan castes are concentrating in unskilled or low paid semi-skilled occupations in the informal sectors (Breman 1990, 1993; Jeffery 2001; Kumar 2008). Analysing all-India data on scheduled castes and scheduled tribes (SCs and STs), Thorat (1993) points out that many of them are agricultural labourers. In comparison, the proportion of agricultural labourers among other castes is substantially smaller. Thus, though rural occupations have now been freed of caste restrictions, there is a marked tendency for certain castes to cluster in particular occupations (Panini 1996).

This tendency is not very different in the urban scene. Available evidence suggests that although urban and industrial occupations and professions have attracted members of diverse castes, here too, certain castes tend to be concentrated in particular occupations. To begin with, the Mandal Commission, in its report published in 1980, estimates that the upper and middle castes form nearly 90% of the Class I services, although according to the commission they constitute not more than about 20% of the total population of the country (GoI 1980). Similarly, Deshpande (2003) has recently calculated the poverty-caste relationship on the basis of the National Sample Survey Organisation (NSSO) consumption data, which reinforce the strong relationship between low-caste status and poverty.

The concentration of various castes in particular professions and occupations is well-documented in recent sociological studies. Navlakha’s (1989) study highlights the concentration of upper and middle castes in modern professions such as engineering, medicine, banking and journalism. Jayaram (1977) shows a continuing high concentration of upper and dominant castes in higher education, including engineering and medical colleges. Holmstrom’s studies (1976, 1985) in Bangalore and Bombay show that the upper and middle castes dominate the supervisory and skilled worker category whereas the unskilled workers are drawn from the “lower” castes. He finds that in the unorganised sector, caste is particularly critical for underprivileged workers because they lack resources other than those offered by their caste linkages.

On the whole, there is no dearth of argument suggesting that the underemployed and poor mostly belong to the lower castes. Social seclusion is said to have led to economic deprivation. And hence the essence of the government policy in an attempt to reduce poverty and rehabilitate the poor-rested on the reservation policy since independence. Social integration was to be achieved through availability of education and employment opportunities to the lower castes.1 However, even after pursuing the reservation policy for more than five decades the percentage of population below and marginally above the poverty line is not negligible. Perhaps this would tend to suggest that poor can belong to higher castes also, and that improvement in economic well-being cannot be attained purely on the basis of caste.

The functioning of the rural labour market is largely castebased, but that is expected to get blurred in the context of an urban job market. In other words, as urbanisation follows and results in commercialisation, it is likely to erode the influence of caste factor in the job market although job seekers may initially access information pertaining to the labour market with the help of caste and kinship bonds (Banerjee 1986; Mitra 2003). In such cases what is then the need to introduce reservation policy on the basis of caste? It is done perhaps to compensate for the discrimination shown in the past, from which the existing social inequality is seen to have emerged. However, to answer some of these questions one may examine the caste and economic profile of those located in the low income households, which in turn can bring out the overlaps or differences, if any, between them. And this would have important policy implications in suggesting whom to mitigate the benefits.

There may lie a point of justification in holding the framework of social and economic dualism because in the rural context, as Srinivas (1969: 268) pointed out, “the ban on contact between castes and the solidarity of a sub-caste, express themselves in the spatial segregation of castes”. Extrapolating the similar situation to the urban context – though one would expect the impact of caste factor to get diluted in the process of urbanisation – spatial segregation of residence, education and skill formation, and sector of employment all may be in relationship, each deducing its root from the manifestation of caste, “the prototype of rigid social inequality” (Beteille 1969: 263).

Migration, Caste and Slum Dwellers in Cities

Landlessness and the lack of assets in villages are believed to be the propelling forces of migration to the urban areas. In other words, slum and pavement dwellers are said to be overwhelmingly poor rural migrants primarily from lower castes or disadvantaged communities who migrate to the city through caste, kinship and village networks in search of better economic opportunities (Singh and D’Souza 1980). The character of urban poverty, in fact, according to Dandekar and Rath (1971) is the “consequence of the continuous migration of the rural poor into the urban areas in search of a livelihood, their failure to find adequate means to support themselves there and the resulting growth of pavement and slum life in the cities.” It is, therefore, worth exploring empirically whether slums are mostly dominated by the lower castes, whether lower castes are mostly absorbed in petty

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activities with meagre earnings compared to others and whether the incidence of poverty is higher among the lower castes than in other categories in the slums.

To examine the dominance of lower castes in slums, Mitra (1988) based on NSSO’s slum survey (1976-77) data considered the proportion of SC population in the slums to the total slum population and compared it with the same ratio for the city as a whole. Next, he examined the proportion of SC population in the slums to the total SC population in the city. Though the percentage of SC population in the slums turned out to be higher than that in the city as a whole for all the four metropolitan cities of Bombay (now Mumbai), Calcutta (now Kolkata), Delhi and Madras (now Chennai), the incidence of SC population in the slums did not turn out to be exceedingly high, particularly in the slums of Bombay and Calcutta (10.75% and 15.02%, respectively). Even in the slums of Delhi and Madras nearly 65% and 60% of the slum population were non-SC. As regards the proportion of SC population in the slums to the total SC population in the city it was noted that except in Delhi and Madras these figures were not very high. Around 67% of the SC population in the city of Delhi resided in the slums, and in Madras the corresponding figure was more than 90%. However, in Bombay and Calcutta slums their prominent presence was not visible (23% and 34%, respectively). One may still hold that it was actually difficult for them to find an entry to secure accommodation in slums and, hence, they might have been residing in other unhygienic localities – not even suited to be called as slums – and pavements. But even in such a situation the converse of the statement, i e, most of the slum dwellers are from lower castes is certainly not correct. The disadvantaged classes may have migrated to the cities from the rural areas either recently or decades back through caste, kinship and village networks, but the existence of other castes in slums due to the shortage of housing, i e, the phenomenon of downward social mobility, certainly cannot be overlooked.

These issues of social and economic inequality, however, need to be studied in a more comprehensive manner, which the present study addresses itself to. Whether the probability of joining certain specific occupations increases with the prominence of a particular caste group, whether the probability of lying below the poverty line shoots up in response to the presence of certain caste groups are some of the important questions that need a thorough investigation. Needless to add that such an analysis along the lines of caste suffers from major limitations as the status of a particular caste varies considerably across regions/states and the places of origin and destination. Hence, in forming groups such as SCs, Other Backward Classes (OBCs) and “General” only at the place of destination, a great deal of reality might have been ignored.

This paper uses demographic and economic data (2006-07) from slums in four major cities (Jaipur, Ludhiana, Mathura and Ujjain) in India to examine if caste still plays an important role in determining people’s choices among occupations, levels of education, place of residence, and income, and whether caste continues to reproduce inequality in contemporary India. This dataset assesses if social and economic inequalities are closely intertwined, and if so, it would then imply that caste-based reservations can

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play an important role in redressing the century old cumulative inequalities of “lower” castes or social groups.

The organisation of the study is as follows. At the end of the present section we discuss the sampling framework used in collecting information on social, demographic and economic attributes of the population from slum households in four cities in India. Section 1 based on bivariate tables looks into some of these attributes along the lines of caste. It also estimates a well-being index and considers the distribution of households from different social categories across various size classes formed on the basis of this well-being index. Section 2 estimates a binomial logit model relating to the probability of falling into higher (or the bottom two) size classes of the well-being index, highlighting the role of caste in explaining such outcomes. Finally, Section 3 summarises the major findings.

The survey of slum households was carried out in 2006-07 in four cities in India, based on a three-stage stratified random sampling technique. It was sponsored by the United Nations Development Programme (UNDP) and the government of India under the Jawaharlal Nehru National Urban Renewal Mission (JNNURM) programme to alleviate urban poverty. Four cities (with a sample size of 500 households from 30 clusters in each city) were selected keeping in view the variability of the cities in terms of population size and other demographic and economic characteristics.

As a first step of the sampling framework each city was divided into several administrative districts or zones, and the slum clusters – the list of which was obtained from the city municipality2

– were distributed across these zones: Z(i)s, i= 1, 2, …..

Second, 30 clusters were selected from these zones on the basis of random draw, using the proportion of the number of clusters in each zone to the total clusters as weight: 30 * C(i)/ΣC(i), where C(i) is the number of clusters in zone Z(i).

Finally, 500 households from each of the four cities were selected from these clusters on the basis of random draw, using the ratio of the number of households in each cluster to the total number of households in 30 clusters as weight: 500 * N(j)/ΣN(j), where N(j) is the number of households in cluster j = 1, 2, …..30.3

In the questionnaire social capital has been conceptualised in terms of social networks from an empirical standpoint. Different channels through which job market information is accessed provide clues to the social capital that an individual possess. Migration status of the household-head and each of the members of the household has been recorded by entering the date of entry of the individual to the place of destination. Finally, information on past and present income and occupation of the workers has been recorded to delineate the inter-temporal changes, if any.

1 Caste and Attributes: Broad Patterns

Under the UNDP-GOI project the survey was undertaken in four cities of Jaipur, Ludhiana, Mathura and Ujjain. The distribution of sample households across various social groups was carried out on the basis of the caste list provided by the respective states.

Table 1 indicates that the relative size of scheduled tribe population except in Jaipur is almost negligible. Similarly, the relative size of those who could not be placed in the specified categories

Table 1: Distribution of Sample Households across Social Categories (%)

Social Categories Jaipur Ludhiana Mathura Ujjain All Four Cities
Combined
General Hindu/Sikh 21.18 26.76 28.52 11.51 22.17
General Muslim 20.21 15.09 4.4 3.22 10.81
Other Backward Classes-Hindu/Sikh 8.02 21.9 26.14 29.86 21.13
Other Backward Classes-Muslim 26.35 4.25 18.49 8.76 15.21
Scheduled castes 18.43 31.42 20.7 45.03 28.13
Scheduled tribes 5.81 0.41 1.17 0.94 2.21
Others 0.0 0.16 0.57 0.67 0.35

Source: Slum Survey under the UNDP-GOI Project (2006-07). Table 2: Distribution of Workers across Social Groups and Nature of Employment (%)

All Cities Combined Daily Wage Regular Self-Employment Wage/Salaried

General Hindu/Sikh 26.7 38.7 34.6
General Muslim 44.4 28.0 27.6
Other Backward Classes-Hindu/Sikh 37.3 32.4 30.3
Other Backward Classes-Muslim 47.4 23.4 29.2
Scheduled castes 44.4 37.0 18.6
Scheduled tribes 31.5 46.6 21.9
Others 25.0 25.0 50.0
All 38.7 33.9 27.4
Jaipur
General Hindu/Sikh 30.6 39.2 30.2
General Muslim 53.8 26.9 19.2
Other Backward Castes-Hindu/Sikh 45.1 22.0 32.9
Other Backward Castes-Muslim 46.3 30.6 23.1
Scheduled castes 37.5 42.8 19.7
Scheduled tribes 22.4 51.0 26.5
Others 0.0 0.0 0.0
All 41.0 34.7 24.3
Ludhiana
General Hindu/Sikh 17.2 42.5 40.3
General Muslim 17.8 31.1 51.1
Other Backward Classes-Hindu/Sikh 20.9 47.6 31.4
Other Backward Classes-Muslim 29.0 25.8 45.2
Scheduled castes 24.5 55.0 20.4
Scheduled tribes 0.0 33.3 66.7
Others 0.0 33.3 66.7
All 20.8 46.5 32.8
Mathura
General Hindu/Sikh 26.5 36.1 37.4
General Muslim 18.2 31.8 50.0
Other Backward Classes-Hindu/Sikh 25.6 34.0 40.4
Other Backward Classes-Muslim 42.2 15.0 42.9
Scheduled castes 44.5 38.4 17.1
Scheduled tribes 41.7 50.0 8.3
Others 50.0 0.0 50.0
All 32.7 32.0 35.3
Ujjain
General Hindu/Sikh 43.5 33.1 23.4
General Muslim 72.7 22.7 4.5
Other Backward Classes-Hindu/Sikh 53.3 24.7 22.0
Other Backward Classes-Muslim 63.9 17.5 18.6
Scheduled castes 59.7 22.8 17.5
Scheduled tribes 77.8 22.2 0.0
Others 20.0 20.0 60.0
All 56.4 24.1 19.5

Each row adds up to 100%. Source: See Table 1.

and have been designated as “others” is nominal. Our analysis, therefore, in the rest of the sections will largely focus on the categories other than the scheduled tribes and “others”. We may note from Table 1 that the general Hindus/Sikhs and general Muslims together constitute a substantive part of the total sample households (combined sample of all the four cities) though the general impression is that the slums are mostly inhabited by the lower castes. In Jaipur and Ludhiana the figure is more than 40% and in Ujjain it is only around 15%, while in Mathura the relative size of general Hindus is the highest (28.5%) among all four.

The nature of employment gauged in terms of daily wage, regular wage and self-employment is structurally different across social groups (Table 2). For example, a relatively higher percentage of st and sc workers are engaged in regular wage employment compared to other social categories. However, at the same time a higher percentage of SC workers along with Muslim (OBC and general both) workers are engaged in daily wage employment. The percentage of general Hindu workers in self-employment is the highest among all if we ignore the category of “others”.

Inter-city variations are substantive. In Jaipur nearly half of the general Muslim, OBC Muslim and OBC Hindus are engaged in daily wage employment. On the other hand, general Hindus and SCs and STs are relatively better off as a comparatively higher percentage of workers from these social categories are employed in regular wage jobs. However, general and OBC Hindus are proportionately more present in self-employment also.

Ludhiana being an industrial city, the percentage of daily wage employment is lowest in most of the social categories. General Hindus, OBC Hindus and SCs are more prominently engaged in regular wage employment than the rest. However, an equally high percentage of general Hindus are also located as selfemployed workers. Nearly half of the Muslims (both general and OBCs) are also employed in this category of employment.

Nearly half of the SC workers are employed in daily wage jobs in Mathura. However at the same time nearly 40% of the Hindu workers (general and OBCs) and around 50 and more than 40% of the general Muslim and OBC Muslim workers respectively are also engaged in self-employment. Ujjain being a purely religious city with a relatively scarce possibility of getting absorbed in high income jobs, a very high percentage of the workers across all social categories are by and large employed in daily wage jobs. Only the percentage of general Hindus is less than 50 in this category of employment while nearly 33% from this social group are located in a relatively better category of employment, i e, regular wage employment. On the whole, evidence across all the four cities seems to be quite mixed and therefore we need to examine many other variables before concluding that lower social categories match the lower economic categories.

The occupational structure varies considerably across caste groups (Table 3, p 59). For example, around 13 and 34% of the general Hindus are engaged as semi-professionals and sales and trade workers respectively whereas among the general Muslims the comparative figures are only 4.2 and 21.2%, respectively. Certain occupations are caste-centric, e g, a very large chunk of the sc workers (around one-fourth) are engaged in one of the most vulnerable categories of occupation – daily wage labour.

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Similarly among the OBC Hindus, around 15.6% are located in this SCs compared to the general or OBC Hindus. Again OBC Hindus category of occupation. Though the ST population is quite nominal and OBC Muslims correspond to a higher incidence of illiteracy in the sample a very significant percentage of them are seen to be than the general Hindus and general Muslims, respectively. At employed as semi-professionals and trade and sales workers. the secondary and higher levels of education, the differences Along the line of religion, Muslim workers, both general and OBCs, between the general and OBCs are evident both among the Hindus correspond to a higher percentage figure in tailoring. Based on and Muslims, indicating that the OBCs are somewhat worse off some of these patterns it would be of great interest to examine if compared to the general category. However, the incidence of illiterthese differences are related to differences in educational levels acy and the percentage of population with low levels of education and access to social capital of workers across different social are sizeable among the higher social categories too. groups.

Table 5: Level of Education – All Cities Combined (%)

Education General General OBC OBC SC ST Others

Table 3: Occupational Structure of the Workers (%)

Hindu/Sikh Muslim Hindu/Sikh Muslim All Cities Combined General General OBC OBC

Illiterates 21.3 35.1 25.6 39.5 31.7 17.9 18.2

Hindu/Sikh Muslim Hindu/Sikh Muslim SC ST Others

Non-formal 2.3 5.2 1.5 7.2 2.1 1.9 0.0

Semi-professional 13.3 4.2 7.9 4.8 7.0 23.3 0.0 Primary 22.0 30.1 31.0 31.2 28.3 19.8 27.3

Sales and trade 34.4 21.1 27.3 28.4 18.1 28.8 66.7 Middle 21.9 16.8 21.4 13.9 21.5 31.9 9.1

Personal services 4.1 2.4 5.6 3.3 15.1 5.5 0.0 Secondary 16.2 7.2 10.9 5.4 10.2 17.9 31.8

Manufacturing and repair 11.7 23.5 16.2 18.5 13.1 9.6 11.1 Higher secondary 6.7 2.9 4.7 1.5 3.0 4.8 13.6

Commercial and security 4.8 1.4 2.7 1.5 2.6 1.4 0.0 Graduate and others 9.6 2.7 5.0 1.3 3.2 5.8 0.0

Transport 8.0 10.4 5.6 12.9 5.3 6.8 11.1 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Tailoring 6.1 16.3 6.6 9.1 3.6 4.1 0.0 Source: See Table 1.

Construction 4.6 12.5 7.8 8.3 7.2 6.8 0.0 Labour 7.9 5.2 15.6 9.3 23.8 12.3 11.1 Table 6: Distribution of Workers across Monthly Income Categories (in Rs)

All Cities Less than 500 501-1,500 1,501-3,000 5,001-10,000 Above 10,000 Total

Others 5.3 3.1 4.7 3.9 4.2 1.4 0.0

Gen Hindu/Sikh 5.2 19.8 43.6 29.7 1.7 100

Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Source: See Table 1. Gen Muslim 5.6 14.6 51.3 25.1 3.4 100

OBC Hindu/Sikh 6.6 28.5 44.7 19.9 0.3 100

Networks, conceptualised to capture social capital, indicate that

OBC Muslim 7.3 23.9 47.6 19.9 1.3 100 among all the caste groups and across all the four cities they play a SC 7.0 29.6 45.2 17.1 1.2 100 significant role in accessing an entry to the urban job market. More ST 1.4 21.1 35.2 35.2 7.0 100 than 90% of the workers seem to have used networks to get an Others 12.5 12.5 12.5 62.5 0.0 100 urban employment (Table 4). These networks could be caste-based Total 6.3 24.8 45.2 22.3 1.4 100

Source: See Table 1.

which means that each social category in the urban context expands due to its own momentum. While from the point of view of an indi-Whether the educational differences translate themselves into vidual, these caste-based networks function as an effective way of income differences is an important question. The income strucproviding accessibility to livelihood, these networks at the same ture at the top level (Rs 5,000 to 10,000 per month) does not time tend to inhibit the process of social assimilation and lead to seem to be much different between Hindus and Muslims, though segmentation and possibilities of conflicts. In fact, urbanisation in OBCs are worse off compared to the general category among both several developing countries has often been accompanied by social the communities (Table 6). On the other hand, the percentage of disturbances of different forms though ideally urbanisation is ex-SCs in the higher income category (5,000 and above) is slightly pected to diffuse the social differences and give rise to a more ho-smaller than that of the OBCs. Though low levels of education and mogeneous set which in turn can facilitate the joint consumption low level incomes are present among higher social categories of public goods and thus help experience upward mobility for all across both the religions, the incidence of low incomes and low sections of the population (Mitra 2006). One way of neutralising levels of education is slightly higher among the lower social catethese caste-based networks is to introduce other effective channels gories. Besides, a religion-based distinction can also be deciof information-flow relating to jobs and shelter.phered – Muslims seem to be worse off compared to the Hindus.

The educational levels however vary substantially across caste On the whole, educational, occupational and income disadgroups (Table 5). The incidence of illiteracy is higher among the vantages are somewhat more pronounced among the socially disadvantaged classes, though it will be

Table 4: Percentage of Workers Who Used Networks

Social Groups All Cities Combined Jaipur Ludhiana Mathura Ujjain equally erroneous to suggest that those Self-Network Self- Network Self-Network Self-Network Self-Network

in higher social categories are all well

Initiative Initiative Initiative Initiative Initiative

General Hindu/Sikh 4.8 95.2 6.2 93.8 3.9 96.1 6.1 93.9 1.7 98.3 off. All this would tend to indicate that

General Muslim 6.5 93.5 3.8 96.2 23.9 76.1 0.0 100.0 4.3 95.7 while a caste-based reservation policy
OBC Hindu/Sikh 6.2 93.8 2.5 97.5 11.8 88.2 8.0 92.0 2.3 97.7 may help the socially disadvantaged
OBC Muslim 1.9 98.1 2.0 98.0 9.4 90.6 0.7 99.3 1.0 99.0 classes reap benefits, it will leave out a
SCSTOthers 2.0 0.0 0.0 98.0 100.0 100.0 1.4 0.0 98.6 100.0 4.5 0.0 0.0 95.5 100.0 100.0 0.0 0.0 0.0 100.0 100.0 100.0 1.4 0.0 0.0 98.6 100.0 100.0 large percentage of the economically weaker section of the population who
Total 3.9 96.1 3.1 96.9 7.2 92.8 4.0 96.0 1.7 98.3 belong to higher social categories, resid-
Source: See Table 1. ing particularly in deprived areas, which
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can be slums, as in the present context or can be any other geo somewhat better off. Though Ludhiana and Mathura are more or
graphical location in a broader sense. However, before drawing less in conformity with this pattern, the percentage of SCs in
any substantive conclusion in this regard we need to pursue a more Jaipur in the top two size classes is around 8%, which is higher
rigorous analysis using the unit level data as attempted below. than the corresponding figure among general Hindus or Muslims.
In Ujjain, the top two size classes are mostly empty for all catego-
Constructing a Well-being Index ries except for SCs. The percentage figures of households in the
In order to assess the overall well-being of the households across bottom two size classes are indeed higher than those in other
various social classes we need to construct a well-being (or depri cities (except among SCs in Mathura) implying that in general the
vation) index at the household level for which various dimensions well-being levels are worse in Ujjain. However, based on the per
of poverty rather than only income or consumption poverty need centage figure of the bottom two size classes, SCs in Ujjain do not
to be considered. However, the other aspects which we could in seem to be worse off in comparison to Muslims or OBC Hindus. In
clude are only those that are quantifiable. Ludhiana and Ujjain, the general Muslims and in Mathura, the
The following variables have been combined to construct the OBC Muslims are the least well off. This may tend to indicate a
household specific well-being index: household size, child religion-based distinction which is more prominent than a caste
woman ratio, per capita consumption expenditure,4 proportion based one. Based on the combined data for all four cities, STs
of persons in the household who reported illness, percentage of seem to be better off than the rest. However, given the limited
household members who acquired at least primary level educa
tion, percentage of members in the age group 15 to 59, which is a proxy for adult potential earners, percentage of working individ- Table 7: Percentage Distribution of Households across Size Classes Formed on the Basis of Well-being Index All Cities Combined General General OBC OBC SC ST Others
uals, age of the household head/principal earner taken as a proxy Hindu/Sikh Muslim Hindu/Sikh Muslim
for experience in the job market, health expenditure per capita, Up to 200 4.9 0.8 6.6 9.4 5.8 8.3 0.0
and per capita household income. Variables such as household 201-400 37.2 42.4 46.4 43.7 46.5 12.8 6.7
size, child-woman ratio, and the percentage of ill members in the household, are likely to reduce the well-being of the household. Health expenditure per capita on an a priori basis may raise the 401-600 29.3 28.7 27.8 30.9 29.7 37.6 40.0 601-1000 21.2 20.3 16.4 14.9 13.5 22.7 36.7 1,001-1,500 4.6 6.7 1.8 0.5 3.2 16.9 0.0 Above 1,500 2.8 1.0 1.0 0.5 1.3 1.7 16.7
well-being of the household if it tends to enhance productivity. Total 100 100 100 100 100 100 100
Alternatively, it may reduce well-being if it is incurred at the ex- Jaipur
pense of consumption of essential items. On the other hand, other Up to 200 3.6 0.0 0.0 0.0 1.8 1.7 0.0
variables would be expected to enhance the well-being. Since 201-400 29.6 37.9 28.5 29.6 21.3 9.2 0.0
these variables are heterogeneous, it is difficult to combine them 401-600 35.4 35.7 41.0 44.7 41.3 38.2 0.0
to indicate an overall living standard of the households. Factor 601-1,000 24.5 20.8 25.5 23.9 27.7 24.9 0.0
analysis has been conducted, and using factor loadings as weights 1,001-1,500 3.1 4.9 2.1 0.9 4.7 23.7 0.0
from the rotated matrix (using varimax rotation technique in Above 1,500 3.9 0.7 2.9 0.9 3.1 2.3 0.0
order to obtain statistically independent factors), variables have Total 100 100 100 100 100 100 0.0 Ludhiana
been combined to generate a composite index of well-being, de- Up to 200 4.0 0.0 2.1 5.8 5.8 60.0 0.0
noted as WELLINDEX(i). This needs to be repeated for each of the 201-400 28.2 48.2 32.2 35.0 36.5 0.0 0.0
significant factors (factors with eigenvalues greater than one): 401-600 32.4 8.8 32.4 25.2 34.5 40.0 100.0
n WELLINDEX (i) = ∑ FLj (i) Xjj = 1 601-1000 24.3 21.1 26.7 30.1 15.5 0.0 0.0 1,001-1,500 7.7 17.5 4.3 1.9 6.7 0.0 0.0
where, FL is the factor loading, j = 1…n corresponds to the Above 1,500 3.4 4.4 2.3 1.9 1.0 0.0 0.0
number of variables, and i represents the ith significant factor. Total 100 100 100 100 100 100 100.0
In the second stage the composite indices generated on the Mathura
basis of factor loadings for each of the significant factors have to be combined using the proportion of eigenvalues as weights: k EV(i)WELLINDEX = ∑ WELLINDEX (i) k < n i = 1 ∑ EV(i) [ ] Up to 200 6.2 5.3 8.7 20.5 5.0 0.0 0.0 201-400 49.5 38.2 51.5 60.1 61.0 34.3 0.0 401-600 20.4 22.9 25.3 18.0 20.2 31.4 44.4 601-1,000 17.6 24.4 12.1 1.5 10.4 34.3 0.0 1,001-1,500 4.0 9.2 1.8 0.0 3.0 0.0 0.0
where i ranges from 1 to k, the number of significant factors. Above 1,500 2.2 0.0 0.6 0.0 0.5 0.0 55.6
Across various social groups we have considered the percent- Total 100 100 100 100 100 100 100
age distribution of households as per the size classes formed on Ujjain
the basis of well-being index (Table 7). Based on the combined Up to 200 6.9 0.0 9.6 16.6 8.2 45.8 0.0
figures for all four cities, the percentage of general Hindus in the 201-400 45.8 79.7 56.7 57.4 57.4 12.5 11.8
bottom two size classes is somewhat lower than the corresponding figures of other social groups. In the top two size classes the percentage figures are again slightly higher in the case of general 401-600 32.7 13.0 23.0 17.0 26.1 41.7 23.5 601-1,000 14.7 7.2 10.7 9.0 7.0 0.0 64.7 1,001-1,500 0.0 0.0 0.0 0.0 0.3 0.0 0.0 Above 1,500 0.0 0.0 0.0 0.0 1.0 0.0 0.0
Hindus and Muslims compared to the OBC Hindus or Muslims or Total 100 100 100 100 100 100 100
SCs, suggesting that those belonging to the general category are Source: See Table 1.
60 december 12, 2009 vol xliv no 50 Economic & Political Weekly
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number of observations on STs in the sample, any conclusion in this regard has to be drawn carefully.

However, these patterns are aggregative in nature. We may therefore like to pursue further experiments. One important line of enquiry could be relating to the factors which explain the overall well-being. Since well-being is measured in terms of a number of variables we need to be careful in selecting the determinants so that they do not include some of the constituents of the well-being index. The right hand side variables include nature of employment, caste categories, duration of migration categories (the long duration migrants of more than 20 years, and the natives constitute the comparison category), nature of construction of the huts and the number of rooms per household, ownership pattern, access to

Table 8: Binomial Logit Model (Maximum Likelihood Estimates)

sanitation and water and ability to remit. As there are mainly three types of employment – daily wage, regular wage and self-employment – two dummies representing the first two categories have been included. In relation to the social groups scheduled tribes and “others” have been taken as the comparison category.

2 Caste-wise Probability of Experiencing Well-being

As mentioned above, the analysis is carried out in a binomial logit framework: households which registered a well-being index of less than 400 are assigned 0 and those lying above the threshold limit are designated as 1. The estimation is carried out for the samples drawn from the four cities separately and also for the pooled sample. Results presented in Table 8 tend to suggest that households in which heads are engaged in regular wage employment correspond to higher levels of well-being while those in daily wage employment are statistically not different from those in self-employment. In individual cities gender dummy does not appear to be significant though in the pooled sample it is significant at 10% level with a negative sign, implying that women headed households have lower well-being index though in terms of marginal effect the probability of having higher levels of wellbeing drops marginally (-0.08) as one moves from male-headed to female-headed households.

Explanatory Variables Jaipur Ludhiana Mathura Ujjain All Cities Combined
Coeff M Eff Coeff M Eff Coeff M Eff Coeff M Eff Coeff M Eff
Gender -0.45 -0.09 -0.52 -0.12 -0.07 -0.02 -0.10 -0.02 -0.33 -0.08
(-0.92) (-0.84) (-1.27) (-1.2) (-0.2) (-0.2) (-0.35) (-0.36) (-1.87)* (-1.86)*
Daily wage 0.11 0.02 0.25 0.05 -0.31 -0.07 -0.10 -0.02 -0.08 -0.02
(0.45) (0.45) (0.85) (0.88) (-1.18) (-1.2) (-0.42) (-0.42) (-0.67) (-0.67)
Regular wage 0.83 0.13 0.54 0.11 0.82 0.20 0.50 0.12 0.63 0.15
(2.69)** (3.04)** (2.08)** (2.18)** (3.31)** (3.37)** (1.84)* (1.81)* (5.04)** (5.25)**
General Hindu/Sikh -0.96 -0.19 1.06 0.21 -0.59 -0.14 0.09 0.02 -0.77 -0.19
(-1.67)* (-1.53) (0.78) (0.84) (-0.74) (-0.77) (0.1) (0.1) (-2.24)** (-2.28)**
General Muslim -1.33 -0.28 0.83 0.14 -0.05 -0.01 -1.11 -0.21 -0.73 -0.18
(-2.23)** (-2.03)** (0.58) (0.72) (-0.05) (-0.05) (-0.95) (-1.27) (-1.93)* (-1.97)**
OBC Hindu/Sikh -0.60 -0.12 1.01 0.19 -0.68 -0.16 -0.34 -0.08 -1.07 -0.26
(-0.92) (-0.84) (0.74) (0.88) (-0.87) (-0.91) (-0.37) (-0.38) (-3.08)** (-3.23)**
OBC Muslim -1.21 -0.25 1.54 0.22 -1.44 -0.30 -0.81 -0.17 -1.16 -0.28
(-2.07)** (-1.89)* (1.06) (1.78)* (-1.75)* (-2.18)** (-0.82) (-0.95) (-3.26)** (-3.54)**
Scheduled caste -0.65 -0.13 0.80 0.16 -0.93 -0.21 -0.08 -0.02 -0.99 -0.24
(-1.11) (-1.03) (0.59) (0.64) (-1.17) (-1.29) (-0.09) (-0.09) (-2.9)** (-3.00)**
Duration of 0.42 0.08 -0.23 -0.05 0.89 0.22 -0.20 -0.05
migration < 1 year (0.35) (0.39) (-0.28) (-0.29) (1.09) (1.11) (-0.41) (-0.41)
Duration of -1.13 -0.25 0.36 0.07 0.55 0.14 -0.03 -0.01 -0.18 -0.05
migration 1-3 years (-1.23) (-1.08) (0.65) (0.7) (1.46) (1.46) (-0.06) (-0.06) (-0.85) (-0.85)
Duration of 0.86 0.12 1.09 0.18 0.14 0.04 0.43 0.10 -0.02 0.00
migration 4 -5 years (0.78) (1.06) (1.39) (1.95)* (0.32) (0.32) (0.87) (0.85) (-0.06) (-0.06)
Duration of 0.15 0.03 -0.05 -0.01 -0.06 -0.01 -0.04 -0.01 -0.25 -0.06
migration 6-10 years (0.32) (0.33) (-0.13) (-0.13) (-0.18) (-0.18) (-0.1) (-0.1) (-1.48) (-1.47)
Duration of -0.26 -0.05 -0.71 -0.16 0.28 0.07 0.53 0.13 -0.12 -0.03
migration 10-20 years (-0.82) (-0.78) (-2.66)** (-2.57)** (0.97) (0.97) (2.04)** (2.02)** (-0.93) (-0.92)
Sending remittance 0.87 0.12 0.68 0.13 0.97 0.24 -1.55 -0.26 0.91 0.20
(1.68)* (2.17)** (2.25)** (2.48)** (2.41)** (2.56)** (-1.29) (-2.13)** (4.85)** (5.62)**
Ownership of house -0.50 -0.09 -0.36 -0.07 -0.14 -0.03 0.40 0.09 -0.01 0.00
(-1.43) (-1.44) (-1.13) (-1.13) (-0.46) (-0.46) (1.66)* (1.67)* (-0.06) (-0.06)
No of room -0.02 0.00 0.22 0.05 0.02 0.00 0.38 0.09 0.09 0.02
(-0.19) (-0.19) (2.28)** (2.3)** (0.24) (0.24) (3.79)** (3.79)** (2.42)** (2.42)**
Concrete roof -0.87 -0.16 -0.08 -0.02 -0.20 -0.05 -0.64 -0.15 -0.36 -0.09
(-2.98)** (-2.92)** (-0.19) (-0.19) (-0.96) (-0.97) (-2.65)** (-2.61)** (-3.31)** (-3.31)**
Brick wall -0.01 0.00 -0.30 -0.07 0.17 0.04 0.08 0.02 -0.11 -0.03
(-0.02) (-0.02) (-0.71) (-0.69) (0.21) (0.21) (0.2) (0.19) (-0.55) (-0.55)
Municipality water 0.75 0.12 0.02 0.00 -0.32 -0.08 -0.81 -0.17 0.06 0.01
(2.38) (2.73)** (0.05) (0.05) (-1.27) (-1.29) (-1.17) (-1.4) (0.45) (0.45)
Individual latrine -0.27 -0.05 -0.40 -0.09 -0.21 -0.05 0.05 0.01
(-0.81) (-0.78) (-0.84) (-0.8) (-0.68) (-0.69) (0.29) (0.29)
Piped water -0.25 -0.04 -0.31 -0.07 -0.68 -0.17 -0.53 -0.13 -0.73 -0.18
(-0.93) (-0.92) (-0.63) (-0.6) (-2.55)** (-2.57)** (-2.28)** (-2.27)** (-6.52)** (-6.57)**
Constant 3.19 -0.04 1.12 -1.14 1.36
(3.46)** (-0.03) (1.16) (-1.06) (3.17)
Chi-Sq 54.14 56.53 67.36 75.96 239.66
No of observations 500 500 500 500 2000
Coeff and M Eff represent coefficients and marginal effects, respectively. ** and * represent significance at 5 and 10% levels, respectively.
Economic & Political Weekly december 12, 2009 vol xliv no 50 61
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In relation to the caste categories we may again refer to the marginal effects. Relative to the scheduled tribes and “others”, most of the categories seem to have a lower probability of experiencing higher well-being index. However, the extent of fall in the case of OBC Hindus, OBC Muslims and SCs measured in terms of marginal effect is higher than that for general Hindus or Muslims. However, these results hold only when we pool the data from all the four cities. Otherwise, based on individual cities this pattern is not uniformly evident. For example, only OBC Muslims in Ludhiana and Mathura have a different impact on well-being while the rest of the categories are statistically insignificant. However, in Ludhiana they tend to have a higher well-being while in Mathura the marginal effect corresponding to OBC Muslims is -0.30. In Ujjain none of the caste dummies is significant, implying that the variations in well-being across categories are not substantive. In Jaipur, both general and OBC Muslims tend to have a lower well-being index than the rest. On the whole, based on the individual city data it is not possible to conclude that lower castes such as OBCs and SCs show lower well-being though based on the pooled data such a tendency is evident.

Duration of migration does not play a significant role in enhancing the well-being level of the households as seen from the pooled data. However, households which are able to remit and are residing in greater space (measured in terms of the number of rooms) are able to acquire higher levels of well-being. The only surprising result is in relation to the access to piped water and the concrete roof, which have negative coefficients. The access to piped water does not mean individual access; it could be the accessibility of the cluster as a whole. Similarly, the households with concrete roof do not seem to be better off in terms of the well- being index. On the whole, while some of the physical characteristics of shelter and the well-being levels of households move in the positive direction, some others do not show any such pattern. This would imply that even in better dwelling structures one may locate households with lower well-being index in terms of income, consumption, education, health and certain social and demographic variables. Land tenure can improve the quality of shelter but that does not ensure improvement in well-being. Similarly, accessibility to piped water can be a function of the residents’ association with political parties and such political contacts are of mutual help only when well-being levels are poor (Edleman and Mitra 2006).5

3 Conclusions

This paper, in an attempt to revisit the caste issue in the Indian context, analyses a sample of households collected from a relatively deprived section of the society, namely, slums. In order to keep in view the variability of city structure which might have an impact on the socio-economic lifestyle of the slum households, four cities of different nature were selected for the study. Based on a stratified random sampling technique, a sample of 2,000 households, 500 from each of the four cities, was selected. The relative size of upper castes conceptualised in terms of general Hindus/Sikhs and general Muslims is not negligible, though the popular belief is that slums are inhabited primarily by lower castes.

Even within the slums, which are often taken to represent a homogeneous lot, variations across social groups in terms of certain important indicators like education, occupation and incomes are evident. However, vulnerability conceptualised in terms of several socio-economic and demographic indicators exists among most of the social categories despite variations in the relative size of deprivation (measured in terms of the incidence of households located below a threshold limit). By and large, the religion-based distinction rather seems to be more prominent than a caste-based one though there are instances of a somewhat higher incidence of deprivation among the socially backward groups.

The econometric analysis pursued on the pooled sample from all the four cities brings out the caste aspect distinctly. In a binomial logit framework the probability of experiencing well-being beyond a threshold limit turns out to be lower for the socially backward classes than the others. However, in individual cities such a pattern is not so conspicuous. In fact, even in a city like Ujjain, which lacks dynamism and where caste factor is expected to have remained dominant, the dummies representing social categories are not statistically significant, implying that all the social categories are equally vulnerable. These findings have important policy implications, indicating that policy initiatives for deprived areas need to be based on economic criteria rather than simply on the caste factor. In fact, in the deprived areas, particularly in the urban space where vulnerability has political lineages as well, caste-based schemes hold the possibility of igniting caste-war or communal tensions instead of smoothing the contours of inequalities.

The other policy conclusion which follows from the analysis is that simply provision of basic amenities or land tenure to improve the quality of shelter does not necessarily improve the overall well-being of the households measured in terms of economic, social, demographic, health and education-specific variables. Developmental expenditure and employment programmes are some of the important dimensions of government initiatives to reduce urban poverty.

Notes

1 The Mandal Commission (1990) reinforced these issues by including Other Backward Classes as beneficiaries.

2 The list of registered or recognised slum clusters is available with the local governments. Information on the unrecognised or unregistered slum clusters is not available as a result of which these had to be left out.

3 On an average 17 households were taken from each of the 30 clusters in each of the four cities. The sample is representative of the slum population in each city.

4 It excludes health expenditure. 5 The clusters act as political vote banks and in return they are assured of legal tenure and accessibility to basic amenities.

december 12, 2009 vol xliv no 50

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Economic & Political Weekly

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