(1)
Institute of Economic Growth, Delhi University, Delhi, India
Abstract
The preceding chapter has highlighted a few socio-demographic attributes of the sample households drawn from selected districts or subdistricts (also known as tehsils) in UP, Rajasthan and Delhi. It was noticed from the analysis of these attributes that the capital city of Delhi has certain advantages over the rest, although there appear to be some notable differences between its slum and non-slum households. The two, for example, differed largely in terms of sex distributions. To be more specific, of all the locations and districts covered in the study, a higher fraction of female population may only be noticed in the slum households in Delhi. In addition, the share of their youth population in the 15–24 age groups is also relatively higher, indicating certain differentials in their fertility behaviour with the re
3.1 Socio-economic Characteristics of Sample Population
The preceding chapter has highlighted a few socio-demographic attributes of the sample households drawn from selected districts or subdistricts (also known as tehsils) in UP, Rajasthan and Delhi. It was noticed from the analysis of these attributes that the capital city of Delhi has certain advantages over the rest, although there appear to be some notable differences between its slum and non-slum households. The two, for example, differed largely in terms of sex distributions. To be more specific, of all the locations and districts covered in the study, a higher fraction of female population may only be noticed in the slum households in Delhi. In addition, the share of their youth population in the 15–24 age groups is also relatively higher, indicating certain differentials in their fertility behaviour with the rest of the sample.
All along these spatial differentials, there is another interesting phenomenon emanating from the same discussion, i.e. a large spread and abounding nuclearisation of families even in villages of UP and Rajasthan where many traditional values are still in vogue. This phenomenon of fast-growing changes in family norms and erosion of traditional forms of living may cause difficulties to many, especially while coping with serious family matters such as prolonged ailments or long-term care provisioning for the aged, diseased or functionally disabled. There may be added complexities if the households and its members are also goaded with poor literacy levels, lack of participation in remunerative economic activities and poor consumption levels and forced to rely on their own to meet expenses arising out of unexpected events like ailments and medications. We try to examine some of these issues focusing on sample of populations described in the preceding chapter. A great deal of this chapter is particularly devoted to discuss overall and health-driven poverty among the sample population.
3.1.1 Educational Status of Sample Population
The educational distribution of sample population in Table 3.1 does in no way contribute to the feelings of any marked improvement over past few decades in social status of populations in districts of both the major states under consideration. The same may as well be true for the slum households in Delhi.
Table 3.1
Literacy level of sample populations (%)
Educational level | Unnao | Jhansi | ||||
|---|---|---|---|---|---|---|
Male | Female | Total | Male | Female | Total | |
Panel 1: UP | ||||||
Illiterate | 23.7 | 41.7 | 32.1 | 22.9 | 45.0 | 33.4 |
Lit. without formal education | 2.1 | 2.0 | 2.0 | 2.4 | 1.0 | 1.7 |
Up to 5th standard (primary) | 34.5 | 27.8 | 31.4 | 28.4 | 28.3 | 28.4 |
7th–8th standard (middle) | 17.7 | 13.7 | 15.8 | 23.8 | 13.7 | 19.0 |
Matriculate | 9.7 | 6.1 | 8.0 | 8.7 | 5.1 | 7.0 |
Higher secondary | 5.7 | 4.9 | 5.3 | 6.5 | 3.7 | 5.2 |
Graduates and above | 5.6 | 3.2 | 4.5 | 6.0 | 3.0 | 4.6 |
Diploma/certificate | 0.6 | 0.2 | 0.4 | 1.1 | 0.2 | 0.7 |
Degree in technical/professional education | 0.4 | 0.4 | 0.4 | 0.2 | 0.0 | 0.1 |
Total literacy level | 76.3 | 58.3 | 67.9 | 77.1 | 55.0 | 66.6 |
Literate + illiterate | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 |
Chi-sq. (9) | Chi-sq. (9) = 136.421; Pr. = 0.000 | Chi-sq. (9) = 153.224; Pr. = 0.000 | ||||
Dausa | Dungarpur | |||||
Male | Female | Total | Male | Female | Total | |
Panel 2: Rajasthan | ||||||
Illiterate | 21.2 | 49.4 | 34.5 | 22.9 | 38.8 | 30.5 |
Lit. without formal education | 1.0 | 1.0 | 1.0 | 1.6 | 2.4 | 2.0 |
Up to 5th standard (primary) | 28.6 | 28.6 | 28.5 | 30.3 | 28.2 | 29.3 |
7th–8th standard (middle) | 27.3 | 15.4 | 21.7 | 19.3 | 14.7 | 17.1 |
Matriculate | 11.8 | 4.5 | 8.3 | 10.1 | 7.9 | 9.0 |
Higher secondary | 5.5 | 0.9 | 3.3 | 6.9 | 4.2 | 5.6 |
Graduates and above | 4.5 | 0.3 | 2.5 | 6.6 | 3.0 | 4.9 |
Diploma/certificate | 0.1 | 0.0 | 0.1 | 0.4 | 0.0 | 0.2 |
Degree in technical/professional education | 0.2 | 0.1 | 0.2 | 2.0 | 0.8 | 1.4 |
Total literacy level | 78.8 | 50.6 | 65.5 | 77.1 | 61.2 | 69.5 |
Literate + illiterate | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 |
Chi-sq. (9) | Chi-sq. (9) = 212.086; Pr. = 0.000 | Chi-sq. (9) = 74.900; Pr. = 0.000 | ||||
Non-slum | Slum | |||||
Male | Female | Total | Male | Female | Total | |
Panel 3: Delhi | ||||||
Illiterate | 9.5 | 19.6 | 14.3 | 25.9 | 44.5 | 35.7 |
Lit. without formal education | 0.4 | 1.4 | 0.9 | 1.5 | 1.3 | 1.4 |
Up to 5th standard (primary) | 25.2 | 20.7 | 23.1 | 43.3 | 39.2 | 41.1 |
7th–8th standard (middle) | 13.0 | 11.5 | 12.3 | 15.2 | 9.4 | 12.1 |
Matriculate | 15.5 | 13.0 | 14.3 | 9.3 | 4.4 | 6.7 |
Higher secondary | 11.9 | 12.0 | 11.9 | 3.3 | 1.0 | 2.1 |
Graduates and above | 16.8 | 16.7 | 16.7 | 1.5 | 0.3 | 0.9 |
Diploma/certificate | 1.0 | 1.5 | 1.2 | 0.0 | 0.0 | 0.0 |
Degree in technical/professional education | 6.8 | 3.5 | 5.3 | 0.0 | 0.0 | 0.0 |
Total literacy level | 90.5 | 80.4 | 85.7 | 74.1 | 55.5 | 64.3 |
Literate + illiterate | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 |
Chi2 (9) = 41.068; Pr. = 0.000 | Chi2 (7) = 38.386; Pr. = 0.000 | |||||
Admittedly, while none of these samples are representative in character and may not therefore be used to make generalisations, there is indeed an indication that a very large percentage of people in smaller towns and low-income residential areas of places like Delhi are either illiterate or semi-literate with their educational attainments perhaps not adequate to prevent poor health and poverty. Table 3.1 brings out these facts very clearly. Broadly, about a third of the total sample population (i.e. between 30 and 36 %) in most of these places is shown as completely illiterate with the highest level of illiteracy being found among the slum residents in Delhi. Another 50 % of them are below matriculate with a large fraction being simply educated up to the primary level or even less. Only about a twentieth of the total respondents were holding a degree from higher educational institutions. There was also a very small fraction of respondents in all the three states with a degree or diploma in professional courses (Table 3.1).
Another significant but a long-drawn observation stemming from Table 3.1 relates to a considerably higher gender gap in levels of educational attainment. That the sex of an individual does have a role in educational attainment is clearly evident from the chi2 test as well (see χ 2 values in Table 3.1).
The usual rural–urban divide in terms of educational status of populations has remained clearly visible from our sample as well, with residents living in urban areas being better educated than their rural counterparts. These details are given in an Appendix Table both for the entire sample and for two major states under consideration. Like sex, individuals’ place of residence is also an important source of differentials in educational status, and the χ 2 values in Appendix Table 3.A.1 reflect this significantly.
Indeed, while most of what has been described in the preceding discussion may not look different from many other studies or help to find an out-of-the-box solution to these long-drawn and well-recognised issues (see, e.g. Probe Team Report 1999; Shah and Rani 2003; Dreze and Murthi 2001), they may nevertheless prove as a marker to substantiate the argument that the country and its planning bodies may not be able to do much in terms of health as long as states like UP and Rajasthan—with a considerably high weightage in country’s overall population—remain educationally weak. In addition, the current regime of the NRHM, believed to work wonders in improving the health status of rural people, may or may not go beyond a certain limit. A more holistic regime covering postprimary education and all other health domains beyond reproductive health may need to be developed.
3.1.2 Work Status of Sample Population
Functional status of the sample population has been obtained by going into the following details. Initially, all the respondents were asked to provide their activity status, namely, working or nonworking. Those who reported working were again classified into ‘main’ and ‘marginal’ workers—with the former including men and women engaged physically or mentally in certain income generating activities for most of the year (those with a lesser duration of paid work were categorised as marginal workers). Finally, all the workers were regrouped into (i) regular workers, (ii) casual workers with uncertain length of employment, (iii) those working on their own or engaged in small family enterprises and (iv) persons employed under the centrally administered National Rural Employment Guarantee Scheme (NREGS).
Drawing upon the criteria noted above, functional status of the sample population is described in the rest of this discussion with two specific points to be highlighted clearly. First, the results of this analysis suggest a somewhat lower activity status of the population under reference; however, in several cases, it matches fairly closely with the Census figures obtained for corresponding districts in 2001 Census (see Appendix Table 3.A.2). And second, the female activity status in our case appears to be at a lower side and may therefore be an underestimate. Such issues however arise in surveys focusing on nonlabour issues.
It appears from the figures given in Table 3.2 that less than a third of the total sample population in majority of cases is economically active with considerable gender differentials. Barring Dungarpur in Rajasthan, nowhere the shares of working women exceed over 13 % of their reported total population. With almost a quarter of the total women engaged in one or the other economic activities, Dungarpur has indeed remained distinct from all other districts under the study (Table 3.2). The χ 2 values also indicate gender as an important distinguishing factor between men and women in their functional status.
Table 3.2
Activity status of sample population (N = 11,063) (%)
Activity status | UP (Unnao + Jhansi) | Unnao | Jhansi | ||||||
|---|---|---|---|---|---|---|---|---|---|
Male | Female | Total | Male | Female | Total | Male | Female | Total | |
Working | 49.1 | 7.7 | 29.6 | 48.3 | 7.0 | 29.1 | 50.2 | 8.9 | 30.6 |
Not working | 50.9 | 92.3 | 70.4 | 51.7 | 93.0 | 71.0 | 49.8 | 91.2 | 69.4 |
Total | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 |
N (number) | 2,972 | 2,631 | 5,603 | 1,833 | 1,603 | 3,436 | 1,139 | 1,028 | 2,167 |
Chi2 | 1.1E + 03 | Pr. | 0.0E + 00 | 709.444 | Pr. | 0.000 | 435.442 | Pr. | 0.000 |
Activity status | Rajasthan (Dausa + Dungarpur) | Dausa | Dungarpur | ||||||
|---|---|---|---|---|---|---|---|---|---|
Male | Female | Total | Male | Female | Total | Male | Female | Total | |
Working | 48.2 | 16.3 | 33.1 | 45.6 | 8.1 | 27.8 | 50.6 | 24.1 | 38.0 |
Not working | 51.8 | 83.7 | 66.9 | 54.5 | 91.9 | 72.2 | 49.4 | 76.0 | 62.0 |
Total | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 |
N (number) | 1,852 | 1,671 | 3,523 | 898 | 806 | 1,704 | 954 | 865 | 1,819 |
Chi2 | 402.014 | Pr. | 0.000 | 297.182 | Pr. | 0.000 | 136.084 | Pr. | 0.000 |
Activity status | Delhi (slum + non-slum) | Slum | Non-slum | ||||||
|---|---|---|---|---|---|---|---|---|---|
Male | Female | Total | Male | Female | Total | Male | Female | Total | |
Working | 48.4 | 11.7 | 30.4 | 49.3 | 10.0 | 28.7 | 48.0 | 12.4 | 31.1 |
Not working | 51.6 | 88.3 | 69.6 | 50.7 | 90.0 | 71.4 | 52.0 | 87.6 | 68.9 |
Total | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 |
N (number) | 986 | 951 | 1,937 | 270 | 299 | 569 | 716 | 652 | 1,368 |
Chi2 | 3.1E + 02 | Pr. | 0.000 | 106.802 | Pr. | 0.000 | 202.194 | Pr. | 0.000 |
Unlike gender, place of residence apparently plays hardly any significant role in pushing families and households to become economically more engaged. The figures given in Table 3.3 do not show too many major differences in activity status of rural and urban households recruited from different districts/tehsils. Barring Dungarpur where differentials in activity status between rural and urban areas are considerably large (see panel 1 of Table 3.3), there is no similar example from any other places covered in the study. In all other cases, the observed differentials remained marginal. This is true for the slums and non-slums in Delhi as well.
Table 3.3
Activity status of sample population by rural–urban and social groups
Analytical variables | Working (%) | Not working (%) | Row total (%) | N (number) | Chi2 | |
|---|---|---|---|---|---|---|
Value | Pr. | |||||
Panel 1: rural–urban | ||||||
Unnao | ||||||
Rural | 28.7 | 71.3 | 100.0 | 2,635 | 0.550 | 0.458 |
Urban | 30.1 | 69.9 | 100.0 | 801 | ||
Jhansi | ||||||
Rural | 31.4 | 68.6 | 100.0 | 1,601 | 1.668 | 0.197 |
Urban | 28.5 | 71.6 | 100.0 | 566 | ||
UP total | 29.6 | 70.4 | 100.0 | 5,603 | ||
Dausa | ||||||
Rural | 28.2 | 71.8 | 100.0 | 1,394 | 0.538 | 0.463 |
Urban | 26.1 | 73.9 | 100.0 | 310 | ||
Dungarpur | ||||||
Rural | 40.6 | 59.4 | 100.0 | 1,311 | 13.386 | 0.000 |
Urban | 31.3 | 68.7 | 100.0 | 508 | ||
Rajasthan total | 33.07 | 66.93 | 100.0 | 3,523 | ||
Delhi slum | 28.7 | 71.3 | 100.0 | 569 | 1.114 | 0.291 |
Delhi non-slum | 31.1 | 68.9 | 100.0 | 1,368 | ||
Delhi total | 30.4 | 69.6 | 100.0 | 1,937 | ||
Panel 2: total sample (UP, Rajasthan and Delhi) by sex and rural–urban | ||||||
Male | 48.7 | 51.3 | 100.0 | 5,810 | 1.8e + 03 DF (1) | 0.000 |
Female | 11.2 | 88.8 | 100.0 | 5,253 | ||
Male–female combined | 30.9 | 69.1 | 100.0 | 11,063 | ||
Rural | 31.5 | 68.5 | 100.0 | 6,941 | 3.202 | 0.074 |
Urban | 29.8 | 70.2 | 100.0 | 2,185 | ||
Rural–urban combined | 30.9 | 69.1 | 100.0 | 11,063 | ||
Panel 3: social groups | ||||||
Scheduled caste (SC) | 30.2 | 69.8 | 100.0 | 2,531 | 17.687 DF (3) | 0.001 |
Scheduled tribe (ST) | 35.5 | 64.5 | 100.0 | 1,361 | ||
Other backward (OBC) | 29.6 | 70.4 | 100.0 | 4,367 | ||
Upper caste (HC) | 31.2 | 68.8 | 100.0 | 2,804 | ||
A distribution of sample population into four social groups—SC, ST, OBC and high castes—reveals that the highest fraction of ‘working’ people belonged to the ST category with more than 35 % of them having reported themselves as economically active (panel 3, Table 3.3). The rest three (in particular SC and OBC) were significantly behind, and the size of their working males and females was in the vicinity of 30–31 % of their respective populations.
About three-quarters (74.2 %) of the working males have reported themselves as the main workers—implying they had paid employment for about 180 days or more during most of the preceding 12 months. The rest however failed to meet this criterion and reported being unemployed for a greater part of the year. They were therefore considered as marginal workers (Table 3.4, panel 1). Women, as usual, suffered from double jeopardy; only a fewer of them were working, and those working were mostly in low-quality unskilled employment (panel 2, Table 3.4).
Table 3.4
Categorisation of workers and nature of activities: gender, rural–urban and social groups
Analytical variables | Type of workers | Nature of work | |||||
|---|---|---|---|---|---|---|---|
Total workers | Main workers | Marginal workers | Regular | Casual | Own accounta | NREGSb | |
Panel 1: total sample | |||||||
Total sample | 3,414 | 74.2 | 25.8 | 29.1 | 35.6 | 7.7 | 27.7 |
Male | 2,827 | 80.4 | 19.6 | 29.5 | 38.3 | 30.3 | 1.9 |
Female | 587 | 44.3 | 55.7 | 26.9 | 22.2 | 15.3 | 35.6 |
Rural | 2,184 | 63.4 | 36.6 | 18.5 | 45.0 | 24.6 | 12.0 |
Urban | 1,230 | 93.4 | 6.6 | 47.9 | 18.8 | 33.3 | 0.1 |
Panel 2: distribution by gender and place of residence | |||||||
Unnao | |||||||
Male | 886 | 73.1 | 26.9 | 24.5 | 35.3 | 2.3 | 37.9 |
Female | 112 | 49.1 | 50.9 | 29.5 | 42.0 | 0.0 | 28.6 |
Chi2 | 27.576 | Pr. | 0.000 | Chi2 (3) | 7.082 | Pr. | 0.069 |
Rural | 757 | 64.9 | 35.1 | 17.8 | 40.4 | 39.1 | 2.6 |
Urban | 241 | 88.0 | 12.0 | 47.7 | 22.4 | 29.9 | 0.0 |
Jhansi | |||||||
Male | 572 | 75.9 | 24.1 | 12.2 | 56.3 | 30.4 | 1.1 |
Female | 91 | 46.2 | 53.9 | 17.6 | 61.5 | 17.6 | 3.3 |
Chi2 | 34.246 | Pr. | 0.000 | Chi2 (3) | 9.544 | Pr. | 0.023 |
Rural | 502 | 67.3 | 32.7 | 10.8 | 64.7 | 22.7 | 1.8 |
Urban | 161 | 85.7 | 14.3 | 19.9 | 32.9 | 47.2 | 0.0 |
Dausa | |||||||
Male | 409 | 74.8 | 25.2 | 20.8 | 56.5 | 22.3 | 0.5 |
Female | 65 | 23.1 | 76.9 | 10.8 | 20.0 | 6.2 | 63.1 |
Chi2 | 68.685 | Pr. | 0.000 | Chi2 (3) | 266.832 | Pr. | 0.000 |
Rural | 393 | 64.1 | 35.9 | 19.3 | 54.5 | 15.3 | 10.9 |
Urban | 81 | 85.2 | 14.8 | 19.8 | 37.0 | 43.2 | 0.0 |
D. Pur | |||||||
Male | 483 | 85.9 | 14.1 | 40.2 | 32.5 | 22.4 | 5.0 |
Female | 208 | 18.3 | 81.7 | 12.0 | 3.4 | 5.3 | 79.3 |
Chi2 | 294.697 | Pr. | 0.000 | Chi2 (3) | 406.866 | Pr. | 0.000 |
Rural | 532 | 56.9 | 43.1 | 25.9 | 25.9 | 12.6 | 35.5 |
Urban | 159 | 94.3 | 5.7 | 50.9 | 16.4 | 32.7 | 0.0 |
Slum | |||||||
Male | 133 | 96.2 | 3.8 | 45.9 | 27.8 | 25.6 | 0.8 |
Female | 30 | 100.0 | 0.0 | 43.3 | 13.3 | 43.3 | 0.0 |
Total | 163 | 96.9 | 3.1 | 45.4 | 25.2 | 28.8 | 0.6 |
Chi2 (1) | 0.164 | Pr. | 0.281 | Chi2 (3) | 4.983 | Pr. | 0.173 |
Non-slum | |||||||
Male | 344 | 99.4 | 0.6 | 60.2 | 7.0 | 32.9 | 0.0 |
Female | 81 | 98.8 | 1.2 | 79.0 | 3.7 | 17.3 | 0.0 |
Total | 425 | 99.3 | 0.7 | 63.8 | 6.4 | 29.9 | 0.0 |
Chi2 (1) | 0.399 | Pr. | 0.528 | Chi2 (3) | 10.070 | Pr. | 0.007 |
Panel 3: distribution by social groups | |||||||
Social group | |||||||
SC | 764 | 72.0 | 28.0 | 29.7 | 44.6 | 19.5 | 6.2 |
ST | 483 | 53.2 | 46.8 | 16.6 | 49.1 | 9.1 | 25.3 |
OBC | 1,292 | 73.3 | 26.7 | 23.0 | 38.2 | 33.8 | 5.0 |
UC | 875 | 89.0 | 11.0 | 44.3 | 16.3 | 36.1 | 3.2 |
Total | 3,414 | 74.2 | 25.8 | 29.1 | 35.6 | 27.7 | 7.7 |
Chi2 (3) | 214.143 | Pr. | 0.000 | Chi2 (9) | 598.717 | Pr. | 0.000 |
A considerably large fraction of the unskilled employment created under the NREGA September 2005 to improve livelihood conditions of rural households has seemingly gone to women, especially in both districts of Rajasthan. In contrast, however, a bulk of employed women in UP is engaged in highly unsecure casual employment. In addition, they were also reportedly working in small home-based activities as self-employed or were own-account workers. Both underscore the earlier argument, suggesting women being a lower partner in economic well-being.
In addition to women, many of those engaged in lower category employment invariably comprise persons from the lower echelons of the caste hierarchy including the SC (29.7 % in regular employment and the rest as casuals, self-employed or NREGS-created activities), ST (16.6 % in regular employment) and OBC (23 % in regular employment) (Table 3.4, panel 3).
3.1.3 Nonworking Population
Table 3.5 presents a few important underlying factors responsible for a big majority of the respondents to be out of the workforce. One of the most significant factors keeping a big majority of the younger population out of workforce is the participation in educational activities. It turns out to be the case in all the districts including slums and non-slums. It may however be interesting to note a big gender gap in reporting education as a reason for non-participation in labour force activities. Also, this gap exists irrespective of the places under study and includes even households from the non-slum areas of Delhi. Another dominant reason for not being able to work is unemployment, especially among the people of Unnao in UP and slums of Delhi. A significant proportion of people at both the places do not work for lack of employment.
Table 3.5
Distribution of nonworking population by states and districts (%)
Unnao | Jhansi | |||||
|---|---|---|---|---|---|---|
Males | Females | Both | Males | Females | Both | |
Retired | 4.5 | 1.9 | 2.9 | 1.6 | 0.1 | 0.7 |
Weak, frail, disabled, mentally weak | 4.0 | 2.3 | 3.0 | 6.9 | 2.8 | 4.3 |
Students | 57.1 | 30.9 | 41.1 | 58.1 | 27.4 | 39.0 |
Unemployed | 11.9 | 8.5 | 9.8 | 8.5 | 4.9 | 6.3 |
Housewives | 0.2 | 44.3 | 27.2 | 0.2 | 48.5 | 30.3 |
Non-school-going children | 21.5 | 11.5 | 15.4 | 21.7 | 13.3 | 16.5 |
Others/voluntarily unemployed | 0.6 | 0.6 | 0.6 | 3.0 | 3.0 | 3.0 |
N | 947 | 1,491 | 2,438 | 566 | 937 | 1,503 |
Chi2 (8) | 577.408, Pr. 0.000 | 406.016, Pr. 0.000 | ||||
Dausa | Dungarpur | |||||
Retired | 1.6 | 0.4 | 0.9 | 3.0 | 0.2 | 1.3 |
Weak, frail, disabled, mentally weak | 6.6 | 4.5 | 5.3 | 4.3 | 4.0 | 4.1 |
Students | 66.6 | 30.9 | 45.1 | 59.7 | 35.2 | 45.4 |
Unemployed | 5.1 | 6.9 | 6.2 | 5.9 | 4.0 | 4.8 |
Housewives | 0.2 | 46.4 | 28.1 | 0.0 | 40.8 | 23.8 |
Non-school-going children | 19.3 | 10.1 | 13.8 | 27.0 | 13.4 | 19.1 |
Others/voluntarily unemployed | 0.6 | 0.8 | 0.7 | 0.2 | 2.6 | 1.6 |
N | 488 | 741 | 1,229 | 471 | 657 | 1,128 |
Chi2 (8) | 340.051, Pr. 0.000 | 284.681 Pr. 0.000 | ||||
Delhi slum | Delhi non-slum | |||||
Retired | 1.5 | 0.4 | 0.7 | 8.6 | 2.1 | 4.67 |
Weak, frail, disabled, mentally weak | 4.3 | 1.9 | 2.7 | 1.9 | 0.4 | 1.0 |
Students | 54.4 | 28.6 | 37.4 | 64.8 | 30.1 | 43.8 |
Unemployed | 15.9 | 9.7 | 11.8 | 5.1 | 4.0 | 4.5 |
Housewives | 0.7 | 33.8 | 22.6 | 0.3 | 44.0 | 26.7 |
Non-school-going children | 21.0 | 19.0 | 19.7 | 13.2 | 8.8 | 10.5 |
Others/voluntarily unemployed | 2.2 | 6.7 | 5.2 | 6.2 | 10.7 | 8.9 |
N | 138 | 269 | 407 | 372 | 571 | 943 |
Chi2 (8) | 71.772, Pr. 0.000 | 259.581, Pr.0.000 | ||||
A more disturbing factor to notice from Table 3.5 is the share of non-school-going children in almost every district and slums. While a big majority of those children (i.e. over three-quarters) were too young and below 4 years of age, almost a fifth of them were grown up and in higher ages as well.1 Their not attending schools, that too in most places, may look problematic. At stake in a situation like this may be the future of the demographic bonus India is expected to harness in coming years to add to its economic prospects.
Those adding to the size of nonworking household population also include a fraction of persons who are mentally or physically challenged. A small number of persons have also reported to withdraw from active workforce because of post-sickness frailty or senescence. Males in most of these cases outnumber females (Fig. 3.1), perhaps partly on account of the reporting biases. Dausa in Rajasthan reports such cases more than UP or Delhi.
Fig. 3.1
Men and women with work disability: district-wise share
3.2 Quality of Life, Consumption Poverty and Inequalities Among the Sample Households
Three broader issues are subjected to a brief examination in the underlying discussion. First is the quality of life of households in terms of selected physical assets owned by groups of people under study and their access to various services relevant on health considerations including domestic power, cooking fuel, sources of water for drinking purposes, toilet system, nearby ponds/river/nullah causing dampness and mosquito breeding, scavenging, waste disposal, drainage facilities through public means, telephone communication and access to banking facilities. The other two issues to be examined in the underlying context include the levels and differentials in per capita consumption expenditure of the sample households, which are later used to draw inferences about existing inequalities, consumption poverty and health outcomes of households drawn specifically from high-poverty locations and states. To the extent possible, most of these issues are examined by allowing for differentials between the rural–urban and the slum–non-slum households. Interpretation of our results must however be within the constraints imposed by a small and purposive sampling procedure.
3.2.1 Quality of Life: Housing Conditions, Possessions and Access to Basic Services
Given the broader concerns of this study—which inter alia require examining the size and burden of self-paid health care accessed by households from low-income rural, urban and slum areas of three selected states—it may not be very unlikely to expect a slant in favour of households with poor or moderate living conditions. This comes out very clearly from the bivariate tables given in most of this section to highlight the quality of houses and the facilities availed by the sample population. Table 3.6 and its two sub-tables (Tables 3.6a and 3.6b) bring out very clearly the poor economic background of most households under consideration. Each of these three tables indicates a very modest living by a big majority of the respondents, most of them residing in non-bricked (kutcha) dwellings and without most of the facilities required for a healthy living.
Table 3.6
Quality of houses and access to daily life services and amenities: total households (%)
Variables | N | Kutcha and semi-kutch | Pucca house | Lighting arrangement | Cooking fuel | Toilet | Drainage (kutcha nali) | Safe drinking water | Scavenging | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Electricity | Kerosene others | LPG | Coal, firewood, kerosene | Flush toilet | Pit toilet | Field and others | Weekly | Monthly | Rarely | ||||||
Total sample | 2010 | 60.3 | 39.7 | 53.6 | 46.4 | 30.9 | 69.1 | 14.6 | 25.9 | 59.5 | 12.8 | 96.3 | 46.9 | 5.8 | 47.3 |
UP | 1,000 | 74.8 | 25.2 | 28.8 | 71.1 | 19.9 | 79.1 | 10.5 | 17.3 | 72.2 | 50.5 | 97.4 | 29.8 | 10.7 | 59.5 |
Unnao | 600 | 70.3 | 29.7 | 28.8 | 71.0 | 24.7 | 74.3 | 17.5 | 12.7 | 69.8 | 51.3 | 96.7 | 22.7 | 13.1 | 64.1 |
Jhansi | 400 | 81.5 | 18.5 | 28.8 | 71.3 | 12.8 | 86.3 | 0.0 | 24.3 | 75.8 | 49.3 | 98.5 | 43.8 | 6.0 | 50.3 |
Rajasthan | 650 | 62.2 | 37.9 | 66.6 | 33.1 | 20.3 | 78.8 | 0.0 | 29.1 | 70.9 | 24.0 | 92.5 | 55.1 | 10.1 | 34.8 |
Dausa | 300 | 65.7 | 34.3 | 60.7 | 39.0 | 10.7 | 88.7 | 0.0 | 21.7 | 78.3 | 20.0 | 96.3 | 61.3 | 8.1 | 30.7 |
Dungarpur | 350 | 59.2 | 40.9 | 71.7 | 28.0 | 28.6 | 70.3 | 0.0 | 35.4 | 64.6 | 27.4 | 89.1 | 51.0 | 11.5 | 37.5 |
Delhi | 360 | 16.7 | 83.3 | 99.2 | 0.8 | 80.3 | 8.9 | 40.3 | 44.2 | 15.6 | 48.6 | 100.0 | 73.4 | 8.3 | 18.3 |
Slums | 102 | 46.1 | 53.9 | 97.1 | 2.9 | 46.1 | 22.6 | 0.0 | 64.7 | 35.3 | 76.5 | 100.0 | 50.0 | 7.3 | 42.7 |
Non-slum | 258 | 5.0 | 95.0 | 100.0 | 0.0 | 93.8 | 3.5 | 54.3 | 36.1 | 9.7 | 37.6 | 100.0 | 80.9 | 8.6 | 10.6 |
Religion | |||||||||||||||
Hindu | 1,789 | 61.1 | 38.9 | 52.8 | 47.0 | 29.7 | 67.5 | 12.3 | 24.2 | 63.5 | 39.1 | 95.9 | 47.0 | 10.4 | 42.6 |
Muslim | 188 | 62.8 | 37.2 | 53.2 | 46.8 | 30.3 | 66.5 | 9.0 | 36.7 | 54.3 | 63.3 | 98.9 | 37.8 | 7.4 | 54.8 |
Social group | |||||||||||||||
SC | 455 | 65.9 | 34.1 | 48.4 | 51.7 | 21.8 | 73.4 | 7.5 | 22.4 | 70.1 | 56.7 | 97.1 | 100.0 | 0.0 | 0.0 |
ST | 249 | 85.1 | 14.9 | 37.8 | 61.9 | 6.4 | 92.0 | 1.2 | 6.8 | 92.0 | 4.8 | 85.9 | 0.0 | 0.0 | 100.0 |
OBC | 777 | 69.0 | 31.0 | 45.1 | 54.7 | 23.9 | 74.4 | 6.7 | 24.5 | 68.9 | 49.2 | 97.2 | 38.9 | 5.6 | 55.6 |
Upper caste | 529 | 31.0 | 69.0 | 78.3 | 21.7 | 60.3 | 36.7
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