The gender ratio is generally more favourable to women in tribal areas, and even in the Dalit dominated villages in Villupuram, it was high.
Table 25: Distribution of total population by sex |
Tamil Nadu
|
Female
|
Male
|
Total
|
Sex Ratio
|
Kalamaruthur
|
188
|
186
|
373
|
1011
|
Kumaramangalam
|
177
|
175
|
352
|
1014
|
Illupur
|
180
|
186
|
365
|
968
|
Keelaiyur
|
161
|
167
|
328
|
961
|
|
705
|
713
|
1417
|
989
|
Maharashtra
|
Sayphal
|
178
|
185
|
363
|
966
|
Hardap
|
162
|
164
|
325
|
988
|
Chinchora (Dara)
|
150
|
149
|
298
|
1007
|
Telkhedi
|
178
|
177
|
354
|
1006
|
|
667
|
673
|
1339
|
991
|
Orissa
|
Kanthi
|
189
|
189
|
378
|
1002
|
Bodhimoha
|
190
|
192
|
381
|
1009
|
Bahubandha
|
167
|
164
|
331
|
986
|
Jharbeda
|
193
|
194
|
386
|
1006
|
Jagada
|
211
|
213
|
424
|
1009
|
|
948
|
951
|
1899
|
1003
|
West Bengal
|
Karnagarh
|
136
|
135
|
271
|
1004
|
Jaambani
|
135
|
134
|
268
|
1009
|
Choukidghata
|
150
|
154
|
303
|
976
|
Kankradara
|
152
|
161
|
312
|
943
|
Tantkanali
|
140
|
148
|
287
|
946
|
Jamda
|
135
|
143
|
278
|
943
|
Salbani
|
846
|
873
|
1719
|
969
|
The other interesting finding is that the proportion of women in total labourforce is above half in all age groups, but especially in the younger and the older age groups. This is particularly so for Tamil Nadu, followed by Maharashtra. This must be distinguished from our earlier data on workforce participation rates. While workforce is the economically active population involved in different economic activities, the labour force includes the unemployed, who are not active workers but seek work.
Women workers are largely concentrated in two activities: working on own or leased farms and in casual wage labour in agriculture. The pattern in Tamil Nadu is different with a number of women engaging in non-agricultural work such as in brick kilns, etc. Gathering minor forest produce, etc. is clearly an important activity feasible in forest areas. The relatively more equitable distribution of land in tribal areas of West Bengal and Orissa have resulted in a high engagement in family farms.
Literacy rates are significantly higher in West Bengal, as is the gender gap in attainment. Both literacy and the gap is the lowest in Maharashtra.
Table 27: Literacy Rate
State
|
Village
|
Literacy Rate
|
Male
|
Female
|
Persons
|
Tamil Nadu
|
Kalamaruthur
|
51
|
38
|
41
|
|
Kumaramangalam
|
52
|
35
|
40
|
|
Illupur
|
49
|
31
|
38
|
|
Keelaiyur
|
56
|
34
|
43
|
|
|
52
|
35
|
41
|
Maharashtra
|
Sayphal
|
38
|
21
|
28
|
|
Hardap
|
41
|
26
|
33
|
|
Chinchora (Dara)
|
33
|
19
|
25
|
|
Telkhedi
|
39
|
23
|
30
|
|
|
38
|
22
|
29
|
Orissa
|
Kanthi
|
42
|
33
|
39
|
|
Bodhimoha
|
39
|
28
|
34
|
|
Bahubandha
|
51
|
37
|
42
|
|
Jharbeda
|
37
|
24
|
30
|
|
Jagada
|
45
|
30
|
37
|
|
|
43
|
30
|
36
|
West Bengal
|
Karnagarh
|
63
|
40
|
54
|
|
Jaambani
|
59
|
32
|
48
|
|
Choukidghata
|
71
|
48
|
63
|
|
Kankradara
|
76
|
49
|
65
|
|
Tantkanali
|
69
|
43
|
59
|
|
Jamda
|
56
|
39
|
50
|
|
Salbani
|
53
|
38
|
48
|
|
|
64
|
41
|
55
|
One of biggest causes of distress and dispossession is the high-interest debt undertaken by rural workers and farmers, which leads to forced migration. This results in a vicious cycle of deprivation, debt and poverty, a cycle the NREGS is expected to break. In the table below, we present the extent of indebtedness, esp. to traditional moneylenders. A large proportion of the sample is indebted, and expectedly those without collateral incur more expensive but smaller quantum of debt from the informal sector’s usurious moneylenders.
The main characteristics related to indebtedness are presented in Table 26. From here, it is clear that by social category, SCs are the most indebted group in terms of incidence. In terms of occupational categories, casual labourers in agriculture have a high incidence of indebtedness. The quantum of debt per household is, not surprisingly, highest for the salaried who spend on consumer durables and cultivators or self-employed in agriculture who borrow to purchase inputs. Indebtedness is clearly a widespread phenomenon, cutting across social category and occupations. However, despite the high incidence of debt across all types of households, there is an inverse relationship between the average quantum of debt per household and social status.
A very striking feature is the continuing grip of non-formal usurious credit, though there is evidence of the emergence of ‘new moneylenders’ who operate along with traditional moneylenders. The new entrants to non-formal credit include government functionaries. Interestingly, West Bengal has a lower average size of household debt and comparatively lower dependence on non-formal usurious debt. However, at 46 per cent, this continues to be high.
Table 28: Percentage households who are Indebted, Source and Average Debt
|
|
|
Households in Debt
|
Non-formal Moneylender @ 3 to 5 % per month
|
Average Debt per HH in Rs
|
Tamil Nadu
|
Villupuram
|
55
|
94
|
6100
|
|
Nagapattinam
|
67
|
89
|
5200
|
Caste
|
SC
|
69
|
93
|
5632
|
|
Others
|
59
|
92
|
5600
|
Primary Occupation
|
Self Employment in Agriculture
|
46
|
71
|
11000
|
|
Self Employment in Non-Agriculture
|
54
|
84
|
10981
|
|
Casual Labour in Agriculture
|
78
|
91
|
5700
|
|
Casual Labour in Non-agriculture
|
63
|
89
|
7500
|
|
Grazing and Gathering
|
74
|
83
|
5900
|
Total
|
|
64
|
82
|
5800
|
Orissa
|
Mayurbhanj
|
74
|
82
|
8800
|
|
Sundergarh
|
51
|
64
|
10982
|
Caste
|
SC
|
68
|
81
|
6163
|
|
ST
|
59
|
67
|
13097
|
|
Others
|
54
|
68
|
11802
|
Primary Occupation
|
Self Employment in Agriculture
|
65
|
49
|
17,000
|
|
Self Employment in Non-Agriculture
|
45
|
74
|
6,091
|
|
Casual Labour in Agriculture
|
63
|
79
|
7,040
|
|
Casual Labour in Non-agriculture
|
58
|
78
|
11,295
|
|
Grazing and Gathering
|
68
|
84
|
7,087
|
Total
|
|
60
|
72
|
9156
|
Percentage households who are Indebted, Source and Average Debt (contd.)
|
|
|
Households in Debt
|
Non-formal Moneylender @ 3 to 5 % per month
|
Average Debt per HH in Rs
|
West Bengal
|
Bankura
|
59
|
44
|
6121
|
|
Midnapur
|
63
|
55
|
5336
|
Caste
|
SC
|
68
|
59
|
3471
|
|
ST
|
58
|
44
|
4120
|
|
Others
|
60
|
35
|
9700
|
Primary Occupation
|
Self Employment in Agriculture
|
71
|
32
|
7,521
|
|
Self Employment in Non-Agriculture
|
64
|
41
|
5,209
|
|
Casual Labour in Agriculture
|
55
|
58
|
4,050
|
|
Casual Labour in Non-agriculture
|
51
|
52
|
3,051
|
|
Grazing and Gathering
|
62
|
51
|
3,121
|
|
Salaried
|
70
|
|
12000
|
Total
|
|
62
|
46
|
5667
|
Maharashtra
|
|
|
|
|
|
Nanded
|
71
|
73
|
5244
|
|
Nandurbar
|
79
|
93
|
4129
|
Caste
|
SC
|
75
|
72
|
3988
|
|
ST
|
80
|
94
|
4537
|
Primary Occupation
|
Self Employment in Agriculture
|
79
|
75
|
5174
|
|
Casual Labour in Agriculture
|
80
|
98
|
5050
|
|
Grazing and Gathering
|
77
|
87
|
2254
|
|
Salaried
|
70
|
|
12000
|
Total
|
|
69
|
89
|
4621
| |