(1966-15) ^ (1976-85)b/
Socio-economic Number Exposed IMR Number Exposed IMR %
factors of deaths births /1000 of death births /1000 change
Mother's education None 1424 9190 155 760 7987 95 -39 ** Some 62 550 113 50 876 57 -50 ** Father's education Illiterate 851 5357 159 411 4280 96 -40 ** Literate / no schooling 389 2641 147 93 1120 83 -44 **
Literate / some schooling 246 1739 141 306 3463 88 -38 **
Mother's work status
Working 173 951 182 48 517 92 -49 ** Not working 1313 8786 149 762 8346 91 -39 ** Contraceptive use Never used 1428 9122 157 692 6816 101 -36** Ever used 58 618 94 118 2047 57 -39 ** Place of residence Rural 1460 9502 154 675 6873 98 -36 ** Urban 26 238 109 135 1990 67 -39 * Land-holding No land-holding 159 1681 94
Under half a hectare 254 2706 93
Half a hectare and above 397 4476 88
Number of cows None 231 2479 93 1-3 233 2395 97 3 + 346 3989 86 Health care Access 202 2677 75 No access 608 6186 98 Overall 1486 9742 153 810 8863 91 -41 **
Notes: Working category of the mother includes only those who worked after marriage in gainful employment.
** and * indicate significant at the one per cent and 5 per cent level respectively in the test of difference in two proportions.
NFFS6/ according to the socio-economic variables of interest to this study. The patterns of relationship between the categories of the variables under study, except for cows possessed by a household from the 1986 NFFS, and child mortality rates based on both surveys (Table 4.2) are similar to those for infant mortality (Table 4.1). Mortality differentials according to socio-economic variables, as expected, are more pronounced for children than for infants. This could be due to differences in exogenous factors such as health, nutrition, food, shelter and environment which are more likely to be important for survival status of children than for infants (Cleland, 1989).
In the literature favourable survival prospects for children of mothers with education have been explained as the consequence of better ability to care for children enhanced by their educational qualifications (Caldwell, 1979; 1981; Caldwell and McDonald, 1981). As children during their first five years of life are likely to be more close to their mothers, every characteristic of mothers could influence the survival prospects of their children. The results in Appendix Table 4.4 suggest that women with some education in Nepal, in general, are likely to represent the higher socio-economic group. So, the influence of mother's education on child survival, in this study, is less likely to be the consequence of mother's ability to care for children enhanced by their educational qualifications. It could rather be a reflection of the favourable familial resources, environment, ability to afford better food, shelter, clothing, health and other child needs among mothers with some education. Similarly, differentials in child mortality by father's education, could also be the consequence of income differentials among the educated and un-educated fathers where those with education are likely to be associated with better income and could afford better services to their children.
The evident higher risk of death among children of working mothers could be the consequence of inadequate time allocated by mothers for child care because of their time commitment to the labour force. A Multipurpose Household Budget Survey (NRB, 1988: 68-69) in Nepal estimated that women in the labour force are likely to work five days less 6/ For child mortality rates the 1976 NFS refers to the period 1961-70 and the 1986 NFFS refers
Table 4.2 Child mortality rates by socio-economic variables: 1961-80, Nepal.
(1971-80)b/
Socio-economic Number Exposed CMR Number Exposed CMR %
factors of death children /1000 of death children /1000 change
Mother's education No education 682 6237 109 399 5832 68 -38 ** Some education 16 328 49 17 648 26 -47** Father’s education Illiterate 461 3684 125 232 3178 73 -42** Literate / no schooling 169 1906 89 61 974 62 -30 *
Literate / some schooling 68 972 70 123 2328 52 -26
Mother’s work status
Working 82 610 134 53 409 129 -4 Not working 616 5953 103 363 6071 59 -43** Contraceptive use Never used 656 6025 109 318 4562 69 -37** Ever used 42 540 78 98 1918 51 -35 * Place of residence Rural 693 6361 109 357 4925 72 -31** Urban 5 204 25 59 1555 37 +48 Land-holding No land-holding 93 1201 77
Under half a hectare 146 1903 76
Half a hectare and above 177 3376 52
Number of cows None 137 1805 75 1-3 98 1607 60 3 + 181 3068 59 Health care Access 69 1727 39 No access 347 4753 73 Overall 698 6565 106 416 6480 64 -40 **
Notes: Working category of the mother includes only those who worked after marriage in gainful employment.
** and * indicate significant at the one per cent and 5 per cent level respectively in the test of difference in two proportions.
than men every month due to their responsibility with domestic work, childbirth and child care. This survey further noted that maternity leave utilized by mothers in the hills was less than a month. The total working days in a year for women in rural areas were estimated to be 254 days (NRB, 1988: 68-69). This suggests that those women who are not working outside the home have an extra 254 days in hand which can be allocated for the care of children according to a child's needs. This pattern clearly indicates that children of working mothers are in a disadvantageous situation in terms of mother's time allocation for child care in comparison to children of mothers not working . On top of this, fatigue due to the long day’s work, as discussed in the earlier sections of this chapter, could have further reduced the quality of child care rendered by mothers after work hours.
Favourable child survival prospects among urban dwellers could be due to the better access to health care and employment opportunity. Even women in the lower socio economic stratum in the urban areas may have adopted some of the child care behaviour of educated women because the chances to observe the behaviour of educated women in the higher socio-economic stratum in the urban areas are likely to be greater than in rural areas. All these factors could have contributed to improved child survival prospects in the urban areas compared with the rural areas.
The differentials in child mortality by size of land-holding and cows possessed by a household, on the other hand, can be attributed to income differentials among these households where the households with higher income are likely to be in a better position in meeting the child's needs. Better child survival prospects among those in a household with more cows was also noted in Bangladesh (D'Souza and Bhuiya, 1982: 763).
Table 4.2 also shows that death rates from both surveys for children whose mothers had ever used contraception were lower than for those whose mothers had never used contraception. Similarly, the lower death rate among children from households which had access to health services is also shown from the 1986 NFFS.
4.5
The effects of joint socio-economic variables
In most societies, educated women tend to marry educated men and enjoy a higher standard of living (Cleland and Van Ginneken, 1989: 83). If both the mother and the father of a child are educated then their education could be operating jointly in influencing child survival. In this case the influence of parents' education on infant and child mortality could be working through income. Majumder (1989: 104) suggested that the effects of mother's education on child mortality rate after controlling for the effects of father's education should be negative if mother's education is influencing child survival through her knowledge and practice of child health care.
Table 4.3 reveals the higher rate of infant loss among mothers with no education after controlling for father's education. Father's education on the other hand does not show a consistent pattern of relationship with infant mortality from either survey after controlling for mother's education. For example, mortality rates among infants of mothers with no education from the 1976 NFS decrease with the increase in father's education while these two variables from the 1986 NFFS show a U-shaped association. Quite different patterns of relationship between father's education and infant mortality between the two surveys are also evident for infants of mothers with some education. However, the results in Table 4.3 suggest higher exposure to death among infants of illiterate parents as against infants of literate parents. This, in general, suggests the possibility of interaction between father's education and mother's education in Nepal in influencing infant mortality.
The higher risk of death for children bom to mothers with no education as against those to mothers with some education, except to those with 'illiterate' fathers, is clearly evident after controlling for father's education (Table 4.3). Similarly, except for the mothers with some education from the 1976 NFS, the risk of death to children declines with the increase in father's education after controlling for mother's education. As is the case with infant mortality, there is a higher rate of child loss among parents with no education as compared to parents with some education.
It can be hypothesized that better educated women are more likely to participate in the labour force and earn more. In this situation the education and the work status of the mother are likely to operate interactively in influencing the infant and child mortality rates. Among infants of mothers with no education, results from both surveys suggest higher mortality rates where mothers are working than where mothers are not working (Table 4.4).