Variables |
Ghana % |
Kenya % |
Zambia % |
Wealth of the household Poorest Poorer Middle Richer Richest |
33.38 22.83 19.58 13.68 10.54 |
34.58 21.2 16.8 14.65 12.77 |
24.02 23.81 22.89 16.94 12.34 |
Occupation of the woman Not working Professional Self employed Manual work |
9.58 2.72 74.19 13.51 |
37.03 7.73 41.28 13.95 |
38.42 2.96 56.11 2.51 |
Source of getting water Pipe Public tap Well Other |
27.81 36.73 22.72 12.73 |
30.83 8.9 53.92 6.36 |
23.42 24.09 51.49 1.0 |
Education of woman No education Primary Secondary Higher |
44.33 20.01 33.74 1.93 |
21.82 52.94 19.02 6.21 |
11.07 55.87 29.56 3.5 |
Partner’s education No education Primary Secondary Higher |
36.2 11.02 45.37 7.41 |
18.56 48.81 23.86 8.77 |
6.63 43.43 43.06 6.89 |
Decision making in the household Respondent alone Respondent and husband Husband/partner alone Someone else |
25.67 53.01 20.88 0.44 |
33.37 40.73 25.6 0.3 |
29.37 43.14 27.02 0.47 |
Marital status Not married Married |
30.16 69.84 |
19.49 80.51 |
17.91 82.09 |
Residence Urban Rural |
39.22 60.78 |
31.31 68.69 |
36.44 63.56 |
Antenatal care visits No visits Between 1 and three visits 4 or more visits |
3.57 12.42 84.01 |
5.86 39.67 54.47 |
1.37 67.25 31.38 |
Source: Authors compilation from the DHS, 2014 data.
Majority of the women in our sample also had a minimum of four antenatal care visits in Ghana and Kenya.
Table 2 shows summary of the mean and the standard deviation of descriptive characteristic of the survey respondents. The mean Z- score was -0.67 with maximum and minimum values of -5.28 and 3.44 in Ghana. In Kenya, the mean Z-score was -0.75 with maximum and minimum values of -5.39 and 4.45. This is slightly different for Zambia where the mean Z-score was recorded as -0.90 with maximum and minimum values of -5.59 and 4.13. The mean age of the child and the mother in Ghana were recorded to be 59 weeks and 30 years respectively. In Kenya and Zambia, the mean age of the child and the mother were recorded to be approximately 2 years and 29 years respectively. The descriptive statistics also reveals that the average Body Mass Index (BMI) of the women in Ghana, Kenya and Zambia were 23.69, 22.92 and 22.56 respectively, with the highest being recoded in Kenya as 58.40. The average child in the study was approximately the fourth child in the three countries, with the minimum being one and maximum values of 13 and 15 children recorded in Ghana, Kenya and Zambia, respectively.
Table 2: Summary result of some characteristics of the respondents across the three countries
Variable |
Observations |
Mean |
Std. Dev. |
Minimum |
Maximum |
Weight for age z score Ghana Kenya Zambia |
1080 8899 10869 |
-0.67 -0.74 -0.90 |
1.14 1.16 1.12 |
-5.28 -5.39 -5.59 |
3.44 4.45 4.13 |
Woman’s BMI Ghana Kenya Zambia |
1080 8899 10869 |
23.69 22.92 22.56 |
4.41 4.39 3.82 |
15.10 13.52 12.24 |
54.35 58.40 55.52 |
Age of child Ghana Kenya Zambia |
1080 10869 8899 |
0.59 1.99 1.97 |
0.76 1.41 1.40 |
0.00 0.00 0.00 |
4.00 4.00 4.00 |
Age of woman Ghana Kenya Zambia |
1080 10869 8899 |
29.59 28.85 28.87 |
6.94 6.98 6.55 |
15.00 15.00 15.00 |
49.00 49.00 49.00 |
Birth order of child Ghana Kenya Zambia |
1080 10869 8899 |
3.50 3.78 3.50 |
2.19 2.45 2.32 |
1.00 1.00 1.00 |
13.00 15.00 15.00 |
Source: Authors compilation from the DHS, 2014 data.
Empirical findings
The results presented in Table 3 indicate that the nutritional status of the child is influenced by the decision-making structure in the household, the wealth of the household, education of the woman and household source of water. The results also indicate that the age of the child, the size of the child at birth, and the nutritional status of the mother are significant factors that influence child nutritional status. Wealth index is positively associated to the nutritional status of children in Ghana, Kenya and Zambia. The positive sign reveals that all the wealth categories are more likely to have a better nutritional status compared to the reference group, poorest. The effect of wealth is stronger in Households in Kenya and Zambia than in Ghana. Also, the results indicated that women with at least secondary education in Kenya are more likely to have well-nourished children than those with no education. Nutritional status of the child was found to affect the nutritional status when households’ decisions are made by someone else compared to households where decision is made by the woman. However, the nutritional status of the child improves with the nutritional status of the mother.
The results equally reveals that the size of the child at birth has a positive effect on the nutritional status of the child. We found that children with average and small size were more likely to have poor nutritional status compared to children with normal size at birth. Age of the child, gender of the child and the birth order of the child have a negative effect on the nutritional status of the child. Female child is more likely to be well-nourished than the male child. The results on birth order suggests that the household nutritional resource allocation is biased towards younger children. We also found that children who were exclusively breastfed, as recommended by WHO, are more likely to have a better nutritional status than children who received supplementary feeds and those who were breastfed beyond 24 months after birth.
Discussion
The wealth of the household is statistically significant at the one and ten percent levels of significance for all the wealth quintiles for Kenya and for the middle and richest wealth quintiles in Zambia. This suggests that the nutritional status of the child is highly related to the wealth of the household in Kenya and Zambia. Thus, compared to the households in the poorest wealth quintiles, all the households from the poorer to the richest wealth quintiles in Kenya and the households in the middle and richest wealth quintiles in Zambia are more likely to have well-nourished children. We also found that poorer households are also more likely to have well-nourished children compared to the poorest households in Ghana. These results indicate that compared to poorer households, richer households are more likely to have well-nourished children. This is not surprising as household wealth status determine nutritional intake of the mother during pregnancy and after birth. Wealth status also affects the ability to provide for the required nutritional needs of the child. This results are in concordance with earlier findings that suggested that the nutritional status of the child is affected by the wealth of the household [9, 13].
The results also indicate that the education of the woman has a positive and significant on child health at the 5 percent level of significance for Kenya suggesting that women with at least secondary education are more likely to have well-nourished babies than women with no education. The finding implies that partner’s educational level is important in influencing the nutritional status of the child in Zambia. Our finding is suggestive that partners with higher levels of education are more likely to have well-nourished children than those with no education. We found the influence of education on child health to be absent in Ghana for the woman as well as the partner. The implication is that educated women and also partners who are educated may have better knowledge of childcare practices, which helps them in the child’s upbringing and feeding. These findings are in conformity with similar findings [9, 10, 11, 12] that finds education correlates with child nutritional practice. Thus, to promote child health, it will be necessary to encourage women to pursue education, at least beyond the primary level as suggested by our findings. Indeed, as Kabubo-Mariaraa et al. posits, maternal education improves nutrition through altering the household preference function and also through better child care practices, indicating the importance of human capital investments in improving children’s nutritional status [16].
Our findings reveals that decision-making structure of the household and the nutritional status of the mother have a significant effect on the nutritional status of the child.
Table 3: Results from the empirical model across the three countries
VARIABLES |
Ghana |
Kenya |
Zambia |
Wealth of household Poorer Middle Richer Richest |
0.193*(0.101) 0.0664(0.125) -0.123(0.189) 0.214(0.210) |
0.167***(0.058) 0.284***(0.066) 0.301***(0.072) 0.442***(0.088) |
0.0749(0.048) 0.095*(0.050) 0.026(0.065) 0.265***(0.084) |
Education of the mother Primary Secondary Higher |
0.106(0.129) 0.0662(0.123) 0.302(0.299) |
0.099(0.067) 0.280***(0.084) 0.412***(0.121) |
0.046(0.053) -0.006(0.063) 0.067(0.169) |
Partner’s education Primary Secondary Higher |
-0.152(0.138) -0.111(0.108) 0.168(0.195) |
-0.071(0.081) -0.032(0.093) -0.121(0.109) |
0.045(0.070) 0.100(0.073) 0.267**(0.121) |
Occupation Professional Self-employed Manual work |
-0.074(0.263) 0.048(0.124) 0.002(0.173) |
0.068(0.093) 0.0144(0.053) -0.020(0.067) |
0.025(0.161) -0.036(0.036) 0.067(0.105) |
Household source of water Public tap Well Other sources |
-0.034(0.119) 0.125(0.117) 0.263**(0.133) |
-0.122(0.077) 0.0523(0.053) 0.103(0.083) |
-0.084(0.066) -0.115*(0.061) -0.376**(0.159) |
Household decision making Woman and partner Partner alone Someone else |
0.0210(0.108) -0.113(0.123) -0.291*(0.175) |
-0.080*(0.044) -0.015(0.047) -0.749***(0.283) |
-0.023(0.039) -0.014(0.044) -0.341*(0.197) |
Marital status of woman Married |
-0.117(0.104) |
0.101(0.079) |
-0.257*(0.139) |
Place of residence Urban |
-0.076(0.139) |
-0.016(0.054) |
0.221***(0.052) |
Antenatal care use Less than 4 visits 4 or more visits |
-0.027(0.297) 0.171(0.300) |
0.018(0.105) 0.082(0.105) |
0.016(0.141) 0.049(0.143) |
Type of breastfeeding (Exclusive) Supplementary feeding More than 24 months |
-0.372***(0.104) -0.547**(0.240) |
-0.461***(0.065) -0.660***(0.089) |
-0.492***(0.054) -0.560***(0.089) |
Size of child at birth Average Small |
-0.192*(0.113) -0.601***(0.136) |
-0.224***(0.045) -0.640***(0.062) |
-0.242***(0.036) -0.600***(0.065) |
Female child |
0.014(0.081) |
0.100**(0.0408) |
0.133***(0.033) |
Birth order of child |
0.015(0.024) |
-0.0199*(0.0107) |
-0.018*(0.010) |
Log age of mother |
-0.182(0.177) |
0.085(0.086) |
0.048(0.076) |
Log age of child |
-0.085(0.092) |
-0.118***(0.018) |
-0.101***(0.017) |
BMI of mother |
0.0312***(0.007) |
0.029***(0.005) |
0.044***(0.005) |
Constant |
0.046(1.180) |
0.381(0.285) |
-0.003(0.303) |
Observations |
910 |
5,169 |
5,833 |
R-squared |
0.183 |
0.176 |
0.130 |
Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1
The nutritional status of the child is poorer in cases where someone else makes the decision in the household compared to when the woman makes such decisions. This is significant at 5 percent in Kenya, but at the 10 percent significance level for both Ghana and Zambia. The nutritional status of the child worsens when both partners make the decision in Kenya compared to when the woman makes the decision alone in the household. The results, therefore, suggests that as a means of improving the nutritional status of the child, it may be better to include the mother in such decisions. In SSA, it is a common norm for other family members to make decision on child’s upbringing in the household. The mother, however, may have better information about the child and this may be necessary to allow her engage in the decision making process regarding the child in the household. In addition, the effect of the mother’s BMI, a measure of the nutritional status of the mother, has a significant and positive effect on the nutritional status of the child in Ghana, Kenya and Zambia. Thus, well-nourished mothers are more likely to give birth to well-nourished babies given the importance of the woman’s nutritional status during pregnancy and after birth. Thus, as a means to improving the nutritional status of the child, it may be necessary to also focus on improving that of the mother.
Our findings indicate that the age of the child is negatively associated with the child’s nutritional status. Thus, the nutritional status of the child worsens as the child grows. This is significant at the 5% level for the Kenya and Zambia. The sign, even though negative in Ghana, is insignificant. Also, we find that the nutritional status worsens for higher order births. This is significant for Kenya and Zambia. This results suggests that the resource allocations in the household are biased towards earlier births, with less focus on later births. This result confirms the findings that older children have poor nutritional status. Indeed, as the child grows, his/her nutritional needs also increase, warranting the necessary supplements to ensure optimal growth. Thus, as a strategy to improve and ensure the optimal growth of the child, it is necessary that equal attention it paid to the older child as well as younger ones. Evidence has shown that health of children that grew up or exposes to dirt or other contaminated objects are affect negatively [19]. This notwithstanding, most children also become very active, coupled with the stress of school. Thus, it becomes important to give proper attention to the child as the child grows and his nutritional requirements increase.
Our results also reveals that the size of the child at birth is positively associated with the child’s nutritional status. Small or average size at childbirth are more likely to have poor nutritional status than children with normal size at birth. This is significant at the one percent level for children in Kenya and Zambia for small and average sized babies. The positive and significant effect of the size of the child at birth on the nutritional status of the child has already been alluded to by a previous study [17]. This positive relation is explained by the fact that normal sized babies’ might have a stronger immune system, which helps resist sickness and prevent anemia. Thus, “smaller-sized” and “average sized” babies need to be given the needed attention to ensure optimal growth like their “normal” counterparts.
Our findings also indicate that the type of breastfeeding has a significant effect on the nutritional status of the child. When compared to children who were exclusively breastfed, children who are given supplementary feeding and those who were breastfed beyond 24 months turn out to be malnourished. This result is significant at the 5 percent level in Ghana, Kenya and Zambia. Thus, exclusively breastfeeding the child contributes to the significant improvement in the child’s nutritional status, compared to supplementary feeding and feeding beyond 24 months. Indeed, as recommended by the WHO, exclusively breastfeeding (breastfeeding the child for six months without food supplements or water) helps children grow better and helps to also improve their immune system, which helps the children fight some diseases. It is therefore important that children are exclusively breastfed for six months before other food supplements are introduced to them. As suggested by Hong, the negative effect of breastfeeding the child over 24 months compared to exclusive breastfeeding may be due to reverse causality whereby mothers of malnourished children breastfed for longer or poorer mothers are more likely to continue breastfeeding as a substitute for appropriate complementary feeding [9]. Thus, it may be optimal for parents to wean off their babies at 2 years and introduce them to appropriate feeds as our results suggests that breastfeeding beyond 24 months worsens the child’s nutritional status.
Conclusion
Certain socio-economics characteristics such as household wealth index, educational level of mother affect child’s nutritional outcome. Policy could thus address the deficits in education in Kenya and perhaps other countries with similar challenges of poor child nutritional outcomes. Parents should also be educated on the importance of providing the appropriate nutritional and dietary requirement for all children irrespective of age and birth order. This is important to ensure that all children in the household maintain an appropriate nutritional status and grow as expected. This will help in the effort to improve child health. Such health education can be provided at the antenatal care points and through informal education using radio jingles and television announcements for behavioral change communication.
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