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Table 1 shows the mean and standard deviations for all the variables used in the analysis. The estimations of the demand for each of the two nutrients for 108 households of a rural area in Bangladesh are presented in table 2. The operational definition of each of the independent variables used in the regression model is as follows.
TABLE 1. Mean and standard deviations
|Education level of wifea||0.223||0.409|
|Education level of husbanda||0.676||1.04|
size (measured in terms of
adult male equivalents)
of minutes given to productive
economic activities (per day per
person) by male members of the household
of minutes given to productive
economic activities (per day per
person) by female members of the household
of minutes given to cooking
(per day per person) by female
members of the household
|Per capita expenditure on food per day (in take)||4.06||0.872|
a. Education score was determined
as follows: 0 = no formal education; 1 = grade 1-5; 2 = grade
6-9; and 3 = grade 10 and above.
b. See footnote a of table 2.
Per Capita Expenditure on Food
Expenditure on food was measured using information on the market price of each of the food items consumed per day by each member of the household, Information on market price per unit of food item was multiplied by total quantity consumed, and this was totalled for all food items for each member of the household to arrive at the total expenditure on food at the household level. This was divided by the total family size to calculate per capita expenditure on food. This is expressed in take per person per day, which was 4.06.
Education was measured in terms of number of years of schooling. The determining question was: "What is the highest grade of education you have completed?" This was coded as follows: 0 = no formal education; 1 = grades 1-5; 2 = grades 6-9; 3 = grade 10 and above. The mean education scores for father and mother were 0.68 and 0.22 respectively.
"Family" included those members of the household who normally ate from the same kitchen The determining question was: "What is the size of your family, i.e. those who normally (almost every day) eat from your kitchen?" Information was obtained from the head of the household. Family size was measured in terms of adult male equivalent, i.e. total calorie requirement of a household divided by calorie requirement of an adult male. An adult was considered to be a person aged 29 to 45 years. The mean family size, i.e. mean adult male equivalent, was 3.87.
Time Given to Productive Activities
Productive activities were defined as those generating income and contributing directly to physical capital formulation. Included in this category were crop production, animal husbandry, trading, wage labour, hut construction and repair, cottage industry and fishing, begging, and other self-employed skilled jobs such as carpentry, midwifery, and all other exchange labour. Numbers of minutes given to productive economic activities (per day per person) by male and female members of the household were 276 and 95 respectively
Time Given to Cooking
Included in this category were washing, processing/peeling, and cooking food. The number of minutes given to cooking (per day per person) by female members of the household was 93.
From table 2, the following findings are evident. Of the seven variables included in the model to explain nutrient adequacy, ail but one had a significant effect on energy adequacy, while only four were significantly related to protein adequacy. Of the six variables significantly affecting energy adequacy, the effects of three (husband's education, mother's employment status, and per capita expenditure on food) were positive, while those of the remainder (family size, number of minutes given to cooking) were negative. Among the variablesnumber of minutes given to productive activities by male members of the household (per day per person), number of minutes given to cooking by female members of the household (Per day per person), and expenditure on food (per day per person)significantly affecting protein adequacy, all but one (expenditure on food) had a negative effect. The variables considered in the model explained 56 to 43 per cent of the variations in dietary adequacy of calories and protein respectively, which is statistically significant.
With the exception of the relationship between husband's education and calorie adequacy, none of the educational measures was significantly associated with nutrient adequacy. This exception, however, is positive and significant. For calories, the implied education (husband) elasticity at the point of sample mean is 0.014. The estimate implies that at the sample mean, each additional level of husband's education was associated with a 3.2 per cent increase in calorie adequacy of a household. Male education has positive effect on nutrition, which is mostly due to the positive association of education and expenditure. Education is associated with lower prices paid by the household, perhaps because ability to choose is enhanced. The overall poor relationship observed between education and nutrient adequacy may be attributed, among other things, to lack of sufficient variability of educational score in the sample for its effect to be picked up by regression.
TABLE 2. Factors affecting nutrient intake for households in a rural area of Bangladesh: regression analysis (OLS) (N = 108)a
|Right-hand side variables (i.e. independent variables)||Nutrient intake expressed as RDAb|
|co-efficient||t values||level||co-efficient||t values||level|
|Education level of wife||0.8166||0.312||NSc||4.56||0.833||NS|
|Education level of husband||2.13||1.99||0.0495||-1.95||-0.869||NS|
|Family sized||- 1.40||- 1.99||0.0493||- 2.60||- 1.76||0.0804|
|Number of minutes given to productive activities by male members of the household (per day per person)||- 0.0195||- 2.51||0.0136||- 0.0519||- 3.19||0.0019|
|Number of minutes given to productive activities by female members of the household (per day per person)||0.0551||3.64||0.0004||0.0497||1.57||0.1193|
|Number of minutes given to cooking by female members of the household (per day per person)||- 0.1118||- 4.04||0.0001||- 0.1876||- 3.24||0.0016|
|Expenditure on food (per day per person) in taka||9.84||7.12||0.0001||18.63||6.45||0.0001|
a. Households consisted of 572
b. This is the ratio of the total nutrient (calorie/protein) intake of a household divided by its total nutrient requirement, times 100.
c. NS = not significant.
d. Family size was measured in terms of adult male equivalent, i.e. total calorie requirement of a household divided by calorie requirement of an adult male. An adult is considered here as one who is in the age group 29-45.
e. R2 gives the percentage of variance in the dependent variable which is accounted for by the independent variables.
f. F value indicates whether or not the independent variables significantly explain the changes in the dependent variable.
Family size had a negative effect on nutrient adequacy, and its relationship to calorie and protein adequacy was statistically significant at 0.05 and 0.08 respectively. The implied family size elasticities at the point of sample means were only -0.05 for calories and -0.07 for protein. The estimations imply that at the sample means, the addition of an adult person to the family reduces family calorie and protein adequacy by 0.36 and 0.67 per cent. One implication of this finding is that in a densely populated, resource-meagre, rural country like Bangladesh, where economic opportunities are very limited, increased family size will have a negative effect on the nutrient intake of a poor rural household. This is contrary to the myth of the economic value of children that obtains in subsistence countries.
Female Participation in Economic Activities and Time Spend on Food Preparation/Cooking
The effects of female participation in productive activities and of food preparation time on the nutrient adequacy of a rural household were contradictory. Females' participation in productive activities was positively associated with nutrient adequacy, while their food preparation time was detrimental. The co-efficient estimates of women's participation in productive activities and food preparation time are statistically significant.
Every additional unit input of time by women to productive activities was estimated to result in an increase of about 0.05 to 0.06 per cent for the protein and calorie adequacy of a family, and these increases were significant. But every additional unit of time given to preparation of food by women resulted in an estimated significant decrease ranging from 0.11 to 19 per cent for these values. Once income was controlled for, these negative findings contradicted predictions of the best use of household time model (i.e. theories of home production). This underscores the fact that efforts to involve women in market activities will have negative effects on human resources due to loss of home production. Greater participation in home production is expected to increase nutrient efficiency, because less time is spent purchasing goods. However, the results showed the opposite.
The co-efficient estimate for women participating in productive/market activities also suggested important specific effects on income. The data from the present survey and other studies in Bangladesh  clearly show disproportionately greater participation of rural poor women in the labour market, and the main reason for their participation is to augment family income to ensure the bare survival of the family. This has the effect of boosting the nutrient adequacy of the family, as the findings from the present study confirm.
Male Participation in Productive Activities
Male participation in productive activities was negatively associated with nutrient adequacy of a household. The implied male market time elasticities at the point of sample means were -0.05 for calories and -0.10 for protein. The estimates indicated that at the sample means an increase of one minute to productive activities by a male member reduced family calorie and protein adequacy by 0.02 and 0.04 per cent, and these reductions are significantly different from zero.
This negative association may indicate, among other things, that the members of poor rural households, who usually work for long hours, do not earn enough additional income to offset the increased calorie requirement of their work. This negative finding, plus the positive relationship between female participation in market activities and nutrient adequacy, may also indicate that, in contrast to working men, working women tend to spend a higher proportion of their income on food, while men spend more on non-food stuffs, as findings from other studies suggest  .
Per Capita Expenditure on Food
The co-efficient estimation of per capita expenditure on food (per day) indicated a strong positive effect on each of the two nutrients. This is to be expected in a low-income rural society like Bangladesh. The implied expenditures on food elasticities at the sample means were 0.38 for calories and 0.52 for protein. The point estimates imply that increased expenditure on food by one take per person per day was associated with 10 and 19 per cent increases in the calorie and protein adequacy of a rural household These increases are significantly different from zero. The coefficient estimates of per capita expenditure on food lent support for a strong income or wealth effect on nutrient intake. This finding reinforces the emphasis of the World Bank on raising income as the critical factor in improving nutrition in poor countries  .
The findings presented here are part of a major study entitled "Determinants of Intra-familial Distribution of Food and Nutrient Intake in Rural Bangladesh." The financial support received from UNICEF to analyse the data is gratefully acknowledged. The author is grateful to Professors Nevin Scrimshaw and Lance Taylor at MIT, Richard Jolly and Dr. Hossein Ghassemi of UNICEF, and Professor Paul Streeten of Boston University for intellectual and moral support. The author also thanks Boston University for providing computer facilities, and Mr. William Marshall, Programme Analyst of the aforesaid university. Mr. Fazlal Karim Chowdhury of Sylhet Government College, who supervised the collection of data, deserves special thanks for his meticulous work.
The views expressed in this article are those of the author and in no way reflect the opinion of the organization to which he is currently affiliated. The author bears sole responsibility for any errors in the paper.
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