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Abstract:
The most common perception is that the prevalence of malnutrition among females is higher than males. To solve this dilemma, the present article analyzes the impact of socioeconomic factors on the nutritional status of under-five children by gender using a binary logistic regression model. Utilizing the Pakistan Demographic and Health Survey [PDHS]-2013 data, a CIAF index is constructed to measure child malnutrition. The disaggregation analysis illustrates that the working status of mothers, mothers not having ownership of assets, women not involved in income decisions, and urban place of residence are found major contributors in male child malnutrition. However, factors such as higher birth order and diarrhea contribute to malnutrition in female children. The study concludes that both male and female children have a higher probability of being malnourished, but effects have been found more in male children in disaggregated analysis than female counterparts
Key Words:
Introduction
In many developing countries, children and adults are in danger to be malnourished because of insufficient nutritional consumption, absence of prevention from infectious diseases, lack of appropriate maternal and parental care, as well as unbalanced delivery of food within the household of that child. Children’s nutritional status is a depiction of their mental as well as physical health. Children with sufficient dietary intakes are not exposed to repeated illness, and they reach their potential growth level (Khan & Raza, 2014). The low family income, maternal illiteracy, and large family size are the significant causes of higher mortality and morbidity rate in under-five children in Pakistan (Khan, 2014). Nutritional problems of women such as iron deficiency, lower body mass index during the pregnancy period affect children’s health inversely. A malnourished mother’s children are more likely to face lower resistance to infection, mental weakening, the higher hazard of diseases and mortality, and short stature in whole life. Malnutrition in girls in their adult age or reproductive age can cause to create a vicious cycle of undernutrition and poverty (Choudhury et al., 2000; Mehrotra 2006).
Habicht et al. (1974) suggested that height or health of children is not as much affected by race or ethnicity in the first five years of life as by the gender difference exclusion. According to Wamani et al. (2007) in wealthy children, the sexual dimorphism normal pattern occurs where males tend to be taller and heavier than females. It can be observed from previous studies that female children are more malnourished compared to male children due to gender discrimination at the household level, i.e. differentiation in caring practices during illness, unequal food distribution among male and female child (Castel et al., 2006; Jawaregowda et al., 2015; Khatun et al., 2004).
The male children are generally considered to be more favored by their parents. Therefore, the probability of their being malnourished is supposed to be less than girls. It may be argued that socio-economically girls are more deprived than males, so the factors towards female malnutrition can be higher than male children. The main consideration of this research is to solve this predicament that whether more socio-economic factors contribute to male child malnutrition or female child malnutrition. The present study hypothesizes that in Pakistan, where more than one-third population lives below the poverty line (Usman, 2016), the socio-economic factors will contribute more to female malnutrition. Therefore, the objective of the current research is to investigate gender impact by observing that whether more socio-economic factors determine malnutrition in either male children or female children.
Data and Methodology
This research utilizes PDHS (2012-13) data for both male and female models. The sample size for male children and female children is 1128, and 1099 respectively. In the logistic regression, the results for CIAF determine the probability to increase anthropometric failure in both male and female children. CIAF is the dependent variable in both male and female children models.
Construction of CIAF
Estimating the prevalence of malnutrition in children, a CIAF index is generated. It is used as an indicator of nutritional value. According to WHO (2006) standards, three indices are measured in the form of Z-Score in CIAF. These three are expressed as follows:
1) Stunning (Chronic Malnutrition) if height for age Z-score<-2 standard deviations (SD)
2) Wasting (Acute Malnutrition) if weight for height Z-score < -2 SD
3) Underweight (Any Protein-Energy Malnutrition) if the weight for age Z-score < -2 SD
But these three indices may not provide a comprehensive estimation of child malnutrition. According to CIAF classification, children are divided into seven groups which are as follows:
A: No Failure, B: Stunted only, C: Wasting only, D: Under-weight only, E: Stunted & Under-weight, F: Wasting & Under-weight, and last is G: Stunting, Wasting, & Under-weight. The total measure of child malnutrition prevalence is calculated by combinations of all except group A. It is binary variable use “1” if the child is malnourished otherwise use “0” if the child is not malnourished.
Construction of Wealth Index
Demographic Health Survey formulated wealth index based on household’s assets data, ownership of different household consumer items like television, bike, cars and other household characteristics like floor type in the household, sanitation facilities in the area and source of drinking water, are also included. It is indicating that the wealth level is consistent with income and expenditure measures (Rutstein, 2005).
Econometric Models
Following two econometric models estimated in this research work:
Model-1(Male child malnutrition)
CIAF (male child) = ?0GOCij+ ?1 CAMij+?2SCAMij+ ?3BONij+ ?4MAGEij+ ?5MELij+ ?6MBMIij +?7MESij+ ?8AOWij+ ?9DSWOEij+ ?10RTIij+ ?11TPRij+ ?12NCFij+ ?13WIij+ ?14HDRij+ ?ij-- (1)
Model-2(Female child malnutrition)
CIAF (female child) = ?0GOCij+ ?1 CAMij +?2SCAMij+ ?3BONij+ ?4MAGEij+ ?5MELij+ ?6MBMIij + ?7MESij+ ?8AOWij+ ?9DSWOEij+ ?10RTIij+ ?11TPRij+ ?12NCFij+ ?13WIij+ ?14HDRij+ ?i----(2)
In equations 1 and 2, coefficients enlightening the amount of relationship with CIAF are ?'s. While in the model error term is ?. The explanations about variables are explained below
Table 1. Variables used in models 1 and 2 (operational explanation)
| Names of variables width="294">Description of variables | > Dependent Variable: width="294">
| > CIAF (Composite Index of Anthropometric width="294">1 = if malnourished child, 0 if not | > Failure) width="294">
| > Explanatory Variables: width="294">
| > Children specific characteristics width="294">
| > Gender of the children width="294">1 = male, 0 = female | > Children’s age in months width="294">1 = ? 6 month, 2 = 7-12, 3 = 13-18, 4 = 18-24, 5 = 25-36, 6 = 37-48, 7 = 49-60 | > Square of age in months of children width="294">Measured as a continuous variable | > Birth order number width="294">Birth order 1=1, 2 or 4= 2, 5 or 6= 3, 7 or above = 4 | > Maternal specific characteristics width="294">
| > Mother age at the first child birth width="294">1 = ? 20 year, 2 = 21-25, 3 = 26-30, 4 = 31-35, 5 = 36-40, 6 = >40 years | > Mother’s education level width="294">Illiterate = 0, primary = 1, secondary = 2, higher = 3 | > Mother body mass index width="294">1 = MBMI > 18.5kg/m2, 0 = MBMI ? 18.5kg/m2 | > Mother’s Employment Status width="294">1 = working, 0 = not working | > Asset Ownership by Women width="294">1 = yes, 0 = no | > The decision to Spend Woman’s Own Earning width="294">1 = involved in decision, 0 = not involved | > Received Tetanus Injection width="294">1 = yes, 0 = no | > Household specific characteristics width="294">
| > Type of Place of Residence width="294">1 = urban residence, 0 = rural | > Number of Children under Five in a household width="294">1 = one, 2 = two, 3 = three, 4 = greater than 3 | > Wealth Index width="294">1 = poorest, 2 = poorer, 3 = middle, 4 = richer,5 = richest | > Disease-specific factors width="294">
| > Had Diarrhea recently width="294">1 = yes, 0 = no |
Results and Discussion
The logistic results in Table 3 for male children depicts that child’s age in months (P-values = 0.010), child age square (P-values = 0.023), low education of mother (p value = 0.019), low mother BMI (P-values = 0.003), working status of mother (P-values = 0.057), mother not received tetanus injections (P-values = 0.021), mother not having ownership of assets (P-values = 0.037), women not involved in income decisions (p value = 0.002), urban place of residence (P-values = 0.023) and poor wealth status (P-values = 0.000) were found major contributors in malnutrition of male children. The results in female children show that child’s age in months (P-values = 0.026), higher birth order (P-values = 0.014), low education of mother (P-values = 0.013), low mother BMI (P-values = 0.001), mother not received tetanus injections (P-values = 0.044), child having diarrhea (P-values = 0.033) and poor wealth status (P-values = 0.002) are main contributors in female child malnutrition (Table 3). By investigating the Pakistan Demographic and Health Survey dataset for each explanatory variable, the malnutrition prevalence percentage in children with respect to diverse characteristics is explained in Table 2 below:
Table 2. Estimation of malnutrition in children (CIAF) by % for each separate variable
| CIAF width="282" valign="bottom">Percentage of CIAF in Children | > Children specific characteristics | > Gender of the children width="282">
| > Male width="282">58.87 | > Female width="282">54.51 | > Birth order number width="282">
| > Birth order 1 width="282">52.80 | > 2 or 4 width="282">52.78 | > 5 or 6 width="282">65.35 | > 7 or above width="282">64.17 | > Age of child in months width="282">
| > ? 6 months width="282">48.88 | > 7-12 width="282">50.61 | > 13-18 width="282">57.64 | > 18-24 width="282">60.00 | > 25-36 width="282">60.35 | > 37-48 width="282">56.18 | > 49-60 width="282">57.06 | > Maternal specific characteristics | > Mother age at the first childbirth width="282">
| > ? 20 years width="282">60.00 | > 21-25 width="282">57.36 | > 26-30 width="282">52.62 | > 31-35 width="282">58.43 | > 36-40 width="282">61.26 | > >40 years width="282">55.75 | > Mother educational level width="282">
| > Illiterate width="282">64.15 | > Primary width="282">58.82 | > Secondary width="282">39.30 | > Higher education width="282">38.22 | > Mother body mass index width="282">
| > MBMI ? 18.5kg/m2 width="282">70.51 | > MBMI > 18.5kg/m2 width="282">54.81 | > Mother employment status width="282">
| > Not-Working width="282">55.48 | > Working width="282">60.72 | > Asset ownership by mother width="282">
| > Yes width="282">51.34 | > No width="282">57.67 | > The decision to Spend Woman’s Own Earning | > Not-involved in decision width="282">57.07 | > Involved in decision width="282">54.89 | > Received Tetanus Injection | > Yes width="282">53.35 | > No width="282">65.58 | > Household specific characteristics | > Place of residence | > In rural width="282">59.69 | > In urban width="282">52.32 | > Number of Children under Five in a household | > 1 width="282">52.88 | > 2 width="282">49.00 | > 3 width="282">56.17 | > Greater than 3 width="282">53.81 | > Wealth Index | > Poorest width="282">69.50 | > Poorer width="282">62.08 | > Middle width="282">58.31 | > Richer width="282">49.26 | > Richest width="282">36.87 | > Disease-specific factors | > Had diarrhoea recently | > Yes width="282">58.19 | > No width="282">56.33 | ||||||||||
Table 3. Disaggregated Binary Logit Regression Results for CIAF (Model 1 and 2)
| Variables width="198" colspan="3">Results for CIAF in Male Children width="180" colspan="4">Results for CIAF in Female Children | > Composite Index for Anthropometric Failure (CIAF) width="66">Coefficient width="66">S. E width="66">p-value width="60">Coefficient width="60" colspan="2">S. E width="60">p-value | > Age of child in months width="66">0.4401 width="66">0.1707 width="66">0.010* width="60">0.3730 width="60" colspan="2">0.1671 width="60">0.026** | > Age of child in months square width="66">-0.0449 width="66">0.0198 width="66">0.023** width="60">-0.0289 width="60" colspan="2">0.0195 width="60">0.139 | > Birth order number width="66">0.0387 width="66">0.0961 width="66">0.687 width="60">0.2352 width="60" colspan="2">0.0955 width="60">0.014** | > Mother age at the first childbirth width="66">-0.0225 width="66">0.0744 width="66">0.763 width="60">-0.0834 width="60" colspan="2">0.0738 width="60">0.259 | > Mother educational level (Illiterate-reference) width="180" colspan="4" valign="top">
| > Primary width="66">0.1535 width="66">0.1967 width="66">0.435 width="60">0.0585 width="60" colspan="2">0.1998 width="60">0.770 | > Secondary width="66">-0.4855 width="66">0.2071 width="66">0.019** width="60">-0.4951 width="60" colspan="2">0.1983 width="60">0.013** | > Higher width="66">-0.206 width="66">0.2616 width="66">0.432 width="60">-0.4930 width="60" colspan="2">0.3109 width="60">0.113 | > Mother body mass index (? 18.5 kg/m2-reference) width="66">-0.6099 width="66">0.2085 width="66">0.003* width="60">-0.6889 width="60" colspan="2">0.2028 width="60">0.001* | > Asset ownership by mother (No-reference) width="66">-0.3708 width="66">0.1774 width="66">0.037** width="60">0.0159 width="60" colspan="2">0.1842 width="60">0.931 | > Decision to Spend Woman’s Own Earning (Not involved-reference) width="66">-0.7982 width="66">0.2544 width="66">0.002* width="60">0.0376 width="60" colspan="2">0.2590 width="60">0.885 | > Received Tetanus Injection (No-reference) width="66">-0.3686 width="66">0.1601 width="66">0.021** width="60">-0.3172 width="60" colspan="2">0.1572 width="60">0.044** | > Place of residence (rural-reference) width="66">0.3561 width="66">0.1563 width="66">0.023** width="60">0.1184 width="60" colspan="2">0.1567 width="60">0.450 | > Number of Children under Five in a household width="66">0.0788 width="66">0.0666 width="66">0.237 width="60">0.0645 width="60" colspan="2">0.0654 width="60">0.324 | > Had diarrhea recently (No-reference) width="66">-0.1463 width="66">0.1592 width="66">0.358 width="60">0.3557 width="60" colspan="2">0.1672 width="60">0.033** | > Wealth Index (poorest-reference) width="180" colspan="4" valign="top">
| > Poorer width="66">-0.1944 width="66">0.2010 width="66">0.334 width="60">-0.2872 width="60" colspan="2">0.1922 width="60">0.135 | > Middle width="66">-0.5239 width="66">0.2213 width="66">0.018** width="60">-0.2781 width="60" colspan="2">0.2170 width="60">0.200 | > Richer width="66">-0.7138 width="66">0.2387 width="66">0.003* width="60">-0.7228 width="60" colspan="2">0.2327 width="60">0.002* | > Richest width="66">-1.2982 width="66">0 .2858 width="66">0.000* width="60">-0.8266 width="60" colspan="2">0 .2706 width="60">0.002* | > Mother employment status (Not working-reference) width="66">0.4386 width="66">0.2340 width="66">0.057*** width="60">-0.1660 width="60" colspan="2">0.2320 width="60">0.474 | > Number of observations = 1128 width="198" colspan="3" valign="top">PROB>Chi2 = 0.0000 width="180" colspan="4" valign="top">Total numbers of observation = 1099 >PROB>Chi2=0.0000 | > LR-Chi2 (19) = 114.55 width="198" colspan="3" valign="top">Pseudo-R2=0.0750 width="102" colspan="2" valign="top">LR Chi2 (19) =102.32 width="78" colspan="2" valign="top">Pseudo R2 = 0.0676 | height="0"> width="66"> width="66"> width="66"> width="60"> width="42"> width="18"> width="60"> | ||||||