Gender bias among children in India in their diet and immunisation against disease.
This paper conducts an econometric analysis of data for a sample of over 4000 children in India, between the ages of 1 and 2 years, with a view to studying two aspects of the neglect of children: their likelihood of being immunised against disease and their likelihood of receiving a nutritious diet. The starting hypothesis, consistent with an universal interest in gender issues, was that girls were more likely to be neglected than boys. The analysis confirmed this hypothesis. In respect of vaccinations, the likelihood of girls being fully vaccinated, after controlling for other variables, was 5 percentage points lower than that for boys. In respect of receiving a nutritious diet, the treatment of girls depended very much on whether or not their mothers were literate: there was no gender discrimination between children of literate mothers; on the other hand, when the mother was illiterate, girls were 5 percentage points less likely to be well-fed relative to their brothers and the presence of a literate father did little to dent this gender gap. But the analysis also pointed to a broader conclusion which was that all children in India suffered from sharper, but less publicised forms of disadvantage than that engendered solely by gender. These were the consequences which stemmed from children being born to illiterate mothers and being brought up in the more impoverished parts of India.
School of Economics and Politics, University of Ulster, Newtownabbey, Northern Ireland, UK. vk.boorooah@ulst.ac.uk
Vani K
VK
Borooah
D002664:Child Nutritional Physiological Phenomena; D002669:Child Welfare / Q000145:classification / Q000191:economics*; D002675:Child, Preschool; D003906:Developing Countries / Q000191:economics; D004032:Diet / Q000145:classification* / Q000191:economics; D004522:Educational Status; D005260:Female; D005783:Gender Identity; D019538:Health Care Surveys; D006801:Humans; D007194:India; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D017059:Models, Econometric; D011203:Poverty; D011287:Prejudice; D014611:Vaccination / Q000191:economics / Q000706:statistics & numerical data*