Social divisions in school participation and attainment in India: 1983-2004

This study documents the size and nature of “Hindu-Muslim” and “boy-girl” gaps in children’s school participation and attainments in India. Individual-level data from two successive rounds of the National Sample Survey suggest that considerable progress has been made in decreasing the Hindu-Muslim gap. Nonetheless, the gap remains sizable even after controlling for numerous socio-economic and parental covariates, and the Muslim educational disadvantage in India today is greater than that experienced by girls and Scheduled Caste Hindu children. A gender gap still appears within as well as between communities, though it is smaller within Muslim communities. While differences in gender and other demographic and socio-economic covariates have recently become more important in explaining the Hindu-Muslim gap, those differences altogether explain only 25 percent to 45 percent of the observed schooling gap.

Economic growth: a panacea for child labour?

In this paper, we test whether economic growth decreases child labour by bringing together data from the National Sample Survey of India and state-level macro data to estimate a bivariate probit model of schooling and labour. Our results lead us to conclude that contrary to popular wisdom, growth actually increases rather than decreases child labour because it increases the demand for child workers. The level of state NDP, village wages and household incomes are seen as the conduits through which growth influences the supply side of the child labour market.

Use of a predictive model for food insecurity estimates in Brazil

In 2004 the National Household Survey (Pesquisa Nacional par Amostras de Domicilios - PNAD) estimated the prevalence of food and nutrition insecurity in Brazil. However, PNAD data cannot be disaggregated at the municipal level. The objective of this study was to build a statistical model to predict severe food insecurity for Brazilian municipalities based on the PNAD dataset. Exclusion criteria were: incomplete food security data (19.30%); informants younger than 18 years old (0.07%); collective households (0.05%); households headed by indigenous persons (0.19%). The modeling was carried out in three stages, beginning with the selection of variables related to food insecurity using univariate logistic regression. The variables chosen to construct the municipal estimates were selected from those included in PNAD as well as the 2000 Census. Multivariate logistic regression was then initiated, removing the non-significant variables with odds ratios adjusted by multiple logistic regression. The Wald Test was applied to check the significance of the coefficients in the logistic equation. The final model included the variables: per capita income; years of schooling; race and gender of the household head; urban or rural residence; access to public water supply; presence of children; total number of household inhabitants and state of residence. The adequacy of the model was tested using the Hosmer-Lemeshow test (p=0.561) and ROC curve (area=0.823). Tests indicated that the model has strong predictive power and can be used to determine household food insecurity in Brazilian municipalities, suggesting that similar predictive models may be useful tools in other Latin American countries.