The Effect of Poverty, Gender Exclusion, and Child Labor on Out-of-School Rates for Female Children

Abstract

In this article, the authors analyze the effect of poverty, social exclusion, and child labor on out-of-school rates for female children. This empirical study is based on a dynamic panel model for a sample of 216 countries over the period 1970 to 2010. Results based on the generalized method of moments (GMM) of Arellano and Bond (1991) and the tests of causality and zero autocorrelation to the panel data show a negative and significant relation between contributing family workers (female) and number of primary school-age children out of school (female) in Europe and Central Asia region. However, the authors cannot find empirical evidence between primary school-age children out of school rates (female) and the variables used to analyze the effect of poverty and social exclusion (poverty headcount ratio at national poverty line and total vulnerable employment). Moreover, the article identifies effects of other variables like proportion of seats held by women in national parliaments. In addition, this article examines geographic regions separately, with the anticipation that differentials in livelihood strategies and opportunities could be reflected in female child schooling decisions.

Keywords:

• child labor
• poverty
• social exclusion
• out-of-school rates

Notes

1. According to the 2012 Education for All Global Monitoring Report, many young people the world—especially the disadvantaged—are leaving school without the skills they need to thrive in society and find decent jobs. The report will focus on skills development, emphasizing strategies that increase employment opportunities for marginalized groups.

2. To achieve these, we need to solve some methodological problems related with the estimation of models by traditional methods, such as Ordinary Least Square (OLS) and Least Squares Dummy Variable (LSDV), that led to ad hoc results. In order to solve this problem, we propose one method (based on Arellano & Bond, 1991) that consists of obtaining consistent estimators.

3. According to Filmer (2006), the gender gap is still substantial in countries in South Asia and North and West Africa. In countries where the gender gap is large among youth in the poorest quintile, it is not nearly as large in the richest quintile.

4. A plausible explanation, according these authors, is that substitution effects helped protect current incomes from the higher school attendance induced by the subsidy.

5. Ordinary least square (OLS) and least squares dummy variable (LSDV) give biased and non convergent values because of inter-relationship between retarded endogenous variable and individual heterogeneity. Under these circumstances, our models should not be estimated by the method of OLS and LSDV due to the fact that estimating by these methods led to ad hoc results.

6. Previously, we tested for every individual of the linear restrictions of type:

7. 7. The method proposed by these authors permit a GMM in two stages, written in the following form:

8. 8. The number of instrument increases in the time for every individual. In the case where explanatory variables exist, is the model correlated with heterogeneity individual .

9. 9. Thus, if distribution is non auto-correlated, this test gives a value of residues differentiated negative and significant to first order and non significant to the second order. This test is based on auto-covariance of residues following a normal law N(0,1) under hypothesis H₀.

10. 10. The evaluation of the models by traditional methods (OLS and within) gives biased and non convergent values because of inter-relationship between retarded endogenous variable and individual heterogeneity. In this context, our models should not be estimated by OLS and LSDV methods due to the fact that estimating by these methods led to ad hoc results. We propose one method that consists of obtaining consistent estimators.

11. 11. Originally, we used other variables to approach child labor, like total economically active children (% of children age 7–14) or economically active children, work only (% of economically active children, ages 7–14). Unfortunately, the available data for these variables do not allow applying the proposed methodology.

12. 12. Also, we used other variables, like GINI Index, in order to approach the inequality. One more time, the available data for these variables do not allow applying the proposed methodology.

13. 13. World Development Indicators Database is the primary World Bank database for development data from officially recognized international sources.

14. 14. Education Statistics Database provides data on education from national statistical reports, statistical annexes of new publications, and other data sources.

15. 15. The World Bank EdStats Query holds around 2,500 internationally comparable education indicators for access, progression, completion, literacy, teachers, population, and expenditures. The indicators cover the education cycle from pre-primary to tertiary education. The query also holds learning outcome data from international learning assessments (Programme for International Student Assessment [PISA], Trends in International Mathematics and Science Study [TIMSS], etc.), equity data from household surveys, and projection data to 2050. EdStats website: http://go.worldbank.org/ITABCOGIV1.