Determinants of the Outcomes of a Household’s Decision Concerning Child Labor or Child Schooling

Abstract

Child labour is an issue of global concern. It assumes more importance when it comes to developing countries like Pakistan. This study attempts to highlight this child labor issue in Mardan District of Khyber Pakhtunkhwa, a province of Pakistan. The analysis collects information through modified questionnaire by randomly interviewing households. Using Multinomial Logit model, the analysis finds that probability of child schooling is high, in case a child is already enrolled in primary school education. Similarly, child schooling is more likely when monthly income of a family head increases. However, with the increase in ‘age’ and ‘monthly income’ of a child, the probability of child labour tends to increase. Additionally, Poor financial position of a family also increases the chances for child’s labour activities. Furthermore, the analysis finds variables like “initiative of work by child himself” and “working capacity” increase the chance for a child to combine school with labour activities. That is, if a child engages himself in labour work on permanent basis, such a child is more likely to combine school with labour work to finance his educational expenses. On the contrary, a household prefers his child neither to attend school nor labour work in case of increasing family’s income. That is, in such a situation a household may prefer his child to engage in homecare activities. Finally, the analysis shows that probability of child schooling is high in case a child is living in rural areas. Based on empirical findings, the study suggests few practicable steps to the government for addressing the child labour issue. Opening more primary schools in remote areas and providing vocational training centers to children whose families cannot afford educational expenses, would be helpful in reducing child labour exclusively.

Appendices

Appendix 1 Multinomial logit Result

		B	Std. Error	t	Sig
1	(Constant)	0.565	0.144	3.928	0.000
	Child Age	-0.136	0.056	-2.420	0.016
	Area of living	0.121	0.053	2.294	0.023
	Education Status	0.188	0.052	3.617	0.000
	Child still studying or not	0.229	0.063	3.614	0.000
	Child's mother education status	0.081	0.056	1.450	0.149
	Family head education	-0.083	0.052	-1.606	0.110
	Family head age	0.087	0.054	1.593	0.113
	Monthly income of the family	-0.759	0.089	-8.539	0.000
	Family head income	0.093	0.050	1.842	0.067
	Is child family head working	-0.001	0.047	-0.024	0.981
	Number of persons in family	0.038	0.053	0.722	0.471
	Is child's family is joint family	0.000	0.044	-0.010	0.992

1. Dependent Variable: Child Schooling only

Appendix B

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig
		B	Std. Error	Beta
1	(Constant)	0.867	0.313		2.766	0.006
	Child Age	-0.197	0.122	-0.101	-1.609	0.109
	Area of living	0.093	0.115	0.053	0.811	0.418
	Education Status	-0.923	0.113	-0.526	-8.154	0.000
	Child still studying or not	-0.458	0.138	-0.214	-3.315	0.001
	Child's mother education status	0.109	0.122	0.056	0.894	0.373
	Family head education	0.077	0.112	0.044	0.684	0.495
	Family head age	0.068	0.119	0.036	0.571	0.569
	Monthly income of the family	0.386	0.194	0.125	1.991	0.048
	Family head income	0.073	0.110	0.041	0.668	0.505
	Is child family head working	-0.093	0.102	-0.057	-0.909	0.364
	Number of persons in family	-0.123	0.115	-0.069	-1.070	0.286
	Is child's family is joint family	0.081	0.096	0.053	0.842	0.401

1. Dependent Variable: Child characteristics

Appendix C Correlation Matrix

	Child characteristics	Child Age	Area of living	Education Status	The child still studying or not	Child's mother’s education status	Family head educated	Family head age	Monthly income of the family	Family head income	Is child family head working	Number of persons in the family	Is child's family is a joint family
Child characteristic	1.000
Child Age	-0.162	1.000
Area of living	0.136	-0.085	1.000
Education Status	0.257	-0.088	0.252	1.000
Child still studying or not	0.293	-0.035	0.096	0.006	1.000
Child's mother education status	0.071	-0.028	-0.062	-0.043	0.159	1.000
Family head education	-0.047	-0.208	0.087	0.183	0.049	-0.027	1.000
Family head age	0.132	-0.140	-0.069	0.014	0.013	0.200	-0.040	1.000
Monthly income of the family	-0.502	-0.045	0.112	-0.025	-0.135	0.114	0.086	-0.022	1.000
Family head income	0.065	-0.062	-0.046	0.087	-0.106	0.029	-0.016	-0.069	0.075	1.000
Is child family head working	-0.011	-0.088	0.018	0.071	-0.129	-0.003	0.000	0.141	0.069	0.134	1.00
Number of persons in family	-0.012	-0.079	-0.185	-0.150	-0.127	-0.010	0.179	-0.018	-0.067	0.046	-0.111	1.000
Is child's family is a joint family	-0.031	0.136	-0.124	0.009	0.159	0.076	-0.083	-0.045	0.064	-0.034	-0.050	-0.115	1.000

The above Table 7, shows Pearson correlation matrix of all the independent variables. The result finds indicates that none of the single variable have correlation greater than 0.75 with all other independent variables. Therefore, the correlation matrix obtained, is an evident of no high collinearity among independent variables. The ‘Rule of Thumb’ is that If correlation between two independent variable is either greater than + 0.75 or less than -0.75, then sever collinearity exist between the two variables. In this case the variance or standard error of the coefficients are no longer reliable (inflated variance of the coefficient). To remove the collinearity drop the variable having weaker correlation with the dependent variable.