Keywords

Child Labour, Microcredit, Pakistan, FINCA.

Introduction

This paper mainly focuses on to find out the impact of microcredit on reduction of child labour. For this purpose, this study discusses the main causes of child labour in start, then the main impact of microcredit with child labour by competing the causes of child labour.

According to International Labor Organization (2010) defines child labor “refers to work undertaken by persons under the age of 18 that harms their mental, physical, or social development and interferes with their schooling by depriving them of the opportunity to attend school, by forcing them to drop out of school early, or by requiring them to combine school attendance and excessively long and heavy work”.

There are 1.6 billion children in the world and child labour is more in Asia and Africa which collectively explained 90 percent of total child labour force throughout the world. Asia has more child labour as compared to anywhere else force (ILO report, 2019). India has 44 million child labours which is biggest country in the Asia in terms of child labour as well as in the world. In Pakistan, 10 percent labour is child labour by total labour force (Zaidi, Javed, & Khan, 2013).

Child Labour in Pakistan

Child labour is the major problem in deleveping countries like pakistan. According to ILO (2019) estimates, Pakistan has been ranked third in the world having largest child labour. Pakistan have upto 1.7 million children from the age of six are working in different industries. It is normally observed that parents have the right to decide about the child activities and whether a child work or not, goes to school or not. But the problem is that poverty is the main reason in which parent’s send their children’s on work to fulfilled their basic needs and survival of their family (Blume & Breyer, 2011).

According to ILO report (2010) explains that poverty is the major cause of high child labour in any country. Parents those are below the poverty line send their children on work due to many reasons which may include to earn extra income for the household, due to lack of child care, unable to afford the costs of schooling, children themselves may also desire to earn money and In some cultures or in some contexts, it is considered normal if children work. Some other important factors of child labour are the income poverty, lack of basic needs, lack of parental education and lack of awareness.

There are many studies reveal that why employers prefer children for word in which major factors include children have fewer egos, less status consciousness, less shame, they are more active and quicker in their work. Furthermore, they almost do the same amount of work in less cost which is done by adults so children are beneficial in terms of profit for the employers (Blume & Breyer, 2011).

How Microcredit can Help to Address the Causes of Child Labour

Microcredit has been working for more than two decades in different regions of the world and now become a very good practices mean of reducing poverty. According to Blume & Breyer (2011) describe in their study that microcredit is often assumed a powerful tool for reducing the income poverty and this also lead to discourage the child labour. Akilova (2015) conclude in his study that micro-loans are effective tool for reducing income poverty and child labour also Figure 1.

Problem Statement

Child labour is a main concern in all over the world special in developing countries like Pakistan (Malik, 2016). According to ILO report (2019), a decline has been observed in all over the world instead of Pakistan and Pakistan still remain at third spot in top ranking countries in child labour. However, empirical findings suggest that access to microcredit can influence child labour in both ways. On the one hand, it can prevent, reduce child labour and on the other hand it can create the child labour because people prefer their children’s in their own business. So, this mix up result necessitates investigating further about microcredit and its impact on child labour that whether the micro loans are effective tool in reducing child labour or not.

Objectives

1. To find out the major causes of child labour
2. To check the role of microcredit in reducing child labour.
3. To provide recommendations for Pakistani community, government and international community for reducing child labour.

Hypothesis

H1: There is a significant relationship between microcredit and child labour.

H0: There is not a significant relationship between microcredit and child labour.

Literature Review

The major reason of child labour is poverty that is supported and proved by various researchers in their studies. Zaidi, Javed and Khan (2013) support this argument and they conclude that majority of child labour work due to poverty and having large family size. Kashif and Hussain (2013) explain in their study to find out the factors affecting the child labor and for this purpose they use primary data in quantitative research framework and filled the questionnaire from 439 respondents from different cities of Pakistan.

Some researchers found that child labour is important problem in Pakistan and socioeconomic problems are reasons of child labor. In contrast to socio economic conditions, Khalid and Shahnaz (2009) describe socio economic conditions about child labour in Pakistan. The result of their study reveals that there are more child labour between the age of twelve to fourteen years and mostly child laborers are male as compared to female. This study also reveals that child labour is found uneducated and low family income. Bhuiyan, Siwar & Hossain (2013) conducted a study on Grameen bank in Bangladesh about microcredit performance on the borrower literacy of children and they found that this bank is doing well to increase the literacy of borrower’s children.

Ashraf (2016) explain in his study that microcredit have a significant impact on reducing child labour but it has indirect effect because if the microcredit reduces the poverty of household then there will be more chances to reduce child labour. The impact of microcredit will be higher on child labour if it is supported by creating awareness about child education among parents.

Akilova (2015) also show a constructive effect of microcredit on child labor as well as child schooling. According to Chakrabarty (2015) found in his study that microcredit has not direct impact on child labour and microcredit is not only the main factor which reduce child labour.

The above literature explains the causes of child labour and relationship of microcredit with child labour. But some literature results are opposite of this. In some situation, microcredit increases the income of household and induces the parents to withdraw their children from work and entered into school but in some situation, microcredit increase income as well as child labour because parents prefer their children in their own business. So, these mixed up results are encountered by literature.

Hazarika & Sarangi (2008) conducted a study to find out the impact of household access to micro loan on child work in rural Malawi. They conclude in their study that as microcredit increases it also raises the chances of child work. Consistent with this results Islam and Choe (2009) found that microcredit programs increase the child labor and decrease the school enrollment. This negative effect is greater in girls as compared to boys. The younger child’s are more negatively affected by their old siblings because if the old siblings are not working so there will be great chances of child labour of that household (Figure 2).

According to Blume & Breyer (2011) describe in their study that microcredit are often assumed a powerful tool for reducing the income poverty and this also lead to discourage the child labour. Furthermore, when the families start their business with microcredit then they generate income from their new business which leads to come out from poverty and poverty is the basic or primary cause of child labour. Akilova (2015) conclude in his study that micro-loans are effective tool for reducing income poverty and child labour also.

Microcredit does not affect the child labour directly; if microcredit reduces the poverty then poverty has an impact on child labour. This relationship among microcredit, poverty and child labour is confirmed by Ashraf (2016); Smith (2011); Bhuiyan et al. (2013); Zaidi et al. (2013), Geneva (2004) and Akilova (2015).

Methodology

The target population of this research is the total active microcredit clients of FINCA micro finance bank in Lahore. This research is based on primary data and for this purpose questionnaire method is used for data collection.

To check the impact of microcredit on child labour two hundred questionnaires are filled from active clients of Kashf micro finance bank from Lahore. The data was processed and analyzed using the SPSS software to generate descriptive results. Total active clients of FINCA micro finance banks are 22035 and Slovin’s Formula n = N/ [1+N(e)²] is used to select the sample size. I have filled the questionnaires from FINCA micro finance bank active clients but my target clients are those who are married or having child less than 15 years of age. The dependent variable of this study is the child labor and independent variable is microcredit.

Model

In this research logistic regression technique is used because the dependent variable is dichotomous variable and this technique is widely used when dependent variable is dichotomous. Chakrabarty (2012) also used this technique to check the impact of microcredit on child labour.

Li = ln [Pi / ( 1 - Pi )] = β1 + β2 X1+ β3X2

In this research, the researcher is actually is interested to know the impact of microcredit on child labour. If micro loans are offered to poor people then what will be the impact of microcredit on child labour either reduced or increases. For this reason logistic regression is very helpful technique to answer this question.

Results and Discussion

Table 1 shows reliability statistics and descriptive result. The value of Cronbach's alpha is 0.70 it means that internal consistency is average and quite acceptable. Table 2 shows the results of means and standard deviation of the questions which is asked from the respondents.

Omnibus test is basically the likelihood ratio and tells about the overall significance of the good fit model and how much all independent variables are collectively improving the model. In Tables 1-6 describe that the fitted model is significant. Nagelkerke R Square is 0.23 which means that microcredit and household characteristics explaining 23% variation in child labour. The Hosmer-Lemeshow (HL) is another test for logistic regression is widely used about the fitness of the model. According to this test if p value is greater than 0.05 than the model is good and significant. In this research (HL) significance value is 0.329 which means model is significant. The last table shows the individual variables B and a significance value in which microcredit is significant whereas household characteristics show insignificant results.

Table 1: Reliability Statistics
Cronbach's Alpha	Cronbach's Alpha Based on Standardized Items	N of Items
0.699	0.674	11

 

Table 2: Item Statistics
	Mean	Std. Deviation	N
Q1	2.137	1.0084	197
Q2	2.589	1.2073	197
Q5	2.127	1.3699	197
Q6	2.650	1.5499	197
Q7	2.112	1.1770	197
Q8	1.817	1.0532	197
Q9	2.360	1.4239	197
Q10	1.909	0.9698	197
Q11	1.934	0.9901	197
Q12	0.883	0.3219	197
Q13	0.893	0.3094	197

 

Table 3: Omnibus Tests Of Model Coefficients
		Chi-square	df	Sig.
Step 1	Step	0.177	2	0.001
	Block	0.177	2	0.001
	Model	0.177	2	0.001

 

Table 4: Model Summary
Step	-2 Log likelihood	Cox & Snell R Square	Nagelkerke R Square
1	133.974a	0.211	0.23

Table 5: Hosmer And Lemeshow Test
Step	Chi-square	df	Sig.
1	9.339	7	0.329

 

Table 6: Variables In The Equation
		B	S.E.	Wald	df	Sig.	Exp(B)
Step 1a	MC	0.046	0.237	0.038	1	0.002	11.048
	HC	-0.244	0.582	0.176	1	0.075	0.784
	Constant	2.658	1.290	4.243	1	0.002	14.268

Conclusion

This paper aimed to find out the key causes of child labour and to find out the impact of microcredit on child labour. On the basis of result it can be concluded that poverty is the major reason of child labour whereas large family size and uneducated parents are also the supported reasons of child labour.

The results show that microcredit has a significant impact on child labour. But microcredit is not a single solution to reduce the child labour, but it can be a supporting instrument to reduce the child labour. Poverty is the basic reason of child labour if microcredit reduces the income poverty then child labour will also reduce of household. The results of some questionnaire also explain that microcredit may also increase the child labour of household because when parents start their new micro business with micro loans than they also prefer their child on work to save the external cost. So mix up result are come from the

Recommendations

1. Microcredit is more helpful in reducing child labour when it is joint with life and health insurance. So, MFIs should focus on this point in elimination of child labour.
2. Many parents are unaware about the importance of child education so some programs should establish in which awareness should be given to parents.
3. Government should also contribute in reducing child labour by strictly implementing the rights of children’s and increasing their education.

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