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Determinants and Working Conditions of Child Labour: A Case Study of Children Working at Automobiles Workshop at Khyber Pakhtunkhwa (KP), Pakistan
ISSN: 2151-6200

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Determinants and Working Conditions of Child Labour: A Case Study of Children Working at Automobiles Workshop at Khyber Pakhtunkhwa (KP), Pakistan

Muhammad Jehangir Khan, Khuram Nawaz Sadozai, Karim Khan, Fida Muhammad Khan, Mubasher Rehman Khattak and Haider Ali*
Faculty of Rural Social Science, Institute of Development Studies, The University of Agriculture, Peshawar, Pakistan
*Corresponding Author: Haider Ali, Faculty of Rural Social Science, Institute of Development Studies, The University of Agriculture, Peshawar, Pakistan, Tel: +923339350722, Email: [email protected]

Received Date: Jan 22, 2018 / Accepted Date: Feb 13, 2018 / Published Date: Feb 19, 2018

Abstract

This study has tried to inspect the determinants and working conditions of child labour in automobile workshops of Tehkal market, Peshawar (KP). Detailed descriptive analysis and multivariate analysis is undertaken to analyze the data and report findings. The results depicts that child labour in the area is a multifaceted phenomenon. Most of the children leave their schools to learn working skills for future employment security. Majority of the sampled children contribute more than 60 percent to family’s income. Most of the children has father alive and live in rental houses with their parents. More than half of the children report physical health and drug addiction problem of their fathers. The data further divulge that only 1 percent of the children are the sole bread earners of their families. The working condition of the automobile workshops is hazardous with no basic facilities. Nearly 85 percent of the children reported punishment at work place by underpaying their salaries followed by physical abuse. The multivariate analysis illustrates that low household income, literacy level, profession, household size, and paternal physical health are the major factors of child labour. The coefficients of all the variables are significant and having correct theoretical signs predicting that these variables greatly influences the wage per hour of the child.

Keywords: Child labour; Working conditions; Automobiles workshop; Case study; Khyber pakhtunkhwa

Introduction

Child labour is an important issue among professional, academic and media [1]. This is a universal problem throughout the world and is worth to investigate [2]. Children have contributed to the household economy in one form or another by means of participating in different activities. From an early age children contribute in earning income at the cost of their own livelihood and deprived of basic opportunities for education, leisure, recreational and intellectual development [3]. The International Labour Organization (ILO), defined child labour as harmful for all children below age of 18 in the labour market or their own household. Defining the term ‘child’ could be compound by many factors like racial and climatic factors related to physical and mental maturity, social norms and practices, socio-economic conditions, educational system and the legal context of the country. The economic activity in which the child is involved has either positive or negative impacts depending upon the age, physical and environmental condition of work [4].

There is common perception that if a child is engaged in economic activity is considered as child labour whereas if child is not being paid comes under child work [5]. But in contrary according to (ILO) if child is economically active without any physical and mental stress is child work while violation of international labour laws refers to child labour. Asia is economically active region of the world and it has the greatest incidence of child labour [6]. Asia and Africa together are responsible for producing ninety percent of total child labour force. Around sixty percent of the children of the world are working under unfavorable conditions. In India 44 million children are working while 12 million children are working in Nigeria [7]. According to 1973 constitution of Pakistan “no child under the age of 14 shall be employed in hazardous jobs [6]. Federal Bureau of Statistics conducted national survey of child labour with collaboration of ILO in 1996 and find that 40 million children age 5 to 14 were economically active among which 73 percent were boys while 27 percent were girls. According to the survey majority of children were working in agriculture sector. Around 33 percent of children never attended school, 46 percent of children had to work more than 35 hours per week. The report concluded that low socio-economic background; poor education system, uneducated household heads and large family size were pushing children into labour [8]. Pakistan has grave problem of child labour and main reasons could be weak socioeconomic and political instability. Thousands of the children have to quit education at intial level to meet the basic necessities of life [9].

The future of every nation is dependent on children skills and their capabilities. The study is more related to the explanation of present child labour in Tehkal area (KPK). It also focuses on the reasons which bring children in to labour. The findings of the study will helpful for the local government to understand the worst condition of child labour. The recommendations if implemented properly can minimize the consequences of worst form of child labour (WFCL). Child labour is the employment of children working between 7 to 18 years on wages or when used for inappropriate or dangerous jobs. According to Keyemuddin and Kayum automobiles workshop is a type of small industry for repairing and maintaining vehicles and Khan categorized workshops into two type’s i.e. heavy vehicle workshops and light vehicle workshops [5,10]. Trucks buses and tractors are repaired at heavy vehicle workshops while rickshaws, cars and motorcycles are repaired at light vehicle workshops. The study will investigate the determinants of child labour working in light automobiles workshops. The objectives of the study are as under:

➢ To evaluate the child characteristics of child working in automobile workshop,

➢ To explore the parental factors leading to child labour in study area,

➢ To examine the household characteristics that forced child to go to work.

Review of Related Literature

Child labor has many determinants ranging at first hand from demographic variables to working condition along with parental and socio-economic determinants. The general perception that child labour is embedded in poverty need not be fit in every context and cultures rather could be viewed as a complex phenomenon which required simplification. The association between the underlying bases of child labour and the surrounding exploitation is not that much simple as the literature demonstrate. An additional effort is needed to determine the root cause of the issue so that to carry out specific measures, as generalization of the issue mean we are avoiding different measures regarding different fundamental causes.

In developing countries child labour and high dropout from school are major issues and poverty being the key factor that forces children to work [11]. Panigrahi conducted a study in rural Orissa (India) and founded that lack of interest in education, unemployment; poor economic status, illiteracy and large family size are the contributing factor of child labour [3]. Emerson and Knabb explained not only the poverty is responsible but child labour is considered as family occupation and transmitted through generation [12]. He feared that by introducing anti child labour policies and compulsory education laws can increase the poverty and income inequality within a society making the condition more worst and eventually can appreciate child labour.

Ahmad conducted a study at Aligarh city to depict the socioeconomic problems of working children by taking 360 sample sizes [13]. The study revealed that most of the parents were either unemployed or had no permanent occupation. It was found that 25 percent of children were in labour because of poverty, 17 percent of children were working because of the parental pressure, 15 percent were uninterested in attending schools, 11 percent had to support family and 6 percent were only source of family income. Siddiqi conducted a case study in Lahore Pakistan where 40 percent of population were living below poverty line and founded that large family size was one of the contributing factor [14]. Ali conducted a study in district Swabi and discussed economic factors which were responsible for child labour [6]. The study was based on a sample of 225 respondents who were interviewed to examine the determinants of child labour in the locale and found 37 percent working children reported that their parents were labours having no good job, 70 percent of children acknowledged that the family income was too low that they had to work for family support. It was also found that half of the respondent’s parents were having income less than PKR 4,000 per month. It can be inferred from the data that parents having low income sent their children to labour.

Dash determined the contributing factor of child labour at Delhi [15]. The study was descriptive in nature in which 120 child labours and 40 parents were selected for interview and found that 62 percent children were working because of parental compulsion on them as well as they had to support the family bearing poor economic background. The same results were also determined by Khan in 2007 while conducting a study in Bannu city of KPK Pakistan and maintain that 20 percent of the children were taken as sample and it was pointed that household income was inversely related to prevalence of child labour. It was also examined that majority of the parents were illiterate and had income lower than PKR 3500 per month. Thus, it is evident from the study that children had to take responsibility by contributing in family income. Grootaert and Patrinos emphasized that parental unemployment pressurized the child to go for work rather than school as well as Parental education directly influence child labour rate [16]. Ampomah conducted study to analyze the child labour condition in areas of Abokobi and Madina east districts of Ghana which interviewed 50 children, 50 parents and three government officials [17]. The study examined the working condition of children, programmers and policies as well as problems faced by these programs and primarily conclude that 60 percent of respondents regarded poverty as the main cause. In addition, majority of parents were unemployed, 70 percent were involved in petty training, 79 percent were getting income from low scale business and 42 percent of parents were uneducated. Thus, the children having low socio-economic status were forced by parents to provide financial assistance.

According to Siddiqi and Patrinos child labour is a problem faced by developing countries throughout the world. Parental decision plays vital role in promoting child labour [18]. Children contribute more in reducing the financial burden as compared to the children of developed nations. School enrollment being a substitute of child labour and parents select the option of schooling and work choice based on utility. The provision of free text books and facilities at primary level can increase the school enrollment. By providing different incentives at the early stage can reduce child labour. Education can improve the skills and opportunities of masses by means of governmental incentive program.

The aforementioned empirical literature shows that factors like socio-economic, cultural aspects, family size, and parental education among others has diverse and mixed effect on child labour. Almost, all of the studies conducted in Pakistan so far examine either economic factors of child labour or socio-political factors. This study intends to examine the three characteristics of child labour including parental, household and child own characteristics. Furthermore, the study chooses the area which is more vulnerable to child labour. Therefore, this study is an extension of the available literature in the direction of examining the three characteristics of child labour. The study also shed light on the working condition of children working in automobile industry, district Peshawar.

Material and Methods

The study is descriptive cum analytical. The collection of primary data is an effort in the form of semi-structured interview conducted. Specifically, the study is structured analytically, present data collection and sampling technique along with the method of research utilized in the data collection process. Tehkal is located in the center of Peshawar; it is one of the main commercial areas of Peshawar having automobiles workshop located on the main roads as well as in the streets. The study mainly focuses on children aged 7-18 working in automobile workshop. There are about 300 automobiles workshops in which most of the work is done by the children; mainly in the car denting, car paint, mechanical and electrical sections. In 300 automobiles workshop 1500 children aged 7-18 were working constitute the population of data. Out of 300 automobile workshops 150 automobiles were randomly taken. From each automobile workshop two children were randomly selected. So, 300 children constitute the sample size of the study.

Data was collected by means of semi-structured interview; comprised of four sections. The first section covered child characteristics consisting age, education, birth order, percentage contribution to family income, reasons of leaving school and work experience. The second section having questions regarding parent’s occupation, education, housing, physical fitness, drug addiction and per month income. The third section inquired household characteristics i.e. earning family members, family income per month, family size, occupational family structure, sibling education, sibling disability and the number of children involved in labour. The last section had questions pertaining to working conditions i.e. total working hours per day availability of basic facilities, duration of mid-day break, type of punishment and different type of injuries.

The proposed study is intended to provide first the descriptive analysis of the study in order to examine the determinants of child labour i.e. child own characteristics, father characteristics and household characteristics. For further evidence on the subject issue the study intended to use the multiple regression technique. The study regressed the working hours per day of the children on variables including birth order, work experience, household head occupation, household head education, physical health, family income per month, number of children involved in labour, wage per hour and family size. The relationship between working hours per day of children and independent variables are represented as follows.

WH= β01 BO+β2 WE+β3 HHO+β4 HHE+β5 PH+β6 FIPM+β7 NCIL+β8 FS+β9 WPH+εi (1)

Where WH=working hours per day

BO=Birth order of the children

WE=work experience of the children

WE=work experience of the children

HHE=Household Head Education

PH=physical health of the fathers

FIPM=income of the family per month

NCIL=number of children involved in labour

FS=size of the family

WPH=wage per hour of the children

While εi is the error term capturing the average effect of omitted variables.

We cannot enter directly the categorical independent variables into regression model because it cannot be meaningfully interpreted and we therefore required an alternative way to deal with such issue. One alternative way is the dummy coding which has the characteristic to transform a k level categorical variable into k-1 level. We transform the categorical variables into dichotomous variables by following the dummy coding technique. The dummy coding for one categorical variable (i.e. birth order) are presented here. We have four levels in birth order i.e. youngest, second to fifth, third to sixth and eldest which could be dummy coded into three variables i.e. youngest, second to fifth and third to sixth. The way in which the dummy is coded is presented (Appendix 1).

Results and Discussion

Descriptive analysis

The descriptive analysis is carried out to achieve the objective of the study along with to examine the demographic and socio-economic characteristics of the sample in the study area. To describe, categorize and summarize the data analytically in a comprehensive form, descriptive analysis is the most widely used technique Nachmias and Nachmias [19-23]. Percentages and classification of data is the center of descriptive analysis.

Table 1 reveals that highest percentage of children that work in automobile workshop were between 11-14 years of age, followed by those children whose ages are in the range of 9-10 and 15-17 (25 percent each). About 28 percent of children working in automobile workshops have no education, 34 percent have primary education, whereas matric and above is the second highest category (32 percent). The eldest children constitute the largest friction of the sample followed by the second youngest. Children who have 1 to 4 years of working experience are 31 percent of the sample [24-27]. Similarly, another 31 percent of the total sample children have 7 to 10 years of experience. About 43.3 percent of the children leaves school due to poor economic background in order to support their families. The second largest percentage (31.3 percent) of the sampled children leaved school due to future employment security. Children whose earnings are inbetween 3000 to 5000 has the largest percentage (41 percent), followed by earnings group of 1500 to 3000. Percentage contribution to family income is conceivably the most significant part of child labour story in the locale, as about27 percent of the sampled children contribute more than 60 percent to family income. Another 58 percent of the sampled children contribute nearly 40-60 percent to family income. This shows that these children are the sole bread earners of their families, ensuring their family survival. The data further shows that being the elder the child the more responsible he is for managing family affairs.

Variable Frequency Percent Cumulative percent
Age
7-8 24 8.0 8.0
9-10 74 25.0 33.0
11-14 128 43.0 75.3
15-17 74 25.0 100
Total 300 100.0  
Level of education
Illiterate 85 28.3 28.3
Primary 99 33.9 61.3
Middle 17 6.0 67.0
Matric and above 99 32.1 100
Total 300 100  
Birth order
Youngest 7 2.3 2.3
Second to fifth 136 45.3 48.0
Third to sixth 20 7.0 55.0
Eldest 137 45.7 100
Total 300 100.0  
Work experience
Up to 1 year 61 20.3 20.3
1-4 years 93 31.0 51.3
4-7 years 54 18.0 69.3
7-10 years 92 31.0 100
Total 300 100  
Income per month (PKR)
Up to 1500 40 13.3 13.3
1500-3000 73 24.3 38.0
3000-5000 123 41.0 79.0
5000 andabove 64 21.3 100
Total 300 100  
Percentage contribution to family income
up to 20% 17 6.0 6.0
20-40% 26 9.0 14.3
40-60% 175 58.3 73.0
60 % andabove 82 27.3 100
Total 300 100  
Reason of leaving school
Lack of interest 25 8.3 8.3
Poor Economic Background 130 43.3 51.6
Future employment Security 94 31.3 83.0
Law and order condition 51 17.0 100
Total 300 100  

Source: Authors own calculation.

Table 1: Distribution of respondents with regards to child characteristics.

Table 2 distributes children by their parental characteristics. Most of the children (88 percent) has father alive. Similarly, most of the children live in rental houses (39.3 percent) with their parents. More than half (69 percent)of the sampled children fathers are not physically fit confirming that physical health is one of the significant determinants of child labour. Further, the data shown that 66 percent of the sampled children fathers are drug addicted.

Correlates/variables Frequency Percentage Cumulative Percentage
Is your father alive
Yes 264 88.0 88.0
No 36 12.0 100
Total 300 100  
Where do you live?
Rental house 118 39.3 39.3
Own house 87 29.0 68.3
Relatives 8 3.0 71.0
Others 87 29 100
Total 300 100  
Does father is Physically fit?
Yes 93 31.0 31
No 207 69.0 100
Does father is drug addicted?
Yes 198 66.0 66
No 102 34.0 100

Source: Authors own calculation.

Table 2: Distribution of respondents with regards to their paternal characteristics.

Table 3 presents household characteristics of the laborer children. Majority of the labourer children are from families having 2-4 or 5-7 bread earners (49 percent each) [28-30]. Those families where children are the sole bread earners are only 1 percent. Most of the children have family income in the range of 10-15 or 20 thousand and above (33 percent each). Child labor is higher in families having larger household sizes. Most of the household heads are wage earners and or unemployed. More than half of the sampled children’s fathers are illiterate, which may be the major factor that pushes children to work in automobile workshop.

Variables Frequency Percent Cumulative Percent
Earning family members
Nil 3 1.0 1.0
2-4 147 49.0 50.0
5-7 146 49.0 99.0
8-above 4 1.3 100
Total 300 100  
Family income per month (PKR)
0-10000 13 4.3 4.3
10000-15000 99 33.0 37.3
15000-20000 90 30.0 67.3
20000 and above 98 33.0 100.0
Total 300 100  
Family size
2-4 23 8.0 8.0
5-7 95 30.0 40.0
8-10 86 29.0 68.3
10-above 96 32.0 100
Total 300 100  
Head occupation
Government employee 67 22.3 22.3
wage earner 102 34.0 56.3
self employed 30 10.0 66.3
unemployed 101 34.0 100
Total 300 100  
Household head education
illiterate 183 61.0 61.0
primary 43 14.3 75.3
middle 32 11.0 86.0
matric and above 42 14.0 100
Total 300 100  
Sibling education
Pre-school 60 20.0 47.0
Primary 141 47.0 67.0
Secondary 34 11.3 78.0
Never went to school 65 22.0 100
Sibling disability
Physical impairment 172 57.33 57.3
Spinal Cord Disability 45 15.0 72.3
Brain disability 61 20.3 92.6
Learning disabilities 22 7.3 100
Number of children involved in labour
Only one child work 122 40.6 40.6
Two children work 93 31.0 71.6
Three children work 65 21.6 93.2
Four children work 20 6.6 100

Source: Authors own calculation.

Table 3: Distribution of Respondents with Regards to their Household Characteristics.

Elder children often choose work rather than go to school to support their sibling education. About 47 percent of the children respond that their sibling education is primary. Nearly 57 percent of the sibling have disability (physically impaired). About 41 percent of children respond that only one child involved in labor.

Table 4 tried to report the working conditions of the children in automobile workshops. About 61 percent of the children responds that there are no basic facilities (water, electric fan, heater, first aid) available at their work place. Nearly 85 percent of the children reported punishment at work place. Most of the children are punished by underpaying their salaries (55 percent) followed by physical abuse (30 percent) [31-33]. About 77 percent of the children respond that their working conditions are hazardous/dangerous to their health. Most of the children (57 percent) faced orthopedics injuries during work followed by cuts and burns (25 percent). Some reported eye or ear infections. Only 5 percent of the children reported protective tools available at the workplace.

Variables Frequency Percent Cumulative Percent
Basic facilities at workshop (water, electric fan, heater, first aid)
Yes 118 39.3 39.3
No 182 60.6 100.0
Total 300 100.0 -
Are you punished at work place
Yes 254 85.0 85.0
No 46 15.3 100.0
Total 300 100.0 -
Type of punishment
Physical Abuse 91 30.3 30.3
Verbal Abuse 28 9.3 40.0
Salary Deduction 165 55.0 95.0
Extra Work 16 5.3 100.0
Total 300 100.0 -
Working condition hazardous or Dangerous?
Yes 232 77.3 77.3
No 68 22.6 100.0
Total 300 100.0 -
Injuries faced during work
Cuts and Burns 76 25.3 25.3
Orthopaedics Injuries 170 57.0 82.0
Skin Allergies 32 11.0 93.0
ENT infection 22 7.3 100.0
Total 300 100.0 -
Protective tools available?
Yes 5 1.6 1.6
No 295 98.4 100
Total 300 100.0 -

Source: Author’s own calculation.

Table 4: Distribution of respondents with regards to their working conditions.

Regression results

We estimate eqn. (1) through ordinary least square (OLS) regression. The results of which is shown in Table 5, we report the coefficients of the model and the probability value (p-value). Table 5 demonstrate the results of the independent variables, most of the coefficients are significant (all the p-values are equal to or less than 5 percent) having correct theoretical signs. The positive sign on the coefficients of age and its magnitude tell us that as compare to base category (age group 7-8) the upper age groups (9-10, 11-14 and 15-17) working hours per day are respectively 8.14, 4.32 and 5.71 times greater. Age is positively and significantly related with working hours per day and old child earning more per hour than young child. This is because old children could be more experienced than the young children along with old have the capability to work more than the young. This evidence are further confirmed by the birth order variable where the relatively older in birth order as compare to the youngest are working 7.48 and 8.58 times more hours respectively. One exception is that, that age group of second to fifth are negatively but insignificantly related to working hours per day. For this one justification could be in fact that this group is very close to the youngest age group. More experienced children are considered to be working more hours than the less experienced children and this is what our results also confirm. Children who had experience of 1-4 years are working 3.59 times higher than the base category that is experience up to only one year. Similarly, those children who had experience of 4-7 and 7-10 years working respectively 1.61 and 5.29 times higher than those who had only one year of experience.

 Variables Coefficients P-value
 Age (7-8)*    
 Age (9-10) 8.14 0.02
 Age (11-14) 4.32 0.001
 Age (15-17) 5.71 0.03
 Birth Order (Youngest)*    
 Birth Order(second to fifth) -3.09 0.12
 Birth Order (third to sixth) 7.48 0.03
 Birth Order (eldest) 8.58 0.04
 Work Experience (up to 1 year)*    
 Work Experience (1-4) 3.59 0.005
 Work Experience (4-7) 1.61 0.020
 Work Experience (7-10) 5.29 0.005
 Wage per hour    
Head is government employs*    
Headis wage earners 4.21 0.05
Head is self employed 2.15 0.00
Head is unemployed 7.19 0.00
Head is illiterate*    
Head is primary 5.61 0.03
Head is middle -3.22 0.00
Head is matric & above -7.32 0.00
father is Physically fit*    
father is not Physically fit 9.52 0.06
Family income per month 0-10000*    
Family income per month 10000-15000 3.58 0.04
Family income per month 15000-20000 -5.04 0.03
Family income per month 20000 and above -1.21 0.21
Family Size 2-4*    
Family Size 5-7 1.34 0.03
Family Size 8-10 2.53 0.05
Family Size 10-above 6.31 0.00
Only one child work*    
Two children work 4.54 0.01
Three children work 1.39 0.04
Four children work 5.55 0.15
Constant 12.22 0.003
R2 0.73
F-Statistics 23.31 0.07

Source: Authors own calculation.
Note: * is the reference category.

Table 5: Regression Results (dependent variable working hours per day).

Three variables from parent’s and household characteristics can also influence the working hours per day of the children including household head occupation, household head education and physical health of the fathers. The result shows that the working hours per day are 4.21 times higher of those children whose heads are wage earners as compared to whose heads are government employees [34-38]. The working hours of the children whose heads are unemployed are 7 times higher as compare to the reference category. Working hours per day of those children whose heads are relatively more educated are less than those whose heads are less educated. Similarly, children having physically unfit fathers working 9.52 times more than those whose fathers are physically fit.

Those children whose family income are higher work less hours per day as compare to whose children whose family income is less. Similarly, working hours of the children belongs to big family size are higher because they must work to meet the higher needs of their family as compare to who has less family members.

Conclusion

Child, parent, and household are important aspects of child labour that are investigated in this study. The child own aspects show that majority of the children working in automobile workshops are in the ages of 11-14 years. Most of the children have primary education. Majority of the children are from families having larger household size. Whereas, most of the children in automobile workshops are the eldest in birth order. Similarly, most of the children acquire considerable experience which are likely to help them in future work. Child earnings constitute nearly 60 percent of the total family income. About 58 percent of the children left school due to poor economic background of their families.

Majority of the household heads are uneducated, which pushes their children to work in automobile workshops. Most of the children are from families who lives in rental house and has low income. A greater proportion of the children reported paternal drug addiction or either physically unfit having disability.

The working environment in automobile workshops was generally not satisfactory. Almost 77 percent of the of the children are working in hazardous conditions which lacked basic facilities. Most of the children reported punishment at work place (85 percent). About 55 percent of the children are underpaid as a penalty. The unavailability of protective tools results in orthopedics injuries (57 percent) during work; followed by cuts and burns (25 percent).

The multivariate analysis shows that old age children are working more hours per day than young age children. The same is the case for birth order and child experience variables. Similarly, children from families whose heads are either unemployed or wage earners and less educated are working more hours per day than households headed by employed and more educated members. Lastly children belong to families having higher income are working less hours per day than lower income families.

The current findings show that child labor is a multifaceted problem. Perhaps the most effective policy is the affordable and accessible education policy as education reduces the time to child work and education enhances human capital, productivity and more income. Raising awareness campaign regarding schooling of victim child along with attention must be diverted of the local government to the said issue to find ways and get rid of this issue on earlier basis.

References

Citation: Khan MJ, Sadozai KN, Khan K, Khan FM, Khattak MR, et al. (2018) Determinants and Working Conditions of Child Labour: A Case Study of Children Working at Automobiles Workshop at Khyber Pakhtunkhwa (KP), Pakistan. Arts Social Sci J 9: 332. DOI: 10.4172/2151-6200.1000332

Copyright: © 2018 Khan MJ, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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