A Sociological Study Of Patterns And Determinants Of Child Labour In India

A sociological study of patterns and
determinants of child labour in India
Barsa Priyadarsinee Sahoo
Abstract
Purpose – The purpose of this paper is to understand the patterns and incidence of child labour in India
and to examine the magnitude of child labour across different social groups. It analyses the impact of the
socio-economic background of the children on their participation in the labour market.
Design/methodology/approach – The paper primarily relies on the data collected from secondary
sources. The census of India data and the National Sample Survey Organisation (NSSO) 66th round data
(2009–2010) on employment and unemployment in India for the study. The dependent variable on child
labour has been computed by the author for the analysis in the paper.
Findings – The findings of the paper suggest that poverty is not the only determinant of child labour, but
gender and caste of a person is also a significant factor for child labour. The study found that children
from lower-caste backgrounds in India seem to participate more in the labour market. In terms of gender,
the study found that boys are more likely to engage in economic activities or paid jobs while girls are more
likely to engage in household activities.
Originality/value – Data used in this paper has been extracted by the author from unit level data
provided by NSSO. The variables used for the analysis in the presented paper has been constructed by
the author and the figures provided are the result of the author’s estimation on data.
Keywords India, Determinants, Child labour, Pattern, NSS, Child work, Incidence
Paper type Research paper
1. Introduction
Child labour is a matter of grave concern in many parts of the world, especially in developing
countries. Child labour is described as a complex and multidimensional issue due to its
features and impact on the well-being of children, inadequate data and the need for a
comprehensive solution. It has a negative impact on the social, economic and
developmental growth of a country. Global estimates of the International Labour
Organisation (ILO) show that 152 million children are working worldwide and the incidence is
very high for the developing countries (International Labour Organisation, 2017). Statistics
reveal that India is one of the countries with the highest numbers of child labourers in the
world (NCPCR Position Paper, 2008; International Labour Organisation, 2013, 2017).
According to the 2011 Census, 10.1 million children in the age group of 5–14years are
employed in India. If we examine the trend from 1971 onwards, we can see an increasing
trend (Table 1). It can be seen that the total number of child labour was declining from 1981
to 1991, but again in 2001, there was an increase in the number of child labourers from
11.28 million in 1991 to 12.66 million in 2001. An increase of over one million child labourers
from 1991 to 2001 signifies that the problem of child labour is escalating. Though the census
2011 figures show a slight reduction in the number of child labour still, the figure is high.
Therefore, it is a matter of grave concern for India, which necessitates immediate attention.
Child labour, as a social evil, did not emerge suddenly. It had always been present in some
form or other. However, it was during the industrial revolution that the problem was much more
Barsa Priyadarsinee Sahoo
is an Assistant Professor
based at School of Law,
Alliance University,
Bangalore, India.
Received 24 October 2020
Revised 24 February 2021
Accepted 11 March 2021
DOI 10.1108/JCS-10-2020-0067 © Emerald Publishing Limited, ISSN 1746-6660 jJOURNAL OF CHILDREN’S SERVICES j

visible with the employment of children in hazardous industries. One of the primary reasons for
hiring children in industries was that they were considered as a cheap substitution to adult
labour (Marx, 1956). Mines were the places with severe child labour. Liten (2010) mentioned
that during industrialisation, 50% of the factory labour force was children and 30% were
working in coal mines. Radhakamal Mukerjee (1945) in his book “Indian Working Class”
mentioned that in 1936, 73.6% of the total child population was working in perennial factories
whereas 26.4% were working in seasonal factories. Many children were employed in cotton
and jute mills and coal mines; they were even employed for underground work (Deshta and
Deshta, 2000). The jute mills of Bengal alone employed as many as 26,500 children in 1925
and in Shellac factories, children constitute around 10% of the total workforce. The situation in
the beedi industry was even worse. Hundreds of young boys were employed for long hours for
beedi making in an unsatisfactory environment and without adequate medical examinations
(Mukerjee, 1945). However, the problem of child labour can not be studied in isolation as it is
influenced by many factors. The socio-economic status of the family of the child, cultural norms
and weak state legislation can also contribute to the increasing number of child labour. In
India, most discussions on the determinants of child labour start with discussing low
household income and poverty. The prominent work of Basu and Van (1998) was framed upon
Table 1 State wise distribution of child labour in India (census 1971–2011)
Sl. no. Name of the states 1971$
1981$
1991$
2001$
2011#
1 Andaman and Nicobar Island 572 1,309 1,265 1,960 1,672
2 Andhra Pradesh 1,627,492 1,951,312 1,661,940 1,363,339 673,003
3 Arunachal Pradesh 17,925 17,950 12,395 18,482 17,029
4 Assam 239,349
327,598 351,416 284,812
5 Bihar 1,059,359 1,101,764 942,245 1,117,500 1,088,509
6 Chandigarh 1,086 1,986 1,870 3,779 4,322
7 Chhattisgarh 364,572 257,773
8 Dadra and Nagar Haveli 3,102 3,615 4,416 4,274 2,055
9 Daman and Diu 7,391 9,378 941 729 881
10 Delhi 17,120 25,717 27,351 41,899 36,317
11 Goa 4,656 4,138 10,009
12 Gujarat 518,061 616,913 523,585 485,530 463,077
13 Haryana 137,826 194,189 109,691 253,491 123,202
14 Himachal Pradesh 71,384 99,624 56,438 107,774 126,616
15 Jammu and Kashmir 70,489 258,437
175,630 114,923
16 Jharkhand 407,200 400,276
17 Karnataka 808,719 1,131,530 976,247 822,615 421,345
18 Kerala 111,801 92,854 34,800 26,156 45,436
19 Lakshadweep 97 56 34 27 81
20 Madhya Pradesh 1,112,319 1,698,597 1,352,563 1,065,259 700,239
21 Maharashtra 988,357 1,557,756 1,068,427 764,075 727,932
22 Manipur 16,380 20,217 16,493 28,836 34,086
23 Meghalaya 30,440 44,916 34,633 53,940 44,469
24 Mizoram 6,314 16,411 26,265 7,778
25 Nagaland 13,726 16,235 16,467 45,874 63,790
26 Odisha 492,477 702,293 452,394 377,594 334,416
27 Puducherry 3,725 3,606 2,680 1,904 2,173
28 Punjab 232,774 216,939 142,868 177,268 176,645
29 Rajasthan 587,389 819,605 774,199 1,262,570 848,386
30 Sikkim 15,661 8,561 5,598 16,457 10,390
31 Tamil Nadu 713,305 975,055 578,889 418,801 284,232
32 Tripura 17,490 24,204 16,478 21,756 13,560
33 Uttar Pradesh 1,326,726 1,434,675 1,410,086 1,927,997 2,176,706
34 Uttaranchal - - - 70,183 82,431
35 West Bengal 511,443 605,263 711,691 857,087 550,092
India 10,753,985 13,640,870 11,285,349 12,666,377 10,128,663
Note:
Census could not been conducted
Source: $: Ministry of Labour and Employment and #: Authors’ calculation of child labour using
census data 2011 (includes main and marginal worker)
jJOURNAL OF CHILDREN’S SERVICES j

the “luxury axiom” and “substitution axiom”. They have argued that child leisure is a luxury
commodity to the poor household and the low adult wage is the main reason for which the
children cannot enjoy that luxury. Kothari (1983), in her study on the matchstick industry in
Sivakasi in the state of Tamil Nadu, found that children were forced to leave the school to
support the family economy. However, some scholars (Jensen and Nielsen, 1997; Remington,
1996; Satyarthi, 2006) argue that poverty is not the only determinant of child labour instead,
there exists a potential link between education and child labour. Weiner (1989) argued that
child labour in India is very much related to dropout rates in schools and the situation is worst
in rural areas, particularly amongst girls in comparison to boys and more specifically amongst
the lower castes. Similarly, Kiran Bhatty (1996) argued against the conventional idea of poverty
as the root cause of child labour. For her, more than poverty, social attitudes and sensibilities
also lead to child labour. Some empirical studies (Chaitanya, 1991; Chamarbagwala, 2008;
Emerson and Knabb, 2006) also suggest that inequality of opportunity is the reason for the
intergenerational transmission of child labour rather than poverty.
Apart from these factors, parents’ level of education also plays an essential role regarding
the decision on child labour (Fors, 2008; Mukherjee and Das, 2007, 2008). Gender of the
child too becomes very crucial in the decision-making process of the parents concerning
child labour and schooling, particularly for a girl child. However, most of the earlier debates
on child labour did not focus on gender roles, particularly for the girl child labour. One of the
reasons behind the absence of work on girl child labour is the type of work they are engaged
in. While one can see boys working in the factories and hazardous conditions, girls mostly
work at home and are, therefore, invisible sometimes (Burra, 1995). Burra argued that a
strong “sex typing” of work existed in relation to the work that female or male children do in
agriculture, household or unorganised industries. Studies suggest that Gender bias
depends on the economic condition of the parents (Kumar, 2013). When parents are better
off, then both male and female children get an equal amount of schooling. However, when
parents are poor, then it creates gender discrimination where female children receive less
education than their male counterparts. The ideas of gender inequality that is rooted in the
Indian mindset affect the magnitude of child labour in the country.
Furthermore, the religious belief system also affects the incidence of child labour of a
country. Though specific religions deliberately do not promote child labour, but different
religions give rise to powerful ideas about the appropriate activities of children. In India, the
Hindu caste system is an important structuring principle and influence on the roles
undertaken by the children (Boyden and Morrow, 2015). The link between caste and
occupation, which has existed for many years, is evident even amongst the child labourers
in India. Although people are moving away from their caste occupation, but because of the
years of suppression and oppression the lower caste communities still have not been able to
come out of their previous status. A study of the relationship between caste and child labour
in urban Bihar found that Scheduled Castes (SC)/Scheduled Tribes (ST) children were hand-
picked to work as ragpickers, barbers and cobblers, amongst other lowly jobs while non-SC/
ST children are chosen to labour at parties and weddings because of the concept of “purity”
promulgated by those who believe in the caste system (Chowdhury, 2020). Even some
empirical studies (Venkateswarlu, 1998; Agrawal, 2004) found support for the argument that
children from a lower-caste group are more likely to work in comparison to the upper castes.
Like the caste system, the age-old tradition and beliefs also influence the parent’s decision
to some extent. For instance, in an ethnographic study on glass industries in Firozabad,
Chandra (2009) found that most of the household interviewed were practicing the bangle
and glasswork since generations and they believe that this occupation is deeply rooted in
their culture and tradition. This bangle and glass making has been so much intermingled
with their life that children were attracted to these occupations from their childhood.
Similarly, Bhatty (1996) says that parents sometimes do not want to send their children to
school because they believe the school will detach them from the village economy and also

education will give them an aspiration for white collar jobs, which is hard to get. Hence, the
beliefs and practices also determine whether children will work or not.
Similarly, sometimes the demographic factors such as locality, geographical area and
ecological condition forces children to work. Bahar (2014) argued that though children work
both in rural and urban areas, but the percent of children are more in rural areas particularly in
agriculture in comparison to urban areas. Kothari (1983) in her study on the match stick
industry in Sivakasi, Tamil Nadu found that children were forced to leave school to support the
family economy. Because, in “Ramanathpuram” (the study area), most of the people, although
have agricultural land, but they largely depend on the rainfall, which affects the cultivation and
makes life difficult in the dry seasons, which pave the way to work in the match and fireworks
industry. Hence, geographical location and climate also sometimes determine child labour.
Thus, there are many factors, which influence the nature and incidence of child labour of a
country. As discussed above, apart from poverty, religious ideas, the value system and the
gender norms also affect the incidence of child labour. However, the value system, religious
belief and gender norms vary across communities and societies. Hence, to understand the
issue of child labour in an Indian context, the author has constructed a socio-economic
paradigm of child labour with reference to the reviewed literature.
2. Socio-economic paradigm of child labour
Socio-economic paradigm shapes perception and practices within the disciplines
according to its subject matter. It shapes what we look at, how we look at things, what we
label as a problem and what problem we consider for worth investigation and what methods
are preferred for the study. Socio-economic paradigm regards child labour as the
consequence of unprecedented changes in socio-economic structure of society. This
paradigm talks about the relationship and interaction between children and economic
status within their immediate setting such as family and social networks. It also discusses
about the social system and economic structure, which impact upon the immediate familial
and social setting in which children are situated and can examine the implementation of
social policies and welfare programmes of government. Socio-economic paradigm
examines various determinants that are responsible for the existence of child labour after
the implementation of various policies and programmes of government. These determinants
of child labour can be understood through a diagram.
This socio-economic paradigm helps the author to understand the issue of child labour as a
multidimensional issue, and hence, any umbrella solution cannot solve the issue of child
labour. To eradicate the problem of child labour every contributing factor needs to be
examined properly and solutions need to be provided. Hence, the nature and problem of
child labour can be understood and analysed with reference to Indian society by using the
socio-economic paradigm (Figure 1).
3. Conceptualising child labour
Defining child labour has always been a contested issue. The concept of “child” and
“childhood” have changed over time and vary across cultures, social class, age and countries
(Dash et al., 2018). Child labour is the combination of two words “child” and “labour”, which
have different meanings that vary across contexts. There exists a debate concerning the use of
terms such as “child labour” and “child work”. Some scholars use these terms interchangeably,
and for them, there is no distinction between these two terms while others think for the need of
distinction between these two terms (Burra, 2005; Liten, 2002). ILO made a distinction between
“child labour” and “child work” in the 1980s. Child labour includes the form of works that are
exploitative and harmful to children, which mainly takes place “outside the family” “for other” and
is “productive”. Child work is not harmful and it is “reproductive” in nature and it is practised at
home for the child’s family (Liebel, 2004). Some studies (Bhukuth, 2008; Liten, 2006a, 2006b)

consider child labour as interference in childhood, which puts an economic burden on the
children and also a form of forced labour, which is “bad” and unacceptable. Child work is the
activities that involve no exploitation, no interference in childhood and not even forced work but
the acceptable form of work, i.e. “good work”. Taking a holistic approach, ILO defines child
labour as, “works that deprive children of their childhood, their potential and dignity and that is
harmful to their physical and mental development” (International Labour Organisation, 2013).
This definition also includes those works that interfere with the education of the child, separate
them from their families and force them to work in hazardous health conditions. In India, children
working in the factories, dhabas (roadside food stalls), in carpet, lock, glass, beedi (country
made cigars), bangle, match industries come under this category. Working in these areas not
only endangers their health but is also harmful to their mental and moral development. In India,
the two major data sources used for child labour research are the Census and National Sample
Surveys (NSS). Both of these data sources do not have any direct question on child labour,
rather they provide data on employment and unemployment. Hence, one has to look at the
employment status and the age of the respondent to construct a variable on child labour.
Moreover, the estimation of child labour depends on the definition of child and age is one of
the significant criteria for the estimation of child labour as this distinguishes child labour from
adult labour. Defining a “child” is as much contentious as defining child labour. Various legal
documents define it differently, and it varies from country to country. Article 1 of the United
Nations Convention on the Rights of the Child sets the age limit of a child at 18years. Various
labour laws in India have different age limits for defining a child. Plantations Labour Act 1951,
The Motor Transport Workers Act (1961), The Apprentices Act (1961), The Beedi and Cigar
Workers (Conditions of Employment) Act, 1966, The Dangerous Machines Act (1983), The
Minimum Wage Act 1984 sets the age limit of a child at 14years. The Factories Act (1948)
keeps it at 15years. Child Labour (Prohibition and Regulation) Act, 1986 and The Child labour
(Prohibition and Regulation) Amendment Act, 2016 sets the age at 14years. The Right of
Children to Free and Compulsory Education Act (2009) defines child between 6 to 14 years,
the Protection of Children from Sexual Offences Act, 2012 and The Juvenile Justice (Care and
Protection of Children) Act, 2015 defines child who has not completed 18years of age.
However, looking at the significant Indian laws relating to children, the present paper
considers persons between 5 to 14years as children and 15 to 18years as adults. These two
age categories have been used for the computation of the variables and analysis of the data.
Figure 1 Socio-economic paradigm of child labour
Socio-economic
Paradigm of Child
Labour
Poverty in Family
Adult Unemployment
Household Size
Geographical
condition/ Locality
Religion of the
Family
Family Social Values
Gender of the Child
Parents level of
Education

Taking into consideration the above-mentioned age categories and the ILO definition of child
labour, the present paper creates two categories of child labour. The paper uses the term
“working children” and “other workers” for analysing the data on child labour. The author
defines “working children” as those children who are economically active and engaged in
any form of paid work. “Other workers” include those children who are not attending any
educational institution and are engaged in household work, begging and prostitution as per
NSS data. A third category of “child non-workers” has been used to refer to those who are
attending an educational institution, are disabled or getting any remittance.
With reference to the literature reviewed the objectives of the present study are to
understand the patterns and incidence of child labour in India, to examine the magnitude of
child labour across different social groups and to analyse the impact of the socio-economic
background of the children on their participation in the labour market.
4. Data and method
4.1 Data source
The present study primarily relies on the data collected from secondary sources. The
census of India data and the National Sample Survey Organisation (NSSO) 66th round data
(2009–2010) on employment and unemployment have been used for the fulfillment of the
objectives of the Study[1].
4.2 Sample
NSSO 66th round (2009–2010) provides questions on the employment and unemployment
situation in India, which has been used for the construction of the variables and empirical
analysis. NSSO adopted the stratified multi-stage sampling design, and the number of
households surveyed in this round was 100,957 (59,129 in rural areas and 41,828 in urban
areas) and the number of persons surveyed was 459,784 (281,327 in rural areas and 178,457
in urban areas). In the present survey, 95,818 children in the age group of 5–14years were
included and 40,702 children were included in the age group of 15–18years.
4.3 Methodology
The analysis has been conducted at two levels. Firstly, cross-tabulation and chi-square has
been done to see if there is an association or not between the independent variables and
the dependent variable. Secondly, binary logistic regression has been conducted to find
out the factors affecting child labour. All the analysis has been carried out in statistical
package for the social sciences (SPSS) software.
Dependent variables: For the regression analysis, two dependent variables have been used
by using the restricted and expansive definition of child labour, and the regression result
presented in Table 4 compares the results on both dependent variables.
Child labour (restricted definition): Child labour is a person between 5–14 years of age who
is engaged only in economic activity.
Child labour (expansive definition): Child labour is a person between 5–14 years of age who
is engaged in economic activity, not attending any educational institution and engaged in
household and other activities.
Independent variables: Gender (male and female), sector (rural and urban), social group (SC,
ST, other backward class (OBC) and general), religion (Hindu, Muslim, Christian and others),
household size (04, 4–6, 6–8, 8–10, 10 and above), household type (self-employed in non-
agriculture, self-employed in agriculture, agricultural labour, regular wage/salary earning,
causal labours and others) and poverty [rural-below poverty line (BPL), urban-BPL, rural-
above poverty line (APL), urban-APL].

5. Empirical analysis
The NSS data for the present study has been analysed using the SPSS software. The basic
descriptive statistics are presented in Table 2, which shows the distribution of child
labourers in India. As mentioned earlier, “working children” includes children engage only in
economic activities or paid work. “Other workers” include children engage in the household
and other activities and not attending any educational institution. “Child non-workers”
includes children attending an educational institution and not working. This category is only
for comparison with the other two categories. These three categories have been used to see
the pattern and incidence of child labour in India.
5.1 Pattern of child labour in India
Table 2 shows that, in India, around 4.2 million children are engaged in economic activity,
which is 1.4% of the total child population. What is interesting is the percentage of the “other
workers” is higher than the percentage of the “working children”. It indicates that more
children are engaged in household and other activities as compared to economically active
children. Hence, when the working children were combined with the other workers, the
percentage got even higher for India, which stands at 8.5%.
The figures on child labour provided by the Ministry of Labour and Employment
(Government of India) do not include “other workers” while calculating child workers, and
some scholars such as Liten (2000) have argued that including these children will magnify
the problem of child labour. However, as per the Government of India (2009), these children
should be at school and not at work. If they are not attending any educational institution and
engaging in some work, they can be counted as child labour. Whether it is the household
activity or begging or prostitution; working at the expense of education can never be helpful
for any child (Orrnert, 2018).
Hence, when including working children with the other workers, the percentage of working
children increased to 8.5%, which is 7.1% higher than the government figures. The reason
for making these two categories is to find out the difference between the government
figures and the present figures so that the problem of child labour can be seen clearly.
Analysis of child labourers is related to the socio-economic background of a child, which
includes the composition of the household, its size, caste, religion, type of household,
monthly per capita expenditure, parent’s education and parents’ occupation.
5.2 Child labour and caste
Child labour and caste unfortunately continue to go hand in hand in India. Most child labour
in India come from a highly impoverished family unit and belongs to a low-caste or minority
community, known as SCs, STs and OBCs (Kara, 2014). Table 3 presents the distribution of
children across social groups in India by age. For the 5–14 age group, though the
percentage of children belonging to STs is higher amongst working children, in the case of
other workers, the participation is more from SCs. The percentage of working children for
Table 2 Child labourers in India: NSS 2009–2010
Categories of children Estimated population In the sample (%)
Working children 4,223,218 1,371 1.4
Other workers 20,188,373 6,780 7.1
Working þ other workers 24,411,591 8,151 8.5
Child non-workers 197,325,856 87,667 91.5
Total children 221,737,447 95,818 100
Source: Derived from unit level data of NSS 2009–2010

OBC stands at 1.4%, which is lower as compared to both SCs and STs but still is greater
than the percentage of working children from others (general category). In the case of
children in the 15–18 age group, the percentage of children is again more from SCs, STs
and OBCs as compared to the general category. It is not surprising that more children from
other or general caste groups constitute the child non-workers. It means more children from
the general caste are attending an educational institution whereas more children from ST
are working. The reason is the opportunities for secure wages, education, access to health
care, fundamental rights, land ownership and inability to access formal credit markets are
seriously lacking for individuals from these sections. They are always at the verge of abuse
and under these situations, it is easier for the parents to send their children for work to
reduce some of the family burden (Kara, 2014).
5.3 Child labour and religion
India is home to 1.2 billion people, which is almost one-sixth of the world’s population that
houses a variety of ethnicities and religions. However, Hindus constitute 78% of the total
population while Muslims constitute 14% and Christian constitutes 2% while other
religions [2] constitute around 6% as per the census of India, 2011. Hence, Hinduism is
professed by a majority population of India and Muslims and other religions are regarded
as minority in India. Though the Indian constitution protects the minorities against any kind
of discrimination on the grounds of religion and caste, communal tensions between Indians
of various religious faiths and castes have long plagued Indian society (Majumdar, 2018).
The empirical analysis also shows similar evidence.
While looking at the distribution of working children across age and religious community in
India, one can see in Table 4 that, amongst the 5–14 age group, 2% Muslim children are
working, whereas only 0.8% Christian children are working. It indicates that 10.7% of
children from the Muslim community are engaging in other activities, whereas the
participation of children from the Christian community is only 3%. These figures explain that
the participation of children from the Muslim community is more in both the work categories
in comparison to the participation of children from the Christian community. It has been
found that 96.2% of children from the Christian community is not working, which means
more children from the Christian community are attending educational institutions.
A chi-square test has also been conducted to find out the association between religion and
working children and the P-value is less than 0.05 in all cases in India, which means religion
does play some role for the participation of children in the workforce.
Chi-square test for other variables such as household type, poverty and education of the
head of the household has been done. The P-value of other variables came less than 0.05
except for the education of the head, which means that there is no statistically significant
relationship between the education of the head and children entering the labour market.
Table 3 Distribution of children across social groups in India by age
Social groups Age Working children Other workers Child non-workers Total
Scheduled tribe 5–14 2.0 (279) 6.9 (954) 91.1 (12,578) 100 (13,811)
15–18 21.4 (1,275) 10.2 (610) 68.3 (4,070) 100 (5,955)
Scheduled caste 5–14 1.8 (300) 9.7 (1,606) 88.4 (14,576) 100 (16,482)
15–18 25.3 (1,697) 16.5 (1,108) 58.1 (3,890) 100 (6,695)
Other backward castes 5–14 1.4 (504) 7.6 (2,815) 91.1 (33,777) 100 (37,096)
15–18 19.4 (2,964) 14. (2,129) 66.6 (10,157) 100 (15,250)
Others 5–14 1.0 (285) 4.9 (1,396) 94.1 (26,662) 100 (28,343)
15–18 14.3 (1,829) 9.7 (1,242) 75.9 (9,688) 100 (12,759)
Source: Derived from unit level data of NSS 2009–2010. N = 136,391

5.4 Determinants of child labour
With the chi-square test of independence, the study found that there is an association between
the independent variables such as age, sex, locality, caste, religion and the dependent variable.
Table 5 presents a comparison of the regression results of the two dependent variables in India.
The first model shows the results from the restricted definition and the second model shows the
results for the expanded definition. The first model shows that when controlled for all other
variables, men in comparison to women have a positive and statistically significant effect, which
means boys are more likely to work than girls. However, the results of the second model show a
negative and significant effect for men when controlled for all other variables, which is the
opposite of the first model. It shows that girls are more likely to work than boys. This difference in
results appears because of the difference in the definition of the dependent variable used.
Controlling for all other variables, rural in comparison to urban has a positive and significant
effect in both the models, which means children from rural areas are more likely to work in
comparison children from urban areas. Moreover, social groups seem to affect the probability of
the working children as all the three variables SC, ST and OBC have a positive and significant
effect in comparison to the general category, which indicates, that belonging to these social
groups increases the probability of a child becoming child labour or joining the labour market.
As per Model 1, being a Hindu does not seem to affect the probability of working as the
coefficient is not significant (even though the sign suggests that children from Hindu have
more probability to work). For Model 2, being a Hindu affects the probability of the children to
work as it is positive and significant in comparison to other religions. Hence, Model-2 shows
that Hindu children are more likely to work than children from other religious communities. The
results for the Muslim community in both the models are positive and significant in comparison
to other religions, which means belonging to the Muslim community increases the chances of
children to work. However, the result for the Christian community is significant but negative in
both the models, which indicates, that children from the Christian community are less likely to
join the labour market in comparison to other religious communities.
Household size too affects the probability of the working of the children as the results for all
the variables under household size in Model-1 is positive and statistically significant. It
indicates that children from the household size with less than 10 are more likely to work in
comparison to the children from the household size of 10 and above.
However, in Model 2, the coefficient for the household size with less than four members is
not significant and negative, which means it does not have any effect on the working of a
child. However, other variables are significant and positive, which is similar to the results of
Model 1. It indicates that children belonging to a small family do not have any effect on the
working decision of the children.
The household type has some effect on the working of the child as all the three variables such
as: self-employed in non-agriculture, self-employed in agriculture, the agricultural labourer and
Table 4 Distribution of children across religious community in India by age
Religious community Age Working children Other workers Child non-workers Total
Hindu 5–14 1.4 (967) 6.8 (4,752) 91.8 (64,154) 100 (69,873)
15–18 19.2 (5,632) 12.1 (3,541) 68.6 (20,085) 100 (29,258)
Muslim 5–14 2.0 (294) 10.7 (1,593) 87.3 (12,958) 100 (14,845)
15–18 22.1 (1,404) 18.9 (1,198) 59.0 (3,743) 100 (6,345)
Christian 5–14 0.8 (51) 3.0 (202) 96.2 (6,408) 100 (6,661)
15–18 14.2 (425) 4.7 (141) 81.2 (2,437) 100 (3,003)
Others 5–14 1.3 (59) 5.2 (233) 93.4 (4,147) 100 (4,439)
15–18 14.9 (312) 10.3 (215) 74.9 (1,569) 100 (2,096)
Source: Derived from unit level data of NSS 2009–2010. N = 136,520

casual labourer have a positive and significant effect in both models. It indicates that children
belonging to these households are more likely to work in comparison to children belonging to
wage labourers. Furthermore, in the case of poverty, both Rural-BPL and Urban-BPL has a
positive and significant effect in comparison to Rural-APL and urban-APL in both models. This
means that children from families below the poverty line in both rural and urban areas are
more likely to work in comparison to children from the above poverty line. Hence, belonging to
a poor household does increase the chances of the children to work.
6. Discussion and conclusion
The present paper distinguishes between the “economically active children” and the “other
workers” to understand the variation in the results and how the effect differs when the definition
changes. This wider definition of child labour shows a higher magnitude of child labour in
India. One may think that a wider definition would of course provide a higher magnitude of
child labour in the country. However, the intention in using the broader definition is to get the
actual figures of the working children, which are not included in the government records. Also,
this wider definition helps to identify the percentage of children engaged in household work,
which is not categorised as child labour. However, the present study found that they are
engaged in household work without attending any educational institution, which hampers their
development. They are also deprived of the leisure, which they are entitled to.
As the paper deals with two different definitions of child labour, the results also came
differently. However, the results have been analysed keeping in mind the socio-economic
Table 5 Determinants of child labour: binary logistic regression models for India: restricted deﬁnition and expansive
deﬁnition (working children = 1)
Independent variables Model-1 Restricted definition Model-2 Expansive definition
Men 0.45*
(0.06) 0.14*
(0.02)
Rural 1.14*
(0.21) 0.89*
(0.09)
Social groups ST 1.0*
(0.10) 0.76*
(0.05)
SC 0.69*
(0.09) 0.72*
(0.04)
OBC 0.25*
0.37*
Religion (0.08) 0.03
Hindu 0.15 (0.13) 0.21*
(0.07)
Muslim 0.76*
(0.15) 0.88*
(0.07)
Christian 0.80*
(0.19) 0.63*
(0.1)
Household
Size 0–4 0.26*
(0.08) 0.00 (0.04)
4–6 0.25*
(0.08) 0.11*
(0.04)
6–8 0.47*
(0.08) 0.41*
(0.03)
8–10 0.48*
0.51*
Household (0.10) (0.04)
Type Self-nonAgri 0.43*
(0.07) 0.10*
(0.03)
Self-agri 0.62*
(0.08) 0.11*
(0.04)
Agri-labour 0.49*
(0.10) 0.35*
(0.04)
Casual 0.60*
0.54*
Poverty (0.13) (0.05)
Rural-BPL 0.55*
(0.08) 0.92*
(0.04)
Urban-BPL 1.44*
(0.20) 1.48*
(0.08)
Constant 7.11*
(0.24) 4.74*
(0.11)
N 1,447 8,151
2 Log likelihood 14,302.812a 52,107.799a
Notes: Self-nonAgri = self-employed in non-agriculture. Self-Agri = self-employed in Agriculture. Agri-labour = agricultural Labour.
Casual = casual labour. Rural-BPL = households below poverty line in rural areas. Urban-BPL = households below poverty line in urban
areas. Rural-APL = households above the poverty line in rural areas. Reference categories: Female, Urban, General, Others, 10 and
above, wage labour, Rural-APL, Urban-APL. *= Statistically significant at 0.05 or below and standard errors presented in the brackets
Source: Derived from unit level data of NSS 2009–2010

paradigm that has been developed by the author and explained in the earlier paragraphs.
Some of the results are not surprising given the multi-dimensional nature of the issue while
others are surprising. It is not at all surprising that poverty continues to have a significant and
robust effect on child labour. This reconfirms “the luxury axiom” provided by Basu and Van
(1998) and tested by many others. This shows that though India is presently regarded as one
of the fastest economically growing countries, its economic policies are not helping the poor
people to come out from the curse of poverty, and they are still sending their children to work.
Though poverty is a significant determinant, it is not the only determinant of child labour. There
are other factors, which also influence the participation of children in the labour market. Given
the prevalence of the caste system in India, it is not at all surprising that caste backgrounds
influence the participation of children in the labour market. Empirical analysis found that
children from lower-caste backgrounds (SCs, STs and OBCs, Table 3) participate more in the
labour market while the children from the general category are attending educational
institutions. Hence, in the Indian context, caste is a significant factor for pushing more children
into labour market. The paper found no gender inequalities in child labour. However, what is
interesting is that there are differences in the results concerning different definitions. In the
case of only economically active children (restricted definition, Model 1), the result shows that
boys are more likely to work, which is similar to the results obtained by Ray (2000). In the case
of the expanded definition (Model 2), the paper found that girls are more likely to work, which
conforms to the results of Roy (2013). This indicates that boys are more likely to engage in
economic activities or paid jobs while girls are more likely to engage in household activities.
The pattern of division of labour between boys and girls showed a similar trend as adults.
As mentioned by Mishra (2014), the reason may be the stereotypical thinking that girls
better handle home-based workers. There are other important factors along with the
factors discussed in this paper that may have a significant impact on child labour. The
socio-economic paradigm too points out that the parent’s level of education, social
values, geographical conditions, also affects the incidence of child labour. Hence,
child labour is a multi-dimensional issue, and one umbrella legislation is not going to
solve the problem. Each dimension needs to be addressed individually and action plan
should be made to deal with specific factors. However, few variables are often difficult
to measure, and NSS does not capture all variables and even if some variables are
present while filtering through conditions, the sample size became smaller. Also, the
effect of certain variables needs to be studied using a qualitative approach such as
social values and orientation of the family, which was not possible in this study due to
the limitations of data used for analysis.
Notes
1. Census of India collects data from Indian population on demographic, economic and social
aspects and this is conducted by the Register General and Census Commissioner of India under
Ministry of Home Affairs, Government of India. It collects data in every 10 years and the last census
of India was done in 2011.
2. Other religions include shikh (1.7%), Buddhist (0.7%), Jain (0.4%), Other Religions and
Persuasions (ORP) (0.7%) and Religion Not Stated 0.29 crores (0.2%) in the Census of India, 2011.
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Corresponding author
Barsa Priyadarsinee Sahoo can be contacted at: barsa2011jnu@gmail.com
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