To read this content please select one of the options below:

Please note you do not have access to teaching notes, a sociological study of patterns and determinants of child labour in india.

Journal of Children's Services

ISSN : 1746-6660

Article publication date: 14 May 2021

Issue publication date: 6 July 2021

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.

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.

  • Determinants
  • Child labour

Sahoo, B.P. (2021), "A sociological study of patterns and determinants of child labour in India", Journal of Children's Services , Vol. 16 No. 2, pp. 132-144. https://doi.org/10.1108/JCS-10-2020-0067

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

Related articles

We’re listening — tell us what you think, something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

Advertisement

Advertisement

A Bayesian Estimation of Child Labour in India

  • Open access
  • Published: 18 June 2020
  • Volume 13 , pages 1975–2001, ( 2020 )

Cite this article

You have full access to this open access article

  • Jihye Kim 1 ,
  • Wendy Olsen 1 &
  • Arkadiusz Wiśniowski 1  

11k Accesses

5 Citations

Explore all metrics

Child labour in India involves the largest number of children in any single country in the world. In 2011, 11.8 million children between the ages of 5 and 17 were main workers (those working more than 6 mo) according to the Indian Census. Our estimate of child labour using a combined-data approach is slightly higher than that: 13.2 million (11.4–15.2 million) for ages 5 to 17. There are various opinions on how best to measure the prevalence of child labour. In this study, we use the International Labour Organization (ILO)‘s methodology to define hazardousness and combine it with the most recent United Nations Children’s Fund (UNICEF)‘s time thresholds for economic work and household chores. The specific aims of this study are to estimate the prevalence of child labour in the age group 5 to 17 and to suggest a combined-data approach using Bayesian inference to improve the accuracy of the child labour estimation. This study combines the National Sample Survey on Employment and Unemployment 2011/12 and the India Human Development Survey 2011/12 and compares the result with the reported figures for the incidence of child labour from the Indian Census. Our unique combined-data approach provides a way to improve accuracy, smooth the variations between ages and provide reliable estimates of the scale of child labour in India.

Similar content being viewed by others

child labour in india research paper

Determinants of child labour practices in Ghana

Lucy Twumwaah Afriyie, Bashiru I. I. Saeed & Abukari Alhassan

child labour in india research paper

Measuring Child Labour: The Indian Scenario

Sourav Chakrabortty, Nilanjana Joseph Roy & Sugata Sen Roy

child labour in india research paper

Neighbourhood effects and the incidence of child labour

Sylvanus Gaku & Emmanuel S. Tsyawo

Avoid common mistakes on your manuscript.

1 Introduction

India has the largest number of working children of any country in the world, with a Census estimate of 12.66 million children aged from 5 to 14 holding this status in 2001, falling to 4.35 million in 2011 (according to the Ministry of Labour and Employment  n.d. ). According to the analysis of ‘child workers’ (ages 5–14), as per the 2011 Census of India, Uttar Pradesh state had the largest number of child workers (2.1 million, 4.1%) and Bihar had the second-largest number (1.1million, 3.9%; Samantroy et al. 2017 , p.46). In terms of incidence, Nagaland and Himachal Pradesh have shown the highest proportion of child workers at 13.2% and 10.1% respectively (ibid.). However, the numbers vary according to datasets and definitions. For example, the proportion of child labourers reached 11.8% among children aged 5 to 14 in 2012, as calculated by UNICEF ( n.d. ), Footnote 1 which is roughly 29 million children in total. Increased household income has led to a reduction over time, but many industries and farms still employ child labourers. Unpaid household services (household chores) are ones of the most prevalent types of child labour and could be increasing, but how this work can be adequately measured as child labour is still debatable.

There has been extensive discussion of how to define child labour. The differentiation between child labour and child work and setting up time boundaries are the key issues. The ILO and UNICEF have moved toward an international agreement on definitions of child labour. However, their definition is still different in some aspects from the definition used at the national level in India. In 2016, the Indian government amended the Child Labour Act (the Amendment Act) to adopt a strict policy of banning children under the age of 14 from work. However, the Amendment Act excludes ‘helping families or working in family enterprises’ from the category of child labour. Also, there is no time limit for children’s weekly working hours in the Amendment Act.

Besides being a matter of definition, there is a need to address the issue of how to measure the number of child labourers with accuracy. A measurement error is a departure from reality in the measurement provided (Groves et al. 2011 , p.52). One possible measurement error is an intentional or unintentional misresponse: for example, parents’ non-responses due to their increasing awareness of the illegality of child labour (Basu 1999 ); and children being involved in farm work but not being recognised as ‘child labour’ (Chaudhri et al. 2003 ; Chaudhri and Wilson 2000 ). In general, ‘child labour’ is a subset of all the forms labouring and child work undertaken by children under the age of 18 (variants are described by Dubey et al.  2017 ). Alternatively, a measurement error might arise owing to limited information, such as precise working hours, industrial categories and working conditions.

Furthermore, child labour is sparse, especially among younger age groups. There could be overrepresentation when we calculate this number using survey weights only. A model-based approach can provide more accurate information regarding the number of child labourers. The National Sample Survey (NSS) and the India Human Development Survey (IHDS) provide qualified data relating to child labour, with each dataset having different strengths. We have chosen to use the Indian Census 2011 as auxiliary information. Despite its large population coverage, it provides only the aggregate number of child labourers based on two broad categories – children who are main (i.e. working more than 6 mo a year in economic activity Footnote 2 ) or marginal (working less than 6 mo a year) workers.

The contribution of this paper is twofold. First, we investigate how the international definition of child labour affects the measured prevalence of child labour. We compare any differences in the measurement of child labour between the major stakeholders – the ILO and UNICEF – and the Indian government’s version before justifying the use of the definition and criteria of child labour. Then we apply these criteria to the estimation of the number of child labourers in India by using a Bayesian hierarchical model. This hierarchical model has layers of parameters; some parameters are relatively innocuous feed into estimates of the key unknowns, such as the number of child labourers. This study is the first attempt to integrate two data sources by using Bayesian inference to estimate child labour disaggregated by age and state. Thus, we overcome the limitations of using a single dataset. Ultimately, this study aims to reveal whether the combined-data approach is efficient in addressing measurement errors regarding estimates of child labour.

This paper is organised as follows. Section 2 introduces the background of the research and discussions on the definition of child labour, which justify our own definition. Section 3 explains our methodology, including a review of Bayesian inference, and describes our models. Lastly, we summarise the results in Section 4 and provide key policy implications in Section 5. We find it informative to use the two sample datasets together alongside the Census.

2 Differentiation Separating Child Labour from Child Work

Earlier research on child labour defined child labour in an extensive way in order to put pressure on legislative interventions. Weiner ( 1991 , p.3007) suggests that children who are not in school are potential child labourers. Economic studies on child labour have shown wide inclusion of various types of work. For example, Basu and Van ( 1998 ) define child labour as any economic activity of children, and Basu ( 1999 ) notes that this definition includes part-time workers. In a later study, domestic chores are also included in the work done by child labourers (Basu et al. 2010 ).

There have been ongoing efforts to distinguish child labour that is harmful to children’s development from socially accepted child work. Since the early 2000s, the ILO definition of child labourers as ‘economically active’ children (Ashagrie 1993 ) has been widely recognised in research. Ray ( 2000 , 2002 ), for example, uses wages to distinguish ‘child labour’ from ‘child work’. Meanwhile, conceptualisations of child labour have gradually expanded and included unpaid and irregular work. For example, the issue of girls being heavily burdened with domestic chores is now more recognised (Das and Mukherjee 2007 , 2011 ; Kambhampati and Rajan 2008 ).

Despite a conceptual expansion of child labour, estimation of its extent has been methodologically limited due to inconsistent standards of calculation and lack of relevant datasets at the national level. The use of working hours to separate child labour from child work is agreed by most international agencies and many other stakeholders. The 20th ILO Conference of Labour Statisticians (ICLS) re-confirms the use of working hours as a threshold for child labour in both the System of National Account (SNA) production boundary and the general production boundary (ILO 2018a ). Footnote 3 However, how many hours of domestic chores are considered harmful for children, and what periods (month, quarter or year) should be regarded as the “usual status”, are still under discussion. Also, the ILO ( 2018a ) has suggested that thresholds of hours are determined by national law, and in the absence of such law, by adult workers’ normal working hours. There is some ambiguity because the maximum hours dictated by national law are different in each country.

Studies in the methodology of estimating the correct number of child labourers are rare. Levison and Langer ( 2010 ) use two datasets to count domestic servants in Latin America. They do not use models but a weighted count. Webbink et al. ( 2015 ) provide a Tobit model of working hours in paid work but do not critically differentiate child labour from child work. Giri and Singh ( 2016 ) attempt to count child labourers in India by integrating economic activities and domestic chores and including ‘nowhere children’ Footnote 4 ; they do not make use of models but multiply the ratio of child labourers by the population. Moreover, considering all ‘nowhere children’ as potential child labourers might bring about misunderstandings of reality (Lieten 2002 ). Thus we argue that it is best to measure the number of actual child labourers and not the potential size of this group. This paper gives advice on how to do so.

2.1 Review of Definitions and Measurement of Child Labour

There has been an effort to define child labour by international agencies. According to the United Nations SNA, economic activity includes some types of family work but excludes household chores (ILO 2017 , p.17). Some economic work, such as production of own-use goods, is within the Gross Domestic Product (GDP), while some forms of domestic work, such as unpaid household services, are outside of the GDP (Hirway and Jose 2011 ). However, the ILO agrees that unpaid household services are part of child labour if they are performed in hazardous conditions (ILO 2017 ). In this section, we describe the widely-used ILO and UNICEF definition and measurement of child labour and then compare these with the Indian government’s current version. UNICEF has used the same definition of child labour as the ILO since 2008 after the 18th ILO Conference of Labour Statisticians (ICLS), Footnote 5 but they have applied slightly different methods in capturing child labour statistics.

2.1.1 Definitions of Child Labour

In the ILO definition, child labour means children in employment, excluding ‘children who are in permitted light work and those above the minimum age’ (ILO 2017 , p.17). The ILO’s first focus is on the hazardousness of work that children are engaged in (Omoike 2010 ). The worst forms of child labour are part of hazardous work. The worst forms of child labour are any work which, by its nature and circumstances, harms children’s health, safety or morality, such as slavery, prostitution, and illicit activities. Footnote 6 The ILO ( 2017 ) also recognises the significance of hazardous unpaid household activities, but it does not provide an explicit method for estimating household chores. The ILO ( 2018a ) has moved in the direction of allowing for hazardous forms of domestic work (notable in the Annex).

According to the ILO’s ( 2017 ) minimum age standard, Footnote 7 the minimum age should not be less than the age of completion of compulsory schooling and in any case, no less than 15 years old (14 for developing countries). The ILO also shows a concern for children who are 16 or 17 years old. The ILO’s Worst Forms of Child Labour Convention prohibits the worst forms of child labour for any children under the age of 18 (ibid.).

UNICEF’s definition agrees conceptually with the ILO’s ( 2017 ) version, but methodologically it shifts interest toward including child’s domestic work. UNICEF emphasises the importance of domestic work performed by children, which is measured by different time boundaries for ages 5–11, 12–14 and 15–17 (Chaubey et al. 2007 , p.2). As a result, the number of child labourers in UNICEF’s standard shows significant growth when compared with the ILO ( 2017 ).

The Indian Government’s Amendment Act, which came into effect in September 2016, defines child labour as any work of a child (up to the age of 14 years) except for helping their families, working in family enterprises after school hours or artistic work, and adolescents (15–17) working in hazardous industries. However, criticism has been raised because family work and family enterprises might allow for exceptions. Also, the Amendment Act considers only three types of work as hazardous – mining, working with inflammable substances or explosives, and working in a hazardous process. The Indian government’s definition of child labour is narrower than the definition of international agencies, as it still allows many hazardous work activities and disregards the effects of long working hours.

2.1.2 Measurement of Child Labour

The table below summarises the ILO estimation procedure of child labour (ILO 2017 ). There are various child labour categories, such as children aged 5–11 in any work, children aged 12–14 who are in more than light work, and children aged 15–17 who are in hazardous work. Hazardousness is specified by industrial and occupational types, working conditions, long-hour work and hazardous unpaid household activities. Table 1 .

The ILO does not specify a limit of working hours for unpaid household activities. Children’s involvement in unpaid household services for long hours or in an unhealthy environment and dangerous locations is child labour, though there are no specific criteria for the measurement of these activities (ILO 2016 , pp.55–57).

We use UNICEF’s most recent time boundaries for child labour in each age group (Table 2 ). According to the current database, UNICEF ( 2019 ) Footnote 8 categorises the criteria as follows: (a) children 5–11 years old who undertook at least 1 h of economic activity or at least 21 h of household chores per week; (b) children 12–14 years old who undertook at least 14 h of economic activity or at least 21 h of household chores per week; and (c) children 15–17 years old who undertook at least 43 h of economic activity per week. UNICEF’s change to their standards of child labour reflects the concerns of the ILO ( 2013 , 2016 ) that more than 20 h of household chores negatively affects children’s education. Previously, the time threshold for household chores for ages 5–14 was 28 h (UNICEF 2017 ), but this has now been reduced to 21 h. Children ‘working in hazardous working conditions’ or children (15–17 years) who spend long hours doing household chores, which were criteria of child labour in the Multiple Indicator Cluster Surveys (MICS) from UNICEF ( 2017 ), are no longer included.

2.2 Conceptual Framework

Hereinafter, we define child labour as including children between 5 and 17 years of age who are engaged in any work that is harmful to their development as well as household chores that require considerable amounts of time (defined below). Our definition is along the same lines as the definition of the ILO. It includes any types of child labour that hampers children’s physical, intellectual and mental development (Weiner 1991 ; Weston 2005 ). Thus we believe that children working in hazardous industries and occupations should be considered to be child labourers regardless of working hours. Time thresholds are applied differently for the age groups depending on the types of work. We follow the ILO’s minimum ages: age 15 for basic work and age 18 for hazardous work.

Our measurement is composed of several steps. Firstly, borrowing knowledge about hazardous occupations and industries from the ILO criteria, we categorise any children involved in harmful areas of work as child labourers regardless of the amount of time spent working. The list of hazardous industries and occupations is shown in Appendix Fig.  9 . We keep time thresholds for economic activity (43 h for ages 15–17, 14 h for ages 12–14, and 1 h for ages 5–11). Then we use UNICEF’s weekly time thresholds for unpaid household services: at least 21 h a week of household chores for ages 5–14. In Table 3 , we summarise our operationalisation for this study of the definition of child labour used by ILO and UNICEF.

3 Methodology

3.1 review of methods.

We have devised a bespoke statistical model that is capable of estimating the number of child labourers using two different sources of data and reconciling the differences between them. We use Bayesian inference to produce the point and interval estimates of the number of child labourers with the accompanying measures of uncertainty in the form of posterior probability distributions. Bayesian inference combines the model for the observed data specified in the form of a likelihood function with the prior distributions for the unknown parameters.

There are many advantages to using the Bayesian approach in this piece of research. Firstly, the Bayesian approach quantifies the uncertainty of conditions that may not be observable, taking it as a prior distribution (Gelman et al. 2013 , p.8). We test several priors and choose the best-performing one to produce our final results. Secondly, the Bayesian approach provides direct interpretations of the posterior probability distribution. While the frequentist approach uses confidence intervals which are the ranges of chances to include the true value of parameters (i.e. 95% confidence), the Bayesian approach uses predictive intervals, which specify the probabilities that the true values lie within them (Gelman 2013, p.95). The predictive intervals allow a clear interpretation of the estimated number of child labourers as well as other model parameters. Thirdly, it is relatively straightforward to fit models with many parameters together with a multi-layered probability structure (Gelman et al. 2013 , p.4).

In this particular application, the Bayesian inferential framework presents us with an efficient way to combine different datasets. The uncertainty of various measurements is translated into model parameters. This is especially useful when multiple datasets provide partial (biased), imprecise or conflicting information about the unobserved (latent) quantity of interest. A statistical model that accounts and corrects for the inaccuracies and biases in the data can be used. The resulting estimates can thus be more precise as information from both datasets is used, compared with using each data source on its own.

This research maximises the accuracy of measurements of child labourer numbers in India, combining both the NSS 2011 and the IHDS 2011, and using the Indian Census 2011 as auxiliary information.

The NSS Employment and Unemployment Survey is the most commonly used dataset on employment as it provides details of work types and industrial categories. The NSS 68th round (July 2011–June 2012) is a large sample survey (number of respondents for ages 5–17 = 122,630, representing 0.04% of the population of the same age group), covering all 35 states in India. A stratified multi-stage design is applied; the first stratum is based on urban-rural characteristics, and the second stratum is based on household wealth (National Sample Survey Office 2013 ).

The India Human Development Survey (IHDS) is a panel survey of two rounds – Wave 1 in 2004/05 and Wave 2 in 2011/12 – but we use only the second wave for this study to match the year with the NSS. The sample size of the IHDS is about half of the NSS’s (number of respondents for ages 5–17 = 51,556, representing 0.02% of the population of the same age group). It covers 33 states in India, excluding Andaman and Nicobar Islands and Lakshadweep (Desai and Vanneman 2015 , p.2). The variables missing from these two States are treated as missing items in our models. The samples in rural areas are drawn from participants in a previous survey by the National Council of Applied Economic Research and the samples in urban areas are selected by sampling proportional to population (ibid.).

The Indian Census 2011 (Ministry of Home Affairs  2011 ) does not include the specific data required to measure child labour according to our definition (Section 2.2), such as industries or working hours; it only gives the aggregate numbers of main and marginal workers defined by the period of work. We use this information as an auxiliary variable. Also, we obtained the population size by age and state from the Indian Census 2011. The date of the Census is close to the date of the other two surveys (2012 vs 2011).

3.3 Undercount Parameter

The IHDS has a clear limitation regarding working hours in domestic work, but it provides accurate working hours for economic activity, while the NSS has an approximation of work intensity, but covers all types of work. The IHDS includes working hours in a family business and household farming work but does not include working hours for household chores. Hence, some of the IHDS sample children who work long hours at home cannot be included as child labourers, which might cause a significant undercount. The descriptive analysis shows that there could be a systematic undercounting of child labourers in the IHDS figures compared to those from the NSS (see Section 3.5).

Considering the overall differences between the IHDS and the NSS data, we suggest using the combined-data model with a parameter that measures any systemic under-counting by the IHDS against the NSS numbers. A systematic over- or under-estimation in one dataset might be solved by applying an over- or under-count parameter (e.g. Wiśniowski 2017 ; Wiśniowski et al. 2013 ). The under- or over-counting parameter in this study informs us about the relationship between two datasets. Thus it is not possible to use it for a single-dataset model (see Appendix Table 5 ).

3.4 Matching Two Key Datasets

To combine the datasets, we have reviewed the variables and questionnaires and then matched the types of work (industries and occupations) and the time-use information. Firstly, the IHDS’s industrial and occupational codes are matched with the NSS’s. The NSS provides five-digit codes for industries of the National Industrial Classification (NIC) 2008 and three-digit codes for occupations of the National Classification of Occupation (NCO) 2004, which correspond to those used by the ILO (ILO 2016 ). The IHDS uses its own industrial codes and the NCO 1968.

Regarding time-use, the IHDS asks respondents how many hours a day they usually work, whereas the NSS asks for a daily time disposition of activity based on a one-week recall. The IHDS provides daily working hours (0 to 16 h) Footnote 9 ; however, the NSS offers the intensity of time disposition for each activity (None = 0, half = 0.5, and full = 1.0) for the last 7 d. (Maximum points are 7.0 per activity.) Footnote 10 In this paper, we use time thresholds on a weekly basis; therefore, the daily hours of the IHDS need to be multiplied by seven, based on an assumption that children work every day in the week. The NSS sets the maximum working hours at seven points in a week, which is converted to 70 points and regarded as equal to 43 h.

This study rigorously follows the concept of ‘usual hours of work per week’ suggested by the ILO (ILO 2018a ). Footnote 11 We use a principal activity status provided by both datasets which indicates the activity on which a person spent a long time during a reference year, as a usual status.

3.5 Descriptive Analysis of the Data

Our criteria for measuring child labour provide time thresholds both for economic activity that is a type of work counted in GDP, including work in family-owned farming or business, and for unpaid household services. In terms of industries and occupations, construction, mining, and waged agricultural or fishery work are the areas with the highest number of child labourers. Children working in textile manufacturing and the service sector, such as street vendors, also appear in large numbers (see Appendix Fig.  8 ). Moreover, many child labourers are found to be unpaid household labourers, and most of these are female children (see Appendix Fig.  9 ).

Figure 1 provides a gross-weighted count of children who are considered to be labourers according to two different measurement methods – one from the ILO and the other from UNICEF – calculated by using the NSS and IHDS in 2011/12. In the ILO standard, the gross-weighted figure of child labourers according to the IHDS is 11.4 million, while the same figure according to the NSS is 13.7 million. Meanwhile, the UNICEF measurement excludes child labour in hazardous activities and adds unpaid household services for long hours to the category of child labour. Accordingly, it generates a huge discrepancy in the numbers of child labour calculated from two datasets: 7.3 million and 13.22 million from the IHDS and the NSS respectively. After applying the UNICEF standard, the IHDS has a significantly reduced number of child labourers because there are no records of children doing household chores and because of the exclusion of those in hazardous work. However, the NSS, even after excluding hazardous work from the definition, still shows many child labourers because of including children who spend excessive time in the recall week doing household chores.

figure 1

Gross Weighted Number of Child Labourers in India Using Different Measurement Schemes. ( a ) ILO measurement ( 2017 ), ( b ) UNICEF measurement ( 2019 )

Figure 2(b) shows the result of applying the newly-constructed integer weights to the counting of child labourers and multiplying it by the population from the Census using the NSS and IHDS 2011/12 under our measurement criteria. The gross-weighted number of child labourers shows an irregular pattern by age (Fig.  2(a) ). However, using a relative weight reduces any sharp decrease between the ages. The relative weights are obtained by taking the sampling weights divided by the mean and rounding them up to the nearest integer greater than 0. The ranges of the new weights are from 1 to 26 for the IHDS, and from 1 to 39 for the NSS, which indicates the number of duplications of each value from the survey used to construct the estimate. Thus, in models, we prefer to use the relatively weighted number of child labour and to multiply it by the population from the Indian census, as it smooths variations by age. A relative weighted count of child labour in the combination of the true population suggests 14.8 million based on the NSS and 11.4 million when using the IHDS.

figure 2

Gross vs Relative Weighted Count of Child Labourers in India (Non-model, Authors’ Definition). ( a ) Gross Weighted Number of Child Labourers, ( b ) Relative Weighted Rate of Child Labour*Population. Notes: weighted counting with the use of measurement criteria of this study (see Table 3 )

An obvious limitation of both datasets is that many children in early childhood are categorised as either “other” or “too young” in their principal activity status, so they are not included as child labourers. A lower incidence of child labour among younger children might be related to who is in those categories. An “other” category includes abandoned work such as begging, waste-picking, etc., but it is difficult to obtain a specific status for each child in that category.

Figures  3 and 4 compare different numbers of child workers and child labourers in India. The Indian Census suggests that the number of child workers between the ages of 5 and 17 was 23.8 million in 2011/12. Our estimation suggests there were 11.4–15.2 million child labourers in the same year (see Section 4). In both results, Uttar Pradesh has the largest number of child workers as well as child labourers because of its large population.

figure 3

Child Workers by Age and State from the Indian Census, 2011. Notes: Main workers (working for more than 6 mo) + marginal workers (working less than 6 mo)

figure 4

Child Labour by Best Estimates, 2011/12

This study uses the aggregated and weighted numbers of child labourers per age i per state j (13 ages * 35 states). In 2011/12, there were 35 states, which have shown different trends in the prevalence of child labour. This study estimates the number of child labourers by using a Bayesian hierarchical Poisson log-normal model and obtaining the posterior distributions for child labour by age and state. Based on these posterior distributions, we produce summaries such as medians and posterior predictive intervals as the point and interval estimates of the number of child labourers, respectively.

The specification of all models is presented in Appendix Table 6 . By μ ij , we denote a key parameter in child labour estimates: the true ratio of children in child labour to all children of age i and in state j . We use n.a ij and n.b ij to denote the sample sizes by age i and state j in the IHDS and the NSS, respectively, weighted to adjust regional differences according to the Indian Census 2001 (Desai and Vanneman 2015 ; National Sample Survey Office 2013 ). Firstly, we estimate μ ij separately for the NSS and the IHDS (Model 1 and Model 2). Then we combine the IHDS and NSS datasets in Model 3 (a Poisson model) and Models 4.1 to 4.3 (Poisson log-normal models). Thus, for each model, the expectation of the Poisson model is the outcome of the product of μ ij and either n.a ij or n.b ij . By using each model, we obtain a suitable expectation parameter for estimating any integer count (Gelman et al. 2013 , pp.42–44). In Models 4.1–4.3, we use a discount parameter υ to capture a possible under-counting of child labourers in the IHDS data compared with the NSS.

In the models, y.a ij and y.b ij represent the observed counts of child labourers in age-state group ij in each survey. The y.a ij and y.b ij are assumed to be drawn from the Poisson distribution with the true ratio of children in child labour to all children (μ ij ) multiplied by the sample size (n.a ij from the IHDS or n.b ij from the NSS). Poisson distribution is a natural candidate for modelling counts of persons or, more precisely, counts of “events” where a child is identified as a labourer by our definition (see Section 2). By ŷ ij we denote the posterior median of the predicted distribution of the number of child labourers aged i and in state j . This value can be obtained by projecting the estimated rate of child labour, μ ij , onto the population, N ij . Then ŷ i+ , the sum of the ŷ ij for all states, shows the number of child labourers by each age.

The models include a few explanatory variables, such as age (x i ) and the log-ratio of main workers (z ij ), obtained from the Indian Census 2011 (defined by its narrow definition of work), which explain the true child labour rate μ ij in a log link function. Parameter β 0 denotes the intercept; β 1 and β 2 are the coefficients of the covariates x i and z ij .

Lastly, we assume over-dispersion in addition to the Poisson variability (see Table 6 , the last column). In rows 4 and 5 of the last column, λ.a ij and λ.b ij , are assumed to be normally distributed to incorporate over-dispersion in each dataset. In row 7, λ ij also allows for overdispersion to predict the true child labour rate (ψ ij ). Overdispersion parameters (λ.a ij , λ.b ij and λ ij ) allow the mean to vary by observation and explain more variability (Lunn et al. 2012 , p.227; see Table 5 ). In Models 4.1–4.3, the true rate of child labour is ψ ij , which is an adjusted mean of the Poisson distribution. Overall, this method of modelling permits a more robust description of the uncertainty of the measured child labour from two data sources.

To obtain posterior distributions, we have used the Markov Chain Monte Carlo (MCMC) method as implemented in the R packages JAGS (Plummer 2003 ) and R2jags (R Core Team 2018 ). After discarding the first 40,000 simulation runs, we implemented 360,000 iterations and thinned them by eight, producing an effective total of 40,000 posterior samples.

3.6 Prior Distributions

The priors for the intercept and coefficients of age and the log-rate of main workers β k and k = 1, 2, 3, respectively, are assumed to be normally distributed with mean 0 and a large variance (i.e. small precision, which is the inverse variance τ = 1/σ 2 ). These non-informative priors allow data to play a dominant role in the inference (Gelman et al. 2013 ).

where 10 −6  denotes precision. In Models 4.1–4.3, we assume a vaguely informative prior. Gelman ( 2006 ) suggests three different priors: uniform, inverse gamma, and half Cauchy. We have tested these priors and compared the sensitivity of the results to various specifications by using the DIC (Deviance Informative Criterion; Spiegelhalter et al. 2014 ) score: a model with a uniform prior (Model 4.1), inverse gamma prior (Model 4.2) and a half Cauchy prior (Model 4.3). As a result, we have chosen to use an inverse gamma prior, as specified below. The effect of the choice of the prior distribution is explained in the next section.

In the combined data model (Models 3 & 4.1–4.3), there is an under-counting parameter, υ, that controls any systematic undercounting of child labour in the IHDS compared to the NSS. The under-counting parameter, υ, is assumed to follow a uniform distribution υ ~ uniform (0, 1) which reflects our lack of knowledge about the undercount.

4.1 Comparing Models

We have reviewed the DIC to compare the goodness of fit as well as the complexity of the models. DIC is the posterior mean of the deviance plus the effective number of parameters (pD). Comparing DIC of Model 1 and Model 2 is not feasible because the NSS and the IHDS have different sample sizes. Model 4.2 has the lowest DIC among the other models (3, 4.1, 4.2, and 4.3). Table 4 .

Both Model 1 (with the IHDS 2011/12) and Model 2 (with the NSS 2011/12) show a narrow posterior uncertainty, as they assume that the variances are equal to the means. The two models generate different predictions for child labour: Model 1 estimates this number at 11.7 million (median) and its 95% predictive interval (PI) is 11.2–12.2 million; Model 2 at 14.1 million (95% PI: 13.8–14.4 million).

The variance of Poisson distribution should be equal to the mean, which may not realistically capture the over-dispersion in the data. Figure 5 shows over-dispersion happening in Models 1 to 3. Several observations are outside of the predicted posterior distribution. Thus a simple Poisson model does not capture the variability of data. Once the data from both surveys are combined, a Poisson model (Model 3) estimates the number of child labourers at 13.5 million (95% PIs: 13.2–13.7 million) for ages 5 to 17, and 3.9 million (3.8–4.1 million) for ages 5 to 14. However, it still does not capture the observed variability very well. No observations lie within the 95% PIs.

figure 5

Posterior Mean Numbers of Child Labourers in India, 2011/12, by Ages (Models 1–3). Notes: the shaded areas indicate 95% intervals

A Poisson log-normal model (Model 4.1–4.3) uses the adjusted mean of the Poisson distribution (ψ ij ), which allows over-dispersion (larger variance) of each parameter. Models 4.1–4.3 reduce deviances compared with Model 3. The result indicates that Models 4.1–4.3 are superior to Model 3, since the DIC value is smaller for these than for Model 3 even after incorporating the large penalty of complexity.

The simulation of different priors implies that the best prior is an inverse gamma prior (Model 4.2), although the difference in DIC between the models is not large. In the next section, we based our predictions of child labour in Indian states using Model 4.2.

4.2 Result of Poisson Log-Normal Model

4.2.1 parameter estimation.

The MCMC algorithm shows proper convergence in the posterior parameters of interest in Model 4.2. Figure 6 shows a histogram of MCMC samples taken from Model 4.2, median and 95% intervals of estimated parameters. The intercept (β 1 ), the coefficient of age (β 2 ), and the coefficient of the log ratio of main workers from the Indian Census (β 3 ) show stable convergence, resulting in statistically meaningful outcomes. The coefficient for age is positive, as the rate of child labour increases with age. The coefficient for the rate of main workers from the Census is also positive. Using the Indian Census as auxiliary information increases the mean rate of child labour and reduces the gap between ages. As a result, it contributes to smoothing the graph of child labour by ages.

figure 6

Histogram of Parameter Estimates. Notes: Model 4.2; burnin = 40,000; iterations kept = 40,000 (2 chains); thin by 8; median and 95% lower and upper bounds

The under-counting parameter (υ) indicates the posterior mean 0.81 (95% PI: 0.77–0.94), which shows that there is a slight undercount (around 2.3 million child labourers aged 5 to 17) in the IHDS. Under-counting of the IHDS is mostly caused by a lack of information on household chores. Accordingly, the combination model puts greater weight on the observations from the NSS.

According to the results of our final model (Model 4.2), the number of child labourers (ages 5–17) is estimated at 13.2 million (4% of the child population aged 5–17) in 2011/2012. The 95% PI for the number of child labourers ranges from 11.4 million to 15.2 million. That is, with a probability of 95%, the true number of child labourers lies within this interval. The estimate for ages 5–14 is around 3.2 million and 95% PIs are 2.7 to 3.8 million. Figure 7 .

figure 7

Results of Bayesian Poisson Log-Normal Model Using a Combination of Datasets (India). ( a ) Posterior No. of Child Labourers by Age, ( b ) Posterior Rate of Child Labour by Age. Notes: the shaded areas indicate 95% intervals; the census data is presented in a graph for purposes of comparison

The number of child labourers is estimated to be higher than the figure proposed by the Indian Census for main workers aged 5 to 17 (working more than 6 mo in any economic activity). The number of child labourers surveyed by the Indian Census is 11.8 million for ages 5 to 17, which is smaller than our point estimate but lies within the 95% PI. The Indian Census figure of main workers for ages 5 to 14 (4.35 million) is larger than the forecast number of child labourers and lies outside the 95% PI. Our estimates do not adequately capture the child labourers under the age of 10, due to there being only a small number of observed child labourers at the early ages (see Section 3.5). Child labourers who are under 10 might be underestimated because some children who work are categorised in “other” or “too young”, so they are not included as child labourers.

4.2.2 Summary of Findings

Combining datasets with a Poisson log-normal model provides a reliable figure for child labour in India and incorporates uncertainty in models supported by the use of available observations. The use of an under-count parameter is a useful way to reduce any systematic error, which might be caused by the lack of information in one dataset compared to the other. In addition, the model shows the clearest age trend of child labour, as it has confirmed the effect of smoothing the variation between ages. The auxiliary variable from the Indian Census ratio of child labour introduces smoothing of the trend of child labour by age; it reduces the gaps between the single-year age groups.

A large increase in the percentage of child labourers appears at age 14 when many more children are likely to be involved in labour compared to earlier ages. This trend is related to the education system in India. In Indian law, the Right of Children to Free and Compulsory Education Act 2009 defines education as free for children from 6 to 14 years, but some children become full-time workers before they move to secondary school. This finding supports the importance of secondary school education in preventing children from becoming full-time workers (Chaudhri et al. 2003 ; Charudhri and Wilson 2000 ).

5 Discussion and Policy Implications

The discrepancies between definitions and measurement of child labour have long been discussed. Our research has tried to reduce the gaps. We have relied on the international definition of child labour and offered a novel solution to estimate it by applying current international criteria within one country, India. Through using the available datasets and accounting for their limitations, we provided authoritative estimates for 2011/12. This study includes a child’s work in household chores as one of the main aspects of child labour if the child worked more than the specified time threshold for their age group. The recent proposition of the ILO is that not only is children’s domestic work undertaken with the aim of creating manufactured goods or any product that competes in the market and counts toward GDP considered child labour, but also domestic work, i.e. services for other household members. This domestic work must also exceed certain time thresholds, relative to the age group, to be counted as child labour. This particular work on domestic tasks is outside GDP but inside the ‘general production boundary’, and is thus non-SNA work (ILO, 2018a ).

Our estimation of child labour reflects the best and most recent knowledge regarding the differing prevalence of child labour across India’s states. Given the definition of child labour using the hazard elements used by the ILO and the time criteria used by UNICEF, the probability distribution represents 13.2 million for child labourers aged 5 to 17 overall, which is larger when compared, for example, to the Indian Census, where this number was 11.8 million. As new datasets emerge, the method can be used further.

The study provides an accurate number of child labourers based on a definition consistent with the one that the international agencies offer. The focus is on any work that is harmful to children’s development, including both economic work and unpaid household services that require considerable time. In addition, time boundaries play a key role in our measurement of child labour. The selected categories of working hours, based on the UNICEF guidelines, help with the capturing of child labour as a category representing work that is harmful to children. However, the most recently suggested UNICEF measures do not include children working in hazardous industries as child labourers and thus might underestimate child labour. Therefore, we suggest using both the concept of hazardous work and the concept of time thresholds to calculate child labour. With the data-combining methods, which are not data-pooling methods, achieving these estimates becomes a feasible calculation task.

There are a few further critical and practical points to make about using hourly thresholds for discerning child labourers. Working hours is a broad term in itself: for example, some datasets use daily working hours, and some use weekly working hours. In the Indian case, the IHDS 2011/12 has daily working hours and the NSS 2011/12 has a roughly weekly basis work intensity. However, seasonality is not handled well in either survey. The best suggestion for any further survey on working hours is to collect hourly information as specifically as possible within a limited time and budget. Daily working hours during a reference week, as well as a relevant period of actually working, perhaps during 2–3 seasons, is required. Also, for weighting, the NSS uses 2001 Census which may be out of date at the time of data collection in 2011/12.

The other obvious concern is the under-reporting of child labour. Although the IHDS provides better time information, it does not cover the domestic sector, so we needed to use an undercount parameter. Furthermore, the observations used in this study fail to capture some of the child labour in early age groups, below the age of 10. Considering that a large number of children’s labouring statuses are not reported or under-reported, relying only on working hours might lead to an underestimation of child labour.

A simplistic counting of child labour relying on one single dataset should be avoided. As a minimum, a clear method of calculation is required that is comparable to international standards and definitions. This piece of research makes a significant methodological contribution to child labour studies in several ways. Firstly, it has introduced the use of a relative weight and multiplied it with a true population, so that we can reduce the amount of error related to the population ratio of any survey.

Secondly, we demonstrate how a Bayesian hierarchical model can be used to combine different datasets to benefit from an increased sample of observed child labourers in two data sources, especially when datasets have different strengths. The combined-data approach can account for any potential systematic under- or over-counting of child labourers and provide more trustworthy estimates for an unknown parameter and the “true” estimated number of child labourers.

Third, we suggest using a Poisson log-normal model, which accounts for over-dispersion of counts. It provides an efficient way to incorporate uncertainty raised by the rare number of observations of child labour. The posterior probability distribution allows reliable estimation as it maximises the use of information using different datasets. In our case, a prediction is smoothed by using age as a covariate and by borrowing information from the Census data, where the less precise definition of child labour is used. Further research can be developed with other multi-dimensional variables to explain the prevalence of child labour.

We suggest the following implications for policymakers. Our results recognise that unpaid household services are non-ignorable aspects of child labour in India. The Indian Child Labour Amendment Act (2016) allows children to help family and work in a family business. However, if unpaid household service work exceeds the time thresholds of a reference age, it is regarded as hazardous according to both UNICEF ( 2019 ) and the ILO ( 2017 ). As a large number of child labourers are engaged in unpaid household services in India, there should be more support for children who spend long hours on housework and so are deprived of education. Secondly, the Amendment Act does not provide time limitations for child work. Setting up maximum working hours will be an important next step towards harmonising with the international standard (e.g. 40 h a week for ages 16–17 in the UK; 35 h a week for ages 15–17 in South Korea). Lastly, the profile of child labourers by age produced by our model shows a clear age trend for when children become labourers. It is found that children might become full-time workers after completing elementary school (ages 13–14) or before entering secondary school at the age of 14. Although further investigation of the relationship between education and child labour is needed, interventions are necessary for children at those ages.

Available at https://www.unicef.org/infobycountry/india_statistics.html (Accessed 16 May 2019). The population of children aged 5 to 14 is estimated at about 260 million by the Indian Census, 2011.

In the Indian Census data, economic activity is working as cultivators, agricultural labourers, household industry workers and other workers.

The SNA is a set of standards to measure economic activity, initiated by the United Nations Statistical Commission. The general production boundary covers all kinds of activities producing goods and services (ILO 2018a ). Own-use production work of services, such as washing and preparing meals, is excluded from the SNA production boundary but included in the general production boundary (ILO 2018a ).

Chaudhri et al. ( 2003 ) and Chaudhri and Wilson ( 2000 ) introduce the concept of ‘nowhere children’ who are neither in school nor work and insist they should be counted as child labourers.

Available at https://www.unicef.org/protection/57929_child_labour.html (Accessed 11 Jan. 2020)

Worst Forms of Child Labour Convention initiated in 1999, No. 182

Minimum Age Convention in 1973, No. 138

This update took place in October 2019 ( https://data.unicef.org/topic/child-protection/child-labour/ accessed 11 Jan. 2020).

‘How many hours did you work in a usual day?’ (Question 7.8, IHDS 2011).

‘Time disposition during the week ended on ...........’ (intensity of activity for 7 d, full mark: 1.0 and half-mark: 0.5) (Question 5.3, NSS 2011).

The ILO defines hours usually worked as ‘hours (actually) worked in a job per a short reference period such as 1 wk, over a long observation period of a month, quarter, season or year’ (ILO 2018b ).

Ashagrie, K. (1993). Statistics on child labor. Bulletin of Labour Statistics, 3 , 11–24.

Google Scholar  

Basu, K. (1999). Child labor: Cause, consequence, and cure, with remarks on international labor standards. Journal of economic literature, xxxvii (3), 1083–1119.

Article   Google Scholar  

Basu, K., Das, S., & Dutta, B. (2010). Child labor and household wealth: Theory and empirical evidence of an inverted-U. Journal of Development Economics, 91 (1), 8–14.

Basu, K., & Van, P. H. (1998). The economics of child labor. The American Economic Review, 88 (3), 412–427.

Chaubey, J., Perisic, M., Perrault, N., Laryea-Adjei, G., & Khan, N. (2007). Child labour, education and policy options . New York: UNICEF.

Chaudhri, D., Nagar, A., Rahman, T., & Wilson, E. (2003). Determinants of child labour in Indian states: Some empirical explorations (1961-1991). Journal of Quantitative Economics, 1 (1), 1–19.

Chaudhri, D. P., & Wilson, E. (2000). The challenge of child labour in rural India: A multi-dimensional problem in need of an orchestrated policy response.

Das, S., & Mukherjee, D. (2007). Role of women in schooling and child labour decision: The case of urban boys in India. Social Indicators Research, 82 (3), 463–486.

Das, S., & Mukherjee, D. (2011). Measuring deprivation due to child work and child labour: A study for Indian children. Child Indicator Research, 4 (3), 453–466. https://doi.org/10.1007/s12187-010-9097-8 .

Desai, S., & Vanneman, R. (2015). India human development survey-II (IHDS-II), 2011-12. ICPSR36151-v2. Ann Arbor, MI: Inter-university consortium for political and social research [distributor], 31 .

Dubey, A., Olsen, W., & Sen, K. (2017). The decline in the labour force participation of rural women in India: Taking a long-run view. The Indian Journal of Labour Economics, 60 (4), 589–612.

Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Analysis, 1 (3), 515–534.

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis . Chapman and Hall/CRC.

Giri, A. K., & Singh, S. P. (2016). Child labour and ‘nowhere’ children in post-reforms India. Indian Journal of Human Development, 10 (1), 97–110. https://doi.org/10.1177/0973703016654562 .

Groves, R. M., Fowler Jr, F. J., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2011). Survey methodology (Vol. 561). John Wiley & Sons.

Hirway, I., & Jose, S. (2011). Understanding women’s work using time-use statistics: The case of India. Feminist Economics, 17 (4), 67–92.

ILO. (2013). Unpaid household services and child labour. In Presented at the 19th international conference of labour statisticians . Geneva: International Labour Office.

ILO. (2016). Methodology: Global estimate of child labour, 2012–2016 . Geneva: International Labour Office.

ILO. (2017). Global estimate of child labour: Result and trends, 2012–2016 . Geneva: International Labour Office.

ILO. (2018a). Resolution to amend the 18th ICLS resolution concerning statistics of child labour. ILO. https://www.ilo.org/wcmsp5/groups/public/%2D%2D-dgreports/%2D%2D-stat/documents/meetingdocument/wcms_648624.pdf . Accessed 11 January 2020.

ILO. (2018b). Hours of work. International Labour Office. https://www.ilo.org/ilostat-files/Documents/description_HRS_EN.pdf . Accessed 11 January 2020.

Kambhampati, U. S., & Rajan, R. (2008). The ‘nowhere’ children: Patriarchy and the role of girls in India’s rural economy. The Journal of Development Studies, 44 (9), 1309,1341.

Levison, D., & Langer, A. (2010). Counting child domestic servants in Latin America. Population and Development Review, 36 (1), 125–149.

Lieten, G. K. (2002). Child labour in India: Disentangling essence and solutions. Economic and Political Weekly, 37 (52), 5190–5195.

Lunn, D., Jackson, C., Best, N., Spiegelhalter, D., & Thomas, A. (2012). The BUGS book: A practical introduction to Bayesian analysis. Chapman and Hall/CRC.

Ministry of Home Affairs. (2011). Census Data,   https://censusindia.gov.in/2011census/population_enumeration.html . Accessed 11 Jan 2020.

Ministry of Labour and Employment. (n.d.). Census Data on Child Labour. https://labour.gov.in/childlabour/census-data-child-labour . Accessed 11 January 2020.

National Sample Survey Office. (2013). India - Employment and Unemployment July 2011–June 2012 . http://mail.mospi.gov.in/ . Accessed 11 January 2020.

Omoike, E. (2010). Child domestic labour: Fostering in transition? Child Slavery Now : A Contemporary Reader, 203.

Plummer, M. (2003). JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. In Proceedings of the 3rd international workshop on distributed statistical computing (Vol. 124, pp. 1–10). Vienna, Austria.

Core Team, R. (2018). R: A language and environment for statistical computing . Vienna, Austria: R Foundation for Statistical Computing https://www.R-project.org . Accessed 11 January 2020.

Ray, R. (2000). Child labor, child schooling, and their interaction with adult labor: Empirical evidence for Peru and Pakistan. The World Bank Economic Review, 14 (2), 347–367.

Ray, R. (2002). The determinants of child labour and child schooling in Ghana. Journal of African Economies, 11 (4), 561–590.

Samantroy, E., Sekar, H. R., & Pradhan, S. (2017). State of child Workers in India. V.V.Giri National Labour Institute.

Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Van der Linde, A. (2014). The deviance information criterion: 12 years on. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76 (3), 485–493.

UNICEF. (2017). Access the data - child labour. UNICEF. https://data.unicef.org/wp-content/uploads/2015/12/Child-labour-database_Nov-2017.xls . Accessed 11 January 2020.

UNICEF. (2019). Child Labour – Notes on the data. https://data.unicef.org/topic/child-protection/child-labour/ . Accessed 11 January 2020.

UNICEF. (n.d.). India statistics. UNICEF . https://www.unicef.org/infobycountry/india_statistics.html . Accessed 16 May 2019.

Webbink, E., Smits, J., & Jong, E. (2015). Child labor in Africa and Asia: Household and context determinants of hours worked in paid labor by young children in 16 low-income countries. The European Journal of Development Research, 27 (1), 84,98.

Weiner, M. (1991). The child and the state in India : child labor and education policy in comparative perspective . Princeton, N.J. ; Oxford: Princeton, N.J. ; Oxford : Princeton University Press.

Weston, B. H. (Ed.). (2005). Child labor and human rights: Making children matter . Boulder, Colorado: Lynne Rienner Publishers.

Wiśniowski, A. (2017). Combining Labour Force Survey data to estimate migration flows: the case of migration from Poland to the UK. Journal of the Royal Statistical Society: Series A (Statistics in Society), 180 (1), 185–202. https://doi.org/10.1111/rssa.12189 .

Wiśniowski, A., Bijak, J., Christiansen, S., Forster, J. J., Keilman, N., Raymer, J., & Smith, P. W. (2013). Utilising expert opinion to improve the measurement of international migration in Europe.  Journal of Official Statistics, 29 (4), 583–607. https://doi.org/10.2478/jos-2013-0041 .

Download references

Acknowledgements

The authors thank the editor and two anonymous reviewers for their valuable suggestions that helped to improve the manuscript. We thank Amaresh Dubey, Abinash Lahkar, Neetha N, Ellina Samantroy, Helen R Sekar, Bupinder Zutshi and all the other interviewees who provided insightful comments and information during the visit by Jihye Kim in India in January 2018.

Author information

Authors and affiliations.

Social Statistics Department, School of Social Sciences, University of Manchester, Oxford Road, Manchester, M13 9PL, UK

Jihye Kim, Wendy Olsen & Arkadiusz Wiśniowski

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Jihye Kim .

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

figure 8

Count of Child Labourers in Hazardous Industries and Occupations from the IHDS and NSS, 2011/12. Notes: Weighted Count, Source: NSS 2011/12 & IHDS 2011/12

figure 9

Child Labour by Economic Activity vs Unpaid Household Services. Notes: Weighted count with our criteria measuring child labour; unpaid household services work is defined by principal activity status code 92 (“attended domestic duties only”)

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Kim, J., Olsen, W. & Wiśniowski, A. A Bayesian Estimation of Child Labour in India. Child Ind Res 13 , 1975–2001 (2020). https://doi.org/10.1007/s12187-020-09740-w

Download citation

Accepted : 15 April 2020

Published : 18 June 2020

Issue Date : December 2020

DOI : https://doi.org/10.1007/s12187-020-09740-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Child labour
  • Bayesian estimation
  • Combining data
  • Time threshold
  • Find a journal
  • Publish with us
  • Track your research

Prevalence and potential consequences of child labour in India and the possible impact of COVID-19 - a contemporary overview

Affiliations.

  • 1 State Legal Services Authority, India.
  • 2 Adelaide School of Medicine, The University of Adelaide, Australia.
  • PMID: 33563103
  • DOI: 10.1177/0025802421993364

Child labour is a global phenomenon occurring predominantly in countries with lower socioeconomic status and resources. Societal and familial poverty, loss or incapacitation/illness of parents, lack of social security and protection, and ignorance about the value of, or limited access to, education are among the myriad reasons for the involvement of children in the workforce. Child labour is a barrier to the development of individual children and their society and economy. Global estimates indicate that 152 million children (64 million girls and 88 million boys) are working, accounting for almost one in 10 of all children worldwide. Currently the COVID-19 health pandemic and the resulting economic and labour market consequences are having a major impact on people's lives and livelihoods. Unfortunately, impoverished families and their children are often the first to suffer, which may push many more vulnerable children into child labour situations. Child labour in India is more prevalent than in many other countries, with approximately 10 million children actively engaged in, or seeking, work. This paper focuses on the issue of child labour, its causes and its ill effects. Further, it also reviews the international legal framework relating to child labour and legislative issues in India. There is clearly an urgent need for this issue to be effectively addressed and resolved.

Keywords: COVID-19; Child labour; child abuse; child development; child health; exploitation; poverty.

Publication types

  • COVID-19 / epidemiology*
  • Child Abuse / statistics & numerical data*
  • Child Labor / statistics & numerical data*
  • Risk Factors
  • Social Conditions*
  • Socioeconomic Factors

A woman sits amid the rubble with her tools-- a hammer and a chisel in Budhpura, Rajasthan, India

How child labour in India makes the paving stones beneath our feet

Despite promises of reform, exploitation remains endemic in India’s sandstone industry, with children doing dangerous work for low pay – often to decorate driveways and gardens thousands of miles away

S onu has one clear instruction from his boss: when you see an outsider, run. In the two years since he started working full time, he has had to run only twice. Sonu is eight years old. His mother, Anita, said that almost every time an outsider comes to their village of Budhpura, in the Indian state of Rajasthan, she receives a phone call telling her not to bring Sonu to work. “Only adults go to work on those days,” said the 40-year-old, cradling her youngest child, who is three.

Sonu and his mother work eight hours a day, usually six days a week, making small paving stones, many of which are exported to the UK, North America and Europe. Sonu began working after his father died of the lung disease silicosis in 2021. “First, he made five stones, then 10, and then he quit school to work full-time,” his mother said. The pair sit on a street close to their home, amid heaps of sandstone rubble, chiselling rocks into rough cubes of rugged stone. Sonu is paid one rupee – less than a penny – for each cobblestone he produces. These stones have a retail value of about £80 a square metre in the UK.

Twenty years of chipping away with hammer and chisel, tossing and turning the hefty rocks, has left Anita with constant back pain, and countless injuries to her hands and feet. She has tuberculosis, which may have been caused by inhaling dust. She can’t hold a hot chapati because her hands are raw and peeling from grasping the stones and handling tools for hours at a stretch. Her income is so small that she has to decide between paying for a doctor or buying clothes and shoes for her five children. When we met last year, in the hot month of August, Sonu was walking barefoot on the stony, unpaved roads of the village.

India is one of the largest producers of natural stone, including granite, marble, sandstone and slate. Rajasthan, a mineral-rich state in the north-west, attracts mining companies from all over the country. Before a business can begin extracting, it must acquire a mine lease from the state government. Rajasthan has issued more than 33,000 mine leases, more than any other state in India – most of them for sandstone mines and quarries – but reports from environmental organisations suggest there are thousands of other quarries operating illegally, without a licence. This means a significant proportion of the Rajasthan mining industry is unregulated.

Sandstone, one of Rajasthan’s top exports, is a coloured sedimentary rock, mainly composed of quartz sand, which is used in construction and paving. In 2020, Rajasthan produced about 27m tonnes . And while a large part of it is for domestic use, hard-wearing sandstone paving is popular in Europe and North America for roads that see a lot of snowfall or heavy vehicles. The biggest consumer of Indian sandstone, though, is the UK. The stone’s combination of patterns and colours – red, tan, brown, grey or white – give an attractive, rustic appearance to garden paths and patios. Although sandstone is produced in Scotland and Cumbria, Indian sandstone is cheaper: in 2021-2022, the UK imported more than 350,000 tonnes of it, worth about £65m.

Reports suggest there are around 2.5 million people working in Rajasthan’s mining industry, the majority of them migrants from marginalised communities elsewhere in India. Some travel to Rajasthan independently, looking for work, but many of them have been recruited from other Indian states by local agents working for or with mining businesses. “The agents tell [the workers] you will work on contracts, make a lot of money,” said Shankar Singh, a social activist and co-founder of the Mazdoor Kisan Shakti Sangathan, an organisation supporting agricultural workers and labourers in Rajasthan. Singh claimed the migrant workers have very little knowledge of the work they are being hired for, or the risks involved. “If they tell them how dangerous the job is, why would anyone take it?” One 2005 report detailed how agents invited migrant workers to Rajasthan on a free trip to Hindu religious sites; when they couldn’t pay the travel expenses, they were forced to work in the quarries.

A mother and daughter making cobblestones in India.

As awareness of modern slavery and trafficking has grown, some countries have passed laws to protect against exploitation of workers. In 2015, the UK passed the Modern Slavery Act, which requires companies with a global turnover of more than £36m to publish a statement every year outlining how they are addressing slavery, including child labour, in their supply chains. But the way the industry works makes it extremely hard to trace shipments of stone back to the mine they came from, or even the area. Sandstone slabs extracted from mines are usually sent to processing centres close to urban areas, and from there, warehoused near transport hubs until they are shipped overseas. “It is very difficult for you to pinpoint which stone is going to which country,” said Madhavan Pillai, an independent journalist and researcher focusing on labour issues. “They have created a lot of layers.”

Some private companies have worked with governments, trade unions and NGOs, such as the Ethical Trading Initiative, to develop programmes that claim to identify and eliminate human trafficking and modern slavery in supply chains. As a result of these efforts, several mine-operating groups in Rajasthan banished children from the mines, and many companies selling stone from India now include anti-slavery declarations on their websites. But my own research has shown that these cleanup efforts have not gone far enough.

During my five-month investigation, I found that many mining businesses are still using child labour. Some had devised a creative workaround for employing children: instead of sending children to the mines, trucks would drop heaps of stone on roadsides close to the children’s homes. I visited five mining villages in Rajasthan, and spoke with dozens of adult and child workers, all of whom shared a similar story of low pay, exploitation and injury. Sonu and his friends, all under 10 years old, are hammering stones instead of going to school. It seems the sandstone paving blocks so beloved of architects and landscape gardeners may still be the fruit of child labour.

F ive decades ago, Budhpura was little more than a sandstone-rich hill with a cluster of underground mines, with a few migrant workers living in shanty ­towns nearby. Munna was one of the workers who came to live on that hill in the 1960s, spending most of his days in the mine, hand-cutting the sandstone and making slabs. It was hot and dusty work, and the pay was terrible. “It was very difficult,” he recalled.

Today, the hill has been levelled. The migrants who arrived to work in the mines have been joined by their families – there are more than 4,000 people living in Budhpura now – but the village isn’t an active mining area any more. The global demand for sandstone for construction and decorative paving has been so extensive that Budhpura’s stocks have been seriously depleted. After the bigger pieces of stone have been taken out, what’s left are broken rocks, or quarry waste.

About two decades ago, when mining operations began to exhaust the extractable sandstone reserves near Budhpura and its neighbouring villages, mining and processing businesses started dumping the waste on the sides of the highway that connects the region to other cities. Here, workers – mostly children, women and older people – would sit all day, turning the waste into cobbles for a rupee per stone. Given the meagre pay, this work was only undertaken by those who couldn’t find work inside the mines, said Rana Sengupta, the CEO of the Mine Labour Protection Campaign Trust , a nonprofit in Rajasthan. “[The businesses] didn’t consider it an illegal thing,” he told me.

Today, all around the village, sandstone waste – lumps of tan and grey rocks and rubble – lies in heaps. It’s hard to find a patch of vacant land that isn’t occupied by piles of dusty stone, or stacked with wooden crates of cobbles waiting to be loaded on to trucks. The crates are unlabelled, and the trucks bear no insignia that would tell the workers who they work for, or the destination of the products of their labour. I asked Munna, who now makes cobblestones, if he knew the name of the company he works for. “We don’t know about the company, but we always hear that [the sandstone] goes to foreign countries,” he said. About 40 other workers told me something similar. The supply chains are long and complex, and hard to monitor.

Piles of stones, lots of small pieces of stone and empty pallet boxes lie discarded.

On a hot afternoon last summer, about 20 women sat in an open area where the hill once stood, working in groups on batches of stones. One of the women, taking shelter from the blazing sun under a tattered umbrella, placed a thin metal plate on top of a sandstone block and drew around its edges to get a near-perfect white rectangle. Then, using a chisel and hammer, she started chopping away around the rectangle, producing a smaller block.

The stonecutters are hired – on a shift by shift basis, without contracts – by local agents. These agents report to, or trade with, processing businesses in a largely informal market, less regulated than the mines. The industry is also heavily tainted by the “mining mafia”, local gangs and agents operating illegally on behalf of mining companies that enjoy political backing and legal protections.

In 2005, Pillai, the journalist and researcher, compiled a widely circulated report that focused on labour issues. Since then, there has been growing pressure on global businesses to check whether there was child labour in their operations. “Some European and British companies visited after the report and saw that [child labour] was a very common practice. They said they wouldn’t buy the stones,” Sengupta said.

One such company was Marshalls, a British supplier of hard landscaping and building materials. In December 2006, the company sent its then marketing director Chris Harrop to tour Rajasthan’s mining villages, and he reported being “ appalled ” by the scale of child labour. Marshalls joined the Ethical Trading Initiative, which, with help from the UK Foreign and Commonwealth Office, helped establish the Sustainability Forum on Natural Stones, a local nonprofit that works on human rights issues, particularly child labour, in supply chains. In 2019, Harrop was awarded an OBE for services to the prevention of modern slavery.

However, when I visited Budhpura last year, I found out that the problem was very far from solved.

F ollowing Pillai’s report, stories appeared in the media about working conditions in the mines, and local mining operations made changes to their working practices. But these changes did not solve the problem; they merely relocated it.

Pillai, who has visited some of these villages several times in the past two decades, told me that businesses used to employ children directly inside the mines, or in workshops. But now, “the entire village has become a workshop,” he said. The stones are dumped outside people’s homes, on intersections, close to where the workers live. “They have turned it into a kind of cottage industry, [where] it becomes easy for them to say that we don’t force [them to work], children just do it [on their own].”

Anita and Sonu now walk a few hundred metres down their street and they find their pile of stones waiting for them. A tractor routinely dumps the rubble. They work under the eye of a supervisor, who counts the finished cobbles, and then the tractor returns to collect them. An adult worker can make somewhere between 100 and 150 stones in a day, for which they are paid about 3,500 rupees (£33) a month.

My investigation into five villages in Rajasthan’s Bundi and Bhilwara districts found that in every one, stones were dumped in a similar fashion around workers’ homes, where children worked alongside their mothers. In India, it’s illegal for children under 14 to be employed in hazardous occupations such as mining. So the stones, the workers say, come with an injunction: don’t tell anyone about the children.

On my first day in Budhpura last year, workers hid their children even before I could speak with them: one of them later confessed that they had seen my car. On another day, as I travelled through the villages on a motorbike, young boys and girls started running away when they saw me. When I finally sat down with workers one afternoon, outside someone’s home in a sequestered corner of a mining village, they told me that they had been advised by the local agents not to speak with me.

A small figure works on the stone surrounded by huge walls of rock.

Dilip Singh, the president of the Rajasthan Barad Khan Mazdoor Sangh, a union of mine workers, said that many mine leaseholders still employ children inside the quarries. Others only employ adult workers, but sell their waste to a processing business that hires child workers to make stones outside the mines. “It allows them to refrain from directly employing children,” Singh said – but still to profit from child labour.

Akshaydeep Mathur, the secretary general of the Federation of Mining Associations of Rajasthan , an organisation representing the interests of mining companies in Rajasthan, said that most mines follow the rules, but acknowledged that processing businesses may have started dumping stones around workers’ homes to avoid scrutiny. However, he added that most companies use machines to cut stones these days and are less likely to need manual labour. Besides, he said, children are not strong enough to do this work. He acknowledged that “there may be some 14- to 18-year-old children who help their parents at the end of the day,” but said that their numbers are low, “less than 2%”. He also said that businesses pay a minimum of 700 rupees as daily wage to workers. If any able-bodied person makes less than that – which was the case for all the workers I spoke to – he said they are “either not good or not working eight hours a day”.

Between September 2023 and January 2024, I sent emails and text messages, and made dozens of phone calls to government bodies responsible for the protection of children’s rights. None offered a meaningful response. The labour department of Rajasthan asked me to speak with India’s labour ministry in New Delhi – which did not respond to my calls – while one of Rajasthan’s Child Welfare Committees and the Rajasthan Directorate for Child Rights either did not respond or declined to talk about the situation.

In response to my findings, Emma Crates, business and human rights manager at Marshalls – which is no longer a member of the Ethical Trading Initiative – noted that the challenges facing the industry are constantly changing, which means the company must continue to evolve. “In 2006, we restricted our Indian natural stone supply chain in order to source from a single, direct supplier. This decision was taken to enable us to build a closer working relationship with that supplier, and ease the rollout of strict protocols, including zero tolerance of child labour.

“We are always looking to develop our strategy, which includes continuing with international site visits from Marshalls staff, and bringing in more independent third-party audits,” she said in an email.

A long white line runs across the back of 14-year-old Amar’s hand, which he got from the jagged edge of a stone. Beside it are two scars, marks of the time when his hammer missed its target and sliced into his hand instead. Injuries like these are so common among cobblestone workers of all ages that they hardly expect any medical support from employers for such “minor accidents”.

Amar avoided work for as long as he could. But he is the oldest child in his family, and when his father got silicosis, he had to start bringing in money. Silicosis is a fatal lung disease characterised by shortness of breath and a cough. It is caused by prolonged exposure to fine silica particles found in sand, quartz and rocks. Sometime before her husband’s death four years ago, when the respiratory illness confined him to bed, Amar’s mother, Sumitra, took a loan that was too big for her to repay while taking care of a sick husband and six children. That’s when Amar, then aged 10, quit school and started work. The 80 rupees he now makes every day doesn’t do much to pay the bills (or repay the debt), but it’s better than nothing.

More than 11 million people living in India have been exposed to silicosis-causing dust. Until a few years ago, silicosis was typically misdiagnosed as tuberculosis, because the two diseases have very similar symptoms. This largely left workers to deal with the illness without employers’ or government support. In Budhpura, I was told, many workers don’t seek treatment for illness or injury, because of the cost of travelling to a hospital. It costs 2,000 rupees to get to Kota, the nearest city. “People simply drop dead if they can’t afford it,” one stonecutter told me.

One way to reduce the amount of dust produced by mining is by wet drilling, where water is applied to the stone through the drill as it works. In 1961, the Indian government ruled that wet drilling would be mandatory in mining operations, but this has not been implemented widely. Activists have also called for workers to be provided with protective gear, such as masks, gloves and helmets. But the people I spoke to told me that that hasn’t happened. “Mask? They can’t even get us drinking water,” one female stonecutter, who lives in Budhpura, told me. Besides, a mask makes it hard for them to work in the hot climate; temperatures in Rajasthan can reach over 45C in the summer.

A truck carries cobblestones out of Budhpura.

Budhpura has been called the “ village of widows ” in some media reports, because of how many men have been killed by silicosis. These widows are raising their children on their own, forced to work in the same industry that killed their husbands. And they take their infants to work. Before they sit down to beat the stones, they sometimes thrust two rods into the ground nearby and tie up a cloth between them to act as a crib for the baby.

Mathur, from the Federation of Mining Associations, told me that fears about silicosis are “blown out of proportion”. He claims that international lobbies have been using misleading and old data to hurt the business interests of the country’s mining industry. He agrees that wet drilling can bring down the risk of respiratory illnesses, and that many companies are adopting it. But doing so isn’t always possible. “At some places there is a shortage of water,” he told me. He also argued that responsibility for preventing silicosis lies with the processing industry, which turns sandstone into different products such as paving blocks. “Processing is a separate industry altogether. You might say silicosis is coming from mining. But it’s coming from the processing industry.”

In 2019, following years of struggle by workers and activists, Rajasthan became the first Indian state to launch a comprehensive policy offering aid to silicosis patients. The state government now provides 300,000 rupees (around £2,800) for treatment that alleviates the silicosis patient’s symptoms, and an additional sum of about 200,000 rupees (£1,900) to their family after death. But most of the amount paid to the bereaved family members, workers say, is spent on their own medical treatments and debt repayment, which offers them little opportunity to move away from the industry and look for healthier jobs. “The cost of human life is only 500,000 rupees,” fumed Shankar Singh, the activist.

As I sat with Amar’s mother, overlooking the hills of depleting sandstone in front of us, I asked her if her son ever complains about having to work. “He does, of course. But what can I do? My hands are tied. We need food on the table,” she said.

The sound of metal striking on a stone in the distance filled the air as a young child in front of us played with his toy trucks and tractors. “Our husbands used to do this work,” she said. “They got silicosis. We’ll eventually get it too. And so will our children.”

A t about 1pm, Pooja comes back from school; it takes her 30 minutes by foot to get to her village of Dhaneshwar, about 20km away from Budhpura. Then there are household chores. By 2pm, she is with her mother, crouched down around a pile of stones with a hammer and chisel. For the next four hours, Pooja makes cobblestones, usually about 50 of them. Pooja, who is 14, wants to be a doctor. Her tests were approaching when we met, so she was putting in extra hours after dinner, which is when she usually sits down to do homework.

Pooja’s father died of silicosis in 2014. She is well aware that it’s going to be an uphill battle to study medicine. Her mother knows it, too. “But against all odds, I am still sending her to school,” her mother said, pride in her voice. She hopes that her daughter can continue in school at least until she turns 16, even though she will have to work at stonecutting after hours.

Many children work in conditions that are “hell on earth”, according to Colin Gonsalves , a senior lawyer at India’s supreme court and the founder of the Human Rights Law Network in New Delhi. India’s Child Labour Act is simply not being enforced, he told me. He blames corruption among labour officers and negligence in the judiciary, as well as Narendra Modi’s government’s aggressive focus on economic progress.

One way to eliminate child labour, some activists say, is to raise the parents’ income. “If you don’t pay [adult workers] the right amount, people will be forced to bring children to the mines,” said Sengupta, the labour campaigner. Yet there is little sign that there will be major wage reform any time soon. Gonsalves said that the only solution here is to take legal action. “Nothing else will work. If you litigate and get a good judge, something may change,” he said.

Many of the workers I met told me that they would quit this work if they could. But they have no other means of support. Shutting down the cobble business would take away many of the workers’ only possible source of income. Agriculture is not an alternative for them. “Who [owns] land here? We are all migrants,” Munna said.

“There’s no other thing in this village, except these stones,” another worker added.

Instead, some of them told me, they want better protective measures such as housing and healthcare. “Shut the cobble business down only if you have another job for us,” another said.

A local activist pointed out that even if they were able to convince local mining and processing businesses to improve conditions, and to spend money on building playgrounds and education centres, it would not solve the problem. The businesses’ costs would go up, and so would their prices. “And when they go to the marketplace, they see the Whites buying from companies that offer the lowest prices. What do you do then?”

Amar dreams of playing cricket someday. Sonu, however, wants to be a doctor like Pooja. He misses going to school, he told me, but it’s expensive and far away. He does hope, though, that he and his friends could catch up for a session of cricket someday. But now he needs to get back to work, where he will beat, pound and craft cobblestones in the heat of Rajasthan for at least four more hours.

The names of the stone workers have been changed. This article was produced with the support of the Journalism Centre on Global Trafficking

  • The long read
  • Child labour
  • Human trafficking

Most viewed

child labour in india research paper

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

  •  We're Hiring!
  •  Help Center

Child Labour in India

  • Most Cited Papers
  • Most Downloaded Papers
  • Newest Papers
  • Save to Library
  • Health and Human Rights Follow Following
  • The Right to Health Follow Following
  • Robotics and automation Follow Following
  • Child Labour Follow Following
  • Interest Rates Follow Following
  • Brain Computer Interface Follow Following
  • Child Abuse Follow Following
  • Machine Learning and Pattern Recognition Follow Following
  • Low Income Countries Follow Following
  • Investment Follow Following

Enter the email address you signed up with and we'll email you a reset link.

  • Academia.edu Publishing
  •   We're Hiring!
  •   Help Center
  • Find new research papers in:
  • Health Sciences
  • Earth Sciences
  • Cognitive Science
  • Mathematics
  • Computer Science
  • Academia ©2024
  • Skip to main content

Advancing social justice, promoting decent work

Ilo is a specialized agency of the united nations, india employment report 2024: youth education, employment and skills.

This is the third edition of the India Employment Report.

IMAGES

  1. Data Story : Child Labour In India

    child labour in india research paper

  2. (PDF) CHILD LABOUR IN INDIA LITERATURE SURVEY

    child labour in india research paper

  3. Uday Publishing House: CHILD LABOUR IN INDIA “PERCEPTIONS AND PROBLEMS”

    child labour in india research paper

  4. literature review on child labour in india

    child labour in india research paper

  5. (PDF) Child Labour -A Product of Socio-Economic Problem for India

    child labour in india research paper

  6. child labour in india research paper

    child labour in india research paper

VIDEO

  1. CHILD LABOUR (a heart touching short film in ladakh)

  2. Annual Learning Festival 2024 Day One

  3. ക്യാഷ്‌ലെസ് ഇടപാടുകളില്‍ അമേരിക്ക ഭാരതത്തോട് അടിയറവ് പറയുമ്പോള്‍

  4. Roll Call

  5. NEW INDIA- बर्बाद होता हुआ बचपन

  6. EFFECTS OF CHILD LABOUR

COMMENTS

  1. Prevalence and potential consequences of child labour in India and the

    Although child labour is most often found in countries with lower socioeconomic resources, it also occurs in developed countries. 5, 6 The latest global estimates indicate that 152 million children (64 million girls and 88 million boys) are engaged in child labour, accounting for almost one in 10 of all children worldwide. While the number of children in child labour has declined since 2000 ...

  2. "A Critical Analysis Of Child Labour In India"

    In our country India child labour constitutes for 13 per cent of the workforce in India (2011 census). ... The main objective of this research paper is to know the major causes of child labour, to ...

  3. Child Labour in India: Causes and Consequences

    It analyses the driving factors responsible for child labour in India and World. The various forms of child labour and health hazards they are faced. ... Future of W ork Research Paper Series No ...

  4. A study of Child labour in India

    1. A study of Child labour in India - Magnitude and challenge s. Sudeep Limaye, ASM's IIBR, Pimpri pune. Dr. Milind Pande, Project Director, MIT School of Telecom, Pune. Abstract -. Children ...

  5. Child labour and its determinants in India

    In India, the farm or agriculture sector employs most of the child labour and it accounts for 51.8 % of child labour, and rest are working in the non-farm sector (Fig. 1).In the non-farm sector, most of the children are employed in manufacturing (16.3%) industry, followed by wholesale and retail trade (13.9%), and construction (11.5%) (Table 2).The average age of children working in these ...

  6. Harmful forms of child labour in India from a time-use perspective

    We chose to analyse child labour in India because it is a large country with a relatively huge informal sector in which child labour has thrived. We also noticed that the use of non-harmonised datasets from 2011/12-2019 would cause non-comparability of results. We offer open-source coding for our analyses so that others can make scientific ...

  7. Prevalence and potential consequences of child labour in India and the

    of child labour in India and the possible impact of COVID-19 - a contemporary overview Navpreet Kaur1 and Roger W Byard2 Abstract Child labour is a global phenomenon occurring predominantly in countries with lower socioeconomic status and resources. Societal and familial poverty, loss or incapacitation/illness of parents, lack of social ...

  8. A sociological study of patterns and determinants of child labour in India

    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 ...

  9. A Bayesian Estimation of Child Labour in India

    Child labour in India involves the largest number of children in any single country in the world. In 2011, 11.8 million children between the ages of 5 and 17 were main workers (those working more than 6 mo) according to the Indian Census. Our estimate of child labour using a combined-data approach is slightly higher than that: 13.2 million (11.4-15.2 million) for ages 5 to 17. There are ...

  10. Prevalence and potential consequences of child labour in India and the

    Child labour in India is more prevalent than in many other countries, with approximately 10 million children actively engaged in, or seeking, work. This paper focuses on the issue of child labour, its causes and its ill effects. Further, it also reviews the international legal framework relating to child labour and legislative issues in India.

  11. Analysis of Child Labour in India by Shreyansh Anand :: SSRN

    India is not an exception. As per the report, India ranks among the top nations of the world for the employment of child labour. The cause of child labour in India is a very complex and deep-rooted issue. Poverty is the main cause of child labour in India and child labour in India is found in both urban and rural areas.

  12. [PDF] Child Labour in India

    Abstract The prevalence of child labour is one of the most important problems confronting the world at large, especially developing countries such as India. In many cases, child labour is mainly necessitated by economic compulsions of the parents. The main reason which gives rise to child labour is widespread unemployment and underemployment among the adult poor strata of the population, inter ...

  13. [PDF] Child Labour in India

    Child labour is the exploitation of children who are denied access to education. In India, one can observe many children below the poverty line engaged in labour to meet their basic needs. Child labour causes social, psychological and physical harm. Access to education is essential for children to improve their standard of living. They must be protected from involvement in hazardous industries ...

  14. Child labour in India: a health and human rights perspective

    The problem of child labour exists throughout the world. According to 2002 estimates from the International Labour Organisation (ILO), about 246 million children aged 5-17 years are working worldwide. ... Many of India's child labourers work long hours for low wages to pay off debts incurred by their families. An estimated 15 million children ...

  15. PDF Child Labour in India A Conceptual and Descriptive Study

    by Amish children, some forms of child work common among indigenous American children, and others. Child labour in India is addressed by the Child Labour Act 1986 and National Child Labour project. Today in India, there are more than after 10.12 million children who are spending their childhood learning carpet-weaving,

  16. Child Labour in India: A Conceptual and Descriptive Study

    Abstract. The issue of child labor is a major concern in India, as early entry into the labor market at an early stage of life means shunning away from proper schooling leading to loss of future ...

  17. PDF Evidence on Educational Strategies to Address Child Labour in India and

    strategies with the potential for reducing child labour in India and Bangladesh. Papers submitted by workshop presenters addressed a variety of topics related to the child labour and education landscape in both countries. Taken together, these pieces represent a valuable contribution to take stock of the knowledge base on child labour and education

  18. PDF Child Labour in India: a Critical Study of Causative Factors and

    Child labour in India has been described as a human problem of enormous magnitude. The Children engaged in economic activity on part-time or full time basis who are deprived of childhood, potentiality and dignity are called child labour. The activity engaged is usually harmful to the physical, physiological and psychological

  19. PDF Child Labour in India

    The incidence of child labour in India has notably decreased by 2.6 million between 2001 and 2011. Exploring the factors that contributed to this decline and understanding the remaining challenges is essential for formulating effective strategies to eradicate child labour. M.C. MEHTA v. ...

  20. Education for Child Labour: Evaluating the National Child Labour Policy

    Transitioning child labourers from work to education is a key component of global efforts to eliminate child labour. In India, the National Child Labour Project is the central programme aimed at achieving this goal. This paper examines the operation of the project in the state of West Bengal using original survey data collected in 2008.

  21. Child Labour in India; Situation and Policy Analysis

    Less than 9% work in manufacturing, services and repairs. Only about 0.8% works in factories. With this paper we wish to throw light upon the prevailing child labour condition in India, the reason for child labour in india,the effectiveness of the existing legislations and the future steps to be taken.

  22. How child labour in India makes the paving stones beneath our feet

    Sonu began working after his father died of the lung disease silicosis in 2021. "First, he made five stones, then 10, and then he quit school to work full-time," his mother said. The pair sit ...

  23. Child Labour in India Research Papers

    View Child Labour in India Research Papers on Academia.edu for free. Skip to main content ... The rate of child labour in India is very differentiated, both over space and by social group, with a national estimate of 12% of children age 5-14 in 2005/6. Recent national child labour estimates have not been published for India from 2011/12 and ...

  24. India Employment Report 2024: Youth education, employment and skills

    The India Employment Report 2024 examines the challenge of youth employment in the context of the emerging economic, labour market, educational, and skills scenarios in India and changes over the past two decades and is primarily based on analysis of data from the National Sample Surveys and the Periodic Labour Force Surveys. India has the ...

  25. THE IMPACT OF EDUCATION ON PREVENTING CHILD LABOUR IN INDIA

    The presented research paper studies the problems of child laborers working in brick kilns in Bhagalpur district. The main objective of this research paper is to study the future of India which is ...