Human Trafficking in the Indian State of Bihar: Prevalence and Characteristics

12 Jun 2020
Inovasi Penyelidikan

Sheldon X. Zhang  | Chair of the School of Criminology & Justice Studies, University of Massachusetts Lowell
Meredith Dank  | Research Professor, John Jay College of Criminal Justice
Kyle Vincent  | Pioneer in the development and application of Adaptive Link-Tracing Sampling methods
Pradeep Narayanan  | Director of Research and Capacity Building, Praxis Institute for Participatory Practices
Sowmyaa Bharadwaj  | Deputy Director of Research and Capacity Building, Praxis Institute for Participatory Practices

Despite the wide attention modern slavery has attracted around the world, the research community continues to struggle with some fundamental issues such as consistency in measurement and rigor in data collection methods. Researchers have been tackling both challenges for many years with mixed results.

In an effort to improve our knowledge and research methods in primary data collection, a team of researchers from the U.S. and India set out to measure the prevalence of human trafficking within the Indian state of Bihar, specifically focusing on: (1) bonded and forced labour; (2) sex trafficking; and (3) worst forms of child labour. The study sought to examine the profiles of trafficking victims and to identify risk and protective factors that shaped their trafficking experiences. The research team hopes the study’s results will be useful to stakeholders by providing empirical data and insights to guide policy making and resource allocation.

Research Methods

Our data collection procedures primarily relied on face-to-face survey interviews. Because of the three distinct subject populations, we used different strategies in our sampling designs. Three survey instruments were constructed, based on international conventions and India’s labour laws, and piloted-tested prior to formal roll-out of the field activities. Briefly, our survey instruments contained a wide range of coercive and abusive working conditions. Our operational definition of human trafficking also included measures of significant exit costs. In other words, to qualify as ‘forced labour’ or ‘human trafficking’, one must find oneself being (1) deceived or coerced into an abusive working condition and (2) unable to exit without incurring significant costs. Further, we separated financial loss from other forms of exit costs.

Our data collection employed several conventional and innovative sampling strategies to reach our target populations. To estimate the prevalence of bonded/forced labour, we applied stratified and multi-stage sampling with probability proportional to size of villages. Our sampling strategy was guided by the Indian Census of 2011 to identify the density distribution of the population in Bihar.

To estimate the prevalence of worst forms of child labour, a time-location-based probability sampling design was used to recruit respondents from 15 urban centers in Bihar.

To estimate the prevalence of trafficking amongst sex workers, we also applied a network/link-tracing sampling design in eight urban centers with known concentrations of sex workers. To search the hotspots where we could develop our entry points for sampling purposes, we were assisted by local non-government organizations and community-based organizations with knowledge of the sex worker population in Bihar.

Forced Labour and Debt Bondage in Bihar

Based on the India 2011 Census, there were an estimated 29.3 million working adults in rural Bihar. Our study found that working adults in rural areas experienced forced labour for reasons other than the fear of financial loss at a much higher rate than their urban slum peers, accounting for 4.5% of the total working adult population. The top four cited factors included physical violence (2.8%), being too far from home (1.5%), emotional violence (1.2%) and harm to family/friends (0.7%). Although low in absolute percentages, when applied to the state’s large population base, the total number of individuals who experienced some form of forced or coerced labour—excluding those citing the fear of financial costs—was estimated to be more than 1.3 million working adults in rural Bihar. Financial costs, operationalized as the loss of accrued earnings, appeared to be the most prevalent factor in preventing working adults from leaving their jobs or accepting other jobs, at the rate of 9.4% or more than 2.7 million in rural villages. The loss of accrued earnings was the largest factor contributing to forced labour for both rural and urban slum working adults.

There were several known risk factors associated with the victimization profiles: (1) men were estimated to be 80% more likely to be subjected to force labour than women; (2) members of the Scheduled Caste (SC)/Scheduled Tribes (ST) were estimated to be 2.67 times more likely to be victims of forced labour than members of other castes; (3) those working for external employers were estimated to be 3.19 times more likely to enter forced labour than those working for family members; and (4) individuals working in farming were estimated to be 1.65 times more likely to enter forced labour than those working in other jobs in rural villages.

Debt bondage was another outcome we measured and estimated. A person is considered in debt bondage if his/her freedom is restricted due to taking on a debt or paying an advance (can be from the employer or a third party, e.g. a loan company). The person who is working off the debt does not have to be a beneficiary of the debt (e.g. the debt could be incurred by another family member.) Restrictions include the inability to refuse work or to switch to another employer offering better wages (or clients offering better prices in case of piecework). We found that 1.1% of the working adults were in debt bondage in rural villages, which would translate to about 322,000 individuals in rural Bihar. The median original loan size was INR 14,080 ($216 USD), with half of the individuals taking on INR 10,000 – 100,000 ($154 – $1,536 USD) of debt. The median annual interest for these debts was 60.0%, with half of them paying an annual rate of 24.0 ~ 99.0%. Borrowing money to cover medical care was the primary reason to go into debt, accounting for 43.1% of these working adults. The second reason was to borrow money to buy food, accounting for 32.9% of working adults who went into debt. Borrowing money for a family wedding was the third most cited reason.

Worst Forms of Child Labour

Based on the India 2011 Census, the total number of working children was estimated to be around 167,000 in urban centers where our survey took place. We found the worst forms of child labour (defined by Article 3 of ILO Convention No. 182, 1999) or illegal child labour (defined by Indian child labour laws) to be widespread and pervasive. Moreover, child workers in Bihar were frequently exposed to abusive labour practices and hazardous work conditions. Many also experienced harmful physical as well as psychological effects.

According to the ILO convention, this study estimated that 61% of all child workers (ages 5-17) were subject to worst forms of child labour. Male child workers were far more likely to be victimized, at the rate of 74%, than their female counterparts (30%). Older child workers (ages 15-17) were more likely to be victimized (72%) than younger child workers (53%). While caste segregation was present in certain job markets such as restaurants, being a member of a particular caste did not necessarily affect a child’s exposure to worst forms of child labour. Most of the worst forms of child labour was attributed to “dangerous working hours,” either long hours in a day or number of days worked in a week. For instance, 42% of all child workers typically worked 43 hours or more per week, and 22% of them worked during dark hours (i.e. before 5 am or after 10 pm).

According to Indian child labour law, this study estimated that 91% of the child workers in Bihar were either in illegal and/or hazardous labour, most of which was due to excessive work hours in a day or days in week. For instance, 82% of the younger child workers typically worked more than 3 hours a day, with an average of 6.48 hours; and 43% of them worked 7 days a week, with an average of 5.49 days a week. We found younger child workers (5-14) were more likely to be involved in illegal and/or hazardous work at the rate of 93% compared to 89% of older child workers.

Sex Trafficking in Bihar’s Sex Industry

There were no official statistics on the number of sex workers in Bihar. Using information compiled by UNAIDS[1], we estimated there were about 56,554 sex workers in Bihar. However, local NGOs and other community agencies estimated the total number of sex workers to be about 9,000.

Sex trafficking among sex workers in Bihar was clearly present. Overall, 18% of the sex workers surveyed reported experiences that met the international definition of sex trafficking. The top three factors that prevented sex workers from leaving abusive work situations included: physical violence, emotional violence and geographical isolation. We believe this was a conservative estimate because of our strict operational definition. When we included the fear of loss of accrued earnings, the proportion of sex workers who felt they could not leave an abusive work environment rose to 40%. Being mostly uneducated or under-educated, these women had few alternatives to earn a living, and the fear of losing their accrued earnings was often sufficient to deter many from leaving an abusive work situation. Those who worked in another township or state experienced the highest rate of trafficking violations, almost 40%, followed by brothel-based sex workers at close to 20%. Women who provided sexual services in the street experienced the lowest rate of sex trafficking at 2.7%.

Policy Implications

Although there are limitations to each of our sampling strategies as we have discussed in our published reports and journal articles, findings from this study provide several important policy recommendations.

For forced labour and debt bondage, findings from this study suggest that both government interventions and community-based programmes need to focus on reducing high-risk but low-cost factors that, if not properly attended, can quickly compel rural families into debt bondage or forced labour situations. This includes improving access to medical care, providing government-backed low-interest loans, ensuring basic food security and organizing government-sponsored wedding ceremonies in rural villages. Once such basic needs are met, more challenging issues can be addressed, such as the enforcement of existing labour laws, public education and awareness campaigns and financial as well as legal punishment of abusive employers.

The most obvious solution to reduce the worst forms of child labour—which may also be the most challenging—is to enforce existing child labour laws. The vast majority of the child workers in Bihar worked excessive numbers of hours in a day and days in a week. Interventions and programmes such as improved enforcement of existing child labour law, public education and awareness campaigns and financial as well as legal punishment of egregious employers are therefore necessary. Efforts to boost grassroots community efficacy are critical to educate parents and children about labour rights and to promote the collective pursuit of fair pay and protection. Because the current reporting and enforcement mechanism, the Childline, relies on the public to report suspected cases, it is imperative to enhance public awareness and the rate of reporting so that more cases of child labour and trafficking can be investigated and addressed.

With regard to sex trafficking in the sex industry, the Indian government and civil society actors have in recent years made a concerted effort to increase India’s anti-trafficking interventions, such as investigating and prosecuting traffickers, raising public awareness and rehabilitating victims in government shelters. However, those measures do not sufficiently address the underlying vulnerabilities. To combat sex trafficking in Bihar’s sex industry, this requires multiple societal efforts that focus on improving women’s education and economic status. Further, a well-regulated and monitored sex industry can also increase the protection of those who choose this work while also ensuring personal freedom to exit.

[1] See p.85 of UNAIDS Data 2017.

Dr Sheldon X. Zhang is Chair of the School of Criminology & Justice Studies at the University of Massachusetts Lowell.  

Dr Meredith Dank is Research Professor at John Jay College of Criminal Justice. Follow her on Twitter: @MeredithDank

Dr Kyle Vincent is the pioneer in the development and application of Adaptive Link-Tracing Sampling methods for estimating the size and distribution of networked hard-to-reach populations.

Pradeep Narayanan is Director of Research and Capacity Building at the Praxis Institute for Participatory Practices. 

Sowmyaa Bharadwaj is Deputy Director of Research and Capacity Building at Praxis Institute for Participatory Practices. Follow her on Twitter: @sowmyaa_b

This article has been prepared by Sheldon X. Zhang, Meredith Dank, Kyle Vincent, Pradeep Narayanan and Sowmyaa Bharadwaj as contributors to Delta 8.7. As provided for in the Terms and Conditions of Use of Delta 8.7, the opinions expressed in this article are those of the authors and do not necessarily reflect those of UNU or its partners.

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