Finding the Forgotten: Field Considerations When Measuring Bonded Labour Prevalence

12 March 2021
Research Innovation

Siddharth Yadav  | Senior Research Associate, Catalyst Atal Incubation Centre
Hephzibah Sunder  | Head, Research and Learning, International Justice Mission-Chennai
Mridulya Narasimhan  | Project Head - STREE, MSME & Entrepreneurship, LEAD at Krea University

In July 2016, the Ministry of Labour and Employment, Government of India announced a revamped Centrally Sponsored Plan for Rehabilitation of Bonded Labourers and articulated a 15-year vision to identify, rescue and rehabilitate 18.4 million bonded laborers. They committed to strengthen the prosecution machinery to achieve a 100 per cent conviction rate to prevent recurrence of bondage. Despite these measures, the cases registered under the Bonded Labour Abolition Act recorded a sharp year-on-year rise between 2016 and 2018. Furthermore, COVID-19 has exacerbated the vulnerabilities of informal and marginalized workers, putting them at greater risk of debt bondage.

The covert nature of the phenomenon poses significant challenges to data collection efforts that seek to measure its prevalence. This is reflected in its exclusion from administrative data collection exercises and survey undertakings. In early 2020, researchers from LEAD at Krea University collaborated with the International Justice Mission (IJM) to develop a robust and comprehensive tool to measure the prevalence of bonded labour in the state of Tamil Nadu. The fieldwork was conducted in eight districts of Tamil Nadu across 12 industrial sectors.

Through consultation with IJM, various markers of forced labour in the state were first identified. These components were then matched with the relevant standardized indicators from the International Labour Organization (ILO). The collaboratively developed survey instrument included questions about debt, working conditions and wages, recruitment and freedom of employment, access to participation in the marketplace, access to government facilities, physical and virtual mobility, and bondage and coercion.

Survey Methods and Inherent Constraints

Locating and surveying the sample population was a challenging task, especially for populations in remote locations. Estimating the prevalence rates for these population segments with a reasonable degree of accuracy requires a large sample size. This degree of accuracy decreases with dis-aggregation of prevalence rates among sub-groups of populations by gender, age and ethnicity. In the last decade, there have been advancements in methods of estimating prevalence with the inclusion of remote sensing and machine learning techniques, as well as adding a participative component to research design.

For a prevalence estimate, the research team chose primary data collection methods. A household survey allows for data collection on the nature of forced labour, family characteristics, education, employment history while both ensuring the safety and anonymity of the respondent and higher data quality since the respondent is not being monitored. An establishment (work-site) survey on the other hand offers opportunity to investigate working conditions first-hand, understand the nature of bondage in each and every industry sector and ascertain the demand side/trafficking traces related to bonded labour. The establishment survey though suffers from a major limitation where workers are inhibited from providing truthful answers to sensitive questions because of the presence of the employer or the manager while the survey is being conducted. For the purpose of our study, we chose to administer the survey at the worksite. While home-based surveys have some advantages, work-site surveys allowed surveyors to make observations about force and lack of facilities that are hard to express for a respondent who may or may not perceive these as strenuous work conditions.

Respondent Tracing

With no listing data available, the researchers faced the added challenge of not knowing the respondent’s location. To address this barrier, we worked with our partner organizations to narrow down the geographies and industries which had a higher probability of bonded labour incidence. Among these selected districts, our enumerator teams then had to conduct a reconnaissance and select worksites where the survey would be undertaken.

The team had a three-step approach to this:

Ensuring Surveyor Safety

As researchers, we train our survey staff to ensure the safety of our respondents, but the imminent harm to our survey team was also something we had to consider. The bonded-labour markets function like cartels–close-knit, airtight, with little space for an outsider to enter.

Our team ensured proper mechanisms were in place including training of the survey staff to identify potential risks, providing a detailed set of procedures to enter a worksite and initiate the survey safely, GPS tracking of the surveyors, designation of a safe place to meet in case of any threat perception and logistical/vehicular support to the team so that they could reach safety as quickly as possible.

In undertaking a challenging study of this nature, strong partnerships with local partners such as the labour department, non-government organizations and community-based organizations was an essential part of the field plan. This included cooperation on information sharing, helping with logistics and ensuring the safety of the surveyors.

Getting Truthful Responses and Tackling Refusals

Conducting a prevalence study with a survey method involves a particular time constraint which impedes the surveyor’s capacity to build trust in order to gain more truthful answers from respondents. In many cases, the respondents were unaware that they were in bonded labour. The concept of fate, limited locus of control and sense of indebtedness informed their assessments of their work and work environment.

Our surveyors were trained to ask a series of proxy questions to gauge aspects of freedom of movement, which helped us in the analysis stage to use methods of triangulation to have a sense of some crucial but sensitive issues about bondage. Furthermore, there were high refusal rates during the survey. Without a government authorization to enter the premise, the enterprise owners found it easy to avoid surveyors they suspected might report labour law violations.

High refusal rates can be mitigated by doing a home-based survey through participative methods. Our study also found that labour force surveys such as Periodic Labour Force Survey (PLFS) should be used to capture socio-economic conditions like caste, debt, nature of work and time-use for cases of bondage.

Ethical Considerations

Due to the nature of the questions asked in the survey, respondents may have believed interviewers have authority to rescue them or improve their working conditions. In such situations, our survey staff were trained to inform the respondent about their limitations as a survey team, record the respondent’s contact details (with their consent) and inform our partner organization to provide rescue and rehabilitation services.

Revisiting the Definition of Bonded Labour

While inter-state and intra-state migration remains prevalent for the survival of industries, digital inclusion has continued to keep families and communities connected. Virtual mobility in the contemporary world becomes an essential freedom that citizens must enjoy. While the government definition of bonded labour accounts for the right to movement, it says very little about an individual’s virtual mobility. Some respondents in our survey, for example, mentioned being surveilled while they use their mobile phones.

There were also other instances where traces of “neo-bondage” were evident: the element of force and coercion has evolved over time, especially in brick kilns where the practice of providing an advance has become a part of the recruitment process itself. Thus, aspects of neo-bondage and virtual mobility should be included in the definitions of bondage and should be researched in a greater detail, especially in the developing country context.

This article has been prepared by Siddharth Yadav, Hephzibah Sunder and Mridulya Narasimhan 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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