Using Big Data to Combat Child Labour

27 September 2018
Research Innovation

Luis Fabiano de Assis  | Chief Research and Data Officer, the Brazilian Federal Labor Prosecution Office

One of the main issues facing law enforcement and policymaking today is how to marry established routines with innovative procedures to better target interventions, improve resource allocation and promote the rule of law. There is room for using big data approaches and data analysis to develop truly innovative initiatives to protect vulnerable populations. Knowledge produced by pulling together information from disparate sources can be used by law enforcement, policymakers and other stakeholders.

The Federal Labour Prosecution Office (FLPO) in Brazil is doing precisely this by using data analysis to strengthen interventions targeted against child labour. At the FLPO, a Decent Work Big Data Platform is in place to gather all relevant data on the working lives of Brazilians, including from censuses and other surveys, government databases on social programs for vulnerable families, and information on the allocation of public resources across all of Brazil’s 5,570 municipalities.

One of the main issues facing law enforcement and policymaking today is how to marry established routines with innovative procedures to better target interventions, improve resource allocation and promote the rule of law.

Using this data and mapping where the worst indicators of child labour are found, the FLPO can determine where resources should be deployed. It can also create a knowledge base to help design efficient, targeted interventions aimed at preventing and combating the problem where it is most prevalent.

Diverse data sources can be used to better understand the prevalence of child labour. In Brazil, traditional sources such as the population census carried out every 10 years offer demographic indicators that show trends on complex social problems such as extreme poverty and low educational attainment. Regular household surveys disclose cyclical (monthly and quarterly) and structural (annual and variable) information, with many indicators related to employment, education, income, informal work, and work of children and adolescents.

There are also non-traditional data sources that can be used to track vulnerabilities, such as the “Prova Brazil”, a biennial large-scale census and academic assessment given to public school children in second, fifth and ninth grades. The objective of the Prova Brazil is to use standardized tests to evaluate the quality of education offered by the Brazilian public educational system. In addition to reading tests and math questions, a socioeconomic questionnaire collects information on contextual factors associated with the performance of the students, such as how many hours a week the students perform domestic activities or other types of work. The de-identified data from the Prova Brazil are publicly available, and help officials track vulnerabilities when analysed in conjunction with local indicators, school attendance, educational achievement and a longitudinal perspective in urban and rural public schools that time-series and cross-sectional data can provide.

The database of the Brazilian conditional cash transfer scheme, Bolsa Família, can be used to assess how successful the programme has been at preventing child labour through access to education and health services. Ultimately, the Bolsa Família programme aims to reduce short-term poverty and prevent long-term poverty by increasing human capital among poor Brazilians. It does so through cash transfers that are conditional on children attending school and receiving basic healthcare. It is the largest conditional cash transfer in the world, reaching 12 million families or roughly 25 per cent of Brazil’s 200 million citizens.

Knowledge produced by pulling together information from disparate sources can be used by law enforcement, policymakers and other stakeholders.

With these data sources, the FLPO is also tracking vulnerable youth. There were 11 million Brazilian young people aged 15-29 outside of the education system and who were not working or being trained for work in the final quarter of 2017. Labour contract databases can show deficits in federally mandated apprenticeship quotas. All medium- and large-sized companies are legally required to have 5 to 15 per cent of their workforce made up of apprentices aged 14 to 24, who combine working and studying to gain skills for jobs that require professional training.

The FLPO turns these diverse data sources into a knowledge base that has strengthened law enforcement interventions. Beyond that, the FLPO is using these resources to provide actionable information and policy insights to stakeholders at the national, regional and local levels through a Digital Observatory on Child Labour, which will be launched in September 2018 by the SmartLab Initiative. Building on the experience of developing other thematic observatories, the FLPO and its partners can help push the boundaries of traditional interventions, moving towards eliminating the root causes of vulnerabilities that lead to child labour.

Information alone may enable but won’t necessarily lead to better policy decisions. Resources like the Digital Observatory are aimed at strategically promoting transparency and data-driven interventions. Such interventions can lead to sustainable social transformation, but they require comprehensive and integrated action from policymakers, law enforcement, mass media and civil society. With better information and greater accountability, municipal policymakers can implement evidence-based policies to combat child labour and protect teenage workers.

Improving the flow of information also increases the public’s awareness of the damages caused by child labour, as well as the need to protect adolescents and youth from exploitative work. In turn, this helps public authorities and civil society define guidelines for training and sensitizing teachers and other education professionals. In turn, they can raise awareness among students, and society in general, of the risks of child labour.

Luis Fabiano de Assis is a Federal Prosecutor, Chief Research & Data Officer at the Federal Labour Prosecution Office (FLPO – Ministério Público do Trabalho, MPT, in Portuguese) and Law & Policy Professor at the National School of Public Prosecutors. He is also head of the Smartlab Initiative.

This article has been prepared by Luis Fabiano de Assis as a contribution 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 author and do not necessarily reflect those of UNU or its partners.

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