The Decent Work SmartLab: A Knowledge Management Initiative in Brazil
Brazil is a highly digital developing country. This creates myriad opportunities for using data in meaningful ways. Brazil’s government produces massive amounts of data and makes national statistics available in open data formats. Despite the availability of government databases, they are scarcely used and generally filed away in the archives of each agency, leading to fragmented information and gaps in the knowledge base that could impact the policymaking process.
Cityscape, Sao Paulo, Brazil. Unsplash/ckturistando
In part to rectify this situation with respect to Decent Work policies and labour law compliance, since 2015 the Federal Labour Prosecution Office (FLPO) in Brazil has been developing a Big Data Platform in which previously collected data on the working lives of all Brazilians are organized, cleaned, harmonized, linked and de-identified, keeping classified information safe. Ultimately, this innovative platform aims at providing information for academics, prosecutors, inspectors, practitioners, policymakers and other stakeholders. As the project evolves, a wealth of scarcely used data are being collected, to create a knowledge hub with disaggregated time-series data on labour contracts, supply chains, social protection programs, occupational safety and health, labour inspection violations, and court cases as well as population, labour, agricultural and educational censuses and surveys.
In 2016, the FLPO and the regional office of the International Labour Organization (ILO) entered into a cooperation agreement to jointly address the root causes of fundamental rights violations and contribute to the achievement of SDG Target 8.7 of taking effective measures to eradicate forced labour, modern slavery, human trafficking and child labour. To address knowledge management issues within the agreement, the Decent Work SmartLab initiative started as a multi-stakeholder collaborative forum. By harnessing the FLPO Big Data Platform’s power to yield policy insights combined with an innovative set of ILO municipal indicators, the initiative highlights decent work opportunities and challenges, and it is thriving.
The work of SmartLab has three guiding elements:
- the promotion of transparency and accountability through the dissemination of open data regarding decent work;
- the development of evidence-based knowledge about fundamental rights at work through empirical research that is able to inform the policymaking process; and
- the development of results-based management tools and frameworks to support the design, monitoring and evaluation of initiatives in the field.
In sum, SmartLab’s goal is to foster effective and efficient decent work policies, programs and projects that are aimed at supporting the development of innovative smart practices that incorporate results-based management and evidence-based research. Accordingly, one of SmartLab’s objectives is to transform data into easy-to-understand reports and data visualization tools that can be used by academics, decision-makers and other practitioners to inform decision making.
Launched in 2017, the Slave Labour Digital Observatory, one of SmartLab’s two current thematic observatories, illustrates SmartLab’s approach. Using de-identified records of unemployment insurance payments to victims of slave labour and records of successful labour inspections, the Slave Labour Digital Observatory presents dynamic maps with geo-referenced data on prevalence and incidence to reveal the geographical distribution, hotspots, migratory routes, and demand and supply curves of contemporary slavery in Brazil. Different layers of information—places of rescue, birth and residence—can be combined or further examined to raise new hypotheses, and these datasets are freely available for download. The administrative records included in the Observatory have been audited and cross-referenced to other databases within the FLPO Decent Work Datahub to maximize their reliability and guarantee the triangulation of findings and documentation of lessons learned.
Multiple social, economic, productive and demographic indicators at the municipal level—such as the evolution of human development indicators, census time series, poverty and GDP per capita—can be found on the Observatory for each of the 5,570 Brazilian municipalities. Used in conjunction with disaggregated data on the profile of victims, such as gender, occupation, education, age and race, these data have revealed vulnerability patterns that were previously hidden. Furthermore, stakeholders can follow the evolution of formal jobs in each territory, which can help improve local qualification, education and employment policies. The Observatory also shows cases of children found in slavery and the risks of exploitation based on census data and national surveys.
Used in conjunction with disaggregated data on the profile of victims, such as gender, occupation, education, age and race, these data have revealed vulnerability patterns that were previously hidden.
In addition to using cutting-edge data visualization techniques and open-source technology, access to the platform is completely open, fostering transparency and accountability. As a knowledge management Swiss-army knife, the Observatory is also an information hub that enables stakeholders to make data-driven decisions, identify data gaps and help governments to improve data collection.
To foster concrete results, information is presented in an impact-oriented way. Based on the data compiled by SmartLab, the government of Maranhão, a region in northern Brazil and the largest source of victims of slavery in the country, entered into a commitment agreement with the FLPO to launch a new policy on slavery prevention to be implemented with ILO support.
Behind the infographics and datasets, we can see not only massive human suffering but also a framework of public policies that must be improved. Likewise, there are new questions to raise, more risks to assess, new surveys to develop in order to collect more data, new forms of exploitation to discover, more vulnerable victims and potential victims to protect, programmes need to be evaluated to assess their impact, taxonomies and methods must be shared and unified to enable comparability, and capacities must be built to make stakeholder coordination flourish. Focus needs to be put on accelerating timelines, conducting research, sharing knowledge, driving innovation, and increasing and leveraging resources.
By collecting and analyzing more data, SmartLab aims to make modern slavery part of the daily public discourse. SmartLab is currently working on studies on targeting and coverage of governmental cash transfers and social assistance programmes towards slavery victims; mortality rates and life expectancy of slavery victims vis-à-vis natural and pseudo control groups; value-chain studies to support private stakeholders in relation to compliance, supplier qualification processes, monitoring, due diligence and risk assessment; studies supported by machine-learning techniques as regards to hotspot prediction, vulnerability detection and recidivism prevention; and mapping the main international migratory routes and flows based on worker’s movements across Brazilian borders, focusing on refugees from Venezuela, Paraguay, Peru, and Bolivia. These studies and their methodologies will be explored in this column in the months to follow.
Perfect and complete data don’t exist. But, any access to reliable data and meaningful information can make a difference in fostering better policies, changing people’s lives and promoting human development. Hopefully, the FLPO Big Data Platform, through the SmartLab initiative can shed some light on the policy road map for eradicating forced labour, modern slavery, human trafficking and child labour.
Luis Fabiano de Assis is Federal Prosecutor, Chief Research & Data Officer at the Federal Labour Prosecution Office (FLPO) in Brazil 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.