Shrimp peeling in Thailand. Thierry Falise for the ILO/Flickr. (CC 2.0 by-nc-nd)
My particular interest is in the business and organisational dynamics of forced labour: how and why it is deployed as part of a business model; how forced labour is managed; how forced labour operates within and between organisations, including the supply chain; the impacts and interrelationships between forced labour and consumers, workers, and other stakeholders of business; and the role of multi-stakeholder initiatives and other mechanisms in combatting forced labour.
These kinds of questions are rich with opportunity for extending our understanding of the phenomenon of forced labour in unique and important ways. After all, most forms of forced labour take place in a business context – mainly informal, but also with clear and significant links to the formal sector. Moreover, forced labour is often driven by business imperatives. Thus, we need to take the business of forced labour seriously if we are to really understand it and address it in a meaningful way.
Unsurprisingly, there are huge gaps in our data on the business of forced labour, perhaps even more so than in other areas of research on the phenomenon. Partly this is due to the lack of researchers in the field with specific expertise in business and management; partly it is because there are particular empirical challenges with researching the business dynamics of forced labour. Siddharth Kara’s book Sex Trafficking: Inside the Business of Modern Slavery is a good example of some of these limitations. Not only is there only half of one chapter devoted to any type of business analysis in the entire book, much of this analysis relies on scant data and poor execution. In one particularly notable passage, Kara estimates the elasticity of demand for sex acts (how much demand for a particular act changes with the price) compared with other products by interviewing four sex worker clients and eighteen of his friends. Though he acknowledges that this is “not nearly enough for a statistically defensible curve” this severely underplays the meaninglessness of such analysis. My sense is that these barriers are not insurmountable, but they will require more and better data. They might also demand a different set of methodologies to those we have been using so far.
Gaps in the data
So what data do we need in order to inform a better understanding of the business of forced labour? The key gaps to me are in the detail of how forced labour works as a business.
For example, there is a common presumption in the popular and academic discourse around forced labour and slavery that perpetrators make huge profits and become rich from their use of the practice. Kevin Bales, for instance, identifies “very high profits” as a defining characteristic of modern forms of slavery compared with historical forms in his book Disposable People. This, however, is a largely untested hypothesis, in large part because we have yet to collect and analyse the financial details of perpetrators in any systematic way. What are the costs of forced labour for perpetrators –in terms of recruiting workers, employing enforcers, paying bribes, etc. – as well as the risks of prosecution?
Similarly, we need to know the margins associated with specific stages of the value chains involved in forced labour. This would allow us to determine how value is distributed through the chain, and subsequently to uncover which actors exactly are able to extract rents and make abnormal profits. What size cut do intermediaries take, for example, and how are recruiting agents’ fees structured in the case of forced labour? Different types of value chain, with different institutional structures, are likely to give rise to different economic complexions.
Another major gap in our data relates to the costs and revenues associated with various ancillary services – accommodation, food, travel, visa services, etc. – that are sold to forced labourers in order to escalate and sustain their indebtedness. While we know that these can be used to develop a different form of business model based more on revenue generation than cost reduction, we do not know with any precision how these transactions work, how prices are set, what the costs are, etc.
Accessing that which is hidden
Because so much forced labour arises through informal and often illegal business, it is little surprise that we face significant obstacles in accessing reliable data compared with researching business in the formal economy.
One solution, of course, is to explore the specific role of formal economy organisations in forced labour. For instance, empirical studies of value chains, labour exploitation, and social auditing can be refined to take account of the business dynamics of forced labour. Niklas Egels-Zandén’s research on suppliers’ compliance with multinationals’ codes of conduct, for example, is based on unofficial interviews with employees of suppliers. This allowed him to identify techniques used to deceive monitoring organisations that might very well be replicated in forced labour-type situations. We might also explore the accounting technologies used to prevent forced labour from being within the purview of regulators, rendering the illegitimate legitimate. A good example of the latter is Dean Neu’s qualitative study of the accounting strategies used by employers and undocumented workers to influence how and where economic transactions with illegal workers are recorded.
Victims themselves then are one potential source of data. But, as my own research has shown, the opaque accounting used by perpetrators to prevent forced labour victims from understanding their own debts makes them unreliable informants in many instances. In this respect there is no real substitute for getting data directly from perpetrators on the details of their businesses, such as their cost and revenue structures. This approach was utilised by Steven Levitt and Sudhir Venkatesh in their empirical analysis of a drug dealing street gang in Chicago. Using a unique data set of detailed financial information on the gang’s activities obtained from a former gang member, the authors demonstrated considerably lower returns to drug selling than had been reported in the literature to date (based primarily on self-reports from drug dealers), a highly skewed wage distribution, and clear evidence of decision-making impossible to reconcile with economic optimisation. As Levitt and his co-author Stephen Dubner describe it in their well-known book Freakonomics, Venkatesh acquired access to the data following a period of immersive ethnographic research where he “practically lived” in the housing project that was home to the gang, watching the gang members “up close, at work, and at home”.
Ethnography is not the obvious way to acquire detailed quantitative data, but it has a long history of use in research on crime. Certainly the need to develop trust with research subjects in (or with experience in) criminal enterprises necessitates a period of deep, long-term engagement with the field. This holds true whether the approach is ethnographic or any other methodology. Such approaches have considerable challenges, especially given the time pressures of academic publishing, but I believe they hold the greatest potential for unlocking new insights into the business of forced labour.
For more on this topic, please see my report ‘Forced Labour’s Business Models and Supply Chains’ for the Joseph Rowntree Foundation, co-written with Jean Allain, Genevieve LeBaron and Laya Behbahani.