Trying to identify possible trafficking from online job advertisements
Perhaps the most obvious finding of this study is just how challenging it is to try to identify potentially trafficking-related advertisements online. The vast majority of online advertisements (98.4%, n = 423) sampled contained at least one indicator of labour trafficking. It is highly unlikely - although theoretically possible - that all such activity was linked to intended trafficking and exploitation. It seems more plausible to conclude that the presence of a single indicator in a job advertisement has little utility in discerning between suspicious and routine activity. Indeed, indicators put forward for identifying trafficking are rarely intended to be used in isolation and the presence of more indicators would generally suggest a higher likelihood of abuse (ILO 2009). In this respect, those advertisements containing numerous indicators - for example, 15% (n = 65) had five or more - may be of particular concern.
Furthermore, exploring online job advertisements is complicated by the lack of consistency and structure of the information contained within them as seen on darbasuzsienyje.org where much of the information on any given indicator was not provided. For instance, information was often not provided on cost deduction from workers’ wages (76.5%, n = 329), transport to work provisions (75.6%, n = 325), sharing accommodation with other workers (65.6%, n = 282), help with settling in (62.8%, n = 270) and support with transfer to destination country (62.1%, n = 267). Similarly, 37.7% (n = 162) of advertisements did not specify whether previous work experience was required or not. We coded indicators as absent where there was an explicit statement contradicting the given indicator or there was no relevant information provided either way (ESM 7 shows how the absent indicators broke down across these two types). Our approach is therefore conservative: for certain indicators (e.g. previous work experience), it might seem reasonable to read the absence of any information on requirements as the absence of the necessity of having any experience or language knowledge. In turn, this may have resulted in our indicator counts being perhaps underestimated. In contrast, there were other indicators where not providing information would be more likely to mean that no such provisions were on offer, for example, support with transfer to destination country, transport to work or help with settling in. Thus, without the ability to clear up such information pre-travel, it is impossible to estimate whether such provisions would pose a risk.
Indicator utility
The task of using existing indicators to differentiate between potential trafficking activity and legitimate jobs is complicated by the fact that the indicators themselves vary in their apparent strength (or utility). Arguably, indicators of violations of national minimum wage and maximum working hours are more informative than the rest because they clearly suggest non-compliance with local labour market legislation. Conversely, such indicators as the provision of accommodation, transport to work, transfer to destination country and help with settling in are not necessarily indicative of criminal activity or exploitative conditions. While they could indicate trafficking, according to such organisations as UNODC (2018) or ILO (2009), they could also be present in legitimate work relations. Such services might be provided by employers in jobs that recruit migrant workers, who sometimes do not speak the language of the destination country or might require assistance navigating a new system. In fact, certain sectors in Western Europe are now dependent on relatively cheap migrant labour (e.g. agriculture, garments, construction, customer service, (Kelly 2005)). Therefore, it seems reasonable that employers (including employment agencies) seeking migrant labour would also be willing to provide additional support.
Nevertheless, such provisions can pose risks if they create additional dependence for workers on their employers. In some cases, this dependence can exacerbate standard power imbalances in the employer-employee relationship and serve to entrap workers in exploitative or harmful work, in most extreme cases in situations of debt bondage, forced labour or labour trafficking (Aronowitz 2001; Hopper and Hidalgo 2006; Skrivankova 2006; Craig et al. 2007). The risk of indebtedness amplifying workers’ vulnerability is particularly relevant when considering that nearly a quarter of advertisements (23.5%, n = 101) contained the indicator that costs would be deducted from workers’ wages.
Moreover, dependence can become particularly acute when the work is sub-contracted through labour market intermediaries as represented in our study. This situation arises because sub-contracting work to labour market intermediaries such as recruitment agencies complicates working relationships (Allain et al. 2013). Complex employment networks spanning multiple legal jurisdictions - as in the case of agency work involving migrant workers - increase the risk of masking exploitative labour practices due to unclear attribution of responsibility and diverse legal frameworks (Davies 2018). However, currently there is a lack of awareness on labour exploitation amongst the stakeholders responsible for labour market regulation and a lack of resources to implement effective monitoring and regulatory practices (Clark 2013). For instance, according to Lithuanian labour law, recruitment or employment agencies are not allowed to charge workers fees for their services and are required to submit a report four times a year on their activities (Law on the Ratification of the Private Employment Agencies Convention 2004). In practice, however, the reports are not available on the Lithuanian Labour Exchange website, which hinders a jobseeker from checking the credibility of a recruitment agency.
The limited job requirements in terms of language and work experience may well be related to the industries and the types of work offered rather than being indicative of exploitation per se. The indicators used in this paper, themselves taken from the human trafficking literature, could be representative of conditions that are offered to people willing to migrate for work in low-skilled industries. Note, however, that deception is said to be more common than outright coercion as a means of recruiting people - online included - into situations of labour trafficking (e.g. Ghinararu and van der Linden 2004; Ollus et al. 2013; Europol 2016; Milivojevic 2012; Dixon 2013; Europol 2014; Hughes 2014). In fact, Cockbain and Bowers (2018) found that out of all individuals from within the European Economic Area who were officially identified as labour trafficking victims in the UK in 2012 (n = 170), all of them were recruited through deception, most commonly relating to wages and the state of living conditions. Therefore, while accommodation, transfer to destination country, transport to work and help with settling in provisions might reflect the conditions that are offered to people willing to migrate, they may also be used as a recruitment method into jobs, where working conditions are not as good as they are advertised to be. After all, it is relatively easy to disguise intent to exploit in an advertisement posted online – a sphere, which provides anonymity and is un-regulated. More research is needed to explore such complexities further.
In the current paper, deception may also be relevant as jobs may be misleadingly advertised as having higher wages or lower hours than will actually be the case; in such instances, using the indicators we used would miss a crucial aspect of labour trafficking. Our analysis did not flag instances where the wage offered in the advertisement is higher than the market norm. Janusauskiene’s (2013) research on Lithuanians as victims of labour trafficking proposes that advertisements used to recruit victims into labour trafficking often offer wage that is too high for the position advertised. The working hours and wages data in the current study proved complex to process and, where ranges were given, we relied on the mean. Conversely, operationalising the wage offered in an advertisement in a way that could account for potentially deceptive wages could be useful in future research. We encourage other researchers to use our data and apply different techniques and transformations to illuminate relationships that were out of the scope of the current investigation.
Indicators are likely to be more useful when used in combination rather than alone. Therefore, the presence of multiple indicators might be taken as a particular red flag, especially if "stronger" indicators (e.g. wage violations) are found among them. For example, ILO (2009) differentiates between strong, medium or weak indicators and proposes a method of assessing individual cases for human trafficking based on the various combinations that the indicators make up. The ILO’s indicators (2009) were developed, however, through consensus from a group of experts and thereby reflect the working knowledge of experts rather than a rigorous empirical assessment. To our knowledge, there has yet to be empirical research evaluating the predictive utility of these indicators in practice in distinguishing between trafficked and non-trafficked populations.
Part of the challenge here is that trafficking is not a clear-cut phenomenon that can be neatly disentangled from neighbouring issues and is better seen as part of a broader continuum that runs from decent work to forced labour (Laczko and Gozdziak 2005; Andrees 2008; O'Connell Davidson 2015; Skrivankova 2010; Quirk 2011; Spencer and Broad 2012; Skeldon 2000; Weitzer 2015; Davies 2018; Cockbain et al. 2018). In between the two extremes are the exploitative labour practices that would not normally be considered severe enough to merit criminal justice responses. Such “routine” exploitation is likely more frequent and subtle than the severe extremes, although both can be embedded within otherwise legitimate business practices (France 2016; Shamir 2012; Davies 2018). However, they can cause considerable harm to workers and reputational damage to industries (Paoli and Greenfield 2015; Davies 2018). Thus, considering how exploitation can be identified and combatted across the continuum of exploitation – rather than focusing solely on practices that might constitute trafficking – could help encourage more holistic and inclusive responses to all abuses.
Embedding indicators in the labour trafficking literature
The choice of characteristics of job advertisements that we investigated (e.g. industry, type of contact, etc.) were informed by the literature on labour trafficking, so might reasonably have been expected to show significant associations with the indicators themselves. Yet, many of our results show inconsistencies and limited consistencies with the existing evidence base on labour trafficking. For instance, men are often found to make up the majority of (identified) labour trafficking victims (e.g. Rijken 2011; UNODC 2016; Cockbain and Bowers 2019). However, in the current sample, where gender requirements were specified, mostly advertised was work for both women and men (17.7%, n = 76). Moreover, Europol (2016) states that 25–50-year-olds are most commonly targeted. Although this constitutes a large range, meaning it would not be surprising if most Lithuanian labour trafficking victims fell into this age category, our analysis found few differences by age of worker sought. Our results showed that the largest category of required age was 18–50 (16.0%, n = 69), which is an even larger range than that suggested by the literature.
Meanwhile, Western European countries are often said to be countries of destination (e.g. Surtees 2008). Although the advertisements in the current sample mostly offered work in the Netherlands (43.0%, n = 185), the UK (30.2%, n = 130) and Germany (12.6%, n = 54), inferential statistics revealed key differences amongst them. Similarly, and in line with opportunity theories of crime (see, e.g. Cohen and Felson 1979; Everson 2003; Farrell and Pease 2001; Brantingham and Brantingham 1993, Brantingham and Brantingham 1984; Felson and Eckert 2015), risk is thought to concentrate in industries such as food processing, agriculture, horticulture, hospitality and construction (e.g. Kelly 2005; Ollus et al. 2013; Strauss 2016). Yet, we found key differences between construction on the one hand and hospitality and food production on the other. The risk of trafficking is thought to be highest in low-skilled (e.g. Dowling et al. 2007) and temporary, part-time or seasonal work (e.g. Ollus et al. 2013), but we found only limited differences on the variables related to job nature and contract type. Overall, few of the advertisements’ characteristics were predictive of the overall number of indicators. Our findings on associations between the advertisements’ characteristics and indicators of national minimum wage and maximum working hour violations suggest these relationships merit further exploration.
While current indicators of labour trafficking (e.g. UNODC 2018; ILO 2009) can be useful in assessing individual cases, this study suggests their utility in risk assessing job advertisements at scale is likely to be modest. Part of the challenge here is the limitations of the empirical evidence base on labour trafficking that could underpin such attempts. For example, a recent systematic literature review of the European evidence base on labour trafficking found that only a handful of publications met even basic criteria for scientific research (Cockbain et al. 2018). Despite growing interest in the topic, the human trafficking literature in general remains notorious for issues such as emotive overclaims, weak research designs, insufficient methodological transparency and questionable assumptions and inferences (e.g. Tyldum and Brunovskis 2005; Denton 2016; Strauss 2016; Cockbain et al. 2018; Zhang 2009; Weitzer 2015). Aside from fundamental definitional and conceptual challenges already discussed (see O’Connell Davidson 2015), accessing relevant participants and data for trafficking research is challenging, especially for quantitative studies. Trafficking victims are widely understood to belong to "hidden populations", meaning that sampling frames cannot be established and convenience samples prevail. Thus, findings are hard to generalise beyond the study samples (Tyldum and Brunovskis 2005; Cockbain et al. 2018, 2019a). In addition, comparison groups are rarely sought and the underdevelopment of the neighbouring literature on the scale and nature of labour rights abuses experienced among the working population at large (Cockbain et al. 2019b ) means there are few baselines against which to compare results.
Future research
Against this backdrop, it is hardly a surprise that the indicators set out in the literature are challenging to use in practice. In order for indicators to be a viable tool in the future, more expansive and reliable underpinning research would be needed. Research on the similarities and differences in experiences between low-wage economic migrants and identified victims of labour trafficking (and other forms of labour exploitation) could help determine which indicators are most useful and in what combination. Additionally, relating specific job advertisements to actual labour trafficking cases could help elicit specific red flags and help train and refine any automated systems, although under-reporting and institutional biases would likely pose challenges (see Cockbain et al. 2019a). Replicating our study in other contexts (including perhaps places where online job advertisements are less commonly used) and on a larger-scale could help identify how existing indicators concentrate in advertisements by industry, occupation, contract type, etc., which could help prioritise sectors and groups for targeted research into the scale and nature of labour rights abuses more generally and labour market enforcement that goes beyond the traditional reliance on a reactive, complaints-based approach (see, e.g., Cockbain et al. 2019b). Since most people who experience labour market abuses do not complainFootnote 15 and complaints are known to be imperfectly related to underlying workplace conditions (Noack et al. 2015; Weil and Pyles 2006), such knowledge could support more effective prioritisation among notoriously under-resourced labour inspectorates.
Overall, this paper highlights that using indicators to detect potential trafficking at the recruitment stage is complicated in practice and we should be wary of studies claiming to have uncovered trafficking when in fact they have simply applied untested indicators uncritically, treating instances with indicators as de facto trafficking. A vital first step before using human trafficking indicators in research, policy or practice is to assess empirically their reliability in distinguishing between instances or individuals that might reasonably be described as trafficking- or non-trafficking related.
An outlook on automated methods
Since stakeholders responsible for labour market regulation are often under-resourced when it comes to actively detecting labour exploitation (Clark 2013), it is worthwhile for researchers to explore computational approaches. Techniques from data science facilitate the data collection process, the extraction of information from unstructured data at scale, and introduce the field to data-driven methods that complement the as yet insufficiently empirically validated tools.
First, data collection could in future studies utilise web-scraping to retrieve data from websites in an automated manner. Using such tools would help to build more extensive and more diverse datasets that are hard to obtain in manual work. For example, web-scraping could be used to expand on our current study and download advertisements over longer time periods (e.g. a whole year), from more websites and across a range of countries automatically, thus supporting comparative research and more extensive analyses. The current study manually collected data for and analysed a relatively small sample of cases (n = 430). Small sample sizes were, in the past years, identified as a major limitation in the behavioural sciences due to the poor generalisation of results beyond the context of individual studies (Yarkoni and Westfall 2017). Aside from limited generalisability of the findings of individual studies, small sample sizes often present snap-shot representations of typically dynamic phenomena. For example, the present study examines labour trafficking in a static manner thereby neglecting a possible temporal evolution in the presence (or absence) of indicators. In order to enable research on temporal variations, larger datasets are needed, which are currently hampered by typically manual data collection. Automating the data collection would greatly help the field to provide high quality, large datasets and thereby open ways to study the problem of labour trafficking in more complexity.
Second, with online ads being in the form of unstructured text data, the dominant approach in many fields is to count the occurrence of indicators manually and assess the agreement between two or more independent judges (e.g. in verbal deception research, see the overviews in Kleinberg et al. 2019). This procedure, however, is costly, hence, it constitutes a key impediment for larger sample sizes and poses a threat to the reliability due to extensive human involvement. Methods from natural language processing are a worthwhile alternative to manual approaches, especially those techniques that help extract information automatically. For example, named entity recognition is a well-established technique to automatically “tag” entities such as persons, locations, languages, mentions of money, dates and organisations (Nadeau and Sekine 2007). Named entity recognition is particularly appealing because it does not rely on hand-crafted word lists but uses machine learning and the grammatical structure of the text to identify relevant information. Many of the indicators used in the current study can be operationalised through named entities and/or keyword techniques (e.g. extracting the offered wage and working hours). Such a hybrid approach uses computational methods to model the constructs that are deemed relevant by theory and has been proven useful in a context with similar challenges (i.e. moving from manual text annotation to automated methods, Kleinberg et al. 2017). As previously mentioned, advertisements containing multiple indicators may warrant particular attention. Thus, automating the identification of indicators could also allow the prioritisation of advertisements with higher overall counts of indicators or where particular combinations of indicators co-occur with one another or with other variables of theoretical relevance.
Third, aside from using information extraction to model already identified indicators on a large scale, another line of future research could explore a data-driven approach. Here, an important step would be the construction of a large dataset that contains cases of known labour abuses and control cases (i.e. a so-called “ground truth” dataset). With such a gold standard of cases, future studies could use supervised machine learning to examine the predictive power of combinations of known indicators (i.e. theory-led investigations). Most importantly, the dataset would also allow for the discovery of patterns in online advertisements that might not be presently formulated as indicators. For example, an oft-used technique in text classification called “bag-of-words” represents a text (here: an online advertisement) through the frequency of all words occurring in the text (e.g. Ott et al. 2011). In supervised machine learning, the goal is to let a classification algorithm learn by itself from examples to separate the two outcome classes (e.g. labour abuse vs. no labour abuse). The performance of this classification function is then typically evaluated on “unseen” data. The latter represents a data-driven approach that might be particularly helpful considering that the validity of the existing indicators is debated. Ultimately, a fruitful way forward could lie in combining automated efforts with human expertise to make use of the distinguishing features of both (fast, reliable processing through computers and small-scale contextualising judgments from human experts). Such a “human-in-the-loop” system could ideally help in prioritising cases and improve detection accuracy to ameliorate the problem.
It is worth noting that predictive modelling efforts are fundamentally reliant on the validity of the labels of the data. That is, a machine learning system might be able to learn with high accuracy to separate two outcome classes that were fed into the system, but it cannot correct or revise the validity of the classes. Furthermore, systems that are self-learning (e.g. through updating databases) might be vulnerable to not only propagating invalid labels but also to zooming in on specific groups or destinations. Especially for a field with potentially far-reaching implications, ethical use of machine learning systems is advised. To avoid such downsides, we encourage joint work between domain experts and computational social scientists to pave the way for a more empirically informed research on labour abuse and trafficking.
The current research did not use any of the automated techniques discussed above because it was outside the aims and remit of this initial scoping study. Early studies like these are useful in testing new ground and identifying potential benefits and challenges in automating the process. The primary utility of using automated methods lies in the fact that they could help process the vast amount of unstructured data that can be found online. There were 679 advertisements posted on darbasuzsienyje.org over 7 days. This is a snapshot of one website. It is practically impossible to commit enough resources to manually go through the process of examining job advertisements for signs of possible labour trafficking activity. Thus, automated methods have considerable potential to make information extraction from labour ads more efficient, uncover patterns in the data that help detect cases of labour trafficking, and thereby facilitate the screening process as a whole.
Study limitations
Our study has some obvious limitations. It dealt with a small sample that was a snapshot from one particular website and the findings are not generalisable. This study lacks ground truth in that the data in our sample did not contain information whether some advertisements - if any - subsequently led to activity that would meet legal definitions of labour trafficking. As such, it remains unclear whether advertisements containing more indicators or certain specific indicators were indeed more likely to involve trafficking-type behaviour. We also only examined those indicators that could be operationalised in the context of online advertisements and even then found that some were challenging to apply. Information on certain variables of interest was rarely specified, so small numbers in sub-categories may have meant some analyses were underpowered to detect differences. Despite these limitations, this exploratory study provides insights into the prevalence, nature and associations of commonly-used indicators of labour trafficking within online job advertisements.