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Under the seemingly normal practice of live-in help, domestic servitude can thrive​. Those who are forced to do house work against their will are domestic.
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It is increasingly clear that human trafficking belongs on a broader spectrum of exploitation, coercion and consent, rather being than a neatly delineated phenomenon that exists in absolute terms [ 50 , 55 , 61 , 65 , 66 , 84 ]. Such considerations notwithstanding, much can be learnt by examining instances identified as human trafficking to advance understanding of this complex phenomenon and support more nuanced and evidence-informed policy and practice see also [ 10 ]. There are solid theoretical and empirical reasons to hypothesise that differences exist between victims of different types of trafficking, for example in terms of their socio-demographic characteristics, trafficking experiences and official responses.

From a theoretical perspective, opportunity theories of crime help explain why crimes are not uniformly distributed but rather concentrate in certain places, times, people and targets see, e. Understanding the distribution of specific crime types often within a general category, such as distinguishing between residential and commercial burglary can help provide insights into their drivers and enablers and inform targeted interventions. The rational choice perspective positions offenders as quasi-rational decision makers who act to maximise rewards, while minimising risks [ 21 ].

In this respect, one would expect the different market realities gaps, demands etc. National and international statistical data highlight important empirical distinctions between the different trafficking types. A classic example is gender: in the UK and internationally, the proportion of men is typically far higher among those identified as trafficked or potentially trafficked for labour than for sex e.

Yet, published comparisons of trafficking types tend to be purely descriptive and made only at a bivariate level; inferential statistics are rarely used to test the relationships observed. These limitations are reflective of wider shortcomings in the trafficking literature, namely the scarcity of robust quantitative research [ 12 , 50 ].

One of the biggest barriers academics face in conducting quantitative research into the differences between trafficking types is securing access to large-scale, individual-level datasets. We address in turn two broad-based and interlinked research questions:. At the time, the NRM dealt only with human trafficking, although it has since been expanded to include other forms of modern slavery [ 40 ]. For context, we briefly explain how the NRM currently works. First responders may have identified potential victims themselves or been notified by third-parties e.

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Our data came from the central repository spreadsheet managed by the NCA, which routinely informs official reports but had not previously been made available to academics. It contains detailed individual-level data on everyone referred into the NRM. The NCA provided us anonymised data covering suspected victims of trafficking: the total population of referrals into the NRM from its inception on 1st April to the date of extraction on 7th October We narrowed down the initial set of suspected victims to our final study sample.

Notably, very similar proportions had been assessed to have been trafficked as not Our final study sample comprised confirmed cases of trafficking for sexual exploitation, domestic servitude and other labour exploitation over the 5. Note that the UK treats domestic servitude as a separate category rather than subsuming it under labour trafficking as some other countries do see [ 12 ].

We cleaned and recoded the raw data, excluding some variables because of high rates of missing data, overlaps with other variables or limited relevance. Due to missing data, we had to drop several variables of clear research interest, such as mode of transport and port of entry. Table 1 shows the final variables used in our analyses, grouped by category.

We began with an exploratory data analysis [ 70 ], examining patterns descriptively and testing whether any differences between trafficking types were significant at the bivariate level. We then ran a multinomial logistic regression analysis to establish which variables were significant predictors of trafficking type the outcome variable.

Victims who do not conform to stereotypes of trafficking victims e. Also, adults, unlike children, must consent to referral so there may be some self-selection bias. Third, the UKVI has been said to be less willing than the NCA to deem people trafficking victims [ 51 , 68 ], which could have introduced systematic bias based on nationality. Fourth, the dataset involves unique cases but not unique individuals: in those relatively rare Footnote 7 instances where individuals are referred into the NRM more than once, they appear as a new entry. Working with anonymised data meant we could not filter out any such duplicates.

Fifth, victims may be connected to one another, which violates the assumption of independence. Since links between cases were not systematically flagged in the data structure, we could not establish the extent of clustering and introduce statistical controls for its possible effects.

Given the large dataset, unless any such clustering was very prevalent it is unlikely to have affected the validity of the findings. Finally, as is common when working with secondary data collected for non-research purposes see, e. We cannot rule out the existence of confounding variables not captured in our dataset.

While it is important to be aware of these limitations and accordingly exercise caution in interpreting our results, the data nevertheless offer many strengths over other sources and meaningful analysis is possible. In this section, we present in turn the results of the exploratory data analysis and the logistic regression analysis. The largest category was labour trafficking Substantially fewer victims were trafficked for domestic servitude Given these size differences, we tend to present results in percentage terms to facilitate comparison.

While the overall median age was Note that all labour trafficking victims aged seven or under were linked to benefit fraud, explaining the presence of infants and young children in this category. Figure 1 shows a much flatter age distribution for labour trafficking, whereas sex trafficking had obvious clustering in the late teens and early twenties and swift decline thereafter.

While These ten nationalities dominated, together accounting for In contrast, the remainder of the sample was diverse and fragmented, with 22 nationalities featuring just once and 54 ten times or fewer. Footnote 8 There were no victims from Oceania in the sample. Of the total victims, just over half came from within the EEA As shown in Fig. For example, four in five labour trafficking victims came from within the EEA Victims in the sample presented to the authorities in eleven different police regions: Wales, Northern Ireland, Scotland and all eight English police regions. Once again, disaggregating the data by trafficking type revealed important distinctions see Fig.

London was the most common region overall Here, we focus on cases referred into the NRM the years to inclusive.

Domestic Servitude

Figure 4 shows the year in which victims were referred into the NRM. It clearly shows how the number of victims identified as trafficked for domestic servitude remained relatively steady year-on-year over the study period. In contrast, a more obvious and sustained increase over time was evident for both sex trafficking and labour trafficking, with particularly sharp increases from to and again from to Most notably, fewer labour trafficking victims were referred in the Spring and Summer months March—August inclusive than at other times of year.

Footnote 10 Disaggregating the data revealed clear differences between trafficking types, shown in Fig. For example, the proportion of referrals from the police was markedly higher for labour trafficking than the other types. Meanwhile, a particularly large proportion of domestic servitude victims was referred by non-governmental organisations. The proportion of referrals from the immigration authorities was markedly higher for sex trafficking and domestic servitude than for labour trafficking, quite possibly linked to the higher proportion of non-EEA victims.

The exploratory data analysis gave a useful overview of our full dataset, helping tease out and patterns and possible relationships. Next, we used multinomial logistic regression to build a model to control for possible relationships between variables and investigate the relative power of the different variables in distinguishing between different trafficking types. As explained previously, the regression analysis was necessarily limited to victims from Africa, Asia or the EEA.

We built the model iteratively, using a main effects modelling strategy. All variables used in the model contributed significantly to model fit, tested using Likelihood Ratio Tests. We ruled out multicollinearity between variables using collinearity diagnostics results available on request. Our final model contained cases: victims of labour trafficking, of sex trafficking victims and of trafficking for domestic servitude.

To end domestic servitude, first fix immigration policy

Descriptive statistics are available in Appendix 1. The model correctly predicted exploitation type for Collectively, the likelihood-ratio test, pseudo R 2 and percentage of cases correctly classified suggested the model had a good ability to discriminate between trafficking types. To understand the extent and direction of changes that each independent variable or category thereof predicts in the dependent variable trafficking type , we examine the exponent of the B co-efficient Exp B.

It covers the three type-wise comparisons in the model: domestic servitude versus labour trafficking; sex trafficking versus labour trafficking; and domestic servitude versus sex trafficking. In the appendices, we provide full parameter estimates for the multinomial logistic regression Appendix 2 and a full forest plot of Exp B values that also includes non-significant results Appendix 3.

Generally speaking the results reflect similar trends to those outlined in the bivariate analysis, but several interesting findings merit closer examination. While all the variables have predictive utility at the overall model-level, the significance, direction and strength of effect often varied considerably at the level of pair-wise comparisons between trafficking types.

The most pronounced results were around gender. All other variables held constant we will not keep repeating this caveat but it is important to bear in mind throughout the results , being female means someone is 75 times more likely to have been trafficked for sex than labour.

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The same trend is evident, although somewhat less pronounced, when comparing domestic servitude and labour: being female means someone is 14 times more likely to have been trafficked for domestic servitude than for labour. The influence of gender is far less keenly felt when comparing domestic servitude and sex: being female means someone is 0.

Introduction: A skewed focus, conflated problems and pronounced knowledge gaps

Region of origin had clear predictive utility too. Most notably, coming from Africa as compared to the EEA meant someone was Meanwhile, coming from Asia as compared to the EEA meant someone was Some variables only predict trafficking type in certain pair-wise comparisons. A good example is age, which was not significant when comparing domestic servitude and labour. For the other two comparisons, the predictive power accumulates as wider age gaps are considered.

For example, for a one-year increase in age, the relative risk of someone having been trafficked for sex rather than labour decreases by 3. For a five-year increase in age, however, the relative risk decreases by Similarly, for a one-year increase in age, the relative risk of someone having been trafficked for domestic servitude rather than sex increases by 2.


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The South East and Scotland had significant results for all three pair-wise comparisons, indicating that the relative composition of the trafficking identified in these regions particularly diverged from that in London. Overall, the regional patterning varied in significance and strength many of the differences were fairly subtle: relative risk ratios ranged from 0.

Seasonal patterning was also complex and typically subtle. There were eight months in which, compared to December, a victim referred into the system was more or less likely to have been trafficked for one form of exploitation than another. While many of the differences were fairly subtle, some seasonal results were comparatively more pronounced: people referred into the system in May and August compared to December were respectively 2.

As with age differences, the effects were most pronounced when considering longer intervals: the relative risk of having been trafficked for domestic servitude rather than sex increases by 0. In other words, considerably longer times to decision are more strongly predictive of someone having been trafficked for domestic servitude than sex.

Finally, source of referral was only significant for one category and one pair-wise comparison of trafficking types: referral from an NGO as compared to the police increases the relative risk of having been trafficked for domestic servitude rather than labour by A key strength of the multinomial logistic regression is that it demonstrates that the individual independent variables have predictive power over and above the other variables in the model. Hence, for example, someone who is older, male, comes from the EEA and identified in various police regions outside of London is even more likely to have been trafficked for labour rather than sex than someone with just one of these characteristics.

As highlighted in the introduction, human trafficking is a broad conceptual umbrella that encompasses various different forms of exploitation. While there are empirical and theoretical grounds to expect differences between key trafficking types, there is very little robust quantitative research on this topic.

Our results clearly support this hypothesis, with significant differences identified through both bivariate and multivariate analyses. These distinctions were not only statistically significant but also often substantial in magnitude.

'I slept on the floor in a flat near Harrods': stories of modern slavery

The most obvious example was gender: boys and men were substantially more likely to have been trafficked for labour than for sex or domestic servitude. This result echo gendered patterns in trafficking frequently reported by various national agencies and transnational bodies e. Using inferential statistics and controlling for the effects of other variables increases confidence in the validity of our findings, providing a clear advantage over purely descriptive comparison of trafficking types.

Other differences were more complex and there was much subtle variation in pair-wise comparisons in terms of significance, direction and size of effect for different variables and their sub-categories. All three pair-wise comparisons yielded significant results, indicating that genuine differences exist between the trafficking types. Overall, our study demonstrates the value of using individual-level data and a quantitative approach in disentangling some of the complex relationships around human trafficking and its various forms.