While measuring the number of people in modern slavery remains a challenge, substantial improvements have been made in this field in recent years. In 2017, the inaugural Global Estimates of Modern Slavery were produced by the International Labour Organization (ILO) and the Walk Free Foundation (WFF) in partnership with the International Organization for Migration (IOM). The regional estimates produced through this collaboration form the starting point for the estimation of national level estimates presented here.

Global Estimates of Modern Slavery

The Global Estimates were comprised of two sub-estimates: an estimate of forced labour and an estimate of forced marriage. The sub-estimate of forced labour was then further broken down into three categories: forced labour in the private economy, forced sexual exploitation, and state-imposed forced labour.

Figure 1Typology of modern slavery
Typology of modern slavery

As no single source provides data that are suitable for the measurement of all forms of modern slavery, a combined methodological approach was adopted for the Global Estimates of Modern Slavery, drawing on three sources of data to calculate the sub-estimates:

  1. The central element of the methodology is the use of 54 specially designed, national probabilistic surveys involving interviews with more than 71,000 respondents across 48 countries. The estimates of forced labour in the private economy (excluding the sex industry) and forced marriage were derived from these surveys. Only cases of modern slavery that occurred between 2012 and 2016 were included in these estimates, and all situations of forced labour were counted in the country where the exploitation took place. In the five-year reference period for the estimates, while surveys were conducted in 48 countries, men, women, and children were reported to have been exploited in 79 countries.1
  2. Administrative data from IOM’s databases of assisted victims of trafficking were used in combination with the 54 datasets to estimate forced sexual exploitation and forced labour of children, as well as the duration of forced labour exploitation. This involved calculating the ratio of adults to children, and also of “sexual exploitation” cases to “labour” cases in the IOM dataset, which contained information on 30,000 victims of trafficking around the world who had received assistance from the agency. These ratios were then applied to the estimates taken from the survey data on forced labour of adults to arrive at an estimate of the number of children in forced labour and another estimate of “sexual exploitation.”
  3. As the surveys focused on the non-institutionalised population, meaning that people in prisons, labour camps or military facilities, and other institutional settings are not sampled, the surveys are not suitable for estimating state-imposed forced labour. Instead, the estimate of state-imposed forced labour was derived from validated secondary sources and a systematic review of comments from the ILO Committee of Experts on the Application of Conventions and Recommendations relating to state-imposed forced labour.

Each sub-estimate was initially calculated as a flow estimate; that is, the total number of persons who were victims of modern slavery during a specified period of time between 2012 and 2016. The flow estimate was then converted into a stock estimate; that is, the average number of persons in modern slavery at a given point in time during the 2012 to 2016 reference period. The stock estimate is calculated by multiplying the total flow by the average duration (the amount of time in which people were trapped in forced labour) of a spell of modern slavery. The average duration of modern slavery was determined from the database of the IOM, containing records of assisted victims of trafficking who were registered during or after 2012.

A detailed explanation of the methodology underpinning the Global Estimates of Modern Slavery is available online.2

From global and regional to national estimates

The national estimates presented in this Global Slavery Index were calculated7 using individual and country-level risk factors of modern slavery. The final set of risk factors were selected from an exhaustive list of variables to optimally predict confirmed cases of forced labour and forced marriage. The model was then used to generate average predicted probabilities of modern slavery by country. The regional totals in the Global Estimates of Modern Slavery were then apportioned based on each country’s average predicted probability of modern slavery. This process involved the following key steps:

  1. Identifying risk factors of modern slavery. Using national surveys that included questions on experiences of forced labour and forced marriage to identify which variables were statistically associated with respondents in the survey who had been victimised, versus those who had not been victimised. This included using a series of statistical tests to identify relationships between instances of victimisation and other variables collected through the survey process (such as age, gender, marital status, education, urban/rural, employment, life evaluation, business ownership, and ability to live on current income). Country-level predictors of risk from the most recent Global Slavery Index Vulnerability Model were also included.
  2. Predicting modern slavery. These risk factors were used to build a statistical model that best predicts occurrence of modern slavery at the individual level.
  3. Estimating prevalence and aligning with Global Estimates of Modern Slavery regional estimates. Individual predictions were aggregated into risk scores at the country level. Whereas survey data on forced labour and forced marriage are not available for every country, a broader set of survey data covering variables such as age, gender, marital status and so on was available for 147 countries.8 Country risk scores were used to estimate country prevalence, based on the extent to which the country risk score deviated from the average regional risk scores. For example, if a country had the exact same risk score as the relevant region in the Global Estimates, then it was assumed that the prevalence in the country was the same as in the region.
  4. Final calculation of estimated prevalence. Number of victims was then estimated by applying the estimated prevalence to population data for each country. To this “base” estimate, an estimate of state-imposed forced labour was added to determine the final estimated prevalence of all forms of modern slavery.

The process followed in each of these steps is detailed below:

1. Identifying risk factors of modern slavery

Data and variables

First, individual and country-level variables that have a significant relationship with forced labour or forced marriage at the individual level were identified. Data for this analysis were taken from Gallup World Poll (GWP) surveys conducted in 2014, 2015, and 2016,9 including a set of surveys with a module on modern slavery used to estimate the risk model, and a broader set of surveys used for prediction purposes, as well as country-level risk variables from the Global Slavery Index Vulnerability Model.

Estimation data and outcome variables

Estimation data were drawn from 54 surveys conducted in 48 countries which included a module on Modern Slavery, with a total sample of 71,158 individual interviews. This included:

  • Fifty-three national surveys conducted through the GWP in 48 countries between 2014 and 2016, with a total sample of 57,158 individual interviews.
  • A 2016 survey covering 15 Indian states: Andhra Pradesh, Bihar, Chhattisgarh, Himachal Pradesh, Jharkhand, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Punjab, Rajasthan, Telangana, Uttar Pradesh, and West Bengal, with a total sample of 14,000 individual interviews.

Cases of forced labour and forced marriage were identified with a series of screening and follow up questions as described earlier. On the basis of these questions, victims of forced labour were identified according to the following criteria:

  • The work was involuntary (“Yes” to any of the screening questions), AND
  • The work was under coercion or the menace of a penalty, AND
  • The work occurred in the last five years.

Victims of forced marriage were identified according to the following criteria:10

  • The marriage was involuntary (“yes” to the screening question), AND
  • The marriage had occurred without their consent (forced marriage).
Prediction data and predictor variables

A broader set of data (“Prediction data”) was drawn from 433 GWP national surveys conducted in 155 countries between 2014 and 2016, with a total sample of 451,161 individual interviews. A total of 157 variables that could potentially be used to predict forced labour or forced marriage status were identified using the five dimensions of the Walk Free Foundation Vulnerability Model as an organising framework. These included:

  • One hundred twenty-two individual-level variables from the GWP core questionnaire, which collects information on basic demographic variables such as the respondent’s age, gender, educational attainment, marital status, employment status, urban/rural location, and number of adults (15 and older) and children (under 15) in the household, as well as development-oriented topic areas including law and order, food and shelter, health, government and politics, business and economics, citizen engagement, education and families, environment and energy, social issues, religion and ethics, work, and well-being.
  • Thirty-five country-level variables from the Walk Free Foundation Vulnerability Model.11

Not all GWP variables were fielded in each survey country12 during each of the three data periods (2014, 2015, 2016), which lead to varying levels of geographic coverage. A list of 19 independent variables with low levels of missing data was identified to maximize geographic coverage of a “base” driver model. In addition to the “base” model, four additional models were created with an increasing number of predictor variables, and corresponding decrease in geographic coverage.

2. Predicting modern slavery

Individual-level models

Several steps were undertaken, using the data noted above to identify a best-fitting prediction model. Forced labour and forced marriage were modelled separately as the two distinct forms of slavery are expected to be predicted by different subsets of variables. The probability of a given respondent13 reporting a case of modern slavery was estimated for each outcome (forced labour and forced marriage) separately, using a logit model14 of the form:

Equation 1

Where the logit of the probability of FL/FM for each individual i is a function of a constant term  (intercept), a vector of individual-level demographic control variables with values varying for each individual , and with unknown coefficients , a vector of individual-level predictor variables , with values varying for each individual , and with unknown coefficients , and an individual error term .

Of the 157 variables available, a subset of variables was selected based on statistical and theoretical criteria in order to enhance the predictive power of the model, while maintaining explanatory relevance. Variables were excluded on the basis of having no significant association with either forced labour or forced marriage:

  • no multivariate significant association with either forced labour or forced marriage when entered within their respective geographic block, and
  • a high degree of multicollinearity, as expressed by variance inflation factors of 3 and above.

Finally, when variables are collinear or multivariate insignificant, priority was given to variables with greater theoretical relevance. For example, “confidence in judicial system,” which relates to issues of regulatory quality that have a direct bearing on modern slavery, is preferred over “confidence in financial system,” which may only have an indirect relationship with modern slavery.

Variables were entered into six models (numbered 1-6 in Table 1) to allow for the inclusion of a successively more exhaustive set of predictors. These models are nested hierarchically, with each successive model including all variables in the prior models, running from the simplest model that includes only seven demographic factors, to an “extended plus” model including 33 predictors of forced labour and 29 predictors of forced marriage (see Table 1 for final list of variables).

Table 1Predictor Variables in Final Models
ModelPredictors of forced labourPredictors of forced marriage

1. Demographic

Age
Urbanicity
Gender
Educational Attainment
Marital Status
Employment Status

Age
Urbanicity
Gender
Educational Attainment
Marital Status
Employment Status

2. Base

Not Enough Money: Food
Life Today (0-10)
Currently Own a Business
Feelings about HH income
Health Problems

Not Enough Money: Food

3. Indices

Negative Experiences
Youth Development
Community Attachment
Civic Engagement
Law & Order

Negative Experiences
Youth Development

4. Medium

Corruption in Government
Confidence in Judicial System
Confidence in National Government
Financial Inclusion (country)

Safe Walking Alone
Regulatory Quality (country)
Disabled Rights (country)
Coming Up with Money (country)
Minorities (country)
International Conflict (country)

5. Extended

City Economy Getting Better
Move Permanently to Another Country
Economic Conditions
Born in Country
Treated with Respect
Smile or Laugh
Experienced Anger Yesterday
Public Transportation Systems
Quality of Water

City Economy Getting Better
Move Permanently to Another Country
National Economy Getting Better
Standard of Living Better
Experienced Enjoyment Yesterday
Move Away or Stay
City: Quality Healthcare

6. Extended Plus

Sent Financial Help

Sent Financial Help
Approval of EU Leadership
Approval of US Leadership

The models were estimated using survey data from the 48 countries where the modern slavery module was included. In order to estimate risk of modern slavery in countries available in the GWP without a modern slavery module, the probability of a positive outcome for each individual in the prediction dataset is calculated and then aggregated into a weighted average predicted probability at the country level. Table 2 shows the sample sizes and number of countries included in each of the estimation and prediction models. The demographic factors-only models showed relatively poor performance, so they were not used for prediction purposes. The “base” models, including a relatively small number of variables, have the widest geographic coverage (152 countries). The “extended plus” models, with the largest set of predictors, have the narrowest geographic coverage (116 countries for forced labour and 110 countries for forced marriage).

Table 2Sample sizes and number of countries for each estimation and prediction model15
Forced labourForced marriage
ModelEstimation samplePrediction samplePrediction countriesEstimation samplePrediction samplePrediction countries
1. Demographics68,628 N/A N/A68,516 N/A N/A
2. Base65,837388,14615267,518434,905152
3. Indices50,946351,49914153,518374,512147
4. Medium47,967315,51212148,457306,176112
5. Extended47,966309,54412048,457289,306111
6. Extended Plus23,148279,17111648,457286,347110

The predictive performance of each model was evaluated using a broad set of post-estimation goodness-of-fit metrics,16 which were calculated on the same set of respondents (i.e. that had data available for all variables) to ensure comparability. Results indicated good predictive power (AUC values greater than .70) for all models. The base model is used as it is most useful for estimation, but the other models, with a greater complexity in terms of predictors but similar predictive performance, are useful to validate the robustness of the base model, which maximises geographic coverage (see Table 1).

Multi-level models

After identifying the “base” model as the best for prediction and maximising geographic coverage, multilevel models (MLM) were fitted to the data in order to enhance the predictions of the individual-level models and take into account the hierarchical nature of these data. MLMs allow for the extrapolation of model results beyond the sample of 48 countries.

All multilevel models were estimated using Bayesian17 applied regression modelling18. The individual-level base model was fitted before being expanded sequentially. First by allowing intercepts to vary across countries according to a random effect:

Equation 2

Equation (2) is the same as the individual-level regression Equation (1), with the addition of a subscript to classify individuals in countries, and an additional coefficient and its associated distribution, representing a random coefficient that is allowed to vary by country.

Next, by modelling country-level variation in order to improve our predictions for countries where there was no modern slavery survey with country-level predictor representing the vulnerability score , with values varying for each country, and with an unknown coefficient :

Equation 3

The Vulnerability Model is ideal for this purpose, as it incorporates a robust set of external datasets aggregated to explain or predict the prevalence of modern slavery. An examination of the association between country-level prevalence estimates and vulnerability scores confirmed a moderate correlation (Pearson r =.33). 

The individual-level models showed that owning a business significantly increases the risk of being a victim of forced labour. Members of the Walk Free Foundation’s Expert Working Group indicated that this result was surprising, as business ownership was expected to be a protective factor and hypothesized that the result could be driven by “necessity entrepreneurs”: individuals who are forced into starting a small-scale business because of a lack of other income-generating opportunities.19  A preliminary set of regional regression analyses confirmed that business ownership was only a significant predictor of forced labour in South Asia and Sub-Saharan Africa, where not owning a business has a protective effect. Finally a cross-level interaction effect for “business ownership” was introduced:

Equation 4

With   representing the interaction between business-ownership dummy , with a coefficient that varies for the two levels of regional dummy variable .

Model Performance

An examination of model performance20 shows that all MLMs perform similarly. In the case of the forced labour models, all MLMs perform similarly, and better than the fixed-effects model. In the case of the forced marriage models, differences were negligible.

Actual vs. fitted prevalence plots for the 48 countries with modern slavery survey data showed a very close fit, without any clear outliers. The introduction of country fixed (and then random) effects represented a major improvement above and beyond the individual-level models, which relied on regional fixed-effects. While the simplicity of the country fixed-effects model is attractive, this approach would not achieve our goal of estimating slavery in countries that are not included in the sample.

In order to exemplify the benefit of the more complex MLMs, country intercepts were removed from the predictions to simulate new data including countries not previously surveyed. The random intercepts model shows a poorer fit with the actual values than the other two models (a perfect fit is exemplified by the red dotted line). A random intercepts model with country level predictors and (in the case of forced labour) a cross-level interaction provides the most comprehensive framework to undertake these inferences and was the model on which estimates were based.

A fuller description of the process by which the final model was achieved is set out in a forthcoming publication.21

3. Estimating prevalence and aligning with regional estimates from The Global Estimates

As summarised above, several concomitant risk factors for modern slavery, including demographic factors such as age, gender, and employment status – but also a variety of socio-economic and psychographic risk factors such as feelings about household income, life evaluation scores, and negative experienced affect – were identified in the analysis. Based on these risk factors, as well as country-level vulnerability scores, a hierarchical Bayes modelling approach was used to accurately predict the forced labour and forced marriage status of individuals and the average prevalence of modern slavery at the country level.

Average weighted predicted probabilities were then calculated for forced labour and forced marriage using this best-fitting predictive model.22 The process to arrive at estimated prevalence of modern slavery was undertaken in several steps, as follows:

  1. Disaggregate regional-level estimates of modern slavery from The Global Estimates into 12 homogeneous subregions (11 broad ILO sub-regions, plus split Southeast Asia & Pacific).
  2. Calculate subregional level prevalence of modern slavery for each subregion (for example, South Asia = 0.77%).
  3. Impute risk factors for 20 countries that are missing GWP data, as an average over several multiple imputation approaches (hot deck, amelia, glm, random forests).
  4. Adjust country risk by country of exploitation. The basic premise is to take a region such as “Receiving- Southeast Asia,” with Singapore, Malaysia, and Thailand, and apply an adjustment factor equal to the ratio of victims identified in the national surveys in Singapore, Malaysia, and Thailand to total exploited victims in the region. If no national surveys were conducted in a given region, we estimate that the prevalence is equal to modelled risk multiplied by population. This is calculated using the following steps:
    1. Calculate number of victims identified by the country surveys who are exploited in a different country.
    2. Code countries as either “net sending” or “net receiving (see Table 3). This was done on the basis of available information from GSI 2016, UNODC Global Report on TIP 2016, US TIP 2017, GRETA, and ILO Global Estimates of Migrant Workers.23Sending versus receiving status was coded by two independent coders. If there was agreement, the common code was maintained. However, if conflicting codes were allocated, the decisions were jointly reviewed and often resolved. In the event that no agreement was reached, a third party would be consulted.
    3. Calculate aggregate number of victims by place of exploitation in sending and receiving areas.
    4. Adjust down the risk score of sending regions that have a lower number of victims being exploited in country.
    5. Adjust up the risk score of receiving regions that have a higher number of victims being exploited in country. For example, the risk in “receiving” Southeast Asia (Singapore, Thailand, Malaysia) is adjusted up by a factor of 1.58, while “sending” Southeast Asia is adjusted down by a factor of 0.94.
  5. Taking adjusted country risks, estimate prevalence in a country based on the regional prevalence and the distance between the adjusted country risk and the weighted average regional risk score, following these steps:
    1. Normalise the adjusted and imputed country risk scores to a 1-100 range, with 1=min risk, 100= max risk.
    2. Multiply the normalised risk score by the country population.
    3. Calculate regional prevalence by dividing aggregates for total modern slavery (excluding state-imposed forced labour) over total population.
    4. Calculate average normalised regional score by dividing the sum of normalised risk scores by the country population.
    5. Calculate country prevalence by multiplying the regional average by the ratio of normalised country risk score over the average normalised regional score.
  6. To simplify, since normalised modern slavery risk in Afghanistan (39.1) is 2.89 times higher than the average risk in the South Asia region (13.6), we estimate that prevalence in Afghanistan is 2.89 times greater than the regional average, or about 2.2 percent.

    Only one exception is made, for Mauritania, where the survey estimate (2.1 percent) is used rather than the modelled risk score due to the distinct nature of slavery in the country. The practice is entrenched in Mauritanian society with slave status being inherited and deeply rooted in social castes and the wider social system. Those owned by masters often have no freedom to own land and cannot claim dowries from their marriages nor inherit property or possessions from their families.24  Given the evidence available that supports the higher survey estimate, that estimate is take from Mauritania and other countries in Sub-Saharan Africa are adjusted down to ensure totals are aligned with the Global Estimates of Modern Slavery.
  7. A final calculation is performed to incorporate existing estimates derived from multiple systems estimation (MSE) in the Europe and Central Asia region. The predictive models are built on information from countries where the World Poll, including the modern slavery module, was conducted face-to-face. Countries where the World Poll is only implemented using computer-assisted telephone interviewing (CATI) have zero chance of selection for a modern slavery survey, meaning that, at present, we are not able to test how the risk factors will behave in CATI countries. Despite this, there is also no evidence to suggest that the risk factors will not act in the same way, for example, being female is very likely to remain a risk factor for forced marriage and poverty a risk factor for forced labour.

    Further, MSE has emerged as a suitable methodology for estimation in countries where surveys are not used and the methodology has been endorsed by several governments and international organisations. Several considerations precluded the use of MSE-estimates alone, notably, (i) the methodology is at an early stage of use in the modern slavery space with several refinements underway, and (ii) some forms of modern slavery, for example, forced marriage are at present unlikely to be captured in administrative records meaning that MSE alone would lead to an underestimate.

    While survey-based estimates are subject to a high level of uncertainty due to sampling and non-sampling errors, the two available MSE estimates employed different approaches and therefore show large variability across countries with similar risk profiles. In light of the considerations set out above, the average of the model-derived prevalence estimates and MSE-based prevalence estimates for the United Kingdom 25and The Netherlands26were set as anchor points within the region. This was applied as follows:
    1. Countries within the Europe and Central Asia subregions were divided into ‘MSE’ and ‘non-MSE’ sub-regions. In practice this aligns with CATI vs. F2F countries in the Gallup World Poll. For example, both Spain and Greece are in “Southern Europe”, but Spain is CATI, and hence its estimate is based on extrapolation from MSE, while Greece is F2F, and hence its estimate is based on non-MSE extrapolation (more below on each type).
    2. Set anchor points and extrapolate to other MSE countries: (i) Average prevalence estimate for the UK was set as the anchor point for Northern Europe (MSE sub-region, i.e. excluding Baltic states) and (ii) the average prevalence estimate for the Netherlands was set as the anchor point for Western Europe (all countries) and Southern Europe (MSE sub-region).
    3. The corresponding anchor point was then extrapolated to other countries in the region based on ratio of risk in the anchor to risk in the extrapolation country. For example, if average prevalence in the UK is 0.20% and modelled risk is 0.775, we estimate that prevalence in Sweden is given by the ratio of its risk to the UK’s risk, multiplied by the UK prevalence, or (0.645/0.775)*0.20%= 0.17%.
    4. Adjust the prevalence of non-MSE countries in Europe and Central Asia to ensure the total aligns with the Global Estimate. The number of victims from the Global Estimates who are unaccounted for in Europe and Central Asia following the MSE adjustment were calculated, then prevalence in non-MSE countries was calculated proportional to the risk of each country relative to the non-MSE population adjusted regional average risk.
Table 3Classification of countries as “net sending” vs “net receiving”
CountryNet sending/net receiving
AfghanistanSending
AlbaniaSending
AlgeriaReceiving
AngolaReceiving
ArgentinaReceiving
ArmeniaSending
AustraliaReceiving
AustriaReceiving
AzerbaijanSending
BahrainReceiving
BangladeshSending
BarbadosReceiving
BelarusSending
BelgiumReceiving
BeninSending
Bolivia, Plurinational State ofSending
Bosnia and HerzegovinaSending
BotswanaReceiving
BrazilReceiving
Brunei DarussalamReceiving
BulgariaSending
Burkina FasoSending
BurundiSending
CambodiaSending
CameroonSending
CanadaReceiving
Cape VerdeSending
Central African RepublicSending
ChadSending
ChileReceiving
ChinaReceiving
ColombiaSending
CongoSending
Congo, Democratic Republic of theSending
Costa RicaReceiving
Côte d'IvoireSending
CroatiaReceiving
CubaSending
CyprusReceiving
Czech RepublicReceiving
DenmarkReceiving
DjiboutiSending
Dominican RepublicReceiving
EcuadorReceiving
EgyptReceiving
El SalvadorSending
Equatorial GuineaSending
EritreaSending
EstoniaSending
EthiopiaSending
FinlandReceiving
FranceReceiving
GabonReceiving
GambiaSending
GeorgiaReceiving
GermanyReceiving
GhanaSending
GreeceReceiving
GuatemalaSending
GuineaSending
Guinea-BissauSending
GuyanaReceiving
HaitiSending
HondurasSending
Hong KongReceiving
HungarySending
IcelandReceiving
IndiaSending
IndonesiaSending
Iran, Islamic Republic ofReceiving
IraqSending
IrelandReceiving
IsraelReceiving
ItalyReceiving
JamaicaSending
JapanReceiving
JordanReceiving
KazakhstanReceiving
KenyaReceiving
Korea, Democratic People's Republic of (North Korea)Sending
Korea, Republic of (South Korea)Receiving
KosovoSending
KuwaitReceiving
KyrgyzstanSending
Lao People's Democratic RepublicSending
LatviaSending
LebanonReceiving
LesothoSending
LiberiaSending
LibyaReceiving
LithuaniaReceiving
LuxembourgReceiving
Macedonia, the former Yugoslav Republic ofReceiving
MadagascarSending
MalawiSending
MalaysiaReceiving
MaliSending
MauritaniaSending
MauritiusSending
MexicoSending
Moldova, Republic ofSending
MongoliaSending
MontenegroReceiving
MoroccoSending
MozambiqueSending
MyanmarSending
NamibiaReceiving
NepalSending
NetherlandsReceiving
New ZealandReceiving
NicaraguaSending
NigerSending
NigeriaSending
NorwayReceiving
OmanReceiving
PakistanReceiving
PanamaReceiving
Papua New GuineaSending
ParaguaySending
PeruReceiving
PhilippinesSending
PolandSending
PortugalReceiving
QatarReceiving
RomaniaSending
RussiaReceiving
RwandaSending
Saudi ArabiaReceiving
SenegalSending
SerbiaSending
Sierra LeoneSending
SingaporeReceiving
SlovakiaSending
SloveniaReceiving
SomaliaSending
South AfricaReceiving
South SudanSending
SpainReceiving
Sri LankaSending
SudanSending
SurinameReceiving
SwazilandSending
SwedenReceiving
SwitzerlandReceiving
Syrian Arab RepublicSending
TaiwanReceiving
TajikistanSending
Tanzania, United Republic ofSending
ThailandReceiving
Timor-LesteSending
TogoSending
Trinidad and TobagoReceiving
TunisiaReceiving
TurkeyReceiving
TurkmenistanSending
UgandaSending
UkraineSending
United Arab EmiratesReceiving
United KingdomReceiving
United StatesReceiving
UruguaySending
UzbekistanSending
Venezuela, Bolivarian Republic ofReceiving
VietnamSending
YemenSending
ZambiaSending
ZimbabweSending

4. Final calculation of estimated prevalence, including state-imposed forced labour

The process outlined in steps 1 to 3 produces prevalence estimates for all forms of modern slavery except state-imposed forced labour. Given the nationally-specific manifestations of state-imposed forced labour where it does occur, it was excluded from the steps outlined above. The final step involves aggregating the estimate resulting from the process set out above, with the estimates of state-imposed forced labour. A final estimate of the prevalence of all forms of modern slavery is then calculated. The resulting estimates are presented in Table 4.

Table 4Estimated prevalence of modern slavery by country
RankCountryEstimated prevalence (victims per 1,000 population)Estimated absolute number of victimsPopulation

1

Korea, Democratic People's Republic of (North Korea)**

104.6

2,640,000

25,244,000

2

Eritrea

93.0

451,000

4,847,000

3

Burundi

40.0

408,000

10,199,000

4

Central African Republic

22.3

101,000

4,546,000

5

Afghanistan

22.2

749,000

33,736,000

6

Mauritania

21.4

90,000

4,182,000

7

South Sudan

20.5

243,000

11,882,000

8

Pakistan

16.8

3,186,000

189,381,000

9

Cambodia

16.8

261,000

15,518,000

10

Iran, Islamic Republic of

16.2

1,289,000

79,360,000

11

Somalia

15.5

216,000

13,908,000

12

Congo, Democratic Republic of the

13.7

1,045,000

76,197,000

13

Mongolia

12.3

37,000

2,977,000

14

Sudan

12.0

465,000

38,648,000

15

Chad

12.0

168,000

14,009,000

16

Rwanda

11.6

134,000

11,630,000

17

Turkmenistan**

11.2

62,000

5,565,000

18

Myanmar

11.0

575,000

52,404,000

19

Brunei Darussalam

10.9

5,000

418,000

20

Belarus

10.9

103,000

9,486,000

21

Papua New Guinea

10.3

81,000

7,920,000

22

Lao People's Democratic Republic

9.4

62,000

6,664,000

23

Thailand

8.9

610,000

68,658,000

24

Swaziland

8.8

12,000

1,319,000

25

Macedonia, the former Yugoslav Republic of

8.7

18,000

2,079,000

26

Congo

8.0

40,000

4,996,000

27

Greece

7.9

89,000

11,218,000

28

Guinea

7.8

94,000

12,092,000

29

Libya

7.7

48,000

6,235,000

30

Philippines

7.7

784,000

101,716,000

31

Timor-Leste

7.7

10,000

1,241,000

32

Nigeria

7.7

1,386,000

181,182,000

33

Uganda

7.6

304,000

40,145,000

34

Madagascar

7.5

182,000

24,234,000

35

Malawi

7.5

131,000

17,574,000

36

Guinea-Bissau

7.5

13,000

1,771,000

37

Liberia

7.4

33,000

4,500,000

38

Syrian Arab Republic*

7.3

136,000

18,735,000

39

Angola

7.2

199,000

27,859,000

40

Djibouti

7.1

7,000

927,000

41

Kenya

6.9

328,000

47,236,000

42

Malaysia

6.9

212,000

30,723,000

43

Albania

6.9

20,000

2,923,000

44

Cameroon

6.9

157,000

22,835,000

45

Togo

6.8

50,000

7,417,000

46

Niger

6.7

133,000

19,897,000

47

Zimbabwe

6.7

105,000

15,777,000

48

Turkey

6.5

509,000

78,271,000

49

Ukraine

6.4

286,000

44,658,000

50

Equatorial Guinea

6.4

7,000

1,175,000

51

Tanzania, United Republic of

6.2

336,000

53,880,000

52

Ethiopia

6.1

614,000

99,873,000

53

India

6.1

7,989,000

1,309,054,000

54

Croatia

6.0

25,000

4,236,000

55

Nepal

6.0

171,000

28,656,000

56

Côte d'Ivoire

5.9

137,000

23,108,000

57

Montenegro

5.9

4,000

628,000

58

Gambia

5.8

11,000

1,978,000

59

Lithuania

5.8

17,000

2,932,000

60

Zambia

5.7

92,000

16,101,000

61

Venezuela, Bolivarian Republic of

5.6

174,000

31,155,000

62

Haiti

5.6

59,000

10,711,000

63

Egypt

5.5

518,000

93,778,000

64

Russian Federation

5.5

794,000

143,888,000

65

Moldova, Republic of

5.5

22,000

4,066,000

66

Benin

5.5

58,000

10,576,000

67

Mozambique

5.4

152,000

28,011,000

68

Armenia

5.3

16,000

2,917,000

69

Uzbekistan**

5.2

160,000

30,976,000

70

Sierra Leone

5.0

36,000

7,237,000

71

Ghana

4.8

133,000

27,583,000

72

Iraq*

4.8

174,000

36,116,000

73

Gabon

4.8

9,000

1,930,000

74

Indonesia

4.7

1,220,000

258,162,000

75

Tajikistan**

4.5

39,000

8,549,000

76

Burkina Faso

4.5

82,000

18,111,000

77

Viet Nam

4.5

421,000

93,572,000

78

Bulgaria

4.5

32,000

7,177,000

79

Azerbaijan**

4.5

43,000

9,617,000

80

Georgia

4.3

17,000

3,952,000

81

Romania

4.3

86,000

19,877,000

82

Cyprus

4.2

5,000

1,161,000

83

Kazakhstan**

4.2

75,000

17,750,000

84

Lesotho

4.2

9,000

2,175,000

85

Kyrgyzstan**

4.1

24,000

5,865,000

86

Cape Verde

4.1

2,000

533,000

87

Dominican Republic

4.0

42,000

10,528,000

88

Kosovo

4.0

8,000

1,905,000

89

Latvia

3.9

8,000

1,993,000

90

Israel

3.9

31,000

8,065,000

91

Cuba

3.8

43,000

11,461,000

92

Bangladesh

3.7

592,000

161,201,000

93

Hungary

3.7

36,000

9,784,000

94

Estonia

3.6

5,000

1,315,000

95

Mali

3.6

62,000

17,468,000

96

Botswana

3.4

8,000

2,209,000

97

Singapore

3.4

19,000

5,535,000

98

Bosnia and Herzegovina

3.4

12,000

3,536,000

99

Honduras

3.4

30,000

8,961,000

100

Poland

3.4

128,000

38,265,000

101

Serbia

3.3

30,000

8,851,000

102

Namibia

3.3

8,000

2,426,000

103

Yemen*

3.1

85,000

26,916,000

104

Trinidad and Tobago

3.0

4,000

1,360,000

105

Slovakia

2.9

16,000

5,439,000

106

Guatemala

2.9

47,000

16,252,000

107

Nicaragua

2.9

18,000

6,082,000

108

Czech Republic

2.9

31,000

10,604,000

109

Senegal

2.9

43,000

14,977,000

110

South Africa

2.8

155,000

55,291,000

111

China**

2.8

3,864,000

1,397,029,000

112

Barbados

2.7

<1,000

284,000

113

Colombia

2.7

131,000

48,229,000

114

Mexico

2.7

341,000

125,891,000

115

Algeria

2.7

106,000

39,872,000

116

Guyana

2.6

2,000

769,000

117

Jamaica

2.6

7,000

2,872,000

118

Peru

2.6

80,000

31,377,000

119

El Salvador

2.5

16,000

6,312,000

120

Portugal

2.5

26,000

10,418,000

121

Morocco

2.4

85,000

34,803,000

122

Italy

2.4

145,000

59,504,000

123

Ecuador

2.4

39,000

16,144,000

124

Spain

2.3

105,000

46,398,000

125

Suriname

2.3

1,000

553,000

126

Tunisia

2.2

25,000

11,274,000

127

Slovenia

2.2

5,000

2,075,000

128

Oman

2.1

9,000

4,200,000

129

Bolivia, Plurinational State of

2.1

23,000

10,725,000

130

Sri Lanka

2.1

44,000

20,714,000

131

Iceland

2.1

<1,000

330,000

132

United Kingdom

2.1

136,000

65,397,000

133

Panama

2.1

8,000

3,969,000

134

Germany

2.0

167,000

81,708,000

135

Belgium

2.0

23,000

11,288,000

136

France

2.0

129,000

64,457,000

137

Korea, Republic of (South Korea)**

1.9

99,000

50,594,000

138

Saudi Arabia*

1.9

61,000

31,557,000

139

Bahrain*

1.9

3,000

1,372,000

140

Norway

1.8

9,000

5,200,000

141

Jordan*

1.8

17,000

9,159,000

142

Brazil

1.8

369,000

205,962,000

143

Netherlands

1.8

30,000

16,938,000

144

Austria

1.7

15,000

8,679,000

145

Lebanon*

1.7

10,000

5,851,000

146

Switzerland

1.7

14,000

8,320,000

147

Ireland

1.7

8,000

4,700,000

148

United Arab Emirates*

1.7

15,000

9,154,000

149

Finland

1.7

9,000

5,482,000

150

Denmark

1.6

9,000

5,689,000

151

Paraguay

1.6

11,000

6,639,000

152

Sweden

1.6

15,000

9,764,000

153

Qatar*

1.5

4,000

2,482,000

154

Luxembourg

1.5

<1,000

567,000

155

Kuwait*

1.5

6,000

3,936,000

156

Hong Kong, China**

1.4

10,000

7,246,000

157

Argentina

1.3

55,000

43,418,000

158

United States

1.3

403,000

319,929,000

159

Costa Rica

1.3

6,000

4,808,000

160

Uruguay

1.0

4,000

3,432,000

161

Mauritius

1.0

1,000

1,259,000

162

Chile

0.8

14,000

17,763,000

163

Australia

0.6

15,000

23,800,000

164

New Zealand

0.6

3,000

4,615,000

165

Taiwan, China**

0.5

12,000

23,486,000

166

Canada

0.5

17,000

35,950,000

167

Japan**

0.3

37,000

127,975,000

*Substantial gaps in data exist for the Arab States region and Gulf countries in particular. These gaps point to a significant underestimate of the extent of modern slavery in this region. As a result, the country-level estimates presented here are considered very conservative and should be interpreted cautiously.

**Substantial gaps in data exist for the Central and East Asia subregions where, with the exception of Mongolia, surveys cannot be conducted for reasons such as (i) survey is only delivered face-to-face, (ii) survey is delivered only in the main language which many migrant workers do not speak, or (iii) national authorities would not, or were unlikely to, consent to the module on modern slavery. Unlike several countries in Western Europe where no surveys were conducted, none of the countries in these subregions were identified as sites of exploitation by respondents in the 48 countries where surveys were implemented.

Data limitations

Limitations of the source data

As with all empirical research, there are some limitations of the data used to produce the Global Estimates of Modern Slavery, within which the findings of this Index should be interpreted.

First, the set of surveyed countries that was used to produce the 2017 Global Estimates of Modern Slavery was treated as a random sample of the world and the global figure was estimated directly from that (that is, without first calculating national estimates). However, the selection of the countries to be surveyed was not random as countries were selected for specific reasons, including:

  • Countries where prevalence is expected to be higher, thereby increasing the chance of identifying cases through a household survey. This leads to the selection of more "developing" and/or "source" countries than "developed" countries as a random sample survey is unlikely to identify cases in the latter;
  • Where the mode of delivery is through face to face surveys, as opposed to telephone interviews, and
  • To ensure regional representation so that the surveys could facilitate extrapolation.

Second, while regional estimates of prevalence of modern slavery were presented in the Global Estimates of Modern Slavery, critical gaps in available data were noted. These are particularly problematic in the Arab States, where only two national surveys were undertaken, none of which was in the Gulf Cooperation Council (GCC) countries despite the incidence of forced labour reported by different sources in such sectors as domestic work and construction in the GCC. Further, measurement of forced marriage among residents of countries within the region is particularly problematic where there are no surveys. Taken together, these gaps point to a significant underestimate of the extent of modern slavery in this region.

Similarly, it is usually not possible to survey in countries that are experiencing profound and current conflict, such as Syria, Iraq, Yemen, Libya, South Sudan, and parts of Nigeria and Pakistan. Yet it is known that conflict is a significant risk factor – the breakdown of the rule of law, the loss of social supports, and the disruption that occurs with conflict all increase risk of both forced labour and forced marriage. The lack of data from countries experiencing conflict means that modern slavery estimates in regions in which conflict countries are situated will understate the problem.

Similar coverage gaps exist for the Central and East Asia subregions where a larger number of surveys (only one in East Asia) were not able to be conducted for reasons that included: (i) mode of delivery was only by telephone, (ii) limited survey languages, (iii) consent of national authorities to the module on modern slavery was not given or was highly unlikely. Further, for countries in these subregions, none were identified as the country where exploitation took place by respondents in the 48 countries where surveys were implemented. As a result of these gaps, the estimates for countries within these subregions are likely to be conservative.

Limitations of the risk modelling

This analysis is not without the limitations inherent to any cross-sectional research endeavour. Our selection of variables is driven by both theoretical and statistical considerations, but unfortunately the field of modern slavery lacks a unifying causal theory that can be used to inform variable selection. Finally, we have a limited sample size of confirmed individual cases, which limits the extent to which we can expand our predictive models and enhance the accuracy of our predictions. Further surveys will lead to an increase in our sample, thereby enabling the study of more complex effects and refinement of the modelling.

Comparability with previous estimates

Due to substantial differences in scope, methodologies, regional groupings, and expanded data sources, the 2018 Global Slavery Index is not directly comparable to the previous edition. These differences are due to the joint development of the Global Estimates of Modern Slavery and, accordingly, the changes in the estimated number of victims at the national level cannot be interpreted against the previous Global Slavery Index. It is important to note the key differences in how the Global Estimates, the 2018 Global Slavery Index national estimates, and the 2016 Global Slavery Iindex estimates were calculated, these include:

  • What we count: In the 2016 Global Slavery Index we identified gaps in the measurement of children across all forms of modern slavery and adults in forced sexual exploitation. These gaps were addressed when developing the methodology for the Global Estimates, which drew on both survey and administrative data from IOM to calculate sub-estimates for forced sexual exploitation and the forced labour exploitation of children. In addition, a more systematic approach to the measurement of state-imposed forced labour was adopted for the Global Estimates and is used here.

    The 2018 Global Slavery Index represents a “stock” estimate; that is, people in slavery on any given day in 2016.
  • Where we count, where exploitation happens: The 2016 Index had too few survey countries to consistently count victims where they were exploited, which is not the case in the 2017 Global Estimates, which are based on a much larger number of survey countries. This change had the impact of increasing the number of victims counted in developed countries, with the exception of the Arab States. As noted previously, measures in that region are hampered by insufficient data.
  • How we measure: While nationally representative surveys remain central to the process, the collaboration on a global estimate necessitated a change from the “bottom-up” approach of first calculating national estimates and then aggregating to a global total.

In the 2017 Global Estimates, the countries surveyed were treated as a random sample of the entire world and global and regional totals were estimated directly from that without first calculating national estimates. In the 2018 Index, national prevalence is calculated on the basis of a predictive model that takes individual and country-level risk factors into account. The results are then weighted such that they aggregate to regional totals from the Global Estimates of Modern Slavery.

Footnotes

1The countries in which forced labour or forced marriage was reported to have occurred by survey respondents were: Afghanistan, Algeria, Argentina, Armenia, Bahrain, Bangladesh, Belgium, Bolivia, Botswana, Brazil, Cambodia, Cameroon, Central African Republic, Chile, Columbia, Cyprus, Czech Republic, Dominican Republic, Democratic Republic of the Congo, Egypt, Ethiopia, France, Georgia, Germany, Ghana, Greece, Guatemala, Haiti, Honduras, Hungary, India, Indonesia, Iraq, Italy, Jordan, Kenya, Latvia, Lebanon, Libya, Malawi, Malaysia, Maldives, Mauritania, Mexico, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Nepal, Netherlands, Nigeria, Oman, Pakistan, Philippines, Poland, Qatar, Romania, Russia, Rwanda, Saudi Arabia, Serbia, Singapore, Somalia, South Africa, Spain, Sri Lanka, Sudan, Tanzania, Thailand, Tunisia, UAE, Uganda, UK, Ukraine, US, Venezuela, Vietnam, and Zimbabwe.
2International Labour Office (ILO) & Walk Free Foundation 2017, Methodology of the global estimates of modern slavery: Forced labour and forced marriage, ILO. Available from: http://www.ilo.org/global/topics/forced-labour/ publications/WCMS_586127/lang--en/index.htm. [10 February 2017]
3Used by large international organizations such as the World Bank, the Organization for Economic Co-operation and Development (OECD), the Food and Agriculture Organization (FAO), and the International Labour Organization, the Gallup World Poll continually surveys in more than 160 countries, representing more than 99 percent of the world’s adult population.
4The questionnaire is translated into the major languages of each country, and interviews are conducted by highly trained enumerators from each country. All face-to-face interviews take place at a person’s home, which can be defined as any dwelling with a cooking facility. A detailed description of the World Poll Methodology is available at: http://www.gallup.com/178667/gallup-world-poll-work.aspx.
5The target population is the entire civilian, non-institutionalised population, aged 15 and older. Samples are probability-based and nationally representative and, with the only exception being areas that are scarcely populated or present a threat to the safety of interviewers.
6Joudo Larsen J, & Diego-Rosell P 2017, 2017 Insight series: Using surveys to measure modern slavery, Walk Free Foundation. Available from: https://s3-ap-southeast-2.amazonaws.com/walkfreefoundation.org-assets/content/uploads/2017/12/01171017/02-Insight-Series-171201_FNL.pdf. [1 March 2018].
7Analyses were conducted by Gallup Inc and Walk Free Foundation and are described in full in a forthcoming paper: Diego-Rosell P & Joudo Larsen J (Forthcoming). Modelling the risk of modern slavery.
8For the remaining 20 countries where no GWP data was available, risk scores were imputed as an average over several multiple imputation approaches.
9All data was collected by Gallup Inc., with most surveys implemented through the Gallup World Poll (GWP), a global research project conducting nationally representative surveys annually since 2005 in more than 160 countries and more than 140 languages. In most of the developing world, GWP surveys are conducted using in-person interviewing and an area sampling frame design. In the developed world, random-digit-dialling or a nationally representative list of phone numbers is used, generally including landline and mobile phones stratified by region. All surveys, either telephone or face-to-face, are probability based and nationally representative of the resident non-institutionalize population aged 15 and older. See http://www.gallup.com/178667/gallup-world-poll-work.aspx for further methodological details.
10The working definition of forced marriage for this purpose deviates in one key aspect from the definition of forced marriage used in the Walk Free Foundation’s  Global Slavery Index (2016) and the International Labour Organization and Walk Free Foundation Global Estimates of Modern Slavery (2017). Our definition does not take into account whether the forced marriage occurred in the five years preceding the survey or took place prior to the reference period but the victim reported their marital status as “married” during the reference period. The exclusion of these clauses is due to the limited sample size available for the stricter definition used by the International Labour Organization and the Walk Free Foundation (see “Statistical Methods” section).
11See Part A of this Appendix for a list of the variables from the Vulnerability Model.
12For example, variables regarding governance issues are not collected in countries such as China, the US and most of the countries in the MENA region, and so the inclusion of these variables in the model effectively excludes a large portion of the population.
13As most variables in the Gallup World Poll are collected at the respondent level, the estimation approach is at the respondent level.
14All estimations take into account the multi-stage clustered nature of the sample, including the effect of sampling design in all variance estimations using the Stata ‘svy’ package with linearization via Taylor series.
15Six sets of projection sampling weights were computed to compensate for the different sample sizes available for each model. Each weight was computed by rebasing the original post-stratification individual weights on the base and extended samples so that the effective samples were projected to the adult population in each country.
16Log-Likelihood Full Model, Wald test, AIC, BIC, Pseudo R2, Nagelkerke R2, AUC (training), AUC (validation).
17Because our data deal with rare events that in a frequentist approach may lead to singularities in matrix inversions, we adopt a Bayesian approach. Besides the computational advantages, a Bayesian approach also allows us to incorporate basic prior knowledge about the prevalence and distribution of modern slavery. Independent weakly informative priors for model intercepts and regression coefficients, using a t density function with 7 degrees of freedom and scale 2.5, based on recommendations from Liu (2004) were assigned. Posterior predictive distributions are sampled using Hamiltonian MCMC, with 3 Markov chains and 1,000 iterations.
18via Stan (rstanarm package, see Gabry & Goodrich, 2016).
19The distinction between “opportunity” and “necessity” entrepreneurs is well-understood in the development community, and the Global Entrepreneurship Monitor (GEM) has found a preponderance of necessity entrepreneurs in developing countries. See further: https://tcdata360.worldbank.org/indicators/aps.ea.nec?country=ESP&indicator=3118&viz=line_chart&years=2001,2015
20In order to compare the predictive accuracy of the different hierarchical Bayes models, we use leave-one-out cross-validation (LOO) to estimate pointwise out-of-sample prediction accuracy using the log-likelihood evaluated at the posterior simulations of the parameter values (Vehtari, Gelman, and Gabry, 2016).
21See Diego-Rosell P & Joudo Larsen J (forthcoming). Modelling the risk of modern slavery.
22Bayes hierarchical linear model with weak priors, 7 demographic predictors, 6 “base” variables from the Gallup World Poll, one country-level predictor (Weighted Vulnerability Score), country-level random intercepts, and a cross-level interaction between currently owning a business and region (South Asia vs rest).
23United Nations Office on Drugs and Crime (UNODC) 2016, Global Report on Trafficking in Persons, UNODC. Available from: https://www.unodc.org/documents/data-and-analysis/glotip/2016_Global_Report_on_Trafficking_in_Persons.pdf. [3 October 2017]. U.S. Department of State 2017, Trafficking in Persons report 2017. Available from: https://www.state.gov/j/tip/rls/tiprpt/2017/. [3 October 2017]. ILO 2015, ILO Global estimates of migrant workers and migrant domestic workers: results and methodology, ILO. Available from: http://www.ilo.org/global/topics/labour-migration/publications/WCMS_436343/lang--en/index.htm. [3 October 2017].
24Anti-Slavery International 2013, Thematic report on slavery in Mauritania for the UN Human Rights Committee, 107th session, 11–28 March 2013. Adoption of the List of Issues on the initial report of Mauritania, Anti-Slavery International, United Nations Office of the High Commissioner for Human Rights. Available from: https://www.ecoi.net/en/file/local/1066447/1930_1371813094_anti-slaveryinternational-mauritania-hrc107.pdf. [3 October 2016].
25Silverman, Bernard. Modern Slavery: an Application of Multiple Systems Estimation 2014, United Kingdom Home Office, Available from: https://www.gov.uk/government/publications/modern-slavery-an-application-of-multiple-systems-estimation. [18 May 2018]
26Van Dijk, Jan, and Van Der Heijden, Peter, G.M. Research Brief: Multiple Systems Estimation for Estimating the Number of Victims of Human Trafficking Across the World 2016, UNODC, Available from: https://www.unodc.org/documents/data-and-analysis/tip/TiPMSE.pdf. [18 May 2018]