Global Slavery Index

Methodology behind the Global Slavery Index

The Global Slavery Index was designed to shed light on the extent of modern slavery and level of vulnerability to modern slavery for 160 countries, as well as the actions taken by 176 governments to address these crimes and human rights violations. The methodology that enables these assessments is described in detail in three parts:

  1. Part A – Estimating prevalence.

  2. Part B – Measuring vulnerability.

  3. Part C – Assessing government action.

Appendix Part A: Estimating prevalence

The regional estimates presented in the 2021 Global Estimates of Modern Slavery (Global Estimates) produced by the International Labour Organization (ILO), Walk Free, and the International Organization for Migration (IOM), are used by Walk Free as the starting point for our independently produced national level estimates, which are presented in the 2023 Global Slavery Index (GSI).

This section summarises the methods used to produce the global and regional estimates presented in the 2021 Global Estimates and details the process to get from the regional estimates to national prevalence estimates. A detailed account of the methodology of the 2021 Global Estimates can be found in the Global Estimates of Modern Slavery report on the Walk Free website.

Global Estimates of Modern Slavery

The 2021 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 commercial sexual exploitation, and state-imposed forced labour.

Figure 1: Typology of modern slavery

As no single source provides data that is suitable for the measurement of all forms of modern slavery, a combined methodological approach was adopted for the 2021 GEMS, drawing on three sources of data to calculate the sub-estimates:

Nationally representative surveys

The estimates of forced labour in the private economy (excluding the sex industry) and forced marriage are derived from 68 nationally representative surveys on forced labour and forced marriage jointly conducted by ILO and Walk Free, and implemented through the Gallup World Poll.1 During the 2017-2021 period, a total of 77,914 respondents aged 15 years and over were interviewed either face-to-face or by telephone across the 68 survey countries. Surveys also collected information on forced labour and forced marriage among immediate family members (spouse, biological parents, children, and biological siblings) of respondents who were alive at the time of interview. As a result, the full network sample (respondents and their family network) was 628,598 persons and included children below 15 years of age as well as individuals 15 years old and over. Only cases of modern slavery that occurred between 2017 and 2021 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 68 countries, men, women, and children were reported to have been exploited in 129 countries (see Figure 2).

Figure 2: Countries of exploitation identified from the countries surveyed

The estimates of forced marriage also draw on national telephone surveys on forced marriage conducted in four countries in the Arab States region: Kuwait, Qatar, Saudi Arabia, and United Arab Emirates.2 During 2021, Arabic speaking residents aged 18 and over were recruited via random digit dialling with quotas based on the most recent national census used to achieve an approximately representative sample of 2,000 respondents per country. Respondents were asked about their own experiences of forced marriage and those of their immediate family members. The inclusion of these surveys brought the total network sample for forced marriage to 109,798 persons.

Estimates for countries in which national surveys were not conducted were produced through an imputation model.3 A weighted linear model on the prevalence rate of forced labour included geographic variables and a variable on the number of international migrant workers in the country as covariates. For forced marriage, the weighted linear model consisted of geographic variables only. Because no national surveys were available for the North America region, that region was assimilated with the Northern, Southern, and Western Europe region.

Counter Trafficking Data Collaborative (CTDC) dataset

Administrative data from IOM’s CTDC dataset4 was used in combination with the 68 survey datasets to estimate forced commercial sexual exploitation of adults and commercial sexual exploitation of children. The CTDC dataset comprises cases of trafficking for both sexual and forced labour exploitation and includes information on the profile of the survivors of human trafficking (e.g., age, gender, citizenship, country of birth) and on the trafficking situation (e.g., country of exploitation, type of exploitation, industry of exploitation, means of control). Statistical models were used to estimate the odds ratios of forced commercial sexual exploitation relative to forced labour exploitation separately for adults and children by gender using the CTDC database. These odds ratios were applied to the corresponding global estimates of forced labour exploitation of adults and children derived from the national surveys.

Comments from the ILO Committee of Experts on the Application of Conventions and Recommendations relating to state-imposed forced labour, and other secondary sources

Because 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, they 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.

The estimates are calculated as stock estimates; that is, the average number of persons in modern slavery at a given point in time during the 2017 to 2021 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.

From global and regional to national estimates

The national estimates presented in this GSI were calculated using individual and country-level risk factors of modern slavery. A risk model was used to generate average predicted probabilities of modern slavery by country. The regional totals in the 2021 Global Estimates were then apportioned based on each country’s average predicted probability of modern slavery. This process involved the following key steps:

Validating individual-level risk factors of modern slavery and predicting modern slavery at the individual-level

During the development of the 2018 GSI risk model to estimate the risk of modern slavery, a set of individual-level risk factors for forced labour and forced marriage were identified using national surveys that included questions on experiences of forced labour and forced marriage. This included using a series of statistical tests to identify relationships between instances of victimisation and other variables collected in the Gallup World Poll.5 In developing the 2023 GSI vulnerability model, the relationship between these individual-level risk factors and modern slavery was assessed. The individual-level risk factors are presented in Table 1. Tests confirmed the variables identified in 2018 remained significantly associated with forced labour and/or forced marriage and were therefore retained as variables in the 2023 risk model. Individual-level risk factors included age, gender, marital status, education level, urban/rural, employment, life evaluation, health, and ability to live on current income.

Table 1: Individual-level predictors of modern slavery
Variable Description FL/FM
Age Age (years) of primary respondent FL & FM
wp12 Residents 15+ in Household FL
wp14 Urban/Rural FL & FM
wp1219 Gender FL & FM
wp3117 Education Level FL & FM
wp1223 Marital Status FL & FM
emp_2010 Employment Status FL & FM
wp16 Life Today FL
wp2319 Feelings About Household Income FL
wp40 Not Enough Money for Food FL & FM
wp23 Health Problems FL

Multi-level models 

Multilevel models (MLM) were fitted to the data to enhance the predictions of the individual-level models and account for the hierarchical nature of these data. MLMs allow for the extrapolation of model results beyond the sample of 68 countries. Model coefficients were estimated using Bayesian hierarchical linear models with random intercepts, with weakly informative normal priors (mean = 0, SD = 2.5). Model coefficients were calculated separately for forced labour and forced marriage which were set as outcome variables. Model coefficients were then applied to survey data in each country to calculate individual-level risk. Individual risks were then aggregated into a country average, which was calculated using post-stratification weights. This followed the same approach that was used in 2018 GSI estimate calculations. This model produced unrealistically high risk of forced labour for several countries in the Latin America and the Caribbean and Eastern Europe subregions. As a result, the sample of surveys on which calculations of individual-level risk were based was limited to those where there was greater confidence based on alignment with the vulnerability model. As a result, 12 countries were removed and the models re-estimated. The final model is represented by equation 1:

Equation 1.


is the logit of the probability  of forced labour or forced marriage for each individual  in a country.

0 is a constant term (intercept).

1ij is a vector of individual-level demographic control variables with values varying for each individual within a country, and with unknown coefficients 1.

2ij is a vector of individual-level predictor variables , with values varying for each individual within a country, and with unknown coefficients 2.

3j  is the vulnerability score , with values varying for each country, and with an unknown coefficient 3.

j is a random coefficient that is allowed to vary by country.

ij is an individual error term.

Model Performance 

The overall accuracy of a model was measured by the area under the ROC curve (AUC), with an AUC of 1 representing a perfect model, and an AUC of .50 representing a model with no discrimination, as good as a random guess. The assessed AUC value of 0.75 indicates the model has acceptable discrimination, according to the Hosmer & Lemeshow (2013) heuristic.

Actual versus fitted prevalence plots for the 55 countries with modern slavery survey data showed a similar or better fit (Pearson’s r = 0.71). The random intercepts model showed a poorer fit with the actual values than the other two models (a perfect fit is exemplified by the red dotted line). Alternate models were examined for improved fit; for example, a model removing random intercepts but leaving country as a fixed effect. However, a random intercepts model with country level predictors provides the most comprehensive framework to undertake these inferences and was the model on which estimates were based.

Estimating prevalence and aligning with Global Estimates of Modern Slavery regional estimates

Individual predictions were aggregated into risk scores at the country level. For the 29 countries that were missing Gallup Word Poll data, risk factors were imputed as an average over several multiple imputation approaches (glm, amelia, multiple imputation by chained equations). Country risk scores were used to estimate country prevalence by apportioning the regional counts of modern slavery from the 2021 Global Estimates based on the risk of modern slavery in a country relative to the risk of other countries in the region. This was undertaken as follows:

First, country risk was adjusted by country of exploitation. The basic premise is to apply an adjustment factor equal to the ratio of victims identified in the national surveys in a subregion, to total exploited victims in the same subregion. If no national surveys were conducted in a given subregion, 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, by country of exploitation.

  2. Code countries as either “net sending” or “net receiving” based on income level of country and international migrant stock as a percentage of the total population (2020).6 Countries with an international migrant stock of less than 5 per cent of their population and grouped as either “low income,” “lower-middle income,” or “upper-middle income” were assigned as sending countries, as were “high income” countries with an international migrant stock of less than 10 per cent of their population.

  3. Calculate aggregate number of victims by place of exploitation in sending and receiving areas.

  4. Adjust down the risk score of regions that have a lower number of victims being exploited in country.

  5. Adjust up the risk score of regions that have a higher number of victims being exploited in country.

Second, 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.

To simplify, since normalised modern slavery risk in Afghanistan (60.1) is 2.28 times higher than the average risk in Asia and the Pacific region (26.3), we estimate that prevalence in Afghanistan is 2.28 times greater than the regional average.

Third, use the survey estimate for Mauritania (3.2 per cent) 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 had no freedom to own land and cannot claim dowries from their marriages nor inherit property or possessions from their families.7 When it abolished slavery in 1981, it was the last country to do so. Hereditary slavery continues to impact the Haratine and Afro-Mauritanian communities, with many survivors and their descendants dependent upon former “masters” because of limited skills and alternate economic opportunities. Given the evidence available that supports the higher survey estimate, that estimate is taken from Mauritania, and other countries in Sub-Saharan Africa are adjusted down to ensure totals are aligned with the Global Estimates of Modern Slavery.

To account for heightened risk experienced by migrants in the Arab States, we made an additional adjustment within the region based on national surveys of returned migrant workers in six origin countries across Asia and Africa.

Final calculation of estimated prevalence

The process outlined in steps 1 and 2 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 and a final adjustment based on publicly available data sources was made to account for this. A final estimate of the prevalence of all forms of modern slavery is then calculated. Additionally, estimates of the number of people in modern slavery for each country are calculated with reference to UN population estimates.8 The resulting estimates are presented in Table 2.

Data limitations

Limitations of the source data

As with all empirical research, there are some limitations of the data used to produce the 2021 Global Estimates, within which the findings of this Index should be interpreted.

First, the use of imputations models introduce some error and, as such, the national estimates should not be interpreted as hard findings.

Second, while the sample of countries on which the estimates were based in the 2021 estimates is larger than in previous editions, there remain some regions where the coverage is limited or lacking — this specifically concerns North America and the Arab States (however, regional forced marriage data gaps have been somewhat addressed). The sample of countries also omits some of the most populous countries, namely China, India, Pakistan. While surveys were conducted in India and Pakistan, fragility of the underlying data led to their exclusion. Fielding of these surveys during COVID-19 restrictions is likely to have had an impact on data quality. 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.

Third, COVID-19 affected the data collected from countries surveyed during 2020 and 2021, during which time data was collected via telephone rather than face-to-face, as had been done exclusively in previous rounds of data collection.

Fourth, the estimates of forced commercial sexual exploitation and forced labour of children were built on models of profiles of assisted cases of human trafficking in the CTDC dataset compiled by IOM and its partners. While the dataset provided rich data for global estimation, the regional distribution must be taken with caution.

Lastly, due to changes in some areas of the methodology and the expansion of the data coverage, the 2021 global and regional estimates on forced labour are not truly comparable with the estimates of the previous edition. The high variability of the estimates, especially, at the regional level also warrants caution when comparing between editions. Forced labour and forced marriage are not only difficult to capture in sample surveys and administrative sources, but also hard to measure through survey questionnaires and administrative reporting systems. The result is that the estimates have relatively high sampling errors and a low degree of replicability. Even without changes in methodology and data coverage, the estimates are likely to exhibit high variability making comparison over time somewhat hazardous.

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.

Table 2: Estimated prevalence and number of people in modern slavery, by country
Country Estimated prevalence of modern slavery (per 1,000 population) Estimated number of people in modern slavery Population
North Korea 104.6 2,696,000 25,779,000
Eritrea 90.3 320,000 3,546,000
Mauritania 32.0 149,000 4,650,000
Saudi Arabia 21.3 740,000 34,814,000
Türkiye 15.6 1,320,000 84,339,000
Tajikistan 14.0 133,000 9,538,000
United Arab Emirates 13.4 132,000 9,890,000
Russia 13.0 1,899,000 145,934,000
Afghanistan 13.0 505,000 38,928,000
Kuwait 13.0 55,000 4,271,000
Ukraine 12.8 559,000 43,734,000
North Macedonia 12.6 26,000 2,083,000
Myanmar 12.1 657,000 54,410,000
Turkmenistan 11.9 72,000 6,031,000
Albania 11.8 34,000 2,878,000
Belarus 11.3 107,000 9,449,000
Kazakhstan 11.1 208,000 18,777,000
Pakistan 10.6 2,349,000 220,892,000
Azerbaijan 10.6 107,000 10,139,000
Papua New Guinea 10.3 93,000 8,947,000
South Sudan 10.3 115,000 11,194,000
Bosnia and Herzegovina 10.1 33,000 3,281,000
Jordan 10.0 102,000 10,203,000
Venezuela 9.5 270,000 28,436,000
Moldova 9.5 38,000 4,034,000
Armenia 8.9 26,000 2,963,000
Syria 8.7 153,000 17,501,000
Kyrgyzstan 8.7 57,000 6,524,000
Bulgaria 8.5 59,000 6,948,000
Haiti 8.2 94,000 11,403,000
El Salvador 8.1 52,000 6,486,000
Cyprus 8.0 10,000 1,207,000
Kosovo 8.0 14,000
India 8.0 11,050,000 1,380,004,000
Republic of the Congo 8.0 44,000 5,518,000
Philippines 7.8 859,000 109,581,000
Guatemala 7.8 140,000 17,916,000
Nigeria 7.8 1,611,000 206,140,000
Equatorial Guinea 7.8 11,000 1,403,000
Colombia 7.8 397,000 50,883,000
Georgia 7.8 31,000 3,989,000
Slovakia 7.7 42,000 5,460,000
Ecuador 7.6 135,000 17,643,000
Gabon 7.6 17,000 2,226,000
Lebanon 7.6 51,650 6,825,000
Romania 7.5 145,000 19,238,000
Burundi 7.5 89,000 11,891,000
Uzbekistan 7.4 249,000 33,469,000
Nicaragua 7.3 49,000 6,625,000
Côte d’Ivoire 7.3 193,000 26,378,000
Jamaica 7.3 22,000 2,961,000
Bolivia 7.2 83,000 11,673,000
Djibouti 7.1 7,000 988,000
Iran 7.1 597,000 83,993,000
Peru 7.1 234,000 32,972,000
Bangladesh 7.1 1,162,000 164,689,000
Serbia 7.0 61,000 8,737,000
Honduras 7.0 69,000 9,905,000
Libya 6.8 47,000 6,871,000
Qatar 6.8 20,000 2,881,000
Bahrain 6.7 11,000 1,702,000
Indonesia 6.7 1,833,000 273,524,000
Dominican Republic 6.6 72,000 10,848,000
Mexico 6.6 850,000 128,933,000
Hungary 6.6 63,000 9,660,000
Gambia 6.5 16,000 2,417,000
Oman 6.5 33,000 5,107,000
Sri Lanka 6.5 139,000 21,413,000
Paraguay 6.4 46,000 7,133,000
Greece 6.4 66,000 10,423,000
Ethiopia 6.3 727,000 114,964,000
Malaysia 6.3 202,000 32,366,000
Somalia 6.2 98,000 15,893,000
Lithuania 6.1 17,000 2,722,000
Timor-Leste 6.1 8,000 1,318,000
Yemen 6.0 180,000 29,826,000
Chad 5.9 97,000 16,426,000
Cameroon 5.8 155,000 26,546,000
Thailand 5.7 401,000 69,800,000
Poland 5.5 209,000 37,847,000
Iraq 5.5 221,000 40,223,000
Cuba 5.4 61,000 11,327,000
Central African Republic 5.2 25,000 4,830,000
Croatia 5.2 22,000 4,105,000
Mali 5.2 106,000 20,251,000
Lao PDR 5.2 38,000 7,276,000
Zambia 5.1 94,000 18,384,000
Kenya 5.0 269,000 53,771,000
Cambodia 5.0 83,000 16,719,000
Zimbabwe 5.0 74,000 14,863,000
Brazil 5.0 1,053,000 212,559,000
Malawi 4.9 93,000 19,130,000
Trinidad and Tobago 4.7 7,000 1,399,000
Panama 4.7 20,000 4,315,000
Niger 4.6 112,000 24,207,000
Madagascar 4.6 127,000 27,691,000
Democratic Republic of the Congo 4.5 407,000 89,561,000
Guinea-Bissau 4.5 9,000 1,968,000
Slovenia 4.4 9,000 2,079,000
Egypt 4.3 442,000 102,334,000
Rwanda 4.3 55,000 12,952,000
Czechia 4.2 45,000 10,709,000
Guyana 4.2 3,000 787,000
Argentina 4.2 189,000 45,196,000
Uganda 4.2 190,000 45,741,000
Angola 4.1 136,000 32,866,000
Estonia 4.1 5,000 1,327,000
Viet Nam 4.1 396,000 97,339,000
Mongolia 4.0 13,000 3,278,000
Guinea 4.0 53,000 13,133,000
China 4.0 5,771,000 1,439,324,000
Sudan 4.0 174,000 43,849,000
Portugal 3.8 39,000 10,197,000
Israel 3.8 33,000 8,656,000
Burkina Faso 3.7 77,000 20,903,000
Eswatini 3.6 4,000 1,160,000
South Korea 3.5 180,000 51,269,000
Sierra Leone 3.4 27,000 7,977,000
Latvia 3.4 6,000 1,886,000
Togo 3.3 28,000 8,279,000
Nepal 3.3 97,000 29,137,000
United States of America 3.3 1,091,000 331,003,000
Italy 3.3 197,000 60,462,000
Costa Rica 3.2 16,000 5,094,000
Chile 3.2 61,000 19,116,000
Liberia 3.1 16,000 5,058,000
Benin 3.0 37,000 12,123,000
Mozambique 3.0 93,000 31,255,000
Senegal 2.9 49,000 16,744,000
Ghana 2.9 91,000 31,073,000
Tanzania 2.9 171,000 59,734,000
Hong Kong 2.8 21,000 7,497,000
South Africa 2.7 158,000 59,309,000
Namibia 2.4 6,000 2,541,000
Tunisia 2.3 27,000 11,819,000
Spain 2.3 108,000 46,755,000
Morocco 2.3 85,000 36,911,000
Singapore 2.1 12,000 5,850,000
France 2.1 135,000 65,274,000
Algeria 1.9 84,000 43,851,000
Uruguay 1.9 7,000 3,474,000
Austria 1.9 17,000 9,006,000
Botswana 1.8 4,000 2,352,000
Canada 1.8 69,000 37,742,000
United Kingdom 1.8 122,000 67,886,000
Taiwan 1.7 40,000 23,817,000
Lesotho 1.6 4,000 2,142,000
New Zealand 1.6 8,000 4,822,000
Australia 1.6 41,000 25,500,000
Mauritius 1.5 2,000 1,272,000
Finland 1.4 8,000 5,541,000
Japan 1.1 144,000 126,476,000
Ireland 1.1 5,000 4,938,000
Belgium 1.0 11,000 11,590,000
Denmark 0.6 4,000 5,792,000
Sweden 0.6 6,000 10,099,000
Netherlands 0.6 10,000 17,135,000
Germany 0.6 47,000 83,784,000
Norway 0.5 3,000 5,421,000
Switzerland 0.5 4,000 8,655,000

Appendix Part B: Measuring vulnerability

Walk Free measures the extent to which a population is vulnerable to modern slavery across 160 countries. The vulnerability model has three main aims:

  • Inform prevalence estimation, contributing to the risk model which allows national estimates to be made at the country-level, including for countries where there is no national survey.

  • Identify and quantify individual, systemic, and structural factors that make people vulnerable to modern slavery, informing the allocation and direction of anti-modern slavery efforts.

  • Help to identify potential data “blind spots” where future research should be targeted.

The vulnerability model maps 23 risk variables across five major dimensions:

  1. Governance Issues

  2. Lack of Basic Needs

  3. Inequality

  4. Disenfranchised Groups

  5. Effects of Conflict

The following section provides an overview of the methodology of the vulnerability model.

Development of the vulnerability model

The vulnerability model methodology has evolved since the 2018 edition of the Global Slavery Index with the input from an independent Expert Working Group. During 2016 and 2017, the Expert Working Group provided feedback on addressing theoretical and empirical gaps, normalisation, and standardisation of the data, dealing with missing data, and weighting of the data. This advice has carried through to the methodology used to calculate the vulnerability model. Further detail on the feedback of the Expert Working Group can be found in the 2018 GSI methodology.

Theoretical framework

The vulnerability model is guided by human security and crime prevention theories. The human security theory was developed by the UN Development Programme to capture seven major areas of insecurity: economic, political, food, community, personal, health, and environment. The most basic shared characteristic of human security as a concept involves a focus on the safety and well-being of individuals regardless of their citizenship status or relationship to a nation state. Importantly, the field of human security allows us to situate our understanding of modern slavery — a complex crime that is both a cause and a symptom of many other global problems such as pandemics, environmental disasters, conflict, and financial crises — within the larger discourse on vulnerability and to ensure that we were not missing significant dimensions of vulnerability to modern slavery. The use of human security theory also emphasises the global importance of the Sustainable Development Goals and links our vulnerability theory and modelling exercises to the developing global discussion on common metrics and goals for international development. Finally, this approach allows for the inclusion and exclusion of variables to be grounded in theory.

Review of the 2018 vulnerability model

Developing the 2018 vulnerability model involved selection of variables based on the human security theory that were published and updated regularly, were transparent about their methodology and source of the data and were the product of a methodology that did not suffer significant limitations that would impact the reliability of the data. A total of 35 variables were collated and, where necessary, normalised to a 1-100 scale and inverted so that higher numbers represented greater vulnerability. The 35 variables were then tested for collinearity, with any variables with a variance inflation factor above 10 and tolerance score below 0.1 dropped from the model. Principal factor analysis was then performed on the 24 variables which were retained. This reduced the number of variables to 23 and grouped them into five factors. The next step involved conceptualising the factors as distinct dimensions based on the final factor loadings and focused on risk of modern slavery. Imputation was used where threshold levels of missing data were met, and subregional averages imputed. Vulnerability scores were finally weighted by eigenvalues to give more weight to dimensions that have the most explanatory power in our overall vulnerability score. Quality assurance checks were performed on data transcription and calculations. A detailed description of the 2018 methodology can be found in Appendix 1 of the 2018 Global Slavery Index.

Data collation

The 23 variables used in the 2018 vulnerability model were assessed for updated data and changes to methodology. Where updated data was available, the most recent version was used, as of 31 December 2021. Six variables were discontinued or covered too few countries. Substitute variables were identified through a literature search and selected based on theoretical and empirical similarity to maintain comparability to the 2018 model. The discontinued variables and their substitutes are listed in Table 3. Table 4 presents the placement of variables within dimensions in the vulnerability model.

Table 3: Variables replaced with substitutes in the 2023 vulnerability model
Discontinued variable from 2018 vulnerability model Variable substituted in the 2023 vulnerability model 
Disabled rights, Gallup World Poll Protection from workplace harassment based on disability, World Policy Analysis Centre
Same sex rights, Gallup World Poll LGBTI Acceptance Index, UCLA School of Law
Judicial confidence, Gallup World Poll Law enforcement reliability (Global Competitiveness Report), World Economic Forum
Acceptance minorities, Gallup World Poll Social Group Equality, IDEA Global State of Democracy Indices
Acceptance immigrants, Gallup World Poll Employers prioritise nationals, World Values Survey
Alt political rights (Polity IV Dataset), Center for Systematic Peace Political rights, Freedom House
Table 4: 2023 vulnerability model with new variables indicated in italics
Dimension Variables
Governance Issues Political instability
Government response
Women’s physical security
Political rights
Regulatory quality
Disability based workplace harassment
Weapons access
Lack of Basic Needs Cell phone users
Social safety net
Ability to borrow money
Access to clean water
Inequality Emergency funds
Violent crime
Gini coefficient
Law enforcement reliability
Disenfranchised Groups Employers prioritise nationals
Social group equality
LGBTI acceptance
Effects of Conflict Internally displaced persons
Impact of terrorism
Internal conflicts fought

Impact of changes to variables on total scores and ranks

The substitution of discontinued variables with theoretically and empirically similar variables had the effect of increasing data availability for some countries which, in addition to methodological differences between the 2018 variable and its 2023 substitute, led to indicator and dimension level changes for some countries that do not necessarily reflect actual changes in vulnerability. Similarly, changes to methodology for three of the variables used in the 2018 vulnerability model saw a reduction in data availability for those variables and thus for some countries. This meant that there were some countries for which imputation was required whereas for others, increased data meant that averages were no longer imputed. This led to significant shifts in scores and ranks for some countries. For example, Papua New Guinea previously benefitted from imputed regional scores based on Australia and New Zealand, however more complete data in 2023 meant imputation was no longer required and led to an increase in overall vulnerability relative to 2018.

Data preparation and analysis

Updated data was cleaned and analysed using Microsoft Excel. The variable Internally Displaced Persons was calculated by summing four different datasets sourced from UNHCR: Internally Displaced Persons (IDPs), New Asylum Applications, Returned IDPs, and Returned Refugees. Internally Displaced Persons was then logarithmically transformed. Where necessary, variables were normalised to a 1-100 scale and inverted so that higher numbers represented greater vulnerability. For more information on normalisation and inversion of variables, please download the 2023 vulnerability data and codebook from the Global Slavery Index 2023 downloads page.

To address issues relating to missing data, the proportion of missing data was calculated at the dimension level for each country. For dimensions Inequality, Disenfranchised Groups, and Effects of Conflict, the threshold was 50 per cent. For dimensions Governance Issues and Lack of Basic Needs, the threshold for missing data was 51 per cent due to the larger number of total vulnerability variables included in the first two dimensions of vulnerability. Subregional averages were imputed for variables within a dimension where the proportion of missing data met the threshold for that dimension. Exceptions to this approach were made in order to maintain variability within the regions where some data may have been more limited and are described in Table 5. Dimension averages were then calculated for each dimension and weighted by the factor eigenvalue determined in development of the 2018 vulnerability model. Weighting by eigenvalue is performed to give more weight to factors that have the most explanatory power in our overall vulnerability score. That is, the factors are not equal, and eigenvalues indicate the amount of variance explained by a certain factor. Factors with greater eigenvalues explain more of the overall model and have thus been weighted accordingly in the overall score which was then calculated by averaging the eigenvalue-weighted dimension scores and was normalised so to scale from 1-100. As a final step, quality assurance checks were performed by external consultants to ensure that no errors were made in the transcription from original sources, nor in calculations made in Excel.

Table 5: Exceptions to the general approach to missing data
Country Dimension Proportion missing Treatment
Hong Kong Governance Issues 57% Maintained at 57%
Papua New Guinea Access to Basic Needs 75% Maintained at 75%
Inequality 67% Maintained at 67%
Burkina Faso Disenfranchised Groups 67% Partially imputed, reduced to 25% missing data
Guinea-Bissau Disenfranchised Groups 67% Partially imputed, reduced to 25% missing data
Syria Inequality 75% Maintained at 75%


The vulnerability model should be interpreted with the following limitations in mind:

  1. How well the vulnerability variables measure the real-world phenomena they are approximating in our model is limited by the need to select variables that cover most of our 160 countries, are published regularly, and explain clearly how these measures were developed.
  2. The lag in administrative data reflecting real world situations on the ground affected the quality of the vulnerability model, as even the most recent information may still not reflect current situations on the ground at this moment.
  3. Collinearity checks on our variables resulted in dropping several empirically redundant but conceptually important variables such as corruption, gender inequality, and environmental performance.
  4. Data imputation ensured that missing data points were supplemented with regionally specific trends and information on affected vulnerability variables. However, imputed values are unlikely to be the true values for those countries.
  5. Comparability between the 2023 vulnerability model and the 2018 vulnerability model is reduced by the substitution of discontinued variables with variables that are conceptually and empirically similar, which nonetheless differ in how they measure the real-life phenomena they approximate or measure slightly different phenomena.

Appendix Part C: Government responses

Governments play a critical role in the developing and implementing the laws, policies, and programs that are needed to prevent and respond to modern slavery. To complement the prevalence estimates and assessment of vulnerability, as with previous editions, the GSI includes an assessment of the actions governments are taking to respond to modern slavery.

This assessment is based on tracking government progress towards the achievement of five milestones:

  1. Survivors of slavery are identified and supported to exit and remain out of modern slavery.

  2. Criminal justice mechanisms function effectively to prevent modern slavery.

  3. Coordination occurs at the national and regional level and across borders, and governments are held to account for their response.

  4. Risk factors, such as attitudes, social systems, and institutions that enable modern slavery are addressed.

  5. Government and business stop sourcing goods and services produced by forced labour.

Theoretical framework: crime prevention theory

Our assessment of government responses is underpinned by situational crime prevention theory (Figure 3).9 This is based on the understanding that in order for the crime of modern slavery to occur, there needs to be a vulnerable victim, a motivated offender, and the absence of a capable guardian. It also recognises that crime does not happen in a vacuum and that broad contextual factors like state instability, discrimination, and disregard of human rights are critical to any government response.

Figure 3: Situational crime prevention theory

Therefore, to reduce the prevalence of modern slavery crimes, governments need to:

  • Reduce the opportunity for offenders to commit the crime.

  • Increase the risks of offending.

  • Decrease the vulnerability of potential victims.

  • Increase the capacity of law enforcement and other guardians.

  • Address the people or factors that stimulate or facilitate slavery.

Development of the conceptual framework

Using this theoretical framework as a starting point and drawing on the UN Trafficking Protocol10 and the European Convention on Action against Trafficking Beings,11 as well as literature on effective responses to modern slavery,12 we first devised (in 2014) a conceptual framework of what constitutes a strong response to modern slavery. We further refined this conceptual framework in consultation with our independent Expert Working Group and scholars in fields related to modern slavery, such as harmful traditional practices, health, social welfare, and migration. It was organised around the five milestones outlined above, which, if achieved, would ensure that governments are taking sufficient steps to address modern slavery, and underpinned our assessment of government responses in the 2014, 2016, and 2018 Global Slavery Indices.

Updating the conceptual framework

After the publication of the 2018 Global Slavery Index and following consultations with several stakeholders from the end of 2018 until data collection began in late 2020, further refinements were made to the conceptual framework in advance of the 2023 Global Slavery Index. After three editions of the GSI, we needed to keep apace of legislative and policy developments and consult with those with lived experience to review and strengthen our framework.

To this end, we held two workshops with our independent Expert Working Group to identify gaps, enhance the ability to measure outputs, and refine the ways issues such as harmful traditional practices were incorporated in the conceptual framework. The Expert Working Group also provided guidance on the processes of data collection and the weighting of milestones. Broadly, the rounds of review with the Expert Working Group culminated in the inclusion of indicators that assess concrete outputs as opposed to processes and of more indicators that focused on labour rights and child rights, as well as aligning our understanding of underlying risk factors and how to address these with international conventions and grey literature.

The need to include survivors in developing solutions to modern slavery has long been identified but rarely actioned.13 To systematically embed survivor perspectives in the assessment of government responses, Walk Free partnered with Survivor Alliance and NGO partners to convene more than 50 survivors in four Lived Experience Expert Groups in Ghana, India, Kenya, and the United Kingdom.

Survivors were asked to share their perspectives on the actions governments should take to end modern slavery, to rank the milestones from most to least important, and any other feedback. The vulnerability of women and children to modern slavery was emphasized across the Lived Experience Expert Groups, as well as the need for sensitisation campaigns to raise awareness of the risks of modern slavery, and for the provision of proactive social safety nets as part of prevention activities. Depending on location, the ranking of which milestone was most important differed: Milestone 1 (UK and Ghana), Milestone 4 (Kenya), and Milestone 5 (India) (see Table 6). Interestingly, despite several groups noting the need for laws to criminalise modern slavery and corruption, no group rated Milestone 2 as the most important part of a government’s response.

Table 6: Lived Experience Expert Groups ranking of government response milestones (ranked from most important (1) to least important (5))
Milestone 1: Survivors identified and supported Milestone 2: Criminal justice mechanisms  Milestone 3: National and regional level coordination  Milestone 4: Risk factors are addressed  Milestone 5: Government and business supply chains 
UK 1 3 2 3 4
India 4 3 5 2 1
Kenya 3 4 2 1 5
Ghana 1 5 2 3 4

Based on the consultations with the Lived Experience Expert Groups, indicators within the conceptual framework were edited to take into account survivor’s assessments of essential services. This included ensuring indicators better measure government action on registering recruitment agencies, ensuring that survivor compensation is delivered in practice, and ensuring that National Referral Mechanisms include providing survivors with the right to work. All survivor groups emphasised the importance of involving survivors in the development of policy and advocacy; in the UK and India, survivors suggested they should be incorporated in the data collection process to overcome data gaps for output indicators. Although this did not form part of the data collection process for the purposes of this report (discussed in greater detail below), Walk Free will consider how to further involve survivors in future rounds of data collection analysing government responses.

In cases where feedback received from the Expert Working Group or the Lived Experience Expert Groups was not incorporated into the conceptual framework, the proposed change either was already captured under another indicator within the framework, there was limited data available, or the suggested indicator was not conceptually consistent. For example, the suggestion to include a specific indicator to capture whether governments had publicly committed to using a survivor-centred approach was not included as it was considered preferable from a conceptual perspective to examine whether a government had streamlined a survivor-centred approach throughout their entire response, rather than simply making a public declaration. To test the robustness and availability of data for suggested indicators, we worked with Regenesys BPO, an offshore ethical sourcing business which employs survivors of modern slavery in the technology sector. From the review of indicators and this round of testing, we excluded 54 proposed indicators where suggestions were not conceptually consistent with the current framework or where data was not available consistently and at the level required for data collection. This included some of the indicators that measured “outputs” as opposed to existence of policies and processes. We will continue to review these suggested indicators to potentially incorporate in the next round of data collection. For examples of indicators excluded in this round, refer to Table 7.

Table 7: Example of suggested indicators which cannot be included in the current conceptual framework due to conceptual or data gaps
Proposed indicator number Description Outcome
Milestone 1, 1.2.7 There has been an increase in number of victims being identified through the hotline If yes to 2.1.1, there has been an increase in number of victims being identified through the hotline. The indicator is met if there has been an increase in number of victims being identified through the hotline
AND this must have occurred since 15 February 2014.
If no to 2.1.1, this indicator cannot be rated as 1 and must be rated as 0.
NOT there hasn’t been an increase in number of victims being identified through the hotline.
NOT the number of victims identified through the hotline decreased.
NOT there is no information regarding the number of victims being identified through the hotline.
Potentially included in the next round of data collection. Review data sources available to ensure that sufficient information exists for majority of countries.
Milestone 2, 2.2.5 Free translation services for victims exist in legislation Free translation services for victims are made explicit in legislation. This means that any type of free translation or interpretation services exists in legislation AND these are either specific to victims of modern slavery OR victims of modern slavery can access translation services, which are available for all victims of crime.
NOT free translation services for victims are not in legislation.
NOT translation services are available and free, but there is evidence to contradict this.
NOT translation services are available, but not free.
NOT free translation services are available only for citizens, not foreign victims.
NOT free translation services are available for certain types of crime (such as violent crime) and modern slavery is not specified.
NOT free translation services are offered by NGOs, but not made explicit in legislation.
Potentially included in the next round of data collection. Review data sources available to ensure that sufficient information exists for majority of countries in the database. To be further refined to be clearly differentiated from existing indicator analysing services available to victims within the courtroom (Milestone 2, 2.1.3).
Milestone 3, 1.1.9 Reports on the National Action Plan are used to inform budget allocations If yes to there is a National Action Plan, annual reports on the NAP inform where the money is spent for the coming yearANDreports to be released during the period 15 February 2019 to 31 August 2022If no to is there an action plan, then this indicator cannot be met. Potentially included in the next round of data collection. Review language to ensure the indicator is not tied to the National Action Plan and reports. Purpose of the update is to look for evidence that reviews of responses inform budget allocations. Covered partially by “Government routinely reviews its response to modern slavery” (Milestone 3, 1.2.2).
Milestone 4, 3.1.10 There are legislative and/or administrative measures to address environmental degradation and climate change SDG Target 13.2.1: Number of countries that have communicated the establishment or operationalisation of an integrated policy/ strategy/ plan which increases their ability to adapt to the adverse impacts of climate change and foster climate resilience and low greenhouse gas emissions development. Potentially included in the next round of data collection.
Milestone 5, 2.3.5 A policy framework exists for eradicating illicit money flows N/a Potentially included in the next round of data collection. Review language to ensure the indicator is clear and linked to a measurable standard such as anti-money laundering legislation. Review also to ensure conceptually consistent with the broader framework.

The Expert Working Group and the Lived Experience Expert Groups identified a greater need to reflect the rights of the child and our understanding of how governments should address underlying risk factors in the conceptual framework. As such, an internal review was conducted to map the conceptual framework to international rights instruments such as the United Nations Convention on the Rights of the Child and Guidelines on International Protection No. 8.14 The purpose of this mapping exercise was to ensure all relevant standards are reflected in the conceptual framework. The conceptual framework was also compared to areas of vulnerability identified by the Alliance 8.7 Migration Action Group15 and joint research by Walk Free and the International Organization for Migration16 to ensure sites of vulnerability, victim characteristics, and guardian responses are all reflected within the framework. Finally, the conceptual framework was mapped against the vulnerability model developed by Walk Free, as discussed in Part B, to ensure vulnerability was comprehensively represented in the conceptual framework.

The full updated conceptual framework can be found at the end of this section.

Process of assessing government responses to modern slavery

Throughout 2020 and 2021, data was collected for 176 countries for the government response component of the GSI. As in previous editions of the Global Slavery Index, this included data on Commonwealth countries17 in our government response database. Where data is limited18 in smaller Commonwealth nations, we have excluded these countries from our analysis. Due to the ongoing conflict and extreme disruption to government, we have excluded ratings for Afghanistan, South Sudan, Syria, and Yemen this year.19

The conceptual framework underpinning our assessment is arranged around the five milestones, which are then broken down into activities, which are further disaggregated into indicators. There are a total of 141 indicators in the conceptual framework and 42 activities. This included five additional indicators that were developed by the Commonwealth Human Rights Initiative (CHRI) in 2018 and are referenced in a report released by Walk Free and CHRI in 2020 that assessed the modern slavery responses of Commonwealth governments. These additional indicators are:

  1. ILO Forced Labour Convention, 1930 (No. 29) is ratified.

  2. Protocol Against the Smuggling of Migrants by Land, Sea and Air (2000) is ratified.

  3. NEGATIVE: Certain groups, such as migrant workers or domestic workers, are not able to unionise.

  4. NEGATIVE: There are lower primary school enrolment rates for girls.

  5. NEGATIVE: Homosexuality is criminalised.

The breakdown by milestone is described in Table 8 below.

Table 8: Breakdown of milestones into activities and indicators
Milestone No. of activities No of indicators
Survivors of slavery are identified and supported to exit and remain out of modern slavery 12 41
Criminal justice mechanisms function effectively to prevent modern slavery 13 40
Coordination occurs at the national and regional level and across borders, and governments are held to account for their response 4 13
Risk factors — such as attitudes, social systems, and institutions — that enable modern slavery are addressed 9 31
Government and business stop sourcing goods and services produced by forced labour 4 16
Total 42 141

Taiwan and Kosovo have 41 activities, not 42, as they are unable to ratify international conventions.

Data collection

Data is collected at the indicator level, where each indicator describes an element of an activity. Take Activity 1.2 under Milestone 1, “Comprehensive reporting mechanisms operate effectively” as an example, set out in Table 9.

Table 9: Activity 1.2, Milestone 1
Milestone 1: Survivors of slavery are identified and supported to exit and remain out of modern slavery
1.2 Comprehensive reporting mechanisms operate effectively
1.2.1 There is a reporting mechanism, such as a hotline
1.2.2 Reporting mechanism is available for men, women, and children
1.2.3 Reporting mechanism is free of charge to access
1.2.4 Reporting mechanism operates 24/7
1.2.5 The reporting mechanism operates in multiple languages or has capacity to provide immediate access to translators
1.2.6 Operators have had specialist training in modern slavery, call-handling and case referrals*
1.2.7 There has been an increase in number of victims being identified through the hotline*

* These indicators were added to the conceptual framework following consultation and review with the Expert Working Group and the Lived Experience Expert Groups. However, data was not collected against them due to limited available sources. These indicators are aspirational and will be retained in the conceptual framework for future rounds of data collection and analysis.

There are seven indicators under this activity, each of which determine the existence of the reporting mechanism, and how well it is operating. Desk research was conducted for five of these indicators and others in the conceptual framework by a team of 17 researchers and research assistants following a strict protocol. Through AnnieCannons, a non-profit that provides technology and software focused vocational training to survivors of modern slavery, and Regenesys BPO, 12 researchers were survivors of modern slavery. Specific protocols were developed on an indicator-level for these researchers to provide further technical support and to ensure consistency in data collection across the global research team. All protocols described both the types of reports and sources to be reviewed and what constitutes “relevant” information. The multilingual global team20 conducted research either by country or by indicator and saved these references in the government response database.21

These data points were then verified, as far as possible, by NGO contacts within each country. NGOs were given the opportunity to either respond via email, hold Zoom interviews, or complete a survey. Over 25 survey responses were received, and a further 51 NGOs responded to individual requests for information via email or Zoom calls.

Data is current as of 31st August 2022.


The 2023 scoring system, which is based on activities, has not been used in previous editions of the GSI. Activities are used to clearly communicate the actions a government is taking, or not taking, to eradicate modern slavery in its simplest form.

Of the 141 indicators in the conceptual framework against which data was collected, 125 are what we have called “positive indicators.” Put simply, these cover the actions the government is taking to implement each activity and ultimately each milestone. The indicators described under Table 8 above are all positive indicators. In some instances, they go beyond implementation to measure effectiveness of an activity (There has been an increase in number of victims being identified through the hotline in the above example).

These indicators are supplemented by 16 standardised “negative indicators,” which attempt to measure implementation of a particular activity (listed in Table 10). For example, if shelters exist for modern slavery victims, the negative indicator victims are held in shelters against their will and do not have a choice about whether or not to remain in a shelter would capture whether victims are detained and experience secondary victimisation despite the existence of these shelters. The negative indicators also cover broader factors, which, if conducted by governments, would increase the risk of human trafficking and child exploitation. These include state-sanctioned forced labour, high levels of government complicity, criminalisation of victims, deportation of potential victims, and policies that tie migrant workers to their employers.

Table 10: Example of negative implementation indicators, Activity 2.1, Milestone 1
Milestone 1: Survivors of slavery are identified and supported to exit and remain out of modern slavery
Activity 2.1: Emergency support is available for identified survivors Indicators:
2.1.1 Survivor support services are available for some suspected survivors of modern slavery (men, women, non-binary, and children where relevant)
2.1.2 NEGATIVE Suspected survivors are held in shelters against their will and do not have a choice about whether or not to remain in a shelter
2.1.3 Government contributes to the operational costs of the shelters and there are no significant resource gaps
2.1.4 Physical health services are provided to survivors of modern slavery
2.1.5 Mental health services are provided to victims of modern slavery
2.1.6 NEGATIVE Survivor support services are not available for all survivors of modern slavery
2.1.7 NEGATIVE No survivors have accessed the services or shelters

Once data had been collected and verified, each indicator was scored on a 0 to 1 scale. On this scale, 0 meant no information was identified or available, or information explicitly demonstrated that the government did not meet any indicators, and 1 meant that the indicator had been met. Negative indicators were scored on a 0 to -1 scale, where 0 meant no information was identified or available, or information explicitly demonstrated that the government did not meet any indicators, and -1 meant that the indicator had been met. As an advocacy tool, we have retained our rating where no information found is rated as “0.” However, we have identified indicators within the government response database that have consistently had no information found since 2014. In future rounds of data collection, we plan to prioritise testing these indicators to ascertain if no information is available, whereupon we will ultimately remove the indicators for future rounds of data collection. As part of the data collection that informed this assessment of government responses, an indicator that assessed whether evaluations of anti-slavery projects were provided to government officials to inform their future programming was removed from data collection due to consistent gaps in available information (Milestone 1, Indicator 3.3.3).

The data and ratings then underwent a series of quality assurance — first by country, where each country was reviewed against the rating descriptions to determine if ratings were sound. Secondly, following data collection being completed, each indicator was reviewed across all countries to check for consistency in the applied logic. Any final edits were then reviewed, and final edits made in the database.

The data was then imported into STATA, where indicators were grouped into activities and scored. Indicators are organised into 42 activities, which are given a score from -2 to 2, dependent on the number of indicators met and the nature of those indicators. Activity scores are outlined in Table 11.

Table 11: Scoring activities
Indicators Activity  Score
No positive indicators within an activity are met OR for negative rated activities (Table 5) no negative indicators are met Activity is not achieved
At least one positive indicator is met, OR all positive indicators are met and at least one negative indicator is met Activity is partly achieved 1
All positive indicators are met, and no negative indicators are met Activity is achieved 2
For negative rated activities only (see Table 5), the negative indicator is met Undermining the response to modern slavery -2

The three negative rated activities in Table 12 are subtracted from the overall score as they typically involve systemic issues that significantly undermine the veracity of the entire government response to modern slavery, rather than increasing vulnerability. For example, these systemic issues are represented by indicators relating to corruption and complicity, the use of state-imposed forced labour and when a government has identified no victims of modern slavery, which indicates that any systems in place to support survivors in the country are ineffective.

Table 12: 16 Negative indicators, and three negative activities in which the government undermine their own response to modern slavery
Negative rated indicators Negative rated activities
M1 1.5.1 There is evidence that victims of modern slavery have NOT been identified between 15 February 2019 and 31 August 2022. M1 Activity 1.5 Victims have not been identified
M1 2.1.2 Suspected survivors are held in shelters against their will and do not have a choice about whether or not to remain in a shelter
M1 2.1.6 Survivor support services are not available for all victims of modern slavery
M1 2.1.7 No survivors have accessed the services or shelters between 15 February 2019 and 31 August 2022.
M1 2.3.5 Foreign survivors are detained for immigration violations
M1 2.3.6 Foreign survivors are deported for immigration violations
M2 2.1.3 There is evidence that survivors of modern slavery have been treated as criminals for conduct that occurred while under control of criminals
M2 3.1.2 Units do not have necessary resources to be able to operate effectively
M2 3.2.4 Judicial punishments are NOT proportionate to severity of the crime and culpability of the offender.
M4 2.1.5 Certain groups, such as migrant workers or domestic workers, are not allowed to unionise
M4 2.2.4 There are laws or policies that prevent or make it difficult for workers to leave abusive employers without risk of loss of visa and deportation and/or security deposits
M4 3.1.3 There are lower primary school enrolment rates for girls
M4 3.1.9 Homosexuality is criminalised 
M4 3.3.1 Reports of individual officials’ complicity in modern slavery cases have not been investigated M4 Activity 3.3 Official complicity is not investigated
M4 3.4.3 Diplomatic staff are not investigated for alleged complicity in modern slavery cases or abuse of survivors
M4 4.1.1 State-sanctioned forced labour exists M4 Activity 4.1 Government places its population, or part of its population, in forced labour

Activity scores are summed to give a total score for each milestone. Milestone scores are then summed to give a total government response score out of 78.

Each activity is weighted equally so that a country can only obtain a total of 78 points, noting that the final score is presented as a percentage. This does lead to an implicit weighting of milestones, where the more activities in a milestone, the more weight it is given. Table 13 describes the milestone weightings below.

Table 13: Implicit weighting of each milestone
Milestone No. of activities Milestone score out of Percentage weight
Survivors of slavery are identified and supported to exit and remain out of modern slavery 12 22 28%
Criminal justice mechanisms function effectively to prevent modern slavery 13 26 34%
Coordination occurs at the national and regional level and across borders, and governments are held to account for their response 4 8 10%
Risk factors, such as attitudes, social systems and institutions that enable modern slavery are addressed 9 14 18%
Government and business stop sourcing goods and services produced by forced labour 4 8 10%
Total 42 78 100%


Collecting data for 141 indicators across 176 countries is a complicated undertaking. Access to data is limited for all indicators where information is not available publicly or available in languages spoken by the research team. The continued absence of Arabic speakers prevented verification with NGOs in countries where these are the primary languages spoken. Limits also remain in measuring the implementation of a response — while the negative indicators and NGO verification are the first steps in measuring this, more remains to be done in getting at the reality of what is occurring on the ground as opposed to what is reported publicly.

Comparability to previous assessments

The government response assessment is broadly comparable with previous iterations of the GSI (Table 14). However, due to the significant changes in both the method of analysis and the conceptual framework, this is limited to comparisons at the indicator rating level rather than comparisons against milestone or total scores.

In 2023, the method of calculating total scores for milestones was updated to better reflect a government’s commitment to completely achieving an activity. Percentage scores for milestones were calculated based on the achievement of activities rather than the number of met indicators as has been done in the past. While the data remains comparable at the indicator level, comparing milestone percentage scores between 2018 and 2023 will be indicative of both changes made to the scoring system and actual government improvement. When seeking government improvement, it is most accurate to compare indicator ratings between 2018 and 2023.

The comprehensive consultations and review of the 2018 conceptual framework led to the addition of 43 indicators and removal of 49 indicators across all five milestones in the conceptual framework. Additionally, some indicators have been moved to different activities in different milestones after a final review of the framework and the shift to activity-based scoring.

Table 14: 2018 rankings when considered under the 2023 framework
Most action in 2023 2023 score 2018 score in 2023 framework Least action in 2023 2023 score 2018 score in 2023 framework
United Kingdom 68 69 North Korea -3 -1
Australia 67 68 Eritrea 5 5
Netherlands 67 67 Iran 8 8
Portugal 67 65 Libya 10 12
United States 67 67 Somalia 18 17
Ireland 63 63
Norway 63 67
Spain 63 59
Sweden 63 64

Other changes at the indicator level involved edits to the existing indicators rather than removing or introducing new indicators: however, across milestones 1, 2, and 3 only 11 indicators were substantially changed. No substantial changes were made to indicators in either milestone 4 and 5. As a result of the additions, removals, and edits there was a related change in the categorisation of indicators at the activity level. Most changes were concentrated in Milestone 2 and were largely the result of regrouping the criminal justice indicators into activities responding to specific types of modern slavery or to support particular vulnerable groups. In 2018, all conventions were grouped into one activity and all criminal justice provisions into a separate activity. In 2023, we organised these by type of modern slavery, so all conventions and domestic legislation related to trafficking were grouped into one activity. We also split out the implementation in legislation versus implementation in practice related to access to justice mechanisms.

Table 15: Conceptual framework for measuring government responses

Methodology for identifying the highest value products at risk of forced labour imported by the G20

The world’s most developed countries are connected to modern slavery not only through exploitation occurring within their own borders but also through the goods they import. In the GSI, we identify the highest value products at risk of being produced by forced labour which are currently being imported into G20 countries. The G20 are the largest importers (and exporters) in the world, accounting for 75 per cent of global trade.22

As a first step we developed a list of products at risk of being produced by modern slavery. This was informed by high-risk countries and industries as well as recent suspected cases of forced labour identified in the production of these goods.23 We then compiled import data for all G20 countries for these products.

Identifying a list of imports at risk of modern slavery

Our starting point was the 2022 US Department of Labor list of goods produced by forced labour and child labour.24 The list was first filtered by “forced labour” to ensure that products suspected of being produced only by child labour were excluded. A simple country count of products was performed to determine a ranking: the product with the highest number of countries listed against it was ranked first, the product with the second highest numbers of countries against it was ranked second, and so on. This produced an initial list of 22 product/source country combinations at risk of modern slavery.

Table 16: Initial list of goods produced by forced labour as reported by the US Department of Labor
Ranking Product with risk of modern slavery Source countries
1 Bricks Afghanistan, Myanmar, Cambodia, China, India, Nepal, North Korea, Pakistan, Russia
2 Cotton Benin, Burkina Faso, China, Kazakhstan, Pakistan, Tajikistan, Turkmenistan
3 Garments Argentina, Brazil, China, India, Malaysia, Thailand, Viet Nam, Bangladesh
4 Cattle Bolivia, Brazil, Niger, Paraguay, South Sudan
5 Sugarcane Bolivia, Brazil, Myanmar, Dominican Republic, Pakistan
6 Gold Burkina Faso, Democratic Republic of the Congo, North Korea, Peru, Venezuela
7 Fish China, Ghana, Indonesia, Thailand, Taiwan
8 Timber Brazil, North Korea, Peru, Russia
9 Carpets India, Nepal, Pakistan
10 Coal China, North Korea, Pakistan
11 Rice Myanmar, India, Mali
12 Brazil Nuts/Chestnuts Bolivia, Peru
13 Cocoa Côte d’Ivoire, Nigeria
14 Diamonds Angola, Sierra Leone
15 Electronics China, Malaysia
16 Coffee Brazil, Côte d’Ivoire
17 Embellished textiles India, Nepal
18 Palm oil Indonesia, Malaysia
19 Shrimp Myanmar, Thailand
20 Stones India, Nepal
21 Textiles China, North Korea
22 Thread/Yarn China, India

As a next step, we conducted a literature review of the product/source country combinations to independently validate the list, using the following parameters: 

  • Reference period: 1 January 2017 to 31 July 2022.

  • Mix of media and non-media sources (peer-reviewed journal articles, research reports, government documents, international organisation reports, NGO reports, etc.), whenever possible.


The following hierarchy of sources was used in conducting this research:

  1. Peer reviewed publications, e.g., articles from journals identified through database searches and, if required, through Google Scholar. 

  2. Reports of international organisations, e.g., ILO, IOM, UN.

  3. Reports of international NGOs, e.g., Human Rights Watch, Amnesty International.

  4. Government websites, e.g., Ministry of Foreign Affairs.

  5. National NGOs.

  6. Media, through Google searches.

It should be noted that this list is not exhaustive, and we performed additional searches where suggested sources did not provide the information required.  

Once the literature review was completed, a product/source country combination was included if it was on the 2022 US Department of Labor list of goods produced by forced labour that are listed in Table 16. In addition, the product/source country combination had to be independently verified by credible secondary sources, such as journal articles, primary research reports, reports from an international organisation or an NGO, or media reports. If no relevant references were found or the information was more than five years old, the product/source country combination was excluded.

The literature review resulted in the final list of at-risk products seen in Table 17. Source countries marked in red were deleted from the list as we could not find recent evidence to verify the occurrence of forced labour. The countries marked in green were added to the final list based on Walk Free primary research into modern slavery in the cocoa sector in Ghana25 and well-known exploitation occurring in the products of solar panels in China.26

Table 17: Final list of products at risk of modern slavery by source country.
Product  Source countries 
Bricks Afghanistan, Myanmar, Cambodia, China, India, Nepal, North Korea, Pakistan, Russia
Garments Argentina, Brazil, China, India, Malaysia, Thailand, Viet Nam, Bangladesh
Fish China, Ghana, Indonesia, Thailand, Taiwan
Cotton Benin, Burkina Faso, China, Kazakhstan, Pakistan, Tajikistan, Turkmenistan
Gold Burkina Faso, Democratic Republic of the Congo, North Korea, Peru, Venezuela
Timber Brazil, North Korea, Peru, Russia
Carpets India, Nepal, Pakistan
Coal China, North Korea, Pakistan
Cattle Bolivia, Brazil, Niger, Paraguay, South Sudan
Sugarcane Bolivia, Brazil, Myanmar, Dominican Republic, Pakistan
Rice Myanmar, India, Mali
Cocoa Côte d’Ivoire, Nigeria, Ghana
Electronics China, Malaysia
Palm oil Indonesia, Malaysia
Textiles China, North Korea
Brazil Nuts/Chestnuts Bolivia, Peru
Coffee Brazil, Côte d’Ivoire
Diamonds Angola, Sierra Leone
Embellished textiles India, Nepal
Shrimp Myanmar, Thailand
Stones India, Nepal
Thread/Yarn China, India
Solar Panels China

Identifying the most valuable imported products at risk of modern slavery

Trade data was obtained for the 19 G20 member countries. South Africa was included in this analysis for the first time: in 2018 it was excluded as it reported trade data via the Southern African Customs Union. The European Union was excluded to avoid double counting trade data from France, Germany, and Italy.

The final list of G20 countries are:

  1. Argentina

  2. Australia

  3. Brazil

  4. Canada

  5. China

  6. France

  7. Germany

  8. India

  9. Indonesia

  10. Italy

  11. Japan

  12. Mexico

  13. Russia

  14. Saudi Arabia

  15. South Africa

  16. South Korea

  17. Türkiye

  18. United Kingdom

  19. United States

Data source and definitions

BACI dataset 

The import data used for this analysis was taken from the BACI dataset.27 BACI is the world trade database developed by the French research centre Centre dÉtudes Prospectives et dInformations Internationales (CEPII) at a high level of product disaggregation.

Original trade data is provided by the United Nations Statistical Division (COMTRADE database). BACI is constructed using a procedure that reconciles the declarations of the exporter and the importer. This harmonisation procedure enables the extension of the number of countries for which trade data is available. The dataset gives information about the value of trade (in thousands of US dollars) and the quantity (in tonnes).

For this project, we used the 2021 BACI trade dataset with the 2017 Harmonized System (HS) nomenclature, which was the most recent available at the time of writing.

Harmonized Commodity Description and Coding System 

The Harmonized System (HS) is an international nomenclature for the classification of products. It allows participating countries to classify traded goods on a common basis for customs purposes. At the international level, the HS for classifying goods is a six-digit code system.

The HS comprises approximately 5,300 product descriptions that appear as headings and subheadings, arranged in 99 chapters, and grouped into 21 sections. The six digits can be broken down into three parts. The first two digits (HS-2) identify the chapter the goods are classified in, e.g. 09 = Coffee, Tea, Maté and Spices. The next two digits (HS-4) identify groupings within that chapter, e.g. 09.02 = Tea, whether or not flavoured. The next two digits (HS-6) are even more specific, e.g. 09.02.10 = Green tea (not fermented). Up to the HS-6 digit level, most countries classify products in the same way (a few exceptions exist where some countries apply old versions of the HS).

The HS was introduced in 1988 and has been adopted by most countries worldwide. It has undergone several revisions in the classification of products, which entered into force in 1996, 2002, 2007, 2012, and 2017.

Data compilation

Each of the products from the final list in Table 17 is represented by multiple HS 2017 product categories within the BACI trade dataset. The relevant categories were identified using the Foreign Trade Online directory.28 Using STATA, import data for all relevant product categories and source countries was extracted from the 2021 BACI dataset for 19 G20 countries.

The products were then ranked from highest to lowest according to import value in US$. The resulting list of top five products at risk of modern slavery (according to US$ value) imported by each of the G20 countries is presented in Table 18. This product list has changed between 2018 and 2023. Textiles, palm oil, and coffee were not included in the top five most valuable products at risk of modern slavery of any G20 country in 2018. Cotton and carpets remain products at risk of modern slavery, however they have dropped off the highest value list since 2018.

Table 18: Top five products at risk of modern slavery according to US$ value imported by G20 countries29
G20 country Imported product at risk of modern slavery Source country Import value (in thousands of US$)
Argentina Electronics China 1,249,673
Malaysia 19,969
Garments Bangladesh 11,673
Brazil 5,914
China 103,034
India 6,338
Malaysia 448
Viet Nam 16,574
Textiles China 82,025
Coffee Brazil 59,467
Solar panels China 55,545
Australia Electronics China 8,499,583
Malaysia 387,144
Garments Argentina 33
Bangladesh 814,958
Brazil 1,830
China 4,847,261
India 298,593
Malaysia 25,397
Viet Nam 400,830
Solar panels China 1,302,216
Textiles China 469,839
Fish China 75,023
Ghana 2
Indonesia 73,488
Taiwan 39,283
Thailand 199,156
Brazil Solar panels China 2,771,297
Electronics China 1,207,778
Malaysia 15,137
Garments Argentina 6,385
Bangladesh 108,725
China 624,307
India 46,369
Malaysia 1,963
Viet Nam 52,986
Palm oil Indonesia 511,464
Malaysia 19,313
Textiles China 269,509
Canada Electronics China 11,203,647
Malaysia 76,024
Garments Argentina 6
Bangladesh 1,278,694
Brazil 1,309
China 3,068,653
India 259,998
Malaysia 12,618
Viet Nam 1,053,657
Gold Peru 2,097,402
Textiles China 482,486
Sugarcane Brazil 427,598
China Palm oil Indonesia 4,902,375
Malaysia 1,432,821
Timber Brazil 322,095
Peru 37,308
Russia 3,564,090
Cattle Brazil 3,907,805
Garments Argentina 24
Bangladesh 404,894
Brazil 358
India 60,244
Malaysia 16,998
Viet Nam 1,173,538
Sugarcane Brazil 1,408,718
France Garments Argentina 577
Bangladesh 1,917,574
Brazil 2,301
China 4,524,306
India 682,554
Malaysia 6,475
Viet Nam 564,909
Electronics China 2,609,763
Malaysia 13,678
Cocoa Côte d’Ivoire 462,434
Ghana 172,531
Textiles China 434,278
Solar panels China 362,406
Germany Electronics China 20,319,762
Malaysia 151,570
Garments Argentina 51
Bangladesh 7,785,869
Brazil 857
China 7,993,142
India 1,301,894
Malaysia 36,160
Viet Nam 1,284,308
Solar panels China 2,425,414
Textiles China 1,620,893
Coffee Brazil 1,064,743
India Electronics China 7,345,027
Malaysia 432,868
Palm oil Indonesia 3,588,848
Malaysia 4,014,696
Solar panels China 3,820,664
Gold Burkina Faso 934,503
Peru 2,143,967
Garments Bangladesh 472,834
Brazil 78
China 753,798
Malaysia 7,523
Viet Nam 47,222
Indonesia Electronics China 2,919,522
Malaysia 77,909
Garments Argentina
Bangladesh 58,744
Brazil 7
China 563,955
India 15,410
Malaysia 21,467
Viet Nam 49,025
Textiles China 663,425
Coal China 432,649
Sugarcane Brazil 353,740
Italy Garments Argentina 143
Bangladesh 1,420,659
Brazil 1,110
China 2,560,544
India 353,801
Malaysia 4,419
Viet Nam 275,277
Electronics China 4,099,594
Malaysia 11,962
Palm oil Indonesia 769,786
Malaysia 416,258
Textiles China 533,455
Coffee Brazil 477,026
Japan Electronics China 29,015,858
Malaysia 111,882
Garments Argentina 400
Bangladesh 1,161,546
Brazil 1,773
China 13,008,247
India 207,494
Malaysia 116,704
Viet Nam 3,129,860
Fish China 1,617,554
Ghana 1,305
Indonesia 189,292
Taiwan 376,438
Thailand 484,290
Solar panels China 1,887,658
Textiles China 1,805,444
Mexico Electronics China 5,553,221
Malaysia 178,698
Garments Bangladesh 394,062
Brazil 1,235
China 1,525,355
India 109,101
Malaysia 5,633
Viet Nam 119,953
Solar panels China 498,915
Textiles China 476,471
Timber Brazil 305,542
Peru 8,671
Russia 12,402
Russia Electronics China 8,699,722
Malaysia 24,478
Garments Argentina
Bangladesh 1,161,231
Brazil 372
China 2,976,065
India 150,554
Malaysia 7,103
Viet Nam 399,252
Palm oil Indonesia 886,351
Malaysia 15,946
Cattle Brazil 129,229
Paraguay 333,732
Textiles China 420,059
Saudi Arabia Garments Argentina 11
Bangladesh 394,959
Brazil 626
China 2,317,822
India 414,568
Malaysia 6,613
Viet Nam 53,416
Electronics China 2,150,220
Malaysia 22,842
Palm oil Indonesia 480,481
Malaysia 369,096
Rice India 812,366
Sugarcane Brazil 340,235
South Africa Electronics China 2,435,721
Malaysia 4,566
Garments Argentina 24
Bangladesh 105,396
Brazil 456
China 1,157,112
India 97,275
Malaysia 3,157
Viet Nam 32,184
Palm oil Indonesia 436,801
Malaysia 116,863
Solar panels China 256,757
Textiles China 142,188
South Korea Electronics China 9,275,468
Malaysia 15,132
Garments Argentina 136
Bangladesh 443,987
Brazil 243
China 4,882,687
India 56,823
Malaysia 8,626
Viet Nam 3,020,662
Solar panels China 1,003,783
Palm oil Indonesia 453,534
Malaysia 373,781
Fish China 533,256
Ghana 1,815
Indonesia 32,753
Taiwan 114,442
Thailand 22,362
Türkiye Electronics China 3,173,722
Malaysia 8,515
Palm oil Indonesia 223,729
Malaysia 659,919
Garments Argentina 65
Bangladesh 190,776
Brazil 171
China 271,493
India 23,756
Malaysia 27,209
Viet Nam 63,508
Solar panels China 374,515
Cocoa Côte d’Ivoire 243,290
Ghana 76,298
UK Electronics China 14,713,414
Malaysia 37,016
Garments Argentina 189
Bangladesh 3,009,806
Brazil 1,294
China 5,257,572
India 1,255,998
Malaysia 22,225
Viet Nam 509,046
Textiles China 538,295
Timber Brazil 130,422
Peru 77
Russia 352,850
Fish China 230,258
Ghana 38,875
Indonesia 18,615
Taiwan 761
Thailand 16,475
US Electronics China 106,158,032
Malaysia 1,427,054
Garments Argentina 950
Bangladesh 7,273,296
Brazil 25,173
China 24,889,568
India 4,657,696
Malaysia 256,474
Viet Nam 15,288,211
Textiles China 4,752,876
Timber Brazil 2,107,498
Peru 8,361
Russia 549,653
Fish China 1,015,843
Ghana 257
Indonesia 406,921
Taiwan 144,891
Thailand 670,363


1Walk Free, International Labour Organization & International Organization for Migration 2022, Global Estimates of Modern Slavery: Forced Labour and Forced Marriage. Available from: [8 November 2022].
2As above.
3As above.
4The Counter Trafficking Data Collaborative n.d., Global Data Hub on Human Trafficking. Available from:
5Diego-Rosell, P & Joudo Larsen, J 2018, ‘Modelling the Risk of Modern Slavery’, SSRN. Available from: DOI: [6 April 2023].
6United Nations Department of Economic and Social Affairs, PD 2020. International Migrant Stock 2020.
7United Nations Human Rights Committee 2019, Concluding observations on the second periodic report of Mauritania CCPR/C/MRT/CO/2, pp. 6-7. Available from: [10 February 2023].
8United Nations, Department of Economic and Social Affairs, Population Division 2019. World Population Prospects 2019, Online Edition. Rev. 1. Available from: [6 July 2021]
9Clarke, R & Homel, R 1997, ‘A Revised Classification of Situational Crime Prevention Techniques’, in Anderson, OCA (ed) Crime Prevention at a Crossroads. Available from: [5 April 2023]; Ekblom, P & Tilley, N 2000, ‘Going equipped: criminology, situational crime prevention and the resourceful offender’, British Journal of Criminology, vol. 40, no. 3. Available from: [5 April 2023]; Lampe, KV 2011, ‘The application of the framework of Situational Crime Prevention to ‘organized crime’’, Criminology and Criminal Justice, vol. 11, pp. 145-163. Available from: [5 April 2023].
10United Nations 2000. Protocol to Prevent, Suppress and Punish Trafficking in Persons, Especially Women and Children, supplementing the United Nations Convention against Transnational Organized Crime. Doc. A/55/383. New York.
11Council of Europe 2005. Council of Europe Convention on Action against Trafficking in Human Beings. 16.V.2005. Warsaw.
12Bales, K. 2007. Ending Slavery: How we Free Todays Slaves, University of California Press, Gallagher, A. & Holmes, P. 2008. Developing an Effective Criminal Justice Response to Human Trafficking: Lessons from the Frontline. International Criminal Justice Review, 18, 318-348, Office to Monitor and Combat Trafficking in Persons 2011. The 3 Ps: Prevention, Protection, Prosecution. United States Department of State. Available from: [19 September 2014].
13See for example, Dang, M 2018, ‘Survivors are speaking. Are we listening?’, in Walk Free 2018, Global Slavery Index 2018, pp. 18-19, Minderoo Foundation. Available from: [17 August 2022]; Rights Lab 2020, Nothing about us, without us, University of Nottingham. Available from: [17 August 2022]; United States Department of State 2021, Trafficking in Persons Report 2021, pp. 32-33. Available from:
14Convention on the Rights of the Child (CRC) 1989, (resolution 44/25), opened for signature 20 November 1989, entered into force 2 September 1990; United Nations High Commissioner for Human Rights 2009, Guidelines on International Protection No. 8: Child Asylum Claims under Articles 1(A)2 and 1(F) of the 1951 Convention and/or 1967 Protocol relating to the Status of Refugees HCR/GIP/09/08, United Nations. Available from: [16 September 2022].
15See Alliance 8.7 workshop notes from the initial assessment of the migrant vulnerability literature review here: Migration Action Group 2018, Workshop Notes: 29-30 November 2018, Geneva, Alliance 8.7, p. 8. Available from: [16 September 2022].
16David, F, Bryant, K & Joudo Larsen, J 2019, Migrants and their vulnerability to human trafficking, modern slavery and forced labour, International Organization for Migration. Available from: [13 January 2022].
17Commonwealth countries included are Antigua and Barbuda, Bahamas, Belize, Fiji, Malta, Palau, Saint Lucia, Saint Vincent and the Grenadines, Seychelles, Solomon Islands and Vanuatu.
18Limited data defined as not included in the United States State Department Trafficking in Persons Report and/or excluded from at least five global datasets. These excluded countries include Dominica, Grenada, Kiribati, Nauru, Saint Kitts and Nevis, Samoa, Tonga and Tuvalu.
19Identified using the 2019 Fragile States Index, where those countries that scored 10.0 on the Security Apparatus indicator were excluded. See
20English, Spanish, Italian, French, German, Chinese, and Portuguese. Arabic speakers were missing from the team but will be included in the next iteration. Team members had a background in law, international relations, international development, or American studies.
21Download available on request from
22G20 Indonesia 2022, About the G20, Available from: [26 August 2022].
23The bibliography used to identify the at-risk products is available online
24Bureau of International Labor Affairs 2022, List of Goods Produced by Child Labor or Forced Labor, US Department of Labor. Available from: [4 Nov 2022].
25de Buhr, E & Gordon, E 2018, Bitter sweets: Prevalence of forced labour and child labour in the cocoa sectors of Cote dIvoire and Ghana, Tulane University & Walk Free. Available from: [22 January 2022].
26Murphy, L & Elimä, N 2021, In Broad Daylight: Uyghur Forced Labour and Global Solar Supply Chains, Sheffield Hallam University Helena Kennedy Centre for International Justice. Available from: [23 March 2022].
27For political reasons, the UN is not allowed to show trade statistics for Taiwan, Province of China. Therefore, Taiwan is included under ‘Other Asia not elsewhere specified’ (country code 490) in the BACI trade dataset. In principle, trade data for territories belonging to Asia, but not specified by country, could end up in code 490. In practice, only trade of Taiwan is included under this code, except for a few countries (e.g. Saudi Arabia, which reports all of their exports to unknown countries). For more information, please see: CEPII 2023, BACI HS17 (2017-2021), CEPII. Available from: [20 February 2022].
28Foreign Trade Online 2021, Harmonized System Codes (HS Code 2017 – Current), Foreign Trade Online Available from: [1 June 2022].
29Imports data is presented for 19 countries of the G20. The European Union was excluded as much of its trade is already captured in the data of Germany, Italy, and France.