Risk Dimensions and Data Sources

The Risk Dimensions, grouped by Risk Categories, are presented in the summary table below, showing the Data Source for each.

Risk Category	Risk Dimension	Data source	Year
Environmental	Ozone-Depleting Substances	UN Environment Programme	2024
	Soil Degradation	European Soil Data Centre	2019
	Pesticide use	FAOSTAT	2023
	Basin (Overall) Water  Risk	WWF Water Risk Filter	2024
	GHG Emissions	Blonk Sustainability	2021
	Biodiversity	WWF Biodiversity Risk Filter	2024
	Climate Change Vulnerability	Index	2025
	Deforestation	Assessment	2025
	Fertiliser use	Environmental Performance Index	2024
	Food Loss & Waste	FAOSTAT	2023
Governance	Institutional Arrangements / Good Governance	Worldwide Governance Indicators	2024
Social	Child Labour	UNICEF	2025
	Working Hours	ILOSTAT	2023
	Migrant Labour	ILOSTAT	2024
	Women’s Rights & Gender Equality	UNDP Gender Inequality Index	2023
	Freedom of Association	ITUC Global Rights Index	2025
	Forced & Bonded Labour	Global Slavery Index	2023
	Working Poverty	ILOSTAT	2025
	Healthy & Safe Workplace	ILOSTAT	2024
	Discrimination	Fundamental rights and Civil rights factors of the World Justice Project
Rule of Law Index	2024
	Indigenous & Community Land Rights	The Global Platform of Indigenous
and Community Lands	2025

The Risk Dimensions, grouped by Risk Categories, are presented in the summary table below, showing the Data Source for each.For each of the Risk Dimensions in this report, the findings and recommendations are presented in the following structure:

Risk Definition: A summary of the nature of the risk that the data source is providing data and/or a rating on.

Data Source: The data source used in the online sustainability risk assessment tool.

Description of the data: A summary of the data used to measure and/or rate the risk and how the data was collected/collated.

1. The Environmental Risk Category

1.1. Ozone Depleting Substances

Risk definition:

Ozone-depleting substances (ODS) are manufactured gases that destroy ozone once they reach the stratospheric ozone layer. The ozone layer reduces the amount of harmful ultraviolet (UV) radiation reaching the Earth. Increased UV exposure can have detrimental effects on human health (e.g., skin cancer, cataracts), crop productivity, and marine ecosystems.

Data source:

United Nations Environment Programme (UNEP), 2024 (link to dataset) – Consumption of Controlled Substances.

Description of the data:

ODS consumption is reported in Ozone-Depleting Potential (ODP) tonnes, a metric that standardises the impact of different chemicals relative to CFC-11. Data are provided per ODS group (Annexes A–F). Country-level consumption reflects the quantities produced, imported, exported, or destroyed each year. Negative values indicate years in which destruction or exports exceeded production or imports, meaning net reductions from stockpiles. For EU Member States, data are reported as an aggregated regional total rather than per individual country.

Notes on the dataset:

This dataset measures absolute national consumption (not per capita), which means countries with larger industrial sectors naturally show higher ODP tonnes. UNEP reporting is subject to national submission cycles, so the most recent year available may differ across countries. The EU provides an aggregated report. Therefore, the EU members all have the same value that represents the per capita ozone depletion potential within the European Union.

1.2. Soil Degradation

Risk definition:

Soil degradation refers to the physical, chemical, and biological decline in soil quality. Causes include unsustainable agricultural practices, overgrazing, erosion, compaction, pollution, loss of organic matter, and long-term climatic shifts. Degraded soils reduce agricultural productivity, diminish ecosystem services, and increase vulnerability to further environmental pressures.

Data source:

European Soil Data Centre (ESDAC) , 2019 (link to dataset) – Global Soil Erosion Model (GloSEM) raster dataset.

Description of the data:

The soil degradation indicator uses modelled global soil-erosion data derived from the GloSEM platform (Version 1.1). The dataset is provided as a 250 m × 250 m raster file showing estimated annual soil loss expressed in Mg ha⁻¹ yr⁻¹. Country-level values are calculated by aggregating the polygonised erosion values for all pixels falling within each country. These aggregated values are then normalised to produce the SRA soil-degradation score.

Notes on the dataset:

This dataset measures soil erosion only, which is used as a proxy for overall soil degradation. Updates to the underlying model are infrequent, so the most recent global dataset remains the 2019 version.

1.3. Pesticide Use

Risk definition: Pesticide use represents the intentional application of chemical substances—including insecticides, herbicides, fungicides, and other pest-control agents—to agricultural systems. The significant volume of chemical contaminants currently found in the air, soil, water, and sediment can have detrimental implications for the environment. While essential for managing pests and diseases, high or inefficient pesticide use increases risks of soil, water, and air contamination and can negatively impact biodiversity, ecosystems, and human health.

Data Source: FAOSTAT Pesticide Use per area of crop-land (kg/ha), 2023  (link to the dataset).

Description of the data: FAOSTAT provides country-level pesticide-use intensity standardised by cropland area (kg of active ingredient per hectare of cropland). The dataset includes total pesticide use, covering insecticides, fungicides, herbicides, bactericides, rodenticides, plant growth regulators, mineral oils, disinfectants, and other classes. The most recent national kg/ha values from the 2023 release are extracted and then normalised so that countries with higher pesticide-use intensity receive higher risk scores.

Notes on the dataset: The indicator measures pesticide intensity rather than absolute tonnage, which better reflects environmental pressure relative to agricultural land availability. FAOSTAT updates this dataset annually, though some countries report inconsistently or with time lags. The environmental impact of a pesticide depends on many factors such as, its toxicological properties, the environmental conditions on application, the application method, its permeance and dispersion in the environment.

1.4. Basin (Overall) Water Risk

Risk definition: Overall (Basin) Water Risk considers the physical quantity, physical quality, and regulatory and reputational water-related risks of a region, specifically weighted for the agricultural sector.

Data Source: The WWF Water Risk Filter, 2024 (link to dataset) includes a broad range of physical quantity, physical quality, regulatory and reputational water risk indicators.

Description of the data: The WWF Water Risk Filter provides basin-level scores for:

• Physical risks
• Regulatory risks
• Reputational risks

Notes on the dataset: Each risk type is first calculated using the agriculture weighting recommended by WWF. These agriculture-weighted scores are then combined using the SIFAV weighting to calculate a composite Basin Water Risk score for each region. The dataset is made up of a combination of model simulation results, predictions, assessments, and satellite data. An overall basin risk score is calculated by aggregating the physical, regulatory, and reputational risks for a basin. In turn, these sub-categories are calculated by consolidating various index scores. For example, water scarcity (a physical risk) is calculated by consolidating scores for seven reputable indices (Aridity Index, Drought Frequency Probability, etc.).

1.5. GHG Emissions

Risk definition: Greenhouse gas (GHG) emissions from human activities strengthen the greenhouse effect, driving anthropogenic climate change. Achieving net zero or net negative GHG emissions is an important objective of ensuring sustainable societies and sustainable agriculture & food systems specifically.

Data Source: Blonk Sustainability, 2021 (link to the dataset). The database provides a good indicator of GHG emissions risk at country and commodity levels. There is a cost to access the data, and Blonk Sustainability will provide IDH with a quotation.

Description of the data: Blonk processed the data for the requested crops and countries from a kg CO2e per kg crop value into a risk rating (0 for low risk and 10 for high risk).

Notes on the dataset: The data considers only primary production and does not include transport to market. International transport (particularly air transport) can be a hotspot in a product carbon footprint. This is not representative of scope 3 emissions.

1.6. Biodiversity

Risk definition: Biodiversity refers to the variety of plant and animal life in the world or a particular habitat or ecosystem. Loss of biodiversity can be driven by pollution, climate change and habitat loss due to land use change as well as other human-induced pressures.

Data Source: WWF Biodiversity Risk Filter, 2024 (link to the dataset) provides a dataset as part of the Biodiversity Risk Filter by considering two main types of risks: physical and reputational risk. It is a tool that is designed to identify and address risks associated with biodiversity loss. The data is industry specific and available at a subdivision level. The tool uses the industry specific weighting for agriculture.

Description of the data: The Biodiversity Risk Filter provides spatially explicit biodiversity-risk scores across multiple indicators that represent both physical and reputational risks. The dataset aggregates 33 indicators related to species, ecosystems, habitat condition, land-use pressures, invasive species, climate impacts, and pollution.

Notes on the dataset: The Biodiversity Risk Filter is a recognised measure of biodiversity used in key research reporting on the state of biodiversity. The dataset covers 33 different impact indicators that assess biodiversity-related risks for a multitude of supply chains, providing a holistic view. It utilises data from various sources including UN institutions, NASA and the World Bank. The data considers species and ecosystem information, locations of protected areas, and pressures on biodiversity like deforestation and pollution. The filter uses a four-level hierarchy to assess risks namely, risk type, risk category, indicator and metric. On WWF’s recommendation, the industry weighting for plant products (agriculture) were incorporated into the final risk ratings for overall pressures on biodiversity.

1.7. Climate Change Vulnerability

Risk definition: Climate change vulnerability was defined by the IPCC as "the degree to which a system is susceptible to and unable to cope with, adverse effects of climate change, including climate variability and extremes”. At a country level, it is a measure of the degree to which a country is exposed to climate change-related “shocks” such as extreme weather events and its capacity to adapt and/or mitigate these shocks.

Data Source: Germanwatch Global Climate Risk Index, 2025 (link to the dataset).  It is an analysis based on one of the most reliable data sets available that assesses the impacts of extreme weather events and associated socio-economic data, the MunichRe NatCatSERVICE. The Climate Risk Index (CRI) analysis ranks to what extent countries and regions have been affected by impacts of climate change-related extreme weather events (heatwaves, floods, storms, drought etc.). 180 countries were analysed for the 2021 CRI, and data from 2000 to 2019 is considered in this database.

Description of the data: To calculate the CRI rating, the following indicators with regard to climate change-related extreme weather events are analysed per country:

1. Number of deaths,
2. Number of deaths per 100 000 inhabitants,
3. Sum of losses in US$ in purchasing power parity (PPP), and
4. Losses per unit of gross domestic product (GDP).

Each country's index score is then derived from a country's average ranking in all four indicating categories, according to the following weighting:

• Death toll (1/6),
• Deaths per 100 000 inhabitants (1/3),
• Absolute losses in PPP (1/6), and
• Losses per GDP unit (1/3).

A lower score indicates adversely affected countries and indicates vulnerability to climate change shocks. This data is updated annually.

Notes on the dataset: The calculation method of CRI has changed – previous capping is no longer applied; the scale is now purely driven by min–max inversion.

1.8. Deforestation

Risk definition: Deforestation refers to the loss of forest area due to conversion for other land uses such as agriculture, settlement, infrastructure or mining activities. Loss of forest cover contributes to biodiversity decline, soil degradation, greenhouse gas emissions, and reduced ecosystem resilience.

Data Source: FAO Global Forest Resources Assessment, 2025 (link to the dataset). The report considers forest losses and gains. The annex containing country-level forest area change was used.

Description of the data: This comprehensive report from FAO covers various aspects of the world’s forests. The Global Forest Resources Assessment (FRA) 2025 is the result of a collective effort by the FAO Forestry Department, FAO member countries, and institutional and resource partners.

The FRA provides two key indicators for each country:

1. Net annual forest-area change (1 000 ha/year)
2. Net annual forest-area change (%)

Notes on the dataset: Each of the two indicators is converted into a score, and the average of these two scores represents the country’s final deforestation risk level. The deforestation scores have been updated to reflect the European Commission’s EUDR Country Risk Classifications, in June 2025. This means that 4 countries are given a score of 10 (Russia, Myanmar, Belarus & North Korea) See full list here.

1.9. Fertiliser use

Risk definition: Fertilisers—particularly nitrogen-based fertilisers—are applied to agricultural soils to support plant growth. When mismanaged or over-applied, they can contribute to nitrogen pollution, eutrophication of water bodies, soil acidification, greenhouse-gas emissions, and broader ecosystem degradation. High fertiliser intensity therefore indicates increased environmental pressure. Fertilisers can lead to adverse biological effects – whether at an individual, population, community, or ecosystem level.

Data Source: The Environmental Performance Index (EPI) Sustainable Nitrogen Management Index (SNMI), 2024 (link to the dataset). The EPI database provides a risk rating, based on nitrogen use.

Description of the data:

The SNMI evaluates countries on two main dimensions:

1. Nitrogen Use Efficiency (NUE) – the ratio of nitrogen absorbed by harvested crops to total nitrogen inputs (including fertiliser).
2. Nitrogen Yield – the amount of nitrogen taken up in harvested crops per year.

These two factors are combined into a 0–100 score, where 100 indicates highly efficient nitrogen management and 0 indicates poor performance. The SNMI is then converted into a fertiliser-use score, where lower EPI performance (i.e., less efficient nitrogen management) corresponds to a higher fertiliser-use risk.

The underlying nitrogen-input data used in the SNMI originates from FAOSTAT fertiliser-use reporting.

Notes on the dataset: The SNMI offers a country-level benchmark but does not disaggregate fertiliser use by crop type, production system, or soil characteristics. The EPI update cycle is not annual, and the 2022 release remains the most recent version.

1.10. Food Loss & Waste

Risk definition: Food loss and waste refers to the edible parts of food that are removed from the food supply chain (including losses at production, storage and processing), as well as inedible parts diverted for recovery or disposal (e.g. composting, bioenergy, landfill).

Data Source: FAOSTAT Food Balances, 2023 (link to the dataset). The Food and Agriculture Organisation of the United Nations statistical database provides access to data at a commodity and country level. Food waste can vary significantly between commodities, and it is thus important that the sustainable risk assessment tool considers this.

Description of the data:

For each relevant commodity included in the SRA, FAOSTAT provides country-level data for:

• Production (tonnes)
• Losses (tonnes)

The two datasets are available per commodity. Countries that report only production or only losses are excluded because they do not allow calculation of a loss ratio. For valid country–product pairs, the Losses / Production (%) ratio is computed.

Observations where the ratio exceeds 100% are removed as likely data inconsistencies.

Each commodity is processed separately, and countries are ranked within each commodity according to their percentage loss. These rankings are then transformed into risk scores.

Notes on the dataset: FAOSTAT provides the broadest global coverage for production and loss statistics, but reporting varies between countries and commodities. Because losses can differ significantly by product type, the Sustainability Risk Assessment treats each commodity independently before aggregating results. The dataset reflects losses across early supply-chain stages and does not include retail or consumer-level waste.

In cases where product-level data is available:

Fruit, vegetables, spices, meat and oil products: 2023 dataset is used.

Fish & seafood - aquaculture and fish & seafood - wild caught: 2022 data is used.

Milk excluding butter: 2023 dataset is used which includes most dairy products (full list found here).

2. The Governance Risk Category

2.1. Institutional Arrangements & Good Governance

Risk definition: Governance consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored, and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them. Good governance requires that all institutional actors involved in managing the social and environmental performance in a country, including citizens, organisations and private entities, work in a common direction. Poor governance leads to increased political and social risks, institutional failure, and lowered capacities to deliver. Therefore, good social and environmental governance requires clear legal frameworks, comprehensive social and environmental policies, enforceable regulations, institutions that work, smooth execution and citizen-based mechanisms of accountability, as well as strong interconnections between these entities.

Data Source: The Worldwide Governance Indicators (WGI) database, 2024 (link to the dataset). ranks a variety of good governance indicators: government effectiveness, voice and accountability, political stability and absence of violence/terrorism, regulatory quality, rule of law, and control of corruption.

Description of the data: The WGI project provides aggregate and individual governance indicators for over 200 countries and territories over the period 1996–2023. The WGI gives a ranking per country with a percentile rank (0-100), and it can compare the rank of a country among all countries in the world. 0 corresponds to the lowest rank, and 100 corresponds to the highest. The ranking is based on a variety of credible sources. On average, at least 7 or more data sources are used per country.

Notes on the dataset: This dataset was the most comprehensive available to cover 212 countries in the tool. Where there is a lack of good governance, buyers should consider additional checks and balances to gauge the impact on their specific supply chains. Good supplier relations and visits are crucial first steps in this regard, and buying from the “general” market in high-risk countries must be reconsidered. Third-party verification and certification that address the specific risk areas may also be required. An average of all six indicators was used to calculate the governance risk rating used within the tool.

3. The Social Risk Category

3.1. Child Labour

Risk Definition: The term “child labour” is often defined as work that deprives children of their childhood, their potential, and their dignity, and that is harmful to physical and mental development. It refers to work that: is mentally, physically, socially, or morally dangerous and harmful to children; and/or interferes with their schooling by depriving them of the opportunity to attend school, obliging them to leave school prematurely, or requiring them to attempt to combine school attendance with excessively long and heavy work.

Data Source: UNICEF Child Labour Statistics, 2025 (link to the dataset) - Percentage of children aged 5-17 years engaged in child labour (by sex)

Description of the data: UNICEF compiles child labour statistics to make internationally comparable datasets publicly available. The data measures the proportion and number of children engaged in child labour per country. The number of children engaged in child labour corresponds to the number of children reported to be in child labour during the reference period. The proportion of children in child labour is calculated as the number of children in child labour divided by the total number of children in the population. For this indicator, children include all persons 5 to 17 years of age. The UNICEF dataset is the primary source because it is regularly updated, and the organisation focuses on children. UNICEF is a source used by the Fairtrade Risk Map.

UNICEF considers children aged 5 to 17.

UNICEF defines child labour as follows:

• Age 5 to 11 years: At least 1 hour of economic work or 21 hours of unpaid household services per week.
• Age 12 to 14 years: At least 14 hours of economic work or 21 hours of unpaid household services per week.
• Age 15 to 17 years: At least 43 hours of economic work per week.

3.2. Working Hours

Risk Definition: Working hours reflect the average number of hours worked per employee per week. Excessively long working hours can increase the risk of work-related injuries, fatigue, poor health outcomes, and reduced well-being. In the agricultural sector, working hours are often longer and more variable due to seasonality and labour intensity, making this an important labour-risk indicator.

Data Source: ILOSTAT, 2024 (link to the dataset).– Mean weekly hours actually worked per employeed person by sex, age and economic activity – Annual. (Agricultural Economic Activity).

Description of the data: ILOSTAT provides national estimates of the mean weekly hours worked per employee by economic activity. Data on hours of work are presented, whenever possible, based on the mean number of hours of work per week, and with reference to hours worked in all jobs of employed persons and in all types of working time arrangements (e.g., full-time and part-time). Data are disaggregated by economic activity according to the latest version of the International Standard Industrial Classification of All Economic Activities (ISIC) available for that year.

Notes on the dataset: The economic activity “Crop and animal production, hunting and related service activities”, both male and female, and the latest period for a particular country, are used. Reporting years vary by country, as ILOSTAT publishes the most recent available figure for each. ISCO-08 ensures that agricultural activity includes crop producers, animal producers, forestry workers, and fishers — confirming that the indicator reflects the full agricultural workforce rather than a narrower subset.

3.3. Migrant Labour

Risk Definition: Migrant workers may face elevated risks in the labour market, including discrimination, job insecurity, limited access to social protection, wage disparities, and more hazardous working and living conditions. These factors can result in higher vulnerability to exploitation and work-related harm compared with native-born workers.

Data Source: ILOSTAT, 2024 (link to the dataset) – Labour force by sex, age and place of birth (thousands) – Annual. There are limited global datasets reporting on migrant labour (as opposed to just migration). The primary data is collected by International Labour Migration Statistics (ILMS) and distinguishes between native- and foreign-born labour in each country.

Description of the data: This dataset reports the size of the labour force disaggregated by place of birth (native-born vs. foreign-born), sex, and age group. For the SRA, the following filters are applied:

• Sex: Total
• Age: 15+
• Place of birth: Native-born and foreign-born groups separately

For each country, the most recent year available is used, ensuring that both the total labour force and the foreign-born labour force figures come from the same year. The proportion of migrant labour is calculated as:

% Foreign-born labour force = Foreign-born labour force / Total labour force × 100%

Notes on the dataset: ILOSTAT coverage spans 130+ countries, but reporting years differ between countries due to varying national data submission cycles. The indicator reflects the share of migrant workers, not their working conditions; however, higher proportions often correlate with increased vulnerability and risk of exploitation.

3.4. Women’s Rights & Gender Equality

Risk Definition: Women’s rights are the fundamental human rights enshrined by the United Nations for every human being on the planet nearly 70 years ago. These rights include the right to live free from violence, slavery, and discrimination, access to education, political participation, economic opportunities and equal treatment before the law. Gender equality is a fundamental human right and a necessary foundation for a peaceful, prosperous, and sustainable world. Despite gains in gender equality, many challenges remain. Discriminatory laws and social norms remain pervasive, women continue to be underrepresented at all levels of political leadership, and 1 in 5 females between the ages of 15 and 49 report experiencing physical or sexual violence by an intimate partner within a 12-month period. Persistent disparities in these areas can impact social stability, well-being, and labour conditions across supply chains.

Data Source: The UNDP Gender Inequality Index, 2023 (link to the dataset). It is derived from the Human Development Index.

Description of the data: The Gender Inequality Index is a composite measure reflecting inequality in achievement between women and men in the following three dimensions:

• Reproductive health (maternal mortality ration, adolescent birth rate)
• Empowerment (share of parliamentary seats, population with secondary education)
• The labour market (labour-force participation rates)

This Index ranges from 0 (women and men are equal) to 1 (maximum measured inequality).

Notes on the dataset: The latest update is in 2023. This dataset covers 195 countries. It reports at the national level.

3.5. Freedom of Association

Risk Definition: Freedom of association is a fundamental human right proclaimed in the Universal Declaration of Human Rights. It is the enabling right to allow effective participation of non-state actors in economic and social policy, lying at the heart of democracy and the rule of law. Ensuring that workers and employers have a voice and are represented is essential for the effective functioning of labour markets and overall governance structures in a country. It underpins collective bargaining, worker representation, and the protection of other labour rights. Restrictions on freedom of association can inhibit fair working conditions, suppress worker voices, and increase vulnerability to exploitation.

Data Source: ITUC Global Rights Index, 2025 (link to the dataset).

Description of the data: The ITUC Global Rights Index ranks countries annually based on the extent to which governments and employers respect workers’ rights. The index evaluates documented violations related to:

• Civil liberties
• Right to establish and join unions
• Trade union activities
• Right to collective bargaining
• Right to strike

Countries are assigned a rating from 1 (best) to 5+ (worst), reflecting the severity and systemic nature of violations.

1. Sporadic violations of rights
2. Repeated violations of rights
3. Regular violations or rights
4. Systematic violations of rights
5. No guarantee of rights

5+. No guarantee of rights due to the breakdown of the rule of law

Notes on the dataset: The dataset is derived from legal analysis, union reporting, and documented cases of rights violations, making it a robust indicator of labour-rights conditions. However, because it captures reported violations, countries with limited press freedom or weaker documentation may be under-represented in reported incidents. Due to this, legal researchers analyse national legislation and identify sections which are not adequately protecting internationally recognised collective labour rights.

3.6. Forced & Bonded Labour

Risk definition: Forced labour refers to any work or service that individuals are compelled to perform against their will under threat of penalty. Bonded labour (or debt bondage) occurs when people are forced to work to repay a debt, often under unfair or exploitative conditions. Both are severe violations of fundamental human rights and may indicate systemic labour exploitation within a country.

Data Source: Walk Free- Global Slavery Index, 2023 (link to the dataset).

Description of the data: The Global Slavery Index provides country-level estimates of the prevalence of modern slavery, expressed as victims per 1,000 population. These estimates draw on nationally representative surveys (including modules within the Gallup World Poll) and the GSI Vulnerability Model.

To maintain consistency with the methodology used in risk assessment, under the instructions of the GSI, the following scoring rules are applied directly to the prevalence value:

• Less than 9 victims per 1,000 → Use datapoint as is
• Between 9 and 23 victims per 1,000 → 9
• More than 23 victims per 1,000 → 10

Notes on the dataset: The GSI is one of the only globally comprehensive and regularly updated sources on forced labour and modern slavery. However, prevalence estimates depend on survey availability and vulnerability modelling, meaning levels of uncertainty can differ between countries.

3.7. Working Poverty

Risk Definition: Working poverty refers to the share of employed individuals whose income remains below the international extreme-poverty line, despite being employed. The international poverty line is a threshold used to measure extreme poverty based on consumption or income levels. A person is considered in extreme poverty if their consumption or income level falls below the minimum level necessary to meet basic needs. High levels of working poverty indicate structural weaknesses in labour markets, limited access to decent work, and economic vulnerability among workers.

Data Source: ILOSTAT’s working poverty rate dataset, 2025 (link to the dataset) – percentage of employed living below US$2.15PPP % - Annual

Description of the data: The dataset records the Sustainable Development Goals (SDG) Indicator 1.1.1 – Working poverty rate (percentage of employed living below US$ 2.15 PPP). Poverty is defined using the international poverty line of US$ 2.15 per day in purchasing power parity (PPP). The ($2.15 a day) poverty line or the critical threshold value, below which an individual is determined to be extremely poor, allows for comparing and aggregating progress across countries in reducing the number of people living under extreme poverty and monitoring trends at the global level. In addition, poverty measures based on an international poverty line attempt to hold the real value of the poverty line constant over time, allowing for accurate assessments of progress toward meeting the goal of eradicating extreme poverty and hunger.

Notes on the dataset: Coverage includes more than 130 countries, though reporting years sometimes vary due to national data submission cycles. The indicator captures income-based poverty only and does not reflect multidimensional poverty factors such as access to housing, health care, or social protection.

3.8. Healthy & Safe Workplace

Risk Definition: A healthy and safe workplace is one in which occupational risks are minimised and workers are protected from injuries, accidents, and work-related ill-health. High levels of occupational injuries may indicate inadequate safety standards, insufficient training, weak enforcement of labour regulations, or hazardous working conditions. Healthy & safe workplaces focus on the following:

• Promotion and maintenance of the highest degree of physical, mental, and social well-being of workers in all occupations.
• Prevention of worker absence due to poor health caused by their working conditions.
• Protection of workers in their employment from risks resulting from factors adverse to health.
• Assessment of an employee’s occupational environment and adapting to their physiological and psychological capabilities.

Data Source: ILOSTAT , 2024 (link to fatal and link to non-fatal occupational injuries datasets), Non-fatal and fatal occupational injuries per 100 000 workers by economic activity.

Description of the data: ILOSTAT provides national statistics on the incidence of fatal and non-fatal occupational injuries, expressed per 100,000 workers. This data is collected from a variety of sources, such as administrative records, establishment surveys and household surveys.

Notes on the dataset: The dataset covers over 120 countries, but reporting quality and frequency vary significantly, and countries may differ in the sources used to compile occupational injury data (surveys, administrative reports, registers). Because the data reflect all economic activities, availability of agricultural-sector–specific injury data may be limited for some countries.

3.9. Discrimination

Risk Definition: Discrimination refers to the unjust or prejudicial treatment of individuals or groups based on characteristics such as gender, ethnicity, religion, socio-economic status, national origin, sexual orientation, or disability. High levels of discrimination can affect access to justice, employment, public services, and basic rights, increasing social and labour-related risks within supply chains.

Data Source: World Justice Project – Rule of Law Index, 2024 (link to dataset). Factors 4.1 (Equal treatment and absence of discrimination) and 7.2 (Civil Justice free of discrimination).

Description of the data: The WJP Rule of Law Index evaluates countries across multiple governance and justice dimensions. For the SRA, two factors are used:

• Factor 4.1 – Equal treatment and absence of discrimination, assessing whether individuals are free from discrimination in public services, employment, legal proceedings, and justice systems.
• Factor 7.2 – Civil justice is free of discrimination, assessing whether the civil justice system operates without discriminatory practices.

Scores range from 0 to 1, where higher values indicate better adherence to the rule of law. For the SRA, the two scores from the 2024 release are averaged and then normalised so that lower WJP performance corresponds to higher discrimination risk in the SRA scoring framework.

Notes on the dataset: Because WJP scores improve as discrimination decreases, normalisation must invert the scale so the risk scores correctly reflects higher discrimination = higher risk. The WJP Rule of Law index evaluates across different dimensions, measuring indicators like corruption, fundamental rights and order & security. For the discrimination indicator we have chosen to only include factors 4.1 and 7.2 because they directly measure discrimination.

3.10. Indigenous & Community Land Rights

Risk Definition: Indigenous and community land rights refer to the legal recognition, protection, and security of tenure for Indigenous Peoples and local communities over their traditional lands. Weak legal protections can increase the risk of land dispossession, conflict, inequitable resource use, and violations of human rights.

Data Source: LandMark, 2025 (link to the dataset) Indicators of the Legal Security of Indigenous and Community Lands – Indigenous Peoples.

Description of the data: The LandMark dataset evaluates national legal frameworks using ten indicators that assess the extent to which Indigenous and community land rights are recognised and protected in national laws. These indicators cover areas such as documentation of rights, due process protections, recognition of community governance systems, and rights related to land use and consent. The average score across the ten indicators is used for each country. Scores reflect legal provisions only, they do not measure enforcement or real-world implementation

Notes on the dataset: Coverage varies by country because LandMark reports the legal situation based on the year in which national laws were enacted or last updated. As a result, reference years are not uniform across all countries, and some countries may still rely on older legal information in the 2025 release.