Gender inequality persists globally, impacting economic participation, education, health, and political empowerment, affecting women’s lives globally. Despite advancements, significant disparities remain. In 2023, UN Women estimated that at the current rate, it will take 286 years to close gender gaps in legal protection, 140 years for women to be represented equally in positions of power and leadership, and 47 years to attain equal representation in national parliaments -—
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The UNCTAD ministerial meeting, in Bridgetown, concluded that policies need to go beyond encompassing a gender perspective and actively promote the inclusion and empowerment of women and youth -—
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—-. The ministers underscored the importance of gender-disaggregated data to build the evidence base for such policies. This SDG Pulse In Focus represents commitment to this work, and UNCTAD has spearheaded efforts to advance this area, including through the release of the first ever set of gender equality in trade indicators in July 2024.

The indicators are calculated based on data derived from international databases include employment and earnings by sex in tradable sectors, trade-intensive and trade-dependent industries. These data enable for the first-time to gain insights about international trade from a gender perspective across the world.

Globally, women employees are underrepresented in tradable sectors, representing only 36 per cent of persons employed in tradable sectors in developed and 39 per cent in developing economies. However, their employment in the trade of services has increased at a faster rate than men’s, highlighting the potential for trade in services to enhance women's economic empowerment, particularly in regions like Africa, Asia, and Oceania. Women’s contribution to domestic value added in exports still lags significantly behind men’s, though it is higher in services exports compared to agriculture and industry. Understanding these emerging patterns to inform effective policy actions will require further country-specific analyses to identify drivers and barriers to women’s participation in high value-added sectors unique to each economy.

Leveraging trade as a catalyst for economic empowerment

Trade plays a crucial role in economic growth and poverty reduction. Thus, inequalities in trade participation and in the distribution of benefits significantly impact people’s lives. In the last three decades, the global poverty rate fell from 38 per cent in 1990 to just below 9 per cent in 2022 -—
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—-. Despite the positive long-term trends, the distributional impacts of trade have not been equal across and within economies and populations.

Furthermore, recently these trends got disrupted by the pandemic, war and crises. An estimated 23 million more people were living in extreme poverty in 2022 compared to 2019 -—
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—-. UNCTAD’s early analysis of the impacts of the pandemic showed that the related changes in international trade led to gendered impacts on employment with women, young and part-time workers as well as those with lower education being most vulnerable to job loss -—
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—-. Understanding the complex relationship between the economy, trade and gender equality is essential for effective decision-making.

UNCTAD's initial analysis reveals an association between trade openness and women’s economic empowerment. According to the GGPI and WEI indices less than 1 per cent of women and girls reside in a country with high women’s empowerment and gender parity (figure 1), mainly in Australia, Belgium, Denmark, Iceland, Norway, and Sweden, while developing economies, such as Iran, Iraq, Lebanon and Pakistan in Asia, and Benin and Nigeria in Africa lag behind. While the impact of trade liberalization on gender inequality depends on multiple factors, research shows several channels by which trade policy can improve gender equality in wages and employment -—
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—-. Liberalization can drive firms to adopt new technologies and reduce discrimination, making jobs less physically demanding and improving opportunities for women. However, changes in the sectoral structure of production due to liberalization can have both positive and negative effects on gender inequality.

Figure 1. Developed economies generally exhibit greater openness to trade together with enhanced women’s empowerment and gender equality

Source: UNCTAD calculations based on -—
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Note: GGPI and WEI were published by the UNDP in 2023. Values of GGPI and WEI greater than 0.6 are considered medium parity, and values greater than 0.8 are considered high parity. The UNCTAD’s trade openness index analyzes countries’ economic dependence on exports and imports. The bubble size refers to the trade openness index. Trade values correspond to the sum of exports and imports of goods and services.

While the importance of women’s economic empowerment for closing gender gaps is widely acknowledged, data to enable effective action to close gender gaps remains rare. This lack of data means that gender equality indices continue to limit the focus of economic empowerment on the labour market and political participation -—
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—- measures empowerment by gender gap in education and political representation. Like GII, WEI and GGPI -—
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—- also omit the dimension of international trade.² The WEF's GGGI -—
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—- measures gender parity across four key dimensions, which do not mention trade. The lack of data makes it challenging to analyse the impact of trade on gender equality hindering effective policy making.

Trade significantly influences employment and business opportunities of women and men, their income, social status, welfare, and equality. Export-oriented industries such as textiles and apparel, often employ a large number of female workers -—
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—-, making up to 33 per cent of the workforce of exporting firms in developing economies, compared with just 24 per cent of non-exporting firms -—
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—-. When employed in export-intensive sectors, women are more likely to hold formal jobs with better benefits, training and security. A study in Bangladesh found that rising exports had a higher impact on lifting women from informal to the formal employment and reduced wage gaps in the garment industry -—
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However, trade can also exacerbate existing gender inequalities, particularly if accompanying trade policies do not include measures to address social and economic inequalities. Women working in global value chains often occupy low-skill and non-managerial jobs, despite being more likely to hold formal jobs -—
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—-. Feminization of labour – increasing women’s employment in labour-intensive sectors, such as textiles or agriculture, – can lead to gender wage discrimination and poor working conditions due to women’s lower bargaining power offering competitive advantage to firms. In contrast, defeminization of labour – declining share of women’s employment as observed in some countries (e.g., the Republic of Korea, Taiwan, Province of China, and Malaysia), – occurs in capital and technology-intensive sectors and may marginalize women due to stereotypes and occupational segregation -—
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—-. Gender equality in trade is highly context-specific and difficult to generalize which is why country-level data and case studies are important.

New data linking initiatives to bridge the gap

Despite the urgent need to analyse trade from a gender perspective, only a few countries regularly compile sex-disaggregated indicators linked to international trade, and some countries do so on an ad hoc basis. For example, Finland and New Zealand linked such data to find that women were underrepresented in international trade both as employees and business owners (See National efforts to produce statistics on gender and trade). Some countries have also collected additional data by specialized surveys on trade and gender, such as Chile -—
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—- and some international organizations, like the World Bank, support countries by carrying out such surveys. These can provide more in-depth information on trade barriers or informal cross-border trade to inform policy but may be costly to carry out.

Prompted by the Buenos Aires declaration -—
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—- and collaboration with pioneering countries and organizations, UNCTAD developed the ‘Conceptual Framework for the Measurement of Gender Equality in Trade' -—
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—- released last year and the 2024 release of a global analytical dataset on gender equality in trade, represent significant strides toward addressing the data gaps with countries and partners (See UNCTAD in Action: Gender and Trade). These indicators and the related analysis are intended to inform more gender-inclusive trade policies.

Women continue to be underrepresented in trade across regions

Women’s underrepresentation in tradable sectors is evident across regions, as indicated by UNCTAD’s data (figure 2). 36 per cent of employees in tradable sectors are women in developed economies compared to 39 per cent in developing economies. Notably, African countries exhibit the lowest gender employment gap in tradable sectors, with women comprising 42 per cent of employees compared to 58 per cent for men.

Figure 2. Women employees are underrepresented in tradable sectors across regions, 2022

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Source: UNCTAD calculations based on -—
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Notes: Classification of tradable and non-tradable sectors is derived based on -—
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—-. Tradable sectors include agriculture (ISIC Rev. 4 - A), industry (B, C, D, E), transport, information and communication (H, J), financial and insurance activities (K), and other services (R,S,T,U). Non-tradable sectors include construction, distributive trade, repairs, accommodation, food services activities (F, G, I), real estate activities (L), business services (M, N), and public administration (O, P, Q). Transportation is also included among tradable sectors, because international transport is considered to be a key enabler of international trade -—
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Women face barriers to trade participation, such as unequal access to resources, limited training opportunities, and cultural constraints. UNCTAD’s studies also highlight the disproportionate burden of care and household work and women’s higher participation in low productivity work are major constraints to women's full economic empowerment -—
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—-, and female business owners face higher trade barriers and limited access to finance, which further restricts their business growth and access to international markets -—
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Analysis of OECD countries revealed that 11 per cent of women-led firms export internationally compared to 19 per cent of men-led firms. However, once involved in exports, women-led firms do so to a similar or larger number of countries than firms led by men, suggesting the particular importance of policies aimed at removing entry barriers for women entrepreneurs -—
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—-. For instance, legal and regulatory barriers, such as restrictions on property ownership or business activities and travel, limit women’s entrepreneurship. The Women, Business, and the Law Index indicates that women have less than two-thirds of the legal rights available to men, particularly in entrepreneurship -—
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Services trade offers increasing potential for women’s economic empowerment

Women are concentrated in the services sector in all regions. Figure 3 shows a rising share of services as an employer of both women and men from 1991 to 2022 in all regions. The shift to services is also mirrored in international trade as growth of trade in services is surpassing that of goods since 2011 -—
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Figure 3. Women's employment in services increased at a faster rate than men's since 1991

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In manufacturing, high shares of female employment typically align with whether an industry is capital-intensive (technology-dependent) or labour-intensive. Low technology-intensive industries which typically employ more women (termed feminization of labour), such as food and beverages and textiles, often face higher tariffs on imported inputs than other industries. Such tariffs can elevate trade costs and hinder the competitiveness of sectors that offer employment opportunities for women -—
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Figure 4. Share of female employees is higher in low technology-intensive manufacturing industries compared to more capital-intensive industries

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Women produce 20-40 per cent of total export value depending on region

Women’s contributions to domestic value added in gross exports lag behind men’s across all regions. This indicates that women’s contribution to the production of goods and services exported worldwide still trails that of men (figure 5). For example, in 2020 women’s largest contribution to domestic value added in gross exports was estimated at 40 per cent in developed economies, while in Africa women only produced one fourth of the total generated exported value. In developing Americas and developing Asia and Oceania, men’s contributions were nearly double that of female generated domestic value added in gross exports.

Figure 5. Women’s contributions to domestic value added in gross exports lag behind men’s across all regions, 2020

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Note: Aggregated figures are based on data on employment and trade in value added for 76 economies. This analysis assumes that there are no differences in gender distribution between exporting and non-exporting firms. The proportions of male and female contributions to domestic value added are calculated assuming homogeneity in labour intensity, skills, etc., thereby stating that women represent a comparable share of value added to their proportion in employment.

Analysis by sectors reveals an intriguing pattern: women’s domestic value added is higher in services exports compared to agriculture and industry in most regions (figure 6). This suggests that trade in services offers greater opportunities for women to contribute to exports in developed economies, developing Africa and the Americas. For instance, in developed economies women’s domestic value added in services nearly equals men’s, whereas their contribution to agriculture and industry exports is approximately one third of that of men’s.

Figure 6. In most regions, women’s domestic value added is higher in services gross exports compared to agriculture and industry, 2020

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Note: Aggregated figures are based on data on employment and trade in value added for 76 economies. This analysis assumes that there are no differences in gender distribution between exporting and non-exporting firms. The proportions of male and female contributions to domestic value added are calculated assuming homogeneity in labour intensity, skills, etc., thereby stating that women represent a comparable share of value added to their proportion in employment.

To further explore women’s contribution to higher value-added exports, the analysis examines exports of goods and services that add more than 50 per cent to the domestic value added in exports. Figure 7 shows that women's share of employment is higher in economies where services exports contribute more than 50 per cent to the domestic value added, compared to economies where goods exports contribute more than 50 per cent. This supports WTO’s argument that services trade may benefit women in the labour market, as services sectors exhibit greater gender balance than manufacturing or mining -—
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Figure 7. Economies with high exported value added from services show greater female employment rates compared to goods-producing industries

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Note: The year for the reported employment varies from 2008 to 2020 based on the latest available with most data reported for 2019 or later. Country data for high-exported value added – i.e. sectors contributing more than 50% to the domestic value added in gross exports. Sectors are aggregated into two groups: goods (primary goods and manufacturers) and services.

While UNCTAD’s first set of trade and gender indicators show that women are still underrepresented in tradable sectors and contribute less to creating domestic value added content in exports across regions, a sectoral analysis reveals opportunities to catalyse trade for women’s economic empowerment. Trade in services presents an opportunity for women to contribute to the growth of exports in most regions. Nevertheless, a further in-depth analysis can help to identify specific drivers and bottlenecks of women’s contribution to high-value sectors unique to each economy. This approach requires country-level linking of micro-data to enable more accurate insights to inform policy action, such as the following examples of UNCTAD’s collaboration with Finland and Georgia.

Women’s contribution to domestic value-added exports in Finland

A small open economy like Finland benefits from globalization and foreign trade significantly. The share of exports in GDP is high up to 30 per cent, but the benefits of trade are unevenly distributed between businesses, employees and consumers -—
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—-. Statistics show a high concentration of exports in the largest enterprises, and higher salaries paid by trading firms compared to others. Importantly, the study showed that women work less often in trading companies, and only one fifth of businesses engaged in exports are female owned.

Gender gap widens as firm size increases.

Statistics Finland releases annually experimental statistics on trade in value added. These can be compiled by linking existing data sources without additional data collection. An analysis of domestic value added embodied in exports, whether through direct or indirect export dependencies, reveals insights into the role of firms and export-supported jobs (figure 8). While larger enterprises in Finland provide many jobs supported by exports, the proportion of jobs that are export-supported is smaller in the largest enterprises, since they often engage with smaller intermediaries to produce intermediate goods for them.³

The global value chain analysis reveals that in Finland women’s share of jobs supported by exports is 32 per cent compared with 38 per cent for men. Interestingly, while export-supported jobs in micro-firms are almost equally distributed between women (20 per cent) and men (21 per cent), the gender gap widens as firm size increases. In large firms, 30 per cent of women’s jobs are supported by exports compared to 43 per cent of men’s jobs.

Figure 8. Finland’s women’s contribution to domestic value added is lower than men’s contribution with widening gap in larger firms, 2021

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Women’s employment, wages and entrepreneurship in trade in Georgia

Gender aspects of trade statistics become very relevant to addressing issues related to welfare and equality. There’s high interest from state institutions, international partners and non-governmental organizations,Gogita Todradze, executive director of Georgia’s National Statistics Office

Georgia was the inaugural pilot country supported by UNECE and UNCTAD in developing sex-disaggregated trade indicators through microdata linking (See UNCTAD in Action: Gender and Trade).⁴ This involved linking annual goods trade data with key sex-disaggregated variables from business statistics and the structure of earnings survey. The linked company-employee data represented over 85 per cent of both exports and imports value in Georgia.

High-skill female workers face less gender inequalities.

The study revealed gender gaps in employment and wages. Women-to-men employment ratios in trading companies ranged from 57 to 64 per cent over the five-year period, while the gender pay gap fluctuated between 30 and 35 per cent. Further analysis by skill levels indicated that high-skill workers had the lowest gender pay gap (18 per cent for importers, 31 per cent for two-way traders), while the gaps for managers and low-skill workers were between 38 and 45 per cent. Gender indicators disaggregated by skill levels highlighted that, generally, high skilled female workers experienced less disparity in trade, both in their employment and pay (figures 9 and 10). Higher education levels could protect women from some gender inequalities.

Figure 9. Gender employment gap in trading companies is lowest among high-skilled workers in Georgia, 2017

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Note: Two-way traders are defined as firms involved in both exports and imports of goods.

Figure 10. Gender pay gap is the highest among low-skilled workers in trading enterprises in Georgia, 2017

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Note: Two-way traders are defined as firms involved in both exports and imports of goods.

The data linking exercise also facilitated the analysis of female and male entrepreneurs in trade. It revealed that men own trading companies five times more often than women (figure 11). Drawing on these data insights, Georgia plans to include gender-in-trade statistics in its regular statistical production to inform design of policies that encourage women entrepreneurship, as well as increase job opportunities and wages for women in international trade.

Figure 11. In Georgia, men own five times more trading companies than women, 2017

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Note: Two-way traders are defined as firms involved in both exports and imports of goods.

More inclusive economies reach higher productive capacities

Enhancing a country’s productive capacities — comprising of human and natural capital, energy, transport and ICT capacities, well-functioning institutions and private sector, and structural change — fuels inclusive economic growth and resilience. Investments in productive capacities yield immediate economic benefits but also lay the groundwork for sustained growth by creating jobs, enhancing skills, and promoting equitable development. Generally, the propensity to build productive capacities is higher in countries with well-crafted and informed economic, trade, industrial, as well as science and technology policies -—
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The strong relationship between inclusive growth and productive capacities is illustrated in figure 12. It also shows that economies with higher PCI scores primarily consist of developed economies that rank high on the IGI equality dimension, which measures labour and political participation, income distribution, education, and gender distribution of social reproduction. Among developing economies, Asia and Oceania demonstrate higher average equality scores, whereas African economies exhibit significant diversity in equality, suggesting that women’s productive capacities have not been optimally developed and utilized -—
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Figure 12. Developed economies reveal higher PCI and IGI equality scores in 2021

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Note: The figure compares overall PCI scores (x-axis) and IGI equality scores (y-axis). The size of the bubbles refers to the overall IGI scores. IGI is composed of four dimensions – economy, living conditions, equality and environment.

Domestic value added in gross exports is closely linked to productive capacities because it reflects the extent to which a country's domestic resources, capabilities and production processes contribute to the final goods and services that are exported. In 2020, total value added, as measured by domestic value added in gross exports, is estimated at almost $15 trillion globally, with 3 per cent generated in agriculture, 56 per cent in industry and 42 per cent in services -—
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Figure 13. Developed economies reveal higher PCI scores and higher contribution of women to domestic value added in gross exports in services

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Note: The year for the reported employment varies from 2015 to 2020 based on the latest available. Country data for high-exported value added – i.e. sectors contributing more than 50% to the domestic value added in gross exports. Sectors are aggregated into two groups: goods (primary goods and manufacturers) and services.

While a clear division between developed and developing economies exists in their productive capacities, some outliers, particularly in the service sector, warrant examination. To understand why some economies follow a different path, PCA was used to assess the main factors affecting gender inequality in trade. This analysis, based on 19 indicators (see table 1) for 62 economies available from the OECD TiVA database -—
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The analysis identifies four principal components, namely education and social conditions (PC1), trade and labour participation (PC2) and political empowerment and participation in decision-making (PC3 and PC4) which explain 71 per cent of the total variance of gender equality. The first component mostly represents preconditions for trade participation for women and men: motivations, aspirations, resources, and constraints. The second points to their degree of involvement in trade. The third one stands for political empowerment and participation in various levels of decision-making.

Figure 14. Developed economies reveal higher gender equality in education and social conditions and in trade participation in contrast to developing economies

Source: Data for the analysis is collected from multiple sources, UNCTAD Gender-in-trade indicators -—
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Note: The figure shows gender equality in education and social conditions (PC1) on the x-axis and gender equality in trade and labour participation (PC2) on the y-axis.

Developed economies stand out as a relatively homogenous group with higher gender equality in education and social conditions and in trade participation with a stark contrast to developing economies (figure 14). They also boast better social conditions and higher women’s empowerment and wages.

Various patterns of gender equality in trade emerge across different countries. In a cluster including Cambodia, Lao PDR, Peru and Viet Nam, women participate significantly in trade, particularly in industries and sectors heavily reliant on trade. Conversely, a group of economies represented on the right side of figure 14 (Bangladesh, Egypt, Jordan, India, Pakistan and Tunisia) faces unfavourable conditions for women’s economic participation, resulting in limited benefits from trade. Nordic and Benelux countries share similarities in high levels of women’s empowerment, institutional representation, education, and wages, grouping them together. Similarly, Eastern European economies demonstrate commonalities in high female labour force and trade participation. These diverse patterns highlight the complex interplay between gender equality, trade participation, and economic and social conditions across different regions.

Table 1. Summary of gender-relevant indicators used for the PCA analysis

Component	Gender equality dimension	Indicator
PC 1	Education and social conditions	Restricted access to productive and financial assets index (SIGI)
		Adolescent birth rate (births per 1,000 women ages 15–19)
		Discrimination in the family index (SIGI)
		Ever-partnered women and girls subjected to physical and/or sexual violence by a current or former intimate partner in the previous 12 months (% ages 15–49)
		Labour force participation rate among prime-working-age individuals who are living in a household comprising a couple and at least one child under age 6, female (% ages 25–54)
		Population with completed secondary education or higher, female (% ages 25 and older)
		Restricted physical integrity index (SIGI)
		Women of reproductive age whose need for family planning is satisfied with modern methods (% ages 15–49)
		Youth not in education, employment or training, female (% ages 15–24)
PC 2	Trade and labour participation	Average monthly earnings of female employees in high exported value added industries (2017 PPP $)
		Average monthly earnings of female employees in tradable sectors (2017 PPP $)
		Share of female employees in high exported value added in industries (%)
		Share of female employees in high export-intensive industries (%)
		Share of female employees in top 5 export-intensive industries (country level) (%)
		Share of female employees in tradable sectors (%)
PC 3 & 4	Political empowerment and participation in decision-making	Restricted civil liberties index (SIGI)
		Share of managerial positions held by women (%)
		Share of seats held by women, local governments (%)
		Share of seats held by women, parliament (%)

Source: Data for the analysis is collected from multiple sources, UNCTAD Gender-in-trade indicators -—
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Note: Indicators are grouped into gender equality dimensions based on the PCA results.

Trade policy as a driving force to bridge gender inequality gaps

Gender equality amplifies human progress, economic growth, and social development. Governments play a crucial role by allocating funds for essential services, investing in education, social support and legal reforms shaping policies and taking actions to enhance economic empowerment, and combatting gender-based barriers and violence.

UNCTAD, jointly with UN Women -—
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Trade policy is increasingly recognized as a means to address gender disparities and promote inclusive trade. Today, approximately one-fourth of RTAs incorporate gender-related provisions -—
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Incorporating stand-alone gender chapters in trade agreements has enhanced the visibility of gender issues in trade policymaking, especially through requirements to assess progress with agreed indicators. UNCTAD has developed ways to link statistical data to assess the gender impacts of trade agreements, with support by the European Commission -—
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Data and analysis are key to understanding the complex dynamics essential for achieving more gender equal trade. These dynamics are shaped by various factors, including domestic labour markets, cultural norms, legal frameworks, international markets and economic fluctuations. Such interactions are highly country-specific, what works in one country may not yield results in another.

Table 2. FTAs with gender chapters

Agreements with gender chapters	Type	Year
Chile-Uruguay FTA	Bilateral	2016
Chile-Argentina FTA	Bilateral	2017
Chile-Brazil FTA	Bilateral	2018
Chile-Ecuador FTA	Bilateral	2020
United Kingdom-Japan FTA	Bilateral	2020
Canada-Chile amended FTA	Bilateral	2019
Canada-Israel amended FTA	Bilateral	2019
United Kingdom-Australia FTA	Bilateral	2021
United Kingdom -New Zealand FTA	Bilateral	2021

Source: UNCTAD Trade, Gender and Development Programme

Trade agreements have the potential to significantly advance gender equality, particularly when they recognize and harness the transformative power of gender equality for our societies. Economies with higher productive capacities tend to achieve greater gender equality and more inclusive growth. Gender equality is essential for long-term structural transformation by improving human capital allocation, but sustainable economic development also relies on aggregate demand and macroeconomic policies. While the global dataset offers new insights into gender and trade, linking country-level micro-data is needed to inform effective policy action. Greater involvement of women and civil society representatives in negotiation and monitoring of trade agreements is critical to enhance positive outcomes of trade policies. UNCTAD supports governments in sensitizing trade officials to gender implications, enhancing data and analytical capacities, and carrying out ex ante impact assessments.

★ UNCTAD in Action ★

Gender and Trade

2 200 people trained on UNCTAD’s e-learning courses on trade and gender in 2015-2023.

Since 2015, UNCTAD’s e-learning courses on trade and gender have addressed the knowledge gap about how gender and trade interlink, and how trade policies can contribute to reducing gender inequalities, targeting especially developing and least developed countries. To date, nearly 2 200 people (62 per cent women) in 154 countries have benefitted from 25 iterations of the online course (figure 15). Participants speak about their experience in this video.

Figure 15. Over 60 per cent of people trained in UNCTAD’s e-courses on trade and gender were from Africa in 2015-2023

Source: UNCTAD Trade, Gender and Development Programme

Six countries compiled new indicators and more than 500 experts were trained.

UNCTAD spearheaded efforts on gender equality in trade statistics following the Buenos Aires Declaration -—
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This informed the development of Compilation Guidelines -—
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Table 3. UNCTAD trained more than 500 people in 11 events on gender and trade statistics

Type of workshop	Number of workshops	Total number of participants
Regional workshop	4	302
National workshops	6	188
Interregional training workshop	1	32
Total	11	522

Source: UNDA project “Data and statistics for more gender-responsive trade policies in Africa, the Caucasus and Central Asia”

Increased capacity of over 500 small-scale traders in six countries.

Informal cross-border trade is largely driven by women as one of the few options available to them due to their time constraints, limited access to resources, and lower education levels. Despite their critical role, they often face challenges, such as regulatory barriers, high duties, poor border facilities, weak border governance, corruption and harassment, resulting in minimal benefits from trading.

Small-scale traders also encounter challenges beyond checkpoints, including information gaps on rules, regulations and market demand. Additionally, supply side obstacles such as difficulties in business registration and limited capital resources further impede their success.

To address these barriers, UNCTAD initiated a capacity-building programme for women in small-scale informal cross-border trade. The programme aims to raise awareness of trade rules and customs procedures, enhance entrepreneurial skills (see UNCTAD in Action on Empretec) and facilitate dialogue with border authorities. In 2019-2023, 18 workshops were delivered focusing on trade rules, customs procedures and entrepreneurship at 9 border crossings in Botswana, Kenya, Malawi, Mozambique, Tanzania and Zambia, benefiting 547 cross-border traders, most of which were women (map 1).

Map 1. Over 500 small-scale traders trained on trade procedures and entrepreneurship skills, 2019-2023

Source: UNCTAD Trade, Gender and Development Programme

International treaties and agreements related to gender equality

Numerous international agreements aim to promote gender equality, urging countries to safeguard women’s rights.

Figure 16. Timeline of international treaties and agreements related to gender equality

Source: UNCTAD

The Convention on the Elimination of All Forms of Discrimination Against Women (CEDAW) is a landmark treaty adopted in 1979, obligating signatory states to combat discrimination against women in various fields, including politics, law, employment, education, and healthcare -—
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Notes

1. For instance, UNCTAD analysis of three major global gender equality indices (The Global Gender Gap Index (GGI), The Gender Inequality Index (GII), The Social Institutions and Gender Index (SIGI) revealed that none of them reported indicators related to trade.
2. WEI is comprised of 10 indicators related life and good health (two indicators); education, skill-building and knowledge (two indicators); labour and financial inclusion (two indicators), participation in decision making (three indicators); and freedom from violence (one indicator). GGPI is a composite index that assesses the relative achievements between women and men in four dimensions: life and good health (one indicator); education, skill-building and knowledge (two indicators); labour and financial inclusion (two indicators); and participation in decision making (three indicators). Source: -—
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3. This analysis is made in relation to the size of the producing firms, not exporting firms. When producing goods for exports, large firms typically use intermediate goods, which are either produced by other (smaller) firms domestically or imported.
4. Georgia’s National Statistical Office was the first to take part in the UNDA project titled "Data and statistics for more gender-responsive trade policies in Africa, the Caucasus, and Central Asia".
5. UNCTAD in partnership with UNDESA and UNDP calculated the costs along six key “transition pathways” that can amplify efforts and speed up progress towards the 2030 Agenda for Sustainable Development. In addition to these six pathways, UNCTAD is working with UN Women to estimate the cost of achieving gender equality for certain gender-related SDG indicators.
6. The study covered the following border crossings: Noe-Elubo border (Côte d'Ivoire–Ghana); Afl­ao–Kodjoviakope (Ghana–Togo); Segbe–Kpoglo (Ghana–Togo); Hillacondji–Sanve econdji (Togo–Benin); Seme–Krake (Benin–Nigeria).
7. In Rwanda, informal cross-border trade is defined as transactions in goods between residents and non-residents of the country that are not documented through official customs clearance system and ultimately not included in the official statistics. They constitute exports and imports of goods and exclude smuggled goods; transit goods; goods properly (100 per cent) declared and verified by the customs officials on declaration documents.

References

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As the world navigates multiple, overlapping crises - from wars and climate disruptions to energy insecurity and fragile supply chains - trade remains a powerful enabler of sustainable development. Developing economies have demonstrated resilience, maintaining a stable two fifths share of global exports in goods and services. Yet this overall strength conceals persistent disparities. LDCs remain far from achieving SDG target 17.11, constrained by structural challenges that limit their integration into global markets. Services trade offers promising new pathways, particularly in digital and knowledge-intensive sectors. But its benefits are unequally shared: over half of all services exports from developing economies are generated by just five economies. Meanwhile, tariff escalation in high-value sectors, such as green technologies, continues to disadvantage countries seeking to diversify and move up the value chain.

These patterns highlight the need for a more inclusive and development-focused global trading system. Trade should be a force for shared prosperity, not geopolitical rivalry, as argued in the UNCTAD SG’s report ahead of UNCTAD 16 -—
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1. Attaining the sustainable development goals through trade
2. Most developing economies lost market share in services exports over the last decade; only a few leading exporters thrive
3. The multilateral trading system has reduced tariffs but not tariff escalation

References

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Donec tincidunt vel mauris a dignissim. Curabitur sodales nunc id vestibulum tempor. Nunc tortor orci, sodales nec eros eget.

This chapter provides some discussion and analyses on a few of the many dimensions of inequality that exist. The chapter begins by outlining some of the typical approaches to measuring inequality before discussing inequality from the specific context of the 2030 Agenda. Some analyses are then presented for global economic and income inequality. Further analyses are presented regarding gender inequalities in the field of international trade and access to banking. The chapter concludes by highlighting an emerging but very important dimension of inequality – access to data and information.

Reducing inequality

Inequality, and how it affects economies and societies, is a growing concern shared by politicians, economists and the global community. There is an emerging consensus that existing levels of inequality are not only morally unacceptable, but also economically and politically damaging and corrosive (Deaton, 2013; UNCTAD, 2013, 2014; Stiglitz, 2012).

Hence the growing interest in trying to assess whether globalization and the emergence of new technologies have exacerbated or improved the situation. Inequality has implications far beyond simple economic development, as it is recognized that it can be damaging to society, even threatening peace and security. Resentment over injustice, unequal access to public goods or social services, or political or social exclusion may all trigger unrest, hostility and violence (Brinkman et al., 2013). From a social justice perspective, discrimination of civil or political rights, of race, ethnicity, language, religion or of legal, political, social freedoms are all sources of inequality. The International Bill of Rights (United Nations and OHCHR, 2003), composed of the 1948 Universal Declaration on Human Rights; the 1966 International Covenant on Economic, Social and Cultural Rights and the 1966 International Covenant on Civil and Political Rights reaffirm the fundamental equality of all human beings. An issue that is also addressed in this chapter is unequal access to data - ‘Unequal access to knowledge and information leads to inequities in the uptake of social protection’ (Roelen et al., 2016, p. 235).

For this reason, the 2030 Agenda, and specifically SDG 10, sets out to reduce inequality within and among countries. In addition to Goal 10, the ambition to reduce inequality is also evident in several other goals. For example, some targets within SDG 4 (equal access to education) and all SDG 5 (gender equality) targets are essentially focusing on inequality. Furthermore, the Global SDG Indicator Framework requires that many of the SDG indicators are disaggregated by sex, age groups, urban/rural or persons with disabilities, thus implicitly targeting inequality.

Definitions of inequality typically refer to an absence of equal dignity, status, rank, privileges, rights or opportunities with others. They often also refer to lack of equal chance and rights to seek success in one’s chosen sphere regardless of social factors such as class, race, religion and sex. Inequality is often a complex amalgam of social, political and economic factors. Goal 10 reflects this broad spectrum, setting a series of targets promoting income growth, social and economic inclusion, equal opportunity, wage and social protection, improved financial regulation, safe migration of people and an improved representation for developing countries in decision-making and global international institutions. Furthermore, Target 10.2¹ explicitly demands equality irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other status.

Measuring inequality

Measuring inequality is typically done by placing specific values along a specific distribution in order to facilitate comparisons with different distributions. While most measures of inequality achieve this to some extent or other, all measures or indices have strengths and weaknesses. For a more detailed discussion, see Deaton (2004) and Milanović (2012). No single measure satisfies all desirable properties, and so the choice of one measure over another involves trade-offs as many of the measurement instruments are not without limitations, problems and biases.² This section outlines some of the indices available and describes briefly their strengths and weaknesses. None can be considered superior, as all are useful given certain contexts. A well-balanced analysis of inequality should look at several measures (United Nations, 2015).

Inequality is often measured using indices. Both the Atkinson’s index³ and the Schultz (Hoover)⁴ index are popular measures. However, perhaps best known of the indices is the Gini index. Named after Italian statistician Corrado Gini, the Gini index or coefficient is a measure of statistical dispersion used to determine inequality among values of a frequency distribution. It can be used to measure the inequality of any distribution. A Gini index of 1 indicates perfect inequality, and 0 (zero) indicates perfect equality. It is a widely used indicator of income inequality or wealth concentration within an economy or society. It indicates how far the distribution of income among individuals (or households) deviates from a perfectly egalitarian distribution.⁵ The Gini index is not a perfect measure of inequality, however. It has been criticized for being more sensitive to movements or changes in the middle of the distribution, rather than the tails where the focus should be placed. It has also been criticized for being difficult to interpret as very different income distributions can have the same Gini index.

Inequality can also be expressed in ratio form. The decile dispersion ratio (or inter-decile ratio)⁶ or the 20/20 ratio⁷ are ratios in common use. Perhaps the best known of the inequality ratios is the Palma ratio of inequality, proposed by Alex Cobham and Andy Sumner (2013). It is based on the proposition by Jose Gabriel Palma that changes in income inequality are almost exclusively due to changes in the share of the richest 10 per cent and poorest 40 per cent. It is the ratio of household incomes of the two tails of an income distribution and it compares the income inequality between the two groups. This index is defined as the ratio of average income per capita of the richest 10 per cent of households to that of the poorest 40 per cent.⁸ The Palma ratio too has its critics, who argue that an increase in the bottom share and an even greater increase at the top would raise the index, despite the poor being better off (Murawski, 2013).

Inequality can also be measured using a variety of statistical units. The various indices and ratios can be calculated at the individual, household, regional, national or even global level. Inequality can also be expressed as within or between comparators. For example, it is not unusual to examine inequality within countries but also between countries.

Measuring inequality in the SDGs

Setting a goal for inequality is conceptually complex as there are many types of inequality and there are many and varied perspectives on inequality as a social and economic problem. Furthermore, there is no consensus among economists as to what level of inequality is acceptable or tolerated (Fukuda‐Parr, 2019). This will most likely change from society to society, from culture to culture. Although there is an emerging consensus that inequality is damaging, there remains a counter perspective that ‘inequality is not always bad. Progress depends on it since society never moves in lockstep’ (Pilling, 2018, p. 117).

Although quite contested during the 2030 Agenda negotiations, inequality was finally recognised as being sufficiently important to deserve a full goal (10). Curiously however, none of the goal 10 targets actually addresses economic, income or wealth inequality directly. Target 10.3⁹, for instance, looks at equality of opportunities by proposing a measure of personally felt discrimination, but there is no data yet. Hence, the IAEG-SDG adopted growth rates of household expenditure or income per capita among the bottom 40 per cent of the population and the proportion of people living below 50 per cent of national median income as the indicator for targets 10.1 and 10.2 respectively. Fukuda‐Parr (2019) and Adams and Judd (2019) have highlighted this issue, asking what are the policy questions being addressed by the targets in Goal 10.

Target 10.4¹⁰, measured by the labour share of GDP¹¹, provides information on the relative share of GDP accruing to workers relative to the share accruing to capital. In 2018, the simple average of labour share in GDP was 53.3 per cent for developed economies, down slightly from 54.3 per cent in 2000. In developing economies, the share fell from 49.8 per cent in 2000 to 33.9 per cent in 2014, when it resumed growth, increasing to 47 per cent in 2018 (United Nations, 2019). To analyse the underlying inequalities, more disaggregated data are required. A more detailed analysis by Schwellnus et al. (2018) found that technological change and greater global value chain participation have reduced labour shares, including by strengthening “winner-takes-most” dynamics among businesses. But this capital-labour substitution has been less pronounced for high-skilled workers, suggesting that policies that raise human capital through education and training will be crucial for better equality and broader sharing of productivity gains.

Global economic inequality

Trying to assess whether global inequality is increasing or decreasing is not a straightforward task. There are myriad forms of inequality. Measuring each type may yield a different answer. If the aim is to determine whether economic inequality is improving or not, then some internationally comparable measures must be agreed upon. These measures must facilitate both spatial and temporal comparison.

Figure 1 shows that over the last 10 years, the global distribution of GDP per capita has become more equal. For example, in 2007, the poorest economies, accounting for 80 per cent of the world’s population, contributed 22 per cent to world GDP. By 2017, their share of GDP rose to 32 per cent. Between 2012 and 2017, however, inequalities in GDP per capita reduced mainly among economies with moderately high income. The relative distance between the richest and poorest economies in the world remained almost unchanged.

Figure 2.a presents a comparison of GDP per capita at constant (2010) prices over a longer time horizon. Here, the contribution of developing¹² and developed economies is benchmarked against a baseline of average world per capita GDP. From this narrow perspective, inequality between developing and developed countries increased steadily between 1970 and 2001, when global economic inequality peaked. This inequality has been driven mainly by changes in GDP per capita of the developed countries. Since 2001 and until 2014, economic inequality between developed and developing countries fell back to levels experienced in the mid 1980’s.

Figure 2.a also illustrates that the fall in economic inequality slowed very considerably around 2015. This pattern is more pronounced if mainland China is excluded. In fact, when China is excluded, the recent trend reverses and economic inequality after 2015 begins to increase again markedly and consistently.

Figure 2.b illustrates the same analysis from a slightly different perspective. GDP per capita in developed economies was between 12 and 13 times higher than that of developing economies in the 1970’s and 1980’s. This ratio rose to 14 times, peaking around the turn of the century, before falling since. In this analysis, the rate of convergence between developed and developing economies has slowed quite considerably since 2014 but has not been arrested. Developments in China had a significant impact on these developments. Excluding China, the ratio between per capita GDP for developed and developing economies was about 10:1 in the early 1970’s. Economic inequality reduced to roughly 9:1 in the mid 1970’s but then began to increase quite steadily until 1999. Thereafter, per capita GDP converged between developed and developing economies excluding China until 2015. Since then economic inequality between the developed and developing economies excluding China has begun to very slowly rise again.

Global income inequality

Considering the prominence of inequality in economic debate at the moment, it is surprising how difficult it was to source and assemble the data presented in figure 3. No globally comparable data exist. Figure 3 below has been assembled carefully from a variety of data sources, but only by making several assumptions, including for example, log normal distributions. Hence the continued importance of GDP per capita for historic international comparisons of inequality – it is one of the only comparable time series that exist (and it should be noted that global GDP estimates are not free of problems). Lakner and Milanovic (2016) have highlighted the need for a globally comparable income survey.

Figure 3 shows how the income distribution has shifted steadily to the right between 1950 and 2016, illustrating a general improvement in global income. But the evolution of the global income distribution over the past 66 years or so has not been linear. A striking feature of figure 3 is the disappearance of the distinctive camel shaped, two peaked, distribution of the 1970’s and 1980’s that begins to merge back into a single peaked distribution from 2000. The 1980 distribution had two peaks, one centred close to PPP US$1 per day and another close to PPP US$30 per day. By 2008, the second peak had begun to flatten, with the distribution centring around PPP US$4.6 per day, had broadened to include more of the population.

During this period a very large, richer minority emerges, reflecting incomes in the advanced economies of the world pulling further away from the rest. By the turn of the century this extreme divide is less evident, but nevertheless still persists. By 2016, the LHS of the distribution has flattened, pulling more of the population towards the centre of the distribution, and suggesting greater equity. Nevertheless, the steeper RHS still shows a large minority with relatively high incomes, signifying persistent global income inequality.

Today, the top percentile (top 1 per cent of the population, accounting for approximately 76 million people) live on an average income of US$172 per day. In contrast, the poorest 50 per cent of the population (approximately 3.8 billion people) live on an average income of only US$4 a day. Comparing 2016 with 1950, the average income of the bottom 50 per cent of the population, slightly more than doubled, from PPP US$1.75 to 3.85. For the top 1 percent, average income trebled, from PPP US$57 to 172.

In recent years, the idea of a living wage has gained some prominence. The Global Living Wage Coalition have published living wages for several developing countries.¹³ By taking an average of these living wages¹⁴, weighted by country population, an average living wage of US$8.1 per day for developing countries is calculated. While this is a crude measure, it provides a threshold with which we can divide the global income distribution (see figure 4).

Figure 4 shows that 3.9 billion people or 52 per cent of the world’s population lived below the average living wage for developing countries or US$8.1 a day in 2016.

Inequality in gender and international trade

While many countries, businesses and socio-economic groups have reaped gains from international trade, billions have been marginalized or excluded. Trade reforms may have contributed to reducing income inequality between countries, but at times these measures have also coincided with widening income inequality within countries. Context-specific factors influence the impact trade has on inequality. For instance, trade affects women and men differently depending on existing gender disparities in production and consumption, through labour market structures, and disparities in access to resources and opportunities (UNCTAD, 2014).

UNCTAD (2018b) has proposed a conceptual framework to measure the gender and trade nexus within official statistics. Subsequently, in 2019, Statistics Finland carried out the first ever study on gender and trade by linking statistical micro-data from various business and social surveys and registers. This study suggests that the benefits from international trade are not distributed equally between women and men in Finland (Lindroos et al., 2019). In 2016, only 18 per cent of entrepreneurs in exporting firms were women, and women accounted for 27 per cent of the labour input of exporting firms on an FTE basis.¹⁵

While trading enterprises are, on average, more productive and generally pay higher salaries compared with other firms in Finland, they employ less women and have a higher gender wage gap. The results show that female business owners hire more women and more highly skilled women than male business owners. Productivity is higher in male-owned exporting firms but wages are higher in female-owned exporting firms (see figure 5). These preliminary results suggest that lower female participation in international trade may also exacerbate differences in capital and salary incomes between women and men.

The 2030 Agenda includes several targets relating to inclusive trade, such as aid for trade (target 8.a), special treatment for developing countries (target 10.a), open and non-discriminatory trade (target 17.10) in addition to targets relating to empowering women (targets 5.b, 5.c and 10.2) etc. More data and statistics are needed to monitor the transformation towards inclusive trade that provides greater and more equitable access to the benefits of global markets.

The 2030 Agenda also addresses financial inequality through Target 8.10¹⁶, which aims to improve access to formal banking and financial services. Although indicator 8.10.2¹⁷ has been classified by the IAEG-SDG as tier I, for many countries, statistics are only available for 2017 and in many cases with no sex disaggregation. However, other statistics on individuals’ and businesses’ access to finance are available.

According to the Global Findex database (World Bank, 2016), in 2017 about 1.7 billion adults remained ‘unbanked’, i.e. without an account at a financial institution or through a mobile money provider. In 2014, that number was 2 billion. Regardless of the increasing share of adults who have an account, inequalities persist as women are still less likely to have an account. In developed economies, 92 per cent of women and 93 per cent of men have an account (see figure 6). The gender gap is also relatively small in transition economies (about 3 percentage points), and largest in Africa where 47 per cent of men, but only 36 per cent of women, have an account. In the developing countries of America and Asia, the gender gap is about 8 percentage points. While the overall proportion of account owners increased between 2011 and 2017, the gender gap in account ownership also increased in all regions, other than developing Asia.

Recently, the spread of mobile money accounts has created new opportunities to close gender gaps especially in African countries were these accounts are becoming common. Mobile accounts appear to be more accessible for women and men alike and for the poorest that may otherwise be excluded from formal financial systems.

Financial inequalities can also be assessed by looking at businesses’ access to and use of funding, e.g. by firm size, industry or gender of the owner. In OECD countries, women-owned or managed businesses used bank loans as a source of financing at significantly lower rates than men in 2018 - 14.5 and 19.5 per cent respectively. This may reflect both gender bias in lending but also the different types of business activities in which women and men engage. Some countries do not have a notable gender gap in businesses’ use of bank loans. This is the case for the Russian Federation, South Africa, Spain, Sweden and Turkey, while a large gap was observed in Germany, Greece and Israel (OECD, 2018).

New dimensions of inequality

In a data driven world, access to data and information is essential. Barriers to access are creating a new dimension of inequality. With the data revolution, a new cold war has begun – a war between individuals, corporations and States for control of our personal data (Wired, 2018). At stake in this war is individual privacy; sovereignty of data ownership; weaponization of data; and the use of algorithms.

While there are a wide variety of elements that contribute to ‘data inequality’, such as language or literacy, this section concentrates on two: (1) lack of access to technology to access data; and (2) lack of access to data itself.

Lack of access to technology

In 2018, the International Telecommunication Union estimated that global Internet penetration was only 51 per cent, although it was as high as 81 per cent in the developed world. Notwithstanding that global coverage is improving rapidly, it still means that in 2018 almost half of the world's population did not use the web or web-based services. Digital divides exist because a wide range of access barriers exist, such as: gender; social; educational; geographic; or economic. The offline population is disproportionately comprised of women, elderly, less educated, people with lower income and those living in rural areas. In developing economies, the proportion of women using the Internet is five per cent lower than for men. A gap almost three times larger is observed between individuals living in urban and rural areas.

While SDG indicators 17.6.2¹⁸ and 17.8.1¹⁹ measure the availability of fixed Internet broadband subscriptions and the use of Internet, more information is needed to understand how people use Internet and what kind of skills they need to maximise the benefits of using it. These data would help policymakers address the related socio-economic inequalities and provide education addressing the skills gaps to avoid exclusion.

The digital divide - limited or no access and connectivity to the web or mobile phones – is creating a data divide. To quote William Gibson: 'The future is already here - it's just not very evenly distributed' (The Economist, 2001). In an increasingly digitized world, anyone who is not connected to the web or using a mobile phone will not only find it increasingly difficult to access data and information, but furthermore, as they will not create a digital footprint, they may also find themselves effectively excluded from many new statistical indicators which increasingly rely on digital data as their source.

Lack of access to and use of data

One of the biggest contributing factors to data inequality is the lack of access to it. Many data are proprietary – typically commercially or privately-owned data are unavailable to the public. For example, data generated from the use of credit cards, search engines, social media, mobile phones and store loyalty cards are all proprietary and are not publicly accessible. While there are sound commercial and privacy reasons for this, the growth in proprietary data is exacerbating the split between ‘the data haves and have-nots’.

This poses some challenging questions for the open data movement as the asymmetry in openness expected of private and public sector data may be inadvertently contributing to the growth in inequality. Many 'open data' initiatives are in fact drives to open government data.²⁰ This of course makes sense, in that tax payers should own the data they have paid for with their taxes, and so those data should be public, within sensible limits. But arguably people also ‘own’ much of the data being held by search engines, payments systems and telecommunication providers too. After all, these data were created through their labour and activities, and so they can legitimately lay some claim to their ownership. Hence the exclusive focus on public or government data is somewhat problematic. The philosophy of open data should be more evenly applied to avoid creating asymmetrical conditions.

This is important as the availability and use of data and statistics contribute to societies with more empowered people, better policies, more effective and accountable decision-making, greater participation and stronger democratic mechanisms (UNECE, 2018).

Data, statistics and information have a central role in 2030 Agenda (United Nations, 2015). The importance of having access to information is clearly recognized. For example, Targets 12.8²² and 16.10²³ set out the aspiration that people should have access to information to help live a sustainable life and to protect fundamental freedoms. Arguably, data are even more central to 2030 Agenda than is immediately obvious. For example, Target 1.4²⁴ aims to give men and women equal rights to economic resources and access to basic services. In a data driven world, data must be considered both an economic resource and a basic service. Furthermore, data should be considered as an integral part of a State's infrastructure. SDG Target 9.1²⁵, which deals with reliable and sustainable infrastructure, although presumably not drafted with data in mind, nevertheless summarises perfectly the requirements of a global statistical system. It should consist of quality, reliable, sustainable and resilient infrastructure, including regional and trans-border infrastructure. In other words, a global statistical system not only contains high quality data and statistics, but well designed and robust codes, identifiers, classifications and mechanisms for transmitting and disseminating those data (MacFeely and Barnat, 2017). Equitable and affordable access to data and statistics is fundamental to supporting economic development and human well-being.

To achieve the ambitions of 2030 Agenda, Governments need access to more statistics, and the capacity to use them, to inform policy formulation and evaluation. Avendaño et al. (2018) use text mining to evaluate the use of statistics in national development plans and poverty reduction strategies by identifying 572 keywords. Their assessment covers 102 developing countries and 199 documents from across Africa, America, Asia, Europe and Oceania, spanning the years 2000 to 2017. Figure 6 shows the increased use of data and statistics between two waves of national development planning to advance progress towards the 2030 Agenda.

While it is difficult to define the monetary value of having reliable statistics, some attempts have been made. Bakker et al. (2014) estimated that each New Zealand dollar invested in the census in New Zealand generated a net benefit of five dollars in the economy. Benefits of a similar magnitude were estimated for the 2011 population census in the United Kingdom. Further, a study comparing developments in Wales and England, after Wales ceased publishing school performance statistics, in 2001, while England continued, estimated a return of US$17 for every dollar invested in school statistics (UNECE, 2018). Manyika et al. (2013) have estimated the potential global economic benefits of open government data and shared private data to be about three trillion dollars annually.

A concluding caution

There are many faces to inequality. Such a complex issue can be difficult to understand. Depending on the variable selected, or the time horizon analysed, global inequality may be said to be falling or rising. For example, looking at global economic inequality over a ten year horizon, inequality can be said to be falling. From a shorter time horizon, say the last three years, the picture is less clear. Furthermore, global economic inequality may be falling while simultaneously economic inequality within countries may be increasing. There are also many faces to data quality. Caution should be exercised when discussing economic inequality in general as the quality of GDP estimates vary enormously, particularly in developing countries with large informal economies (Jerven, 2015; Pilling, 2018). It is quite possible that current estimates of GDP in many developing countries are underestimated, thus overstating global economic inequality. In other countries, particularly those where MNEs dominate the domestic economy, GDP may be a particularly uninformative indicator of economic development and may serve to only distort international comparisons. For global income inequality the converse may be true, as often the highest incomes prove very difficult to measure using traditional survey instruments, thus introducing the risk of understating global income inequality. The same is true of every other measure - caution should always be exercised. As Muller (2018) wisely counsels "measurement is not an alternative to judgement: measurement demands judgement" (p. 176). Rosling et al. (2018) have also highlighted the need to be skeptical about conclusions derived purely from number crunching.

Caution should also be exercised regarding the ownership of, and access to data. The concentration of power in this field, must surely be a cause of growing concern (Reich, 2015; Lagarde in Reuters, 2019).

Notes

1. Target 10.2 – By 2030, empower and promote the social, economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other status.
2. For example, household surveys may exclude households of linguistic minorities, those without telephones or fixed addresses, nomadic households, and households in distant or difficult-to-reach locations. They typically exclude the homeless or those without a fixed address.
3. A popular welfare-based measure of inequality. It presents the percentage of total income that a society would have to forego in order to have more equal shares of income between its citizens.
4. It shows the proportion of all income which would have to be redistributed to achieve a state of perfect equality. In other words, the value of the index approximates the share of total income that has to be transferred from households above the mean to those below the mean to achieve equality in the distribution of incomes.
5. This is typically done using the Lorenz curve. Developed by American economist Max Lorenz, the Lorenz curve is a graphical representation of the distribution of income or wealth. It shows the proportion of overall income or wealth held by the bottom x per cent of households. Many economists consider it to be a good measure of social inequality.
6. It is the ratio of the average income of the richest decile of the population to the average income of the poorest decile.
7. It compares the ratio of the average income of the richest 20 per cent of the population to the average income of the poorest 20 per cent of the population. Used by the United Nations Development Programme Human Development Report (called “income quintile ratio”).
8. Palma index (per capita) = [(Income share held by the highest 10 per cent)/10] / [(Income share held by lowest 40 per cent)/40].
9. Indicator 10.3.1 – Percentage of the population reporting having personally felt discriminated against or harassed within the last 12 months on the basis of a ground of discrimination prohibited under international human rights law.
10. Target 10.4 – Adopt policies, especially fiscal, wage and social protection policies, and progressively achieve greater equality.
11. Indicator 10.4.1 – Labour share of GDP, comprising wages and social protection transfers.
12. Including UNCTAD transition economies.
13. Bangladesh, Brazil, China, Colombia, Dominican Republic, Ghana, Guatemala, India, Kenya, Malawi, Nicaragua, Pakistan, South Africa and Vietnam.
14. In countries where one or more living wages are published per country, the lowest living wage was taken.
15. These figures are based on the FOLK data on occupational status that also include detailed data on employees' earnings and their formation as well as background information on the employer.
16. Strengthen the capacity of domestic financial institutions to encourage and expand access to banking, insurance and financial services for all.
17. Indicator 8.10.2 – Proportion of adults (15 years and older) with an account at a bank or other financial institution or with a mobile-money-service provider.
18. Indicator 17.6.2 – Fixed Internet broadband subscriptions, by speed.
19. Indicator 17.8.1 – Proportion of individual using the internet.
20. For example: the OECD Open Government Data (OECD, 2019) is a philosophy, and increasingly a set of policies, that promotes transparency, accountability and value creation by making government data available to all. In the United States, Data.gov (2019) aims to make government more open and accountable. Opening government data increases citizen participation in government, creates opportunities for economic development, and informs decision making in both the private and public sectors. In the European Union, there is a legal framework promoting the re-use of public sector information (EU, 2013).
21. The HDI includes three components: income (gross national income per capita), education (years of schooling) and health (life expectancy at birth).
22. Target 12.8: By 2030, ensure that people everywhere have the relevant information and awareness for sustainable development and lifestyles in harmony with nature.
23. Target 16.10: Ensure public access to information and protect fundamental freedoms, in accordance with national legislation and international agreements.
24. Target 1.4: By 2030, ensure that all men and women, in particular the poor and the vulnerable, have equal rights to economic resources, as well as access to basic services, ownership and control over land and other forms of.
25. Target 9.1: Develop quality, reliable, sustainable and resilient infrastructure, including regional and trans-border infrastructure, to support economic development and human well-being, with a focus on affordable and equitable access for all.

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Trade multilateralism plays a crucial role in fostering global economic stability, promoting inclusive growth, and ensuring a predictable trading environment. The WTO has been undergoing reform discussions to address challenges such as dispute settlement inefficiencies, special and differential treatment for developing countries, and the need for updated trade rules in areas like digital trade and sustainability. Strengthening multilateral cooperation remains essential to counter protectionism and to support a fair, rules-based global trading system. The report to the Sixteenth Conference of UNCTAD, emphasizes the need for enhanced multilateralism and a stronger UNCTAD role in global consensus-building, including UN-wide initiatives, regional and multinational organizations, and South-South cooperation frameworks, such as GSTP, AfCFTA, the G20 and ASEAN -—
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Two-thirds of world trade is tariff-free, but significant duties remain in agriculture and industry

Figure 1. In 2023, about two-thirds of international trade are free of tariffs

Percentage (SDG 17.10.1)

Source: UNCTAD calculations based on -—
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Rising tariffs in green sectors make it harder for countries to move up the value chain

In sectors, such as renewable energy and electric mobility, tariff structures affecting critical minerals play a crucial role in shaping supply chain dynamics, investment flows, and the competitiveness of developing economies in global markets.

Table 1. MFN applied simple average tariffs in trade of raw critical minerals are lower than those in electric vehicles that utilize these minerals

Percentage (SDG 17.10.1)

Group of economies	Stage 1: Raw minerals	Stage 2: Processed minerals	Stage 3: Battery materials	Stage 4: Battery packs	Stage 5: Electric vehicles
Developed economies	1.0	1.9	2.3	3.6	4.6
Developing economies excluding LDCs	3.9	3.8	4.4	9.4	17.0
Least Developed Countries (LDCs)	6.6	6.0	6.8	14.1	16.5
All economies	4.2	4.1	4.7	9.8	15.0

Source: WTO, ITC, UNCTAD, -—
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Note: Simple average of the three latest available years, except for Switzerland, for which the reference year is 2024.

Recent developments in tariff wars have cast uncertainty over the future of multilateralism. In early April 2025, the United States imposed a baseline 10% tariff on almost all imports to the country and sharply raised duties on certain Chinese products to rates exceeding 100%. These new measures build on earlier protectionist policies, which were intensified during the United States–China trade tensions of 2018–2019 and further reinforced amid the global supply chain disruptions caused by the COVID-19 pandemic. Tariff escalation underscores a shift towards a more fragmented global trading environment, thereby reinforcing the urgent need to strengthen multilateral efforts to safeguard open trade flows.

References

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