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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—-. Ensuring fairer rules, broader participation and stronger international cooperation will be essential to expand opportunities and make trade work for all, especially for countries still striving to overcome structural barriers and fully participate in the global economy. These goals were stated in the UNCTAD Bridgetown Covenant -—
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—- and remain relevant today; the following sections describe related challenges:

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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The persisting lack of financing for sustainable development is worsening in 2025. With ODA falling for the first time in five years (down 7.1% in 2024), and a stagnant overall FDI to developing economies, with declines in 2024 observed in Latin America and the Caribbean (-12%) and Asia (-3%), as well as a drop of SDG-related investments (-26%), heads of state and government gather at FfD4 this year to discuss how to reshape the global financial architecture for sustainable development. South-South cooperation, grounded in peer-to-peer partnerships, knowledge exchange and non-financial support, plays a critical role in sustainable development for all, reinforcing other mechanisms, especially through mutual support, cooperation and knowledge sharing. The Bridgetown Covenant -—
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—- strongly emphasized the essential contribution of ODA, private investment, and South-South and triangular cooperation, in addressing the challenges related to development finance, including mounting debt of the most vulnerable economies.

Approaching UNCTAD 16, redefining international support to developing countries is high on the agenda. The following chapters address related challenges:

1. Bringing South-South cooperation into focus: the journey to measurement
2. With falling foreign direct investment, investment-promoting regimes increasingly more important to reach the SDGs
3. Costlier debt servicing undermines the achievement of the SDGs
4. Measuring illicit financial flows for stronger domestic resources

References

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The Bridgetown Covenant underlines the importance of “boosting resources, private and public, and domestic and international” and their “effective employment” in delivering on the 2030 Agenda -—
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—-. In the SDG progress report -—
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—-, the UN Secretary-General urged world leaders to come together at the SDG Summit in September 2023 to deliver a Rescue Plan for People and Planet centred around three major breakthroughs: equipping governance and institutions for sustainable and inclusive transformation; prioritizing policies and investments that have multiplier effects across the goals; and securing a surge in SDG financing and an enabling global environment for developing economies. This SDG Pulse In Focus offers analytical, data-driven input to these efforts based on experimental costing of SDG indicators using official statistics.

The 2030 Agenda can be seen as a universal agenda for investment in sustainable development. But progress towards the SDGs is off track and slipping increasingly out of reach. Effective action is hampered by the lack of overall understanding of the financing needs required to achieve the SDGs. It is difficult to align the national budgets, investment and financing flows, or debt relief with the needs for achieving the SDGs when there is no data to guide decisions.

In recent years, approximately 50 countries have estimated the costs of achieving selected SDGs reflecting national priorities based on guidance developed by the Inter-agency Task Force on Financing for Development -—
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—-. These efforts have shown that better data and understanding of the costs can strengthen financing for national sustainable development priorities and feed effectively into national planning. These estimates are, however, tailored for specific national purposes and the global picture of the need for SDG financing remains weak and incomplete.

The 2023 SDG Pulse In Focus discusses experimental cost estimates calculated for 60 countries, including 21 developing economies¹, initially covering 20 SDG indicators including their breakdowns, and spanning across the transition pathways. The study covers more than 45 per cent of the global population. This analysis, however, focuses on developing economies covering over 35 per cent of their total population. The SDG transition pathways on energy, education, jobs and social protection, digitalization, food systems and climate, as communicated by the UN Deputy Secretary-General -—
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—-, are the focus of the analysis. UNCTAD has developed an SDG costing methodology that considers synergies and trade-offs, i.e., the impact of spending on one SDG across the other goals (see Annex I). This method can be used to identify strategies and best practices to maximize the effect of spending on the achievement of the SDGs. When effectively implemented, systematic financing through the pathways can serve as catalysts for change to focus on sustainable solutions and put human rights and equality at the centre.

The work is inspired by the UN Secretary-General’s SDG Stimulus which calls for a massive increase in financing for development, including humanitarian support and climate action, and is part of the related UN-wide efforts, led by UNCTAD with UN DESA and UNDP to pool expertise across the UN and provide effective tools and methods to cost the achievement of SDGs across the transition pathways (Figure 1). That effort is carried out in collaboration with UN Women to ensure gender focus and with IFAD, IEA, ILO, ITU, ESCWA, UNEP, UNESCO, UN-Habitat, UNICEF and other interested partners.

The method used in this study is based on countries’ official statistics on government expenditure by sector² compared to development outcomes measured using countries’ SDG indicator data. Indicators of government effectiveness, political stability and absence of violence and terrorism, as well as FDI (net inflows) were used as control variables. While the input data consist of official statistics, the analysis is based on SFM and as such the results are estimates that include uncertainty (see Annex I). However, we hope that even provisional estimates regarding optimal spending have the potential to inform policies on how to accelerate progress towards SDGs.

While this study focuses on the total costs of achieving SDG transition pathways for the covered SDG indicators, UNCTAD World Investment Report has previously estimated the SDG investment gap to finance capital expenditure -—
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—-. This effort focused on private, cross-border investment and linked SDGs to investment sectors, such as energy, infrastructure or food and agriculture to facilitate SDG-aligned investment. The 2023 World Investment Report -—
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—- will provide an update of the estimate of the SDG investment gap, at the midpoint of the 2030 Agenda.³ This work responds to a request by the General Assembly -—
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—- for the World Investment Report “to focus on promoting investments for sustainable development as well as concrete recommendations, including on strategic sectors to invest for the implementation of the 2030 Agenda”.

The work presented in this study stems from a UN-wide collaboration -—
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—- to develop SDG costing methods and tools, and the role of UNCTAD “as the focal point of the United Nations for the integrated treatment of trade and development and interrelated issues in the areas of finance, investment, technology and sustainable development” -—
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—-. The study provides the first preliminary estimates using a new methodology based on SDG indicators and official statistics and offers a starting point for further work to expand these estimates and enhance the methodology. In a world without data gaps, this analysis could be done for all SDG targets and countries. Thus, the resulting analysis of critical data gaps is also a key value-added highlighting this exercise for much needed investment in key statistics.

The United Nations has identified the below six transition pathways (Figure 1) to maximize efforts towards achieving the 2030 agenda and to enable translating global commitments to support for country-level implementation.

Figure 1. Six transition pathways to sustainable development include 12 SDGs

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The costing exercise covers 20 SDG indicators to date

The aim of this experimental study is to cover SDG indicators included within the six transition pathways with sufficient country coverage and in a way that can be costed. The SDG indicator framework includes 231 unique SDG indicators, of which 163 are classified as tier I -—
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Out of 163 tier I SDG indicators, 109 fall within the transition pathways and 91 of these could in principle be costed at a country level. Indicators that are global in scope, like ‘the number of countries…’, cannot be costed for national achievement, and some are not relevant to all countries globally. Furthermore, even for some tier I indicators country coverage is low, especially for developing economies. Therefore, this first analysis estimates the costs of meeting the target values of the 20 SDG indicators and their breakdowns within the pathways (Table 1).

This study analysed a time series spanning from 2005 to 2021. The indicators spread across nine SDGs among the twelve included in the transition pathways. This analysis could potentially be extended to other SDG indicators and countries following further data collection efforts.

This study applies an SFM method which is valuable for analysing the structure and interactions of producer performance, its determinants and synergies. It is interesting when applied to SDG achievement since countries’ performance is subject to strong influence by government decisions, priorities and expenditures. The public sector plays a central role in steering the pursuit of the 2030 Agenda and seeking financing to that end, especially in health and social sectors, but arguably also in environmental protection. This study incorporates both recurrent and capital expenditures. However, due to the focus on government expenditures, the role of private investment is not fully captured, and it is significant for specific SDGs, like investment in digital technology, and varies by country, e.g., as some countries may have a notable share of privately provided health or education services. This is mitigated by control variables on government effectiveness, political stability and absence of violence and terrorism, and FDI net inflows. Furthermore, the selection of categories included is based on the statistical significance between government expenditure and the related policy measures. The 2023 World Investment Report will complement the picture with its focus on the private investment perspective.

Major increases needed to support social protection and decent jobs

Critical to the advancement of SDGs 1, 2, 3, 4, and 5, this pathway is about pursuing wellbeing, access to essential services, equality and human rights. This exercise considers the costs of achieving the targets for access to basic services (target 1.4), investment in rural infrastructure and agriculture (SDG indicator 2.a.1), education completion and proficiency in reading and mathematics (target 4.1), and reducing child mortality (target 3.2), fighting tuberculosis (SDG indicator 3.3.2) and ensuring access to vaccines (3.b.1). The study also considers the requirements of achieving gender equality in the parliaments and local government (5.5.1) and in time use on unpaid domestic and care work (5.4.1).

From 2023 to 2030, achieving these indicators is estimated to require an average annual per capita cost of US$1 194 (or 13.7 per cent of GDP) in the 21 developing economies covered.⁴ This value illustrates the total financing required to achieve the SDG indicators covered for the social protection and decent jobs pathway and could be funded from various sources of financing.

Achieving the indicators costed⁵ presents unique challenges and opportunities for countries. Afghanistan grapples with elevated neonatal mortality rates for neonatal (SDG indicator 3.2.2) and under-five year-olds (3.2.1), coupled with restricted access to vaccines (3.b.1) and low education results (4.1.1 and 4.1.2). Addressing these challenges in Afghanistan would demand significant yearly spending, approximately 22 per cent of GDP or around US$3.3 billion per year. Some countries, like Armenia, have made commendable progress with near-universal access to basic services (1.4.1) and an impressive vaccination rate (3.b.1) but challenges remain with the high mortality of under-five year-olds (SDG indicator 3.2.1) and the low gender parity in national parliaments (5.5.1). In Azerbaijan, prioritizing education has resulted in high completion rates (SDG indicator 4.1.2), but further improvements are needed in health (3.2.1, 3.2.2, 3.3.2 and 3.b.1) and women's political participation (5.5.1).

Bolivia, along with other countries, faces challenges in health and education results, marked by high under-five year-olds’ mortality (3.2.1) and prevalent tuberculosis (3.3.2). Other countries, such as China and Thailand, have made substantial progress while focused efforts are still needed. China stands out with its robust agriculture orientation of government spending (SDG indicator 2.a.1). Additionally, 94 per cent of China's vast population has access to basic services (1.4.1). Kazakhstan has excelled in education results (4.1.1 and 4.1.2) and vaccination rates (3.b.1). The Maldives, for instance, have achieved complete access to basic services (1.4.1). Thailand, despite challenges, is making promising strides toward eradicating tuberculosis (3.3.2).

It is essential to note that improvements in unpaid care and domestic work participation (SDG indicator 5.4.1) present a long-term challenge for the countries surveyed. For instance, Bolivia's score stands at 1.69,⁶ indicating a significant imbalance in unpaid care responsibilities. Afghanistan, with a score of 8.57, shows a stronger need for considerable efforts in achieving gender equality in unpaid domestic and care work. This goal, crucial for gender equality and economic productivity, may not be attained until after 2030, underscoring the need for persistent focus and strategic actions.

Digitalization is a key driver of progress across the 2030 Agenda

This pathway promotes tech-driven economies, enhancing innovation, job creation, and economic outcomes contributing to SDG 9. Digitalization influences structural change through its impacts on productivity, employment, sectoral linkages and trade -—
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—-, and has therefore important effects across the 2030 Agenda. Digital divides and related skills bias may also hamper progress in structural transformation towards higher value-added activities.

On the digital transition pathway, the two first indicators costed include the manufacturing share of value added in GDP (SDG indicator 9.2.1), and the universal coverage of mobile networks (9.c.1). Their achievement from 2023 to 2030 is estimated to cost US$325 per capita annually (3.4 per cent of GDP) for the 21 developing economies covered.

There are large country differences in progress within this pathway, and in the role of the government therein. For instance, Afghanistan, despite its needs, shows promise with a manufacturing share of value added at 27 per cent of GDP (SDG indicator 9.2.1) and a 57 per cent mobile network coverage (9.c.1). China and Türkiye show strong progress within the digital transition pathway with high manufacturing value added share and near-universal mobile network coverage. Smaller economies like Armenia and Mongolia impress with 100 per cent mobile network coverage and have in practice bridged the digital divide for mobile access.

However, achieving universal mobile network coverage and increasing manufacturing share to over 20 per cent of GDP presents challenges for many countries. Sustained efforts, including bolstering digital literacy, digital infrastructure investment, and creating a favourable environment for innovation and entrepreneurship, are vital for harnessing digital transformation's potential for growth. UNCTAD’s efforts to support entrepreneurs in this regard are discussed in UNCTAD In Action.

Education to unlock innovation for sustainability

The education transition pathway underlines the significance of quality education (SDG 4), empowering individuals with the knowledge and skills essential for sustainable development and related research and development (SDG 9). The study considers the costs of achieving 90 per cent proficiency in reading and mathematics (SDG indicator 4.1.1), 97 per cent education completion rate (4.1.2), higher manufacturing value added share (9.2.1) and universal mobile network coverage (9.c.1) (the latter two are also included in the previous section).

Achieving these SDG indicators as part of the education transition pathway is estimated to cost US$422 per capita annually for the developing economies studied (corresponding to 4.8 per cent of their GDP). Armenia, and Kazakhstan show high achievement levels, with near-universal education completion rates (SDG indicator 4.1.2) and impressive digital inclusion metrics for mobile network coverage (9.c.1). Conversely, countries like Somalia, and Mauritius lag significantly behind on both indicators. According to -—
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—-, it was estimated that the cost of achieving the key SDG 4 targets, including universal pre-primary, primary, and secondary education in low- and lower-middle-income countries by 2030, would amount to US$461 billion on average between 2023 and 2030. These figures highlight the significant financial resources required to meet the targets and emphasize the importance of adequate funding to support the implementation of education initiatives in these countries.

The later discussion on synergies, underlines the important effect on education spending across the 2030 Agenda, including through enhanced human capital, socio-economic development, reduced inequality, higher innovation, and sustainable industrialization.

Food system transition remains challenging for many developing economies

Overall, the food system transition pathway aligns with the zero-hunger goal (SDG 2) and underscores the essential role of agriculture for food security, to improve rural livelihoods, and to bolster resilience. This study considers the cost of developing rural infrastructure and agriculture (SDG indicator 2.a.1), and protecting important sites for terrestrial, freshwater and mountain biodiversity (15.1.2 and 15.4.1). This costing exercise, however, does not consider the achievement of the zero-hunger target and could be extended in the future.

The estimated average annual per capita cost for developing economies to achieve indicators covered for this pathway is US$225. These contribute to enhanced food security, sustainable agricultural practices, mitigating climate change impacts, and conserving biodiversity.

While some countries have made significant strides in agricultural expenditure orientation (SDG indicator 2.a.1) and protected area coverage for biodiversity (15.1.2 and 15.4.1), others are far from the targets. The agriculture orientation index for government expenditures indicates efforts in supporting the agricultural sector (2.a.1). Some countries, like El Salvador and Thailand, have high scores, demonstrating their prioritization of agricultural development. However, there is room for improvement in many countries where the index remains low.

While some countries, like Thailand, have made notable progress in biodiversity conservation (SDG indicators 15.1.2 and 15.4.1), others struggle to reach higher levels of protected area coverage. The food system pathway presents an opportunity for countries to address the complex interplay between food production, climate change, and biodiversity conservation, and is an important area for further efforts to fill data gaps and enable an extended analysis of indicators and countries.

Energy transition has the potential to boost sustainability across sectors

The energy transition pathway aims to enable access to electricity, renewable energy and reduce energy intensity towards SDG 7 and taking urgent action on climate change towards SDG 13. This study considers the cost of achieving universal access to electricity (SDG indicator 7.1.1), increasing the share of renewables (7.2.1) and reducing energy intensity (7.3.1). The cost of taking action to address climate change merits an extended study and has not been considered yet.

Achieving these indicators requires yearly spending of US$586 per capita (6.7 per cent of GDP) in the 21 developing economies considered. Earlier the Sustainable Energy for All consortium -—
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—- estimated that the requirements are in the range of US$1.0–1.2 trillion annually from 2012 to 2030 to achieve universal access to modern energy services, double the global rate of improvement in energy efficiency, and double the share of renewable energy in the global energy mix. This would have required tripling the level of investments in 2010. But as years have gone by, and as we have also seen some setbacks, the annual spending needs have increased. The report estimated that the bulk of the resources would be needed for energy efficiency and renewable energy interventions.

The -—
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Among developing economies studied, ten have achieved universal access to electricity (SDG indicator 7.1.1), while additional efforts are needed in Myanmar (70 per cent) and Somalia (80 per cent) to get closer to 100 per cent -—
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US$1 839 per capita needed each year

The overall cost estimate for reaching the selected SDG indicators is US$1 839 per capita for the 21 developing economies. This does not consider the available financing sources or the potential remaining financing gaps, but rather the overall spending needs per year.

The sum of costs for pursuing the selected SDG targets across the pathways, discussed above, and if pursued individually, would be US$2 751 per capita. Considering synergies across goals and pathways, the actual overall cost, US$1 839 per capita, is only two thirds of the separate costs. This speaks to the systemic nature of the 2030 Agenda. While some SDG indicators are overlapping between pathways, even removing them would leave higher costs for individual pathways. This underscores the importance of leveraging synergies between the SDGs and the transition pathways. This chapter will also discuss those synergies in maximizing positive spill over effects and minimizing trade-offs across the 2030 Agenda.

A comparison of several SDG costing studies reveals a large variation in the estimates of the funding needs to achieve the 2030 Agenda. Estimates from different studies range from about US$250 to over US$1 839 per capita annually (see Annex II). The variation does not seem to be driven only by country or SDG coverage, but also by the choices made related to methodology, sector, and data sources. It should also be noted that the financing needs depend on the time left to pursue the SDGs and the current distance from them. The closer we get to 2030, the higher the annual cost to bridge the gap.

Dollars spent with a gender focus yield better results

Financing for SDGs should consider gender perspectives specifically, because if it does not, it risks reinforcing existing gender biases, or leading to otherwise unexpected outcomes. Gender equality and the empowerment of women and girls is recognized under the 2030 Agenda not only as a standalone goal but also as a prerequisite for addressing the world’s most pressing concerns, including poverty, inequality and climate change, and for greater peace and prosperity. Empirical evidence shows a robust connection between greater gender equality and improvements across key economic outcomes, including growth, productivity, and competitiveness -—
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Gender equality, however, is still a distant goal that the world has yet to achieve. More women and girls live in extreme poverty today than men and boys, and hunger and food insecurity are a routine part of the lived reality of millions of women and girls. By the end of 2022, around 383 million women and girls lived in extreme poverty compared to 368 million men and boys -—
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Targeting financing across the SDGs with a gender focus, including for social protection and decent jobs, is crucial to get the world back on track. UN Women et al. -—
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—- estimates that over 100 million women and girls could be lifted out of poverty if governments were to employ a comprehensive strategy aimed at improving access to education and family planning, fair and equal wages, and expanding social transfers. Moreover, the analysis presented in the SDG Pulse In Focus shows a synergistic relationship between government spending on key sectors such as agriculture, transportation, energy and housing to accelerate progress towards gender equality.

According to this study, equal representation of women and men in parliaments and local governments and in unpaid domestic and care work would be attainable by 2034 for 50 per cent of the developing economies included in this study and, with a few exceptions for the 39 developed economies, with the right spending and policy mix. In agriculture, for example, support for women’s access to and control over resources can boost progress on SDG 2 to eradicate hunger in addition to SDG 5 on gender equality. Similarly, public transportation and housing are essential for improving lives and the wellbeing of those left furthest behind, especially the estimated 560 million women and girls living in slum settings around the world -—
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Efforts to develop sustainable agriculture and clean energy technologies⁷, including modern cooking stoves, can reduce women’s unpaid domestic work burden -—
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—-. Among developed economies, the model points to many different ways to optimize government spending to boost progress towards target 5.4 on the value of unpaid work and target 4.2 on pre-primary education, including the synergies of spending on housing and health.⁸ Spending on social protection, including maternity benefits, parental leave and childcare subsidies are key drivers of accelerated progress on target 5.5 to ensure women’s full and effective participation and equal opportunities in political, economic and public life among developed economies in the sample.⁹ Interestingly, the synergistic relationship of spending on agriculture and housing appeared more effective to advance female political empowerment in developing economies.¹⁰

While having a gender lens in designing stimulus packages is important, so is a commitment to stable and consistent spending on gender equality over time. More efforts are needed to track resource flows in support of gender equality and women’s empowerment in countries. In 2021, only one in every four countries had such monitoring in place. However, translating resource flows into systems change is crucial for sustainable outcomes -—
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—-. These findings reaffirm the importance of developing gender-sensitive tools to support decision-makers in identifying priorities for accelerated and inclusive progress towards the 2030 Agenda.

Optimizing spending - more with less and sooner?

The above cost estimates are based on an analysis of two scenarios for spending on SDGs. Business-as-usual, BAUS, assumes that the current spending trajectory continues. We arrive at the costs of achieving the targets without any reorientation. This means the costs may be high, and fewer goals are achieved with the same cost. In the optimal scenario, OS, we consider the best practice identified by the SFM. This methodology is useful in analysing producer performance and investigating its determinants -—
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—-, and can provide useful insights on best practices to increase efficiency and achieve SDGs with less costs. It helps to determine optimal growth rates for sectoral spending. The cost estimates discussed above in this chapter are based on the OS scenario.

With a strategic reallocation of sectoral spending, more countries can achieve SDGs sooner with less costs. Figure 2 shows the number of countries achieving the selected SDG indicators by 2030, and how their number increases from the BAUS to the OS. Certain SDG indicators are likelier to be achieved than others, namely those located on the right of the figure. For example, 17 of the 21 developing economies analysed are likely to achieve the targets for under-five and neo-natal mortality rates (SDG indicators 3.2.1 and 3.2.2) in the BAUS option, and up to two countries more in the OS scenario. Universal access to basic services (1.4.1) is challenging as only six countries will achieve it by 2030 unless OS solutions are found which would enable 19 countries to reach the target. Strategic allocation of investment is particularly important for achieving universal vaccination coverage (3.b.1), higher education completion rates (4.1.2), and to reach higher manufacturing value added (9.2.1) and renewable energy share (7.2.1.), as well as universal access to electricity (7.1.1). Here the difference of BAUS and systematic OS strategies that consider synergies are the largest.

Deep cultural transformation to boost SDG achievement

Effective investment combinations can drive the achievement of some SDG indicators very close. In 2024, Egypt and El Salvador are on the brink of achieving universal access to basic services (SDG indicator 1.4.1), with over 80 per cent already achieved in 2021. Azerbaijan and Thailand show promise in excelling at education completion (4.1.2). Kyrgyzstan and Uzbekistan have made significant strides in ensuring universal access to electricity (7.1.1). China, Egypt, and South Africa have demonstrated progress in mobile network coverage (9.c.1). Challenges remain for universal coverage of populations with vaccinations planned in the national programme (SDG indicator 3.b.1). (Figure 3)

Even if not immediate, several SDG indicators are on track for achievement before 2030 assuming efficient allocation of spending. This could happen for Egypt, Thailand, and China with universal access to vaccines (SDG indicator 3.b.1), Bolivia, Maldives, and Myanmar in education completion (4.1.2), and Guatemala, Indonesia, and Uzbekistan in access to electricity (7.1.1). El Salvador, Indonesia, and Kazakhstan have the potential to achieve broad mobile network coverage (9.c.1). However, challenges lie ahead for other developing economies in achieving targets measured by these indicators.¹¹ Regardless of optimal spending scenarios, some indicators on the far right and out of sight remain challenging to reach. Optimal scenarios need to be supported with other policy measures to address many cultural and other barriers to progress.

High public spending helps but is no guarantee

Higher government spending is generally associated with better outcomes in achieving SDGs. To assess this, we compared government spending with the -—
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—- scores (Figure 4). However, this relationship is not consistent across all countries, as highlighted by Afghanistan's high government spending (48 per cent relative to GDP) but low SDGs Index score (49). Finland, with high government spending, equalling 56 per cent of GDP, emerges as the top performer also in terms of the SDG Index score of 88. Notably, Ireland stands out as interesting case, where government spending is relatively modest (25 per cent) compared to GDP, but the country achieves a comparatively high SDGs Index score (80). This highlights the importance to consider factors beyond spending, such as governance and policy implementation. Adequate funding combined with strategic planning and stakeholder collaboration are necessary for achieving the SDGs.

Understanding synergies is key to SDG achievement

The analysis shows strong interconnections between government spending across sectors and SDGs. This underscores the critical need for an integrated, holistic approach to financing the SDGs. Spending on education is particularly effective in fostering achievement of outcomes across all SDGs. Similarly, spending on 'information and telecommunications' and 'health' can substantially enhance ‘education’ outcomes, creating mutual benefits.

The highest synergy coefficient of 7.5 is found for 'health' and 'education' spending. A synergy coefficient of 7.5 implies that a unit increase in combined spending on 'health' and 'education' is associated with a 7.5-fold improvement in achieving the SDGs. This suggests that the combined output of the education and health sectors has a significantly stronger impact on the SDGs compared to the sum of their individual outputs. Working on these two sectors together can lead to enhanced performance in achieving the SDGs, surpassing what could be achieved by focusing on them independently. Similarly, combined spending on 'agriculture' and 'education', or on 'housing' and 'education' yield notable synergy coefficients of 6.8 and 6.0, respectively. Synergies are also found in combined spending on 'health' and 'social' sectors, and on 'transport' and 'social' sectors, with coefficients of 2.5 and 1.7, respectively.

Systematic strategies that consider spillover effects and trade-offs can multiply effectiveness. The following figure illustrates the synergies of sectoral spending.

The analysis finds positive synergies between spending on ‘environment’ with that on ‘agriculture’, ‘housing’ and ’transportation’, illustrating the cross-cutting synergies of an environmental focus for the achievement of many SDGs. Yet, a trade-off is observed for spending on ‘fuels and mining’ and ‘environment’, primarily due to the environmental degradation caused by fossil fuels.

Understanding these synergies is essential for strategic public spending decisions, to optimize positive impacts and minimize trade-offs.

Investing in data can help target scarce resources

Halfway through the 2030 Agenda, many data gaps, specifically in developing economies hamper the analysis. This study is thus limited to 60 countries, 21 of which developing, where data for selected SDG indicators are available (Map 1). Albeit providing for 45 per cent of world population and accounting for 80 per cent of world GDP in 2021, when developing economies are considered, these values drop to 35 and 58 per cent, respectively. Currently, the coverage of countries in Africa and Latin America is critically low and remains to be extended where complementary national data sources can be found.

Costing the key targets of the SDGs is even more urgent in the present context, as multiple policy priorities, be they short, medium- or long-term priorities, imply greater trade-offs in the allocation of scarce resources. The forecast of financial costs for time-bound and target-based development goals is at the core of the methodology underpinning the SDGs.

Figure 6 shows that data for 28 SDG indicators had to be dropped because they did not have the required time series from 2005 to 2021 and covered less than half of the countries. This was 34 per cent of the 91 SDG indicators that otherwise were included within the pathways and could be costed at the country level. In total, 45 unique SDG indicators (yellow in Figure 4), or nearly 50 per cent, were dropped since for countries where these indicators were available, other input data, like government expenditure statistics were not available, and country coverage dropped too low for the inclusion of the SDG indicator. To date, it was only possible to estimate costs for the achievement of 26 per cent of SDG indicators.

This preliminary work is being extended by trying to find complementary national sources especially for government expenditure statistics. The findings also point to the critical need to invest in data and statistics to fill gaps and produce more comprehensive analysis of SDG financing needs.

Further work to inform efforts to accelerate progress towards the 2030 Agenda

The findings of this study provide early insights into pursuing more systematic and planned financing for sustainable development and gives indications of the financing needs for the countries and the SDG transition pathways for SDG indicators covered in this study. While the full analysis of the costs by SDG and optimal spending allocations are still pending, this study serves as a foundation for further research and extension. The complex nature of the work, involving numerous countries and indicators, necessitates more time for a comprehensive assessment of costs and synergies.

Credible cost estimates can only be compiled with methodologies that rely on reliable data and statistics. This bottom-up estimation starting at country levels and based on analysis of progress using SDG indicators and official statistics on government expenditure by sector enables a detailed analysis of financing needs by SDGs and their indicators or by pathway. The results are far from comprehensive due to the lack of statistics on government spending and owing to the remaining gaps in SDG indicator reporting. Further efforts to examine complementary national data sources will be taken. However, eventually the robustness and comprehensiveness of cost estimates depends on countries’ capacity to compile these key statistics.

This study serves as a starting point by providing the first experimental cost estimates covering selected SDG indicators across the SDG transition pathways. This analysis can be further refined and extended in a UN-wide collaboration and jointly with partner organizations. This could mean extension of country or SDG indicator coverage where data can be identified, focused regional or thematic analysis by different UN entities and others, and a more detailed analysis of gender equality considerations in financing SDGs.

To further improve the methodology, it would be beneficial to explore methodological refinements that incorporate determinants of efficiency related to tax revenue, FDI, debts, and ODA. By examining these factors, this costing exercise can gain a better understanding of their impact on SDG financing and identify opportunities to optimize resource allocation and improve efficiency in achieving the goals. For future analysis, it would be useful to select the most relevant SDG indicators for each SDG transition pathways thus avoiding overlap across pathways as far as possible.

This study informs the formulation of financing strategies by highlighting the importance of joining forces, finding synergies, and fostering collaboration among stakeholders. It emphasizes the need for policymakers to try to understand how different sectors and targets interact with each other. Attempts to identify areas for spill over effects and avoid trade-offs will pay off by accelerating progress towards the SDGs.

Such strategies will consider the central role of education as a trigger for progress, factor in the synergies across health, environment, and other sectors, avoid trade-offs that hamper progress in environmental sustainability and pursue the multiple benefits of gender considerations to accelerate progress by involving everyone.

Annex I. SDG costing methodology

Methodology description

The methodology of this report, based on the conclusions of Schmidt-Traub -—
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—-, emphasizes the importance of harnessing synergies within and across sectors to reduce overall financing needs and maximize the impact of spending. These sectors are interconnected through a dynamic system of feedback loops, leading to jointly significant effects on key indicators. For example, spending on transportation can enhance the effectiveness of education spending, while improved education outcomes can reinforce the impacts of the health sector and enhance livelihoods.

In the context of assessing the achievement of SDGs by countries, this study adopts a standardized frontier technology that is applicable to all countries. It employs the same specification function to transform the sectoral government spending of each country into the attainment of specific SDG indicators. The relationship between inputs and outputs in the SFM is captured through a translog cost function.

To quantify the costs required to achieve the targets of the 2030 Agenda, this study adopts a cross-sectoral approach. This approach allows for the inclusion of multiple fiscal indicators, such as sector-specific expenditures as a percentage of GDP, and it considers the interaction terms between these indicators.

This approach offers several advantages. Firstly, it mitigates the issue of overestimated costs that arise from neglecting policy synergies, ensuring a more accurate estimation of the actual costs involved. Additionally, it addresses the limitations of underestimating costs by accounting for nonlinear relationships, particularly the diminishing marginal returns.

To capture these cross-sectional synergies and nonlinear relationships between multiple policy inputs and outcomes of various SDGs, the model employs a joint production estimation. This allows for a comprehensive assessment of the interdependencies and interactions among different policies and SDG targets.

In line with the work of UNICEF -—
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—-, the cost requirements for achieving SDGs are estimated by using a stochastic frontier model based on a translog production function specification taking the following form:

where:

Ln Y_jt = Selected SDG targets for country j (in log form) – for example, Primary Net Enrolment Rates

Ln X_ijt = Spending per GDP on sector i for country j (in log form)

Ln Z_jt = controle variable of country j (in log form)

u_jt = random errors, it assumed to be independently and identically distributed, vi ∼ N (0, σ_v2)

v_jt = inefficiency term, it assumed to be positively defined with an asymmetric distribution independent of those of the u_t

The β_i (i=1,...,11) are the coefficients which illustrate the estimated elasticity of a key indicator with respect to its own-sector spending. These coefficients capture the direct effect of an increase in per GDP sectoral expenditure on the return of a key indicator.

Furthermore, the translog models include quadratic terms for each sector, represented by β_ik with i=k. These terms are included to reflect the fact that increased cost in the same sector often results in diminishing marginal returns.

The last set of coefficients β_ik with i<k, represent the synergy effects of spending in multiple sectors simultaneously, meaning the extent to which one sector’s spending impacts the effectiveness of another sector’s spending.

In order to evaluate whether a cross-sectoral impact is significant, F-test for an incremental contribution is applied.

Calculating business as usual and optimal spending scenarios

Beyond the complicated mathematical theoretical foundations of transcendental logarithmic models, the econometric tanslog models of panel data of BAUS study the asymptotic properties of a vector of significant parameters based on the underlying stochastic representation of growth of each individual. In this same space, the evolution of each SDG is studied as an endogenous variable explained by the forecast of several exogenous macroeconomic variables. So, we deduct the corresponding costs.

For the OS models which focus more on the properties of the best practices using the statistical convergence of stochastic frontiers of each country class (developed and developing). In general, econometricians choose an optimal scenario by penalizing the quality of adjustment of a model by its complexity, ex-post, during the validation or choice phase. This can sometimes result in actions that reduce the efficiency of the system being measured. Whereas in this study, the optimal scenarios are based on the objective function of a country which is considered as best practice. Then the idea is to take optimal combination of scores of an individual reference in space of expenditures as a measure reference for each country.

This approach consists of:

1. Running the Model: The Translog model specification is run under panel data, considering the efficiency term as an error term along with the random term. This allows establishing a frontier and identify the best practice. The results of this model reveal the key determinants of sectoral spending, including direct impacts of sectors, squared terms, and sector interactions - Since the model is non-linear, it provides valuable insights into the relationships between variables.
2. Forecasting the Indicator: To forecast the indicator using the retained model, the input variables need to be incorporated in the model, specifically sectoral spending. There are two alternative approaches:
   1. Assumption 1 (BAUS): In this approach, we assume that sectoral spending (the selected sectors determined by the model) will continue to grow at the same pace. This forecast data is used to calculate the indicator over time and identify the total spending required to achieve the SDG target.
   2. Assumption 2 (OS): In this approach, we consider the best practice identified by the model, using the SFM method. We use the growth rate of spending in this country to forecast the selected sectoral spending and, consequently, the indicator's evolution. This allows calculating the necessary resources needed to achieve the target.

Data sources

The dependent variables, as in the key indicators, were obtained from various databases, including the Global SDG Database -—
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—- and from other international and national sources. Other inputs data, namely, government effectiveness, political stability and absence of violence and terrorism and FDI (net inflows) which were used as variables of control are sourced from and World Bank Governance Indicators -—
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Estimating the precise requirement for achieving the SDGs is a challenging endeavour. Consequently, both recurrent and capital expenditures have been considered in the costing analysis. The analysis incorporates fiscal inputs in the form of government expenditures as a percentage of GDP across various service categories. The selection of these categories was based on data availability and the statistical significance of the connections between fiscal inputs and different policy measures. The inputs encompass a wide range of sectors, including agriculture; fishing, forestry and hunting; fuel and energy; mining, manufacturing and construction; transport; communication; environment protection; housing and community amenities; health; education; social protection; and other sectors. These have been identified using the COFOG.

Annex II. SDGs costing studies

Notes

1. The developing economies covered are Afghanistan, Armenia, Azerbaijan, Bolivia, China, Egypt, El Salvador, Guatemala, Indonesia, Iran, Kazakhstan, Kyrgyzstan, Maldives, Mauritius, Mongolia, Myanmar, Somalia, South Africa, Thailand, Türkiye, and Uzbekistan.
2. Sectorial spending refers to the government expenditures classified according to the COFOG classification. For the purpose of this study, 11 sectors (or functions based on COFOG) have been defined as follows: Agriculture, forestry, fishing, and hunting; Fuel and energy; Mining, manufacturing, and construction; Transport; Communication; Environment protection; Housing and community amenities; Health; Education; Social protection; and Special others. These sectors (also referred to as functions) were selected to represent specific areas of government spending and to analyze their potential impacts on achieving the SDGs. It is important to note that the terms 'sector' and 'function' are used interchangeably in this study. The terms sector and pathway, as used in this study, should not be confused. The sectors represent the specific areas of government spending that were used as inputs in the model, while the transition pathways refer to the interconnected thematic pathways towards achieving the SDGs. The pathways provide a framework for understanding the holistic progress towards sustainable development, while the sectors highlight the specific components of government spending considered in the analysis.
3. The estimates derived in this study are based on selected countries’ SDG indicators and official statistics on government expenditures, while the WIR figures are derived by SDG-sector from the most recent studies published by specialized agencies, institutions and research entities in their respective areas. This study focuses on selected 21 developing economies while WIR covers all developing economies. Furthermore, we quantify the overall financing requirement to achieve the SDGs, while WIR estimates the investment gap. i.e., the additional investment needed to achieve the SDGs. This study does not consider available financing.
4. The model endeavors to optimize the attainment of the goals by 2030; however, it recognizes that the desired optimization has not been fully achieved within the specified timeframe. Particularly regarding SDG indicators 5.5.1 and 5.4.1, it is anticipated that many countries will not reach these objectives before 2030, despite the allocated investments.
5. This study estimates the costs of achieving selected SDG indicators of the pathway, as listed in Table 1.
6. The ratio is derived by rescaling indicator SDG 5.4.1, proportion of time spent on unpaid domestic and care work by sex, for 2001-2021, to a female-to-male time use ratio, where the target is set at 1.03 for equal time use. Scores exceeding 1.03 refer to a higher female share of unpaid domestic and care work.
7. The synergy coefficient between agriculture and clean energy for developing economies is 0.013 and statistically significant at the 10 per cent level.
8. The synergy coefficient for developed economies is 4.4 for spending in education and is statistically significant at the 10 per cent level; the synergy coefficient for the combined spending in health and education is 1.7 and is statistically significant at the 5 per cent level.
9. The synergy coefficient for developed economies is 2.6 for spending on social protection and is statistically significant at the 10 per cent level.
10. The synergy coefficient for the combined spending on agriculture and housing is 1.5 and is statistically significant at the 10 per cent level.
11. This analysis reflects an optimal scenario based on efficient allocation of government spending, highlighting the positive outcomes of optimized spending. See note on BAUS and OS.

References

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“We used to think of progress as if economy, society and environment were separate spheres and that mindset led to the sustainability and exclusion crisis which we are still in now. In reality, they overlap almost completely, and our mindset is changing. We must improve people’s lives while at the same time we protect the environment. That’s why we have 17 goals with 169 targets.” said Ola Rosling when presenting the progress across all the SDGs for world leaders at the first UN SDG Moment event held in September 2020 -—
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—-. The SDG Moment takes place every September as part of the UN General Assembly, to highlight successes and identify where more action is needed to achieve the Agenda 2030.

One of the challenges facing the achievement of the Agenda 2030 is sustainable economic progress. To this end, UNCTAD has developed an inclusive growth index (IGI) to contribute to the goals equal and inclusive prosperity for all. This chapter presents the index which combines aspects of living conditions, inequalities and environment with the economy. The new UNCTAD IGI builds on earlier work by UNCTAD and EEC inclusive growth -—
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—-, first published in 2019, and aims to inform analyses and discussions on the inclusiveness of economies, particularly when taking into account social and gender inequalities. Moreover, as inclusive economy must also be sustainable to meet the needs of future generations, by preserving natural resources, the index includes a number of environmental indicators.

What is inclusive growth? Concept and background

Rising inequality and its impact on economies and societies have raised concerns among politicians, economists and the global community. Economic performance and in particular well-being should no longer be assessed by economic growth only; equality and environmental sustainability should also be considered. Many emphasize that existing levels of inequality are not only morally unacceptable, but also economically and politically damaging and corrosive -—
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Economic growth is still considered the most powerful instrument for reducing poverty and improving the quality of life -—
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—-. Research suggests that growth in average income explains 70 per cent of the variation in poverty reduction in the short run, and as much as 97 per cent in the longer term. Any remaining poverty reduction is accounted for by changes in the income distribution.

But similar rates of economic growth can have different effects on poverty, employment opportunities and human development depending on the country, the underlying conditions and governance. The extent to which economic growth reduces poverty depends on the degree of equal opportunities and freedom to participate in activities generating economic growth and to benefit from those. Thus, both the pace and pattern of growth matter for reducing poverty and inequality. The challenge for policy is how to combine growth promoting policies with measures that build and enhance equal opportunities to participate in the economy and benefit from it. This includes policies to make labour markets work better, reduce discrimination, support equal access to education and skills, and increase economic and financial inclusion in all parts of society in a sustainable manner that protects the planet.

Translating inclusive growth into a measurable concept is not an easy task, however. Information on whether changes are happening and whether those changes are moving towards inclusive growth cannot be easily captured by one indicator. The challenge is that inclusive growth is a multifaceted phenomenon, the main characteristics of which are not easily presented in a statistical form.

In 2012, the United Nations Secretary-General Ban Ki-Moon, speaking at a High-Level meeting on ‘Happiness and Well-being: Defining a New Economic Paradigm’, noted the importance of establishing ‘a Sustainable Development Index, or a set of indicators to measure progress towards sustainable development’ -—
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—-. There are initiatives to measure the inclusiveness and sustainability of wealth by taking into account human, social, produced and natural assets. For instance, the United Nations University’s International Human Dimensions Programme on Global Environmental Change in collaboration with UNEP has developed an Inclusive Wealth Index -—
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—- Comprehensive Wealth Index considers produced, human, and renewable and nonrenewable natural capital, as well as net foreign assets. There is a proliferation of other summaries of inclusiveness, well-being and sustainability. These summaries propose either a composite index or a dashboard approach and have different perspectives, data sources and approaches. This reflects the complexity of measuring the world and the issues that now are included in the ‘progress’ umbrella, such as environmental sustainability; economic stability and sustainability; social equality and health, life satisfaction, and general well-being.

Inclusive growth in the 2030 Agenda

The 2030 Agenda emphasizes the development of productive capacity as the basis for achieving inclusive and sustainable development. As part of that broad shift in emphasis towards economic, equality and environmental issues, the notion of inclusive growth has received a prominent place in the 2030 Agenda, reflected by SDG 8 in particular – ‘promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all’ and Target 17.19 – ‘ by 2030, build on existing initiatives to develop measurements of progress on sustainable development that complement GDP, and support statistical capacity-building in developing countries’.

While the global SDG indicator framework does not directly measure inclusive growth, it includes a number of elements relevant to the concept, including related indicators of economic growth and environmental sustainability which is important from a temporal equality perspective. SDG 8 addresses full employment and decent work; SDG 16 aims to build just and peaceful societies; SDG 10 addresses reducing inequalities by closing socio-economic gaps within and across nations, generations and households; SDG 5 aims to reduce gaps between men and women; SDG 1 and SDG 2 aspire to build a more caring community that protects vulnerable population groups and provides for their most basic needs. Thus, linkages between SDG 8 and other SDGs are several and taken together are consistent with the concept of inclusive growth.

Beyond the 2030 Agenda, there are several dashboards, indicators or analytical approaches that set out to measure inclusive growth or elements of inclusive growth. For instance, increasing economic inclusion is set as one of the goals of the ADB Strategy 2020 -—
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—- defines inclusive growth as economic growth with equality of opportunity. Their indicator framework proposes a set of 35 indicators centered on poverty, social inclusion, social safety and governance. Achieving inclusive growth is one of the three priorities of the -—
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—- strategy called "Europe 2020". In this approach, inclusive growth is defined as support for the population through the provision of high employment rates, investment in acquisition of skills, fighting poverty and modernising the labour market.

The definition of inclusive growth must, therefore, go beyond equal participation and consider how the benefits are shared equally. In this study, we define inclusive growth by building on the 1948 Universal Declaration of Human Rights and in line with the overarching principle of the 2030 Agenda to leave no-one behind. Thus, inclusive growth is defined as equal and non-discriminatory opportunities, for everyone, to both participate in the economy and to benefit from economic growth with consideration of environmental sustainability and emphasis on gender equality.

UNCTAD IGI considers economy, living conditions, equality and environment

The first iteration of IGI consisted of three pillars, namely economy, living conditions and equality -—
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—- and was composed of 21 indicators, including one environmental indicator (CO₂ emissions under pillar 2). This new, expanded IGI includes more equality metrics addressing gender inequality more broadly (see gender section), and includes a new separate pillar dedicated to environmental issues (see environment section). These were highlighted as potential development areas of the original index, and can now be addressed also due to progress with data availability for countries. The extended IGI is comprised of four pillars and 27 indicators (see Table 1).

Taking relevance and general statistical quality into account, a review of data availability was conducted of all the major global statistical databases. As a result, the 27 indicators were selected as being the most relevant to inclusive growth from this angle and offered the best availability of robust data across countries and time (see Box 1).

Developed countries surprisingly different in inclusive growth

The new composite index of inclusive growth can provide insights about country performance regarding gender equality, living conditions or environmental sustainability as compared to economic development.

In general, higher levels of inclusive growth are associated with more economically advanced countries. Luxembourg, Iceland and Norway are among the highest-ranked countries¹ based on the overall index score. The top-ranking 30 countries are all developed economies (see Map 1). Luxembourg (the highest) and Lesotho (the lowest) were not maintained across the other three pillars. Although, Luxembourg also ranked highest in pillar 4, environment as well. The country has been successful in promoting sustainable transport and was the first country in the world to completely abolish public transport fares in February 2020 -—
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—-. Free public transportation is intended to reduce private car traffic, which is a major driver of climate change including CO₂ emissions and pollution, and also to address income inequality.

The overall index shows that more inclusive growth is often not achieved until a certain level of economic growth and prosperity is reached. However, some developing countries score higher than many developed countries. Developing countries in Africa tend to show the lowest index scores, with an average of 35.

Developed countries appear to be the most heterogeneous group characterized by the largest gap in the overall index scores. For example Luxembourg was ranked highest for the economy pillar (100) whereas the republic of Moldova was ranked lowest (11.6). Gaps between developing economies can also be large, for example Singapore scored the highest, with 81.8, compared with only 2.8 in Sudan. Even more noticeable is the difference in living conditions between countries, with Singapore scoring 88.6 and Niger at 7.0. LDCs are the most homogeneous group, with differences in index scores not exceeding 16 points for pillar 1, economy. Both developing and developed country groups are heterogeneous in terms of living conditions, equality, and environment.

The world is highly unequal in living conditions

As noted by UNCTAD member States in the Bridgetown Covenant -—
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—-, high levels of inequality are the main obstacle to sustainable economic growth and poverty reduction. Economic recovery requires evolving policies at all levels to address these issues. Prosperity gaps between and within countries have been widening for decades. Even Before the pandemic, nearly 700 million people were living in extreme poverty, and this vulnerability meant billions of people lacked access to modern technologies, including some now considered essential, such as the internet. Women around the world remain, on average, poorer and more vulnerable than men, regardless of their country of origin. Unfortunately, the remarkable expansion of global trade, investment and technology in recent decades has not benefited everyone.²

An analysis of the living conditions pillar reveals, indeed, large disparities between regions reflecting a lack of opportunities. The wide spread of countries in Figure 1 shows that living conditions in different parts of the world today are very unequal. All developed countries score above 60, except for Romania, Albania and Greece. In the rest of the world, countries like Turkey, Malaysia, Chile and Mauritius all score above 70, while others, such as Nigeria or Guinea reach a living condition score of 13 and 10, respectively. In developing Africa, Mauritius, South Africa, Egypt, Tunisia, Algeria, Morocco and Libya scored between 50 and 70. All the remaining African countries scored below 38 for living conditions. The figure also shows the relation between living conditions and equality, with better living conditions often relating to better equality scores as well, and vice versa.

Breaking down the analysis into indicator levels, the biggest challenge for Africa appears to be access to safe water. In 42 African countries, half of the population has no access to clean and safe water. The challenges of inclusive growth cannot be met without infrastructure reforms, such as ensuring sustainable and safe water systems. For developing countries in the Americas, Asia and Oceania, the challenges lie in the areas of income and gender inequality. The availability of Internet connection and logistics performance vary greatly, for example Tajikistan, Afghanistan, Turkmenistan and Bhutan have an average of 1 in 100 inhabitants with a broadband internet connection. The top three developing countries on pillar equality are Kazakhstan, Argentina and Chile. Many African countries exhibit high gender inequality in terms of labour force participation with the ratio of female to male labour force participation below 0.5. At the same time, to reduce inequalities, all people need an equal opportunity to make a living, no matter who they are and where they are located, and most importantly, have their basic human rights met, including with access to safely managed drinking water.

Developing countries differ greatly in environmental performance

At least since the 1960s, ecological economists and researchers, such as Malte Faber, Nicholas Georgescu-Roegen, Kenneth Boulding, and Herman Daly, have developed economics that take into account limited natural resources and consider sustainable development and issues of intergenerational equity. More recent studies have raised discussions over the potential exhaustion of economic growth in a system where natural resources are limited and keep being depleted. These discussions -—
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—- draw attention to the damaging effects of economic growth to natural life and resources, and the growing waste and pollution problems, not to mention climate change. This discussion is increasingly fueled by growing concerns over social inequality, and the intergenerational sustainability of induced lifestyles.

Countries have responded to these challenges by proposing an ambitious vision for the future in the form of the 2030 Agenda for Sustainable Development -—
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—-. The United Nations Member States pledged to ensure more sustainable and inclusive economic growth, elimination of extreme poverty, reduction of inequalities, and environmental protection.

It is undeniable that we are currently facing some of the most complex and widespread environmental challenges in history. Rapid population growth, climate change, increasing urbanization, and unsustainable consumption have all led to increased stress on the environment and increased conflict over land, water, and energy resources. To effectively address these challenges, it is necessary to measure the interrelations of economy and the environment. However, it is a difficult and complex task, requiring consideration of environmental impact (e.g., environmental impact on biodiversity, human health, ecosystem function, economic production), the spatial and temporal dimensions of environmental degradation (e.g., severity of environmental diseases, occurrence of extreme weather events), and the variety of stakeholder perspectives (e.g., environment activists, corporate executives, farmers, indigenous community members).

According to the IGI concept, economic growth and socially inclusive co-production associated with greater economic opportunity will be unsustainable without efficient and sustainable use of natural resources (water, land, energy, etc.). The key is to create more economic value with fewer resources in order to not compromise people's future well-being. -—
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—-. In its environment pillar, the IGI considers energy intensity, carbon dioxide emissions, water productivity, and protected land area. These address only a subset of ecological challenges, selected based on a literature review and the availability of environmental indicators for a large number of countries. See Box 1 for more details about these indicators.

The environment pillar differentiates developing countries more than economy. Bangladesh and Lesotho, for example, have almost the same score for economic performance (around 8), but around 50 and 25, respectively, for the environment pillar. On the contrary, for developed countries, differences within the economic pillar are greater than those within the environment pillar. On the same environmental score of 40, Iceland and the republic of Moldova scored around 82 and 12, respectively, on the economic pillar. The top 5 developed countries for the environment pillar are Luxembourg (100), Malta (71.4), United Kingdom (68.8), Ireland (68.2) and Switzerland (67.7). Maldives (70.5), Singapore (69.0) and Seychelles (68.6) are the top 3 countries in the developing region. This is largely due to their good performance in water productivity and energy intensity. Switzerland also shows good performance with relatively low CO₂ emissions per unit of GDP.

As mentioned above, this is not representative of all environmental aspects as only 4 indicators are included. Furthermore, these 4 metrics only capture 60 per cent of the total variance. This pillar will be subject to development as countries start producing more environmental statistics and indicators.

Developed countries generate twice as much waste per capita as developing countries

Solid waste management has been gaining importance, especially as the population continues to grow and cities expand. Many developed countries face problems with solid waste because of its high volume and the low rate of recycling. Developing countries tend to have more individual households who manage their own waste, which leads to less reliance on centralized systems. According to the data from World Bank -—
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—-, the amount of solid waste generated per capita in developed countries is around two times that generated in developing countries. This difference is not just due to the higher population density of developing countries, but also due to the higher levels of consumption of developed countries. In developed countries, the main sources of solid waste are from consumption (43 per cent), waste from households (30 per cent), urban development (14 per cent), and industrial production (12 per cent). In contrast, the main source of solid waste in developing countries is from agriculture (58 per cent), with the remainder coming from urban development (26 per cent), waste from households (12 per cent), and industrial production (4 per cent).

Figure 3 shows a positive correlation between economic development and the intensity of waste generation relative to population. Countries that score high on the economic pillar generate more waste per capita than others. These countries are almost all developed countries. Luxembourg has the highest score in the economic pillar, yet it generates almost 800 kg of waste per capita, annually (over 2 kg per person per day). Japan and the Republic of Korea have a better score for waste generation considering the high urbanization and population levels. The republic of Moldova, among some other eastern European economies, scores low in economic performance and generates high amounts of waste per capita -—
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—-. By contrast, countries of developing Africa generate the lowest amount of waste with an average of 0.54 kg per person per day. The same pattern is observed for the rest of the developing countries, including China with high economic performance compared to other developing countries but low waste generation (0.77 kg per person per day). However, there are few exceptions, Singapore has a relatively high economic performance with low waste generation (0.89 kg per person per day). Singapore set up a Zero Waste Masterplan aiming to increase the overall recycling rate to 70 per cent and reduce waste-to-landfill per capita per day by 30 per cent by 2030 -—
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Solid waste affects the quality of water, land and air and large amounts of domestic waste have many negative impacts on humans and ecosystems. Developing countries therefore face the unprecedented challenges of how to avert the low road unfortunately taken by developed countries, and marry economic growth with environmental sustainability. But the question is how?

Rather than following the same unsustainable path that associates higher economic growth with greater solid waste per capita, developing countries may find, with the support of the international community, new paths to move straight from the top left to the top right quadrant of Figure 3, representing higher economic growth with low waste generation.

Decoupling economic growth from natural resource consumption, pollution and waste generation remains a thorny issue for which global actions and solutions are required and pressing. A sustainable future may require not only technological change but also changes in consumption and social practices -—
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Enhanced gender equality analysis of inclusive growth by considering care and social reproduction³

The new IGI puts more emphasis on gender equality than its predecessor by incorporating more gender equality indicators. It takes into consideration the key role of care, drawing on the framework defined by Braunstein, Bouhia and Seguino -—
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Care can be defined as both a process and an output. As a process, it is primarily perceived as a work activity that involves close personal or emotional interaction with those being cared for -—
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—-. As an output, it refers to all paid and unpaid care activities used as inputs in the production and in the maintenance of the labour force. Care activities, whatever form they may take, have considerably contributed to generating, exacerbating and perpetuating inequality between women and men. Everywhere, women are, or have been until very recent times, reported to spend exorbitantly more time on unpaid work and care than men, essentially due to deeply entrenched stereotypes according to which they are more ‘nurturing’ or biologically better endowed to do this work -—
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—-. As a result, forms of social determinism which give women greater responsibility for this work, have prevailed, with social penalties for failure to conform to these gender norms. Gendered norms and stereotypes about care work have been acknowledged as a key contributor to gender inequality in both the market and the home -—
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Inequality stemming from the gender division of care within societies is detrimental as such to women, who have been carrying this ‘invisible’ burden at the expense of their personal, educational and professional growth, but it has also major adverse effects on economic development, especially through its impact on social reproduction. Social reproduction refers to the efforts it takes to produce, maintain and invest in the labour force, and is often expressed in terms of time and money. Braunstein, Bouhia and Seguino -—
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—- illustrate how social reproduction is organised—the extent to which reproduction takes place in the household, public or market sectors, and the gender distribution of the labour in each— and influences current aggregate demand and long-run productivity growth. Those advocating for greater female education and labour force participation assume that women’s care work is solely an impediment to their participation in paid labour. This ignores the labour as a resource that is produced. Care work that women disproportionately perform also has economy-wide benefits by raising human capacities and thus productivity. In the framework of inclusive or comprehensive wealth indices, care work would be seen as an investment in human capital, rather than a cost or a non-productive activity.

The IGI pillar 3 on equality includes conventional measures of gender parity in school and the labour market, through UNESCO’s Gender Parity Index, ILO’s rates for unemployment and labour force participation and the representation of women in national parliaments. The role of care was not directly captured in pillar 3 originally but two important aspects of it, namely public provisioning for care and reproductive infrastructure, were implicitly taken into account in the overall index, through the inclusion in pillar 2 (on living conditions) of several variables reflecting these dimensions. Infrastructure is an often-neglected aspect of the relationships between social reproduction, gender inequality and growth, but a key determinant and outcome of the gender system. It refers to goods like roads, electricity, sanitation and water that decrease the opportunity cost of market work, mostly by lowering the time intensity of care work by women, but also by lowering the price and increasing the availability of care commodities -—
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The first element added to the new IGI is men’s relative contribution to social reproduction, which refers to the gender distribution of both the time and financial costs of social reproduction. The aim is to capture gender differentials in unpaid care time. Regardless of increasing availability of time use studies, there is not even nearly enough data to carry out a historical analysis. The female-to-male ratio of mean age at first marriage was selected from available proxies with the logic that the greater the gap, the greater the gender inequality embodied in intra-household gender relations, and therefore, the more unequal the distribution of unpaid care time. The female-to-male ratio of mean age at first marriage was shown to be highly correlated with the female-to-male ratio of hours spent on domestic work -—
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The gender wage gap is the second element enhancing the new index. It was proxied by the female-to-male ratio of the share of wage and salaried employment in total employment to capture the relative quality and productivity of employment. For developing countries in particular, where self-employment and contributing to family work is often an indicator of residual unemployment, using relative access to wage employment was deemed a reasonable proxy for gender-based wage inequality in the labour market -—
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The last element included is the extent and quality of the market care sector. As women’s service sector work tends to be concentrated in the caring professions, women’s services employment as a share of total employment (men plus women) was used as a proxy for the extent of the market care sector. This measure was discounted by the extent of income inequality in the economy (by raising it to the power of the inverse of the Palma ratio) on the argument that the more inequality, the lower the quality (and pay) of care sector work.

This new gender pillar therefore aims at measuring gender equity as the equal representation of males and females in key social activities such as education and labour participation with a view of ensuring equal opportunities and treatment at the individual level, but also capturing those countries where gender equity is most likely to entail increases in human capacities and settle a genuine virtuous circle of gender equality, through an approach which level up women rather than a race to the bottom. Such virtuous circle of gender equality unlocks the full potential of economies to grow through a better quality and allocation of human capital, but the extent to which this effectively turns into sustainable economic development also depends on aggregate demand and macroeconomic orientation, as pointed by Braunstein, Bouhia and Seguino -—
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Regional disparities in gender inequality persist

Figure 4 reveals contrasted situations with gender equality of inclusive growth across regions. As expected, developed economies tend to show higher scores and are located closest to each other. Nordic countries perform particularly well: all these countries are found at the very top of the distribution, along with some Eastern European economies like Slovakia, Slovenia and Belarus . In contrast, Hungary, Greece and Romania bring up the rear.

Developing Asia is the region with the widest gaps across countries. At the top, some of these countries do better than several developed countries, including a few Central Asian economies (like Azerbaijan and Kazakhstan) and high-income countries like Singapore and the United Arab Emirates. At the same time, a significant number of Asian countries is found at the bottom of the gender equality ranking, including Iran, Jordan and Bangladesh.

The bottom of the distribution is also populated with several African countries, including Egypt, Guinea and Chad. Africa is the continent with the lowest score on average on gender equality (34). However, it hides, like in Asia, stark disparities. Rwanda, Ethiopia and South Africa are the most gender egalitarian, with scores above the median for all countries.

Latin America and the Caribbean appears to be, on average, the most gender egalitarian developing region in the light of this index, with a score reaching 52. It is also more homogeneous. These countries tend to be located in the centre of the world distribution, with Chile, Argentina and Mexico at the top and Paraguay, Honduras and Uruguay at the bottom.

The position of regions relative to each other has not changed much since 2009 (see Figure 4). Although some developing countries clearly improved their situation, the overall catching-up process of the South, observed in previous decades, did not seem to have occurred within these 10 years, pretty much in line with what was observed for other types of inequality. Developed economies have paradoxically reinforced their leading positions, especially with significant improvements in Eastern Europe. African countries have also slightly improved their rankings. Gaps across countries in the African continent have somewhat narrowed, mostly with countries at the bottom of the 2009 distribution catching up those at the top, such as Ethiopia, Ghana and Mozambique (see Figure 5). While developing Americas have maintained their overall position, Asian countries are those which have lost most ranks on average, given few remarkable upgrades combined with significant deterioration notably in Thailand and Kyrgyzstan. Overall, 12 developing countries show outstanding progression by climbing more than 10 ranks. Seven of these countries are located in sub-Saharan Africa.

Gender equality a challenge for low-income countries in particular

Figure 6 reveals a logarithmic pattern: the more economically developed a country, the more gender equality is observed, with marginally declining gains as economic development increases. However, this does not entail a strict causal relationship between gender equality and economic development. Countries showing higher economic development are not necessarily the best gender performers. This is consistent with gender equality, defined with a particular emphasis on care, being a necessary, albeit not sufficient, condition for long-term structural transformation.

Countries that have achieved their structural transformation had to pass stages of development where minimum levels of equal contribution between males and females to social reproduction were required. However, in the shorter term, the extent to which gender equality will foster stable and sustained growth depends on demand and the macroeconomic structure, in particular whether growth is predominantly driven by either the wage or the profit share. In the former case, and if gender equality in care is sufficient enough to contain the adverse effect that greater participation of women may induce on human capacities, and thereby investment, higher wages for women are likely to be good for growth. Gender equality and growth reinforce one another.

The interplay between gender equality and economic structure is particularly challenging for low-income developing countries, as illustrated by the horn shape of the curve in Figure 6. These countries generally struggle to find the right mix to kickstart the virtuous circle where both gender equality and economic orientation contribute to sustainable growth in the same direction, ensuring progress in the SDGs. In this regard, social policies which increase investment in care, going hand in hand with macroeconomic policies targeting aggregated demand, can offer solutions.

Conclusion

The broad concept of inclusive growth envisages economic growth that simultaneously contributes to improving everyone’s quality of life equally. In practical terms however, it remains an open question as to what precisely that means. This has implications for how it should be measured and how it can be achieved.

For the purposes of IGI, inclusive growth was defined as a convergence in the quality of life for all population groups within countries, achieved not only through the governmental redistribution of economic performance outcomes, but also through the creation of favorable, non-discriminatory economic conditions, that allow each population group to achieve self-sufficiently quality of life comparable to other groups and contributing to the improved quality of life of the entire population and in a sustainable manner.

The IGI, accompanied by sub-indices and its pillars, can facilitate an understanding of trade-offs and produce rankings, which can be useful in understanding the impact of policy choices as countries need to prioritise elements of economic development, education, labour and political participation, depending on local circumstances. In this study, a dual approach is adopted whereby inclusive economic growth is examined, using a global composite indicator with rankings, combined with principal components or pillars, and assessing challenges and successes by reviewing results for individual subindicators.

As multiple factors affect the inclusiveness of economic growth, policymakers struggle to design effective measures. Statisticians too face challenges in trying to quantify inclusive growth with its complex interrelations with wellbeing and sustainability. Overall, the IGI shows the wide spread of countries’ performance in living conditions, equality and environmental sustainability, underlining the insufficiency of economic growth as a sole measure of progress of nations. It calls for more comprehensive and balanced policies to advance wellbeing that is sustainable and equal, and strong enough to close persisting differences between countries and regions, and address pressing inequalities of opportunity and outcome.

Notes

1. The overall IGI has been compiled for 96 countries for which all information needed for the 4 pillars were available. Thus, the ranking does not take into consideration the missing countries.
2. Conclusions of UNCTAD's fifteenth session (TD/541/Add.2, paras. 28 and 29).

References

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Overcoming the tyranny of distance to achieve sustainable development

Little has been said about the challenges and opportunities associated with remoteness for achieving the 2030 Agenda for Sustainable Development. Remoteness or isolation is an important dimension of vulnerability; one that is not always negative. Isolation or geographic remoteness can create unique, resilient communities with strong traditions and cultures, help preserve rare or fragile ecosystems; and as witnessed over the last 18 months, shield communities from the worst effects of global pandemic.

Building a strong economy may require more innovation in a distant location without natural trade relations with bordering countries and with long distances to markets that offer higher volumes of demand. Remoteness results in higher costs of connecting to global value chains that need to be overcome to ensure competitiveness. Remoteness can also be especially challenging for small economies where domestic demand is insufficient for sustained economic growth, forcing businesses to access far-away destinations to reach larger markets.

Remoteness has many attributes other than just geographical distance. A standard dictionary definition of remoteness is typically comprised of two parts: The first focuses on physical distance (the geographic dimension) and the second on a lack of connection. Due to its multidimensional nature, remoteness can influence all aspects of sustainable development.

The 2030 Agenda -—
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—- set 17 goals for sustainable development addressing economic, social and environmental development challenges with the principle of leaving no one behind. In view of that principle, it is important to consider the specific challenges and opportunities faced by remote economies, such as small island developing states, some LDCs or LLDCs that must start their pursuit of sustainable development from a more challenging baseline.

The plight of island nations has been an issue of analyses and concern going back to the 1960’s. The SIDS, that set of countries recognized as being particularly vulnerable to economic and environmental shocks, was first formally recognized at the Earth Summit -—
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—-, held in Rio de Janeiro, Brazil in 1992. But the international community had recognized that developing island countries were a special category from a developmental perspective long before that.

For instance, -—
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—- discusses the characteristics of island societies, noting that remoteness and smallness are their most distinguishable characteristics. He uses the term ‘tyranny of distance’ and lists the related challenges: high transport and communication costs; barriers to market access; fragile environments; dis-economies of scale and scope; limited division of labour; segmented market; remoteness or insularity; high-cost economy; over-blown public sector; and a high dependency on tourism. Kakazu finds that because of their smallness, remoteness and openness, island economies have a distinctive economic structure.

These findings are reflected by the -—
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—- “Development and Globalization: Facts and Figures” which provides statistical analysis of the economic, environmental and social situation of SIDS. The report notes that goods production in agriculture and manufacturing has declined in relative terms in many SIDS, while services like tourism, financial intermediation and the public sector have gained prominence.

Among the many challenges faced by SIDS, remoteness remains one the most formidable and deserves a comprehensive in-focus analysis in relation to the SDGs. Greater distance from markets translates into increased costs, including transportation and insurance, weakening the competitiveness of domestic products in international markets and increasing the import bill. It typically means isolation from the main transportation routes or corridors, potentially making supply of resources more costly and unreliable. Additionally, infrastructure projects, such as those enabling connections to energy and communication networks, are more costly to implement and maintain.

Nevertheless, some small island economies have achieved high income levels based on exports, not of goods, but of financial, logistical or tourism services, for instance Singapore and the Bahamas. Indeed, an analysis of the new -—
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—- PCI shows that SIDS’ productive capacities are highly correlated with human development, and that GDP per capita is highest in SIDS which have succeeded in transforming from agriculture to service activities, not necessarily through industrial transformation. Moreover, in the context where financial flows can move from one side of the planet to the other instantaneously and where a growing share of value added comes from the digital economy and intangibles, physical distance is no longer the impediment it once was. This illustrates how digital connectivity can alleviate at least some of the obstacles brought about by geographic isolation.

Cantu-Bazaldua -—
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Remoteness relates to more than just geographical distance from markets resulting in higher transportation costs. It also involves integration into transport networks, as well as political and cultural linkages. Thanks to the greater importance of the digital economy, access to and performance of digital networks is gaining greater importance. This chapter presents the main dimensions of remoteness and proposes indicators for measuring them in the context of the sustainable development of SIDS.

In the outcome document of the most recent global conference on SIDS, signatory countries called on the United Nations, its specialized agencies and relevant intergovernmental organizations to “elaborate appropriate indices for assessing the progress made in the sustainable development of small island developing States that better reflect their vulnerability and guide them to adopt more informed policies and strategies for building and sustaining long-term resilience”, as well as requesting “the tracking of progress and the development of vulnerability-resilience country profiles” -—
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—-. The indicators proposed herein represent a contribution to this direction.

This chapter studies remoteness as geographical distance adjusted for connectivity. All things being equal, a greater distance imposes additional costs and increases the isolation from markets and people. However, better connectivity could considerably reduce the distance premium. An economy can be distant from others yet well connected (Australia, for instance). While a country cannot control its physical location, it can influence its connectivity through targeted investment in infrastructure and through greater participation in cultural and political networks.

Remoteness has multiple dimensions

From a policy perspective, the broader analysis of remoteness introduces a more complete monitoring of sustainable progress, fully taking into consideration one of the most salient challenges faced by SIDS. More importantly, although location and geographical distance cannot be changed, the expanded definition of remoteness considers factors that can be improved through targeted investment and appropriate policies. This can serve as guidance when analysing the approaches taken by some small island economies to reach a high national income level in spite of their geographic remoteness.

Distance could be measured with respect to main populated areas, markets or sources of financing, for instance. Connectivity could refer to transport routes, socio-cultural linkages or digital networks, among others. Cantu-Bazaldua -—
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—- provides a discussion of dimensions of remoteness (see figure 1) and proposes a set of indicators for measuring them.

Figure 1. Dimensions of remoteness

1. Geographical distance from markets. This traditional dimension of remoteness indicates geographical proximity to other territories and separation from economic centres. It will be measured using three variables: distance to nearest neighbour, distance to economic centres, and distance to trading partners.
2. Distance from financing sources. While distance is not an obstacle for financial flows, financial activity tends to cluster around centres, where most business and investment decisions are made. Countries far from these centres risk falling off the radar. The indicators include distance to business centres, distance from sources of FDI, and distance from ODA donors.
3. Distance from cultural and political centres. A frequently neglected aspect of remoteness is the potential isolation from the centres of cultural and political power. These are the countries with a great deal of influence in defining international rules, shaping global discourse and setting cultural trends. This dimension will be assessed as the distance to the main centres of global soft power¹ and the countries with the strongest global presence, as measured through international indicators available in the literature.
4. Transport connectivity. Well-developed transport links could ease the burden of distance, facilitating the inflow and outflow of products and people. Maritime, air and land connectivity are measured in this dimension.
5. Social and political connectivity. It is important to consider also the cultural or social connections of a country with the rest of the world. This dimension is studied through indicators on the number of immigrants in the country and the stock of nationals living abroad, foreign (tertiary) students registered in the national education system and nationals studying (tertiary education) abroad, foreign diplomatic representations in the country, and membership in economic, trade, defence or other alliances.
6. Digital connectivity. For digital economy to mitigate disadvantages of geographic remoteness, ICT infrastructure needs to be well developed with widespread access to these tools among businesses and individuals. This dimension will be assessed through three indicators: Internet access of the population; international bandwidth per Internet user, as a proxy of the available Internet infrastructure; and the latency rate, a measure of network performance.

Source: UNCTAD deliberations based on Cantu-Bazaldua -—
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Cantu-Bazaldua -—
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—- includes complete information on the variables considered, including their definition, data sources, and details on imputation methods. His paper also includes summary statistics for all the variables. The variables considered vary considerably in terms of data ranges and units of measurement and are thus transformed to a 0-100 scale through a min-max transformation to facilitate comparison. The variables will be presented for all SIDS, as well as aggregates for relevant comparison groups.² The visualizations use lighter colours to indicate a higher relative remoteness. Unless otherwise indicated, data refer to 2019.

SIDS are situated far from their main markets

SIDS are situated in remote locations as measured by distance to their nearest (non-SIDS) neighbour (figure 2, column 1). While the global (weighted) average is a distance of only eight km to the nearest neighbour, an average citizen from a SIDS has to travel 371km to the closest non-SIDS country. Moreover, the distance ranges from zero for those SIDS sharing a border with another country, to 3 264km required to cover the distance from the Marshall Islands to its nearest non-SIDS neighbour (Indonesia). Tuvalu, Nauru and Samoa also register a high remoteness according to this variable.

It is also important to consider the distance to the largest countries to appreciate the economic opportunities for trade, investment, cross-border interactions and spillovers. SIDS are located far away from the main economic centres (figure 2, column 2), as measured by the average distance to countries weighted by their GDP. Different SIDS regions are situated in relative proximity to some large economic centres (e.g. Caribbean islands) but far from others. On average, SIDS are more remote than other country groups, such as LDCs or LLDCs, and especially when compared to all middle and high-income countries. According to this indicator, the most remote SIDS is Tonga, with an average (weighted) distance of 12 175km, followed by Fiji, Vanuatu and Samoa. However, the top 5 most remote countries according to this variable are not SIDS, but are mostly located in Oceania and South America, including New Zealand, Australia, Chile, Argentina and Uruguay, in that order; Tonga is ranked sixth.

SIDS are not necessarily more remote than other country groups when distance to trading partners (figure 2, column 3), weighted by their bilateral trade (exports plus imports of goods) has been taken into account. In fact, the average distance for all groups is remarkably similar, suggesting that countries tend to specialize in products and services tailored to nearby markets. However, for SIDS, there is a relatively high dispersion, ranging from the Bahamas (3 806km) to the Marshall Islands (8 864km), with Suriname, Cuba and Mauritius also registering high trade-weighted average distances. While the Marshall Islands is the SIDS economy most distant from its trading partners, it is only twelfth in the world rankings. The top 5 most distant countries using this variable are Chile, Brazil, Peru, New Zealand and Argentina, in that order.

SIDS are more distant from financing sources than others

The three distance variables from financing sources are correlated as the countries with the largest companies are also the main sources of other types of financing (in this case, private foreign investment and development assistance). Across all three dimensions, SIDS are on average more distant from financing sources than other country groups. High-income countries and LLDCs tend to be closer in proximity to origins of financial flows.

In terms of distance from main business centres (figure 3, column 1), measured by the revenues of the largest 500 firms, Tonga is the most isolated SIDS, followed by Fiji, Mauritius, Vanuatu and Samoa. However, from a more global perspective, the extremes are located in South America (Uruguay, Argentina, Chile, Paraguay, Brazil, Plurinational State of Bolivia), Oceania (New Zealand and Australia) and Southern Africa (Lesotho, South Africa).

The five SIDS with the greatest distance from FDI sources (figure 3, column 2) are Tonga, Fiji, Vanuatu, Samoa and Solomon Islands. In terms of distance to ODA donors (figure 3, column 3), the first four SIDS are also the most remote, with Tuvalu taking fifth place. According to both metrics, New Zealand and Australia are the most remote countries in the world, followed closely by the SIDS mentioned here.

SIDS remain distant from cultural and political centres

SIDS are also located far away from soft power centres (figure 4, column 1), as measured by the Global Soft Power Index published by -—
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—-. This group’s average is significantly above those of all other comparison groups. The most remote country according to this indicator is New Zealand, but six SIDS are ranked in the top 10: Tonga, Samoa, Fiji, Vanuatu, Tuvalu and Solomon Islands.

SIDS are also situated at a greater distance from centres of global presence (figure 4, column 2) than most countries, although less so than in the case of soft power centres. Here too, the most remote countries in the world are New Zealand and Australia, and in addition the top 10 includes a mix of SIDS, such as Tonga, Fiji, Vanuatu, Samoa and Tuvalu, and some South American nations, such as Chile, Argentina and Uruguay.

SIDS are well connected by air – less so by sea

For island economies, land connectivity is (mostly³) non-existent so other means of transport gain a greater relevance. For maritime connectivity (figure 5, column 1), Singapore is a clear outlier within SIDS, with a score almost three times higher than the second ranked small island economy, the Dominican Republic. In fact, Singapore is ranked second globally, after the most connected country in maritime networks (China) and just above the third placed country (Republic of Korea). Maritime connectivity is estimated through the liner shipping connectivity index, which indicates a country’s level of integration into global liner shipping networks.

In addition to Singapore and Dominican Republic, mentioned above, only three more SIDS exceed the average connectivity for middle income countries: Jamaica, Mauritius and Bahamas. On average, SIDS are not very well integrated into shipping connections. For countries with a high dependence on the sea, this low maritime connectivity could further aggravate the challenges of geographical remoteness -—
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For air connectivity, as measured by the number of international flights per year relative to population (figure 5, column 2), some SIDS with a high reliance on tourism are among the best connected in the world: Antigua and Barbuda, Saint Kitts and Nevis, Bahamas, Dominica, Nauru, Barbados and Palau. In addition to these SIDS, most of the top ranked countries are either micro-States (Luxembourg) or other island economies (Iceland, Malta, Cyprus). On average, SIDS are comparatively well connected by air transportation, with international flights per capita at a level comparable with high-income countries. However, not all SIDS are as well integrated. Papua New Guinea, Haiti and Guinea-Bissau are among the lowest ranked economies in this variable.

Most European micro-States (landlocked, with extensive land borders relative to their area and excellent roadways) are the best ranked considering land connectivity, constructed from the length of land borders, relative to total area, weighted by road infrastructure.⁴ Unsurprisingly given their lack of land borders, SIDS mostly scored zero, with a few exceptions, but nevertheless low scores (Timor-Leste, Belize, Dominican Republic and SIDS that are not islands or that share an island with another country).

SIDS are relatively well connected socially

Contrary to centrally located countries, working with neighbours over common border issues or tackling regional challenges, SIDS could lack opportunities to join alliances or shared initiatives, movement of persons and ideas. This dimension of remoteness is broader and more difficult to measure than the others. A full account would involve monitoring all spaces that allow exchanges between individuals, societies and governments. Given data limitations, this dimension is estimated using the seven indicators included in figures 6 and 7. These include immigration and emigration, cross-border exchange of students, diplomatic representations and participation in defence and trade agreements. While cultural and political links clearly extend beyond the areas measured by these variables, they are difficult to conceptualize and measure, especially through internationally comparable indicators with worldwide coverage.⁵ Cross-national trust can be important for connectivity and cultural spillovers, and is sometimes used as an indicator of cultural ties -—
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Migrants take with them ideas, traditions, practices and businesses. They build networks and bridges between their communities of origin and destination. For this reason, it is important to consider rates of both immigration and outward migration. Foreign immigrants constitute a sizable share of the population in several high-income SIDS, such as Bahrain, Singapore, and Antigua and Barbuda. However, other SIDS feature some of the lowest immigration rates in the world: in Cuba, Haiti, Papua New Guinea, Solomon Islands, Timor-Leste and Jamaica, immigrants constitute less than one per cent of the population. Overall, the average immigration ratio in SIDS is higher than in low and middle-income countries, although still at about one third of the levels observed in high-income countries.

A similar story is told by emigration (figure 6, column 2). One SIDS, Saint Kitts and Nevis, has the largest emigration rate in the world, with 2.4 nationals living abroad for each person living in the country. Other countries with high outward migration are Dominica, Suriname, Tonga, Grenada, Guyana and Samoa. Other SIDS, such as Maldives or Solomon Islands, exhibit a very low ratio in this variable. Nonetheless, with an overall emigration rate of 33.6 per cent, SIDS are significantly above the world average in this aspect.⁶

An interesting group of migrants, for which detailed statistics are available, are students that move to another country to pursue a tertiary education. The inbound mobility rate, measured as the percentage of students from abroad enrolled in a tertiary education program at a local university (figure 6, column 3), is very high in Grenada and Saint Kitts and Nevis, where 85 and 73 per cent of tertiary students are foreigners. Although these are clear extremes, the SIDS average remains well above the average for low and middle-income countries. In terms of outbound mobility rate (figure 6, column 4), SIDS are on par with high-income countries, although far from the high student mobility rates observed in some cases.

Many SIDS are less well connected politically

The number of foreign nations with at least one diplomatic representation (embassy, consulate or permanent mission) in a SIDS (figure 7, column 1) ranges from 50 in Singapore to two in Antigua and Barbuda, Dominica, Nauru, Saint Kitts and Nevis, Saint Vincent and the Grenadines, and Tuvalu. Based on the Global Diplomacy Index -—
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Inter-country linkages can also be analysed through agreements, pacts and other alliances. Defence agreements, some of the oldest international pacts in existence, are one manifestation of this. By using the somewhat outdated database from -—
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—-, which only includes data up to 2012, the most connected nations are the United States of America and Canada, having some type of defence agreement in force with 56 and 51 nations, respectively. Conversely, 45 countries have no such alliance in force. According to this variable, the average SIDS has defence agreements with 15 countries (figure 7, column 2), above the world average but still limited compared to other cases, particularly high-income countries.

A similar situation is observed when trade agreements are examined. According to the -—
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—- database, Egypt has the highest number of active bilateral or plurilateral trade pacts in force. They have active trade agreements with 105 countries, closely followed by members of the European Union, who have a common international trade policy involving trade agreements with 98 countries. On the other hand, a handful of nations have no active agreements covering trade, including two SIDS (Palau and Sao Tome and Principle). The average SIDS has a trade agreement with 34 partners, less than the average for middle and high-income countries (40 and 67, respectively).

Digital connectivity of SIDS varies while some of them reach the world top

The first indicator of digital connectivity, the share of population that has access to the Internet (figure 8, column 1), shows that SIDS are well connected, although with a great deal of variability. Indeed, this variable ranges from 99.7 per cent in Bahrain, the highest digital connectivity in the world, to only 3.9 per cent in Guinea-Bissau, the country with the fifth lowest Internet access. On average, SIDS have similar outcomes than middle-income countries and better scores than LDCs and LLDCs.

International bandwidth per Internet user (figure 8, column 2) shows a skewed distribution for SIDS, with a few countries (Singapore, Bahamas, Saint Vincent and the Grenadines, and Saint Kitts and Nevis) among the best performers in the world, while many other SIDS' score is very close to zero. This mirrors the world distribution of this variable, which serves as a proxy for the Internet infrastructure in place. On average, SIDS have a relatively good attainment in this variable, outperforming the average for low- and middle-income economies, although still behind the high-income group.

The average SIDS performs as well as the average middle-income country and LLDC in the latency rate (figure 8, column 3), based on all tests conducted in each country in 2019. SIDS perform significantly better than low-income countries or LDCs. This average hides a large variance, with one SIDS at the bottom of the world rank (Tuvalu, with a median latency of 1 821 milliseconds), whereas other SIDS have some of the best Internet connections worldwide, like the Bahamas or Singapore.

An overall remoteness index – top 15 featuring mostly SIDS

The previous analysis presented 21 variables that can provide a comprehensive assessment of remoteness across six dimensions. This shows that traditional measures of geographical distance to markets are not sufficient to give a complete panorama of the challenges of distance. Moreover, a large number of connectivity factors could mitigate or accentuate remoteness, and they should be taken into account. This section presents the steps for calculating a remoteness index and the results for SIDS and relevant benchmarks. The methodology is discussed in more detail by Cantu-Bazaldua -—
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For some of the variables presented in the above analysis, a higher score indicates greater remoteness, whereas for others the opposite was the case. To overcome this problem, all variables were transformed so that a higher value corresponds to greater remoteness. The index for each dimension was calculated through a simple average of the variables included, and the results were adjusted to a 0-100 scale through a min-max transformation. This way the most remote country takes a value of 100 and the most proximate country a value of zero. The overall remoteness index was calculated as a simple average of the aggregate indicators for all six dimensions.

According to the overall remoteness indicator (figure 9), the most remote SIDS is Tuvalu, closely followed by Tonga and Vanuatu. Samoa and the Solomon Islands complete the top 5. The top 10 is composed of nine Pacific SIDS, which are remote on all or most dimensions. New Zealand is the only country that makes top 10 and is not SIDS.

For some SIDS, the overall index is improved by positive scores in one or a few dimensions of remoteness. For example, while Timor-Leste and Papua New Guinea score high in most dimensions, their overall index score is reduced by their geographical location, as they are relatively close to their main markets and trading partners. A similar situation is observed in Nauru, although in this case it is the relatively high transport connectivity, mostly based on air transport, which lowers the overall remoteness score. Mauritius’ score is significantly improved by its well-developed digital connectivity.

Figure 9 also shows some SIDS that are more proximate across most dimensions, but whose score is penalized by a poor result in one dimension. For Suriname, Cuba, Guyana, and Trinidad and Tobago, the area lagging behind is transport connectivity. For the Maldives and Palau, it is their social and political isolation.

The least remote SIDS are at the bottom of the figure starting with the Bahamas which compensates for a relatively low social and political connectivity with shorter average distances to markets and an excellent digital infrastructure. Following closely are Singapore, Bahrain and some of the high-income SIDS in the Caribbean, such as Saint Kitts and Nevis, Antigua and Barbuda, and Barbados.

Comparing SIDS’ scores to the world distribution, they are indeed among the most remote economies in the world, particularly Pacific SIDS. All top-15 most remote countries are Pacific SIDS except New Zealand (8th), Australia (13th) and Madagascar (15th).⁷ The most remote SIDS outside the Pacific is Comoros, ranked 18th in the world. For scores for all countries see Cantu-Bazaldua -—
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Figure 10 presents the aggregate results for SIDS with several benchmarks. A first highlight of this graph is the strict ordering of each of the six dimensions of remoteness according to income level. This indicates a clear link between remoteness and economic performance, as well as a clustering effect. SIDS’ a score in the remoteness index is comparable to low-income economies.

Another striking result is that SIDS are not worse off than LDCs or LLDCs in terms of remoteness. While they are located at a greater distance from markets, financing sources and cultural centres, they partially compensate for this disadvantage through better connectivity, especially in terms of ICT and digital technologies. This draws attention to the importance of connectivity and considering all aspects of remoteness beyond just geographical distance when studying the development of SIDS.

As shown in the country-level results (figure 9), the SIDS’ average hides some important differences between countries. SIDS in the Pacific are distinctly more remote, with a higher score in most dimensions, particularly transport and socio-political connectivity. SIDS in the Atlantic and Indian Ocean are the least remote, thanks in part to their improved digital and transport connectivity.

The figures presented in this chapter also include an aggregate for analytical SIDS -—
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Does remoteness hamper sustainable development?

To study the relationship of remoteness to the economic, social and environmental pillars of sustainable development, we compare the remoteness index with some composite indicators broadly representing these themes. The 231 SDG indicators designed to measure the 17 goals and their respective 169 targets are rather narrow in scope when looked at individually, and comprehensive data coverage is not available. Therefore, six indicators are selected to evaluate their interaction with the remoteness index and dimensions of sustainable development. These include GDP per capita, PCI, Gini index, GII, HDI and EVI.

Data for SIDS show that GDP per capita is negatively correlated with remoteness (ρ = -0.61) (see figure 11). The more remote the country, the lower their GDP per capita. Singapore had the highest GDP per capita in 2020, and the lowest overall remoteness score, together with the Bahamas and Saint Kitts and Nevis. The negative correlation between GDP per capita and remoteness is even higher (ρ = -0.66) among the rest of the SIDS excluding Singapore. When looking at poor connectivity only (dimensions 4-6 of the overall index on transport, socio-political and digital connectivity), the negative correlation with GDP per capita is notably higher (ρ = -0.79).

We also look at the new UNCTAD PCI as another proxy indicator of the economic pillar (figure 12). It provides a more comprehensive measure than GDP per capita as it assesses productive capacities from the perspective of eight categories: natural capital, human capital, energy, institutions, private sector, structural change, transport and ICT -—
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Overall remoteness is negatively correlated with income inequality (ρ = -0.45) in SIDS, as measured by the Gini index (figure 13). Geographic remoteness i.e., distance (dimensions 1-3 on distance from markets, financial and cultural-political centres) is more strongly negatively correlated with income inequality (ρ = -0.51) than poor connectivity (ρ = -0.21). People living in the most geographically remote SIDS experience lower income inequality. Remote locations may offer less opportunities for achieving high income levels, especially small rural communities. It should be noted, however, that the Gini index is not available for the eight least remote SIDS, including Bahamas, Saint Kitts and Nevis, Singapore, Bahrain, Antigua and Barbuda, Barbados, Dominica and Grenada.

For SIDS that have GII data, gender inequality has a relatively high positive correlation (ρ = 0.68) with poor connectivity (dimensions 4-6), but not with geographic remoteness (ρ = 0.13). The overall remoteness index is positively correlated with gender inequality (ρ = 0.46) (figure 14). In general, SIDS with higher transport, social, political and digital connectivity provide a more gender equal environment, but geographic distance does not mean increased gender inequality. GII data are available for 19 SIDS only. Data gaps are somewhat more common for the most remote SIDS.

The overall remoteness index correlates negatively with the HDI (ρ = -0.57) (figure 15). The negative correlation of human development and poor connectivity (dimensions 4-6 of remoteness) is significantly higher, -0.76, with little correlation with geographic remoteness only (dimensions 1-3), -0.22. Small island economies with good transport, social, political and digital connectivity have achieved higher human development.

Due to their geography, SIDS face a unique and varied mix of environmental concerns, ranging from increased exposure to storms and floods, to the loss of their actual land. SIDS account for three of the top five most environmentally vulnerable countries according to the EVI in 2020. Kiribati, Marshall Islands and Tuvalu are the most vulnerable countries globally according to the EVI. These small island economies are also among the most remote countries in the world. Overall remoteness is positively correlated with economic and environmental vulnerability (ρ = 0.58).

Geographic remoteness adds an extra barrier but can be mitigated

The analyses presented here show that remoteness has a negative impact on the economic, social and environmental aspects of sustainable development and places additional demands on countries. Figure 17 summarizes the correlations of overall remoteness, geographic remoteness and limited connectivity across the themes covered by the indicators analysed in figures 11 to 16. The analyses show that geographic distance correlates most positively with environmental vulnerability and most negatively with income inequality. They also show that the correlations with geographic distance are weaker than for limited connectivity, and distant location can, thus, be mitigated by improving transport, social, political and digital connectivity. Limited connectivity has the strongest negative correlation with GDP per capita, human development and productive capacity, and a strong positive correlation with gender inequality and vulnerability in SIDS.

There are examples of highly geographically remote countries outside of SIDS that have also managed to mitigate the impacts of geographic isolation. Across all geographical indicators (the first three dimensions), New Zealand is the most remote country in the world, sometimes by a large margin. However, it partially makes up for this disadvantage through a well-developed connectivity infrastructure, especially ICT. A similar situation can be observed in Australia. Uruguay, for instance, compensates for its location with excellent digital and transport connections, whereas Chile has well developed social and political networks, including one of the world’s highest number of defence and trade pacts. The remoteness ranks for these four selected countries are shown in table 1, where top ranks (i.e., high relative remoteness) in the first three dimensions are offset by good performance in the connectivity dimensions, therefore improving the overall remoteness score.

These four cases strengthen the message that geographic remoteness is not an insurmountable obstacle. While geographical distance does entail higher transportation costs and hinders participation in global decision-making, this can be offset by targeted investments in transport and ICT connectivity, as well as an active participation in cultural and political networks. SIDS have already done important progress in this front and, on average, according to the index, they are not more remote in digital connectivity than other groups of countries, such as LDCs or LLDCs.

Remoteness is a gap that needs to be bridged to progress towards SDGs (see figure 18). It brings challenges, many of which can be mitigated by investing in transport and digital connectivity and cultural and political networks. But those investments naturally require sufficient resources and finances. It seems that highly remote countries do not start their journey towards the 2030 Agenda on equal footing, and this should be taken into consideration in global development assistance and finance.

Figure 18. Remoteness as a factor in sustainable development

Source: UNCTAD deliberations.

The broader study of remoteness presented herein also highlights the heterogeneity within SIDS. While most SIDS located in the Pacific are objectively remote in all dimensions, SIDS in the Caribbean or in the Atlantic and Indian Oceans are no more remote than the average middle-income country. This illustrates the importance of having detailed, disaggregated statistics for SIDS that reflect the most pressing challenges they face and highlights the usefulness of regrouping SIDS for analytical purposes, reflecting discussions in -—
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A remoteness index, along the lines presented here, could be used as a measure to evaluate the challenges faced by SIDS due to their isolated location. The index reflects the importance of geography, but also of attenuating factors stemming from targeted policies for improving connectivity. Moreover, it reflects key aspects of remoteness, including the limited options for transport connectivity with no land borders for most SIDS, but also lack of access to maritime transport for most LLDCs. As suggested by Cantu-Bazaldua -—
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Notes

1. An idea originally developed in -—
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   —-, “soft power” refers to the ability to influence the behavior of others to get the desired outcomes through attraction and co-option rather than coercion (or “hard power"). According to the author, it relies on three pillars: political values, culture and foreign policy.
2. Note that country aggregates are calculated as a weighted average of the corresponding variables, using population as weight.
3. Some islands, however, have a better land connectivity, e.g., in the case of shared islands, mainland islands and connected islands -—
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4. This indicator is only a proxy for land connections and does not consider important factors affecting cross-border transportation, including geographical features (mountainous or fluvial borders), border-crossing infrastructure, customs and border-crossing administrative efficiency, or other obstacles.
5. For instance, an interesting indicator would be the share of the world population that share the same language. A shared language facilitates exchange and transmission of ideas, and gives access to larger knowledge pool and more media sources, therefore reducing isolation. Although there are specialized databases for this variable (for instance, CEPII or Ethnologue), they present important data gaps, particularly for some SIDS.
6. Some cases could be affected by practices where countries grant citizenship by investment. This could have an ambiguous relationship with social connectivity, but the available data do not allow a more detailed disaggregation.
7. On the other hand, the 30 least remote countries in the world are all located in Europe. The five less remote countries are Luxembourg, Belgium, the Netherlands, Cyprus and the United Kingdom.

References

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Timeline of a pandemic

On 31 May 2020, the WHO reported that more than 5.9 million people had been confirmed infected with COVID-19. That same day, 367 166 deaths globally were attributed to the virus.¹

Five months earlier, on 31 December 2019, the WHO country office in China was notified that a new strain of pneumonia of unknown cause had been detected in the Hubei Province. On 7 January 2020, the Chinese authorities identified this pneumonia as a new strain of coronavirus. By mid-January, ministries of health in both Thailand and Japan confirmed imported cases of the novel coronavirus. The Republic of Korea reported their first case on 20 January. The following day, the WHO began issuing daily situation reports² and confirmed 282 cases across the four affected countries, with six deaths in China.

Thereafter, events unfolded quickly (see figure 1) and, by the end of January, the day after the WHO designated “2019-nCoV acute respiratory disease” as the interim name of the disease, their Emergency 2019-nCoV Committee declared a PHEIC under the 2005 International Health Regulations (WHO, 2005). That day, the WHO reported 9 826 confirmed cases across 20 countries and 213 deaths (all in China).³ The first confirmed cases in Italy were also reported that day.

Figure 1. The first five months: Some key events, until end-May 2020

• December 31

  First report to WHO of new strain of pneumonia of unknown cause in Hubei Province, China

• January 7

  New strain of coronavirus isolated

• January 12

  China shares genetic sequence of the novel coronavirus

• January 13

  Confirmed case of novel coronavirus in Thailand

• January 15

  Confirmed case of 2019-nCoV in Japan

• January 20

  Confirmed case of 2019-nCoV in Republic of Korea/WHO start daily Situation Reports/282 cases and 6 deaths

• January 21

  1st confirmed case in South-East Asia region (Thailand)

• January 23

  1st confirmed case in region of the Americas (USA)

• January 25

  1st confirmed case in Europe region (France)

• January 31

  WHO Declares PHEIC / Italy reports 1st cases of 2019-nCoV

• February 1

  UK reports 1st cases of 2019-nCoV

• February 2

  1st 2019-nCoV related death outside China (Philippines)

• February 6

  1st cases reported on a cruise ship (Japanese waters)

• February 11

  WHO names the disease

• February 26

  1st case in Africa region (Algeria)

• February 28

  WHO increases assessment of Global risk of spread and impact from HIGH to VERY HIGH / 83 652 confirmed cases/2 791 deaths/54 countries

• March 6

  Number of cases globally passes 100 000 (101 254)

• March 7

  Number of countries reporting confirmed cases passes 100

• March 11

  WHO declares COVID-19 a Pandemic / 118 319 cases / 4 292 deaths / 114 countries / Italy passes 10 000 cases (10 149)

• March 20

  Global death toll passess 10 000 (11 186)

• March 26

  Number of countries reporting confirmed cases passes 200 (201)

• March 29

  Italy passes 10 000 deaths (10 023)

• April 3

  Spain passes 10 000 deaths (10 003)

• April 4

  Number of cases globally passes 1 000 000 (1 051 697)

• April 8

  France and United States of America pass 10 000 deaths (France 10 313) / (United States of America 10 845)

• April 12

  Global death toll passes 100 000 (105 952)

• April 13

  United Kingdom passes 10 000 deaths (10 612)

• April 17

  Number of cases globally passes 2 000 000 (2 074 529)

• May 6

  UK passes out Italy to become country with most deaths in Europe region (29 427)

• May 11

  Brazil passes 10 000 deaths (10 627)

• May 23

  Number of cases globally exceed 5 000 000

Source: UNCTAD derived from WHO (2020a).

On 26 February, the first cases of COVID-19⁴ were reported on the African continent (all in Algeria), at which point COVID-19 was present in 45 countries or territories across all six WHO regions⁵ (see figure 2). Two days later, the WHO (2020a) increased their assessment of the global risk of spread and impact from high to very high. At this point, there were 83 652 confirmed cases spread across 54 countries.⁶

On 6 March, the number of global confirmed cases attributed to COVID-19 passed the 100 000 mark (see figure 3). The following day, the number of countries reporting confirmed cases exceeded 100. Four days later, the WHO declared COVID-19 a pandemic. In doing so, the Director General of WHO expressed concern at both the alarming levels of spread and severity, and the alarming levels of inaction. He explained that the WHO had assessed that COVID-19 could now be characterized as a pandemic, clarifying that this did not change the threat level (WHO, 2020b).

By the end of May 2020, the aggregate cumulative number of confirmed cases and deaths reported by countries to the WHO was 5.9 million and 367 thousand, respectively. As of 31 May 2020, Europe and the United States of America combined accounted for 65 per cent of all confirmed cases and 77 per cent of all COVID-19 deaths, as shown in figure 4 (readers should be aware that there are particular measurement problems with COVID-19 statistics as currently reported by all sources (see section Measurement issues below)).

In the first three or four months of the pandemic, the global cumulative total deaths was led by European countries (notably Belgium, France, Germany, Italy, Spain and the United Kingdom) as well as by the United States of America. But since then, as shown in figure 5, it has been the Americas that have accounted for most of the growth (most notably Brazil and Mexico, in addition to the United States of America).

At a country level, the spread and prevalence of COVID-19, as well as the measures taken to contain its spread, have varied considerably. For a variety of reasons, a number of countries showed much higher prevalence rates than others. The trajectory of the number of confirmed cases in a selection of hardest hit countries is compared in figure 6.

A curiosity of COVID-19, in the early months at least, is that it has hit developed countries much harder than developing countries, in terms of prevalence, with the notable exceptions of the Islamic Republic of Iran and, more recently, Brazil and Mexico. In figure 6, the time axis is normalized to the start date (i.e. the date when a confirmed case was first reported to the WHO by a country) so that the trajectory of COVID-19 spread in the first 100 days can be compared.

Comparing the worst hit countries in Europe with badly hit countries elsewhere in the world, the patterns are immediately different in both timing and scale. Although Italy rose to prominence in the media, Spain and Belgium have been the worst affected countries to date on a per capita basis. The spread accelerated early and rapidly in Italy, Spain and Belgium, peaking in Italy and Spain around day 62 (i.e. approximately two months after the first confirmed cases were reported). The number of new cases peaked about two weeks later in Belgium (day 74).

In France and Germany, acceleration started about 10 days later than in Italy and Spain. Both countries experienced similar trajectories and prevalence to each other, with the spread of COVID-19 peaking around days 69 and 70. Initially, the United Kingdom had an almost identical trajectory to France, albeit lagged by a few days; however, new cases peaked on day 70 in France, whereas the spread continued accelerating in the United Kingdom and did not reach its maximum for another week. Furthermore, unlike France and Germany, the peak was not followed by a rapid decline. Rather, the number of new cases continued at a slightly reduced rate, until around day 91, when the number of cases began rising again, and then finally began to decline around day 100.

Some of the worst hit countries elsewhere in the world have had a markedly different experience to that in Europe. Acceleration was much more varied, beginning around day 13 in the Islamic Republic of Iran, day 40 in Brazil but not until around day 60 in the United States of America. To date, the per capita number of cases per day in the United States of America and Brazil are a little over half of what was experienced in Spain at its peak.

The Islamic Republic of Iran was hit by COVID-19 relatively early and rapidly. When most countries were just beginning to experience an acceleration in spread (around day 45), the spread in the Islamic Republic of Iran was already at its maximum. Unlike most European countries, however, this country did not experience a sharp downturn, but rather a gradual deceleration which troughed around day 76 and then began slowly rising again.

The United States of America, once acceleration began, experienced quite a steep trajectory similar to that observed in Italy. By day 83, cases in the United States of America peaked at 95 cases per million people (higher than Italy’s peak of 91 per million people) and then, similar to the United Kingdom, the number of new cases did not reduce significantly, but fell back to a slightly reduced rate of spread. Again, like the United Kingdom, the trajectory began increasing again around day 97.

The number of cases in both the Russian Federation and Brazil have increased steadily along a similar curve, albeit with acceleration in the Russian Federation lagging by about 20 days. By day 100, the number of cases per capita in Brazil, at 110 per million, had far surpassed the peak in the United States of America. Cases in Mexico have been rising slowly but inexorably.

An important aspect to note about the outbreaks within each of these countries is their highly heterogenous and, at least initially, concentrated nature, both in terms of geography and demography. Most countries initially experienced severe outbreaks in one or several geographic areas, for example Lombardy in Italy or New York in the United States of America, rather than a uniform development across the country. Specific communities or groups have also been affected differently by the virus, with many countries experiencing outbreaks in care-homes, meat-processing plants, or low-income communities. This has also led to second-wave developments in some countries as at-risk communities experience outbreaks amidst an otherwise “under control” situation, such as it has been the case with meat-processing plant workers in Germany or migrant workers in Singapore, or as previously spared geographic areas succumb to the pandemic, as with the southern United States of America.

Measurement issues

One of the challenges of analysing COVID-19 statistics is that their quality is unproven and considerable methodological differences exist across countries. They likely suffer from problems considering that organizing a new global data collection during a pandemic, at both national and international level, on a disease about which relatively little is known, is not going to be without teething problems. There is also always the risk that some countries may inaccurately or not report COVID-19 related statistics at all (BBC, 2020). The WHO notes "Differences are to be expected between information products published by WHO, national public health authorities, and other sources using different inclusion criteria and different data cut-off times. While steps are taken to ensure accuracy and reliability, all data are subject to continuous verification and change. Case detection, definitions, testing strategies, reporting practice, and lag times differ between countries/territories/areas. These factors, amongst others, influence the counts presented, with variable underestimation of true case and death counts, and variable delays to reflecting these data at global level."⁷ Furthermore, when making international comparisons, one should also be cognisant that a range of factors not directly related with the state of a country’s health system likely impact infection rates. These include: the age structure of populations, the density or urbanization of populations, prevalence rate of chronic diseases and perhaps also ethnicity.

The two principal variables, ‘confirmed cases’ and ‘deaths’, are to some extent problematic, and this impacts on the veracity and general quality of derived variables, such as mortality rates. The number of ‘confirmed cases’ is based on the number of laboratory-confirmed cases, which rely on the quantity and consistency of testing in countries. This varies enormously, as experience has shown (see figure 7). Some countries undertake large-scale population testing, whereas others have adopted less comprehensive approaches. As countries have learned more about COVID-19, some have changed their testing methods and schemes, causing methodological breaks and discontinuities in time series. For example, several countries have changed their reporting ‘day’, i.e. the 24-hour period that comprise the reference period, as did the WHO themselves on 18 March.⁸ Furthermore, uncertainty regarding the numbers of asymptomatic and undiagnosed cases, and of course misdiagnosed cases, means that the actual number of cases may be quite different to the number of officially confirmed cases. An early study from China suggests that almost 80 per cent of cases of infection were classified as mild or asymptomatic (Day, 2020). In a pre-print study published in April 2020, Lu et al. (2020) estimate the proportion of asymptomatic cases to be lower, ranging from 18 to 50 per cent. Therefore, it is important when using the ‘confirmed cases’ metric to understand that this statistic is the number of cases reported by each country, and that the reference date may not always accurately reflect the date of the event.

Figure 7 illustrates the variation in the numbers of tests undertaken by countries, which as noted above, will immediately impact the number of confirmed cases reported. From a surveillance and control perspective, it should also be noted there is an important distinction between the number of tests performed and the number of tests analysed and reported. The time delay between the two is also of critical importance.

Almost certainly, both ‘confirmed cases’ and ‘deaths’ are undercounted, probably to different degrees, which no doubt will, with time, explain some of the apparently high mortality rates. Given the problems with the reported statistics, the actual prevalence of COVID-19 in populations remains for the time being unknown. In a February 2020 interview, Neil Ferguson, Professor of epidemiology at Imperial College London, estimated that China had only detected around 10 per cent or less of its coronavirus cases (Ferguson, 2020). In France, a recent study by Salje et al. (2020) estimated that on 11 May⁹ about 2.8 million¹⁰ people (or 4.4 per cent of the population) had been infected by COVID-19 – some 20 times more than the official estimate of 137 073 reported to the WHO for that day.¹¹ A serological antibody test conducted in the canton of Geneva in Switzerland (Hôpitaux Universitaires de Genève, 2020) found seroprevalence in the population to be 5.5 per cent (or 27 000 people) on 17 April 2020, or some five times higher than official estimates. Although using different approaches, these studies yield similar results, which suggest that the proportion of the target populations infected in April/May was between four and six per cent.

From a policy perspective, these studies suggest that countries are a long way from developing herd immunity, which in turn implies that population immunity is probably insufficient to avoid a second wave. On 25 April, the WHO (2020d) warned there was no evidence that people with COVID-19 are immunised. They noted, on 12 May, that the concept of herd immunity is generally used for calculating how many people will need to be vaccinated in a population to protect others, not for calculating the occurrence of immunity through infections (Independent, 2020). Many studies support the conclusion that a relatively small proportion of the population has been infected to date. A study using three different approaches (Lu et al., 2020) estimated that as much as 10 per cent of the population in the United States of America may have been infected by mid-April 2020. On 20 April, the WHO noted that early studies suggest that only two to three per cent of the global population had been inflected (WHO, 2020b). The ONS in the United Kingdom reported on 5 June that, as of 24 May, 6.8 per cent of people who provide blood samples tested positive for antibodies to COVID-19 (ONS, 2020).

At first glance, ‘deaths’ statistics appear to be less problematic, but on closer examination, a number of problems are also evident. In several countries it has emerged that deaths (initially at least) only included deaths in hospitals, and that deaths in other institutional or private households had not been included. There have been several revisions to official reports, as causes of death have been re-evaluated as more is learned about the disease. This too has led to reporting lags, and problems matching events to dates properly. Furthermore, analyses of ‘excess deaths’, i.e. the deviation in mortality from the expected level, suggests that deaths attributed to COVID-19 are being undercounted.

EuroMOMO (2020) monitors mortality for several countries in Europe.¹² Their data suggest that between weeks 12 and 20, i.e. between the weeks beginning 16 March and finishing 26 April, there were 142 577 excess deaths in Europe (see figure 8). The excess mortality in 2020 is notable, both in scale and in seasonal pattern. The weeks in which excess mortality was unusually high during the first quarter of 2020 were quite distinct from the typical seasonal flu patterns associated with the winter months. For the same countries and during the same period, the WHO reported that deaths attributed to COVID-19 rose from 13 786 on 16 March to 116 029 on 26 April, an increase of 102 243. This number is 40 000 lower than the number of excess deaths reported by EuroMOMO (2020). While most of these excess deaths can, in all probability, be attributed to COVID-19, caution must again be exercised: it is likely that other medical treatments were postponed or cancelled, as people avoided doctors and hospitals. This in and of itself may have had led to a spike in excess mortality.

A false sense of security?

In October 2019, a new GHS Index was launched jointly by Johns Hopkins University and the Nuclear Threat Initiative, with the purpose of conducting a first comprehensive assessment and benchmarking of health security and related capabilities across the 195 countries that are signatories to the WHO International Health Regulations. The index was constructed by the Economist Intelligence Unit, in consultation with Nuclear Threat Initiative and the Johns Hopkins University Center for Health Security and advised by an international panel of experts (Johns Hopkins University et al., 2019).

The GHS Index assesses not only countries’ health security capacities, but also the existence of functional, tested and proven capabilities for stopping outbreaks at the source. It also tests whether that capacity is regularly tested and shown to be functional in exercises or real-world events. It was not designed to warn specifically against COVID-19, but to assess the readiness of countries to deal with a biological event or pandemic, such as COVID-19, in general.

In their 2019 inaugural report, the authors issued some stark warnings, reporting that countries were not prepared for a globally catastrophic biological event, nor were they fully prepared for epidemics or pandemics. Collectively, they note, international preparedness was weak. Many countries did not show evidence of the health security capacities and capabilities that needed to prevent, detect, and respond to significant infectious disease outbreaks. Prophetically, they warned: "knowing the risks, however, is not enough. Political will is needed to protect people from the consequences of epidemics, to take action to save lives, and to build a safer and more secure world". They also noted that "unfortunately, political will for accelerating health security is caught in a perpetual cycle of panic and neglect”.

The GHS Index is described as a multidimensional analytical framework, commonly known as a benchmarking index. It is essentially a composite, comprising six categories: (1) prevention; (2) detection and reporting; (3) rapid response; (4) health systems; (5) compliance with international norms; and (6) risk environment. Those categories are populated with 34 indicators and 85 sub-indicators. The overall index for each country is the weighted sum of the category scores, where the weights are agreed by an expert panel. In constructing the index, three other weighting types were tested: neutral weights; equal weights; and weights derived from a principal component analysis.

One would hope that the GHS index never need be tested in a live situation. But it has been, and like many metrics, it has been confounded by COVID-19. In retrospect, the GHS (as is often the case with composite indices) may have hidden as much as it has revealed. It highlights again the question of whether country rankings have any real utility or simply distract readers from important underlying messages. Although the report issued many stark warnings, the indices themselves may have conveyed a different message; at least for countries ranked near the top, with scores in excess of 70, the indices may have given a false sense of security. Developments in the first half of 2020 have made some of the GHS country rankings appear incongruous. It is too early to conduct any definitive analyses of COVID-19, thus any assessment is necessarily premature. Perhaps in the longer term, the index rankings may correlate better with events. That said, the first six months have generated some noteworthy comparisons.

The index ranked the United States of America as the best prepared country in the world, followed by the United Kingdom. Additionally, included in the top 20 best prepared countries were Belgium, France, Netherlands, and Spain. It is striking that these are some of the hardest hit countries by the COVID-19 pandemic in both absolute and per capita terms. It also ranked Brazil and Mexico in the top 30 and placed New Zealand only 35th.

Four measures of ‘worst affected’ are presented: confirmed cases; confirmed cases per million of population; deaths; and deaths per million of population. The United States of America, the United Kingdom and Spain are in the top 20 hardest hit, no matter which measure is used. Sweden, France and Belgium are in three of the four measures. Canada and Germany feature in two (see table 1).

It is of course easy to be wise with hindsight. But the particular importance of indicators for Political and Security Risk and Public Health Vulnerabilities are striking. In their commentary, the authors portentously noted the importance of ‘political will’ – this seems to have been the critical factor in how well countries have dealt with COVID-19 to date. Unfortunately, it is extremely difficult to measure political will. Furthermore, in light of developments, this dimension might warrant a higher weight in the overall index. Perhaps the overall risk environment needs to be supplemented as well, as the index doesn’t seem to adequately address potential transmission vectors. For example, some connectivity and globalization indices would arguably strengthen the robustness of the index.

The importance of public health systems is also now clear. COVID-19 has graphically illustrated the importance of government and public infrastructure and services more generally and the critical role they play during a time of crisis. Thus, a wider reflection of public services generally, including the strength and investment in national statistical systems, but in particular investment in public health systems, might also improve the index.

Another composite index, compiled using an AI approach, has been published by the Deep Knowledge Group (2020). This index is more bespoke, targeting COVID-19 directly. The latest version, from June 2020, also provides country rankings, which in light of events appear more credible. However, it should be stressed that this index is updated contemporaneously and is specific to COVID-19, so this should not be surprising. Unlike the GHS, the purpose of which was to conduct an ex-ante assessment of countries preparedness for a biological event, such as, a pandemic, the purpose of the index from the Deep Learning Group is to inform government decisions during the current pandemic, helping them to optimize current and post-pandemic safety and stability, in order to maintain the health and economic well-being of their populations and alleviate the collateral damage caused by COVID-19.

Policies implemented during the COVID-19 pandemic

Person-to-person contagion of COVID-19 depends on the characteristics of the virus itself, including how easily it can infect a new host and how long it can survive outside the human body. But it also depends on the number of potential opportunities of transmission provided by social interaction between people. Since contagion can be rapid, and carriers may unwittingly spread the virus, as COVID-19 appears to have a long lag before symptoms manifest themselves, it has turned out to be essential to contain the spread of the disease at an early stage, before it affects larger shares of the population and the number of patients exceeds the capacity of health systems.

Although facing many unknowns about the virus and its transmission mechanisms, governments around the world started implementing containment measures aimed at reducing the probability that an infected person transmits the virus. These measures included, but were not limited to: school closures; limiting non-essential business activity and promotion of remote work; restrictions on public or private gatherings and cancellation of public events; stay-at-home requirements; restrictions on domestic or international travel; obligatory or recommended use of masks, gloves and other physical barriers; and information campaigns. These measures were applied broadly to the entire population or targeted to specific population groups (for example, in highly affected geographical areas or for most at-risk groups).

The curve in figure 9 measures the application of physical distancing measures worldwide since the outbreak of the disease. It is constructed as a population-weighted average of country-level scores on the Oxford COVID-19 Government Response Tracker’s Stringency Index.¹³ There was a first wave of policies in late January and early February, primarily concentrated on China and other countries in East and South-East Asia that responded to the first cases of the disease. The implementation of such measures was more widely adopted around mid-March, after the number of affected countries passed 100 and the disease was declared a pandemic by the WHO (see section Timeline of a pandemic). Since early May, we see a gradual decrease in the index, as some of the containment measures are rolled back in areas where the disease is considered to be under control.

The global trend observed in the first months of 2020 hides significant different patterns at the country level. As shown in figure 10, some countries swiftly implemented distancing measures and successfully contained the spread of the disease. In all these countries, there were already strict measures in place by the time there were 100 confirmed cases, with a resulting slowdown in the contagion rate. In some cases, such as El Salvador, New Zealand or the Philippines, some measures were active even before the first case was detected. Other countries delayed the onset of these policies (see figure 11) until the number of cases was already high and rapidly increasing, with a resulting surge in the spread of the disease. It is worth noting the case of Singapore, one of the first countries to put in place containment measures against COVID-19. This resulted in slower infection rates already in February; however, the country was affected by a second wave beginning in mid-March forcing it to scale up their policy response.

Figure 10. Stringency of confinement measures and cumulative COVID-19 cases, selected countries

Source: UNCTAD calculations based on University of Oxford, Blavatnik School of Government (2020) and WHO (2020c).
Note: This index ranges from 0 to 100, with higher numbers indicating more stringent confinement measures. Cumulative number of cases are in logarithmic scale. The red dotted lines indicate the date when the number of confirmed cases reached 100.

Figure 11. Stringency of confinement measures and cumulative COVID-19 cases, selected countries

Source: UNCTAD calculations based on University of Oxford, Blavatnik School of Government (2020) and WHO (2020c).
Note: This index ranges from 0 to 100, with higher numbers indicating more stringent confinement measures. Cumulative number of cases are in logarithmic scale. The red dotted lines indicate the date when the number of confirmed cases reached 100.

In some cases, neighbouring countries chose different policies to contain the spread of the virus. Figure 12 shows the situation in four Nordic countries. While Denmark, Finland and Norway took strict measures (the three of them scored above 60 on the Stringency Index by mid-March), Sweden adopted a more relaxed containment policy. As of 10 June 2020, Sweden had 4 547 confirmed cases of COVID-19 per million people, compared to 2 072 in Denmark, 1 268 in Finland and 1 580 in Norway. In terms of confirmed deaths, Sweden has registered 467 deaths per million people, in comparison with 102, 58 and 44 in Denmark, Finland and Norway, respectively.

Although the pandemic remains active and it is too soon to conduct a full evaluation of the impact of containment measures, early evidence seems to indicate that they were effective in slowing down the infection rate of COVID-19 and reducing the number of deaths. The timing of the measures has also proven crucial, with those implemented faster resulting in stronger effects (Deb et al., 2020).

Figure 12. Stringency of confinement measures and cumulative COVID-19 cases, selected countries

Source: UNCTAD calculations based on University of Oxford, Blavatnik School of Government (2020) and WHO (2020c).
Note: This index ranges from 0 to 100, with higher numbers indicating more stringent confinement measures. Cumulative number of cases are in logarithmic scale. The red dotted lines indicate the date when the number of confirmed cases reached 100.

It quickly became evident that, while the containment measures could be effective in slowing down the rate of infection, they also had serious economic and social consequences. With international trade collapsing, domestic economic activity at a standstill and unemployment soaring, the pandemic could also bring long-lasting harm to the economy. And the detrimental economic effects are not distributed evenly. Because of their lower diversification, more limited capacity to hedge risks and less resources in general, smaller firms were particularly affected. Also, poorer families, households in rural areas, workers in the informal sector and certain population groups were more impacted than others. The health crisis could, therefore, exacerbate existing sources of inequality. Governments proposed and started implementing policy packages covering fiscal, monetary and macro-prudential measures, along with employment preservation, income support and social protection policies.

The Oxford COVID-19 Government Response Tracker’s Economic Support Index provides a quantitative indicator of such measures. Because it only covers policies related to income support and debt/contract relief for households (and does not include fiscal stimulus for firms, for instance), it only provides a partial picture of the full spectrum of economic measures taken as a response to the pandemic. However, it can still give an indication of how reactive the governments were when faced with the supply and demand shocks brought by the pandemic.¹⁴ A GDP-weighted global average of this index is presented in figure 13.

Figures 14 and 15 show the implementation of economic support against the evolution of the PMI in the manufacturing sector, a timely indicator of economic activity in this sector. The first graph covers developed economies, while the second includes developing and transition economies. We see a strong response since mid-March or early April in many countries, as soon as economic indicators signalled a slowdown. But other countries have implemented more muted economic stimulus. The capacity of countries to implement stimulus policies depends on factors such as the available fiscal space and the degree of development of the financial sector. Because of this, the crisis could also deepen pre-existing inter-country inequalities, affecting poorer or less financially-integrated economies to a larger degree.

Figure 14. Economic support measures and manufacturing activity, selected countries

Source: UNCTAD calculations based on University of Oxford, Blavatnik School of Government (2020) and Refinitiv (2020).
Notes: The Economic Support index ranges from 0 to 100, with higher numbers indicating more economic support measures. PMIs are diffusion indices, with values above (below) 50 indicating an expansion (contraction).

Figure 15. Economic support measures and manufacturing activity, selected countries

Source: UNCTAD calculations based on University of Oxford, Blavatnik School of Government (2020) and Refinitiv (2020).
Notes: The Economic Support index ranges from 0 to 100, with higher numbers indicating more economic support measures. PMIs are diffusion indices, with values above (below) 50 indicating an expansion (contraction).

COVID-19 and the SDGs

COVID-19 has had a dramatic impact on the global economy, environment and society. This section presents a small flavour of developments since the outbreak. One indicator has been selected to represent each of the three key pillars. For economy, developments in international trade are examined, which relate directly to SDG targets 17.11 and 17.13; for social, the likely impact of COVID-19 on extreme poverty, target 1.1, are highlighted; for environment and climate change we examine changes in greenhouse gas emissions, target 9.4.

Economy: Severe decline in global trade

At the end of 2019, global merchandise trade volumes and values were showing modest signs of recovery. But in 2020, as the world adopted a range of measures to contain the COVID-19 pandemic, the global economy grounded to a halt, and international trade with it. In early May 2020, the monthly UNCTAD Trade Nowcast (UNCTAD, 2020b) estimated that the value of global merchandise trade would fall in the second quarter of 2020 by 27 per cent year-on-year (see figure 16). As economies start to reopen after containment, a rebound in June is anticipated. However, as no data are available yet to reflect this upturn, the nowcast is still extrapolating prior trends. Consequently, the June edition of the UNCTAD Trade Nowcast was suspended, as UNCTAD statisticians were concerned that their models were overshooting, as data picking up impacts of decontainment were not yet available.

The UNCTAD nowcasts incorporate a wide variety of data sources to capture the diverse determinants and indicators of trade. To help users understand this, UNCTAD also publishes, alongside the headline nowcast, a time series, showing how the nowcast has evolved on a weekly basis, as the model incorporates new information (see figure 17). For value estimates, one can see a clear deterioration since late April as new data became available.

Social: Worsening impact on global poverty

In May, the World Bank (Gerszon-Mahler et al., 2020) estimated that COVID-19 could push between 40 and 60 million into extreme poverty (CCSA, 2020). Since then, the epicentre of the pandemic has shifted from Europe to the Americas and the Global South, increasing the death toll in low- and middle-income countries. As a result, they have updated their assessment of the impact of COVID-19 on global poverty.

Based on the updated growth forecasts presented in their Global Economic Prospects, the World Bank (2020) has updated their impact assessment on global poverty. They present two scenarios, a baseline scenario (global growth contracts by five per cent in 2020) where the outbreak remains at currently anticipated levels, with economic activity recovering later in the year. The more pessimistic downside scenario (global growth contracts by eight per cent in 2020) anticipates a more persistent outbreak, forcing prolonged containment measures, resulting in vulnerable firms closing, vulnerable households sharply reducing consumption, and several low- and middle-income countries experiencing heightened financial stress.

Based on these deteriorating economic forecasts, the World Bank have updated their assessment of the impact of COVID-19 on poverty. They estimate that the baseline scenario will result in 71 million people being pushed into extreme poverty (measured by the international poverty line of US$1.90 per day), whereas the downside scenario would see this rise to 100 million people.

Environment: Reduction of CO₂ emissions due to COVID-19 not enough to reach climate targets

In the first quarter of 2020, global CO₂ emissions were more than five per cent lower compared with the same period in 2019 according to estimates by IEA (2020). Depending on the scenario used, 2020 global CO₂ emissions are forecast to decline by around eight per cent; the equivalent of 2.6 Gt. This will be the largest reduction ever recorded and will bring us back to levels last seen a decade ago. The last significant decline, caused by the global financial crisis in 2009, only yielded a reduction of 0.4 Gt.

Early in 2020, global demand for energy fell sharply owing to containment measures taken against the COVID-19 pandemic. Significant contributors to this slump in demand were the fall in demand for air and road travel (see Make or break for green economy). The fall in demand, combined with changes in the global energy mix in favour of renewables, in turn, contributed to notable short-term improvements in air quality, particularly falls in NO₂ (Carbon Brief, 2020; NASA, 2020; European Data Portal, 2020; CCSA, 2020).

Although record-breaking, the forecast reduction of CO₂ emissions caused by the COVID-19 outbreak will not be enough to achieve even the weakest of the targets set out by the Paris Climate agreement. Global emissions would need to be cut by almost eight per cent every year for the next ten years to keep us within reach of the Paris Climate agreement. Even if COVID-19 has induced fast reductions of CO₂ emissions in 2020, it will not be enough to win the fight against climate change. More effective and lasting efforts are needed to reduce CO₂ emissions and other greenhouse gases to limit global warming below 2°C or especially below the 1.5°C target by 2100. As populations and GDP per capita continue to grow, a drastic reduction in carbon intensity will be required. Rising energy efficiency serves as an important step in that direction, as well as renewable and cleaner energy.

Trade shocks and gender equality in employment

¹⁵

Business cycles are not gender neutral (e.g. Hoynes et al., 2012; Peiro et al., 2012; Razzu and Singleton, 2016), as a consequence of gender-segregation into different industries and occupations (Razzu and Singleton, 2018). Economic downturns usually affect men more than women since men tend to work in industries that are more closely tied to economic cycles (e.g. construction and manufacturing). However, the COVID-19 economic downturn may be different as sectors most exposed to the collapse absorb a sizeable share of female employment (ILO, 2020b). Therefore, women are likely to be more affected, at least in the short-term (Alon et al., 2020). As the economic consequences of COVID-19 unfold, the effects may spread. As outlined above, the latest UNCTAD (2020b) nowcast anticipates that the world trade will fall by 27 per cent during the second quarter of 2020. This will have differing effects on women and men in the labour markets which will be important to consider in the crisis response.

To analyse the link of gender and trade in these conditions, we estimate the response of women’s and men’s employment to changes in international trade. A set of gender balanced indicators in employment, as proposed by Van Steveren (2012), shows how gender equality has evolved during previous economic fluctuations. These indicators also provide early signs of changes in the labour market by gender in response to changing international trade. Data from the EU and the United Kingdom, comparable by EU regulation, make an interesting case study, noting the synchronizing effect of the common economic area on business conditions.

Figure 20 compares year-on-year changes in male and female unemployment rates with year-on-year changes in international trade for EU countries and the United Kingdom. The ratio of female to male unemployment seems to follow similar patterns to international trade, meaning that male employment increases more than female’s as trade increases. From this viewpoint, international trade benefits men more than women (Luomaranta et al., 2020).

To inspect the relationship of trade and employment in selected groups in the labour markets, we estimate a set of panel-VAR regressions using Abrigo and Love (2015) as:

where X_{i,t} = \big[\ln Y_t \ln T_t \ln U_t \big]^{\prime} vector includes the labour force indicator of interest, international trade growth rate, and unemployment growth rate, all in logs. \Theta(L) is the matrix polynomial in the lag operator L . \pi_i captures the country fixed effects and \varepsilon_{i,t} is the error term. As in Clark and Summers (1980), unemployment rate is used to capture the state of the economy, distinguishing overall economic conditions from international trade.

Figure 21. Impulse responses of employment and unemployment rates in selected groups in the labour markets, EU and United Kingdom

(year-on-year change)

Source: UNCTAD calculations based on the Eurostat (2020).
Notes: We apply a set of homogeneous panel VAR models, useful for analysing cyclical responses of the variables, all of which are entered in a stationary form and are de-trended and de-seasonalized natural logarithms of year-on-year growth rates. The impulse-response plots provide a short-term response to one per cent increase in the impulse variable (international trade in goods) on the response variables (selected employment variables) in each country. The lag length is selected by the Akaike information criterion. Notice that the plots should only be used to consider the very short-term responses. The titles placed above each plot provide the impulse variable and the response variable, respectively.

The first two charts in figure 21 illustrate the differing responses of male and female employment rates to a one per cent increase in international trade in goods. Indeed, based on the estimated model, male employment rate reacts more strongly to an increase in trade than female rates: 0.24 per cent increase for men compared with only 0.13 per cent for women. Similarly, male employment will drop by 0.24 per cent for every one per cent decrease in international trade. This reinforces the observation that male employment is more pro-cyclical than female employment.

The remaining four charts review responses to a one per cent increase in unemployment to capture the labour market responses to worsening economic conditions in a number of gender balance indicators. The third chart compares women’s unemployment to men’s unemployment among youth, with a declining development referring to men’s unemployment rate increasing faster than women’s among young workers (20-24 years). In part-time employed, gender balance in employment shifts for the benefit of men, when economic conditions deteriorate. Similarly, women would gain relative to men when the economy picks up.

The opposite is true among employees with a lower education, as gender balance in employment shifts for the benefit of women when the economy deteriorates. Men are relatively more hit in low-skill jobs when the economy plummets. The gender balance in employment in the high-skill category is not strongly responsive to economic shocks.

Taken together, the results provide evidence that international trade has gendered impacts on employment and points out that young, part-time workers and those with a lower education are most vulnerable to shocks, such as those related to the COVID-19 pandemic. According to ILO (2020c), over one in six young people (aged 15 to 24) surveyed have stopped working since the onset of the COVID-19 pandemic, and for those remaining in employment, working hours have dropped by 23 per cent.

Gender balance in the labour markets can be significantly affected by international trade and economic fluctuations and should, therefore, be closely monitored. UNCTAD (2018) provides a conceptual framework for analysing the interconnections of gender equality and trade. Countries should collect and analyse gender statistics linked to trade to inform crisis response and recovery plans, since it looks like the most vulnerable are likely to suffer the strongest effects of the COVID-19 related economic downturn.¹⁶

Impact on global statistics

The global COVID-19 crisis has disrupted the compilation of official statistics across the global statistical system, throwing up a wide range of methodological, conceptual and data collection challenges. National and international statistical organizations have had to implement a variety of innovative actions to ensure the continuity of key statistical collections and outputs.

COVID-19 has posed challenges for some longstanding statistical concepts, not least, the definition of unemployment. The internationally agreed statistical concepts and definition of unemployment, set out in the 1982 ILO Resolution Concerning Statistics of the Economically Active Population, Employment, Unemployment and Underemployment¹⁷, have been strained by confinement. In summary, to be classified as unemployed, a person must be without work, available for work, and seeking work during a reference period. But what happens when an economy closes? Curiously, strict application of ILO rules, despite the difficulties presented for job search amid COVID-19 restrictions, and the variety of government social protection and furlough schemes put in place to protect labour that have fully or partially replaced wages and salaries usually paid by employers, could yield a counter-intuitive result, whereby the numbers employed and unemployed would be little impacted by the pandemic. Consequently, some countries have made special adjustments, in respect the ILO standards, to yield credible results. For example, in Ireland the Central Statistics Office presents their traditional (or standard methodology) monthly unemployment estimates alongside an alternative COVID-19 adjusted unemployment measure that estimates the share of the labour force that were not working due to unemployment or who were out of work due to COVID-19 and were in receipts of special COVID-19 related social protection or unemployment payments. In May 2020, the traditional measure for unemployment was estimated to be 5.8 per cent, whereas the COVID-19 adjusted rate was 26.1 per cent (CSO, 2020).

The compilation of national accounts is also facing similar conceptual challenges, not least how to treat or account for COVID-19 related payments to enterprises, employees and self-employed in the system of national accounts and GDP. The Intersecretariat Working Group on National Accounts (2020) advise that government supports to employers to maintain businesses and keep employees on payroll, and government supports to self-employed to support business, should be recorded in the SNA as ‘other subsidies to production’. Government supports to households to maintain income (depending on whether they are considered as social benefits or not), should be recorded as social security benefits, social assistance benefits or miscellaneous current transfers. For example, the Coronavirus Job Retention Scheme implemented in the United Kingdom, where employers of furloughed staff are paid 80 per cent of salaries by government, will be treated as a subsidy to business, to be netted off the income measure of GDP (Athow, 2020).

COVID-19 has additionally thrown up a whole host of methodological issues. For example, many national statistical offices have had to either temporarily suspend face-to-face interviews or switch very quickly to other modes of data collection, such as telephone or web-based collection, web scraping, or greater use of administrative or privately held data. Important household surveys, such as labour force, consumer price index, household budget, income and living condition surveys have suffered from disruptions. This presents not only logistical and infrastructural challenges but also significant statistical challenges. For example, creating telephone databases or adopting dual or multiple frame sampling (a challenge if surveying both landline and mobile phones) are significant complications. Furthermore, if NSOs switch from CAPI to CATI, then they will also need to adjust for ‘mode’ as each mode of collection has its own inherent biases. They may also need to deal with suddenly reduced response rates (ILO, 2020d). Many traditional imputation and seasonal adjustment procedures, which rely on historic patterns, will have been rendered redundant by containment.

Equally, enterprise surveys too have been impacted as many businesses are closed or have ‘relocated’ to new addresses as business owners and employees work from home. The crisis is likely to pose very particular challenges for the quality of statistical business registers, as enterprise churn, the washing machine of enterprise births and deaths, is likely to be much higher and less predictable than usual. In turn, this will impact both sample selection and the weighting of many other business surveys. NSOs have also had to grapple with the knotty problem of compiling price indices when markets have shut down. For example, how to continue residential and commercial property price indices when there are no transactions, and consequently no reported prices for some products. How do you impute for a market that does not exist? These are important questions for the indices themselves, but also for the derived deflators – the basis for volume and constant price measures. COVID-19 will also disrupt normal seasonal patterns, introducing a set of new challenges for statisticians hoping to present consistent time series and provide timely information.

From an official statistics perspective, COVID-19 hit at a particularly unfortunate time, as 2020 was the beginning of the next round of the decennial census of population. More than 120 countries were scheduled to conduct census enumeration between 2020 and 2021. Censuses are expensive, and if delayed, many of the sunk costs cannot be recouped and may result in cancellations rather than just postponements. By early May 2020, UNFPA reported that already 64 countries had reported adverse impacts of COVID-19 on their population and housing censuses (CCSA, 2020). In a recent survey, ‘Monitoring the state of statistical operations under the COVID-19 Pandemic’ jointly conducted by UN DESA and the World Bank (more below), 58 per cent of the 61 countries who were planning a Population and Housing Census in 2020 reported impacts on their preparatory activities, with more than half (53 per cent) postponing fieldwork to later in 2020 or to 2021 or beyond (UNDESA and World Bank, 2020). If the global census round is disrupted, this will ripple through the entire statistical system, as not only will many minority and vulnerable populations go uncounted, but as the denominator for so many other indicators, the impact will be felt in every statistical domain – social, economic and environmental.

National statistical systems and international statistical offices around the world have risen to the challenge. Like many other industries, they have switched rapidly to working from home, while simultaneously introducing new data collection methodologies, adapting existing conceptual frameworks to incorporate government interventions and yield technically accurate but plausible results. There has also been considerable innovation, with many offices having introduced new data sources, surveys and statistics.

Statistics Canada (2020), for instance, introduced a monthly flash GDP estimate in April 2020 to provide a faster approximation of the scale of economic disruption in March 2020. Statistics South Africa (2020) and the ONS in the United Kingdom (Athow, 2020), among others, have introduced online surveys on the business impact of COVID-19 and surveys to assess the impact on people, households and communities, similarly to the new Household Pulse Survey of the United States (United States Census Bureau, 2020). Many offices have partnered with government and private organisations to access timely data sources, such as big data on ship tracking, road traffic sensors, credit card transactions and mobile phone use. Statistics Netherlands improved the timeliness of many statistics, including mortality, retail trade, use of energy, bankruptcies statistics and introduced new statistics on emergency measures and mobility among others. Statistics Estonia and the Ghana Statistical Services, for example, have been measuring mobility under the confinement period using anonymized mobile phone data (Migration data portal, 2020; Ghana Statistical Services and Vodafone Ghana, 2020). A quick adaptation of data collection methods has also been necessary under confinement, including in South Africa, where a large proportion of price data collection was moved online (Statistics South Africa, 2020), and offices like the United States Census Bureau and Statistics New Zealand have started using credit card purchase and supermarket price data directly for statistical production.

As noted above, the UN DESA and the World Bank's Development Data Group, in coordination with the five UN regional commissions, recently conducted a global online survey to monitor the nature, scale, and scope of the impact of the coronavirus crisis on statistical agencies, as well as to identify new data needs. The survey results, covering 122 responding countries, highlight the tremendous challenges being faced by national statistical offices as a result of the COVID-19 crisis, but also illustrate the range of measures being taken to mitigate negative impacts and meet new data demands. 65 per cent of NSO headquarters offices are partially or fully closed, 90 per cent have instructed staff to work from home, and 96 per cent have partially or fully stopped face-to-face data collection. The results also show that NSOs in low- and lower middle-income countries have been hardest hit, where nine out of ten offices report impediments to their ability to meet international reporting standards and additional funding constraints. Unsurprisingly, the survey has reinforced the importance of technological infrastructure and skills, which has allowed some offices to find substitute modes of data collection for face-to-face interviews. Worryingly, at a time when good quality statistics are needed, 38 per cent of responding NSOs reported funding cuts.

UNCTAD Statistics responded quickly by introducing a new quarterly nowcast for merchandise and services trade (UNCTAD, 2020b), providing up-to-date information on global trade (see section COVID-19 and the SDGs – Economy). The online statistical capacity development that UNCTAD provides in cooperation with WTO and UNSD has continued uninterrupted (see UNCTAD in Action TRAINFORTRADE), bringing capacity to developing and developed countries all around the world.

36 international organisations also quickly came together, under the aegis of the CCSA, and assembled a report in May 2020, ‘How COVID-19 Changed the World: a statistical perspective’, providing a wide range of statistics to illustrate how COVID-19 has impacted different aspects of our lives (CCSA, 2020).

There is a lot of work to be done. The fast spreading COVID-19 pandemic shows the interconnectedness of countries and underlines the need for more granular, interlinked and timely official statistics. There is, most likely, no return to ‘business as usual’ for official statistics. The statistical community will need to reshape future official statistics by exploring new partnerships, integration of surveys, registers and alternative data sources for the provision of timely, agile and more bespoke statistics to inform policies with a rich picture of the economy and society – be it on health, employment, production, trade, globalisation, technology, inequality, skills, environment or their interactions. Interesting debates are underway on what this future might look like on the Statistical Journal of the International Association of Official Statistics discussion platform¹⁸ and on the UN DESA COVID-19 Response page¹⁹.

2020 hindsight

In recent years, there has been much debate surrounding the ethics of using personal data and what are the acceptable trade-offs vis-a-vis privacy. Captains of industry 4.0, such as Mark Zuckerberg (Facebook), Scott McNealy (Sun Microsystems) and John McAfee (McAfee Associates) have all argued that the concept of privacy is extinct (Kirkpatrick, 2010; Noyes, 2015; McAfee, 2015). Many disagree and have voiced concerns over the loss of privacy (Pearson, 2013; Payton and Claypoole, 2014; Zuboff, 2019). New data protection legislation in Europe (EU, 2016) and in California (State of California, 2020) suggest that at least some legislators still see a value in privacy. Nevertheless, it is difficult to see how the concept of privacy can survive unscathed with the relentless drive towards the Internet of Things - smart phones, smart TVs, smart cars, smart homes and smart cities, and harvesting of personal data. Soon it seems everything we do will be monitored. One cannot help but wonder whether privacy as an ‘ideal’ might still be alive and well, but privacy in ‘practice’ is on life-support; day after day, we read about enterprises and institutions failing to protect personal records.

There is a risk that COVID-19 may exacerbate this situation. In a time of crisis, populations expect their governments and public services to adapt and provide new services (and information) without delay. At the same time, populations tend to have elastic ethical frontiers. Thus, social license typically contracts in good times but loosens in emergencies, with the result that populations are less concerned about the how job gets done as long as it gets done. While this is understandable, reactions to recent crises have arguably permanently stretched the limits of the pre-crisis ethical frontiers. For example, following 9/11 many legal barriers to data sharing were quickly swept aside as the political focus shifted from privacy to security (Lyon, 2001); many were never reinstated. The COVID-19 pandemic may do the same. In March 2020, it was reported that 19 countries were accessing citizen data to track the virus (Cozzens, 2020; Doffman, 2020), including Austria, Germany, Italy, the United Kingdom and the United States of America, while Liechtenstein is even planning to electronically tag and monitor its citizens (Financial Times, 2020). Furthermore, Google began publishing detailed statistics, harvested from their applications and platforms, on population movements (Kelion, 2020; McGrath, 2020). Yale’s professor Sudhir neatly sums up the situation: ‘Privacy concerns are on the back burner during this emergency’ (Sudhir, 2020). While this is understandable, it raises the question of what happens after the crisis? Can we put the genie back in the bottle afterwards?

COVID-19 may have additionally unwittingly exposed tensions between community and individual rights. Many will argue that the growth of the Internet of Things and the ability to measure everything is a good thing. But good for who? As Sen (1999, p. 150) reminds us, ‘in judging economic development it is not adequate to look only at the growth of GNP or some other indicators of overall economic expansion. We have to look also at the impact of democracy and political freedoms on the lives and capabilities of the citizens’. There are some who now fear the growth and centralisation of technology as a direct threat to democracy (Reich, 2015; Taplin, 2017; Zuboff, 2019). Data can both be a tool and a weapon; used for good or evil. As noted in the 2019 In Focus of SDG Pulse The many faces of inequality, (UNCTAD, 2019) equal access to data is of central importance to achieving the 2030 Agenda. The growth in proprietary data is exacerbating the split between ‘the data haves and have-nots’ and is creating a new dimension of inequality.

Notes

1. WHO (2020a, report #132).
2. WHO (2020a, report #1).
3. WHO (2020a, report #11).
4. On 11 February 2020, the WHO, in consultation and collaboration with the OIE and the FAO, named the novel coronavirus as COVID-19.
5. Africa, Americas, Eastern Mediterranean, Europe, South East Asia, and Western Pacific.
6. WHO (2020a, report #37).
7. WHO (2020a, report #133).
8. WHO (2020a, report #58).
9. The date of the first stage reopening.
10. Range around the estimate: 1.8 – 4.7 million people.
11. WHO (2020a, report #112).
12. 24 participating countries or territories: Austria, Belgium, Denmark, England, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Luxembourg, Malta, the Netherlands, Northern Ireland, Norway, Portugal, Scotland, Spain, Sweden, Switzerland and Wales.
13. This composite index provides a quantitative score of the implementation of nine types of containment and physical distancing measures in over 150 countries. It is updated daily from a variety of data sources. It is scaled to a 0-100 range, with higher numbers indicating more “stringent” containment measures. For a complete description of these index and its methodology, see Hale et al. (2020).
14. For a more complete compilation of economic measures, see IMF (2020) and OECD (2020).
15. This note is based on ongoing research and should be taken as preliminary. More developed research report will appear as an UNCTAD research paper.
16. These tests provide only a partial view as we are not capturing the complex interrelationships between the labour market and, for example, policies imposed during the COVID-19 lockdowns. Instead, we measure, in a simple way, the responses of the selected employment variables to trade and unemployment, as a marker of the state of the economy, to provide insights on gendered economic structures which can help anticipate future developments.
17. Adopted by the Thirteenth International Conference of Labour Statisticians, see ILO (1982).
18. Official Statistics in the context of the COVID-19 crisis, see Statistical Journal of the International Association of Official Statistics (2020).
19. UNDESA (2020).

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Heads of state and government gather at FfD4 this year¹ to reshape the global financial architecture for sustainable development. Amid mounting global challenges, from geopolitical tensions and related refugee crises to climate shocks and systemic financial risks, the conference seeks to catalyse a renewed commitment to equitable and resilient financing. While ODA to developing economies is failing to reach commitments, new solutions are sought. South-South cooperation, grounded in peer-to-peer partnerships, knowledge exchange and non-financial support, plays a critical role in sustainable development for all, reinforcing other mechanisms.

Official Development Assistance turning downwards, increasing the gap to agreed targets

Despite recent growth, longstanding aid commitments remain unmet. In 2024, ODA fell by 7.1% in real terms compared to 2023 – for the first time in five years – declining to $212.5 billion -—
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Figure 1. The falling ODA has increased the gap: disbursements now less than half of what developed economies have committed to

ODA as a percentage of GNI (SDG 17.2.1)

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Notes: 2024 is not shown in the LDCs graph as ODA data by detailed recipient are not yet available.

The previous increases of ODA were driven by conflict-related aid, e.g., to Ukraine, and in-donor refugee costs, and their decrease has contributed to the fall in 2024. This decline was further impacted by reduced contributions to international organisations and lower levels of humanitarian aid. Amidst these shifts, ODA with climate objectives has gradually increased over the past decade, reaching nearly $50 billion in 2021/2022 -—
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South-South data supporting strategic allocation of resources to reach the 2030 Agenda

South-South cooperation, alongside other international development support, is key to achieving the 2030 Agenda, but is the only form of development cooperation lacking systematic data. This hampers its strategic management and the effective allocation of flows to achieve sustainable development.

Figure 2. UNCTAD has engaged in close collaboration with partners to advance work on SDG indicator 17.3.1

Source: UNCTAD.

Momentum to measure South-South cooperation is rapidly building following the endorsement of SDG indicator 17.3.1 in March 2022 at the UN Statistical Commission -—
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Interest in measuring South-South cooperation is quickly increasing. The first expert meeting in July 2023 in Brasilia brought together 16 member States, and in June 2024, 66 developing economies met in Doha. Four countries have reported preliminary South-South data in the ’Framework’ and eleven are pilot testing it in 2025 (map 1). Early data by pioneering countries confirm non-financial support as essential to South-South cooperation, with scholarships, humanitarian assistance and technical cooperation reported most frequently, targeting (ordered by number of reported activities) SDGs 4 (quality education), 9 (industry and innovation), 8 (decent work and economic growth), 17 (partnership for the goals) and 3 (enhancing health), showcasing the diversity of South-South cooperation.

Map 1. Pioneering and pilot countries are driving South-South data measurement

Source: UNCTAD, ECA, ECLAC, ESCAP, ESCWA.

Note: Situation reflected on the map as of April 2025.

Notes

1. 4th International Conference on Financing for Development Sevilla, Spain | 30 June – 3 July 2025; https://financing.desa.un.org/ffd4
2. Developed economies have committed to a target of 0.7% of ODA/GNI and 0.15 to 0.20% of ODA/GNI targeted to the LDCs -—
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3. The cost estimates presented are extrapolated values derived by scaling a population-weighted median of per capita costs from sampled LDCs to total LDC population.
4. Project website: https://stats.unctad.org/measuringSSC.

References

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

Bridging the financing gap to achieve the SDGs and facilitate long-term economic transformation requires effective mobilization and utilization of various financing sources. Many developing economies face challenges in mobilizing sufficient funds, often hindered by their inability to secure affordable borrowing for investment. As they transition to higher income groups, losing eligibility for concessional finance (or part thereof) can exacerbate these challenges, creating a greater incentive to engage in South-South cooperation, but also reliance on private financial markets.

Continued decline in foreign direct investment to developing economies

Global FDI flows reached an estimated $1.5 trillion in 2024, an apparent increase of 4%. However, this headline figure is distorted by financial flows routed through European conduit economies¹. Excluding these intermediary flows, global FDI actually fell by about 11% year-on-year -—
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—-. Global volatility of FDI appears to be flatlining in recent years, however FDI flows to developing economies, after having exhibited a flat trend since 2010, experienced a sharp increase in 2021-2022 (figure 1). 2023 saw a drop in FDI and 2024 remained flat compared to the year before for the global South, undermining progress on the SDGs, as these economies rely heavily on international financing. FDI declines in 2024 were observed in Latin America and the Caribbean (12%) and Asia (3%), while FDI increased in Africa, a record 75% rise -—
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Figure 1. Since the start of the 2030 Agenda, the only major FDI inflow jump to developing economies (2021-2022) is now losing momentum

FDI inflows, billions of dollars (SDG 17.3.1)

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Greenfield project announcements, primarily in industrial sectors, saw a moderate increase of 3% in number, yet fell 5% in value. Despite the drop, the value of greenfield projects remained high ($1.3 trillion), second only to the 2023 record value, driven primarily by investments in data centres and data processing. -—
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Investments in SDG-related sectors dropped sharply, by more than a quarter in 2024. Investment flows to developing economies for infrastructure fell 35%, renewable energy 31%, water and sanitation 30%, and agrifood systems 19%. Only the health and education sector saw growth (25%) -—
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Facilitating outward foreign direct investment key to achieving the SDGs

Mobilizing private sector finance is crucial to achieve the SDGs, with FDI playing a key role. FDI promotion is not solely the objective and responsibility of host economies; home economies can also support investment in developing economies and LDCs through dedicated OFDI promotion schemes. In this regard, SDG indicator 17.5.1 tracks the number of economies with OFDI promotion schemes for developing economies, including LDCs.

In 2024, at least 51 economies, including 21 emerging or developing economies, had in place at least one type of investment promotion mechanism for OFDI. This represented 71 per cent of developed economies and 15 per cent of developing economies. Among the economies with OFDI promotion mechanisms, an increasing number (27) had adopted schemes specifically targeting developing economies, including least developed economies. Globally, the most common mechanisms supporting OFDI were investment facilitation services (44 economies), followed by fiscal and financial support (38 economies), investment guarantees (35 economies) and State equity participation in foreign investment projects (25 economies) (figure 2).

Figure 2. Also in 2024, outward FDI promotion schemes towards developing economies, including LDCs, are predominantly provided by developed economies

Number of economies with an investment promotion mechanism for OFDI, by mechanism (SDG 17.5.1)

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Notes

1. Several European economies, including Ireland, Luxembourg, the Netherlands and Switzerland, where FDI statistics are significantly affected by conduit financial flows, reported large fluctuations and negative numbers in 2023 and 2024. Fewer negative numbers in 2024 exerted a net positive effect on global flows of about $230 billion. -—
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References

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

Digitalization has changed the way people produce, consume, trade and live. Narrowing the technological gap and closing the digital divide between and within developed and developing countries provides opportunities for improving incomes and resilience, as well as reducing the vulnerabilities, of the poorest – and in particular of women and youth. Past and ongoing crises have highlighted the role of digital technologies in diversifying economies and building resilient systems that are open, inclusive, and secure and benefit everyone.

It all starts with getting people and businesses online

To be able to engage in and benefit from the digital economy and digital trade, individuals and businesses must first be online. This means being covered by Internet infrastructure that is sufficiently fast and reliable, and furthermore by electricity infrastructure to power digital devices. By 2024, 92% of world population was covered by 4G mobile networks, double the share in 2015 -—
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Map 1. More than half of the world’s population now covered by 5G, but deployment lags in some regions

Percentage of population covered by mobile network, by technology, 2023 (SDG 9.c.1)

Source: UNCTAD calculations based on -—
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Coverage alone is not enough

While 96% of the world population is covered by mobile broadband (3G or above) networks, many other factors create a gap between those who could access the Internet and those who do use it. In 2024, two thirds of the world’s population used the Internet, leaving 2.6 billion people offline. Furthermore, while almost all people in developed countries are online, only 35% of those in the least developed countries (LDCs) use the Internet -—
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One key reason is that the costs involved in getting online can be prohibitive for many. In 2024, the annual cost of a mobile broadband subscription was equivalent to 4.6% of per capita GNI in LDCs while a fixed broadband subscription equated to one sixth of GNI per capita -—
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—- – both exhibiting a slight decrease compared to previous year. Given disparities in income distribution within countries, for many people connectivity will be even less affordable. Furthermore, the digital devices required to access the Internet, such as smartphones, also need to be available and affordable.

Additionally, the skills required to use the Internet must be sufficiently widespread and available amongst the population, and people need to be aware of the opportunities of the digital economy and of digital trade, especially those working at firms that stand to benefit from digital transformation. Finally, the speed provided by Internet connections and fixed line technologies, such as the fixed broadband, matters -—
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Digital technologies offer a vital opportunity for economic diversification

Digital technologies, including the Internet, underpin e-commerce – in which buyers place and sellers receive orders online. Additionally, they enable instantaneous remote delivery of services directly into businesses and homes. Both digitally ordered and digitally delivered transactions increasingly take place across borders. The possibility of engaging in such digital trade offers new opportunities for the diversification of developing economies. Digitally deliverable services now account for over half of all services exports worldwide. Their share grew especially during the disruptions of the COVID-19 pandemic, then declined as exports of other services recovered, and stabilized in 2023, at a higher level than before the pandemic (figure 1).

Figure 1. Digitally deliverable services have increased their share in total services trade over the last 5 years

Exports of digitally deliverable services as percentage of total services exports

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Note: Digitally deliverable services are services that can be delivered remotely through computer networks. The figures in the graph cover: insurance and pension services; financial services; charges for the use of intellectual property; telecommunications, computer and information services; other business services; and audio-visual and related services.

Seizing the opportunities of digital trade requires not only investments in ICT connectivity but also actions to boost digital skills and awareness of the opportunities and risks associated with digital trade. Measures to facilitate digitally ordered goods transiting the border and regulatory actions to encourage digital payments, ensuring privacy and data protection, as well as the establishment of channels for recourse in case of loss or detriment related to digital trade, represent further enabling factors -—
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References

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The external debt of developing economies reached $11.7 trillion in 2024, up from $11.4 trillion in 2023. Even though the value of total external debt continues to rise, the pace of that increase has slowed significantly since the COVID-19 pandemic, leading to moderate annual growth of 2.6% in 2024 – less than a third of the long-term average annual growth rate (8.4%) prior to the pandemic.

Figure 1. Debt servicing costs have risen sharply for the poorest economies

Long-term external public and publicly guaranteed debt service as a percentage of exports of goods and services (SDG 17.4.1)

Source: UNCTAD calculations based on -—
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Note: Values in 2024 are estimated.

For developing economies as a group, the ratio of debt service on long-term external PPG debt to exports has been on an upward trend since 2010 (figure 1). The increase was particularly pronounced for the poorest: in 2024, the ratio was 13.5% for LDCs as a group, more than three times as high as in 2010 (3.5%) and also significantly higher than before the pandemic (9.7% in 2019). For the group of SIDS excluding Singapore, a group of particularly climate-vulnerable economies, this ratio increased sharply in 2020 as a result of decreasing services exports mainly due to falling tourism at the times of the lockdowns. After returning to its pre-pandemic level, the indicator has increased again since 2022, primarily due to higher debt servicing costs.

Figure 2. External debt has become more expensive to service, especially for low-income economies

Ratio of external debt service to external debt by income groups (percentage)

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—- classification by income level.

The ratio of external debt service to external debt – which considers the impacts of both maturity and interest costs – has been on a rising trend in the groups of the low, lower-middle and upper-middle income developing economies over the past decade (figure 2). The ratio was highest for upper-middle income economies, reaching 15.2% in 2024, as compared to 9.9% in 2014. Lower-middle income economies recorded a steady increase after the pandemic, with total external debt service rising to 12.5% of total external debt in 2024, up from 10.3% in 2020. The increase over the past decade was most pronounced in low income developing economies where this ratio rose from 3.8% in 2014 to 10.5% in 2024.

References

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