# The Global Cost of Reaching a World Without Hunger: Investment Costs and Policy Action Opportunities

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Bezawit Beyene Chichaibelu, Maksud Bekchanov, Joachim von Braun and Maximo Torero

## 1 Introduction

At the heart of the 2030 Agenda<fnref n="1" /> was a promise to prioritize two objectives: to eradicate poverty and end hunger in all their forms. Worldwide, over 650 million people are estimated to have been undernourished in 2019. World hunger increased further in 2020 to 720–811 million people, exacerbated by the impact of COVID-19 (FAO, IFAD, UNICEF, WFP, & WHO 2021). Recent global projections of hunger show that the world is not on track to achieve zero hunger<fnref n="2" /> by 2030. Estimates in 2020 projected that the number of people affected by hunger will surpass 840 million by 2030, or 10% of the global population (FAO, IFAD, UNICEF, WFP, & WHO 2020). Updated estimates in 2021 projected lower, but still alarming levels of hunger

Bezawit Beyene Chichaibelu, Maksud Bekchanov, Joachim von Braun and Maximo Torero 2021. The global cost of reaching a world without hunger: Investment costs and policy action opportunities. In Food Policy. Volume 104, October 2021, 102151. https://doi.org/10.1016/j.foodpol.2021.102151_34.

B. B. Chichaibelu (✉) · J. von Braun

Center for Development Research, University of Bonn, Bonn, Germany
e-mail: bchichai@uni-bonn.de

M. Bekchanov

Research Unit Sustainability and Global Change, Center for Earth System Research and Sustainability, University of Hamburg, Hamburg, Germany

M. Torero

Food and Agriculture Organization (FAO), Rome, Italy

<footnote n="1">¹In 2015, all of the UN member states adopted seventeen goals as part of the 2030 Agenda for Sustainable Development, which set out a fifteen-year plan to achieve the goals.</footnote>

<footnote n="2">²Most studies define ‘zero hunger target’ as the reduction of hunger levels to less than the 5 or 3% of population. ‘Absolute zero hunger target’ is used throughout this manuscript to refer to a complete eradication of hunger.</footnote>
that will affect about 660 million people by 2030 (FAO, IFAD, UNICEF, WFP, & WHO 2021). The world is also not on track to achieve the 2025 and 2030 targets for child malnutrition. In 2019, 21.3% of children under five years of age were stunted globally, or 144 million (UNICEF, WHO & World Bank 2020). In 2021, the number of children suffering from stunting increased further to 149.2 million. Although there has been some progress globally since 2000, rates of stunting reduction are far below what is needed to reach the targets of 40% reduction for 2025 and 50% reduction for (FAO, IFAD, UNICEF, WFP, & WHO 2021).

COVID-19 is expected to further worsen the overall prospects for food security and nutrition. Food insecurity may appear in countries and population groups that were not previously affected. In 2020, the number of undernourished people increased by about 118 million people compared to the 2019 level as COVID-19 disrupted economies, job markets and supply chains, and inflated food prices. The pandemic is also expected to have a lasting effect beyond 2020, adding about 30 million more people to the total number of undernourished in the world in 2030 (FAO, IFAD, UNICEF, WFP, & WHO 2021). Additionally, early estimates on the impact of COVID-19 on child malnutrition suggested an additional 6.7 million children suffered from wasting in 2020 as compared to pre-COVID-19 projections. The combined effect of the increase in wasting and a 25% reduction in the coverage of nutrition and health services due to COVID-19 could cause an additional 128,605 deaths in children younger than five years in 2020 (Headey et al. 2020). While these projections are an early estimate and may not fully capture the impact of COVID-19 on food security and nutrition, they emphasize the urgent need for actions to get back on track towards achieving targets 2.1 and 2.2 of Sustainable Development Goal No. 2 (SDG2).

Investments needed to end hunger are anticipated to be extensive, costly and difficult to implement, even without considering the impacts of the COVID-19 pandemic. As policymakers still need to prioritize the allocation of resources, identifying optimal and least-cost investment options is important for practical policy. In this regard, this study developed a marginal abatement cost curve (MACC) as an original contribution to identify a mix of least-cost investment options with the highest potential for reduction in hunger. Twenty-two different interventions are considered for reducing hunger. The information about the interventions are drawn from the findings of various model- and cost-benefit and impact evaluation studies on hunger reduction measures. Some of them are more short-term interventions (such as social protection), and some are more long-term (such as agricultural R&D, or soil fertility management). The MACC of hunger reduction can be considered when asking “what are the costs of ending hunger?”, depending on the number of people who are to be brought out of risk of hunger by 2030. The assessment can broadly guide global and country efforts to achieve the SDG 2 targets by 2030.

The results from the MACC indicate that ending hunger would not be prohibitively expensive, provided that a mix of least-cost measures with large hunger reduction potential are prioritized. Ending hunger by 2030 is estimated to require US$ 39–50 billion annually until 2030. Of that, the G7 would need to contribute US$ 11–14 billion to meet their Elmau commitment of lifting 500 million people out
of hunger by 2030,<fnref n="3" /> effectively doubling current aid flows for agriculture, food and rural development. A bundle of promising investments that deliver short-term and long-term impacts would meet the goal of ending hunger by 2030. Short-term measures are needed to reach the hungry soon – including those adversely affected by COVID-19 related job losses and other socio-economic consequences – with social protection and nutrition programs. Long-term measures, such as agricultural R&D and expansion of small- and large-scale irrigation, which require high up-front investments but also have a high long-term impact, also need to be included. Optimally phasing investments is crucial: those with longer-term impact should be frontloaded to reap their benefits soon before 2030.

## 2 Review of Selected Estimates of the Cost of Ending Hunger

Here we review several estimates of the cost of achieving SDG 2, in particular, ending hunger and improving nutrition. We focus on the five most up-to-date estimates conducted by FAO, IFAD, and WFP (2015), Rosegrant et al. (2017), Torero and von Braun (2015), Laborde et al. (2016), and Shekar et al. (2017). Since some of these studies, for example, the studies by FAO, IFAD and WFP (2015) and Torero and von Braun (2015), use the same methodology as earlier works, i.e. Schmidhuber and Bruinsma (2011) and Hoddinott et al. (2013), these earlier works are not included in our review. In the case of others, for example, in the paper by Laborde et al. (2016) titled “Ending Hunger: What Would It Cost”, the applied methodology is similar to another ongoing work by Ceres2030 research group; we therefore present only the studies for which the final results are readily available (see also Fan et al. 2018). Summary of the findings of the reviewed studies are presented in Table 1. We further discuss the details and interpretations of these studies in the remaining part of this section.

The “Achieving Zero Hunger” study by the Food and Agriculture Organization of the United Nations (FAO), the International Fund for Agricultural Development (IFAD), and the World Food Programme (WFP) (2015) presents the most extensive, but also most costly set of measures, including extensive social protection programmes and targeted pro-poor investments. The basic premise of the ‘Achieving Zero Hunger’ framework is that hunger is a result of lack of purchasing power which translates into a lack of access to sufficient and nutritious food, and therefore the target of eliminating hunger (SDG 2) can be achieved only by eliminating poverty (SDG 1). Unlike other models, it aims for absolute-zero levels of hunger globally by

<footnote n="3">³G7 heads of states at their Summit in Elmau in 2015 committed to lifting 500 million people out of hunger and malnutrition by 2030 as part of a broader effort to be undertaken with partner countries to support the 2030 Agenda for Sustainable Development, i.e. Sustainable Development Goal 2 (SDG 2) to end hunger and malnutrition by 2030.</footnote>
<table><caption>**Table 1** Overview of selected costing studies</caption><thead><tr><th>Model/ framework and institution/author (s)</th><th>Research question/ time frame</th><th>Undernourishment reduction target</th><th>Investments/ interventions considered to achieve the target</th><th>Method used in the analysis</th><th>Discussed financial sources to reduce hunger</th><th>Total annual cost (billion US$) of reducing hunger</th><th>Per capita total cost of hunger eradication (US$) over 2015–2030 *</th></tr></thead><tbody><tr><td>Achieving Zero Hunger (FAO, IFAD &amp; WFP 2015)</td><td>What are the additional investments needed to end poverty and hunger in all countries by 2030?</td><td>Zero hunger; eradicating extreme poverty</td><td>Pro-poor investments: primary agriculture and natural resources, agro-processing operations, infrastructure, institutional framework, R&amp;D, extension; Social protection</td><td>Global partial-equilibrium model of country-wise projections of food supply and demand (called GAPS)</td><td>Public and private</td><td>265, out of which 198 for pro-poor investments</td><td>4035</td></tr><tr><td>IMPACT (IFPRI/ Rosegrant et al. 2017)</td><td>How much would hunger decrease given investments to achieve target yield increases by 2030?</td><td>5% hunger</td><td>Agricultural R&amp;D; irrigation expansion; water use efficiency; soil management; transport and energy infrastructure</td><td>Agricultural sector partial-equilibrium model linked to biophysical models and CGE model; impacts of climate change included</td><td>Public</td><td>52</td><td>929</td></tr></tbody></table>
<table><tr><td>Toward a Zero-Hunger by 2030 (Torero and von Braun 2015)</td><td>What is the global cost to accelerate undermourishment reduction to a level that would almost eliminate hunger by 2030?</td><td>3% hunger; improved nutrition</td><td>Accelerating yield enhancements (agricultural R&D); market innovations (information and communication technologies, increasing competition in the fertilizer market); interventions that reduce micronutrient deficiencies (vitamin A, iodine, iron, zinc) and reduce stunting</td><td>Builds up on the Hoddinott et al. (2013) which used agricultural partial-equilibrium model (called IMPACT) for assessing the impacts of R&D investments and cost-benefit analysis for the remaining options</td><td>Public, including ODA</td><td>30, out of which 15 for ending hunger</td><td>312</td></tr><tr><td>MIRAGRODEP (IFPRI& IISD/ Laborde et al. 2016)</td><td>What is the minimum cost to end hunger for vulnerable households by 2030?</td><td>5% hunger</td><td>Social safety nets: food subsidies; farm support: production subsidies, fertilizer subsidies, investment grants, R&D, extension; rural development and infrastructure: reduction of post-harvest losses, irrigation, roads</td><td>Global multi-regional CGE model combined with household surveys for targeted interventions</td><td>Public, including ODA</td><td>11</td><td>368</td></tr></table>

(continued)
<table><caption>**Table 1 (continued)**</caption><thead><tr><th>Model/ framework and institution/author (s)</th><th>Research question/ time frame</th><th>Undernourishment reduction target</th><th>Investments/ interventions considered to achieve the target</th><th>Method used in the analysis</th><th>Discussed financial sources to reduce hunger</th><th>Total annual cost (billion US$) of reducing hunger</th><th>Per capita total cost of hunger eradication (US$) over 2015–2030 *</th></tr></thead><tbody><tr><td>Investment Framework for Nutrition (WB/ Shekar et al. 2017)</td><td>What is the minimum cost to meet the World Health Assembly targets on nutrition by 2025?</td><td>40% reduction in child stunting; 50% reduction in anaemia in women; 50% increase in exclusive breastfeeding rates; 5% child wasting</td><td>Targeted nutrition interventions (micronutrient and protein supplementation, public provision of complementary food, promoting good health and hygiene) and selected nutrition-sensitive interventions (staple food fortification and pro-breastfeeding policies)</td><td>Investment cost minimization ad cost-benefit-analysis</td><td>Public, including ODA and private, including household contributions and innovative financing mechanisms</td><td>7</td><td>Not applicable</td></tr></tbody></table>

Source: Adapted from Mason-D'Croz et al. (2019)

Note: * Total cost per person is calculated as total net discounted cost over the fifteen-year period (only for the 'Achieving Zero Hunger' study, the time frame is fourteen years, i.e. 2016–2030). The discount rate is assumed to be 5% (Hoddinott et al. 2013). For each modelling framework, the absolute number of people lifted out of hunger by the proposed investments is calculated as the difference between the projected number of hungry people in the 'business as usual 2030' scenario and the projected number of hungry people in the '2030 investment' scenario. These figures are retrieved from each model. The per capita total cost of hunger eradication is then calculated as the total net discounted cost divided by the number of people lifted out of hunger. We calculate only the cost per person for the investments towards hunger reduction, but not for the investments towards improvement in nutrition due to the very specific nature and outcomes of each intervention
2030. Note that hunger is measured here by the prevalence of undernourishment (PoU), defined as chronically inadequate dietary energy intake, in line with the methodology adopted in the FAO's 'The State of Food Security and Nutrition in the World 2019' report (FAO, IFAD, UNICEF, WFP, & WHO 2019). The 'Achieving Zero Hunger' study draws upon a methodology previously used by Schmidhuber and Bruinsma (2011) and employs the partial-equilibrium model called Global Agriculture Perspectives System (GAPS). According to the modelling simulations, the twin-track approach of social protection and pro-poor development is expected to bring relatively fast but also sustainable eradication of poverty and hunger. In the short-term, public investment in social protection is expected to close the poverty gap and increase incomes, both directly and through increased productivity. In the long run, the effects of social protection will be reinforced and sustained by targeted private and public pro-poor investments, especially in rural areas, and particularly so in agriculture (see Table 1). The overall cost of achieving zero hunger would be US$ 265 billion annually, out of which US$ 198 billion will cover pro-poor investments and US$ 67 billion social protection.

The International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) by the International Food Policy Research Institute's (IFPRI) was applied to analyze the potential contribution of agricultural investments to achieving SDG 2, and proposes a comprehensive investment package that can reduce hunger to 5% of the global population (Rosegrant et al. 2017). These investments focus on agriculture and include agricultural research and development (R&D); resource management, especially water and irrigation; and infrastructure, mainly transportation and energy. Out of the five estimates this is the only framework explicitly modelling the impact of R&D on agricultural productivity and hunger reduction; it is also the only one to account for climate change impacts. The IMPACT model is a highly disaggregated, global partial-equilibrium multi-market model. To overcome the limitations of a partial-equilibrium model, it is linked to a global computable general equilibrium (CGE) model (GLOBE) which allows for estimating the impacts of investment in agriculture on the broader economy. Hunger is proxied by the risk of hunger based on the estimated calorie availability per day per capita. The cost of the agricultural investment package is estimated at US$ 52 billion annually for the developing world. These investments are expected to result in a reduction of the share of the population at risk of hunger to 5%, except for Eastern and Central Africa where hunger will remain at 10% level (Rosegrant et al. 2017).

The "Toward a Zero-Hunger by 2030" study by Torero and von Braun (2015) provides global estimates for the investments necessary to reduce hunger to near zero by 2030, with the assumption that transitory undernourishment at around the 3% level, related to conflict and crises, would require different measures. The estimates are to a great extent extrapolated from Hoddinott et al. (2013) and consider hunger reduction through investing in: (i) accelerating yield enhancements, i.e. investments in agricultural R&D; (ii) market innovations, i.e. information and communication technologies (ICTs) and improving the functioning of fertilizer markets; (iii) and interventions that reduce micronutrient deficiencies (vitamin A, iodine, iron, zinc) and reduce stunting.
This framework of the study is somewhat similar to the work of Rosegrant et al. (2017) presented above, as both studies rely on IMPACT modelling assessments. Hunger level is also measured using a similar approach as in the “Achieving Zero Hunger” study (e.g., Rosegrant et al. 2017). However, the conceptual framework and the underlying assumptions vary to some extent. Agricultural R&D is expected to increase productivity, and the elasticity of yields to R&D expenditure is estimated based on a literature review. This yield growth entails both income and price effects, which will then affect the incidence of hunger. The original cost estimates for agricultural R&D in the underlying paper by Hoddinott et al. (2013) show that it would cost US$ 733 per person to reduce the number of undernourished by 210 million by 2050 (the original time frame of the baseline paper), which translates into a prevalence of hunger reduced to 5.9%. Torero and von Braun (2015) suggest to accelerate these investments up to 2030, and couple them with the remaining investment strategies, i.e. food markets and ICTs, as well as with programmes to reduce micronutrient deficiencies and stunting, which would lift 500 million people out of hunger and attain the objective of near-zero hunger. The total cost of all measures addressing hunger and malnutrition would be US$ 30 billion annually; out of which the cost of ending hunger would be US$ 15 billion annually.

The “Ending Hunger: What Would It Cost” study by IFPRI and the International Institute for Sustainable Development (IISD) combines micro-, meso- and macro-level inputs (Laborde et al. 2016). This modelling framework is based on a dynamic multi-country multi-sector CGE model (MIRAGRODEP) combined with household surveys. The framework that combines modelling with household surveys allows for more efficient targeting of interventions in the model assessment due to more detailed classification of households at risk of hunger. Based on the surveys, households are differentiated in accordance with their location (urban or rural), income sources and levels (farm or non-farm), and farm ownership. Consequently, strategies of social protection are matched with the households with lower income, measures of supporting farm production are considered for households which owns farm, and rural support were proposed to the rural households. This household-level targeting in the model is expected to result in estimations of improved spending efficiency of the hunger reduction investments in comparison to the other models which are based on national averages (Laborde et al. 2016). As noted by Fan et al. (2018), the MIRAGRODEP model’s targeting approach, together with the narrow focus on reducing hunger in isolation of other SDGs, produces one of the lowest cost estimates, US$ 11 billion annually. Hunger is measured by the PoU, as defined above in the discussion of the “Achieving Zero Hunger” study. Rather than targeting absolute zero hunger the MIRAGRODEP’s objective is set to reducing the PoU to 5% or less. Two other sub-goals of SDG 2, i.e. raising agricultural productivity and doubling smallholders’ income (target 2.3) and ensuring sustainable agricultural systems (target 2.4) are also accounted for in the design of interventions. Three types of interventions are included in the MIRAGRODEP model: social safety nets, directly targeting consumers through food subsidies; farm support to increase farmers’ productivity and incomes; and rural development, mainly through infrastructure investments (see Table 1). These interventions are expected to affect calorie
consumption by increasing poor households' incomes, as in 'Achieving Zero Hunger' study, or by decreasing food prices. The importance of interventions addressing nutrition are also acknowledged, however because of household data limitations, they are not accounted for in the modelling framework (Laborde et al. 2016).

Finally, the “Investment Framework for Nutrition” was proposed by the World Bank (WB) (Shekar et al. 2017). This framework has a narrow scope in comparison to the other models and frameworks presented here, because its adopted methodological framework is very simple and transparent. Rather than aiming at reducing hunger, as in the other models, the WB framework estimates the financial needs for improved nutrition targets. More specifically it aims to (i) reduce the number of stunted children under five by 40%, (ii) reduce the number of women at reproductive age affected by anaemia by 50%, (iii) increase the rate of exclusive breastfeeding in the first six months up to at least 50%, and (iv) reduce and maintain childhood wasting to less than 5%. These targets correspond to the World Health Assembly's Targets for Nutrition, but also contribute to SDG 2 (Shekar et al. 2017). The case for investing in nutrition is very strong: ending malnutrition is critical for long-term human capital, labor productivity and broad economic development (Fink et al. 2016; Horton and Steckel 2013; Hoddinott et al. 2008). At the same time, nutrition interventions are considered to be among the most cost-effective (Horton and Hoddinott 2014). The interventions included in the framework are identified based on two criteria: (1) strong evidence of their impact; (2) relevance for low- and middle-income countries. The selected interventions range from staple-food fortification and micronutrient supplementation to public provision of supplementary food and behavior promotion campaigns. To estimate the total cost of scaling up the selected nutrition interventions, financial needs are first analyzed for the highest-burden countries based on the unit-cost data obtained from a literature review; these results are then extrapolated to all low- and middle-income countries. The estimates suggest that to reach the nutrition targets it will cost around US$ 7 billion annually between 2015 and 2025; more than half of this amount targeted at reducing stunting (Shekar et al. 2017).

The five estimation approaches presented above provide a very wide range of estimates for the total investment necessary to achieve SDG 2, i.e. ending hunger and improving nutrition. These differences are largely attributable to the different objectives and policy questions asked, interventions and investment strategies considered, as well as definitions, methods and assumptions used (Mason-D'Croz et al. 2019; Fan et al. 2018). The differences in the approaches adopted by the costing frameworks make it difficult to directly compare the resulting estimates. We calculated the estimated cost per person of hunger eradication for all the modelling frameworks except the WB's Investment Framework for Nutrition that only provides estimates of nutrition-specific interventions (Table 1). These estimated costs per person vary widely, from more than US$ 4,000 in the 'Achieving Zero Hunger' study to just above US$ 300 in Torero and von Braun (2015). The number of people lifted out of hunger also differs substantially, from 650 million in the 'Achieving Zero Hunger' study, 580 million in the IMPACT modelling study, 500 million in Torero and von Braun (2015), to only 290 million in the MIRAGRODEP modelling study. These
differences are accounted for by differences in modelling assumptions, and the scope of each framework in terms of suggested investments and interventions. Rather than providing clear-cut answers, the studies suggest that a variety of diverse investment strategies can contribute to ending hunger.

Although all five estimation approaches address the issue of financial needs for the achievement of SDG 2, the scope of each framework is narrower than the scope of SDG 2 itself. SDG 2 has five targets, the first two concerned with ending hunger and ending all forms of malnutrition by 2030. The remaining three targets concern doubling agricultural productivity and the income of small-scale food producers by 2030, ensuring sustainable food production systems by 2030, and maintaining the genetic diversity of seeds, plants and animals, including wild species by 2020. Three of the models focus on either eradicating or substantially reducing hunger. However, the definitions of hunger vary between the studies, and are based either on food access, as in the ‘Achieving Zero Hunger’ and the MIRAGRODEP modelling studies, or food availability, as in the IMPACT modelling study; none consider all four dimensions of food security, i.e. food availability, access, utilization, and stability. Only two frameworks, the WB’s Investment Framework for Nutrition and the estimates by Torero and von Braun (2015), explicitly model the nutrition outcomes; with the latter being the only one to address both objectives of hunger eradication and improved nutrition in one framework. The other four studies only assume that investment to reduce hunger will also help to reduce malnutrition. Finally, only one of the studies, MIRAGRODEP based ‘Ending Hunger: What Would It Cost’, factors in the question of sustainability in agriculture.

There are important trade-offs between the scope of a modelling framework and the complexity of the methodology used. Looking at the five frameworks reviewed here, it seems that the narrower the scope of the study, the more detailed and accurate the estimates, as in the case of the MIRAGRODEP model. The combination of macro-level and household-level data in the MIRAGRODEP model is an interesting methodological development in comparison to studies based on national averages of dietary intake, as it allows not only for assessing the cost-effectiveness of interventions but could also better capture the distributional effects of investments across heterogeneous households based on their specific socio-economic characteristics, which are largely omitted in most analyses. Also, only a few models explicitly include the investments necessary to create enabling environments for achieving SDG 2; admittedly, these are relatively difficult to present in monetary terms.

Last but not least, the financing strategy with respect to the pacing of investments, allocation of financial resources between competing objectives, distribution of the burden of investment between various financing sources, and the sustainability of results beyond 2030, especially in the context of large economic, climatic or political shocks, is rarely considered in detail in the reviewed frameworks. In particular, the issue of how to spread investments over time is not discussed in much detail in any of the models; instead, the costing estimates are presented in terms of annual averages. However, this has serious implications not only for the resource mobilization strategy and therefore the feasibility of timely investments, but can also affect the economy-wide outcomes of the intervention.
Another question is how to allocate limited financial resources between the various SDGs and the development targets specific to SDG 2. Of course, the case for investing in zero hunger target is evident, as the right to food is considered to be among the most basic of human rights. However, in the context of scarce financial resources, the potential synergies between different objectives, as in the case of eradicating hunger (SDG 2) and poverty (SDG 1), need to be found. Also, potential conflicts, for example between doubling agricultural productivity (SDG 2.3) while preserving the natural environment (Sachs et al. 2019), e.g. ensuring sustainable food production systems (SDG 2.4), need to be addressed to make the proposed investment strategies efficient. Additionally, the long-term sustainability of the proposed investment frameworks are rarely explicitly addressed. The time horizon of the models ends in 2030, aside from the 'Investment Framework for Nutrition' which ends in 2025 (Shekar et al. 2017). The latter is the only one to include a 5-year maintenance period (2021–2025); in general, however, the question of how to sustain the results beyond 2030 is not discussed. In the broader frameworks, like the 'Achieving Zero Hunger' or MIRAGRODEP frameworks, the implicit assumption is that pro-poor investments in agriculture and their expected long-term economy-wide growth effects will be sufficient to maintain zero or 5% hunger levels worldwide. While this might hold if the proposed frameworks' scenarios hold, the reduction in hunger might be reversed in the case of major economic, climate or political shocks, as the last decade has proven (FAO 2018). Only the IMPACT model-based study includes the effects of climate change in its modelling framework (Rosegrant et al. 2017); and none of the models discuss the challenges of achieving zero hunger in fragile states, i.e. conflict and post-conflict states.

## 3 Overview of Approaches to Reducing Hunger

Despite continued global agricultural output growth and considerable reduction in hunger since the 1960s, food insecurity still persists, albeit with huge differences between countries, within countries and even households (FAO et al. 2015). The nature of food insecurity has been also changing as increasing demand for processed food and consequently higher consumption of unhealthy fats, sugars and salts are exacerbating obesity and micronutrient deficiency (Barrett 2021). Thus, interventions focused on agricultural productivity improvement alone will not be enough to achieve the goal of sustainable food security. Achieving global food security would require not only improvements along food supply chains but also additional efforts in health, education, information and research systems. A sustainable development of the food system should go along with ensuring food and nutrition security and without compromising the social, economic and environmental futures for the generations to come (HLPE 2014). This study looks within and beyond agricultural system to identify the intervention options and investments needed to alleviate
hunger or undernourishment.<fnref n="4" /> Performing food systems analysis considering multiple interventions entails an assessment of the relevant processes that influence the four dimensions of food security food availability, access, utilization and stability.<fnref n="5" /> Food availability emphasizes the need to address the supply side of food security to ensure sufficient quantities of food is available to individuals either through food production or imports. Food access on the other hand points to the importance of ensuring individuals have the resources necessary to obtain sufficient quantities of food. Going beyond availability and access to food, food utilization focuses on dietary quality and highlights the importance of ensuring individual's ability to utilize the energy and nutrients in the food they consume. Food stability reflects the stability of the three dimensions and reminds us of the importance of taking into consideration seasonal or temporary food prices and shocks in hunger prevention policies.

Food security can be enhanced through multiple investment options that intensify agricultural production and improve agricultural productivity. For instance, agricultural R&D and extension efforts to enhance crop and livestock production can boost food supply. Innovations in improved crop varieties, methods to improve soil fertility, and efficient irrigation technologies can also increase agricultural productivity and address food availability. The resulting increase in agricultural productivity further contributes to increased agricultural income, improved purchasing power and reduced food prices, which when combined with innovations in post-harvest technologies can improve access to food, increase calorie consumption, increase dietary diversity, and thus enhance food accessibility and utilization.

Market platform and infrastructure improvements help in reducing post-harvest losses and enhance access to food. Improved storage systems, better roads, availability of food processing, and equitable food distribution systems can greatly improve access to food by consumers. Trade rules at the international and intra-national level also greatly impact on food access. Some infrastructural improvements such as electricity access and information and communication technologies (ICTs) can improve food supply, distribution and access systems. For example, using mobile phones, farmers can access information about the weather and market conditions, allowing them to better manage water resources and fetch higher prices for their produce. ICT and storage systems are also important to plan and predict food supplies and hence, stabilize food market prices. At the same time,

<footnote n="4">⁴This study uses the prevalence of undernourishment (PoU) as the main indicator for hunger. The PoU identifies the proportion of the population whose habitual, daily, per capita dietary energy consumption (DEC) level is lower than their dietary energy requirement (Cafiero and Gennari 2011). It is computed from aggregated country-level data on food availability that is annually compiled in FAO's Food Balance Sheets and data on food consumption from surveys which is available for certain countries.</footnote>

<footnote n="5">⁵Future work on estimating the cost of ending hunger could further consider interventions that can address the two additional dimension “agency” and “sustainability” that have become crucial for transforming food systems towards the direction needed to meet the SDGs (HLPE 2020)</footnote>
interventions that can enhance the incomes and purchasing capacity of the popula-
tion can improve food affordability, which is an important aspect of food access. For
some marginalized groups with inadequate income and informal jobs, social security
programmes such as food vouchers and financial assistance can be considered.

Severe cases of child malnutrition, caused by nutrient insufficiency and certain
diseases, require nutrition-specific interventions. According to Bhutta et al. (2013),
at least 20.3% of the current child stunting rate could be averted if ten evidence-
based nutrition-specific interventions were scaled up to cover 90% of the population
in countries with high stunting burden. These interventions include periconceptional
folic acid supplementation or fortification, maternal balanced energy protein sup-
plementation, maternal calcium supplementation, multiple micronutrient supple-
mentation in pregnancy, promotion of breastfeeding, appropriate complementary
feeding, vitamin A and preventive zinc supplementation in children aged 6–
59 months, management of severe acute malnutrition, and management of moderate
acute malnutrition.

The quality of maternal- and child-care practices is also one of the non-nutritional
factors that affect the nutritional outcomes of children (Smith and Haddad 2015).
Women play a key role in children's nutritional outcome as they give birth to them,
breastfeed them and are their primary caretakers. Hence, maternal education has
numerous positive impacts on the quality of maternal care that mothers receive
during and after pregnancy and consequently on the quality of care that their children
receive, ranging from the amount of breastfeeding to seeking health care in case of
illnesses (Ruel et al. 2013). The strong link between female education and nutritional
outcomes of children, particularly for stunting, has been well established (Headey
2013; Smith and Haddad 2015). The specific intervention options considered in this
study are described in the next section.

4 Marginal Abatement Cost Curve Approach
and Investment Scenario Assumptions

4.1 The Marginal Cost Curve and Key Steps of the Process

Policymakers need to prioritize the allocation of resources to competing hunger
reduction measures by identifying the sets of least-cost investment options that have
the potential to yield the greatest reduction in hunger in a defined time horizon. It is
therefore essential that policymakers and practitioners can compare the different
hunger reduction measures and make economically efficient investment decisions. In
this regard, MACCs can be helpful as a policy tool in ranking investments options.
Applications of MACCs are common in the economic assessment of climate change
mitigation options (Schneider et al. 2007; Kesicki and Ekins 2012; Bockel et al.
2012; IPCC 2014; Eory et al. 2018) and have been also extended into the assessment
of effective water policies (Addams et al. 2009). This study implements MACC
approach in hunger reduction research. By developing a realistic and policy-relevant global MACC of different hunger reduction measures, the study allows to assess their cost-effectiveness and contributes to the evaluation of actions that should be prioritized and implemented to achieve target 2.1 of SDG 2 by 2030.

MACCs are developed based on either modelling outcomes or multiple expert opinions (Kesicki 2013). MACCs derived from top-down modelling provides internally consistent estimations, follows to smooth and continuous dynamics but do not account for the effects of specific interventions (Klepper and Peterson 2006). Expert based MACCs, despite being criticized for double counting and interaction possibility, are richer in terms of reflecting technology details as they are constructed by summarizing and synthesizing the average costs and abatement effects of multiple interventions. As our assessments are based on the cost and hunger reduction effects of multiple interventions from multiple studies, the framework of expert-based MACC is relevant here.

The global hunger reduction MACC represents the relationship between the cost-effectiveness of different hunger reduction interventions and the hunger reduction potential of each intervention. It reflects the additional costs of lifting people out of hunger by each intervention. Elaboration of the MACC were conducted step-by-step, by first identifying the variety of intervention options that can effectively reduce hunger, and then by determining the cost and hunger reduction potential of the interventions. The related assessment is conducted through a literature review and an integrated evaluation of model-based, econometric or cost-benefit analysis studies. While the cost and hunger reduction potential (number of people lifted out of hunger) were readily found in the literature for some of the interventions, additional calculations or assumptions based on expert assessments were considered for others. Particularly, hunger reduction potential was derived through additional calculations considering conversion factors and elasticity coefficients when food security enhancement effects such as additional food supply, income, or prevented levels of undernourishment due to the interventions are available. The cost of implementing some interventions were also estimated considering the prices of food, costs of social protection, or transaction costs of trade (details of these calculations and assumptions behind are provided in the Supplementary Material of the original publication). Finally, the interventions are ranked from the cheapest to most expensive, based on their marginal costs (average cost of lifting an individual out of hunger) to represent the cost of achieving incremental levels of hunger reduction.

## 4.2 Reference Scenarios of Hunger Trend and Investment Options

In a MACC based economic assessment of investment options, reference scenarios are built either based on a “business as usual” scenario, using historical trends to identify future developments, or based on alternative scenarios that consider climate
<figure><img src="image_135.png" /><figcaption>**Fig. 1** Hunger levels expected under various socio-economic and climate change scenarios. (Source: Authors' own elaboration based on Hasegawa et al. (2018) and FAO et al. (2020))</figcaption></figure>

change impacts and socio-economic developments of the future. Model-based foresight exercises highlight how food and agricultural systems could evolve in an inherently uncertain future. These foresight exercises provide alternative scenarios on food security in which challenges are addressed to varying degrees, building on historical trends of factors that determine the performance of socio-economic and environmental systems. According to the bio-economic model-based assessments of AIM/CGE, GLOBIOM and IMPACT, under various climatic and socio-economic development scenarios the world will be home to between 251 to 842 million undernourished people in 2030 (Fig. 1; Hasegawa et al. 2018). If population growth were to be largely controlled, high economic growth rates (SSP1) were maintained and climate change effects were neglected (dark green, blue and brown lines in the first bunch of the lines), the number of undernourished people would be reduced to between 251 to 437 million. Yet, when climate change (RCP6.0) is considered in the modelling assessments the number of undernourished people is expected to be between 288 and 443 million (light green, blue and orange lines in the first bunch of the lines). Under the worst scenario, with high population growth, economic stagnation, high-income inequality (SSP3), and a climate change impact (RCP6.0), the number of undernourished people is expected to be between 617 and 842 million (light green, blue and orange lines in the third bunch of the lines). All three modelling assessments indicate similar trends of hunger reduction under various socio-economic and environmental changes. Yet, the magnitude of the reduction differs across the modelling assessments.
The worst scenarios of the projected number of undernourished people in 2030 lie close to the projection presented in the recent report of the state of food security and nutrition in the world without considering the impact of the COVID-19 pandemic, i.e. about 840 million (Fig. 1; FAO et al. 2020). The pandemic is expected to further accelerate the projected increase in the number of people facing hunger, at least in the immediate future. As the global economy contracts due to containment measures of COVID-19, it is anticipated that hunger will also increase globally, hampering the progress of global efforts geared towards achieving the SDG 2 targets. A 4.9 to 10% decline in global GDP growth is estimated to lead to an additional 100 to 194 million people into hunger globally in 2020 and 2021. In the worst-case scenario, the pandemic could potentially increase the number of undernourished people to 909 million by 2030 (FAO et al. 2020).

In the MACC analysis, the reference scenarios of hunger trends presented above serve in determining the number of the undernourished people that would need to be lifted out of hunger to achieve target 2.1 of SDG 2 by 2030 and hence the investments required to reach the target. In this study, the projection that consider the impact of COVID-19 is used as the reference scenario for the population at risk of hunger in 2030, since it is the recent authoritative foresight study on hunger that considers the impact of the pandemic on hunger.

The cost and hunger reduction potential of the various investment options considered in this study are also analyzed relative to a “business as usual” or reference scenario of investments, wherein the costs of investments are assumed to remain frozen or grow following historical trends. The costs in the reference scenario include all investments required to achieve the projected level of implementation of the intervention options by 2030, including the capital, operational, and programme costs where applicable. For instance, the IMPACT model-based projection, from the study by Rosegrant et al. (2017), is used as a reference scenario for the interventions such as agricultural R&D, water resource management, and infrastructure. Rosegrant et al. (2017) used IFPRI’s IMPACT model together with a global computable general equilibrium model (GLOBE) and several linked post-solution models to evaluate investment requirements, land-use changes, greenhouse gas (GHG) emissions, biodiversity, water quality, and micronutrient availability and dietary diversity under the business as usual scenario. In addition to the climate change assumptions, Rosegrant et al. (2017) consider investment in agricultural R&D, water resource management, and infrastructure under the business as usual scenario. The projections of these investments under the business as usual scenarios are based on historical trends and expert opinions of long-term developments in the agricultural sector. Investments in water resource management are modelled endogenously combining the IMPACT model with a suite of water models. Similarly, all investment options considered in this study are compared to a reference scenario to identify the incremental cost of implementing the investments.
## 4.3 Opportunities of Investments in Policies and Programs for Hunger Reduction

For estimating hunger reduction potential, 22 interventions were selected based on the framework described in Sect. 3. Details of these interventions and overview of data and approaches used in calculating their hunger reduction potential and implementation costs are presented briefly in Table 2 to make all of the assumptions transparent. This would address the common critique, inherent in MACC, of a lack of transparency. Twelve of the twenty-two interventions are related to interventions for enhancing crop yields at farm levels through improved technologies, extension services, crop protection measures, soil fertility management and irrigation development. Five of the interventions are related to improved ICT, infrastructure and trade that improve food distribution efficiency. Three of the interventions consider supporting marginal groups of society increasing access on food. The last two interventions serve to reducing child malnutrition through enhanced child and maternal care. The details of the calculations to estimate the hunger reduction potential and costs of the interventions are presented further in the Supplementary Material of the original publication.

## 4.4 Investments to Reduce Hunger: Marginal Cost Curve Results

After ranking the considered interventions in accordance with their average cost per undernourished, a MACC of hunger reduction potential was elaborated (Fig. 2). Specific parameters of MACC such as the width (number of people lifted out of hunger) and length (annual cost per individual lifted out of hunger) of each bar (intervention) and additional indicators such as cumulative costs and cumulative hunger reduction potentials are provided in Table 3. According to the estimation, overall, the measures included in the MACC have the potential to lift over a billion people out of hunger over ten years between 2020 and 2030. To meet the G7 commitment of lifting 500 million people out of hunger by 2030, an average annual investment ranging between about US$ 11 to 14 billion will be required.<fnref n="6" /> This would be achieved through a mix of least-cost intervention options –agricultural R&D efficiency enhancement, agricultural extension services, agricultural R&D, ICT – agricultural information services, small-scale irrigation expansion in Africa, female literacy improvements, and scaling up existing social protection. Following the 2030 hunger projection by FAO et al. (2020) and taking the preliminary estimates on the impact of COVID-19 on hunger (based on the less pessimistic

<footnote n="6">⁶As can be seen from Fig. 2, the per capita cost estimate of lifting 500 million people out of hunger is within range of the prior estimates by Torero and von Braun (2015) and Laborde et al. (2016) that vary between US$ 30 to 38 per person lifted out of hunger.</footnote>
<table><caption>**Table 2** Investment options for hunger reduction and investment scenarios assumptions</caption><thead><tr><th></th><th>Interventions</th><th>Sources</th><th>Modelling Framework</th><th>Calculations and assumptions</th></tr></thead><tbody><tr><td>1</td><td>Agricultural R&D</td><td>Rosegrant et al. 2017</td><td>IMPACT 3 modelling suite</td><td>This option considers the hunger reduction potential of increased investments in the CGIAR plus increased complementary investments in national agricultural research systems (NARS), where US$ 1.97 billion and US$ 0.99 billion per year are invested by the CGIAR and NARS respectively.</td></tr><tr><td>2</td><td>Agricultural R&D efficiency enhancement</td><td>Rosegrant et al. 2017</td><td>IMPACT 3 modelling suite</td><td>This option considers the hunger reduction potential of higher CGIAR agricultural R&D efficiency so that the yield impact of investments is 30% higher. Agricultural R&D efficiency enhancement scenario is assumed to cost 30% of the annual average incremental investment in agricultural R&D with a total of US$ 0.89 billion.</td></tr><tr><td>3</td><td>Agricultural extension services</td><td>FAO et al. 2019; Ragasa &amp; Mazunda 2018; Ecker &amp; Qaim 2011; Blum and Szonyi 2014; World Bank 2020a</td><td>Econometric model</td><td>Hunger reduction potential of increased investment in extension service is estimated for 38 low and lower-middle-income countries using the methodological note for calculating PoU (FAO et al. 2019) and the impact of extension services on Dietary Energy Supply (DES). The DES is estimated based on Ragasa and Mazunda's study (2018) that shows 36% increase in value of farm production due to the extension services, and Ecker and Qaim (2011) that indicates the elasticity of DES to income to be 0.66. Based on Blum and Szonyi (2014), the implementation cost is assumed to be 1% of the 38 low and lower-middle-income countries GDP in 2019 (based on WDI in 2019 (World Bank 2020a)).</td></tr><tr><td>4</td><td>Irrigation expansion – Large-scale irrigation expansion</td><td>Rosegrant et al. 2017</td><td>IMPACT 3 modelling suite</td><td>This option reflects the hunger reduction potential of large-scale irrigation expansion in developing countries by 2030, with projected irrigated area expansion of 20 million hectares by transforming rainfed areas.</td></tr></tbody></table>
<table><tr><td>5</td><td>Irrigation efficiency enhancement</td><td>Rosegrant et al. 2017</td><td>IMPACT 3 modelling suite</td><td>This measure considers the hunger reduction potential of a 15 percentage increase in basin efficiency by 2030 due to increased water infrastructure investment and water management improvement in food production units.</td></tr><tr><td>6</td><td>Irrigation expansion – Small scale irrigation expansion in Africa</td><td>FAO 2020; You et al. 2011; Passarelli et al. 2018; Ecker & Qaim 2011</td><td>Econometric model</td><td>Hunger reduction potential of increased investment in small-scale irrigation expansion in Africa is estimated using the methodological note for calculating PoU (FAO 2020) and the impact of the expansion on DES. The DES is estimated based on Passarelli et al. (2018) that finds 2.5 times increase in agricultural income, and Ecker and Qaim (2011) that indicate an elasticity of DES to income of 0.66. The total annual cost of the expansion is assumed to be US$ 3.8 billion per year based on the estimate by You et al. (2011).</td></tr><tr><td>7</td><td>Soil-water management</td><td>Rosegrant et al. 2017</td><td>IMPACT 3 modelling suite</td><td>This measure considers the hunger reduction potential of water availability enhancement technologies such as no-till agriculture and water harvesting with an investment of about US$ 4.6 billion annually.</td></tr><tr><td>8</td><td>Crop protection – insects</td><td>Rosegrant et al. 2014</td><td>Decision Support System for Agrotechnology Transfer (DSSAT) crop model and IMPACT 2 modelling suite</td><td>This measure simulates hunger reduction potential of investments that promote the adoption of crop protection technologies for insects. To calculate the cost, we assume the technology is implemented on 175 Mha with US$ 50 per ha cost.</td></tr><tr><td>9</td><td>Crop protection – diseases</td><td>Rosegrant et al. 2014</td><td>DSSAT model and IMPACT 2 modelling suite</td><td>This measure simulates hunger reduction potential of investments that promote the adoption of crop protection technologies for diseases. To calculate the cost, we assume the technology is implemented on 175 Mha with US$ 40 per ha cost.</td></tr><tr><td>10</td><td>Crop protection – weeds</td><td>Rosegrant et al. 2014</td><td>DSSAT model and IMPACT 2 modelling suite</td><td>This measure simulates hunger reduction potential of investments that promote the adoption of crop protection technologies for weeds. To calculate the cost, we assume the technology is implemented on 175 Mha with US$ 60 per ha cost.</td></tr></table>

(continued)
<table><caption>**Table 2 (continued)**</caption><thead><tr><th></th><th>Interventions</th><th>Sources</th><th>Modelling Framework</th><th>Calculations and assumptions</th></tr></thead><tbody><tr><td>11</td><td>Nitrogen-use efficiency</td><td>Rosegrant et al. 2014</td><td>DSSAT model and IMPACT 2 modelling suite</td><td>This measure simulates hunger reduction potential of investments that promote the adoption of agricultural management practices and improved crop varieties to enhance crop nitrogen-use efficiency. To calculate the cost, we assume the technology is implemented on 175 Mha with US$ 500 per ha cost.</td></tr><tr><td>12</td><td>Integrated soil fertility management</td><td>Rosegrant et al. 2014</td><td>DSSAT model and IMPACT 2 modelling suite</td><td>This measure simulates hunger reduction potential of investments that promote the adoption of integrated soil fertility management. To calculate the cost, we assume the technology is implemented on 175 Mha with US$ 100 per ha cost.</td></tr><tr><td>13</td><td>ICT – Agricultural information services</td><td>Hoddinott et al. 2013; FAO et al. 2019</td><td>Econometric model and cost-benefit analysis</td><td>Hunger reduction potential of improved access to market information through ICT is estimated by extending Hoddinott et al. (2013); poverty reduction assessments in six countries were extrapolated to cover 69 low and lower-middle income countries. The estimated poverty reduction levels are then converted into the corresponding hunger reduction levels using an estimated equivalence coefficient of 0.68 (FAO et al. 2019).</td></tr><tr><td>14</td><td>Infrastructure (Road, Rail, Electricity)</td><td>Rosegrant et al. 2017</td><td>IMPACT 3 modelling suite</td><td>This option simulates the hunger reduction potential of a mix of infrastructure improvements in developing countries, focusing primarily on improvements to transportation infrastructure (road building, road maintenance, and railroads) and increased rural electrification.</td></tr><tr><td>15</td><td>Food loss reduction along the value chain</td><td>Rosegrant et al. 2015</td><td>IMPACT 2 modelling suite</td><td>The hunger reduction potential of increased investments in post-harvest reduction is estimated assuming a scenario where a 10% reduction in the post-harvest loss is maintained globally by 2030 through increased investments in infrastructure.</td></tr></tbody></table>
<table><tr><td>16</td><td>International trade – Completing the Doha Development Agenda (DDA)</td><td>Anderson 2018; FAO et al. 2019</td><td>Cost-benefit analysis</td><td>Hunger reduction potential of enhancing international trade is estimated converting Anderson's (2018) poverty reduction estimate of about 160 million using an estimated equivalence coefficient of 0.68 (FAO et al. 2019). Following Anderson (2018), 5% of the estimated annual benefit in 2025 is assumed to be the adjustment cost of the trade reform for the period of ten years, amounting to an annual total investment of US$ 30 billion.</td></tr><tr><td>17</td><td>Intra-African trade – African continental Free Trade Area (AfCFTA) agreement</td><td>World Bank 2020b; FAO et al. 2019; Anderson 2018</td><td>Global dynamic CGE model and cost-benefit analysis</td><td>Hunger reduction potential of AfCFTA is estimated converting World Bank's (2020b) poverty reduction estimate of 30 million by 2035. The poverty reduction by 2030 is first calculated using linear interpolation and converted into hunger reduction using an estimated equivalence coefficient of 0.68 (FAO et al. 2019). To estimate the implementation cost of AfCFTA, we follow Anderson (2018) in assuming 5% of the economic gains from the continental free trade agreement estimated to be US$ 450 billion by 2035 in the study by World Bank (2020b) over a ten-year period. Then, the adjustment cost of the trade reform is then assumed to be US$ 2.25 billion per year.</td></tr><tr><td>18</td><td>Social protection – Scaling up existing programmes</td><td>FAO et al. 2020; Hidrobo et al. 2018; reviewed papers in Table A2</td><td>Cost-effectiveness analysis</td><td>Based on a review of cost-effectiveness studies of social protection programmes across different countries, the minimum per dollar cash transfer cost of per capita is identified at US$ 35.7 and used to calculate the annual per capita cost of scaling existing programmes. Based on the review of the current coverage of social protection programmes, we estimated that about 103.1 million people could be targeted.</td></tr><tr><td>19</td><td>Social protection – Establishing new programmes</td><td>FAO et al. 2020; Hidrobo et al. 2018; reviewed papers in Table A2</td><td>Cost-effectiveness analysis</td><td>Based on a review of cost-effectiveness studies of social protection programmes across different countries, the maximum per dollar cash transfer cost per capita is identified at US$ 88.9 and used to calculate the annual per capita cost of establishing new programmes. Based on the review of the current coverage of social protection programmes, we estimated that about 103.1 million people could be targeted.</td></tr></table>

(continued)
<table><caption>**Table 2 (continued)**</caption><thead><tr><th></th><th>Interventions</th><th>Sources</th><th>Modelling Framework</th><th>Calculations and assumptions</th></tr></thead><tbody><tr><td>20</td><td>COVID-19 – Social protection</td><td>FAO et al. 2020; Hidrobo et al. 2018; reviewed papers in Table A2</td><td>Econometric model estimates-based simulation and cost-effectiveness analysis</td><td>Following the less pessimistic COVID-19 impact scenario estimated by FAO et al. (2020) and based on the reviews of cost-effectiveness studies of social protection programmes across different countries, the maximum per dollar cash transfer cost per capita is identified at US$ 88.9. This is further used to calculate the annual per capita cost of social protection coverage for individuals that would fall into hunger due to COVID-19. Based on the less pessimistic scenario of COVID-19 impact on hunger estimated by FAO et al. (2020), we estimated that about 137.9 million people could be targeted.</td></tr><tr><td>21</td><td>Nutrition program</td><td>Shekar et al. 2017</td><td>Lives Saved Tool (LiST) for nutritional outcomes</td><td>This option considers increased investment in scaling up 7 nutrition-specific interventions to 90% coverage in 37 countries that account for 90% of the stunted children globally to reduce stunting among children below five years of age. The estimated stunting reduction levels are then converted into the corresponding hunger reduction levels using an estimated equivalence coefficient of 0.997.</td></tr><tr><td>22</td><td>Female literacy improvement</td><td>Smith & Haddad 2015; Shekar et al. 2017; World Bank 2020a</td><td>Econometric model and cost-effectiveness analysis</td><td>Stunting reduction potential of investment in women's education is estimated using Smith and Haddad's (2015) elasticity of stunting to female secondary school enrolment (−0.166) for 37 countries that account for 90% of the stunted children globally. It is also assumed that the female secondary enrolment rate between 2011 and 2015 is maintained over the next ten years, which is about 6.66 million additional female students enrolled at a per capita cost of US$ 130. The estimated stunting reduction levels are then converted into the corresponding hunger reduction using an estimated equivalence coefficient of 0.997.</td></tr></tbody></table>
<figure><img src="image_136.png" /><figcaption>**Fig. 2** Marginal cost curve of the suggested interventions to eradicate hunger</figcaption></figure>

Note: The MACC for hunger shows the cost of each hunger reduction measure such that each bar represents a single intervention where the width shows the number of individuals lifted out of hunger, the height its associated annual per capita cost, and the area its associated total annual cost. The total width of the MACC reflects the total hunger reduction possible from all interventions, while the sum of the areas of all of the bars represents the total annual cost of reducing hunger through the implementation of all interventions considered. The positions of the bars along the MACC reflect the order of each intervention by their cost-effectiveness based on the annual per capita costs. When moving along the MACC from left to right, the cost-effectiveness of the interventions worsens as each next intervention becomes more expensive than the preceding. It is important to note that this figure is subject to considerable uncertainty given various assumptions made in the calculation, missed synergies and potential overlap between interventions and impact of extreme events not considered when estimating the costs

scenario) into consideration, the global goal of ending hunger by 2030 may require an investment of about US$ 39 to 50 billion to lift about 840 to 909 million people out of hunger.

As illustrated in Fig. 2, investing in agricultural R&D efficiency enhancement, agricultural extension services, ‘ICT – Agricultural information systems’, are low cost options that have a relatively large hunger reduction potential. Scaling up existing social protection programmes and establishing new programmes to serve food insecure households can reduce the number of people at risk of hunger by about 206.2 million at an annual per capita cost of about US$ 35.7 and US$ 88.9 per undernourished. To address the potential increase in the number of people at risk of hunger estimated in 2020 and 2021 of about 137.9 million, an additional US$ 12.3 billion will need to be spent in social protection. While investing in women’s education also provides a least-cost option to reduce hunger, investment in
<table><caption>**Table 3** Hunger reduction potential of interventions and cost of implementation from 2020 to 2030</caption><thead><tr><th>Least-cost Rank</th><th>Interventions</th><th>Number of people lifted out of hunger (Million)</th><th>Cumulative number of people lifted out of hunger (Million)</th><th>Annual cost (US $ Million)</th><th>Cumulative amount of annual cost (US$ Million)</th><th>Annual cost per individual lifted out of hunger (US $)</th><th>Total cost per person lifted out of hunger (US$) over 2020-2030</th></tr></thead><tbody><tr><td>1</td><td>Agricultural R&D efficiency enhancement</td><td>69.9</td><td>69.9</td><td>888</td><td>888</td><td>12.7</td><td>98</td></tr><tr><td>2</td><td>Agricultural extension services</td><td>81.5</td><td>151.4</td><td>2096</td><td>2984</td><td>25.7</td><td>199</td></tr><tr><td>3</td><td>ICT – Agricultural information services</td><td>26.6</td><td>178.0</td><td>698</td><td>3682</td><td>26.2</td><td>114</td></tr><tr><td>4</td><td>Small-scale irrigation expansion in Africa</td><td>142.3</td><td>320.3</td><td>3790</td><td>7472</td><td>26.6</td><td>206</td></tr><tr><td>5</td><td>Agricultural R&D</td><td>92.0</td><td>412.3</td><td>2960</td><td>10,432</td><td>32.2</td><td>249</td></tr><tr><td>6</td><td>Female literacy improvement</td><td>2.6</td><td>414.9</td><td>87</td><td>10,518</td><td>33.1</td><td>261</td></tr><tr><td>7</td><td>Social protection – Scaling up existing programmes</td><td>103.1</td><td>518.0</td><td>3677</td><td>14,195</td><td>35.7</td><td>154</td></tr><tr><td>8</td><td>Crop protection – Insects</td><td>10.1</td><td>528.0</td><td>700</td><td>14,895</td><td>69.7</td><td>536</td></tr><tr><td>9</td><td>Social protection – Establishing new programmes</td><td>103.1</td><td>631.1</td><td>9158</td><td>24,053</td><td>88.9</td><td>385</td></tr><tr><td>10</td><td>COVID-19 – Social protection</td><td>137.9</td><td>769.0</td><td>12,255</td><td>36,308</td><td>88.9</td><td>165</td></tr><tr><td>11</td><td>Crop protection – Diseases</td><td>8.8</td><td>777.8</td><td>875</td><td>37,183</td><td>99.4</td><td>768</td></tr></tbody></table>
<table><tr><td>12</td><td>Integrated soil fertility management</td><td>16.6</td><td>794.4</td><td>1750</td><td>38,933</td><td>105.1</td><td>814</td></tr><tr><td>13</td><td>Crop protection – Weeds</td><td>9.4</td><td>803.8</td><td>1050</td><td>39,983</td><td>111.7</td><td>863</td></tr><tr><td>14</td><td>Trade – African Continental Free Trade Area (AfCFTA)</td><td>15.3</td><td>819.1</td><td>2250</td><td>42,233</td><td>147.1</td><td>1136</td></tr><tr><td>15</td><td>Nitrogen-use efficiency</td><td>56.5</td><td>875.6</td><td>8750</td><td>50,983</td><td>154.9</td><td>1196</td></tr><tr><td>16</td><td>Nutrition-specific interventions</td><td>30.9</td><td>906.6</td><td>4950</td><td>55,933</td><td>160.0</td><td>1237</td></tr><tr><td>17</td><td>Food loss reduction</td><td>36.0</td><td>942.6</td><td>8580</td><td>64,513</td><td>241.7</td><td>1841</td></tr><tr><td>18</td><td>Irrigation efficiency enhancement</td><td>18.6</td><td>961.2</td><td>4590</td><td>69,103</td><td>246.3</td><td>1906</td></tr><tr><td>19</td><td>Trade – Doha Development Agenda</td><td>108.8</td><td>1070.0</td><td>30,000</td><td>99,103</td><td>275.7</td><td>2129</td></tr><tr><td>20</td><td>Infrastructure (Road, Rail, Electricity)</td><td>33.8</td><td>1103.8</td><td>10,810</td><td>109,913</td><td>320.0</td><td>2470</td></tr><tr><td>21</td><td>Soil-water management</td><td>12.2</td><td>1116.0</td><td>4580</td><td>114,493</td><td>374.5</td><td>2899</td></tr><tr><td>22</td><td>Irrigation expansion – Global large-scale irrigation expansion</td><td>7.6</td><td>1123.6</td><td>3520</td><td>118,013</td><td>473.4</td><td>3577</td></tr></table>

Note: Number of people lifted out of hunger and annual cost of each intervention are compiled and computed based on the studies and assumptions presented in Table 2. For each intervention, the number of people lifted out of hunger by the proposed investments is calculated as the difference between the projected number of hungry people in the business as usual 2030 scenario and the projected number of hungry people in the 2030 investment scenario. The annual cost per individual lifted out of hunger is then calculated as the annual cost divided by the number of people lifted out of hunger. The cumulative figures for the number of people lifted out of hunger and annual costs across the interventions reflect the total hunger reduction possible from all interventions and the total annual investments required. Total cost per person lifted out of hunger is calculated as total net discounted cost over the ten-year period (with the exception of COVID-19 social protection, ICT and scaling new and existing social protection programmes where the time frame is 2–5 years respectively, i.e. 2020–2021 and 2020–2024). The discount rate is assumed to be 5%, following Hoddinott, et al. (2013). The total cost per person lifted out of hunger is then calculated as the total net discounted cost divided by the number of people lifted out of hunger
nutrition-specific investments can significantly reduce hunger by about 30 million at a total incremental average cost of about US$ 5 billion per year.

Investments in ‘African Continental Free Trade Area (AfCFTA) agreement’, ‘Food loss and waste reduction’, ‘Irrigation efficiency enhancement’, improvements in international trade (completion of the DDA), ‘Infrastructure’, ‘Soil-water management’, and ‘Large-scale irrigation expansion’ can considerably decrease undernourishment by about 232.2 million. These hunger reduction measures are relatively expensive investment options that require a longer time for implementation and hence would need to be frontloaded earlier in the decade to have a large effect soon before 2030.

It is also important to note that the marginal cost curve elements include many investments that contribute to long-term development and sustainability, beyond 2030 and not restricted to hunger reduction. For instance, investments in agricultural R&D and research efficiency, irrigation expansion and water use efficiency, soil water management and infrastructure all have a long-term impact going further to 2050 and also have much broader development impacts beyond the reduction of hunger, like poverty, child malnutrition, and the environment (Rosegrant et al. 2017). The composition of the investments facilitates an increase in resilience for populations affected by hunger today or at risk of hunger in this decade. Since it is beyond the scope of this study, such lagged benefits of investments and their impact on other development outcomes beyond hunger has not been considered in this study and hence the estimates presented might understate the full benefits of these investments.

# 5 Uncertainties and Caveats of the Assessment

## 5.1 Uncertainties Associated with Data and Assumptions

The developed MACC of hunger reduction can considerably contribute to debates over the prioritization of efforts and allocation of investments to achieve the global goal of ending hunger. However, it is important to note that the cost assessments and rankings of the interventions are subject to various levels of uncertainties due to various assumptions made in the calculations. Thus, these estimates should be perceived as only the best possible estimations based on available data. Due to the limited availability of data for conducting a proper quantitative analysis of uncertainty, we here present a qualitative analysis of uncertainty (Table 4). This assessment is related to the description of calculation steps presented in Table 2. We further use narrative analysis of potential impacts of various uncertainties on the levels of costs and hunger reduction potential and shape of the MACC.

According to the assessments, despite the very low cost of ‘Agricultural R&D efficiency enhancement’, this cost level is subject to ‘high’ uncertainty and hunger reduction potential is subject to ‘moderate’ uncertainty (Table 4). The cost of ‘Agricultural extension services’ is characterized with ‘low’ uncertainty but its hunger reduction potential is characterized with ‘high’ uncertainty. Most of the
<table><caption>**Table 4** Uncertainties of potentials and implementation costs of the hunger reduction interventions</caption><thead><tr><th>Rank</th><th>Interventions</th><th>Uncertainties for number of people lifted out of hunger (Million)</th><th>Uncertainties for annual cost per individual lifted out of hunger (US$)</th></tr></thead><tbody><tr><td>1</td><td>Agricultural R&D efficiency enhancement</td><td>++</td><td>+++</td></tr><tr><td>2</td><td>Agricultural extension services</td><td>+</td><td>+++</td></tr><tr><td>3</td><td>ICT – Agricultural information services</td><td>++</td><td>+</td></tr><tr><td>4</td><td>Small-scale irrigation expansion in Africa</td><td>+</td><td>+</td></tr><tr><td>5</td><td>Agricultural R&D</td><td>++</td><td>++</td></tr><tr><td>6</td><td>Female literacy improvement</td><td>+</td><td>+</td></tr><tr><td>7</td><td>Social protection – Scaling up existing programmes</td><td>++</td><td>+</td></tr><tr><td>8</td><td>Crop protection – Insects</td><td>++</td><td>+++</td></tr><tr><td>9</td><td>Social protection – Establishing new programmes</td><td>++</td><td>+</td></tr><tr><td>10</td><td>COVID-19 – Social protection</td><td>+++</td><td>+</td></tr><tr><td>11</td><td>Crop protection – Diseases</td><td>++</td><td>+++</td></tr><tr><td>12</td><td>Integrated soil fertility management</td><td>++</td><td>+++</td></tr><tr><td>13</td><td>Crop protection – Weeds</td><td>++</td><td>+++</td></tr><tr><td>14</td><td>Trade – African Continental Free Trade Area (AfCFTA)</td><td>+</td><td>+++</td></tr><tr><td>15</td><td>Nitrogen-use efficiency</td><td>++</td><td>+++</td></tr><tr><td>16</td><td>Nutrition-specific interventions</td><td>+</td><td>++</td></tr><tr><td>17</td><td>Food loss reduction</td><td>+++</td><td>++</td></tr><tr><td>18</td><td>Irrigation efficiency enhancement</td><td>++</td><td>++</td></tr><tr><td>19</td><td>Trade – Doha Development Agenda</td><td>+</td><td>+++</td></tr><tr><td>20</td><td>Infrastructure (Road, Rail, Electricity)</td><td>+++</td><td>++</td></tr><tr><td>21</td><td>Soil-water management</td><td>++</td><td>++</td></tr><tr><td>22</td><td>Irrigation expansion – Global large-scale irrigation expansion</td><td>++</td><td>++</td></tr></tbody></table>

Note: Levels of uncertainty are defined with “+” for ‘low’, “++” for ‘moderate’ and “+++” for ‘high’. Evidence-based data estimated through econometric assessment or obtained from reliable statistical sources were considered with ‘low’ uncertainty. We assume ‘moderate’ uncertainty if the data was found out from simulation modelling studies or derived through additional calculations considering data with low uncertainty. ‘High’ uncertainty emerges in case value was obtained through additional calculations based on data with ‘moderate uncertainty’ or based on pure assumptions
remaining interventions except ‘Small-scale irrigation in Africa’ and ‘Female illit-
eracy improvement’ are subject to ‘moderate’ or ‘high’ levels of uncertainty.

**5.2 Missed Synergies, Overlaps and Other Investment Options**

As each intervention in the MACC is considered independently with its marginal
costs and hunger reduction effects, beneficial synergies among interventions are not
captured. For instance, interventions such as constructing irrigation systems and
implementation of ICT in water distribution systems may have additional synergetic
benefits. Yet, the presented MACC indicates conservative estimates of mixes of
interventions. Consequently, it is possible that the costs are overestimated and
hunger reduction impacts are underestimated for such cases.

Some overlap or double counting may also exist between the considered inter-
ventions as there are likely a group of people who have already been lifted out of
hunger through one policy yet may benefit from the second policy. It may occur for
example in places where many people are quite near the threshold of undernourish-
ment. This implies that a certain group of people could be counted multiple times
and hunger reduction potential of the interventions might be overestimated. Also, in
places where the gap between adequate and actual nourishment is high, a bundle of
complementary interventions may be required to lift people out of hunger rather than
a single policy (Banerjee et al. 2015; Barrett et al. 2020). Hence, there may be some
undercounting in the event of bundled programs which could instead lead to an
underestimation of the hunger reduction potential of interventions and
overestimation of the costs. However, since the MACC is built based on an aggre-
gated assessment of interventions at the global level, our study does not highlight
such details. Further studies should differentiate hunger levels, their exact causes and
precise solutions to address this issue. Bottom-up integrated assessment models may
capture such synergies and reduce double counting consequently allowing for
developing consistent MACCs.

Analyzing the hunger reduction potential and costs of the selected twenty-two
options available from the recent studies, it is likely that we omitted other hunger
reduction interventions where costs and hunger reduction potentials were either not
available or not widely discussed in the literature. As an example, food production
and harvesting in marine environments including the production and harvesting of
seafood and seagrasses were excluded as the option was not widely assessed at the
global level for hunger prevention. Likewise, alternative foods produced from
insects and non-traditional food crops were not considered as their health safety
and upscaling potential have not been properly examined. With more advances in the
sciences, new interventions can come into the scene and they may change the shape
of the MACC for hunger reduction.
## 5.3 The Impact of Extreme Events on the Cost of Hunger Reduction

Investment options reviewed in this study did not explicitly include climate change or the effects of extreme events like conflicts, pandemics and extreme weather events on hunger reduction. Even though it is understood that important drivers of acute food insecurity in 2020 were conflict, economic fallout of COVID-19, and extreme weather events. In 2020 an estimated 99 million people were in acute food insecurity because of conflict situations (Food Security Information Network & Global Network Against Food Crises 2021). This statistic may only be roughly compared with the statistic of undernourishment (768 million) as the two statistics are based on very different concepts.

In 2019, six out of ten people eligible for global humanitarian food assistance were residing in countries with ongoing conflicts (Development Initiatives 2020). Also, the share of children suffering from stunting residing in conflict zones has considerably increased within the last two decades (FAO et al. 2017). Conflicts directly and indirectly impact on food insecurity. For instance directly through resource loss when fields are rendered unusable due to mines, and indirectly through disruption of markets and trade. Barrett (2021) makes the argument that the hunger crises today and of the future are fundamentally humanitarian and conflict resolution issues rather than shortcomings with agri-food systems, and addressing hunger requires targeted humanitarian and conflict resolution efforts instead of agri-food innovations. While it is widely regarded that conflict resolution can have a profound impact on hunger reduction, estimating the cost, such as diplomatic and peace keeping engagements, and impacts on other interventions would be multifaceted and quite complex (Kemmerling et al. 2021), and hence was not considered in this study.

Studies reviewed in our analysis were conducted before the COVID-19 pandemic and did not consider the impacts of such an event or similar pandemics on the goal of hunger reduction and the cost to other interventions. Nonetheless, it is noteworthy that as a pandemic event like COVID-19 impacts all aspects of society and the economy it surely would have a considerable impact on the effort to achieving zero hunger before 2030, given that all the resources needed to achieve this goal would be directed towards the fight against the pandemic. This is especially true for financing, which is mainly from wealthy nations but immediately repurposed and prioritized for healthcare investments and economic stimulus for their citizenry, thus leaving the fight against hunger in peril, howbeit temporarily. The extent and impact of this temporary neglect due to the pandemic are not assessed in this study.

Climate change impacts not only hunger levels but also the costs and potential of the targeted hunger prevention interventions. Rising temperatures and consequent drought increases the value of water and reduce the efficiency of the projects related to irrigation improvement (IPCC 2019). Also, intensified flooding events induced by temperature rise destroys agricultural production systems as well as affect hydraulic infrastructure reducing the resilience capacity to cope with temperature anomalies
and systems, exposing the farmers and rural community to hunger. Meanwhile, climate change mitigation efforts are increasingly and deservedly prioritized, but sometimes at the expense of the efforts to reduce hunger. For instance, available land likely to be used for solar power generating projects instead of agriculture, hydro-electric power dams are prioritized regardless of the impact on surrounding farms, and available public funds are allocated to climate change mitigation efforts limiting finance to hunger reduction programmes. In our study, these linkages and the extent of the impact on the interventions were not considered.

## 5.4 Effects of Scaling on Marginal Costs

As the marginal cost of each intervention is assumed to be fixed per undernourished person lifted out of hunger, the aggregated marginal cost curve appears like a staircase (step) function. Growing marginal cost in the aggregated marginal cost curve is due to the ranking of the individual interventions. In reality, increasing (not fixed) marginal costs of hunger reduction are expected for each intervention. Due to the scaling effects, it is not surprising that the additional cost of reducing the number of undernourished people increases with the number of undernourished people lifted out of hunger. Lifting the first group of people out of hunger requires less investment than the last group of hungry people since the cost of reaching the most vulnerable and hard to reach populations is a lot more than to reach those that are less in need of food assistance and support. Based on our data collected from the literature review of contributions and costs of various interventions, continuous aggregated MACC can be derived by replacing the staircase graph with the polygon graph (See Supplementary Material available in the original publication). The polygon-type graph can be fitted to obtain smoothly growing cost functions that are similar to the ones obtained through modelling. Combining interventions considering their varying marginal costs is an alternative option to assess aggregated MACC yet it may require top-down modelling application (Klepper and Peterson 2006). Such assessment would most likely change the shape of the aggregated MACC, having more people lifted out of hunger at a low price at the beginning but increasing the costs of interventions even further for the remaining groups of people who require food support the most.

## 5.5 Other Limitations and Some Strengths of the Marginal Cost Curve Approach

MACC can be used to identify promising policies and programmes for investment. This facilitates priority setting by governments and investment stakeholders from the private sector and civil society. An advantage of MACC analysis is also its transparency. However, the concept has several limitations which have been already
highlighted in previous studies (Kesicki and Ekins 2012; Bockel et al. 2012; Eory et al. 2018). One of the limitations relates to the fact that the MACC presents the incremental cost of reducing hunger for a single point in time. Hence, it cannot capture intertemporal dynamics and technological inertia. Education and R&D investments for example yield gains after sometime. In a static MACC, lagged effects for such investments are not effectively captured in the MACC.<fnref n="7" /> Another aspect is that the MACC concentrates on hunger reduction and thus attributes the entire cost of the interventions only to hunger reduction. This is an overestimation in terms of economic cost-benefit considerations, as most of the interventions considered in this analysis generate various ancillary benefits, including reducing poverty and enhancing health, environmental sustainability, and education. Nevertheless, the MACC can be considered useful for an assessment of various potential interventions to reduce hunger based on a synthesis of studies from different fields based on multiple methodologies.

Additional analysis – for example at regional or perhaps national levels – is also warranted for prioritizing the measures for implementation and setting policies to promote them. Additional studies could focus on extending the analysis by identifying additional cost-effective measures in specific country contexts which can further contribute to hunger reduction. Technical and behavioral challenges to implementing the identified least-cost measures need to be considered in the prioritization process, despite their economic attractiveness.

While most of the parameters used in building the global hunger MACC are compiled from system- and economy-wide model-based studies, the cost and hunger reduction potential of several interventions were assessed based on a specific and large-scale cost-effectiveness studies. A next step, in this respect would be to evaluate the various measures using bottom-up integrated assessment modelling that could capture synergies and trade-offs between the different measures, as well as risks and uncertainties. Theoretically, that would be an advantage, but it remains difficult to embed the level of granularity and programmatic detail in such modelling, as pursued with the 22 interventions considered in the MACC approach here. Yet, additional quantitative sensitivity analysis and interpretation of the different results would be helpful to policymakers to support their decision-making.

<footnote n="7">⁷However, Rosegrant et al. (2017) capture the lag effects of investments in agricultural R&D in the investment-yield estimation model using a perpetual inventory method, where investments in agricultural R&D contribute to the stock of knowledge over time. The lag structure in the perpetual inventory method used in the study followed a gamma distribution where the impact of R&D investments peaked after ten years from initial investment and then sunk to zero after ten years from its peak.</footnote>
# 6 Policy Implications of MACC Analyses

This study has synthesized the findings of various model- and cost-benefit analysis-based studies on food and nutrition security interventions to assess the expected levels of the hunger reduction and the costs of achieving zero hunger by 2030. The most recent 'State of Food Security and Nutrition in the World' report estimated levels of undernourishment by 2030 to be about 630 million without considering the impact of COVID-19, or 660 million when considering the impact of COVID-19 on hunger.

MACC analyses are a basis for policy strategies and policy mobilization. The MACC for hunger reduction developed by synthesizing the outcomes of multiple studies indicates the overall potential of the interventions identify what it takes to end hunger by 2030. Considerable investment is required, but it is a question of political commitment to get the finance mobilized at national and global levels and the actual investments implemented in sound ways. Compared to the hundreds of billions of US$ for economic rescue packages to mitigate COVID-19 in many OECD countries, the investments to end hunger presented in this analysis are rather modest. The results from the MACC indicate that:

* Achieving target 2.1 of SDG 2 need not be prohibitively expensive, provided that a mix of least-cost measures with large hunger reduction potential are prioritized.

* Investments with long-term effects should be frontloaded in the decade to have a large effect soon before 2030.

* To end hunger by 2030, options that require high up-front investments but also have a high long-term impact need to be in the investment mix.

* Overall, the measures included in this MACC analysis have the potential to lift about a billion people out of hunger over ten years until 2030.

Yet, given the finding that investments to end hunger are rather modest, the troublesome question arises, what political economy forces prevent the required actions? Obviously, the spending priorities of those who could mobilize the resources seem not sufficiently oriented toward overcoming hunger, and the voice and influence of the undernourished seem too weak to enforce the investment action. Attempting to comprehensively answer this question goes beyond the scope of this paper. However, research into assessing the political economy for each of the considered interventions in the MACC might help to identify a set of politically acceptable second-best MACC elements that might differ from the marginal costs.

**Acknowledgment** This chapter was published in Food Policy, 104, October 2021. The supplementary material is also provided in the online version of this paper.
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# Financing SDG2 and Ending Hunger

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Check for
updates

Eugenio Díaz-Bonilla

## 1 Introduction

The adequate functioning of food systems is crucial to achieving multiple Sustainable Development Goals (SDGs) by 2030 (von Braun et al. 2020b). This chapter focuses on financing the transformation of food systems to help achieve crucial components of SDG2,<fnref n="1" /> including ending hunger by 2030. Given those objectives, the analysis centers mainly on agricultural production and rural development, as well as on poor and food-insecure consumers, both rural and urban, as part of the more general collection of issues around production and diets in food systems. Even with that focus, the interventions<fnref n="2" /> considered have important implications for a variety of other nutritional and environmental objectives.

The chapter compares the additional costs of achieving SDG2, including zero hunger (as estimated by von Braun et al. 2020a and studies referenced therein), with potential financial sources. The estimates of potential funding use the framework in Díaz-Bonilla et al. (2021), which identifies two flows of funds “internal” to food systems (consumer food expenditures, which are the sales/revenues that the agents in the agri-food system use to finance their operations), and four that are “external” to food systems (international development flows, public budgets, banking systems, and capital markets) (Fig. 1).

<footnote n="1">¹The focus is on ending hunger (2.1); doubling agricultural productivity and the incomes of small farmers (2.3); and fostering sustainability and resilience within food production (2.4). In the text, references to SDG2 must be understood in this vein.</footnote>

<footnote n="2">²“Interventions” refer to public sector actions, including policies, programs, investments, expenditures, taxes and subsidies, laws and regulations, and institutional aspects, that seek to address a specific problem.</footnote>
<figure><img src="image_139.png" /><figcaption>**Fig. 1** Flow of funds for food systems. (Source: Díaz-Bonilla et al. (2021))</figcaption></figure>

The main questions analyzed here are: given the estimated costs involved in such a transformation, what are the options for financing the interventions needed, what is their quantitative availability, and how can those potential sources of finance be reallocated and mobilized to achieve SDG2, including ending hunger?

Adequate macroeconomic policies, a supportive business environment, and peace are basic requisites for the operation of food systems. Also, different policy interventions can influence the size and allocation of consumer expenditures<fnref n="3" /> and the production outlays of the operators of food value chains (the internal flows) in ways that help achieve different SDGs (see a discussion in Díaz-Bonilla et al. 2021). However, the internal flows are not the focus of this chapter, but rather the availability and mobilization of external flows to food systems, which can augment the internal flows to finance the additional costs of reaching SDG2 and ending hunger.

This chapter is structured as follows. Section 2 focuses on the costs of achieving SDG2, based on the work referenced in von Braun et al. (2020a). Section 3 presents estimates of the current values of the external funds that can complement the internal flows and help finance the additional expenditures and investments needed to achieve the desired objectives. Section 4 compares the costs in Sect. 2 with the availability of funds estimated in Sect. 3 and evaluates different financial alternatives for effectively mobilizing the additional resources needed. The analysis considers the total amount of financial resources available; whether some of them can be reallocated towards the desired objectives; and, if that is not enough, where the additional money may come from, considering overall budget constraints. A conclusion is that, in the aggregate, there are enough financial resources to achieve

<footnote n="3">³Consumer food expenditures are estimated within a range of 8–10 trillion dollars annually (updated from Díaz-Bonilla 2021).</footnote>
SDG2. However, Sect. 5 argues that it is not only a matter of overall availability of financial resources (which must be further assessed at the level of individual countries as well), but of adequately designing and implementing national programs. Therefore, the section presents the idea of a Zero Hunger Alliance & Fund (based on suggestions advanced by different global leaders<fnref n="4" /> and by Action Track One of the United Nations Food Systems Summit, UNFSS) to help developing countries design, finance and implement zero hunger programs. Section 6 summarizes all proposals and Sect. 7 concludes.

## 2 Costs of Interventions to Achieve SDG2 and End Hunger

The estimates of the costs related to SDG2 and ending hunger are based on the work reported in von Braun et al. (2020a, b), with the background of two other studies, ZEF and FAO (2020) and IFPRI et al. (2020). Those studies consider a variety of interventions to end hunger, increase agricultural incomes, and achieve certain environmental outcomes, mainly related to mitigation and adaptation to climate change. The number of people estimated to avoid hunger depends on the costs and range of the interventions considered (Table 1).

The costs of eliminating hunger are not linear, with each further reduction in the number of people affected becoming more expensive (ZEF and FAO 2020). The largest estimate, of about 163 billion dollars annually, would save about 1050 million people from hunger by 2030.<fnref n="5" /> ZEF and FAO (2020) reckons that, without the interventions considered and under intermediate climate scenarios, the number of hungry people in 2030 would be about 900 million. But, as the study clarifies, this projection does not consider the possibility of additional humanitarian, health, or

<table><caption>**Table 1** Estimates of ending hunger and other SDG2 goals</caption><thead><tr><td>Source</td><td>People lifted from hunger (million)</td><td>Additional cost per year (billion dollars)</td></tr></thead><tbody><tr><td>IFPRI et al. (2020) (Ceres 2030)</td><td>490</td><td>33</td></tr><tr><td>ZEF and FAO (2020)</td><td>870</td><td>56</td></tr><tr><td>ZEF and FAO (2020)</td><td>1050</td><td>163</td></tr></tbody></table>

Source: Based on the cited studies

<footnote n="4">⁴Pope Francis, for instance, advocated a “Global Fund” to end hunger http://www.vatican.va/content/francesco/en/messages/food/documents/papa-francesco_20201016_messages-giornata-alimentazione.html</footnote>

<footnote n="5">⁵FOLU (2019) estimates the costs of 10 “transitions” needed for the transformation of food systems at 300–350 billion dollars per year until 2030. Those transitions involve several SDGs; but considering only those more directly related to SDG2, the costs would be about 170–190 billion dollars (close to the high estimates in ZEF and FAO 2020).</footnote>
environmental crises. The matrix of financing in Sect. 4 considers the intermediate estimate of lifting 870 million people from hunger, as well as and in addition to the target of about 1 billion people avoiding hunger (both as a cushion against future crises and because the additional interventions support climate change adaptation and mitigation as well).

# 3 Possibles Sources of Funding

Each of the following subsections discusses quantitative estimates of the annual current values of the external sources.<fnref n="6" /> They will be compared later with the additional costs shown in Table 1.

## 1. International development flows

International development flows include concessional development assistance and non-concessional lending by bilateral agencies, multilateral development banks (MDBs), and some large private philanthropic funds. Using disbursements<fnref n="7" /> in current values (from FAOSTAT), the annual average for the period 2014–2018 has been some 256 billion dollars for all uses/sectors, and 11.1 billion for agriculture,<fnref n="8" /> forestry and fishing (AFF), or some 4.3% of all development flows. If development flows to other sectors related to SDG2 (such as water and sanitation) are included, then disbursements in 2018 were estimated to be about 15 billion dollars (ZEF and FAO 2020).

## 2. Public budgets

Many public policies and expenditures influence the operation of food systems. Considering the interventions related only to SDG2, the analysis centers on two main types of public expenditure: on AFF and on social protection. There is also a brief discussion of additional fiscal expenditures related to the COVID-19 pandemic.

<footnote n="6">⁶Remittances are an important flow of funds. However, they are basically intra-family flows, which may not be possible or desirable to reallocate through public policies.</footnote>

<footnote n="7">⁷The values of disbursements are different from the net flows in the case of loans (concessional or not), because repayments of the principal of the previous loans must be deducted.</footnote>

<footnote n="8">⁸Agriculture includes Agro-industry, General Environment Protection, Food and Nutrition Assistance, and Rural Development. Some countries report expenditure for the General Government, others only for the Central Government, and some of them report both. Table 2 has been calculated with the larger of the two values reported.</footnote>
### (a) Agriculture, forestry, and fishing

Table 2 shows total government outlays (current US dollar average 2014–2019) and outlays on AFF,<fnref n="9" /> using FAOSTAT data.<fnref n="10" />

Developing countries,<fnref n="11" /> not including China, show total outlays of some 5 trillion dollars, and 125 billion for AFF, which represents about 2.5% of total expenditures. While developing countries spend a larger percentage of their budgets on AFF than developed ones, it is also necessary to consider those expenditures against the size of the agricultural sector, using the Agricultural Orientation Index (AOI; last column of Table 2 with the median values by regions). It is calculated as the percentage of agricultural expenditures over total expenditures divided by the share of agricultural GDP in total GDP. A number smaller (greater) than 1 indicates that the share of government spending on agriculture is less (more) than the share of agriculture in GDP, suggesting that there would be under- (over-) spending in the sector relative to its economic relevance. Clearly, developed countries spend more as a proportion of their agricultural sectors than developing countries (excluding China).

Although the levels of public spending alone do not determine the performance of the agricultural sector, different studies show that the types of expenditure matter, particularly their orientation toward the provision of public goods (see, for instance, Fan, ed. 2008).

Also, as noted, these numbers do not include other public expenditures relevant for agriculture, such as rural infrastructure, or for the food system as a whole. These considerations suggest the need to utilize a broader food-system focus to analyze the level and composition of public expenditures at the country level that are relevant for achieving the desired SDGs.

### (b) Social protection

Another important type of expenditure related to SDG2 and ending hunger is for programs of social assistance (i.e., those more directly linked to poverty and vulnerability that are financed by general revenues from the government and not

<footnote n="9">⁹Using the distinction made by FAO (2012), they cover public outlays in agriculture (aimed specifically at enhancing primary production), but not for agriculture (which are government expenditures in other sectors that can also have a positive impact on the agricultural sector). Further, the classification does not include all of the expenditures that can support the whole food system (Díaz-Bonilla 2015).</footnote>

<footnote n="10">¹⁰The OECD compiles producer support estimates in agricultural products only (not including fisheries and forestry) and for a more limited number of countries than FAOSTAT, but with a very useful disaggregation of interventions that include budgetary and non-budgetary transfers involving consumers (see https://www.oecd.org/switzerland/producerandconsumersupportestimatesdatabase.htm). Suggestions about reallocating or “repurposing” 600–700 billion USD in agricultural “subsidies” are based on those estimates. However, the OECD database shows that not all the transfers are agricultural “subsidies” that can be repurposed.</footnote>

<footnote n="11">¹¹What is considered a “developed” or “developing” country varies across datasets. Therefore, the numbers presented must be considered approximations for those groups.</footnote>
<table><caption>**Table 2** Government outlays (current dollars, average 2014–2019)</caption><thead><tr><th></th><th>Total Government Outlays (Billion USD dollars)</th><th>Outlays for Agriculture, Forestry, and Fisheries (AFF) (Billion USD dollars)</th><th>AFF as Share of Total Government Outlays (%)</th><th>AOI median (coefficient)</th></tr></thead><tbody><tr><td>Africa</td><td>483.5</td><td>9.6</td><td>2.0</td><td>0.15</td></tr><tr><td>Asia developing</td><td>4937.0</td><td>365.7</td><td>7.4</td><td>0.32</td></tr><tr><td>(of which China)</td><td>2987.0</td><td>285.9</td><td>9.6</td><td>1.19</td></tr><tr><td>LAC</td><td>1730.9</td><td>23.4</td><td>1.4</td><td>0.32</td></tr><tr><td>Oceania</td><td>569.1</td><td>3.4</td><td>0.6</td><td>0.19</td></tr><tr><td>Northern America & Europe</td><td>16966.2</td><td>94.2</td><td>0.6</td><td>0.42</td></tr><tr><td>Developing</td><td>8013.2</td><td>410.9</td><td>5.1</td><td>0.28</td></tr><tr><td>Developing w/o China</td><td>5026.2</td><td>125.1</td><td>2.5</td><td>0.28</td></tr><tr><td>Developed</td><td>19044.8</td><td>136.9</td><td>0.7</td><td>0.41</td></tr><tr><td>Total</td><td>27058.0</td><td>547.8</td><td>2.0</td><td>0.3</td></tr></tbody></table>

Source: Author, with data from FAOSTAT

by contributions from beneficiaries—known as “non-contributory programs”).<fnref n="12" /> Here, we focus on the social assistance programs using data from the World Bank’s ASPIRE database. It is based on household surveys, and therefore it may not capture all governmental programs. Also, the database focuses on developing and emerging countries only. On the other hand, it provides a useful disaggregation of social protection programs and of the distribution of benefits across the population.

Table 3 shows an estimate of the money allocated to those programs in current dollars for the period 2014–2018, using the categories in ASPIRE, except Cash Transfers and Social Pensions (CT+SP), which aggregates the three separate categories of conditional cash transfers, unconditional cash transfers, and social pensions.<fnref n="13" />

For the countries in the ASPIRE database, the median of social assistance expenditures is less than 1.2% of their GDP. Another key characteristic to consider

<footnote n="12">¹²These are part of the broader category of social protection, which includes programs financed by the beneficiaries (“contributory”). Developing countries spend about 1.1 trillion dollars (916 billion without China) in social protection (annual average 2010–2017; based on IFPRI’s SPEED database using data from the IMF), or about 3.5–4.0% of the GDP.</footnote>

<footnote n="13">¹³The ASPIRE database covers 125 countries, 43 from Africa, mostly from sub-Saharan Africa (AFR), 15 from East Asia and the Pacific (EAP) (including China), 29 from Europe and Central Asia (ECA) (including Russia, Hungary, Ukraine), 22 from Latin America and the Caribbean (LAC), 10 from the Middle East and North Africa (MENA), and 6 from South Asia (SAR) (including India).</footnote>
<table><caption>**Table 3** Estimated expenditures for social assistance programs</caption><thead><tr><th>Billion USD</th><th>Total</th><th>CT +SP</th><th>School feeding</th><th>Public works</th><th>Food and in-kind</th><th>Health fee waivers</th><th>Other social assistance</th><th>Total excluding health fee waivers</th></tr></thead><tbody><tr><td>AFR</td><td>20.2</td><td>12.6</td><td>0.9</td><td>2.8</td><td>1.3</td><td>0.5</td><td>1.9</td><td>19.7</td></tr><tr><td>EAP (China)</td><td>164.9</td><td>47.1</td><td>0.8</td><td>13.8</td><td>9.1</td><td>92.3</td><td>1.7</td><td>72.5</td></tr><tr><td>ECA</td><td>72.8</td><td>59.1</td><td>0.3</td><td>1.5</td><td>0.6</td><td>5.8</td><td>5.5</td><td>66.9</td></tr><tr><td>LAC</td><td>84.8</td><td>44.2</td><td>3.9</td><td>1.2</td><td>2.9</td><td>25.9</td><td>6.6</td><td>58.9</td></tr><tr><td>MENA</td><td>24.7</td><td>10.5</td><td>0.0</td><td>0.0</td><td>9.5</td><td>3.7</td><td>0.9</td><td>21.0</td></tr><tr><td>SAR (India)</td><td>40.3</td><td>4.9</td><td>1.5</td><td>6.4</td><td>24.5</td><td>2.2</td><td>0.7</td><td>38.1</td></tr><tr><td>Total</td><td>407.7</td><td>178.4</td><td>7.5</td><td>25.7</td><td>48.0</td><td>130.5</td><td>17.3</td><td>277.1</td></tr><tr><td>Total w/o China</td><td>260.9</td><td>139.2</td><td>7.5</td><td>12.0</td><td>42.6</td><td>42.0</td><td>17.3</td><td>218.9</td></tr></tbody></table>

Source: ASPIRE and WDI/WB

is the distribution across the population.<fnref n="14" /> Social assistance is intended for the poorest segments of a population, and if properly targeted, the largest percentages should go to the poorest quintile, with no benefits accruing to the richest ones. However, in the case of Africa, the poorest quintile receives 11.3% of the benefits (the average for the countries; the median is 8%), while the richest quintile receives 41.5% (average) and 38.9% (median). The East Asia and Pacific region also shows a distribution that is biased toward the rich, with the poorest quintile receiving about 17% (average and median), far less than the richest quintile (average of 33.4% and median of 22%). Other world regions show a better distribution, with the poorest quintile receiving somewhat more than 30% (average and median), but the richest quintile still getting 10–16% of the benefits. These numbers suggest significant problems with the targeting of these programs that are intended to help the poor and hungry.<fnref n="15" /> In particular, countries in Africa seem to suffer the dual problem of both lower levels of expenditure overall (a median of about 0.9% of GDP) and ineffective targeting of the poorest groups.

<footnote n="14">¹⁴The estimates are from the author using all the annual household surveys for all the countries in the database (several countries have more than one household survey, and the years for each country vary; the average year of the surveys in the database is 2011). Benefit incidence is calculated as the percentage of benefits going to each quintile relative to the total benefits going to the population (Sum of all transfers received by all individuals in the quintile)/Sum of all transfers received by all individuals in the population).</footnote>

<footnote n="15">¹⁵These are data from household surveys, which do not capture the wealthier segments of the population well; therefore, what appears as the richest quintile in the survey may not be so in real life.</footnote>
(c) Brief consideration of expenditures related to COVID-19

The current pandemic is posing further challenges for fiscal accounts. Governments have implemented a variety of policies and investments in health, social protection, and support for employment and production, all of which require the use of a variety of unconventional monetary and fiscal instruments. As reported by the IMF policy tracker for governmental COVID-19 actions, developing and emerging countries made a strong additional fiscal outlay, surpassing 1.2 trillion dollars in 2020 (counting only additional public expenditures as of this writing), with 1.1 trillion dollars spent on non-health measures of social protection and maintenance of employment (excluding China, the respective values are 700 billion dollars and 680 billion dollars). It will be difficult for those levels of expenditure to be sustained in the future, considering the debt already accumulated. These considerations will determine whether developing countries have the flexibility to increase public expenditures for SDGs in the aftermath of the pandemic.

3. Banking system

While, in the previous sections, the focus was on public flows, the transformation of food systems will also require significant private investments from all operators in the food value chains. The internal cash flows from food operations (based on consumers' food purchases) can be expanded by loans from the banking system (which is discussed here) or by operations in capital markets (analyzed in the next subsection).

Table 4, also based on FAOSTAT data, shows the total amount of loans out-
standing at a point in time,<fnref n="16" /> which was provided by the banking sector to producers
in agriculture, forestry, and fisheries (including household producers, co-operatives,
and agri-businesses)<fnref n="17" /> and for all sectors (the average for 2014–2019, in current
dollars).

There are no data on net disbursements (loans minus repayments of principal), but the change in stocks may be an indicator of net flows. For total credit, the yearly average change in stocks for 2015–2019 is about 1.6 trillion dollars globally; but the average for developing countries (excluding China) is only 87 billion dollars. The average annual change in loans for AFF during 2015–2019 is 24 billion dollars worldwide. The estimated flows for AFF in developing countries would be around 14.2 billion dollars, or some 9.5 billion dollars if China is excluded.<fnref n="18" />

Table 4 also shows the percentage of AFF loans as a share of total loans. In the case of developing countries without China, the coefficient is about 4% of total loans. But, as with public expenditures, a more revealing indicator of the importance of lending to the AFF sector is the Agricultural Orientation Index (AOI) (calculated

<footnote n="16"><sup>16</sup>It should be noted that this is a stock, while the data in the previous sections were flows.</footnote>

<footnote n="17">¹⁷There is no information about loans to other operators in food systems.</footnote>

<footnote n="18">¹⁸The actual annual flow of loans for AFF may be larger, considering that some short-term credit may be extended and liquidated within the year, and thus does not affect stocks from one year to the next.</footnote>
<table><caption>**Table 4** Value of loans outstanding, total and for AFF (Current Dollars; Average 2014–2019)</caption><thead><tr><th></th><th>Total loans (Billion USD dollars)</th><th>Loans to Agriculture, Forestry, and Fishing (Billion USD dollars)</th><th>% of AFF over total loans</th><th>AOI (median)</th></tr></thead><tbody><tr><td>Africa</td><td>402.5</td><td>16.3</td><td>4.1</td><td>0.2</td></tr><tr><td>Asia developing (of which China)</td><td>17043.6</td><td>427.9</td><td>2.5</td><td>0.4</td></tr><tr><td>LAC</td><td>11612.3</td><td>180.5</td><td>1.6</td><td>0.2</td></tr><tr><td>Oceania</td><td>995.7</td><td>93.3</td><td>2.0</td><td>0.5</td></tr><tr><td>Northern America & Europe</td><td>20978.8</td><td>404.4</td><td>9.4</td><td>1.4</td></tr><tr><td>Developing</td><td>18879.3</td><td>473.1</td><td>2.5</td><td>0.3</td></tr><tr><td>Developing w/o China</td><td>7267.0</td><td>292.6</td><td>4.0</td><td>0.3</td></tr><tr><td>Developed</td><td>22878.6</td><td>531.3</td><td>2.3</td><td>1.3</td></tr><tr><td>Total</td><td>41757.9</td><td>1010.2</td><td>2.4</td><td>0.4</td></tr></tbody></table>

Source: Author, based on FAOSTAT

as the percentage of AFF credit in total credit, divided by the percentage of agricultural GDP in total GDP). The last column in Table 4 provides the median AOI for the countries in each region. As in the case of public expenditures, developing countries show far smaller AOIs than developed countries,<fnref n="19" /> and values for Africa are lower than for other developing regions.

## 4. Capital markets

Capital markets at the global and national levels offer another source of external funds. Here, the focus is on socially- and environmentally-oriented investments,<fnref n="20" /> a potentially relevant source of funds for the transformation of food systems, considering the global trend toward investments that consider broader objectives along with financial returns.

However, definitions of these new investments are evolving, and therefore data on the actual volume of operations vary. Just as an indicator, Díaz-Bonilla (2021) cites that the issuing of Green Bonds in 2019 was 260 billion USD and, of Social Bonds, some 131 billion USD in 2020.<fnref n="21" />

The largest shares of investments in those categories take place in developed countries, and the amounts oriented towards agriculture and the transformation of

<footnote n="19">¹⁹ In Oceania, the values are dominated by Australia and New Zealand.</footnote>

<footnote n="20">²⁰ Díaz-Bonilla (2021) also included an estimate of foreign direct investments (FDI) for AFF and Food, Beverage and Tobacco. FDI for agriculture and agro-industries, in the aggregate, is part of the internal flows within food systems, but for individual countries, they can be considered additional financing.</footnote>

<footnote n="21">²¹ Aimed, respectively, at specific environmental and social objectives. When both objectives are combined, they are called sustainable bonds. There are also other themed bonds, such as “blue bonds” for sustainable fisheries.</footnote>
food systems are small. For instance, the survey of impact investments in GIIN (2020) shows that only 8.1% of the funds (average 2018–2019) were allocated to food and agriculture.<fnref n="22" />

The challenge is to mobilize these resources for investments in support of the transformation of food systems to achieve SDG2, including ending hunger.

## 4 Matrix of Financing and Interventions to Shape the Flows of Funds

Table 5 is an indicative matrix of financing that compares the current levels of the different sources discussed in Sect. 3 with the costs identified in Sect. 2. It assumes certain percentage of financing for each group of interventions from the individual flow of funds: for instance, some of them may only be financed by public expenditures, while others could receive credit from the banking system or investments from capital markets.

Table 5 shows the current values of flows of funds in those categories for developing countries calculated in the previous section (excluding China).<fnref n="23" />

Overall, there seem to be enough aggregate resources (except, perhaps, in the case of banking systems for the 1 billion target).<fnref n="24" /> The next subsections discuss policy options for each source to ensure that those resources can be mobilized to achieve SDG2 and end hunger. As mentioned, the analysis considers several questions: what the amount of available financial resources is; whether some of them can be reallocated towards the desired objectives; and, if that is not enough, then where the additional money may come from, considering the overall availability of financial resources (“budget constraints”).

A general constraint is defined by global aggregate savings: they amount to about 21.6 trillion dollars (average of 2015–2019), but are distributed very unevenly across regions (see details in Díaz-Bonilla 2021). Further, global savings are the counterpart to world investments. Therefore, any proposal to increase investments in certain activities would require adjustments in other investments and/or consumption, with

<footnote n="22">²²There are also several bonds issued by MDBs with agri-food components. But that money is then lent to developing countries as part of the international development flows discussed above and should not be (double) counted here.</footnote>

<footnote n="23">²³The estimate for capital markets is a rough approximation, partially combining (to avoid double counting) the value of social bonds issued by developing countries, as surveyed in the Climate Bond Initiative and HSBC (2021) (although they were not necessarily financing aspects of SDG2) and of the results for impact investment flows into agriculture, according to the survey in GIIN (2020).</footnote>

<footnote n="24">²⁴The financing matrix in Table 5 is just an example; different percentages of financing by sources can be considered that also depend on the instruments to be utilized. For instance, if governments decide to scale up cash transfers that include grants for productive activities and environmental sustainability, then, the additional costs of improved technologies would be financed by the public sector, instead of loans from the banking system.</footnote>
<table><caption>**Table 5** Matrix of incremental costs and financing, and reference flows (USD billions)</caption><thead><tr><th></th><th>Average annual<br>incremental<br>investment cost</th><th>International<br>development<br>flows</th><th>Public<br>expenditures:<br>AFF</th><th>Public<br>expenditures:<br>social</th><th>Public<br>expenditures:<br>infrastructure</th><th>Banking<br>system</th><th>Capital<br>markets</th></tr></thead><tbody><tr><td>AgR&D, extension and ICT</td><td>6.6</td><td>1.3</td><td>5.3</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>Small scale irrigation Africa</td><td>3.8</td><td>0.8</td><td>1.1</td><td>0.0</td><td>0.0</td><td>1.5</td><td>0.4</td></tr><tr><td>Crop protection (Insects, Diseases, Weeds), integrated soil fertility management</td><td>4.4</td><td>0.9</td><td>0.9</td><td>0.0</td><td>0.0</td><td>2.2</td><td>0.4</td></tr><tr><td>Infrastructure: roads, rail, electricity</td><td>10.8</td><td>2.2</td><td>0.0</td><td>0.0</td><td>8.6</td><td>0.0</td><td>1.1</td></tr><tr><td>Gender programs and nutrition</td><td>5.0</td><td>1.5</td><td>0.0</td><td>3.5</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>Social protection</td><td>25.1</td><td>5.0</td><td>0.0</td><td>20.1</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>TOTAL (870 million lifted from hunger)</td><td>55.8</td><td>11.7</td><td>7.3</td><td>23.6</td><td>8.6</td><td>3.7</td><td>1.9</td></tr><tr><td>TOTAL (1 billion lifted from hunger plus other interventions)<sup>a</sup></td><td>163.1</td><td>16.3</td><td>41.8</td><td>27.1</td><td>8.6</td><td>56.6</td><td>12.6</td></tr><tr><td>CURRENT FLOWS</td><td></td><td>11–12</td><td>86</td><td>293</td><td>n/a</td><td>9.5</td><td>9.9</td></tr></tbody></table>

Source: Author, using data from Sect. 3 and ZEF and FAO (2020).

<sup>a</sup>"Other interventions" include: Efficiency enhancement in irrigation; Global large-scale expansion of irrigation; Nitrogen-use efficiency; Food loss reduction; Soil-water management; Optimal crop planting and varieties (Adaptation); Soil Carbon Sequestration (Mitigation)
economy-wide repercussions that must be considered. Also, there may be “budget constraints” at the level of each individual flow of funds that need to be analyzed.

### (a) International development flows

International development flows for food systems should be increased by about 15 billion dollars above current levels (within the range suggested in IFPRI et al. 2020). Here, it is further suggested that 2 billion dollars of that increase be allocated to support the Zero Hunger Alliance & Fund (outlined below). If total international development flows cannot be increased (because bilateral development aid is limited by budgetary and political factors in donor countries, and net flows of non-concessional loans from MDBs are also constrained by their capital base and restrictive financial policies), this implies a reallocation of funds from other activities. For example, some of the development funds are supporting investments with high green-house gas (GHG) emissions, such as coal-based energy (UNFCCC 2021). At COP26,<fnref n="25" /> 25 countries and public finance institutions committed to ending financing abroad of projects with unabated (i.e., without carbon-capture) fossil fuel energy by the end of 2022, and those funds can then be reallocated to ending hunger. Similarly, other funds can be reallocated from activities with lower priority.

Another option currently being discussed is to reallocate a percentage of the new issue of Special Drawing Rights (SDRs) in the IMF to support developing countries (the new allocation has been about 650 billion dollars, of which about 60% went to developed countries).<fnref n="26" /> At the time of this writing, there is a discussion at the IMF about options for developed countries to donate or lend part of the SDRs that they do not need to support low-income countries. Here, an additional alternative is suggested to use the SDRs in a way that further multiplies their impact for broader objectives: the allocation of, for example, 2% of SDRs to a fund to guarantee “zero hunger bonds” issued by developing countries (explained later).<fnref n="27" />

In general, international development funds should be used more strategically to leverage and mobilize private funds. In addition, multilateral and bilateral organizations should better coordinate their own operations to avoid the fragmentation of relatively isolated initiatives and competition across international agencies at the national level.

### (b) Public expenditures

Table 5 estimates that public expenditures for agriculture and rural development and for social assistance would have to be increased by about 6% and 11%, respectively, to eliminate the risk of hunger for about 870 million people. If the objective is lifting

<footnote n="25">²⁵ https://unfccc.int/news/end-of-coal-in-sight-at-cop26</footnote>

<footnote n="26">²⁶ A smaller reallocation already happened to the IMF's Poverty Reduction and Growth Trust (PRGT), which provides concessional loans to low-income countries (https://www.imf.org/en/About/FAQ/special-drawing-right).</footnote>

<footnote n="27">²⁷ A larger allocation of 10% of the SDRs has been suggested in Díaz-Bonilla (2021) to guarantee “pandemic recovery bonds.”</footnote>
1 billion people from hunger, along with other climate mitigation and adaptation measures, then public expenditures in agriculture and rural development will need to expand by almost 20% and those in social assistance by about 12%.

These are aggregate numbers. Focusing on individual countries, indicators such as the AOI for agricultural expenditures or the percentage of social assistance expenditures in total GDP show that developing countries in general, and particularly in Africa and Asia (not counting China), devote comparatively fewer resources than other regions to those crucial interventions. Here, it is suggested that individual developing countries should try to increase their AOIs to about 0.5 and social assistance expenditures to at least 2% of the GDP.

Specific public expenditure reviews can help determine the adequacy of both the level and composition of public expenditures dedicated to SDG2, as well as their efficiency, efficacy and equity. Certainly, targeting could be improved in social and agricultural programs, and better instruments can be utilized, such as the evolving type of enhanced social safety net.<fnref n="28" />

Part of the additional resources can come from reallocation of agricultural subsidies with negative impacts on poverty, nutrition, and the environment (see, for instance, Laborde et al. 2020). Using data from OECD, the total amount of expenditures that can be subject to that repurposing in developing countries (excluding China) was estimated at about 52 billion dollars (Díaz-Bonilla 2021).<fnref n="29" />

Other expenditures with negative effects that should be phased down, following the Glasgow Climate Pact document agreed to at COP26 (paragraph 36), are subsidies to fossil fuels, globally estimated at some 800 billion dollars (Parry et al. 2021); the money saved can be reallocated to activities linked to SDG2 and ending hunger.

However, reallocating/repurposing, along with better targeting, even with improved instruments, may not be enough to reach the levels needed to achieve SDG2 and end hunger, and therefore expenditures and revenues may have to be increased.

One way to achieve this is by improving tax administration so as to reduce tax evasion. Also, developing countries should reassess the multiple exemptions to value-added and sales taxes: in several countries, they represent an important loss of revenue, help rich, as well as poor, consumers, and do not address challenges of nutrition or environmental sustainability. Implementing taxes on unhealthy and/or environmentally damaging food products can shift incentives while collecting

<footnote n="28">²⁸Cash transfers in the rural sector have been expanding to include poverty, nutrition, environmental, and productive payments (De La O Campos et al. 2018). Recent work by the World Bank has extended the framework for social inclusion to multidimensional programs that include social safety nets, livelihoods and jobs, and financial inclusion (see Andrews et al. 2021).</footnote>

<footnote n="29">²⁹The total amount of agricultural subsidies that can be repurposed (average 2014–2018 in current dollars) is less than 240 billion dollars: about 132.5 billion dollars in OECD countries, of which the EU represents 82.5 billion, and 105.8 billion dollars for non-OECD countries, of which China represents 62.1 billion dollars. For developing countries (excluding China, but including OECD members that are developing countries), the value is about 52 billion dollars (Díaz-Bonilla 2021).</footnote>
additional revenue. Taxes on international trade, including with impacts on fiscal accounts and on production and consumption incentives, should be analyzed considering SDG2 and hunger. Further, more progressive taxation of incomes and wealth will strengthen revenues. Finally, pricing the externalities of fossil fuels should be implemented, not only to shift incentives away from high GHG emissions, but as a source of revenue (Parry et al. 2021).

The Zero Hunger Alliance & Fund, discussed below, can help developing countries conduct the specific fiscal analyses involved in the reallocation, refocusing and scaling up of public expenditures needed to support programs to end hunger, considering the constraints posed by the fiscal response to the pandemic.

Additionally, all countries, but particularly the developed ones that have greater influence on the operation of global financial markets, must be more active at the international and national levels to implement stronger controls on money laundering and tax havens that facilitate illegal financial outflows and tax evasion from developing countries. Also, proposals for a more unified system of taxation of international corporations, with an established formula to allocate the taxable base and a common minimum corporate tax, must be implemented.<fnref n="30" /> These initiatives would help many developing countries to increase fiscal revenues that are currently being lost through corruption and tax evasion.

### (c) Banking systems

Expansion of irrigation and the adoption of improved agricultural practices (needed to reach SDG2 and end hunger) will require financing from the banking system. Estimates in Table 5 suggest that, in the aggregate, credit to the agricultural sector in developing countries (excluding China) will have to increase by some 40% in flows (for the central estimates of 870 million people avoiding hunger). While this is an aggregate estimate, for individual countries, it is suggested that they target an AOI for credit of at least 0.5.

For the banking sector to play this role, the systemic barriers that limit the supply of financial services for agriculture, small farmers, and the poor and vulnerable (women, disadvantaged ethnic groups, and youths) must be addressed. This requires a country-level analysis of the banking system considering the following aspects.<fnref n="31" /> First, the adequacy of the overall macroeconomic and regulatory framework. Lending to the agricultural sector is affected by macroeconomic volatility, and by regulations that are designed for the urban sector and for activities with more regular cash flows than agriculture. Second, the origin and use of the funds that are to be intermediated (such as deposits; budget allocations by the government; rediscounts by the monetary authorities; regulatory mandates to lend to the agricultural sector; loans from international organizations; and others). In particular, it is suggested here

<footnote n="30">³⁰Some advances were made in October 2021 at the G20 presidential meeting in Rome (https://www.reuters.com/business/g20-leaders-endorse-global-minimum-corporate-tax-deal-2023-start-2021-10-30/).</footnote>

<footnote n="31">³¹What follows is based on Díaz-Bonilla (2015); Díaz-Bonilla et al. (2019).</footnote>
that an updated version be used of the monetary policies that sustained agri-food
development in the 1960s and 1970s, implemented then by what have been called
“developmental central banks,” and which, with the 2008 global recession and the
current pandemic, have been revived mainly in developed countries under the name
of quantitative easing (Díaz-Bonilla 2015).<fnref n="32" /> Such an approach must be
implemented within a consistent monetary program that maintains control of
inflation.

Additional funds may also come from reallocation of credit that now supports activities with negative externalities such as deforestation or fossil fuels. In that regard, it would be important to properly implement the “Glasgow Leaders’ Declaration on Forests and Land Use” at COP26, and implement the disclosure recommendations concerning climate-related financial risks, as suggested by the Task Force on Climate-Related Financial Disclosures (TCFD) created in 2015 by the Financial Stability Board (FSB).<fnref n="33" />

The third component of the analysis is the types of banking and financial institutions that can intermediate those funds. There is a wide variety of formal and informal banking and financial operators, with their own advantages and disadvantages. Here, it is suggested that the role of public development banks (PDBs) be strengthened. They were dismantled in many developing countries during the 1990s because of concerns about corruption, inefficiency, and fiscal costs. However, several of them, including those with an agricultural orientation, have been reformed to operate with incentives, performance metrics, and controls to avoid the problems of the past, while pursuing developmental objectives. In fact, during the UNFSS, and following the November 2020 “Finance in Common Summit” of PDBs, a coalition of those banks to help finance the transformation of food systems was announced.<fnref n="34" /> That coalition can help PDBs address the pervasive market failures in agricultural and climate credit markets that affect small farmers and firms, particularly those operated by women, youths and vulnerable ethnic groups.

The fourth aspect to consider relates to financial instruments. A central one is
credit, particularly longer-term operations, which face agricultural-sector-specific
problems, such as the dispersion and small scale of customers, and weather and other
risks. Innovative insurance schemes, technical assistance, and better weather and
market information can mitigate some of those risks. Supply-chain and value-chain
lending offer a flexible form of financing that can include small farmers; input and
equipment suppliers should also be considered as potential vehicles for lending to
small and family farmers. In any case, credit for long-term investment may require
funding from public fiscal or monetary sources (as suggested above).

<footnote n="32"><sup>32</sup>The U.S. Federal Reserve operated as a developmental central bank to help the U.S. economy in the 1930s. More recently, with the recent crises, central banks, mainly in developed countries, have revived the use of those dedicated lines of credits to buy both public and private credit instruments.</footnote>

<footnote n="33">³³Established by the G-20 in 2009, to ensure that the financial system is resilient to all forms of risk.</footnote>

<footnote n="34">³⁴https://foodsystems.community/public-development-banks-coalition/</footnote>
Beyond the obstacles to credit, there is a dearth of other financial products and services for small farmers, rural populations, and SMEs. This is true both on the financing side (such as leasing, warrants, and the discount of invoices, all of which require the adaptation of regulations and operational mechanisms) and on the payments and savings side (for instance, simplified checking and savings deposits, which are an important risk mitigation tool for rural households). In general, digital technology can reduce transaction costs and generate more information about potential customers, lowering risk for financial institutions.

(d) Capital markets

Table 5 suggests that capital market operations will have to increase significantly above current estimated levels to achieve SDG2 and zero hunger, plus other environmental and health objectives. The net-zero deforestation commitments and emissions disclosures already mentioned in the banking section can also help shift incentives and financial flows in capital markets. Additionally, we present two other ideas here.

First, it is necessary to develop a robust pipeline of investable opportunities (including individual projects, impact investment funds, and/or thematic bonds) with the adequate profile of risk/reward to attract investors, and clear, measurable, and monitorable impact objectives, aligned with achieving SDG2 and ending hunger.

An international project preparation/incubation/acceleration facility could be set up to link investable opportunities for small farmers and rural populations in social and environmentally relevant activities with private capital, based on CGIAR technologies and leveraging the presence of its centers in more than 100 developing countries, where they work with a variety of national agricultural research institutes (NARIs) (see the detailed proposal in Díaz-Bonilla et al. 2018).<fnref n="35" />

The facility would identify projects involving small and family farms; aggregate and structure them (as different types of investable vehicles), with adequate rates of return and risk profiles, and with value sizes that compensate investors for the transaction costs and due diligence requirements; provide technical assistance to both small farmers and investors, particularly in relation to sustainable technologies (based on the work of the CGIAR centers and participant NARIs); and define and monitor metrics for the impacts desired. This facility can also support enhanced environmental lending by public and private banks. International development funds, as well as some national public expenditures, can be used more strategically in this facility as blended finance with private sector funds, including for the purpose of de-risking investments.

<footnote n="35">³⁵ More recently, there have been other similar ideas regarding the need for an institutional device to link investable opportunities and investors (see, for instance, Millan et al. (2019) and Finance for Biodiversity Initiative (2021)).</footnote>
A second proposal is to guarantee “zero hunger” bonds to finance related public programs. In particular, as mentioned, it is suggested here that 2% of the new allocation of SDRs of 650 billion dollars be assigned to a fund, which could be set up within the IMF, to guarantee the interest rate payments of zero hunger bonds issued by developing countries as part of the Zero Hunger Alliance described later.

Although the specific design of those bonds will have to be discussed with potential private and institutional investors, some features to consider are the following: they will be a “consol” or perpetual bond;<fnref n="36" /> possibly issued in dollars; paying an adjustable rate with a cap (say, 5%, which is close to the average nominal yield since the 1950s); and may be callable, with call protection (for example, until 2050). Other official development aid and private philanthropic funds could be utilized as well to guarantee the interest payments, and thus eliminate country risk for the countries that join the international alliance to eliminate hunger (discussed below).

This alternative will greatly increase the impact of the SDRs: for instance, 13 billion dollars can guarantee an issuance of between 52 and up to 130 billion dollars in zero hunger bonds (under the assumptions in the footnote<fnref n="37" />). The funds raised, in addition to the zero hunger objective, would also help finance sustainable agricultural technologies and other environmental interventions addressing climate change mitigation and adaptation.

Certainly, the financial scheme suggested here can also be utilized for special bonds with other purposes, such as financing pandemic-related expenditures (a “pandemic recovery bond” is discussed in Díaz-Bonilla 2021 and von Braun and Díaz-Bonilla 2021).

# 5 The Need for Country-Based Institutional Arrangements: A Zero Hunger Alliance & Fund<fnref n="38" />

The quantitative estimates and the financing matrix discussed above suggest that, in the aggregate, there are enough additional financial resources to save anywhere from 870 to 1 billion people from hunger by 2030, effectively achieving zero hunger.

However, the potential sources of financing and whether they are sufficient cannot be judged solely at the aggregate level; they also need to be assessed in each individual country. And even if the domestic resources exist and can be

<footnote n="36">³⁶ Alternatively, 100-year bonds can be considered, with payment periods during the last 10 years.</footnote>

<footnote n="37">³⁷ The guarantee fund holds between 2 and 5 years in interest payments at the rate of 5%; the default rates of the interest payments that have to be covered by the guarantee fund are similar to those of the IMF or the World Bank; and the erosion that those payments inflict on the guarantee fund is covered by additional international public money. The multiplier effects changes with different assumptions.</footnote>

<footnote n="38">³⁸ A more detailed explanation is in Díaz-Bonilla (2021).</footnote>
mobilized, they can only be transformed into solid programs to end hunger and achieve SDG2 if individual countries are willing and capable to do so.

However, institutionally weak governments may not be able to design the programs and coordinate the work of their own Ministries and agencies, as well as the international organizations operating in their countries. They could benefit from the establishment of institutional mechanisms to help developing countries design, finance, and implement their programs. The fiscal constraints imposed by the public responses to the current pandemic reinforce the need for these country-based arrangements.

Therefore, it is suggested that a “Zero Hunger Alliance and Fund” (ZHAF, or “the alliance”) be established, with the objective of operating as an international mechanism to assist the developing countries that formally join the alliance in the design, financing, and implementation of their zero hunger plans.

The proposal outlined here builds on the idea of a Zero Hunger Fund, which was suggested by Action Track One of the UNFSS and has considered the experiences of other initiatives.<fnref n="39" /> It also expands the scope of the announcement by the UN Secretary-General in his “Chair Summary and Statement of Action on the UN Food Systems Summit” about appointing Resident Coordinators and UN Country Teams to develop and implement national programs, while enjoining the Rome-based agencies (FAO, IFAD, and World Food Program) with the coordination of a UN-based hub to support the follow-up activities to the Food Systems Summit.

The proposed ZHAF would go further than the UN system, as a public-private partnership with the capacity to support, both institutionally and financially, country-owned and country-coordinated programs to end hunger. The ZHAF would have personnel seconded from international organizations (with specific assignments of, say, 3 years) focusing on poverty, food security and nutrition issues. It can work with (and even within) the UN coordinating mechanism suggested by the Secretary-General. It would support participating countries, which, after formally joining the Alliance, should designate a coordinating group of high-level officials with the relevant authority to design national initiatives and mobilize the financial and institutional resources needed to implement them.

The ZHAF will receive $2 billion a year for 5 years from the annual increase of $15 billion in international development funds, while also expecting to mobilize an additional $500 million per year from the private sector, with targeted commitments from at least 50 companies in food and other sectors.

<footnote n="39">³⁹Such as the Global Agriculture and Food Security Program (GAFSP), the Poverty Reduction Strategy Papers or Programs (PRSPs), and GAVI, the Vaccine Alliance (more details are in Díaz-Bonilla 2021).</footnote>
To expand the financial resources available, it is also suggested that the devel-
oping countries joining the ZHAF be allowed to issue the new “zero hunger bonds”
guaranteed by the SDRs.

The fund would be used to cover operational costs; hire technical and operational experts to support the countries in defining the programs and mobilizing the human, financial, and institutional resources to carry them out; facilitate the issuance of zero hunger bonds and other financial support; and, eventually, possibly finance some interventions directly.

In addition to helping eliminate hunger by 2030, the ZHAF, through specific policy interventions and investments supported at the national level, would help achieve other crucial nutritional and environmental objectives, which will be monitored and documented.

It is recognized that the establishment of a new global hunger fund is not without risks. Potential public and private sector contributors may suffer “donor fatigue.” Also, some developing countries may not join the alliance, and others may not have the necessary capabilities, even with the support of the ZHAF, to coordinate and sustain the effort to eliminate hunger.

Yet, the institutional arrangement outlined here (and detailed in Díaz-Bonilla 2021) has several important advantages. It has a flexible public-private institutional structure, and it supports participating countries as they implement country-owned, country-coordinated, integral programs. It also focuses on the single and measurable objective of ending hunger by 2030, while, at the same time—given the type of agricultural technologies and environmental interventions supported—it will contribute to crucial objectives related to climate change mitigation and adaptation. The ZHAF would also mobilize a significantly larger volume of funds than those directly allocated to it. And, by relying on personnel from existing organizations, it reduces the risks of creating another permanent international bureaucracy.

Ending hunger is within reach, and the Zero Hunger Alliance & Fund can play a
central role in achieving it.

# 6 Summary of Proposals

Table 6 summarizes the proposals discussed above.
<table><caption>**Table 6** Summary of proposals</caption><thead><tr><td>Topics</td><td>Proposals</td></tr></thead><tbody><tr><td rowspan="3">International development flows</td><td>Increase by 15 billion dollars annually the international development funds dedicated to agricultural and rural development, food and nutrition security, and environmental aspects of food systems. Reallocate development funds currently supporting fossil fuels and other environmentally damaging activities</td></tr><tr><td>Allocate yearly 2 billion dollars of the additional 15 billion dollars to the Zero Hunger Alliance &amp; Fund</td></tr><tr><td>Allocate 2% of the future issue of Special Drawing Rights (SDRs) of 650 billion dollars to offer guarantees for a new “zero hunger bond” to help finance the economic, social, and environmental interventions needed to achieve SDG2 and end hunger. These instruments can be perpetual or very long-termed bonds, with an adjustable but capped coupon</td></tr><tr><td rowspan="4">Public budgets</td><td>Implement public expenditure and tax reviews in developing countries to increase and reallocate inefficient agricultural expenditures and fossil fuel subsidies and scale up, better target, and redesign social safety nets using new and evolving cash transfers that combine poverty, productive, nutritional, environmental, and financial inclusion components</td></tr><tr><td>Increase the Agricultural Orientation Index (AOI) of expenditures for agriculture, forestry, and fisheries (for example, to not less than 0.5) and social protection expenditures as a percentage of GDP (for example, to at least 2%)</td></tr><tr><td>Revenues in developing countries should be strengthened through better tax administration and the revision of sales, income, wealth, and trade taxes, and by implementation of international initiatives to control corruption, tax evasion, and other practices that erode those countries’ tax bases. Pricing fossil fuel externalities should also be implemented</td></tr><tr><td>Reactivate the tools of the “developmental central banks,” using rediscounts to offer credit to small farmers, rural populations, and SMEs in food value chains (within a consistent monetary program that maintains control of inflation)</td></tr><tr><td rowspan="3">Banking systems</td><td>Revitalize and modernize public development and agricultural banks (with incentives, performance metrics, and controls to avoid the problems of the past in this type of institution) to increase credit (supported by central bank discounts) and offer other financial services to small farmers, rural populations, and SMEs in food systems, with particular consideration for women, vulnerable ethnic minorities, and youths</td></tr><tr><td>Increase the AOI of agricultural credit to at least 0.5</td></tr><tr><td>Implement the zero deforestation and emissions disclosures discussed as part of COP26 and the work of the Task Force on Climate-Related Financial Disclosures (TCFD)</td></tr><tr><td>Capital markets</td><td>Create a project preparation/incubation/acceleration facility to structure productive opportunities for small farmers into investable opportunities for impact investors, using economic, social, and environmentally sound technologies with the support of One CGIAR and national agricultural research institutes (NARIs) and partners in more than 100 developing countries</td></tr></tbody></table>

(continued)
<table><caption>**Table 6** (continued)</caption><thead><tr><th>Topics</th><th>Proposals</th></tr></thead><tbody><tr><td></td><td>Support countries that participate in the Zero Hunger Alliance &amp; Fund with the design, guarantee (using 2% of the new allocation of SDRs and other public funds) and launch of a new type of social and environmental bond, called a “zero hunger bond”</td></tr><tr><td></td><td>Implement the zero deforestation and emissions disclosures discussed as part of COP26 and the work of the Task Force on Climate-Related Financial Disclosures (TCFD)</td></tr><tr><td>Zero Hunger Alliance &amp; Fund</td><td>Create a public-private international institution, with a dedicated fund, to organize country-based alliances to eliminate hunger by 2030. This institution can work within the UN coordinating mechanism suggested by the UN Secretary-General to follow up on the UNFSS</td></tr></tbody></table>

Source: Author

# 7 Conclusion

This chapter analyzed the costs and potential financial mechanisms for achieving SDG2, including the end of hunger, and made a series of specific proposals for mobilizing the resources to achieve those objectives. The proposals include the creation of a Zero Hunger Alliance & Fund, in support of country-owned and country-coordinated integral programs to end hunger by 2030. It is hoped that this chapter has shown that it is feasible to achieve the SDG2 and end hunger in an improved global food system, if we decide together to do it.

**Acknowledgements** The author acknowledges the detailed and very useful comments from Joachim von Braun, Johan Swinnen and Rob Vos. Those comments significantly improved the different versions of this document. However, they are not responsible for any of the remaining errors and omissions. This chapter is an abridged and updated version of Díaz-Bonilla (2021).

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Andrea Zimmermann and George Rapsomanikis

## 1 Introduction

Trade is an integral part of our food systems. It connects people at all stages of agricultural and food value chains, promotes food security, is inherent to economic growth, and interacts with society and the environment. Since 1995, agricultural and food trade has more than doubled in value, quantity, calories, and land used for export (FAO 2020b; Qiang et al. 2020; Traverso and Schiavo 2020). Today, about one-third of agricultural and food exports in the world are traded within global value chains that encompass at least three countries (Fig. 1; FAO 2020b).

Agricultural and food trade links the food systems of countries and plays a crucial role in providing consumers worldwide with sufficient, safe and nutritious food, while generating income and employment for farmers, workers and traders in agriculture and the food industry.

Trade is closely related to economic development. Developed countries make up more than 60% of agricultural and food trade. Emerging economies, such as Brazil and China, have been increasing their market shares since the early-2000s and play an increasingly important role in global agricultural and food markets (FAO 2018a, b).

At the same time, and as the interdependence among nations strengthens, the role of trade in society and income distribution becomes more important (FAO 2020b). This, together with the emergence of new players in global markets, has induced lively debates on what economic, environmental and social outcomes trade and global markets generate. These debates have been intensified and broadened through significant concerns about inequality, growing environmental consciousness,

<footnote>A. Zimmermann (✉) · G. Rapsomanikis
Commodity Markets and Development Team, Markets and Trade Division, Food and Agriculture Organization of the United Nations (FAO), Rome, Italy
e-mail: Andrea.Zimmermann@fao.org</footnote>
<figure><img src="image_142.png" /><figcaption>**Fig. 1** Participation in global value chains in food and agriculture. (Source: Based on analysis from Dellink et al. (2020); adapted from FAO (2020b))</figcaption></figure>

changing lifestyles and diets that have been attributed to globalization and the related concerns about health risks associated with increasing shares of overweight and obese people (FAO 2016, 2018b, 2020b).

The COVID-19 pandemic has fueled fears about the functioning of global agricultural and food trade, and the discussion about reshoring tendencies in manufacturing and services and shortening global value chains has also reached food and agriculture.

However, agricultural production strongly depends on specific natural resource endowments and environmental conditions, such as soil characteristics, altitude, water availability and climate. These are distributed unevenly across the world and, together with differences in technology, shape trade flows. This distinguishes agricultural and food trade from trade in manufacturing and services. In fact, since the Neolithic period, agricultural and food trade has evolved in line with the comparative advantage derived from these immutable characteristics (see, for example, Smith et al. 2015).

At the same time, the demand for food is most rapidly increasing in regions where population and income growth are strongest, but which may not always be the most productive. These developments may reinforce the role of trade in ensuring food security and providing nutritious and healthy diets for all.

This chapter highlights the role of agricultural and food trade in moving food globally from surplus to deficit regions, thus ensuring food security and serving a fundamental food system function. It further addresses the interlinkages among trade and economic development, the environment and societal shifts in food consumption. Ultimately, the chapter illustrates the role that trade can play in balancing different aspects of sustainability from a global perspective and points out the scope for further research and novel policy approaches.
## 2 Trade, Food Security and Nutrition

Trade in food and agriculture can help balance food supply and demand globally by moving food from surplus to deficit areas. Higher food imports can increase the availability of calories and nutrients in a country. Through increased food supply, food prices would fall, thus improving access for net consumers. At the same time, decreasing food prices induced by import competition can also affect incomes and livelihoods of domestic farmers and food processors who are net producers. However, for a country, increased trade openness may also allow for better access to other countries' markets and promote exports of agricultural products to these markets, thereby creating and expanding employment opportunities and raising workers' incomes (Dithmer and Abdulai 2017; FAO 2016).

By moving food from surplus to deficit areas at times of shortages, which might, for example, be caused by natural disasters or seasonal growing patterns, trade can also contribute to more stable food supplies and prices, and thus to the stability dimension of food security. The exchange of foods that are produced under specific climate, soil and other natural conditions can contribute to the diversity of diets (Remans et al. 2014) and improved food utilization (FAO 2016, 2018b).

Although the theoretical pathways of how trade can affect food security and nutrition are well established, the linkages between agricultural and food trade and food security and nutrition are complex, and some of the impacts can offset each other. This makes the identification of the effects in empirical assessments difficult. In fact, there has so far been only little empirical evidence on these relationships (FAO 2018b; Mary 2019).

A relatively new strand of literature contrasts trade openness with direct nutritional outcomes such as undernourishment. At the global level, it was shown that agricultural trade openness has, on average, a positive net impact on food security measured as dietary energy supply adequacy. It also increased dietary diversity measured as the share of calories from non-staple foods and protein consumption (Dithmer and Abdulai 2017). However, the exact mechanisms and impacts can vary by context and stage of development (FAO 2016). For example, in a sample of 52 developing countries, food trade openness was associated with an increase in the prevalence of undernourishment. In fact, it was found that food supply increased as a result of increased trade openness, but, in net food-importing countries, the negative effect on agricultural producers and the food sector caused by import competition prevailed. This result could point to efficiency constraints in net importing countries with large agricultural sectors (Mary 2019).

Besides trade openness, the ease with which trade takes place also matters. For example, poor trade facilitation, with high bureaucratic requirements and lengthy export and import times, can negatively affect various dimensions of food security, as shown in a study of 45 African countries observed between 2006 and 2015 (Bonuedi et al. 2020).

Among the most-researched relations within the area of agricultural trade and food security are the linkages between trade and price volatility. Price volatility,
which is described by episodes of large and unexpected price changes, can intensify
and contribute to risks to food security (Kalkuhl et al. 2016). In particular, the food
price crisis of 2007/08 has triggered a plethora of studies on its causes. While a
whole set of macroeconomic and sector-specific drivers for the price surges has been
identified (Tadesse et al. 2014), it is now well established that trade restrictions that
were imposed by many countries in response to rising food prices exacerbated food
price volatility.

Trade-restricting measures, such as high import tariffs and export bans, reduce the volume traded in international markets, and thus constrain the exchange mechanism between surplus and deficit areas. This makes markets more vulnerable to shocks and increases price volatility at times of crisis (Anderson 2012). To insulate themselves from sudden food price surges, countries tend to impose new or heighten existing export restrictions and/or lower import barriers so that the domestic price does not rise as high as the world market price (Rapsomanikis 2011), with the effect that world markets become even thinner, market uncertainty increases and international food prices become more volatile (Anderson and Nelgen 2012; Anderson et al. 2013; Martin and Anderson 2012).

Export restrictions, especially when applied by major exporters, can significantly
harm their trading partners, in particular, net food-importing developing countries.
For example, export restrictions implemented by various countries between 2006
and 2011 increased international price volatility for wheat and rice. In fact, the
contribution of export restrictions to price volatility appeared to be on the same order
of magnitude as that from key macroeconomic variables (Rude and An 2015).

At the same time, export restrictions also affect domestic markets (FAO 2016). For example, export restrictions on wheat applied by major wheat exporters during the 2007/08 food price crisis not only harmed their trading partners, but also reduced prices for domestic producers and increased domestic market instability. The negative market effects discouraged private investors and prevented the countries that imposed the export restrictions from achieving their production potential (Götz et al. 2013).

Dietary diversity is important for the adequate provision of nutrients and human
health. As natural conditions do not allow for the production of all foods everywhere, trade is an important means to help diversify diets. A number of studies investigate the relationship between trade and dietary diversity.

Since the beginning of the 1960s, trade in crops has expanded and diversified.
This process has been identified as the main driver of globally diversifying supply of
vegetable products (Aguiar et al. 2020). In fact, the diversity of foods produced is a
strong predictor of food supply diversity only in low-income countries, which are
less integrated in international trade. In middle- and high-income countries, food
supply diversity was shown to be independent of production diversity, and other
factors, including international trade, contributed more to the diversity of a country's
supply (Remans et al. 2014).

Although lower-income countries are often not well integrated in global markets,
a study found that they still tend to improve their nutrient supply through trade, in
particular, the supply of energy, protein, zinc, calcium, vitamin B12 and vitamin A
(Wood et al. 2018). However, in another study, it was found that, while trade distributes substantial volumes of nutrients, its role in bridging the nutrient adequacy gap<fnref n="1" /> was only marginal in low- and lower-middle-income countries. International trade helped close the nutrient gap in most high- and upper-middle-income countries, even where domestic production ensured only a very low nutrient adequacy (Geyik et al. 2021).

Taken together, the evidence shows that trade is indispensable to ensure food security in all its dimensions. Without trade, the availability and accessibility of foods and nutrients would be more unevenly distributed, any form of domestic production disruptions would cause serious concern for food security, and diets would be less diverse.

However, increased competition through rising imports may be challenging for farmers in developing countries that are characterized by low efficiency and productivity constraints associated with poor physical infrastructure, weak institutions and low skills.

## 3 Trade, Growth and Inequality

The global trade regime – as reflected by the WTO rules and a multitude of trade agreements – has contributed to increasing trade significantly since the last decades of the twentieth century. Population growth and urbanization, rising incomes and improvements in transport and communication technology have colluded with lower policy-induced trade barriers to fuel trade (FAO 2020b).

Most economists would agree that openness to international trade promotes economic growth (Irwin 2019). Trade results in efficiency gains, as resources are allocated in line with comparative advantage, which is shaped by differences in technology and relative factor endowments. In agriculture, where differences in land and water endowments and climate are significant across countries, gains from openness and market integration can be large (Martin 2018). These gains can add to the rate of growth of the economy, but are difficult to estimate.

Isolating the impacts of trade openness, whether it comes from a reduction in trade costs or trade policy reforms, is challenging, given the myriad factors that affect economic growth. In addition, focusing the analysis on single sectors, such as food and agriculture, can be complex. Using structural models to test counterfactual scenarios is the analysts' preferred method for untangling the role of trade and trade policy in economic growth. For example, a study looking at the effect of market integration across US counties between 1880 and 1997 suggests that such gains are

<footnote n="1">¹The nutrient adequacy gap describes the difference between nutrient requirements and actual availability, referring to six essential nutrients (protein, iron, zinc, vitamin A, vitamin B12 and folate) (Geyik et al. 2021).</footnote>
substantial as agricultural production is allocated according to comparative advan-
tage (Costinot and Donaldson 2016).

In addition to the effect of efficiency gains, trade facilitates technology and knowledge spillovers across countries which promotes growth by improving the production process, increasing product quality and innovating new products (Grossman and Helpman 1991). Indeed, since 1995, the growth in food and agricultural trade has taken place together with increases in agricultural productivity per capita, particularly in emerging and developing economies (FAO 2018a).

This conventional wisdom concerning the effects of trade openness on growth and productivity is being questioned by many practitioners. Gains from trade are asymmetrically distributed. Trade openness affects the prices of goods and those of production factors, including labor, and thus can result in winners and losers. In agriculture, a major concern relates to the ability of smallholder farmers from developing countries to compete effectively in open markets.

A handful of studies focus on the impact of trade openness on agricultural productivity, with the underlying hypothesis being that trade facilitates the diffusion of technology and knowledge spillovers. Focusing on how agricultural productivity in 44 countries – both developed and developing – converges at higher levels, one study finds that openness to trade increases labor productivity growth rates in agriculture within an analytical framework that also takes into account the costs of technology diffusion and adaptation (Gutierrez 2002).

Additional evidence suggests that trade openness can have a short-run negative impact on agriculture's efficiency (Hart et al. 2015). However, in the long run, it is found to increase efficiency in agriculture, reflecting the ability of the sector to adapt to global markets and increased competition through technology adoption, but also through the withdrawal of inefficient farms from the sector. In Chile – a country that liberalized trade in the 1990s after a period of import-substitution policies – an analysis of 70,000 farms suggests that trade openness is positively related to farm yields (Fleming and Abler 2013).

Downstream, a study of more than 20,000 food firms in Italy and France suggests that import penetration in both final food products and intermediate inputs systematically contributes to firm-level productivity growth (Olper et al. 2017). Participation in agricultural and food global value chains, either through imports of inputs or exports of intermediate products, is also found to promote agricultural labor productivity (FAO 2020b; Montalbano and Nenci 2020). The main mechanism for this lies in how value chains unbundle the production process, allowing farms and firms to leverage their comparative advantage in global markets and facilitating the transmission of improved technology, leading to better farm practices and improved labor productivity.

These linkages between trade openness and technology are unwrapped by a
micro-level data study of the impact of trade in agricultural inputs on the produc-
tivity of 1.1 million fields across 65 countries. Since the 1980s, trade openness in
agricultural inputs has been found to result in significant shifts from traditional farm
technologies to modern ones, thus having distributional implications for productivity
and welfare across the world (Farrokhi and Pellegrina 2020).
In addition to the efficiency gains from better resource allocation in agriculture and the dynamic effects on agricultural productivity through the transmission of technology and knowledge, trade openness in food and agriculture can generate significant effects on the broader economy by facilitating structural transformation. Trade in food, especially imports, can help meet domestic food requirements and allows for labor to be allocated to non-agricultural sectors, thus promoting economic growth and development (Tombe 2015). Analyzing the process of structural transformation in the UK in the nineteenth century and, more recently, in South Korea, a study finds that agricultural imports played a crucial role in the transformation process of both economies (Teignier 2018).

Trade openness, either by intensifying competition or through fueling the struc-
tural transformation process, can promote growth, but can also affect income
distribution and inequality. A recent analysis of the impacts of eliminating tariffs
on agricultural products across low- and middle-income countries pointed to
increases in both income and inequality (Artuc et al. 2019). The results suggest
that, on average, liberalizing agricultural trade would increase household incomes.
At the same time, eliminating import tariffs was found to have highly heterogeneous
impacts across countries, and, within countries, across households. In most coun-
tries, the top 20% of the richest households would gain more from liberalization than
the bottom 20%, thus exacerbating inequality.

In the context of food systems, trade openness highlights the trade-offs between promoting economic efficiency and generating positive social outcomes. Integrating smallholder farmers in global markets is challenging. Policies that promote trade openness often tend to underplay market failures, and complementary actions to address inequality are necessary. Inclusive business models, such as contract farming, can address the constraints that farmers in developing countries face in entering markets and global value chains (FAO 2020b). However, a range of public policies and investments, such as carefully designed input subsidies targeted at smallholder farmers, skill upgrades and education, the removal of labor market rigidities, and improvements in infrastructure and regulation, can complement the market mechanism and promote a fair structural transformation.

4 Trade, Environment and Climate Change

Agriculture builds one complex with the environment. Natural resources and climate
are inputs to agricultural production, and a part of the human impact on the
environment is transmitted through this production process.

While expected changes in climatic and environmental conditions over the
coming decades will affect food security and nutrition, short-term shocks, such as
natural hazards, pests, diseases and extreme weather events, already lead to harvest
losses and supply chain disruptions. In regions with limited access to international
markets and where food production and consumption are tightly coupled, these
shocks can more readily translate into local shortages of (specific) foods (Davis et al. 2021).

At the same time, changes in trade flows are associated with changes in agricul-
tural production, which can influence greenhouse gas (GHG) emissions, land and
water use and biodiversity through positive and negative externalities. Because of
the spatial heterogeneity of resource availability, resource productivity, and farming
practices, the environmental impact of producing food is localized and highly
dependent on its origin. Depending on whether the environmental impact of agri-
cultural production is greater or lesser in the exporting region than in alternative
production sites, agricultural and food trade can therefore either increase or reduce
the aggregate impact of agriculture on the environment globally (Dalin and
Rodríguez-Iturbe 2016).

By contributing to a better allocation of production across countries, trade can
improve the utilization of natural resources in agriculture at the global level, which,
in the aggregate, can be beneficial to the environment (Roux et al. 2021). Without
trade, some countries would have to produce a wider range and larger quantities of
foods, even if their natural endowment was not compatible with such an expansion,
placing additional pressure on their ecosystems.

For example, increased agricultural production in net food-importing countries in
the Middle East and North Africa would likely be at the expense of further water
depletion in an already water-scarce region (Biewald et al. 2014).

However, greater import demand and demand for specific products in some
regions of the world can also lead to the depletion of natural resources and/or
increased pollution in exporting countries.

In particular, if comparative advantage is derived from differences in environ-
mental regulation, production might shift to countries with relatively laxer regula-
tion, leading to worse environmental outcomes in the aggregate (Grossman and
Krueger 1991).

Moreover, trade can induce technological change, including through the transfer
of technology and best practices between trading partners, and lead to increased
productivity and more efficient resource use (Grossman and Krueger 1991). For
example, greater agricultural output per hectare may release some agricultural land
from production (land sparing), which thus becomes available for natural habitats
and species, contributing to wildlife biodiversity (Phalan et al. 2011).

In order to analyze the impact of trade on resource use and pollution, a growing
literature expresses trade flows in terms of the resource inputs and emission content
they carry (virtual resource trade, carbon/land/water footprint). In fact, while trade
was not found to be a major topic in ecosystem research based on a survey of
ecological journals published in 2017 (Pace and Gephart 2017), the literature on
interactions between trade and the environment has been rapidly expanding since
then. The analysis of impacts of agricultural trade on the environment mainly centers
on climate change and the use of water and land, also covering deforestation (Balogh
and Jámbor 2020).
# 4.1 Climate Change

Agricultural trade can play a role in both adjusting to the effects of climate change (adaptation) and reducing GHG emissions from agriculture (mitigation).

## 4.1.1 Trade as Adaptation Mechanism

Climate change may lead to significant trade disruptions in the short term (through extreme weather events) and to long-term changes in trade patterns through the altering of countries' comparative advantages. Trade could help countries adapt to short-term supply disruptions and long-term changes in comparative advantage triggered by climate change (FAO 2018a).

As climate change is expected to have an uneven effect across regions, trade can be an important avenue for ensuring food security. In studies on climate change impacts on agriculture in the time period 2050 to 2100, low-latitude regions such as the Near East, North Africa, sub-Saharan Africa and South Asia are often projected to be adversely affected, whereas high-latitude regions such as North America, parts of South America (e.g., Chile), Central Asia and Eastern Europe are expected to experience largely positive impacts on agricultural production (FAO 2018a; Reilly 1995; Wheeler and von Braun 2013).

Under deteriorating conditions for agricultural production due to climate change, food imports by relatively more adversely affected (often developing) countries will have to come from those countries (often developed) that are relatively less adversely affected.

In fact, most studies integrating biophysical and economic models project a stronger role for trade as a result of climate change at the global level (Ahammad et al. 2015; Baldos and Hertel 2015; FAO 2018a; Havlík et al. 2015; Janssens et al. 2020; von Lampe et al. 2014, 2014; Nelson et al. 2014; OECD 2015; Schmidhuber and Tubiello 2007; Fig. 2).

However, the adaptive role of trade in ensuring food security could be constrained by trade restrictions and structural barriers to adjustment.

While a substantial role in mitigating adverse effects from climate change in agriculture would be played by endogenous production adjustments, such as shifts in production patterns, in line with evolving comparative advantage (Costinot et al. 2016; Gouel and Laborde 2021), freer trade could indeed offset part of the welfare losses from climate change (Costinot et al. 2016; Gouel and Laborde 2021; Stevanović et al. 2016; Wiebe et al. 2015). Open markets could also contribute towards food security, especially in adversely affected regions that are already characterized by a high prevalence of undernourishment (Baldos and Hertel 2015; Janssens et al. 2020).

The aggregate patterns of climate change effects at the global and regional levels can mask differences in the distribution of gains and losses within countries and regions. Through the balancing mechanism of international trade, agricultural and
<figure><img src="image_143.png" /><figcaption>**Fig. 2** Projected changes in agricultural net trade in 2050: climate change scenario relative to a no-climate change baseline (in billion USD, 2011 constant prices). (Source: Based on analysis from Cui et al. (2018); adapted from FAO (2018a))</figcaption></figure>

food prices in adversely affected regions would be relatively lower under free trade, compared to a scenario in which trade is restricted. This would benefit net food consumers, while agricultural producers could lose out. At the same time, farmers in less affected or even benefiting regions could gain from relatively higher prices under free trade, while consumers would face welfare losses (Stevanović et al. 2016).

As labor productivity in agriculture would be more affected by higher average temperatures than that in other sectors of the economy, affected countries could adapt to climate change by importing food and shifting labor towards non-agricultural sectors. However, under limited market integration, subsistence food requirements in many developing countries could even drive specialization towards, rather than away from, agriculture, thus exacerbating losses from climate change (Nath 2020).

### 4.1.2 Trade in Climate Change Mitigation

Foresight analyses suggest that, between 2012 and 2050, agricultural production will have to increase by 50% to provide food for a growing and progressively wealthier population (FAO 2018c). Such increases in production could also result in increases in global GHG emissions, unless food systems become ‘emissions efficient’ and produce lower emissions per unit of output. And, as trade expands to contribute to climate change adaptation, increased transport will also add to emissions (FAO 2018a; Pendrill et al. 2019; Schmitz et al. 2012, 2015). The ultimate impact on global emissions depends on whether imports are sourced from systems that operate at lower emission efficiency or from ones that operate at higher emission efficiency (Table 1).