The State of Food Security and Nutrition in the World 2024

S3.1 Comparison of definitions of financing for food security and nutrition: official development assistance

As highlighted in Section 3.1 of the main report, the current state of financing for food security and nutrition is hard to measure as there is neither a unified definition of what constitutes financing for food security and nutrition, nor a common approach to measuring what comprises it, no matter whether the financing stream is public or private, domestic or foreign. This is easiest to illustrate in studies that estimate levels of official development assistance (ODA) financing, where perhaps the greatest efforts to define financing for food security and nutrition have been made. Yet here, different groups still use varying measures to identify ODA financing relevant to food security and nutrition. Table S3.1 provides a comparison of the various definitions of financing for food security and nutrition from the studies presented in Figure 14 of the main report, whereby the blue-coloured boxes indicate which ODA OECD Development Assistance Committee (DAC) codes are included in their measures. While there are some convergences on the DAC coding applied, there are significant differences, resulting in a wide variation of estimates of levels of ODA financing for food security and nutrition (see Figure 14 of the main report).

TABLE S3.1Comparison of definitions of financing for food security and nutrition: official development assistance Organisation for Economic Co-operation and Development Development Assistance Committee codes

NOTES: ZEF = Center for Development Research of the University of Bonn; NCD = non-communicable disease; STD = sexually transmitted disease; SALW = small arms and light weapons. The table includes all Organisation for Economic Co-operation and Development Development Assistance Committee (OECD DAC) purpose codes. Blue-filled cells indicate that 100 percent of the resources recorded under the associated OECD DAC code are included in the financing for food security and nutrition definition. Where “10%” or “22%” appears in a blue-filled cell, this indicates the percentage of the resources recorded under the associated OECD DAC code which are included in the official development assistance definition and indicate the percentage of these resources that contribute to food security and nutrition. Keyword(s) indicates that a keyword search was also conducted at a lower level to the purpose code (e.g. project descriptions).
SOURCES: Comparison of previous studies is adapted from Eber Rose, M., Laborde, D., Lefebvre, L., Olivetti, E. & Smaller, C. 2024.Towards a common definition of aid for food security and nutrition.. Rome, FAO and Geneva, Switzerland, Shamba Centre for Food & Climate; AFSI (L’Aquila Food Security Initiative). 2012a. Tracking the L’Aquila Food Security Initiative pledge and related funding. L’Aquila, Italy. https://2009-2017.state.gov/documents/organization/202955.pdf; AFSI. 2012b. L’Aquila Food Security Initiative (AFSI) 2012 Report. L’Aquila, Italy. https://2009-2017.state.gov/documents/organization/202922.pdf; Laborde, D., Murphy, S., Parent, M., Porciello, J. & Smaller, C. 2020. Ceres2030: Sustainable solutions to end hunger - Summary report. Cornell University, IFPRI and IISD. https://ceres2030.iisd.org/wp-content/uploads/2021/03/ceres2030_en-summary-report.pdf; ODI (Overseas Development Institute). 2012. Measuring aid to agriculture and food security. Briefing Paper 72. London. https://cdn.odi.org/media/documents/7588.pdf; G7 France. 2019. Biarritz Progress Report – G7 Development and Development-Related Commitments. Biarritz, France, Ministry for Europe and Foreign Affairs. https://www.diplomatie.gouv.fr/IMG/pdf/rapport-g7-v8_cle852e6f-1.pdf; Yaagoub, Z. & Jaeger, K. 2021. Hunger in times of crisis: Trends in donor financing for food and nutrition security. Seek Development; Bharali, I., Zoubek, S., McDade, K.K., Martinez, S., Brizzi, A., Yamey, G., Brownell, K. & Schäferhoff, M. 2020. The financing landscape for agricultural development: An assessment of external financing flows to low- and middle-income countries and of the global aid architecture. Durham, USA, Duke World Food Policy Center, Duke Center for Policy Impact in Global Health and Open Consultants. https://wfpc.sanford.duke.edu/wp-content/uploads/sites/15/2022/05/AgDevFinancing-WFPC-Dec2020.pdf; ZEF (Center for Development Research of the University of Bonn) & FAO. 2020. Investment costs and policy action opportunities for reaching a world without hunger (SDG2). Rome and Bonn. https://doi.org/10.4060/cb1497en

For example, to track commitments made under the L’Aquila Food Security Initiative – a pledge to spend USD 20 billion over three years following the 2009 food price crisis – donors recorded spending under the agriculture, forestry and fisheries purpose codes, adding agro-industries, nutrition and development food aid/food security assistance. They also added purpose codes related to transport, storage, safety nets, rural development, and “other”.^15–17 However, each of the G7 donor countries individually decided which purpose codes and what volume of investments are related to food security, with limited guidance or methodology.¹⁸

Alternatively, to measure the resources relevant to the G7 Elmau commitment to “lift 500 million people in developing countries out of hunger and malnutrition by 2030”,¹⁹ there are three indicators. The first tracks the “percentage of G7 member programmes on agriculture and rural development that include objectives and expected results to increase the incomes of smallholder farmers”;²⁰ the definition considers the number of projects recorded under the codes for agriculture, forestry and fishing, adding agro-industries and rural development.²¹ The second measures direct assistance to agriculture, fishing, food security and nutrition defined by the OECD DAC codes for agriculture (311), fishing (313), agro-industries (32161), development food assistance (520), emergency food assistance (72040) and basic nutrition (12240). The third – “other assistance with explicit objectives to improve people’s food security and/or nutrition”²⁰ – captures the broadened scope of resources that affect food security and nutrition, using a keyword search in a series of codes.^{d, 22}

The most expansive definition of ODA is that adopted by the European Commission to track expenditure on commitments related to food security and nutrition. This definition of ODA to food security and nutrition counts ODA recorded under 75 Creditor Reporting System (CRS) purpose codes, ranging from vocational training and education to reproductive health care, human rights, and democratic participation and civil society. The breadth of the codes included is designed to capture any expenditure and action undertaken by European Union Member States that may contribute towards food security and nutrition and sustainable agriculture.²³

In the last two columns of this table, the definition of food security and nutrition presented in this report is provided for comparison; however, please note that in the application of this new definition in the ODA estimates presented in Section 4.1 of the main report, the methodology does not apply only CRS purpose codes, but a combination of purpose codes with other recovery keywords (indicated by “keyword” in Table S3.1). The only other study which took a similar approach is the G7 study, though this was applied in a more limited way. Therefore, the comparison of this report’s new definition of financing for food security and nutrition in the last two columns of Table S3.1 is not strictly comparable to the other studies presented in Table S3.1.^e

Challenges: specific and supportive contributions to food security and nutrition

It is important to distinguish between specific and supportive financing for food security and nutrition. There are important financial allocations that contribute to food security and nutrition without supporting only food security and nutrition outcomes. For example, a road construction project will have positive benefits for food security and nutrition; however, the financial resources involved will also have broader development benefits, such as enhancing the availability of imported commodities or tourism. Thus, not all the resources would entirely contribute to only food security and nutrition outcomes. This concept of a supportive impact on food security and nutrition is particularly important for the extended definition, as not all the financing for interventions to address the major drivers will have direct impacts on just food security and nutrition.

The methodology used to estimate the volume of public external resources for food security and nutrition must therefore distinguish between specific and supportive financing for food security and nutrition to enable a better and more accurate estimate of the entirety of financing relevant to food security and nutrition. A percentage or weight can be applied to different financing components included in the core and extended definitions of financing for food security and nutrition to distinguish between specific and supportive contributions. Under this approach, keywords directly relevant to food security and nutrition are weighted at 100 percent, that is 100 percent of the resources identified as related to these keywords will be included in the estimate of food and security and nutrition financing, whereas other keywords, which relate to resources more indirectly contributing to food security and nutrition, would be allocated a lower percentage.

However, there is little precedent on which to calculate an appropriate percentage to capture the indirect financing of resources. There have been two prior attempts in efforts to estimate external public resources for food security and nutrition. Under the L’Aquila Food Security Initiative, G7 donors were able to add ODA recorded under non-specified purpose codes providing their main purpose was to improve food security.^15–17 Each G7 country individually decided which purpose codes and what volume of investments were related to food security, with limited guidance or methodology, leaving significant subjectivity in the definition and final volumes of ODA and limiting what can be learned from this approach.¹⁸

In its definition of ODA for agriculture and rural development and food security, the Overseas Development Institute calculated a relevant share of some OECD DAC purpose codes to include the definition. For example, the share of agricultural value added in developing countries’ GDP was calculated to be 10 percent; therefore, 10 percent of ODA for financial services, business support services and general budget support was included in the measure.

To estimate an appropriate weighting for each financing flow, certain assumptions must be made. The first is whether the financing for each category, as a general rule, will have a direct or indirect impact on food security. In this regard, each category of financing was either specific to or supportive of food security and nutrition (see Section S3.2). The resources under categories such as food aid or food safety and storage were deemed to be specific to food security and nutrition, while the resources under the affordability of and access to health services or water and sanitation were deemed to be supportive of food security and nutrition. That is, not all the resources related to these categories would entirely contribute to food security and nutrition outcomes.

Identifying appropriate weights for each category requires assumptions about relative contributions of financial allocations to food security and/or nutrition. All aspects of financing for food security and nutrition have varying levels of impact, and establishing an appropriate weighting requires a generalization of impact and an estimation of what the impact is. Different approaches and weights will be relevant to different funding sectors, each requiring that assumptions be made about relative contributions.

For specific financing categories, it was decided to accept the commonly applied assumption of 100 percent weight, meaning that the full financial resources contribute to food security and/or nutrition. This is a common assumption in most existing studies (for example, see Figure 14 of the main report and Table S3.1); however, even this requires strong assumptions. For example, certain aspects of domestic production related to agriculture are included as specific contributions to food security and nutrition and are therefore included at 100 percent. However, it is a strong assumption, albeit a common one, that all spending on agriculture will have a direct impact on food security. Investment in a tea plantation in Kenya, which would be classified under domestic production, may have negative specific impacts on food security and nutrition if, for example, resources are used to relocate displaced smallholder farmers in the area or to hold consultations with local communities about the planned development, with limited impact on food security and nutrition. Therefore, even the contribution of specific financing to food security and nutrition needs qualifying.

For supportive financing, the weightings should arguably be more nuanced, with different weightings applied to different sectors and purposes, depending on their relative contribution to food security and nutrition outcomes. Yet, this is even more complicated, as all aspects of finance that indirectly support food security and nutrition have varying levels of impact, and establishing an appropriate weighting requires a generalization of the impact and an estimation of what the impact is. Different approaches and weightings will be relevant to different financing sectors. For example, an appropriate contribution of social protection to food security and nutrition could be estimated using household food expenditure as a percentage of GDP in developing countries. For women’s empowerment, youth inclusion, equitable access to productive resources and assets, access to essential services, and fiscal reform to improve income distribution, a more appropriate proxy might be a measurement constructed from the percentage of people employed in agriculture and the contribution of agriculture to GDP. For energy, the relative use of energy in the agriculture and food sector could be used. However, more research is needed to establish more nuanced and sector-specific weights.

Introducing several sector-related weights without sufficient evidence of impact research and testing is risky, as it can destabilize the results, creating more biases and errors. Furthermore, data gaps and data limitations in current financing flow databases prevent a full application of the definition (see Section 4.1 of the main report), which can further compound the biases and errors introduced by the addition of more nuanced weights. For example, this report considered applying a unique weight for the supportive elements of health services and environmental health (see Section S3.2). The rationale was to apply a weight estimate based on the percentage of total current health expenditure that was spent on “nutrition deficiencies” defined by the System of Health Accounts²⁴ and estimated by the World Bank based on the available data between 2017 and 2019 in the WHO Global Health Expenditure Database.^f However, this estimate covers expenditure on both specific and supportive elements under health services and environmental health, as well as practices (see Section S3.2). Thus, applying this nuanced weight would introduce further errors due to its mismatch with the definition level of aggregation, as well as the financing flow data disaggregation limitations.

For the reasons cited above, as a best option at this time, this report applies a single weight of 22 percent applied to all supportive elements (see Table S3.1 and Section S3.2) to reflect the impact of supportive expenditures on food security and nutrition as closely as possible. The weight, 22 percent, was calculated based on household spending on food as a percentage of GDP in low-income countries (LICs), lower-middle-income countries (LMICs) and upper-middle-income countries (UMICs). The source for share of food consumption in household consumption in LICs, LMICs and UMICs is FAOSTAT,²⁶ while for household consumption as a percentage of GDP in LMICs it is the World Bank.²⁷ Further research and testing is needed to identify the contribution of financial allocations to improved food security and nutrition (beyond the agriculture sector), as well as advancements in financial data and data mining methodologies to achieve higher resolution of financing flows contributing to improved food security and ending all forms of malnutrition.

S3.2 A new definition of financing for food security and nutrition: mapping classification, keywords, weights and decision rules

Table S3.2 provides the four classification levels in tabular form, based on the conceptual framing of the new definition of financing for food security and nutrition presented in Section 3.1 of the main report (see Figure 15 of the main report). This four-level classification makes up the foundational classification structure.

TABLE S3.2New financing for food security and nutrition definition: four-level mapping classification

NOTE: Four-level classification based on the new definition of financing for food security and nutrition presented in this report, see Figure 15, Figure 16, Figure 19 and Box 7 of the main report.
SOURCE: Authors’ (FAO) own elaboration.

Table S3.3 is the elaboration of the four-level classification with keywords identified to clarify financing flows linked to each classification level. Keywords are defined based on food security and nutrition expenditures and intervention areas for each level of classification defined at level 3 or level 4 (column 5). Keywords in bold are the purpose code description names of ODA/other official flows (OOF) CRS codes; these are added to ensure coherence in applying sector and subsector purposes across different financial databases.

TABLE S3.3New definition of financing for food security and nutrition: mapping of keywords to four-level classification and identification of specific and supportive indicators, and corresponding weights

NOTE: NCD = non-communicable disease; SALW = small arms and light weapons; STD = sexually transmitted disease; WASH = water, sanitation and hygiene.
SOURCE: Authors’ (FAO) own elaboration.

TABLE S3.4New definition of financing for food security and nutrition: mapping decision rules

SOURCE: Authors’ (FAO) own elaboration.

The table also distinguishes financial allocations between those that are “specific” and “supportive” (column 6). “Specific” refers to financing that contributes wholly (100 percent) to food security and nutrition. “Supportive” refers to financing that only partially contributes to food security and nutrition; therefore, a weight is applied to account for the percentage allocation that contributes to food security and nutrition. The weight rule applied is 22 percent. See Section S3.1 for a full discussion of the definition of “specific” and “supportive” and the estimation of supportive weights.

S3.3 Countries affected by major drivers: list of countries, methodology and data sources

The analysis of “countries affected by major drivers” (i.e. conflict, climate extremes and economic downturns) applies a risk–hazard–vulnerability–capacity analytical framework:

Risk of food insecurity = (hazard (H), vulnerability (V)/capacity (C)/resilience (R))

The hazard is defined as the occurrence of the driver (e.g. a climate extreme), and vulnerability is defined as a country’s vulnerability (to food insecurity) as a result of exposure to the driver event. The variables used to detect the occurrence of the hazard and identify the degree of vulnerability are specifically identified and analysed for each individual driver based on the nature of the driver, the conceptual understanding of the determinants of food security, and evidenced analysis of the impact of the driver on these determinants.

Update of countries affected by major drivers

This report provides an update of countries affected by combinations of major drivers that were provided in The State of Food Security and Nutrition in the World 2021³¹ report. Figure S3.1 shows the global map of the updated analysis for low- and middle-income countries, and Table S3.6 provides a full list of the countries by combination of drivers.

FIGURE S3.1Countries by combination of major drivers of food insecurity and malnutrition, 2013–2022

NOTES: The boundaries and names shown and the designations used on this map do not imply the expression of any opinion whatsoever on the part of the Food and Agriculture Organization of the United Nations (FAO), the International Fund for Agricultural Development (IFAD), the United Nations Children’s Fund (UNICEF), the World Food Programme (WFP) and the World Health Organization (WHO) concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers and boundaries. Dashed lines on maps represent approximate border lines for which there may not yet be full agreement. Dotted line represents approximately the Line of Control in Jammu and Kashmir agreed upon by India and Pakistan. The final status of Jammu and Kashmir has not yet been agreed upon by the parties. Final boundary between the Republic of Sudan and the Republic of South Sudan has not yet been determined. See Table S3.5 for definitions and methodology of countries affected by multiple drivers. See data sources for drivers in Table S3.5.
SOURCE: Authors’ (FAO) own elaboration.

https://doi.org/10.4060/cd1254en-figS3-1

TABLE S3.6List of countries by combination of MAJOR drivers, 2013–2022

NOTES: The table shows the list of 119 low- and middle-income countries with information on prevalence of undernourishment that are affected by different combinations of major drivers (conflict, climate extremes and economic downturns). Yellow highlighting denotes low-income food-deficit countries, while the asterisk (*) denotes countries with high income inequality. See Table S3.5 for definitions and methodology of countries affected by multiple drivers.
SOURCES: Authors’ (FAO) own elaboration based on FAO. 2024. Low-Income Food-Deficit Countries (LIFDCs) – List updated June 2023. In: FAO. [Cited 6 June 2024]. https://www.fao.org/countryprofiles/lifdc for the classification of low-income food-deficit countries; World Bank. 2022. World Development Indicators. In: World Bank. [Cited 31 October 2023]. https://datatopics.worldbank.org/world-development-indicators for country income group data. See Table S3.5 for data sources on drivers.

TABLE S3.7Countries affected by the major drivers

NOTES: PoU = prevalence of undernourishment. The table refers to 119 low- and middle-income countries with information on the PoU that are affected by different combinations of major drivers (conflict, climate extremes and economic downturns). Percentage of population unable to afford a healthy diet is not available for all countries. The values in the table are unweighted averages. See Table S3.5 for definitions and methodology of countries affected by multiple drivers.
SOURCES: Authors’ (FAO) own elaboration based on FAO. 2024. FAOSTAT: Suite of Food Security Indicators. [Accessed on 24 July 2024]. https://www.fao.org/faostat/en/#data/FS. Licence: CC-BY-4.0 for PoU; FAO. 2024. FAOSTAT: Cost and Affordability of a Healthy Diet (CoAHD). [Accessed on 24 July 2024]. https://www.fao.org/faostat/en/#data/CAHD. Licence: CC-BY-4.0 for the percentage of population unable to afford a healthy diet. See Table S3.5 for data sources on drivers.

FIGURE S3.2The majority of undernourished people and stunted children live in countries affected by multiple MAJOR drivers

NOTES: Figure S3.2A shows the total number (bars) and the average prevalence of undernourishment (PoU) (circles) in 2023. Figure S3.2B shows the total number (bars) and the average prevalence of stunted children (circles) in 2022. For both figures, the analysis is for 119 low- and middle-income countries exposed to no driver, a single driver or multiple drivers. Only conflict, climate extremes and economic downturns are considered. See Table S3.5 for definitions and methodology of countries affected by multiple drivers.
SOURCES: Authors’ (FAO) own elaboration based on FAO. 2024. FAOSTAT: Suite of Food Security Indicators. [Accessed on 24 July 2024]. https://www.fao.org/faostat/en/#data/FS. Licence: CC-BY-4.0 for PoU; UNICEF, WHO & World Bank. 2023. Levels and trends in child malnutrition. UNICEF / WHO / World Bank Group Joint Child Malnutrition Estimates – Key findings of the 2023 edition. New York, USA, UNICEF, Geneva, Switzerland, WHO and Washington, DC, World Bank. https://data.unicef.org/resources/jme-report-2023, http://www.who.int/teams/nutrition-and-food-safety/monitoring-nutritional-status-and-food-safety-and-events/joint-child-malnutrition-estimates, https://datatopics.worldbank.org/child-malnutrition for child stunting data. See data sources for drivers (conflict, climate extremes, economic downturns) in Table S3.5.

https://doi.org/10.4060/cd1254en-figS3-2

FIGURE S3.3Countries affected by economic downturns experienced large increases in the prevalence of undernourishment in all country income groups, but for lower-middle-income countries the largest increase is in conflict-affected countries

NOTES: The figure shows the difference in the PoU, measured in percentage points, from 2019 to 2023 for low- and middle income countries affected by conflict, climate extremes and economic downturns, and for countries with high income inequality. Categories are not mutually exclusive, as a country can be affected by more than one driver and also face high income inequality. The figure also shows the difference in the PoU by different combinations of drivers (no driver, single driver, multiple drivers), excluding high income inequality. The analysis is shown by country income group for a sample of 119 low- and middle income countries with available PoU information. See Table S3.5 for definitions and methodology.
SOURCES: Authors’ (FAO) own elaboration based on FAO. 2024. FAOSTAT: Suite of Food Security Indicators. [Accessed on 24 July 2024]. https://www.fao.org/faostat/en/#data/FS. Licence: CC-BY-4.0 for PoU; World Bank. 2022. World Development Indicators. In: World Bank. [Cited 31 October 2023]. https://datatopics.worldbank.org/world-development-indicators for country income group data. See Table S3.5 for data sources on drivers.

https://doi.org/10.4060/cd1254en-figS3-3

FIGURE S3.4Change in the prevalence of undernourishment between 2019 and 2023 by type of driver and geographical region

NOTES: The left axis shows the difference in the PoU, measured in percentage points, from 2019 to 2023 for low- and middle income countries affected by conflict, climate extremes and economic downturns, and for countries with high income inequality, and grouped by region (bars). Categories are not mutually exclusive, as a country can be affected by more than one driver and also face high income inequality. The figure also shows the difference in the PoU by different combinations of drivers (no driver, single driver, multiple drivers), excluding high income inequality. The right axis shows the percentage of countries in each region that were affected by at least one driver (circles). The analysis is shown for a sample of 119 low- and middle-income countries with available PoU information. See Table S3.5 for definitions and methodology.
SOURCE: Authors’ (FAO) own elaboration based on FAO. 2024. FAOSTAT: Suite of Food Security Indicators. [Accessed on 24 July 2024]. https://www.fao.org/faostat/en/#data/FS. Licence: CC-BY-4.0 for PoU.

https://doi.org/10.4060/cd1254en-figS3-4

FIGURE S3.5Protracted major food crisis countries affected by three major drivers experience the highest level of food insecurity, 2023

NOTES: The figure shows the total number of undernourished people in 2023 in the 18 countries facing protracted major food crises (left axis – orange bars) by different combinations of major drivers (conflict, climate extremes and economic downturns). The figure also shows the unweighted average prevalence of undernourishment in the same countries (right axis – blue circles). See Table S3.5 for definitions and methodology on countries affected by major drivers and the Global Report on Food Crises 2024³⁸ for a definition of countries in protracted major food crisis.
SOURCES: Authors’ (FAO) own elaboration based on FAO. 2024. FAOSTAT: Suite of Food Security Indicators. [Accessed on 24 July 2024]. https://www.fao.org/faostat/en/#data/FS. Licence: CC-BY-4.0 for prevalence of undernourishment; FSIN (Food Security Information Network) & GNAFC (Global Network Against Food Crises). 2024. Global Report on Food Crises 2024. Rome. https://www.fsinplatform.org/report/global-report-food-crises-2024 for list of countries in protracted major food crisis. See Table S3.5 for data sources on drivers.

https://doi.org/10.4060/cd1254en-figS3-5

FIGURE S3.6Change in the prevalence of undernourishment between 2019 and 2023 in protracted major food crisis countries by type of driver

NOTES: The figure shows the average difference in the prevalence of undernourishment (PoU), measured in percentage points, from 2019 to 2023 in the 18 countries facing protracted major food crises and affected by conflict, climate extremes and economic downturns, and for countries with high income inequality. Categories are not mutually exclusive, as a country can be affected by more than one driver and also facing high income inequality. The figure also shows the difference in the PoU by different combinations of drivers (no driver, single driver, multiple drivers), excluding high income inequality. See Table S3.5 for definitions and methodology.
SOURCES: Authors’ (FAO) own elaboration based on FAO. 2024. FAOSTAT: Suite of Food Security Indicators. [Accessed on 24 July 2024]. https://www.fao.org/faostat/en/#data/FS. Licence: CC-BY-4.0 for PoU; FSIN & GNAFC. 2024. Global Report on Food Crises 2024. Rome. https://www.fsinplatform.org/report/global-report-food-crises-2024 for list of countries in protracted major food crisis. See Table S3.5 for data sources on drivers.

FIGURE S3.7Prevalence of undernourishment in protracted food crisis countries affected by the major drivers and faced with high income inequality, 2013–2023

NOTES: Figure S3.7A shows the prevalence of undernourishment (PoU) between the years 2013 and 2023 for 33 countries in protracted food crisis affected by any of the three major drivers (conflict, climate extremes and economic downturns), and for countries with high income inequality. Figure S3.7B shows the PoU between the years 2013 and 2023 for 15 countries in protracted food crisis, but not in protracted major food crisis. Categories are not mutually exclusive, as a country can be affected by more than one driver and/or face high income inequality. PoU estimates are unweighted. See Table S3.5 for definitions and methodology.
SOURCES: Authors’ (FAO) own elaboration based on FAO. 2024. FAOSTAT: Suite of Food Security Indicators. [Accessed on 24 July 2024]. https://www.fao.org/faostat/en/#data/FS. Licence: CC-BY-4.0 for PoU; FSIN & GNAFC. 2024. Global Report on Food Crises 2024. Rome. https://www.fsinplatform.org/report/global-report-food-crises-2024 for list of countries in protracted major food crisis. See Table S3.5 for data sources on drivers.

https://doi.org/10.4060/cd1254en-figS3-7

Differences between the list of countries affected by major drivers in the 2021 edition of this report and the updated list in this year’s report

Among the 108 countries that were classified in both reports, 60 countries changed driver classification. Eleven countries were not classified in The State of Food Security and Nutrition in the World 2021,³¹ but they are in The State of Food Security and Nutrition in the World 2024: Guinea, Guinea-Bissau, Libya, Mauritius, Niger, Republic of Moldova, Syrian Arab Republic, Tajikistan, Uganda, Zambia and Zimbabwe. Two countries are not classified in The State of Food Security and Nutrition in the World 2024, but they were in The State of Food Security and Nutrition in the World 2021:³¹ Guyana, which is now a high-income country, and Lesotho, which does not have PoU data.

Nine countries affected by conflict (alone or with other drivers) based on The State of Food Security and Nutrition in the World 2021³¹ are not affected by conflict based on The State of Food Security and Nutrition in the World 2024 (Angola, Côte d'Ivoire, Georgia, Indonesia, Liberia, Nepal, Rwanda, Sri Lanka and Uzbekistan). This is due to a change in methodology that now looks only at more recent conflict (i.e. happening in the last two five-year subperiods rather than the last four five-year subperiods). On the other hand, 12 more countries affected by conflict (alone or with other drivers) based on The State of Food Security and Nutrition in the World 2024 were not affected by conflict or not included in the analysis in The State of Food Security and Nutrition in the World 2021³¹ (Azerbaijan, Brazil, Burkina Faso, Haiti, Iran [Islamic Republic of], Kenya, Lebanon, Libya, Mexico, Mozambique, Niger and Syrian Arab Republic). The high level of PoU in Liberia and Rwanda (average 35 percent in 2019) and low level of PoU in Azerbaijan, Brazil, the Islamic Republic of Iran, Lebanon, Libya and Mexico (average 5 percent in 2019) explain the higher level of PoU for countries affected by conflict in Figure 21 of The State of Food Security and Nutrition in the World 2021³¹ compared with Figure 18A in The State of Food Security and Nutrition in the World 2024.

Twelve countries affected by economic downturns (alone or with other drivers) based on The State of Food Security and Nutrition in the World 2021³¹ are not affected by economic downturns based on The State of Food Security and Nutrition in the World 2024 (Afghanistan, Central African Republic, Democratic Republic of the Congo, Dominica, Ecuador, Iran [Islamic Republic of], Lebanon, Lesotho, Madagascar, Vanuatu, Venezuela [Bolivarian Republic of] and Yemen). This might be due to a more restrictive use of the PoU change point analysis. On the other hand, 18 more countries affected by economic downturns (alone or with other drivers) based on The State of Food Security and Nutrition in the World 2024 were not affected by economic downturns in The State of Food Security and Nutrition in the World 2021³¹ (Bolivia [Plurinational State of], Chad, Comoros, Cuba, Fiji, Gabon, Ghana, Mali, Mauritania, Morocco, Namibia, Pakistan, Paraguay, Somalia, Syrian Arab Republic, Türkiye, Zambia and Zimbabwe). Although there is a high level of PoU in Somalia, Zambia and Zimbabwe (average 41 percent), eleven countries have a PoU level less than 15 percent (average 6 percent). In The State of Food Security and Nutrition in the World 2021,³¹ there was a high level of PoU in the Democratic Republic of Congo, Madagascar and Yemen (average 36 percent in 2019) and a low level of PoU only in Dominica, Ecuador, the Islamic Republic of Iran, Lebanon and Vanuatu (average 9 percent). This explains the higher level of PoU for countries affected by economic downturns in Figure 21 of The State of Food Security and Nutrition in the World 2021³¹ compared with Figure 18A in The State of Food Security and Nutrition in the World 2024.

Fourteen countries affected by climate extremes (alone or with other drivers) based on The State of Food Security and Nutrition in the World 2021³¹ are not affected by climate extremes based on The State of Food Security and Nutrition in the World 2024 (Albania, Armenia, China, Colombia, Costa Rica, Georgia, Guatemala, Kyrgyzstan, Lesotho, Myanmar, Nigeria, Paraguay, Serbia and Venezuela [Bolivarian Republic of]). On the other hand, 16 countries affected by climate extremes (alone or with other drivers) based on The State of Food Security and Nutrition in the World 2024 were not affected by climate extremes in The State of Food Security and Nutrition in the World 2021³¹ (Algeria, Angola, Benin, Bulgaria, Ecuador, Gabon, Guinea-Bissau, Iraq, Jordan, Papua New Guinea, Syrian Arab Republic, Tajikistan, Togo, Tunisia, Uganda and Zimbabwe). The difference in the classification is also due to a new more comprehensive and longer dataset on cereal production and imports together with improvements to the methodology used to identify the vulnerability of a country to climate variability and extremes (see Table S3.5).

TABLE S3.5Countries affected by combination of MAJOR drivers: methodologies and data sources

NOTES: PoU = prevalence of undernourishment; GDP = gross domestic product. Primary data sources are provided in the table. For full elaboration of the methodologies, see FAO et al. (2021).³¹ For full bibliographic references relative to numbered endnotes, see the Notes in the end matter.
SOURCES: FAO, IFAD, UNICEF, WFP & WHO. 2017. The State of Food Security and Nutrition in the World 2017. Building resilience for peace and food security. Rome, FAO. https://openknowledge.fao.org/handle/20.500.14283/i7695en; FAO, IFAD, UNICEF, WFP & WHO. 2018. The State of Food Security and Nutrition in the World 2018. Building climate resilience for food security and nutrition. Rome, FAO. https://openknowledge.fao.org/handle/20.500.14283/i9553en; FAO, IFAD, UNICEF, WFP & WHO. 2019. The State of Food Security and Nutrition in the World 2019. Safeguarding against economic slowdowns and downturns. Rome, FAO. https://doi.org/10.4060/CA5162EN; FAO, IFAD, UNICEF, WFP & WHO. 2021. The State of Food Security and Nutrition in the World 2021. Transforming food systems for food security, improved nutrition and affordable healthy diets for all. Rome, FAO. https://doi.org/10.4060/cb4474en

Thirteen countries not affected by a driver based on The State of Food Security and Nutrition in the World 2021³¹ are now affected by climate extremes (Benin, Bulgaria, Gabon, Papua New Guinea, Togo and Tunisia), conflict (Azerbaijan and Burkina Faso) or economic downturns (Bolivia [Plurinational State of], Comoros, Fiji, Gabon and Namibia). For four of them (Gabon, Namibia, Papua New Guinea and Togo) the average PoU in 2019 was 21 percent. On the other hand, fifteen countries are not affected by a driver in The State of Food Security and Nutrition in the World 2024. In The State of Food Security and Nutrition in the World 2021,³¹ these countries were either missing (Guinea, Mauritius and Republic of Moldova), or affected by climate extremes (Albania, Armenia, China, Costa Rica, Georgia, Guatemala, Kyrgyzstan, Serbia and Venezuela [Bolivarian Republic of]), economic downturns (Dominica, Vanuatu and Venezuela [Bolivarian Republic of]) or conflict (Liberia and Georgia), and their average PoU was 8 percent. This is why the level of PoU for countries affected by no driver in Figure 18A of The State of Food Security and Nutrition in the World 2024 is slightly lower compared with the level of PoU for countries affected by no driver shown in Figure 20 of The State of Food Security and Nutrition in the World 2021.³¹

Eighteen countries are exposed to a higher number of drivers based on The State of Food Security and Nutrition in the World 2024. In particular, countries affected by none or one driver (Algeria, Brazil, Cuba, Gabon, Ghana, Iraq, Jordan, Kenya, Mali, Mauritania, Mexico, Morocco and Mozambique) or two drivers (Chad, Haiti, Pakistan, Somalia and Türkiye) now face the negative impact of a higher number of drivers. On the other hand, sixteen countries were affected by a higher number of drivers based on The State of Food Security and Nutrition in the World 2021.³¹ Among these sixteen countries: i) conflict was identified as a driver in The State of Food Security and Nutrition in the World 2021,³¹ but not in The State of Food Security and Nutrition in the World 2024 for seven of them; ii) six are no longer identified as affected by economic downturns; and iii) five of them are no longer identified as affected by climate extremes.