Photo: Adapted from image by Tomás Guardia Bencomo, via iStock.
Ceres2030 built a cost model—a mathematical and economic model of the world over time—to answer these questions. Prior work had found that we are not on track to end hunger by 2030, but we can meet that goal if we have additional resources, prioritize countries with the highest need, and use the right balance of our best interventions.
The Ceres2030 cost model includes the costs for three critical dimensions of SDG 2: ending hunger, ensuring sustainability and doubling small-scale producer productivity.
The cost model estimates the total additional spending needed to reach SDG 2, including from private investors, domestic governments, and international donors. The goal of the Ceres2030 cost model is to inform international donors how much additional spending is needed to reach SDG 2, which countries are highest priority for additional commitments, and what mix of investments will yield the best results.
The above figure gives a breakdown of how the increase in donor’s public spending, averaging 14 billion annually until 2030, should be directed based on the World Bank income classification of the recipient country. Public investment in low-income countries should account for 35 percent of the additional donor spending.
The majority of the additional donor spending, 58 percent, should be directed to lower-middle-income countries. While hunger is less common in these countries than in low-income countries, this income group accounts for a much larger share of the population (2.9 billion versus 0.7 billion in 2019 [citation information: World Bank Open Data. “Population, total – Low income, Lower middle income.” Accessed 29 Jun 2021).
The remaining 6 percent should go to upper-middle-income countries. (Ceres2030 is not designed to assess the cost of ending hunger in high-income countries, and not all countries are included in the estimate due to lack of data usable for the modelling exercise.)
What could we estimate?
Modeling can help us to see how different parts of an economy are related, and how spending on agriculture affects a wide range of economic and social outcomes. For example, when a farmer’s net disposable income increases because a storage bin is put at her disposal, reducing post-harvest losses and allowing her some control over when to sell the crop, the model can simulate the effects of the intervention and the resulting income gains on the wider economy, from the household to the national level.
Any estimate for additional spending needed for SDG 2 can only be as good as the data available. For this reason, we cannot provide a cost estimate with exact coverage of all the aspects of SDG 2.
We also cannot simulate the political contexts that can affect hunger. We do our best with available data, and we focused our work on the three major SDG 2 targets: SDG 2.1, 2.3, and 2.4.
Available data is not perfect, but the world cannot wait for perfect information to act. SDG 2 donors need a baseline now to build an action plan for additional spending. We do our best to provide estimates.
How was the cost model built?
Determining how much money is needed for SDG 2, where it should be spent, and how it should be spent means we needed to account for economic relationships from the micro level to the macro level from now until 2030. To account for these complex relationships, the team used a dynamic multi-country, multi-sector Computable General Equilibrium model built on household data, adapting from the MIRAGRODEP economic model.
The model is a set of equations linking inputs to outcomes, where targets are achieved using numerical optimization. It processes information for each country and for each year, infusing public spending and allocating it among countries and interventions so that the three targets are reached by 2030 at minimum cost.
Micro Level: People are the base of the model. It is built on household survey data, a bottom-up approach that allows the model to identify and target hungry, poor, and small-scale producer households directly. This household targeting is key to spending efficiency.
Meso Level: Our model also incorporates “meso-level” data. This includes variables, like people’s food consumption and incomes, which affect and are affected by regional and sectoral circumstances, represented by other meso variables such as food prices, fertilizer prices, and wages.
Macro Level: Our model also includes macro level linkages to account for how changes in markets or government policies affect a small-scale producer’s income or the food a hungry household can afford. For example, an increase in maize yields in Tanzania could lower prices for maize in Ethiopian markets through trade, helping hungry households in Ethiopia meet their caloric needs. At the same time, Ethiopian farmers who grow maize could suffer income losses from the competition.
Interventions are represented by parameters in the model’s equations and can influence other variables through these parameters directly or indirectly affecting variables at any level. The numerical optimization process allows the model to choose the best bundle of interventions to reach the three targets (SDG 2.1, 2.3 and 2.4) at minimal cost.
After the model was run, the team extracted its dependent variables to answer our three questions. These variables tell us:
|1| The additional amount of public spending the model used to accomplish the costed SDG 2 targets
|2| How the model distributed the additional spending among countries
|3| How the model allocated the money among types of interventions in each country
How were the results used?
The results of the costs model exercise are available in detail in the report, Ending Hunger, Increasing Incomes and Protecting the Climate: What would it cost donors? The results are also embedded in the Ceres2030 Summary Report and other project outputs.
The results from Ceres2030 can help donor governments determine where to direct their investments and how this spending will affect economies. It is up to donor governments to consult with recipient government experts and other stakeholders to refine how much money to allocate for each type of intervention.
Ceres2030 aims to improve people’s lives in a sustainable way through effective public spending on SDG 2. The model reflects this through the targets it sets and the mathematical relationships it includes. The model’s results therefore provide an initial strategy for additional spending by SDG 2 donors. There is potential to refine and adapt results in future projects by including new data as available and consulting with SDG 2 donors, national governments, and other stakeholders.