the cost of SDG 2

We need more public spending to achieve the United Nations’ Sustainable Development Goal 2—Zero Hunger—by 2030.

But how much more?

And how do we distribute the spending?


Photo: Adapted from image by Tomás Guardia Bencomo, via iStock.

Ceres2030 is building a cost model—a mathematical and economic model of the world over time—to answer these questions. Early work on the cost model found that we are not on track to end hunger by 2030; but, we can end hunger by 2030 if we have additional resources, prioritize countries with the highest need, and use the right balance of our best interventions. 

But Sustainable Development Goal 2 is about more than ending hunger: The Ceres2030 cost model will also include the costs for two critical dimensions of SDG 2: sustainability and small-scale producer productivity.

While the cost model estimates the total additional spending needed to reach SDG 2—by private investors, domestic governments, and international donors—Ceres2030 focuses its policy recommendations on additional spending by international donors.

Country profiles suggested by the cost model are intended to be a first iteration. The allocation by type of intervention will need to be refined in consultation with recipient government experts and other stakeholders.

What can we estimate?

Any estimate for additional spending needed for SDG 2 can only be as good as the data we have today. For this reason, we cannot provide a cost estimate with exact coverage of all the aspects of SDG 2. We do our best with available data, and we will cost targets relating to three major SDG 2 sub-goals.

While these targets are modest compared to the targets set by SDG 2, 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 is 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 will need 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 uses 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 goals 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: Micro variables, like people’s food consumption and incomes, affect and are affected by regional and sectoral circumstances, such as food prices, fertilizer prices, and wages. Our model incorporates this meso-level data.

Macro Level: Meso variables are intertwined with national and international changes in policies, markets, and other circumstances. 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. Our model includes these kinds of macro linkages to account for implications on what food a hungry household can afford or how much income a small-scale producer makes.

Interventions influence other variables through a set of parameters. Interventions are represented by parameters in the model’s equations. They can directly or indirectly affect variables at any level. The numerical optimization process allows the model to choose the best bundle of interventions to reach the three goals at minimal cost.

After the model is run, the team can extract its dependent variables to answer our three questions. These variables will 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.

Data Sources: The model uniquely leverages harmonized data from a variety of sources. Household data relies on the World Bank’s Living Standards Measurement Study and other sources, while meso- and macro-level data comes primarily from the GTAP database and FAOSTAT.

How will the results be used?

Ceres2030’s goal is 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 relationships it includes. 

While results will need to be developed through consultation with national governments and other stakeholders, they provide a first-iteration strategy for additional spending by SDG 2 donors.

Ceres 2030 is a partnership between Cornell IP-CALS,  the International Food Policy Research Institute (IFPRI), and the  International Institute of Sustainable Development (IISD)