With inequality reduction now being officially and broadly recognised as a key development objective with its own Sustainable Development Goal (SDG 10), there is a need for simple, economical and quick methodologies with which to focus on this area and assess progress. This paper presents such a methodology, which allows a rough assessment of the potential impacts of development cooperation on income, consumption and wealth inequality. This is important, as a rigorous causal analysis of the contribution development cooperation makes to reducing a partner country’s inequality is complex and costly. First, the relative contribution of targeted development cooperation programmes and projects to the economies of partner countries tends to be small (though admittedly not in all cases). Second, a myriad of factors contribute to changes in inequality in any given country, and assessing the impact of all of them is a complex, imprecise, time-consuming and resource-intensive exercise. The proposed methodology therefore makes use of SDG 10’s focus on the poorest 40% of the population to assess whether development cooperation in a given partner country has been directly targeted at them. This Briefing Paper presents a simple methodology to support donors or multilateral development cooperation institutions in assessing, addressing and mainstreaming inequality in their operations. The first step of the methodology recommends that development agencies identify a country’s needs in terms of inequalities as a basis for providing support for policies and interventions to address them. The second step consists of making sure that inequality has been taken into account in key strategic documents. Subsequent steps aim to assess whether the design and implementation of specific programmes, projects and budget support operations targets inequalities. In the case of projects and programmes, the recommended assumption is that if their direct beneficiaries are in the bottom 40%, then these projects and programmes can be considered to address inequality. For the sake of simplicity and practicality, this does not account for general equilibrium or indirect effects. In the case of budget support of any kind, any indication of the distributional profile of government expenditure in the area of support can be used as a proxy for the support’s distributional profile. As a complement to this, it may be possible in many cases to analyse whether the subnational geographic allocation of funds corresponds to the location of the national bottom 40%. Despite many good reasons why funding should not always go to poorer areas, this information may provide important insights. A key limitation of this approach is that disregarding indirect or general equilibrium effects does not establish any causal link between targeting and macroeconomic effects on inequality. Yet it does allow an assessment of the degree to which portfolios (or parts of them) are potentially addressing inequality, thereby providing important feedback for development actors.
