Invest in data for evidence and indicators
Key Finding: There are few instances of donors using baseline studies or indicators to assess how well states are meeting their obligations on TJ, to judge whether civil society actors are playing positive social roles, or to understand how well TJ is meeting the needs of victim groups.
TJ actors lack data—or easy access to what little data exists. It would be useful, for example, to understand victims’ (and others’) perceptions of the state and of justice before a TJ process begins, to track changes in perceptions as a TJ process unfolds.
"[Our] donor roundtable…really articulated donors' dilemmas when it comes to funding…transitional justice processes… The biggest is demonstrating impact - but also the fact that they are short-term project cycles which funders are tied to, which makes it difficult especially to show [that] impact."
One reason for this gap, however, is that data sets are expensive to produce: they require teams of researchers in the field to interview hundreds of people. They can also present risks to the researchers conducting the survey, especially in conflict and post-conflict contexts. Civil society groups may be populated by lawyers and activists, who may lack the social scientific skills to do them well.
There are instances of surveys to assess victim perspectives on justice in Uganda, Democratic Republic of Congo, Cambodia, Iraq, Côte d’Ivoire, and elsewhere. It is not clear that the results have been used effectively by donors or other TJ actors, despite the surveys being conducted by credible social scientists. Follow-up surveys to assess changes over time are rare. A rare exception is Cambodia, where GIZ and other donors funded a baseline survey in 2008 and a follow-up survey in 2010 (to assess change after the first trial at the ECCC).
Generating data and evidence requires commitment specifically from donors, and developing standardized assessment tools can be a difficult collective process. If donors invest sufficient resources in producing data—and develop a culture of using it—the result would likely be more responsive and relevant programming.