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After a bitter fight between the World Bank's board of directors and Bank President Paul Wolfowitz, Congo-Brazzaville was allowed to reach decision point in the HIPC program on March 9. The deal was almost held up after reports that Congo’s President Denis Sassou-Ngueso spent $300k at a New York hotel, but this scandal wasn’t enough to convince debt relief diehards that Congo wasn’t perhaps the most worthy recipient. But the IMF still seems to be of two minds. Its press release opens by declaring that Congo: "Must address serious concerns about governance and financial transparency."
This reflects the extra demands from the Bank that Congo’s oil accounts be properly audited and other anti-corruption safeguards be put in place if the country is ever to reach HIPC completion point (when the debt relief becomes irrevocable). But two paragraphs later the Fund, which has just indicated that the government is still deeply corrupt, inexplicably justifies letting Congo through because: "Interim debt relief will increase resources available to the government to….fight corruption."
So, which is it? Is the government so corrupt that it needs unprecedented international oversight? Or does Sassou really deep-down want to fight corruption but (despite over $1 billion in annual oil revenues), he just needs a bit more money from HIPC to finally tackle corruption within?
CGD blog posts reflect the views of the authors, drawing on prior research and experience in their areas of expertise. CGD is a nonpartisan, independent organization and does not take institutional positions.
Despite improvements in censuses and household surveys, the building blocks of national statistical systems in sub-Saharan Africa remain weak. Measurement of fundamentals such as births and deaths, growth and poverty, taxes and trade, land and the environment, and sickness, schooling, and safety is shaky at best. The Data for African Development Working Group’s recommendations for reaping the benefits of a data revolution in Africa fall into three categories: (1) fund more and fund differently, (2) build institutions that can produce accurate, unbiased data, and (3) prioritize the core attributes of data building blocks.