The statistical challenges (not tragedy) faced by some countries highlighted in this blog post are interesting conversation starters. But there are also African success stories in statistical capacity building. Commensurate efforts to reflect on these would be useful to inform the imperative scaling up of efforts to replicate these successes across the continent. The resources currently allocated by the Africa region of the Bank for strengthening statistical systems are a tiny fraction of our overall portfolio and are insufficient to meet the needs of our clients. Regarding poverty measurement issues raised, the decline in Africa’s $1.25-a-day poverty rate estimates from 59 percent in 1996 (not 1995) to 50 percent in 2005 are based on survey data collected in countries mainly between 1995-1997 and 2004-2006; collectively these respectively covered 60% and 67% of the population in the region at the time. Thus the suggestion that extrapolation (based on national accounts growth data) over long periods of time was used extensively and undermined data quality are somewhat exaggerated. You can readily verify this from PovcalNet: the on-line tool for poverty measurement developed by the Bank’s Development Research Group (http://go.worldbank.org/WE8P1I8250). Extrapolation spanning longer time periods was only necessary for some countries, such Botswana (an upper-middle income economy with a population of less than 2 million which is home to less than 0.2 percent of the poor in the Africa region), for which the only household survey data made available to the Bank’s researchers who work on poverty measurement dates back to1993. The problem is less with the data underlying poverty estimates up to 2005. Rather, the pertinent challenge is that 3 out of 4 countries in Sub-Saharan Africa have: (a) not collected any household survey data to measure poverty during the past 5 years, or (b) collected these data but either not yet processed these, not yet analyzed these, or not yet made these data available to the Bank’s research department to produce $-a-day estimates. A key challenge are the capacity constraints faced by many of our clients in Africa to process, analyze and provide access to recently collected household survey data in a timely fashion; for instance, as per the IMF’s General Data Dissemination Standard (GDDS) see: http://dsbb.imf.org/images/pdfs/gdds_oct_2003.pdf. Botswana, for example, collected household income and expenditure survey data in 2002/2003 that could be used to improve recent $-a-day poverty estimates; but these have not been made available to the Bank. Another example is Nigeria, which collected a household living standards survey in 2008/2009, but has as of today not yet completed the processing of these data (i.e., transferred the information recorded on pencil and paper questionnaire forms into an electronic database) so that they can be analyzed. The Africa Chief Economist could use the influence and resources of his office to engage in a dialogue with our clients on such matters. Systematically providing support to clients who have already collected recent survey data or are in the process of doing so, but who face analytical or other capacity constraints would be one pragmatic step towards helping to improve the quality and timely dissemination of data available across the continent. I would submit that in many African countries the harsh reality of statistical capacity constraints, not statistical politics, is the fundamental problem that we need to tackle head on. Many countries in Latin America faced very similar challenges in the early 1990s, but today most have statistical systems in place that produce poverty estimates annually or every two to three years. Africa can learn from these successful statistical capacity building experiences too. Statistical politics is an unfortunate possibility only once statistical systems are strong but governance remains weak.