The Anarchy of Numbers: Aid, Development, and Cross-country Empirics

Recent literature contains many stories of how foreign aid affects economic growth: aid raises growth in countries with good policies, or in countries with difficult economic environments, or mainly outside the tropics, or on average with diminishing returns. The diversity of these results suggests that many are fragile. I test 7 important aid-growth papers for robustness. The 14 tests are minimally arbitrary, deriving mainly from differences among the studies themselves. This approach investigates the importance of potentially arbitrary specification choices while minimizing arbitrariness in testing choices. All of the results appear fragile, especially to sample expansion. The Anarchy of Numbers: Aid, Development, and Cross-country Empirics David Roodman Research Associate, Center for Global Development Center for Global Development Working Paper #32 Final version: May 2007 Abstract: Recent literature contains many stories of how foreign aid affects economic growth: aid raises growth in countries with good policies, or in countries with difficult economic environments, or mainly outside the tropics, or on average with diminishing returns. The diversity of these results suggests that many are fragile. I test 7 important aid-growth papers for robustness. The 14 tests are minimally arbitrary, deriving mainly from differences among the studies themselves. This approach investigates the importance of potentially arbitrary specification choices while minimizing arbitrariness in testing choices. All of the results appear fragile, especially to sample expansion. Recent literature contains many stories of how foreign aid affects economic growth: aid raises growth in countries with good policies, or in countries with difficult economic environments, or mainly outside the tropics, or on average with diminishing returns. The diversity of these results suggests that many are fragile. I test 7 important aid-growth papers for robustness. The 14 tests are minimally arbitrary, deriving mainly from differences among the studies themselves. This approach investigates the importance of potentially arbitrary specification choices while minimizing arbitrariness in testing choices. All of the results appear fragile, especially to sample expansion.