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poverty data

From Jordan to Liberia: Imputing, modeling and measurement in a world of imperfect data

Kristen Himelein's picture

Simply stated, we never have enough data. This is true from smallest low income countries in Africa to the largest more complex economy in the West.  And the need grows continuously as interconnected world markets and leapfrogging technologies smash through any remaining notions of a standard path to prosperity. For many countries in the developing world, the unfortunate paradox is that they have the greatest needs but the fewest resources, both financial and in terms of capacity.  In this setting, researchers in statistics and economics have been developing new techniques to expand the usefulness of limited data. The broad body of work is collected under the umbrella “survey-to-survey imputation” and includes two recently-published papers in the World Bank Policy Research Working Paper series, “Updating Poverty Estimates at Frequent Intervals in the Absence of Consumption Data: Methods and Illustration with Reference to a Middle-Income Country,” by Hai-Anh Dang, Peter Lanjouw, and Umar Serajuddin, and “Estimating Poverty in the Absence of Consumption Data: The Case of Liberia,” by Andrew Dabalen, Errol Graham, Kristen Himelein, and Rose Mungai. (Fortunately the authors are much more creative in their approach to analysis than in their approach to naming papers.)