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Olivier Dupriez

Olivier Dupriez is a Lead Statistician in the Development Data Group. He leads a team in charge of the World Bank’s Microdata Library, and is the learning and innovation coordinator for the data group. His current interests include research and applied work in statistical disclosure control, machine learning, synthetic data and dynamic micro-simulation, and the analysis of household consumption patterns. Prior to joining the Data Group in 2004, Olivier worked as a Poverty Statistician at the Asian Development Bank, as a Survey Statistician in the Bank’s Africa Region, and as a resident expert in population census in Cape Verde, Chad, and FYR Macedonia.

Featured Research

Simulation of Synthetic Complex Data: The R Package simPop

Matthias Templ, Bernhard Meindl, Alexander Kowarik, and Olivier Dupriez, Journal of Statistical Software, 2017.

Synthetic population datasets have become an important instrument for policy micro-simulation. The performance and acceptability of such data rely on the statistical similarity between the synthetic and the true population of interest. We provide an overview of the approaches used for generating synthetic data, and introduce simPop, an open source data synthesizer.

Improving Household Consumption and Expenditures Surveys to Enable More Evidence-Based Nutrition Policies

John L. Fiedler, Calogero Carletto, and Olivier Dupriez, Food and Nutrition Bulletin, 2012

The constrained evidence base of food and nutrition policy-making compromises nutrition programs. Although Household Consumption and Expenditures Surveys have shortcomings, they are increasingly being used to address this information gap. Elements of a possible approach and process for strengthening the surveys are outlined.

Purchasing Power Parity Exchange Rates for the Global Poor

Angus Deaton and Olivier Dupriez. American Economic Journal: Applied Economics, 2011.

The price indexes that underlie the PPPs used for the global poverty count are constructed for purposes of national income accounting, using weights that represent patterns of aggregate consumption, not the consumption patterns of the global poor. We use household surveys from 62 developing countries to calculate global poverty-weighted PPPs and to calculate global poverty lines and new global poverty counts.


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