Kraay, Aart

Development Research Group, The World Bank
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Macroeconomics, Debt management, Economic growth, Inequality and shared prosperity
Development Research Group, The World Bank
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Last updated: January 31, 2023
Aart Kraay is Director of Research in the Development Research Group at the World Bank. He joined the World Bank in 1995 after earning a Ph.D. in economics from Harvard University (1995), and a B.Sc. in economics from the University of Toronto (1990). His research interests include international capital movements, growth and inequality, governance, and the Chinese economy. His research on these topics has been published in scholarly journals such as the Quarterly Journal of Economics, the Review of Economics and Statistics, the Economic Journal, the Journal of Monetary Economics, the Journal of International Economics, and the Journal of the European Economic Association. He is an associate editor of the Journal of Development Economics, and co-editor of the World Bank Economic Review. He has also held visiting positions at the International Monetary Fund and the Sloan School of Management at MIT, and has taught at the School of Advanced International Studies at Johns Hopkins University.
Citations 714 Scopus

Publication Search Results

Now showing 1 - 2 of 2
  • Publication
    Doing the Survey Two-Step: The Effects of Reticence on Estimates of Corruption in Two-Stage Survey Questions
    (World Bank, Washington, DC, 2015-05) Karalashvili, Nona; Kraay, Aart; Murrell, Peter
    This paper develops a structural approach for modeling how respondents answer survey questions and uses it to estimate the proportion of respondents who are reticent in answering corruption questions, as well as the extent to which reticent behavior biases down conventional estimates of corruption. The context is a common two-step survey question, first inquiring whether a government official visited a business, and then asking about bribery if a visit was acknowledged. Reticence is a concern for both steps, since denying a visit sidesteps the bribe question. This paper considers two alternative models of how reticence affects responses to two-step questions, with differing assumptions on how reticence affects the first question about visits. Maximum-likelihood estimates are obtained for seven countries using data on interactions with tax officials. Different models work best in different countries, but cross-country comparisons are still valid because both models use the same structural parameters. On average, 40 percent of corruption questions are answered reticently, with much variation across countries. A statistic reflecting how much standard measures underestimate the proportion of all respondents who had a bribe interaction is developed. The downward bias in standard measures is highly statistically significant in all countries, varying from 12 percent in Nigeria to 90 percent in Turkey. The source of bias varies widely across countries, between denying a visit and denying a bribe after admitting a visit.
  • Publication
    Predicting Conflict
    (World Bank, Washington, DC, 2017-05) Celiku, Bledi; Kraay, Aart
    This paper studies the performance of alternative prediction models for conflict. The analysis contrasts the performance of conventional approaches based on predicted probabilities generated by binary response regressions and random forests with two unconventional classification algorithms. The unconventional algorithms are calibrated specifically to minimize a prediction loss function penalizing Type 1 and Type 2 errors: (1) an algorithm that selects linear combinations of correlates of conflict to minimize the prediction loss function, and (2) an algorithm that chooses a set of thresholds for the same variables, together with the number of breaches of thresholds that constitute a prediction of conflict, that minimize the prediction loss function. The paper evaluates the predictive power of these approaches in a set of conflict and non-conflict episodes constructed from a large country-year panel of developing countries since 1977, and finds substantial differences in the in-sample and out-of-sample predictive performance of these alternative algorithms. The threshold classifier has the best overall predictive performance, and moreover has advantages in simplicity and transparency that make it well suited for policy-making purposes. The paper explores the implications of these findings for the World Bank's classification of fragile and conflict-affected states.