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Lanjouw, Peter Frederik

Poverty and Inequality Team, Development Economics Research Group, World Bank
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Poverty and Inequality Analysis; Rural Development; Small Area Estimation; Village Studies
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Poverty and Inequality Team, Development Economics Research Group, World Bank
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Last updated: January 31, 2023
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Peter Lanjouw, a Dutch national, is Research Manager of the Poverty and Inequality Team in the Development Economics Research Group of the World Bank. He is also an Honorary Fellow of the Amsterdam Institute of International Development, Netherlands. He completed his Ph.D. in economics from the London School of Economics in 1992. From August 2003 until August 2005, he was a visiting scholar at the Agriculture and Resource Economics department at UC Berkeley, and he held the appointment of Professor of Economics at the VU University of Amsterdam between September 1998 and May 2000. He has taught in the Masters in Development Economics program at the University of Namur, Belgium and has also taught at the Foundation for the Advanced Study of International Development in Tokyo, Japan. His research focuses on various aspects of poverty and inequality measurement as well as on rural development issues.  
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Now showing 1 - 3 of 3
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    Reinterpreting Between-Group Inequality
    (2008) Elbers, Chris; Lanjouw, Peter; Mistiaen, Johan A.; Ozler, Berk
    We evaluate observed inequality between population groups against a benchmark of the maximum between-group inequality attainable given the number and relative sizes of those groups under examination. Because our measure is normalized by these parameters, drawing comparisons across different settings is less problematic than with conventional inequality decompositions. Moreover, our measure can decline with finer sub-partitioning of population groups. Consequently, the exact manner in which one groups the population acquires greater significance. Survey data from various countries suggest that our approach can provide a complementary perspective on the question of whether (and how much) a particular population breakdown is salient to an assessment of inequality in a country.
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    Revisiting Between-Group Inequality Measurement: An Application to the Dynamics of Caste Inequality in Two Indian Villages
    (2011) Lanjouw, Peter; Rao, Vijayendra
    Standard approaches to decomposing how much group differences contribute to inequality rarely show significant between-group inequality, and are of limited use in comparing populations with different numbers of groups. We apply an adaptation to the standard approach that remedies these problems to longitudinal household data from two Indian villages-Palanpur in the north and Sugao in the west. In Palanpur we find that the largest Scheduled Caste group failed to share in the gradual rise in village prosperity. This would not have emerged from standard decomposition analysis. However, in Sugao the alternative procedure does not yield any additional insights because income gains have applied relatively evenly across castes.
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    How Good Is a Map? Putting Small Area Estimation to the Test
    (2008) Demombynes, Gabriel; Elbers, Chris; Lanjouw, Jean O.; Lanjouw, Peter
    This paper examines the performance small area of welfare estimation. The method combines census and survey data to produce spatially disaggregated poverty and inequality estimates. To test the method, predicted welfare indicators for a set of target populations are compared with their true values. The target populations are constructed using actual data from a census of households in a set of rural Mexican communities. Estimates are examined along three criteria: accuracy of confidence intervals, bias and correlation with true values. We find that while point estimates are very stable, the precision of the estimates varies with alternative simulation methods. Precision of estimates is shown to diminish markedly if unobserved location effects at the village level are not well captured in underlying consumption models. With well specified models there is only slight evidence of bias, but we show that bias increases if underlying models fail to capture latent location effects.