Person:
Lanjouw, Peter Frederik
Author Name Variants
Fields of Specialization
Poverty and Inequality Analysis; Rural Development; Small Area Estimation; Village Studies
Degrees
Departments
Poverty and Inequality Team, Development Economics Research Group, World Bank
Externally Hosted Work
Contact Information
Last updated
January 31, 2023
Biography
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.
Publication Search Results
1 - 4 of 4
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Publication
Imputed Welfare Estimates in Regression Analysis
(World Bank, Washington, D.C., 2004-04)The authors discuss the use of imputed data in regression analysis, in particular the use of highly disaggregated welfare indicators (from so-called "poverty maps"). They show that such indicators can be used both as explanatory variables on the right-hand side and as the phenomenon to explain on the left-hand side. The authors try out practical ways of adjusting standard errors of the regression coefficients to reflect the error introduced by using imputed, rather than actual, welfare indicators. These are illustrated by regression experiments based on data from Ecuador. For regressions with imputed variables on the left-hand side, the authors argue that essentially the same aggregate relationships would be found with either actual or imputed variables. They address the methodological question of how to interpret aggregate relationships found in such regressions. -
Publication
How Good a Map? Putting Small Area Estimation to the Test
(World Bank, Washington, DC, 2007-03)The authors examine the performance of small area welfare estimation. The method combines census and survey data to produce spatially disaggregated poverty and inequality estimates. To test the method, they compare predicted welfare indicators for a set of target populations with their true values. They construct target populations using actual data from a census of households in a set of rural Mexican communities. They examine estimates along three criteria: accuracy of confidence intervals, bias, and correlation with true values. The authors find that while point estimates are very stable, the precision of the estimates varies with alternative simulation methods. While the original approach of numerical gradient estimation yields standard errors that seem appropriate, some computationally less-intensive simulation procedures yield confidence intervals that are slightly too narrow. The 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 the authors show that bias increases if underlying models fail to capture latent location effects. Correlations between estimated and true welfare at the local level are highest for mean expenditure and poverty measures and lower for inequality measures. -
Publication
Micro-Level Estimation of Welfare
(World Bank, Washington, DC, 2002-10)The authors construct and derive the properties of estimators of welfare that take advantage of the detailed information about living standards available in small household surveys and the comprehensive coverage of a census or large sample. By combining the strengths of each, the estimators can be used at a remarkably disaggregated level. They have a clear interpretation, are mutually comparable, and can be assessed for reliability using standard statistical theory. Using data from Ecuador, the authors obtain estimates of welfare measures, some of which are quite reliable for populations as small as 15,000 households--a "town." They provide simple illustrations of their use. Such estimates open up the possibility of testing, at a more convincing intra-country level, the many recent models relating welfare distributions to growth and a variety of socioeconomic and political outcomes. -
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How Good Is a Map? Putting Small Area Estimation to the Test
( 2008)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.