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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
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.  
Citations 52 Scopus

Publication Search Results

Now showing 1 - 9 of 9
  • Publication
    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.
  • Publication
    Imputed Welfare Estimates in Regression Analysis
    (World Bank, Washington, D.C., 2004-04) Elbers, Chris; Lanjouw, Jean O.; Lanjouw, Peter
    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 Accurate Is a Poverty Map Based on Remote Sensing Data?: An Application to Malawi
    (World Bank, Washington, DC, 2022-09) Van Der Weide, Roy; Blankespoor, Brian; Elbers, Chris; Lanjouw, Peter
    This paper assesses the reliability of poverty maps derived from remote-sensing data. Employing data for Malawi, it first obtains small area estimates of poverty by combining the Malawi household expenditure survey from 2010/11 with unit record population census data from 2008. It then ignores the population census data and obtains a second poverty map for Malawi by combining the survey data with predictors of poverty derived from remote sensing data. This allows for a clean comparison between the two poverty maps. The findings are encouraging - although that assessment depends somewhat on the evaluation criteria employed. The two approaches reveal the same patterns in the geography of poverty. However, there are instances where the two approaches obtain markedly different estimates of poverty. Poverty maps obtained using remote sensing data may do well when the decision maker is interested in comparisons of poverty between assemblies of areas, yet may be less reliable when the focus is on estimates for specific small areas.
  • Publication
    How Good a Map? Putting Small Area Estimation to the Test
    (World Bank, Washington, DC, 2007-03) Demombynes, Gabriel; Elbers, Chris; Lanjouw, Jean O.; Lanjouw, Peter
    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
    Poverty Alleviation through Geographic Targeting: How Much Does Disaggregation Help?
    (World Bank, Washington, D.C., 2004-10) Elbers, Chris; Fujii, Tomoki; Lanjouw, Peter; Özler, Berk; Yin, Wesley
    Using recently completed "poverty maps" for Cambodia, Ecuador, and Madagascar, the authors simulate the impact on poverty of transferring an exogenously given budget to geographically defined subgroups of the population according to their relative poverty status. They find large gains from targeting smaller administrative units, such as districts or villages. But these gains are still far from the poverty reduction that would be possible had the planners had access to information on household level income or consumption. The results suggest that a useful way forward might be to combine fine geographic targeting using a poverty map with within-community targeting mechanisms.
  • Publication
    Re-Interpreting Sub-Group Inequality Decompositions
    (World Bank, Washington, DC, 2005-08) Elbers, Chris; Lanjouw, Peter; Mistiaen, Johan A.; Özler, Berk
    The authors propose a modification to the conventional approach of decomposing income inequality by population sub-groups. Specifically, they propose a measure that evaluates observed between-group inequality against a benchmark of maximum between-group inequality that can be attained when the number and relative sizes of groups under examination are fixed. The authors argue that such a modification can provide a complementary perspective on the question of whether a particular population breakdown is salient to an assessment of inequality in a country. As their measure normalizes between-group inequality by the number and relative sizes of groups, it is also less subject to problems of comparability across different settings. The authors show that for a large set of countries their assessment of the importance of group differences typically increases substantially on the basis of this approach. The ranking of countries (or different population groups) can also differ from that obtained using traditional decomposition methods. Finally, they observe an interesting pattern of higher levels of overall inequality in countries where their measure finds higher between-group contributions.
  • Publication
    Brazil within Brazil : Testing the Poverty Map Methodology in Minas Gerais
    (World Bank, Washington, DC, 2008-02) Elbers, Chris; Lanjouw, Peter; Leite, Phillippe George
    The small-area estimation technique developed for producing poverty maps has been applied in a large number of developing countries. Opportunities to formally test the validity of this approach remain rare due to lack of appropriately detailed data. This paper compares a set of predicted welfare estimates based on this methodology against their true values, in a setting where these true values are known. A recent study draws on Monte Carlo evidence to warn that the small-area estimation methodology could significantly over-state the precision of local-level estimates of poverty, if underlying assumptions of spatial homogeneity do not hold. Despite these concerns, the findings in this paper for the state of Minas Gerais, Brazil, indicate that the small-area estimation approach is able to produce estimates of welfare that line up quite closely to their true values. Although the setting considered here would seem, a priori, unlikely to meet the homogeneity conditions that have been argued to be essential for the method, confidence intervals for the poverty estimates also appear to be appropriate. However, this latter conclusion holds only after carefully controlling for community-level factors that are correlated with household level welfare.
  • Publication
    Micro-Level Estimation of Welfare
    (World Bank, Washington, DC, 2002-10) Elbers, Chris; Lanjouw, Jean O.; Lanjouw, Peter
    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.
  • Publication
    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.