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Attrition in Longitudinal Household Survey Data : Some Tests for Three Developing-Country Samples

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dc.contributor.authorAlderman, Harold
dc.contributor.authorBehrman, Jere R.
dc.contributor.authorKohler, Hans-Peter
dc.contributor.authorMaluccio, John A.
dc.contributor.authorCotts Watkins, Susan
dc.date.accessioned2015-01-22T21:13:39Z
dc.date.available2015-01-22T21:13:39Z
dc.date.issued2000-09
dc.description.abstractFor capturing dynamic demographic relationships, longitudinal household data can have considerable advantages over more widely used cross-sectional data. But because the collection of longitudinal data may be difficult and expensive, analysts must assess the magnitudes of the problems, specific to longitudinal, but not to cross-sectional data. One problem that concerns many analysts is that sample attrition may make the interpretation of estimates problematic. Such attrition may be especially severe where there is considerable migration between rural, and urban areas. And attrition is likely to be selective on such characteristics as schooling, so high attrition is likely to bias estimates. The authors consider the extent, and implications of attrition for three longitudinal household surveys from Bolivia, Kenya, and South Africa that report very high annual attrition rates between survey rounds. Their estimates indicate that: 1) the means for a number of critical outcome, and family background variables differ significantly between those who are lost to follow-up, and those who are re-interviewed. 2) A number of family background variables are significant predictors of attrition. 3) Nevertheless, the coefficient estimates for standard family background variables in regressions, and probit equations for the majority of outcome variables in all three data sets, are not significantly affected by attrition. So attrition is apparently not a general problem for obtaining consistent estimates of the coefficients of interest for most of these outcomes. These results, which are very similar to those for industrial countries, suggest that multivariate estimates of behavioral relations may not be biased because of attrition. This would support the collection of longitudinal data.en
dc.identifier.doi10.1596/1813-9450-2447
dc.identifier.urihttps://hdl.handle.net/10986/21336
dc.language.isoen_US
dc.publisherWorld Bank, Washington, DC
dc.relation.ispartofseriesPolicy Research Working Paper;No. 2447
dc.rightsCC BY 3.0 IGO
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/igo
dc.subjectaged
dc.subjectbiases
dc.subjectChild Development
dc.subjectdata set
dc.subjectdata sets
dc.subjectdependent variable
dc.subjectdeveloped countries
dc.subjectdeveloping countries
dc.subjectDevelopment Research
dc.subjectemployment
dc.subjectequations
dc.subjectfamily planning
dc.subjectfathers
dc.subjectFood Policy Research
dc.subjecthousehold characteristics
dc.subjecthousehold data
dc.subjecthousehold income
dc.subjecthousehold level
dc.subjecthousehold survey
dc.subjecthousehold surveys
dc.subjectindividual level
dc.subjectinformal networks
dc.subjectintervention
dc.subjectinterviewer
dc.subjectinterviews
dc.subjectlabor force
dc.subjectlabor force participation
dc.subjectlabor market
dc.subjectLiving Standards
dc.subjectLiving Standards Measurement
dc.subjectmigration
dc.subjectMortality
dc.subjectmultivariate analyses
dc.subjectmultivariate analysis
dc.subjectmultivariate regression
dc.subjectNutrition
dc.subjectnutritional status
dc.subjectoutcome variables
dc.subjectparents
dc.subjectpolicy research
dc.subjectpopulation size
dc.subjectPopulation Studies
dc.subjectprecision
dc.subjectpreschool children
dc.subjectprobabilities
dc.subjectprobability
dc.subjectradio
dc.subjectrandom sample
dc.subjectResearch Institute
dc.subjectResearch Working Papers
dc.subjectresearchers
dc.subjectrural areas
dc.subjectsiblings
dc.subjectsignificance level
dc.subjectsocial networks
dc.subjectsocial scientists
dc.subjectSpecification tests
dc.subjectsurvey data
dc.subjecturban areas
dc.subjecturban communities
dc.subjectweighting
dc.subjectHousehold surveys
dc.subjectLongitudinal method
dc.subjectDemographic indicators
dc.subjectLongitudinal data
dc.subjectSample surveys
dc.subjectHousehold size
dc.subjectReduction in force
dc.subjectMigrations
dc.subjectCase studies
dc.subjectFamily background
dc.subjectVariables (mathematics)
dc.subjectMultivariate analysis
dc.subjectBehavioral responses
dc.titleAttrition in Longitudinal Household Survey Data : Some Tests for Three Developing-Country Samplesen
dspace.entity.typePublication
okr.crossref.titleAttrition in Longitudinal Household Survey Data: Some Tests for Three Developing-Country Samples
okr.date.disclosure2000-09-30
okr.date.doiregistration2025-04-10T09:19:01.286108Z
okr.doctypePublications & Research
okr.doctypePublications & Research::Policy Research Working Paper
okr.globalpracticePoverty
okr.guid224291468757762797
okr.identifier.doi10.1596/1813-9450-2447
okr.identifier.reportWPS2477
okr.language.supporteden
okr.region.administrativeAfrica
okr.region.administrativeLatin America & Caribbean
okr.region.countryBolivia
okr.region.countryKenya
okr.region.countrySouth Africa
okr.topicEducation::Educational Sciences
okr.topicHealth, Nutrition and Population::Health Monitoring & Evaluation
okr.topicHealth, Nutrition and Population::Public Health Promotion
okr.topicPoverty Reduction
okr.topicPoverty Reduction::Poverty Assessment
okr.topicScience and Technology Development::Scientific Research & Science Parks
okr.topicScience and Technology Development::Statistical & Mathematical Sciences
okr.unitOff of Sr VP Dev Econ/Chief Econ (DECVP)
relation.isSeriesOfPublication26e071dc-b0bf-409c-b982-df2970295c87
relation.isSeriesOfPublication.latestForDiscovery26e071dc-b0bf-409c-b982-df2970295c87
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