提高纵向研究调查估计的质量：在LSMS面板调查中的应用（英）

Improving the Quality of Survey Estimatesfrom Longitudinal Studies An Application to LSMS Panel Surveys Piero FalorsiPaolo RighiGiulia Ponzini Development EconomicsDevelopment Data GroupJanuary 2025 A verified reproducibility package for this paper isavailable athttp://reproducibility.worldbank.org,clickherefor direct access. Policy Research Working Paper11026 Abstract Longitudinal surveys are an invaluable source for analyzingthe current state of and changes in human populationsover time. However, maintaining the accuracy of estimatesfrom a panel sample becomes more difficult as the lengthof the panel survey increases. Key concerns are the lackof sample representativeness, due to the sample erosioncaused by deaths and movers and the impact of new births,and migration flows. Moreover, sample fatigue introducesan increasing measurement error. Correct design, imple-mentation, and use of a panel survey considers a set ofmethods to deal with these problems at different stages ofthe statistical process: the sampling design, the data collec-tion, and the estimation. This paper focuses on the case ofpanels with a rotating sample design. This case representsa powerful hybrid solution for facing the impact of panel dynamics on sample representativeness. Empirically, thepaper focuses on the estimation procedures. Using datafrom the Uganda National Panel Survey, the longest LivingStandards Measurement Study panel survey, it experimen-tally evaluates the suggested technique. In summary, thefindings show that the calibrated generalized weight sharemethod base estimator yields individual-level statistics thatappear to be more accurate than those produced by thecurrent Uganda National Panel Survey estimator. Addi-tionally, the calibrated generalized weight share methodbase cross-sectional estimates on the transition matrix showa generally higher degree of stability when the sample ischanged compared to the current Uganda National PanelSurvey estimates. The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about developmentissues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry thenames of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely thoseof the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank andits affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Improving the Quality of Survey Estimates from Longitudinal Studies:An Application to LSMS Panel Surveys Piero Falorsi1, Paolo Righi2, Giulia Ponzini3 1.INTRODUCTION Longitudinal surveys, based on repeated observations of the same statistical units over time,are an invaluable source for analyzing the current state and changes in human populations overtime. The Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) initiative demonstrates the value of longitudinal surveys by disseminating householdpanel data on issues such as health, agriculture, access to basic services, nutrition, povertystatus, and much more, for countries across Sub-Saharan Africa. The European Union’sStatistics on Income and Living Conditions (EU SILC) and the US Bureau of Statistics’ Survey ofIncome and Program Participation (SIPP) are other key examples of the value of longitudinalstudies. In panel surveys, accuracy depends on many of the same factors as in cross-sectional surveys.However, maintaining the accuracy of estimates from a panel sample becomes more difficult asthe length of the panel survey increases. One key concern is the panel attrition of respondentswho drop out of the survey, which can become increasingly troublesome for long-runningpanels. Additionally, movers – that is, people who have moved away from where they wereinterviewed in the first observation of the longitudinal study – represent a dynamic sub-population and are complex and costly to interview. Finally, new entrants to the population,such as immigrants and newborns, are particularly difficult to capture in traditional panelsurveys. The correct design, implementation, and use of a panel survey considers several methods toaddress these problems at different stages of the statistical process, namely the sample design,data collection, and estimation stages. To the extent possible, sample design should minimizethe loss of representativeness of panel data due to attrition, leavers, and new entrants. Practicalapproaches to this include the use of refreshed samples (i.e., rotating panels or split panels)and protocols for incorporating individuals not previously involved in the study to capturepopulation dynamics. Based on tracking rules to follow the movers, the data collection stageshould be designed to retrieve essenti