Enriching surveys with supplementary data and its application to studying wage regression

• Main Authors: LEUNG, Denis H. Y., YAMADA, Ken, ZHANG, Biao
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2015
• Subjects: empirical likelihood; inverse probability weighting; selection bias; supplementary data; surveys; regression analysis; Econometrics
• Online Access: https://ink.library.smu.edu.sg/soe_research/1992; https://ink.library.smu.edu.sg/context/soe_research/article/2991/viewcontent/EnrichingSurveys_SJS_2015.pdf
• Institution: Singapore Management University

We consider the problem of supplementing survey data with additional information from a population. The framework we use is very general; examples are missing data problems, measurement error models and combining data from multiple surveys. We do not require the survey data to be a simple random sample of the population of interest. The key assumption we make is that there exists a set of common variables between the survey and the supplementary data. Thus, the supplementary data serve the dual role of providing adjustments to the survey data for model consistencies and also enriching the survey data for improved efficiency. We propose a semi-parametric approach using empirical likelihood to combine data from the two sources. The method possesses favourable large and moderate sample properties. We use the method to investigate wage regression using data from the National Longitudinal Survey of Youth Study.