How Data Quality Affects Our Understanding Of The Earnings Distribution

Preface
Acknowledgements
Contents
About the Author
List of Figures
List of Tables
1 Introduction
	1.1 The Income Construct in Household Surveys
	1.2 Objectives and Chapter Typology
	References
2 A Framework for Investigating Microdata Quality, with Application to South African Labour Market Household Surveys
	2.1 Introduction
	2.2 Framing the Discourse on Data Quality
		2.2.1 Data Quality Elements in the Data Production Process
		2.2.2 The Total Survey Error (TSE) Framework
	2.3 The Interaction Between TSE and Data Quality
		2.3.1 Validity of the Construct of Interest
		2.3.2 Measurement Error
		2.3.3 Processing Error
		2.3.4 Coverage Error
		2.3.5 Sampling Error
		2.3.6 Nonresponse Error
		2.3.7 Adjustment Error
	2.4 Data Quality and Survey Errors in Statistics South Africa Household Surveys
		2.4.1 Representation of the Population of Interest
		2.4.2 Measurement of the Construct of Interest
	2.5 Discussion
	2.6 Conclusion
	References
3 Questionnaire Design and Response Propensities for Labour Income Microdata
	3.1 Introduction
	3.2 Questionnaire Design and the Income Question
		3.2.1 The Response Process and the Cognitive Burden of Answering Income Questions
		3.2.2 Different Types of Income Questions
		3.2.3 Analysing Response Groups in the Income Question
		3.2.4 Questionnaire Design Changes in SA Labour Market Household Surveys
	3.3 Methodology
		3.3.1 Response Propensity Models for the Employee Income Question
		3.3.2 Questionnaire Design Changes and the Resulting Structure of Income Data in Publicly Released Datasets
		3.3.3 Estimation, Specification and Testing
	3.4 Results
		3.4.1 A Descriptive Analysis of Employee Income Response Type
		3.4.2 Sequential Response Propensity Models
		3.4.3 Diagnostics of the Sequential Response Models
	3.5 Conclusion
	References
4 Univariate Multiple Imputation for Coarse Employee Income Data
	4.1 Introduction
	4.2 Preliminaries
		4.2.1 Coarse Income Data
		4.2.2 Multiple Imputation
	4.3 Setup of the Problem
		4.3.1 Data Preparation
		4.3.2 The Imputation Algorithm
		4.3.3 Estimation and Inference from Multiply Imputed Data
	4.4 Results: Univariate Multiple Imputations for Coarse Income
		4.4.1 Quantiles and Moments Across Four Imputation Models
		4.4.2 The Distribution of Multiply Imputed Bounded Income Values
		4.4.3 The Distribution of Multiply Imputed Missing Income Values
		4.4.4 The Distribution of Multiply Imputed Refusals and Don\'t Know Income Values
		4.4.5 Unspecified Responses as a Source of Error
		4.4.6 Stability of Parameter Estimates as the Number of Multiple Imputations Increase
	4.5 Conclusion
	References
5 Conclusion: How Data Quality Affects Our Understanding of the Earnings Distribution