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Understanding and improving data quality relating to low-income households. / by Johnson, David | Scutella, Rosanna. Publisher: [Parkville, Vic.] Melbourne Institute of Applied Economic and Social Research 2003Description: 74 p.Online Access: Electronic copy Summary: In this report the issues concerning the reliability of data and the implications for the measurement of inequality, poverty and social welfare are explored. There are both conceptual and practical issues of concern. The conceptual issues relate to the choice of the most appropriate unit of observation, period of observation, metric of measurement and source of data. The most natural unit of observation is the income unit, but since income units may share resources such as housing, the most practical
unit is the household. Income data are generally available on both a current weekly basis and an annual basis. Some expenditure data are available on a current basis but
lumpiness of expenditure in relation to durables and housing often mean that annual data will be the most reliable measure for small groups. There are enduring debates about the usefulness of concepts of poverty, relative and absolute; and of measures of inequality in describing the circumstances of people. There are also longstanding debates about the limitations of measurement relying on income and expenditure
which are narrow measures of circumstance, and which ignore the benefits of in-kind provision of health, education and other services.Availability:
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Longitudinal Surveys of Australian Youth (LSAY) 2006 Cohort : user guide / by National Centre for Vocational Education Research. Publisher: Adelaide, S.A. National Centre for Vocational Education Research 2010Description: PDF.Other title: Longitudinal Surveys of Australian Youth. Technical report 55.Online Access: Electronic copy Notes: June 2010 Includes bibliographical references INTO AND OUT OF WORK SCHOOL TO WORKSummary: This user guide has been developed for users of the LSAY data. It consolidates existing technical documentation and other information into one document. The guide aims to address all aspects of the data including: data restrictions, variable naming conventions, the structure of the data, classifications and code frames used, weights and derived variables.Availability:
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