Please use this identifier to cite or link to this item: https://hdl.handle.net/10620/17184
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dc.contributor.authorVinh, Aen
dc.contributor.authorGriffiths, Wen
dc.contributor.authorChotikapanich, Den
dc.date.accessioned2019-04-13T03:33:08Zen
dc.date.accessioned2011-04-21T01:22:34Zen
dc.date.available2011-04-21T01:22:34Zen
dc.date.issued2010-02en
dc.identifier.isbnISBN: 978 0 7340 4446 4en
dc.identifier.urihttps://hdl.handle.net/10620/17184en
dc.identifier.urihttp://hdl.handle.net/10620/3242en
dc.description.abstractAs indicators of social welfare, the incidence of inequality and poverty is of ongoing concern to policy makers and researchers alike. Of particular interest are the changes in inequality and poverty over time, which are typically assessed through the estimation of income distributions. From this, income inequality and poverty measures, along with their differences and standard errors, can be derived and compared. With panel data becoming more frequently used to make such comparisons, traditional methods which treat income distributions from different years independently and estimate them on a univariate basis, fail to capture the dependence inherent in a sample taken from a panel study. Consequently, parameter estimates are likely to be less efficient, and the standard errors for between-year differences in various inequality and poverty measures will be incorrect. This paper addresses the issue of sample dependence by suggesting a number of bivariate distributions, with Singh-Maddala or Dagum marginals, for a partially dependent sample of household income for two years. Specifically, the distributions considered are the bivariate Singh-Maddala distribution, proposed by Takahasi (1965), and bivariate distributions belonging to the copula class of multivariate distributions, which are an increasingly popular approach to modelling joint distributions. Each bivariate income distribution is estimated via full information maximum likelihood using data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey for 2001 and 2005. Parameter estimates for each bivariate income distribution are used to obtain values for mean income and modal income, the Gini inequality coefficient and the headcount ratio poverty measure, along with their differences, enabling the assessment of changes in such measures over time. In addition, the standard errors of each summary measure and their differences, which are of particular interest in this analysis, are calculated using the delta method.en
dc.subjectFinanceen
dc.subjectFinance -- Poverty and disadvantageen
dc.titleBivariate Income Distributions for Assessing Inequality Under Dependent Samplesen
dc.typeReports and technical papersen
dc.identifier.urlhttp://www.melbourneinstitute.com/hildaen
dc.identifier.surveyHILDAen
dc.description.urlhttp://www.melbourneinstitute.com/hildaen
dc.description.urlhttp://www.melbourneinstitute.com/downloads/hilda/Bibliography/wp/Vinh_etal_Bivariate_Income_Distributions_WP1093.pdfen
dc.description.institutionDepartment of Economics, the University of Melbourneen
dc.title.reportDepartment of Economics, the University of Melbourne, Working Paper Series Research Paperen
dc.identifier.rishttp://flosse.dss.gov.au//ris.php?id=3498en
dc.description.pages32en
local.identifier.id3498en
dc.identifier.edition1093en
dc.subject.dssIncome, wealth and financesen
dc.subject.dssmaincategoryFinanceen
dc.subject.dsssubcategoryPoverty and disadvantageen
dc.subject.flosseIncome, wealth and financeen
dc.relation.surveyHILDAen
dc.old.surveyvalueHILDAen
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.openairetypeReports and technical papers-
item.fulltextNo Fulltext-
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