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A Model for Longitudinal Employment Status of Immigrants to Australia

Pettitt, Anthony N., Haynes, Michele A., Tran, Thu, & Hay, John L. (2002) A Model for Longitudinal Employment Status of Immigrants to Australia. .


This paper investigates appropriate statistical methodology for modelling categorical longitudinal data, representing the employment status of immigrants to Australia. The Longitudinal Survey of Immigrants to Australia (LSIA)is the most comprehensive survey of immigrants ever to be undertaken in Australia and is a reliable source of data that may be analysed to provide information for assisting the immigrant settlement process. of particular interest is the relationship between an immigrant's Visa Category and employment status and how this varies with time following arrival in Australia. We consider both a multinomial logistic approach using maximum likelihood estimation and a binary logistic approach using generalised estimating equations to model the correlation structure which is typical of longitudinal data. Our results show that immigrants were more likely to be employed at the end of the Survey (approximately three and a half years after arrival) than at six months after arrival in Australia. Immigrants who speak Enlgish very well and immigrants with a Visa Category of Business Skills or Employer Nomination Scheme (ENS) were more likely to be employed than all other immigrants. Although the probability of being employed is much lower for immigrants with a Visa Category of Independent at the first Wave of interview, this probability increases substantially by the final Wave of interviews approaching that of immigrants with a Visa Category of Business Skills/ENS.

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332 since deposited on 10 Jun 2004
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ID Code: 186
Item Type: Journal Article
Additional Information: QUT Contact Author: Prof Tony Pettitt, School of Mathematical Sciences
Keywords: employment status, generalised estimating equations, immigrants, longitudinal surveys, multinomial logistic model
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2002 (Please consult author)
Deposited On: 10 Jun 2004
Last Modified: 09 Jun 2010 12:21

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