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Detecting economic insecurity in Italy: a latent transition modelling approach

[Zeitschriftenartikel]

Giambona, Francesca
Grassini, Laura
Vignoli, Daniele

Abstract

Economic insecurity has increased in importance in the understanding of economic and socio-demographic household behaviour. The present paper aims to analyse patterns of household economic insecurity over the years 2004-2015 by using the longitudinal section of the Italian SILC (Statistics on Income... mehr

Economic insecurity has increased in importance in the understanding of economic and socio-demographic household behaviour. The present paper aims to analyse patterns of household economic insecurity over the years 2004-2015 by using the longitudinal section of the Italian SILC (Statistics on Income and Living Conditions) survey. In the identification of economic insecurity statuses, we used indicators of economic hardship in a latent transition approach in order to: (i) classify Italian households into homogenous classes characterised by different levels of economic insecurity, (ii) assess whether changes in latent class membership occurred in the selected time span, and (iii) evaluate the effect of employment status and characteristics of individuals on latent status membership. Empirical findings uncovered five latent statuses of economic insecurity from the best situation to the worst. The levels of economic insecurity remained quite stable over the period considered, but a non-negligible worsening can be detected for the unemployed and individuals with part-time jobs.... weniger

Thesaurusschlagwörter
Italien; Längsschnittuntersuchung; Privathaushalt; soziale Faktoren; demographische Faktoren; ökonomisches Verhalten

Klassifikation
Allgemeines, spezielle Theorien und "Schulen", Methoden, Entwicklung und Geschichte der Wirtschaftswissenschaften

Freie Schlagwörter
economic insecurity; latent transition analysis; longitudinal data; EU-SILC 2004-2015

Sprache Dokument
Englisch

Publikationsjahr
2022

Seitenangabe
S. 815-846

Zeitschriftentitel
Statistical Methods & Applications, 31 (2022) 4

DOI
https://doi.org/10.1007/s10260-021-00609-y

ISSN
1613-981X

Status
Veröffentlichungsversion; begutachtet (peer reviewed)

Lizenz
Creative Commons - Namensnennung 4.0


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© 2007 - 2025 Social Science Open Access Repository (SSOAR).
Based on DSpace, Copyright (c) 2002-2022, DuraSpace. All rights reserved.