Patterns of flexible employment careers. Does measurement error matter?

Dimitris Pavlopoulos; Mauricio Garnier Villarreal; Roberta Varriale

Patterns of flexible employment careers. Does measurement error matter?

Dimitris Pavlopoulos, Mauricio Garnier Villarreal, Roberta Varriale

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

24 Downloads (Pure)

Abstract

In recent years, the debate on flexible employment has been at the center of political and scientific discussion in Europe. Findings on mobility from flexible to permanent employment can be severely biased due to measurement error, usually present in the data used for analysis. The aim of this paper is to use a mixed hidden Markov model (MHMM) to study the effect of measurement error on the role of flexible employment in the life course. Specifically, we employ a MHMM with two indicators for the employment contract, coming from linked data from the Labour Force Survey and the Employment Register of the Netherlands for the period 2007-2015.

Original language	English
Title of host publication	SIS 2023 (SEAS IN)
Subtitle of host publication	Book of the short papers
Editors	Francesco Maria Chelli, Mariateresa Ciommi, Salvatore Ingrassia, Francesca Mariani, Maria Cristina Recchioni
Publisher	Pearson
Pages	985-990
Number of pages	6
ISBN (Print)	9788891935618
Publication status	Published - 2023
Event	Statistical Learning, Sustainability and Impact Evaluation SIS 2023 - Ancona, Italy Duration: 21 Jun 2023 → 23 Jun 2023 https://meetings3.sis-statistica.org/index.php/ancona/ancona

Conference

Conference	Statistical Learning, Sustainability and Impact Evaluation SIS 2023
Country/Territory	Italy
City	Ancona
Period	21/06/23 → 23/06/23
Internet address	https://meetings3.sis-statistica.org/index.php/ancona/ancona

Keywords

mixed hidden Markov model
latent variable model
multisource data
employment careers

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

SIS_paperSubmitted manuscript, 439 KB
Patterns of flexible employment careers Does measurement error matterFinal published version, 4.33 MBLicence: Article 25fa Dutch Copyright Act

Persistent URL (handle)

Persistent link

Cite this

@inproceedings{5b0bfdd3cb254ba095154a34133370e1,

title = "Patterns of flexible employment careers. Does measurement error matter?",

abstract = "In recent years, the debate on flexible employment has been at the center of political and scientific discussion in Europe. Findings on mobility from flexible to permanent employment can be severely biased due to measurement error, usually present in the data used for analysis. The aim of this paper is to use a mixed hidden Markov model (MHMM) to study the effect of measurement error on the role of flexible employment in the life course. Specifically, we employ a MHMM with two indicators for the employment contract, coming from linked data from the Labour Force Survey and the Employment Register of the Netherlands for the period 2007-2015.",

keywords = "mixed hidden Markov model, latent variable model, multisource data, employment careers",

author = "Dimitris Pavlopoulos and \{Garnier Villarreal\}, Mauricio and Roberta Varriale",

year = "2023",

language = "English",

isbn = "9788891935618",

pages = "985--990",

editor = "Chelli, \{Francesco Maria \} and Ciommi, \{Mariateresa \} and Ingrassia, \{Salvatore \} and Mariani, \{Francesca \} and Recchioni, \{Maria Cristina \}",

booktitle = "SIS 2023 (SEAS IN)",

publisher = "Pearson",

note = "Statistical Learning, Sustainability and Impact Evaluation SIS 2023 ; Conference date: 21-06-2023 Through 23-06-2023",

url = "https://meetings3.sis-statistica.org/index.php/ancona/ancona",

}

Patterns of flexible employment careers. Does measurement error matter? / Pavlopoulos, Dimitris ; Garnier Villarreal, Mauricio; Varriale, Roberta.
SIS 2023 (SEAS IN): Book of the short papers. ed. / Francesco Maria Chelli; Mariateresa Ciommi; Salvatore Ingrassia; Francesca Mariani; Maria Cristina Recchioni. Pearson, 2023. p. 985-990.

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review