Righting the Wrongs. A Life Course Dynamics Approach for Non-Standard Employment

Project Information

DYNANSE

Grant agreement ID: 864471

DOI

10.3030/864471

Project closed

EC signature date 20 December 2019

Start date 1 September 2020

End date 30 April 2026

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost

€ 1 996 377,00

EU contribution

€ 1 996 377,00

Coordinated by

STICHTING VU
Netherlands

Periodic Reporting for period 3 - DYNANSE (Righting the Wrongs. A Life Course Dynamics Approach for Non-Standard Employment)

Reporting period: 2023-09-01 to 2025-02-28

Summary of the context and overall objectives of the project

Aim: To break new ground in the field of Non-Standard Employment (NSE), I develop a novel method for the study of employment trajectories and an integral life course framework that explains why NSE leads to positive or negative career outcomes.
Background: NSE has become a common feature of modern labour markets. Research provides contradictory results on whether NSE leads to prosperous or precarious careers. This research suffers from three shortcomings: 1) the description of how NSE affects the career is only based on single events, 2) the explanation of why and when NSE leads to positive or negative career outcomes is based only on static factors, 3) the positive outcomes of NSE are overestimated as information on NSE is contaminated by measurement error.
Innovation: I present the Life Course Dynamics Approach for Non-Standard Employment (DYNANSE) that assesses how dynamic micro (individual career-choices), meso (organisational dynamics) and macro (institutional change) factors determine why NSE leads to prosperous or precarious careers. This is done with a novel method that corrects for measurement error and treats trajectories, rather than single events, as the unit of analysis.
Plan: This project consists of four interconnected subprojects: 1) I develop the DYNANSE method, which then is applied in the study of NSE-outcomes in two life stages 2) young workers and 3) older workers, and of 4) how institutional change shapes the role of NSE using linked survey-register data from the Netherlands, Norway and Italy.
Impact: DYNANSE will lead to a long-sought understanding of the dynamic factors determining whether NSE leads to positive or negative career outcomes. In this way, I will resolve a theoretical debate on whether NSE is a factor of dynamism or a source of labour market inequality, while adding knowledge on the role of micro, meso and macro dynamics. The novel method that is developed in this project will also benefit the analysis of other social phenomena.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

Concerning the development of the method, 2 papers are completed and submitted for publication, another 2 are in the phase of completion while another one has just started. The first of these papers illustrates the effect of measurement error in the clustering algorithms that are used in sequence analysis and Latent Profile Analysis. The second paper compares the generic version of mixed hidden Markov models (MHMMs) with sequence analysis on whether they can correctly classify subjects in the respective pathways using simulated data. The third paper compares MHMMs with sequence analysis in whether they can identify the real number of clusters using simulated data. The third paper applies an MHMM to create an error-corrected typology of employment trajectories using Dutch and Italian register data. The fourth paper goes one step further and compares the results of the MHMM in predicting the employment contract type with machine learning techniques.
The PhD projects started in September 2021. The first substantial paper of each one of these project is completed and submitted for publication. The first paper of PhD project 1 studies whether the initial type of the employment contract lead to different pathways on the labour market for school leavers using an MHMM on Dutch register data. The first paper of PhD project 2 produces a novel insight on the pathways that lead older workers to retirement (and some of them to work alongside retirement). For this purpose, it uses an MHMM on Dutch register data for older workers and early pensioners.
The subproject on the effect of institutions started in February 2021. 1 article has been already submitted to an English-language international journal, while 2 articles have been submitted to a Dutch-language scientific journal. The first article focused on organizations and creates a typology of employers according to the strategies they employ in the use of non-standard employment contracts. The second article deals with the same topic but focusses on sector differences in the employer strategies. The third article studies whether the typology of employer strategies determines workers’ careers. A fourth article is in the phase of analysis. In this article, we investigate how concrete institutional arrangements, i.e. arrangements agreed in national law and in collective employment agreements, affect the careers of workers.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

We have shown that measurement error cannot be ignored when studying typologies of employment trajectories. Failing to correct for measurement error groups careers in more unclear clusters while individuals are often placed to an incorrect group. Until the end of the project, we will study whether measurement error biases the number of groups in which employment trajectories are classified. All in all, this research will show that using Mixed Hidden Markov Models is a better and more theoretically informed choice in producing and explaining employment trajectories than the standard algorithmic approach that is currently used, i.e. sequence analysis.
Studying the way firms use non-standard employment contracts shows that firms can be classified in 4 groups: the 2 largest groups (32% and 25%) follow a core-periphery strategy. The core consists mainly of high- or medium-paid workers that are employed with permanent contracts, while the periphery consists by workers in temporary agency contracts or by low-paid workers in fixed-term contracts. The third group of firms (23%) consists of firms that make extensive use of on-call contracts. The remaining firms (20%) belong to a group where firms make extensive use of fixed-term contracts. These firms seem to use these contracts mainly with a cost-reduction strategy. Further research till the end of the project will test these findings in different countries and will also study how employers strategies determine the quality of workers’ careers.
For young workers, we show that they follow 4 distinct career patters. 36.7% of them are entrapped in low quality jobs, 24.6% experience upward mobility, 22.6% move sooner or later to moderately- or low-paid permanent jobs, while 16.1% move gradually out of employment. Later, we will show how the careers of young workers are affected by their education discipline and how return to formal education affects their careers. For older workers, we show that they follow 5 distinct patters towards retirement. 41% follow a standard retirement path from employment towards and medium to high pension. 43% follow a rather precarious path from non-employment to low pension. A considerably smaller group (7%) follows a pathway where individuals often work alongside receiving a rather high pension income, while another one (5%) includes individuals who alternate between employment and pension. A final small group (4%) consists of individuals retiring from employment with low pensions.