Do generous unemployment benefits facilitate transition into more-secure labour market positions? Insights from a multilevel latent Markov model

Abstract

A large body of research suggests that generous welfare provisions for jobseekers create a disincentive to work. Other scholars argue that generous benefits can reduce unemployment by serving as a job-search subsidy. One caveat in this literature is that, when testing the two hypotheses, many scholars conceive of labour markets as homogeneous entities or they theoretically assume a certain insider/outsider divide. In this article, we claim that the employment effect of generous benefits varies between labour market segments. Analysing EU-SILC panel data of 27 European countries, we find that more-generous unemployment cash benefits enhance the transition from unemployment into more-secure work while discouraging transition into less-secure work in terms of temporal, economic and organisational security. Contrary to existing research, welfare generosity is measured by aggregated information on individual benefit receipt. Labour market segments are identified by latent class analysis and transitions between segments are estimated by Multilevel Latent Markov Models.

Conditional Generosity and Deservingness in Public Support for European Unemployment Risk Sharing*

“…This means that the generosity of EURS schemes would determine the preferred level of conditionality attached to receiving unemployment benefits and vice versa. More‐generous unemployment benefits alleviate the material needs of the unemployed, allow for a longer job search and enhance the transition from unemployment into more‐secure work (Otto and Lukac, 2021). When welfare support to the unemployed is more generous and thus more costly, people may prefer the receipt of unemployment benefits to be conditional on stricter requirements for the unemployed to make efforts at re‐integration.…”

Cooperation sustainability in small groups: Exogenous and endogenous dynamics of the sustainability of cooperation

“…The latent Markov Model is a mixture model with dynamic categorical latent variables or classes (Nagelkerke, 2018; Otto and Lukac, 2021; Vermunt, 2010). A key assumption of the model is that the observed data originates from an unobserved process evolving over time (Nagelkerke, 2018; Otto and Lukac, 2021). The respondents are classified into several distinct latent classes (Nagelkerke, 2018).…”

“…The multilevel extension of the latent Markov Model captures the hierarchical data structure of decisions per round, individuals, and groups. Incorporating parametric random effects allows for modeling a group-level time-varying effect on the transition structure (Otto and Lukac, 2021: 9). For longitudinal data, the MLMM method is particularly advantageous as it accounts for unobserved heterogeneity, autocorrelation, and measurement error (see Otto and Lukac, 2021; Vermunt et al, 2008).…”

“…While level 2 and 3 decision rules remain constant over time, individual decision rules can change over time. Following existing research, we aim to choose the model that reveals the best trade-off between model fit and parsimony (Otto and Lukac, 2021). Once we determined the final number of latent states across different levels, we re-estimated the model with the effects of the manipulations and control variables (Otto and Lukac, 2021).…”

Correction to “Conditional Generosity and Deservingness in Public Support for European Unemployment Risk Sharing”

“…This means that the generosity of EURS schemes would determine the preferred level of conditionality attached to receiving unemployment benefits and vice versa. More-generous unemployment benefits alleviate the material needs of the unemployed, allow for a longer job search and enhance the transition from unemployment into more-secure work (Otto and Lukac, 2021). When welfare support to the unemployed is more generous and thus more costly, people may prefer the receipt of unemployment benefits to be conditional on stricter requirements for the unemployed to make efforts at re-integration.…”