Funded by the Excellence Initiative of the German Federal and State Governments

Liuben Siarov

Bremen International Graduate School of Social Sciences (BIGSSS)
Jacobs University Bremen
Campus Ring 1/South Hall          
PO Box 750 561
28759 Bremen
Germany

Room: 307

Phone: +49 (0)421 200 3954
Fax: +49 (0)421 200 3955

E-Mail: lsiarov(_a_)bigsss-bremen.de


Liuben Siarov is pursuing a Ph.D. in Social Sciences and is specializing in the field of Life-Course and Lifespan Dynamics.

Dissertation Topic

The economics of Lifelong Learning: Tracing Individual Choice over the Life Course. The Case of Germany. (working title)

Abstract

Since Becker’s original work on the Human Capital model, a wealth of empirical and theoretical research followed to lend weight to the view of human capital as a productive resource, the accumulation of which is subject to the confines of the economic rationale. Perhaps surprisingly, despite the theory being firmly grounded in the investment approach, most extant studies have focused on only one component of the investment decision – the return to training - and both empirical and theoretical work have driven the flip-side of the coin, namely risk – to the fringes. With the increasing importance of lifelong learning as a key value driver for individual, firm, industry and societal competitiveness, it is ever more important to have a good grasp of the fundamental basis of the training investment decision in order to be able to formulate sustainable and effective policy. This thesis project therefore examines the effect of risk on training investments – both in terms of opportunities training unlocks (upside risk) and the threats it faces (downside risk), using a theoretical and empirical approach to the quantitative study of individual investment decisions over the life course.

Academic Supervisors

Research interests

  • Labour market imperfections
  • Lifelong learning and career progression
  • Rational choice and investment rationale with uncertain information
  • Multivariate latent variable modelling
  • Discrete-time survival models