Mark Harris, Curtin University - Threshold Effects, nonlinear models and unbalanced panel data: Over-, under- and required-education and effects on earnings

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Title: Threshold Effects, nonlinear models and unbalanced panel data: Over-, under- and required-education and effects on earnings

Abstract: Over the past few decades, a substantial body of research looking at the returns to education has generally shown that education is a worthy investment, with strong monetary benefits conferred by the attainment of higher levels of education. Aside from the monetary returns to education, educational attainment has also been associated with other positive individual and societal benefits such as improved health, increased civic engagement and lower crime rates. As such, education has been a priority of policy-makers across the globe. Educational attainment rates up to and including the tertiary level have increased globally, particularly for more developed economies.

In more recent times, however, studies challenging the notion that education always generates the best returns have arisen. In particular, this literature studies the phenomenon of overeducation and raises doubts about the absorptive capacity of economies to properly utilise increased human capital arising from expansionary education policies. By establishing a reference or ‘required’ level of education for a worker’s occupation, it is possible to decompose an individual’s actual level of education into years of required education and years of overeducation or undereducation relative to that occupational norm. Following pioneering studies by Freeman (1976) and Duncan and Hoffman (1981), an extensive ‘ORU’ (over-, required- and undereducation) literature has developed providing a more nuanced picture of wages determination and the consequences of skills mismatch.

Three main methods have been used to define the ‘required’ level of education for a job, against which to measure under- and overeducation: job-analysis; worker self-assessment; and the ‘realised match’ or ‘empirical method’. Hartog (2000) notes that the choice of method is usually dictated by data availability, and despite the literature having been around for multiple decades, there is still ongoing debate around methodological issues. This paper explores the validity of assumptions underlying the realised match method and presents new evidence on the implications of skills mismatch for earnings in Australia.

Presenter: Mark Harris, Curtin University

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