Working in the wrong job or in the wrong industry? Skill mismatch, technology and production

The Information and Communication Technology (ICT) revolution has led to important organizational changes, accompanied by increased demand for skilled workers (O’Mahony et al. 2008, Silva and Lima 2017, Falck et al. 2020). Moving into the fourth industrial revolution, a wider variety of skills is in high demand, including technical (STEM) and soft skills (Black et al. 2021, Deming 2017). Within this context, the rise in the number of graduates in the UK, whose supply has steadily increased since the 1990s (O’Leary and Sloane 2005, Green and Zhu 2010, Savic et al. 2019), should be considered an important step towards ensuring positive labour market outcomes during a period of fast-changing employment opportunities. Graduates generally develop a variety of ‘soft’ skills (for example social and communication skills), next to acquiring knowledge related to the specific subject of choice. In addition, the increasing number of graduates is believed to help address the issue of skill deficiencies, considered one of the contributing factors to the UK productivity slowdown (Mason et al. 2018, Augar et al. 2019). 

However, despite the complementarity between graduates’ skills and technology, a high proportion of graduates are overqualified, or overeducated, as they possess higher qualifications than required for the job. This type of skill mismatch, often defined as vertical mismatch, has been widely discussed in the literature and the evidence consistently shows that overqualified graduates suffer a wage penalty, that is they earn less than graduates who find employment in a graduate job. [1] There is also evidence showing that the skill mismatch may negatively affect innovation (Igna and Venturini 2019), exacerbating concerns about its contribution to the productivity slowdown (Augar 2019).

The first question that this project addresses is whether current measures of vertical mismatch fully capture the extent of the phenomenon. Addressing this question requires the construction of alternative measures of the skill mismatch, for example horizontal mismatch, which describes the situation where a graduate finds employment in an occupation that requires skills different to those associated with their degree field. Another measure is the mismatch between graduates’ skills and the skills required in the industry of employment.

A second question is whether technological changes have had an impact on the skill mismatch. Computers and software are replacing tasks previously performed by workers, particularly those requiring low and intermediate skills. There is evidence that the intermediate skilled have been particularly affected by the adoption and diffusion of ICT in what has become known as the phenomenon of job polarisation (Goos and Manning 2007, Goos et al. 2009 and 2014). However, an in-depth analysis of the relationship between skill mismatch and technology is missing.

The presence of a skill mismatch has consequences for individuals, as they suffer a wage penalty. How the size of this penalty changes depending on the different types of mismatch is an unresearched areas of research and an important question to address. The skill mismatch also has macroeconomic consequences. It is often considered as an inefficient use of resources that can lead to lower productivity. However, evidence on the relationship between skill mismatch and productivity, measured at the macro level as output per worker or Total Factor Productivity, is scant. Understanding this relation is a major policy concern as Government wish to know whether investments in education lead to the expected gains in terms of productivity performance and economic growth.

PhD Project Aims  

1. Undertake a thorough literature review on the skill mismatch among graduates, the relationship between skills and technology and between the skill mismatch and productivity
2. Collect the necessary data for the UK, accessed via the ONS secure data environment. Different data sets (Annual Population Survey, LEO, ASHE) may need to be linked to address some of the questions in the project.
3. Construct different indicators of skill mismatch and compare their trends over time.
4. Develop an analytical strategy to address the questions highlighted above.

This project will provide an excellent opportunity to develop skills in the areas of data management, generation of new indicators, econometric modelling, and analysis. The PhD student will gain experience in a very active area of research and will have the opportunity of working with experts in the fields

[1] See, for example, Hartog 2000, Savic et al. 2019, Verdugo and Verdugo 1989, Alba-Ramirez 1993, Dolton and Vignoles 2000 Hartog 2000, Bauer 2002, Leuven and Oosterbeek 2011, among others.