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Estimating the Effects of Technological Progress Estimating the effects of technological progress: A data-analytic and agent-based approach M.P. Ingwersen Master Thesis Delft University of Technology Faculty Technology, Policy and Management FUTURE OF WORK Estimating the effects of technological progress: A data-analytic and agent-based approach by M.P. Ingwersen to obtain the degree of Master of Science at the Delft University of Technology, To be defended publicly on Friday August 30, 2019 at 15:00 AM. Student number: 4149459 Project duration: February 1, 2019 – August 30, 2019 Thesis committee: Prof. dr. C P. van Beers, TU Delft, Head of committee Dr. S. T. H. Storm, TU Delft, first supervisor Dr. Ir. I. Nicolic, TU Delft, second supervisor Internship supervisors: Drs. L. Akkermans, PwC Prof. Dr. R. Wetzels, PwC Preface Dear reader, This thesis marks the end of my journey at the Delft University of Technology. While the end of a thesis is often experienced as a moment of joy and relief, for me, ending this research leaves me with mixed feelings. Looking back to the months writing this thesis, nothing but warm memories and gratefulness to the many learning experiences arise. Yet, it is this feel- ing of fortune that makes this end somehow bittersweet. Like all good stories, the beginning of my thesis started in a bar. Here, my brother told me about his doubts about becoming a surgeon. Knowing the many hours of study and hard work he had put into his career, his hesitation surprised me. However, never could I have known that his motivation for doubting and the underlying reasons would continue to intrigue me for many months. ‘Seeing the increasing use of robots on the operating table, how would the content of my job develop in the future’ my brother asked. ‘Am I putting all this effort into a job that eventually will be replaced by robots?’. For my thesis, I have analyzed how technological progress can influence the Dutch labour market. Motivated by how the doubts of my brother could eventually influence his deci- sions, this research distinguishes itself from other related research by including the notion of uncertainty. Building on strong research of other scholars, I was able to perform a very interesting study about how the consequences of advances in technology can affect Dutch jobs in the future. While writing a thesis is often seen as an individual process, for me, it would be ignorant to ignore all the insights and support that others gave me. For starters, I would like to thank my supervisors at the TU Delft. With his comments and his provision of new angles of ap- proach, Mr Storm has supported me and, above all, inspired me during the whole process. Besides the provision of new insights, with his support, Mr Nikolic has helped me with the many technical aspects of my thesis and made sure that this thesis really is the result of the combination of an economic and technical perspective. Furthermore, I would like to thank the supervisors of my graduate internship at PwC. Next to the comfortable and inspiring environment for writing my thesis, they gave me valu- able support that encouraged me to always take that extra step and to deliver the quality I pursued. Lau Akkermans has been an inspiring and enthusiastic supervisor who, despite his full schedule, always made time to challenge and to stimulate me on a both scientific and personal level. I would also like to thank Ruud Wetzels for his very knowledgeable comments on the data-analysis and his help with my doubts about my future. Dear reader, I hope that you will enjoy reading this thesis as much as I enjoyed making this. With this thesis, I truly hope that I can contribute to the understanding of the consequences of technological progress and the challenges we face, including that of my brother’s. M.P. Ingwersen Amsterdam, July 2019 iii Executive Summary While much has been written about the possible consequences of technological progress on the labour market, there is still no consensus among scholars about how technological progress and its consequences will manifest themselves on the future of work. This disagree- ment among scholars reveals itself mostly concerning the question ‘how many workers will be replaced due to technological progress?’. While famous research of Frey and Osborne (2013) predicts that 47% of all US workers will be replaced, other research of Arntz, Gre- gory, and Zierahn (2017) predicted that only 9 % of the US workers would be replaced due to technological progress. Despite whether it was caused by technological progress, the Dutch labour market al- ready suffers from a gap between labour demand and supply, and when no appropriate ac- tion is undertaken, the chances are that this gap will increase in the future. However, when considering the current rapid technological developments, one should incorporate these de- velopments when assessing the right future policies. This thesis aims at providing more insights into the effects of technological progress on the Dutch labour market. However, as mentioned, these effects –and technological progress itself– are by no means certain. This uncertainty, in turn, can causes firms’ and workers to behave differently and thereby influencing the ‘real’ consequences of technological progress. This research, therefore, tries to make this uncertainty and its effects explicit by answering the following research question: How will uncertainty about technological progress eventually influence the effects of tech- nological progress on the Dutch labour market? Estimating the true effects of technological progress: a multi-method approach To answer the research question, this study applied a so-called multi-method research ap- proach. That is, it first conducted a data-analysis of the competencies of Dutch workers and their possibilities of being automated. This data-analysis was followed by the development and analysis of an agent-based model that was, in contrast to the data-analysis, able to take heterogeneous low-level factors into account –such as the uncertainty and behaviour of firms and workers. For the data analysis, the model of Frey and Osborne (2013) (FO) was applied to Dutch data. To apply the model of Frey and Osborne (2013), US data about jobs and competencies had to be mapped to Dutch data. Consequently, a data-set was constructed that incorpo- rated data about the competencies of Dutch jobs and workers. Besides the application of the FO model, we used this newly created data-set to analyze the current competencies on the Dutch labour market. Nevertheless, since this data-set was not yet constructed, the creation of this data about the competencies of Dutch workers has opened up great opportunities for further research. Findings of the FO application turn out not to be that catastrophic as it may have seemed The application of the FO model has shown that approximately 12% of the Dutch workers are at high risk of being replaced due to technological developments. Moreover, when simulating v vi Executive Summary this risk, this study found that on average, 36% of the Dutch workers can lose their jobs within 20 years due to technological progress. However, while these numbers are shocking, they do not reveal the whole story. In the FO application, the study did not account for the possibility of retraining of workers and the strategic behaviour of workers and firms stemming from their uncertainty due to technolog- ical progress. To take these factors into account, an agent-based model was developed. By experimenting with this model, the average unemployment rate after 21 years dropped to 3%, a difference of 33% with the results of the FO model. However, due to policy and scenario testing, the underlying unemployment rates of this average unemployment rate of 3% varies greatly. This variance is mostly due to different levels of labour market flexibility. That is, our model found that the higher the proportion of flexible contracts, the higher the final unem- ployment rate will be. Additionally, this study found that this effect of flexibility is much stronger in determining unemployment due to technological progress than other factors – such as the possibility of retraining. Besides, this study found that the higher the number of years firms and workers consider in their planning, the better they anticipate on future changes due to technological progress and hence; the lower unemployment will be. Finally, this study has shown that uncertainty does indeed matter since it almost determines 90% of the outcomes of the model and thereby shapes the ‘true’ consequences of technological progress. Recommendations for the formulation of labour market policies and further research From a policy perspective, these findings suggest some valuable lessons. At first, this study found that firms are more prone to financial incentives than workers. Next, since workers do consider other non-financial factors into their choices –such as age and working experiences– policies concerning workers –for example stimulate retraining– should take these other fac- tors into account. At last, for all policies to be effective, it should aim at stimulating decision making for the long-term, instead of encouraging firms and workers to formulate their strat- egy on an ad-hoc basis. By developing, analyzing and combining both models –the FO model and the agent-based model– this study was able to investigate the real consequences of technological progress on the Dutch labour market. While both models open up many possibilities for further research –such as the analysis of specific labour market activating policies– their limitations should be taken into account. For both models, the most significant limitation is the issue of valid- ity. This study, therefore, recommends further validation of both model–for example through expert opinion– when they are applied for new research.
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