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Different countries across Europe and the world have tried to adopt heterogeneous and non-coordinated reactive measures to cope with the highly transformational automation wave of the 1990s. Among the most discussed proposals five main solutions were identified reviewing studies such as The European House Ambrosetti, Berg,A. et al and Servoz, M. : 1) the introduction of a minimum salary 2) the establishment of a universal basic income (UBI) 3) robot taxation 4) incentives to investments in innovation and in industry 4.0 and 5) enabling education. Among these different responses, the last two seem to be the most reasonable with higher probability of long-term effects on the future structure of labor markets and on the empowerment of current and future generations.
The majority of reports reviewed on the topic indeed concur in advancing solutions 4) and 5) as the most valid and this research is in line with such an opinion. The first three proposals are discussed in what follows to understand their major weaknesses. Firstly, the introduction of a minimum salary, despite providing individuals with greater economic availability and being already widespread across developed economies, doesn’t assist against a technology driven displacement and is consequently unable to bring balance in the future labor market as far as technological unemployment is concerned.
Moving to the so-called universal basic income, this model is still under testing and its effects are still to be assessed . UBI would grant citizens a monetary inflow independent from the profession covered. However, in this way all individuals employed in activities with a social value who do not perceive a salary would be remunerated (housewives, volunteers, caregivers etc.
). Furthermore, like the introduction of a minimum salary, this measure wouldn’t have an impact on the future labor market as it wouldn’t positively affect the ability to catch opportunities arisen from the fourth industrial revolution. Moving to proposal 3), the taxation of robots proposes a shift of the object of taxation from people to robots in order to both maintain public inflows which would otherwise be lost and allow for redistributive policies. Despite attractive for the public opinion, this measure seems impractical and doesn’t directly solve the issue for the displaced workers. Its first controversy lies in the complexity of assessing the unit of taxation. A robot could indeed work in many different functions at the same time or, vice versa, more than one robot could be used to perform the same function. Moreover, when taxing robots, the object of the levy is the computational power of the machine rather than the robot itself. But this raises the question of whether all computation-endowed machines (smartphones, PCs..) should be taxed to grant a taxation parity. Last but not least, this measure could slow down the introduction of new technologies, blocking progress. Coming to the last two proposals, these appear as the best positioned to manage a correct transition to automation ensuring long term gains. However, enabling education (proposal 4) is a necessary condition for the investing in innovation (proposal 5) to make sense. It is of utmost importance to invest in new technologies and 4.0 industries fully embracing the change and spurring the growth rate of reinstatement effect. Deploying money to the financing of innovative projects and 4.0 industries would not only improve productivity and efficiency of current companies, thus leading to more hires, but could also bring about the creation of new tasks and jobs, concurring to sustain job creation against job destruction. These investments are essential if European countries want to take the lead during this 4th industrial revolution and include various policies such as: enabling measures for startups, taxation incentives for filing patents or investing in R&D, favorable amortization for the introduction of highly innovative machinery and equipment, better access to credit and public guarantee funds for SMEs. However, this solution wouldn’t work without properly training those directly affected by automation in order for them to benefit from innovation itself. Financing of innovation and industry 4.0 would thus be irrelevant if the final intended beneficiaries are not prepared to deal with the newly introduced technologies and, in the worst scenario, may even lead to a total substitution of capital to labor. Indeed, as Goldin and Katz advance in their book: “the reason why human labor has prevailed relates to its ability to adopt and acquire new skills by means of education”. Such a thought is shared by Frey and Osborne 2017 which point at two main open debates when looking at the future of current technologies being i) the ability of labor to win the race against machines by means of education and ii) the possibility that rising technological unemployment establishes a permanently higher job turnover and thus a higher natural rate of unemployment. The existence of responsive and structured educational systems stands out as a strong prerequisite for automation to produce its expected beneficial effects. Considering the state of current European educative systems and comparing them with the needs of the future however, these aren’t aligned and equipped enough to manage the quickly transforming labor market according to the Commission Report on AI by Servoz. Given the entity of the challenge faced, this shouldn’t come as a surprise. Indeed, the fast pace of technological development brought about by the 4th industrial revolution, renders particularly difficult for educative programs to keep up to date. Regulators and investors willing to promote reforms and sustain projects in the educational sector, are brought on a completely unknown field which requires them to perform a hard task. That of sensing the type of skills needed in future, still unknow, jobs brought about by the endless application of AI. Despite challenging, this venture might be made possible by the enormous data availability characterizing this industrial revolution. Regulators could indeed turn to the numerous labor platforms and Massive Open Online Coursers (MOOCs) and aggregate data available regarding job supply, job demand and available skills, ultimately using them to make predictions regarding the future. Some evidence for the feasibility of this challenge are available when looking at LinkedIn’s Economic Graph. A digital representation of the global economy able to virtually represent all data on LinkedIn mapping 610 million members, 30 million companies, 20 million open jobs, 84 thousand schools and 50 thousand skills . Such tools are currently more needed than ever in order to sense the skills of the future and their continuous evolvement. However, there is the urgency to complement skills anticipation with a proper preparation of the future and current labor market force. Indeed, according to the estimates reported in Servoz, M (2019), approximately 50% of knowledge gained after the first year of studies of a four-year technical degree, becomes outdated by the time the degree is achieved. Business Europe (2018) further sustains the urgency of reforming European current education systems reporting impressive figures from The World Economic Forum 2016 and Cedefop (European Centre for the Development of Vocational Training). Indeed, a survey conducted by the forum shows that the majority of employers expect the skills required to perform their jobs to radically change by 2022. Likewise, Cedefop’s Euroepan skills and jobs survey (ESJ) shows that 10% of current EU adult workers possess technological skills at high risk of obsolescence while 21% believe their skills will be updated in a five-year time span. But, why aren’t European educational system functioning as they should? Before starting with an accurate analysis of the average educational systems across members states aimed at disentangling their major shortcomings, it is essential to define a framework of reference for education. In the wide panorama of educational formats, this paper finds it relevant to distinguish between two macro categories: the institutionalized formal education and the adults’ education and training. Starting from the institutionalized formal education, this is mandatory up until a certain age which varies depending on the country and can be of a general form (i.e. Middle School, High School, University) or technical (technical institutes/programs). As for the latter, this is also called Initial Vocational Education and Training (I-VET) and refers to those teaching programs than can be undertaken to learn professional skills directed to specific careers and happening in a pre-work phase. Moving to adults’ education and training, this form is also known as Continuing Vocational Education and Training (C-VET) and is of extreme importance for adults in order to keep on re-training themselves, avoiding skill obsolescence. This type of learning typically refers to adults more than 25 years old so to exclude those still participating in the formal educational system and can be provided by either public or private recognized entities or by companies themselves. Public Employment Services are the usual points of reference, be it as providers or partners of adult education and trainings.
Turning to the European shortcomings’ analysis and starting with formal education, one major issue is the divide between the general and technical pathways (except for Switzerland and Germany) along with the stigma associated to apprenticeship and Initial Vocational Education and Trainings which are valued as second-rate choices when compared to the university. This results in general schools providing students with general skills and the other way around for technical schools, leaving them unprepared to correctly face an uncertain and flexible environment for which they would need a more comprehensive and tailored background rather than a too generic or too specific one. Another important weakness of current EU-systems most certainly lies in the lack of structured programs for the teaching of soft skills such as problem solving, teamwork and communication. These are indeed deemed to play an increasingly important role as they are too complex for algorithms to internalize and could be a key lever to exploit for human labor to be considered as essential in the long term. Future reforms should be able to initiate a convergence between general and technical education able to equip the future worker to adequately face the labor market with an increased focus on the skillset of the future being linguistical, mathematical, technological and digital literacies along with strong soft skills. Coming to adult education, this presents relevant issues as well. Firstly, it doesn’t take into account the different learning needs of adults as it is often structured
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