Technical change, EMPloyment & Inequality. A Spatial analysis of households & plant data

According to the London Futures Deloitte report (Frey and Osborne, 2014), 35 per cent of the current workforce in the UK is at risk of being made redundant over the next two decades as a result of the introduction of digital robots that will replace their tasks. For those that manage to remain employed, it is difficult to predict whether and how they are able to adapt their skills to the changing demand for occupational tasks. One emerging trend is that, after the introduction of new digital capital, firms dismiss large shares of medium-skilled workers, while seeking either low skilled workers to perform highly routinised tasks, or very high skilled people who provide creative ideas and apply sophisticated knowledge to maximise the benefits of digital capital. The polarisation of demand for tasks and the skills required to perform them is likely to be reflected in a similar polarisation of wages. This will depend on how firms decide to increase digital mechanisation, for instance what type of hardware and software services they purchase, and what types of new occupations this new capital requires. Also, as pointed out by Piketty (2014), rising share of capital in production goes hand in hand with decreasing share of labour, favouring top income concentration. Overall, innovative firms might be responsible for increasing income inequality, both through higher concentration of capital returns in the hands of a few creative CEOs and a higher proportion of the wage bill going to a proportionally smaller share of very high-skilled workers. This research aims to provide comparative evidence on the core mechanisms behind the effects of technical change on income inequality, by looking at the actors directly involved in their occurrence: firms investing in tangible, digital capital and R&D, and households providing skilled and unskilled workforce respectively employed in un-routinised and routinised tasks, and the associated distribution of wage and non-wage income. The novel contribution of this research compared to the extant literature is in the following aspects. First, we will uniquely combine, at the spatial level, plant-level data on tangible, digital and R&D investments of firms located in a certain area, defined as Travel-To-Work-Area (TTWA) and data on occupational categories, wage and non-wage earnings of household living in the same TTWA. TTWAs are defined by the Office for National Statistics as self-contained local labour markets. Second, we will study how technical change, through creative destruction, changes top income shares, wage distribution, and capital income distribution, at the level of TTWA. Third, we will analyse the temporal and spatial associations between the level and composition of investments in tangible, digital capital and R&D in firms and (i) changes in individual's occupational choice across job categories, and (ii) changes in wages at different quantiles of the wage distribution. This research adds to a debate on pressing social and policy issues: income inequality and unemployment. It is therefore particularly relevant not only for the academic community, but also for policy makers, innovative employers, public, social and private enterprises, trade unions, training institutions and young and old members of the workforce that seek and use information on employment and investment decisions. Our dissemination plan ensures that the findings of our research reach all the above stakeholders to inform their decision-making processes.