Innovation and wage polarisation in Europe

Abstract

In this article we improve on the literature dealing with the polarising effects of technological change on wages by proposing more rigorous definitions of wage dispersion within industries and of the different types and effects of innovation.\ud We carry out an analysis across 10 manufacturing and service sectors in seven\ud European countries (France, Italy, Germany, the Netherlands, Portugal, Spain and\ud the UK), for two time periods. In addition to structural economic variables, we\ud draw data from two waves of the Community Innovation Surveys (CIS 2, 1994–\ud 1996 and CIS3, 1998–2000) and from two waves of the European Community\ud Household Panel (ECHP, 1994 and 2001) providing information on employment,\ud wages, education and other individual’s characteristics, that we grouped in three\ud skill groups: managers and professionals, white-collar and blue-collar workers.\ud We set up econometric models to study the impact that different technological\ud strategies, labour market patterns, education and training have on the levels of\ud wage polarisation within industries. Higher wage polarisation is found in\ud industries with strong product innovation and high shares of workers with\ud university education. Wage compression is associated to the diffusion of new\ud process technologies and to high shares of workers with secondary education

‘Global’ Financial Crisis, Earnings Inequalities and Gender: Towards a More Sustainable Model of Development

“…As Adair Turner, Chair of the UKs Financial Services Authority, argued with respect to high level finance workers 'in the years running up to 2007, too much of the developed world's intellectual talent was devoted to ever more complex financial innovations, whose maximum possible benefit in terms of allocative efficiency was at best marginal' and further that high market returns and associated earnings 'can just as easily reflect market imperfections rather than proof of social value' (Turner 2009, 5). This last remark has resonance with product innovation industries discussed earlier where managers and professionals were able to appropriate the productivity gains (Angelini, Farina and Pianta 2009).…”

Policy complexity and the green transformation of the economies as an emergent system property

“…The availability of dedicated skills for green jobs plays in fact a crucial role in triggering change and facilitating sustainable transition (CEDEFOP 2013;Vona et al 2015). Previous general finding of the literature is that the diffusion of technologies might have strong skill bias and wage polarisation effects (Chennells and Van Reenen 2002;Acemoglu 2002;Pianta 2005;Croci Angelini et al 2009). These issues might be of particular relevance for policy design as the speeding up of transition processes may contrast with important social challenges such as reducing inequalities and promoting inclusive growth.…”

Three decades of research on innovation and inequality: Causal scenarios, explanatory factors and suggestions

“…(Bourdieu, 1987;Tilly, 1998;Fragkandreas, 2012;Dorling, 2019), as well as being shaped by various (multi-scalar) factors and forces, such as education, gender, race, international trade, immigration, declining union membership and minimum wages, financialization, unequal organizational structures, neoliberal policies, and welfare state retrenchment (Neckerman and Torche, 2007;DiPrete, 2007;Lemieux, 2008;Donegan and Lowe, 2008;Piketty, 2014;Tomaskovic-Devey, 2014;Stockhammer, 2017;Cavanaugh and Breau, 2018;Munir, 2021). From these factors, however, it is innovation which, according to a growing number of contributions, constitutes one of the most significant causal determinants of rising inequality in contemporary societies (e.g., Fernandez, 2001;Acemoglu, 2002;Angelini et al, 2009;Cozzens and Kaplinsky, 2009;Van Reenen, 2011;Lazonick and Mazzucato, 2013;Lee, 2016;Pianta, 2018).…”

“…One possible explanation for this is the availability of firm-level and sectoral data in the 2000s owing to the wide circulation of international (firm-based) surveys on innovation (e.g., Community Innovation Survey) (Smith, 2005;Hong et al, 2012). Nonetheless, innovation is measured in a narrow manner (e.g., computer usage, R&D intensity, patents) (e.g., Xu and Li, 2008;Weinhold and Nair-Reichert, 2009), with only a very small portion of studies using alternative measures, such as the percentage of high-tech employment (e.g., McCall, 2000;MacPhail, 2000), indicators of product and process innovation (e.g., Angelini et al, 2009;Bogliacino, 2009). Additionally, and in line with several studies in the early phase, 25% of all studies treat innovation either as a latent (background) causal factor (e.g., Wheeler, 2005;Kim and Sakamoto, 2008;Xu and Li, 2008;Dustmann et al, 2009) or as export intensity (e.g., Meschi and Vivarelli, 2009).…”