Journal Article

This article investigates the link between environment-related innovation and job creation at firm level. Employing Italian data on 4507 manufacturing firms, matched with patent records for the period 2001–2008, we test whether “green” innovation, measured by the number of environment-related patents, has a positive effect on long-run employment growth that is specific with respect to non-environmental innovation. Results show a strong positive impact of “green” innovation on long-run job creation, substantially bigger than the effect of other innovations. Our findings are robust to a number of additional tests including controls for patents’ quality and cost differential between generic and “green” innovation and endogeneity.

Purpose- This paper sets out to examine the link between technological change and continuing training at a workplace level. Design/methodology/approach- The paper hypothesises that workplaces subject to technological change have an increased demand for skills, which induces an increased provision of training. UK data from two waves (1998 and 2004) of the Workplace Employment Relations Survey (WERS) are used to investigate this hypothesis. Findings- Workplaces undertaking technological change are more likely to train their workers and also to provide more days of training per worker. Team working is also associated with a greater number of days spent on training, as are the setting of training targets and the keeping of training records. Training intensity decreases with an increasing share of part‐time and manual employees. Conversely, where workplaces face difficulties in filling skilled vacancies, they provide more days of training. Research limitations/implications- The WERS training questions refer only to core experienced employees which, since this group may vary from one workplace to another, may not give a completely consistent measure of either absolute or relative training provision. Because the WERS panel (1998 and 2004) excludes both the dependent variable (training intensity) and the variable of interest (technical change), the analysis is restricted to cross‐section estimation. Causal implications of this analysis should be regarded as correspondingly tentative. Practical implications- The findings suggest that one way to induce firms to provide more training is by enhanced incentives for firms to undertake more rapid technological change. In addition, if the current global economic downturn persists, evidence that operating in a declining market is associated with the provision of fewer training days may be of particular concern to training professionals and policy makers. Originality/value- The paper provides empirical evidence concerning the interaction between technological change and training.

In warehouses of food retailers, handlers workers work with headphones on ears and a microphone in front of the mouth. This is a digital voice that tells them tasks to perform and they validate every gesture made by voice recognition, pronouncing keywords. When asked about the content of their work, they do not compare themselves fail to "robots", stripped of their ability to act outside the imposed script. Outside observers often do the same, stunned by this strange dialogue between a digital tool and humans that meet its injunctions. If this conception of "automation" is not satisfactory in the light of the investigation immersion in warehouses, critical analysis of this terminology and its symbolic spring brings new light on how the working class is perceived and perceives. [googletranslate_en]

The aim of the paper is to analyse the features of the digital labour connected with the so-called platform economy. Many platform-based business models rely on a new composition of capital capable of capturing personal information and transforming it into big data. Starting with the example of the Facebook business model, we explain the valorisation process at the core of platform capitalism, stressing the relevance of digital labour, to clarify the crucial distinction between labour and work. Our analysis differs from Fuchs and Sevignani’s thesis about digital work and digital labour and seems consistent with the idea that Facebook extracts a rent from the information produced by the free labour of its users.

The allure of an environmentally benign, abundant, and cost-effective energy source has led an increasing number of industrialized countries to back public financing of renewable energies. Germany’s experience with renewable energy promotion is often cited as a model to be replicated elsewhere, being based on a combination of far-reaching energy and environmental laws that stretch back nearly two decades. This paper critically reviews the centerpiece of this effort, the Renewable Energy Sources Act (EEG), focusing on its costs and the associated implications for job creation and climate protection. We argue that German renewable energy policy, and in particular the adopted feed-in tariff scheme, has failed to harness the market incentives needed to ensure a viable and cost-effective introduction of renewable energies into the country’s energy portfolio. To the contrary, the government’s support mechanisms have in many respects subverted these incentives, resulting in massive expenditures that show little long-term promise for stimulating the economy, protecting the environment, or increasing energy security.

We examine how susceptible jobs are to computerisation. To assess this, we begin by implementing a novel methodology to estimate the probability of computerisation for 702 detailed occupations, using a Gaussian process classifier. Based on these estimates, we examine expected impacts of future computerisation on US labour market outcomes, with the primary objective of analysing the number of jobs at risk and the relationship between an occupation’s probability of computerisation, wages and educational attainment.

The present work tackles the issue of the effects of digitalisation on employment. This issue has been attracting a growing interest, in particular because of the anxiety generated by the idea that digital technologies could cancel a large number of jobs. Although I agree with argument put forward in opposition to the existence of a causal link between technological innovation and increased productivity at the macroeconomic level, I believe that the novelty and pervasiveness of digital technologies require more in-depth micro-level analysis in order to understand the extent to which new digital technologies are currently employed by leading manufacturing companies and the ways new technologies are affecting employment. The empirical findings show that among the different technologies included under the umbrella of Industry 4.0, mainly robots have received a great deal of attention so far, while the current application and employment impact for other emerging technological opportunities such as 3D printing, Internet of Things, Augmented reality, Big data Analytics have not been studied yet. In relation to the qualitative changes of the labour market, our empirical research confirms that there are new types of skills that will be demanded in the future in manufacturing, in particular in relation to service provision and software development.

Robots, that is any sort of machinery from computers to artificial intelligence programs that provides a good substitute for work currently performed by humans, can increasingly replace workers, even highly skilled professionals, and thus reduce opportunities for good jobs and pay. But, with appropriate policies, the higher productivity due to robots can improve worker well-being by raising incomes and creating greater leisure for workers. Consider the way Google reduces the need for reference librarians and research assistants, or the way massive open online courses reduce the need for professors and lecturers. How these new technologies affect worker wellbeing and inequality depends on who owns them.

Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets. While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change. In this paper we discuss the barriers that inhibit scientists for measuring the effects of AI and automation on the future of work. These barriers include the lack of high-quality data about the nature of work (e.g., the dynamic requirements of occupations), lack of empirically informed models of key micro level processes (e.g., skill substitution and human–machine complementarity), and insufficient understanding of how cognitive technologies interact with broader economic dynamics and institutional mechanisms (e.g., urban migration and international trade policy). Overcoming these barriers requires improvements in the longitudinal and spatial resolution of data, as well as refinements to data on workplace skills. These improvements will enable multidisciplinary research to quantitatively monitor and predict the complex evolution of work in tandem with technological progress. Finally, given the fundamental uncertainty in predicting technological change, we recommend developing a decision framework that focuses on resilience to unexpected scenarios in addition to general equilibrium behavior