While the impact of increasing concentrations of greenhouse-gases in the atmosphere on the climate system cannot be accurately predicted, there is a non-trivial risk that beyond some ex-ante unknown tipping points – in terms of greenhouse-gas concentration and/or global temperature – irreversible and highly expensive events might unfold. This calls for quick action to reduce the probability that such tipping points will be passed2. Consequently, annual greenhouse gas emissions will have to be reduced dramatically before 2050. In order to stabilize CO2 concentrations at about 450 ppm3 by 2050, global emissions would have to decline by about 40-70% by 2050.
Such aggressive decarbonisation on a global scale will require an international agreement because otherwise fossil fuels not used in some countries will simply be used in other countries4. And we cannot wait until low-carbon technologies become cheaper than expiring fossil fuels because, in particular, the last percentage points of cost advantage that fossil fuels have will be difficult to overtake without a price on carbon5. But an agreement is only feasible and stable if the climatebenefit for each country exceeds the cost. The cost of decarbonisation essentially depend on the cost of low-carbon technologies. Consequently, reducing the cost of these technologies in Europe not only allows for cheaper domestic decarbonisation and for a competitive edge to be gained in selling these technologies overseas, but most importantly it would strongly facilitate an international agreement.
Without public intervention, European companies will under-invest in low-carbon innovation for three reasons: (i) in all sectors, innovators cannot reap the full benefits of their innovation because good ideas might be used to enhance productivity beyond the product made by the original inventor (e.g. by inspiring new innovation or being merely copied by competitors). No company will invest in a project for which the expected return is below the upfront investment, even if the societal benefits exceed the initial investment cost. (ii) The European carbon price is likely to be below the social cost of carbon and there is no sufficient long-term visibility of the carbon price-signal. As companies will
only invest in technologies that mitigate CO2 emissions at a cost below the carbon price, investments in technologies with higher abatement cost (e.g. carbon capture and storage) are not brought forward, even though they might be needed to mitigate climate change. (iii) Low-carbon technologies are most competitive in markets where greenhouse-gas emissions are regulated.
Hence, even though emissions outside the EU are as bad for the planet as emissions within the EU, companies invest at less than socially-optimal levels in developing low-carbon technologies, because they do not receive extra remuneration for the ability of their technology to reduce emissions in markets that do not regulate emissions.
The policy question hence is: how can the EU overcome in the most efficient way these market failures that hold back low-carbon innovation. We first provide some evidence that companies that do ‘green innovation’ appear not to be different from other innovators. Then we discuss what public policies are used to support low-carbon innovation and where we see room for improvement.
This paper adds to the literature by studying the impact of firms’ own technology and non-technology innovations as well as of innovation spillovers from vertically linked manufacturing and services industries on firms’ employment growth and skill upgrad-ing. We exploit unique representative samples of micro data for Spain and Slovenia for the period 1996 -2008. Our results show a substantial heterogeneity of innovation effects on employment and skill composition of labor. This implies that, first, the effects depend a lot on the specific structure of each economy, whereby results can vary substantially across industries that generate spillovers and across firms that are potential beneficiar-ies of the spillovers. And second, innovations in service industries do not seem to have a different spillover effect on employment and skill structure when compared to innova-tions in manufacturing industries.
Our recent work (Dechezlepretre, Martin & Mohnen, 2014, DMM) shows that clean innovation generates higher spillovers than dirty innovation. This could imply that shifting resources from dirty R&D to clean via a policy intervention can generate higher growth at th emacro level. However, the question arises what is driving this spillover gap? DMM explore explanations such as the suggestion that clean technologies are more general purpose or more original without much success. This leaves the explanation that spillovers – per innovation – are larger simply because clean technologies are a relatively un-explored field and there are decreasing returns to spillovers. Hence, what we measure is the higher marginal effect, however this advantage will dissipate once clean expands.
However, an implication of the presence of spillovers with decreasing returns is the possibility of multiple market equilibria some of which are inferior. Hence, if the economy is locked in an inferior equilibrium a policy intervention can lead to sustainable welfare improvement. In this note we develop a simple model that illustrates this.
The paper investigates the impact of innovation and globalization on observed Ushaped labour market polarization in 33 European countries in the period 1995- 2013. For these purposes Eurostat’s Labour Force Survey data is combined with data on R&D activity and international trade as well as with UNCTAD’s data on foreign direct investment. Three main findings can be deduced from our empirical research. First, innovation and R&D expenditures contribute to polarization in the higher end of the wage spectrum, but less so at the lower end. However, both became more important during the recent crisis period. Second, general imports seem to accelerate polarization at the lower end of the wage spectrum, while imports of Chinese low-tech products dampen theses effects by reducing employment of the lowest-paying occupations. And third, inward FDI seem to foster polarization in the labour market by increasing demand for labour at both sides of the wage spectrum, while relocation of production abroad via outward FDI moderates these polarization effects by reducing the demand for labour. These effects are aggravated during the crisis.
We estimate the welfare effects of government support to private R&D using comparable R&D project level data from Belgium, Finland, and Germany. In a counterfactual analysis we evaluate the existing policies against alternative policies, including first best. There is considerable heterogeneity in R&D investments, R&D participation rates, spillovers, and profits across firms. Socially optimal R&D participation rates are only marginally higher than those observed in the data, suggesting that most of the benefits from activist policies come from increasing R&D in firms already doing R&D rather than from enticing new firms to start R&D. We find that activist policies increase r&D substantially, but have essentially no effect on welfare. We also find that the gap between laissez-faire and first- and second-best policies is narrow at 3-4 per cent. EU-wide innovation policy is clearly more effective than national ones.
This report specifies the major results from the micro and the macro parts of the SIMPATIC project, the calibration that has been used in the macro part and the importance of the incorporation of spillover effects.
The high debt and deficit burden in some EU countries leaves no choice but to continue the course of fiscal consolidation. At the same time, the growth performance in the EU remains subdued. The dangerous cocktail of high debt and low growth calls for smart means of public investment. The search is for public investment that fosters long-term growth while at the same time minimise the potentially negative short-term effect on public finances and economic activity. R&D is an area typically identified as a candidate for smart spending, because of its growth effects. What is the case to be made for public expenditures on R&D? Is this an area of smart spending in times of low growth-weak public deficit?
Identifying R&D spending as an area of smart government spending requires several issues to be cleared. A first question is: does R&D contribute to growth? At present, it is widely acknowledged that innovation is an important force behind long-run economic growth. Particularly the models using an endogeneous growth framework make a strong case for the growth power from R&D and innovation (eg Aghion (2006), Conte (2006)). But this does not yet make the case for public R&D investments. Will public R&D lead to innovation and growth, sufficiently to cover the opportunity costs of using public funds for R&D? To address these questions, we review the evidence and analysis on the impact of public R&D spending. We first look at the evidence from micro-analysis of the impact of public intervention on private R&D and innovation, with a special focus on the latest results from cross-country micro-research performed within SIMPATIC. To analyse the impact from public R&D on growth, we need to take a macro-perspective. To this end, we look at how public R&D performs in affecting GDP growth and jobs in applied macro-models most commonly used in EU policy analysis. We focus particularly on the NEMESIS model in development within the SIMPATIC project. We conclude with some policy recommendations from the reviewed micro and macro (SIMPATIC) evidence for designing public R&D projects and programs.
This paper summarizes the results from theoretical modeling of the R&D subsidy process as well as from microeconometric analysis on how firms apply for R&D subsidies, and how governments grant them, using data from 5 EU countries. The two key lessons for macro-modeling are: First, additionality is not a sufficient statistic on which one could build the micro-input into macro models. Second, an important feature of firm application and government subsidy rate decisions is heterogeneity. This heterogeneity manifests itself across firms/applications, across countries, and to some extent also across time. One source of heterogeneity are differences in institutions across countries and time.
It is increasingly evident that complex problems, such as pressing societal challenges (aging population, climate change and other environmental problems, energy issues, etc.) call for a systemic approach to solutions and shift to a combination of multiple types of innovation. The quest is reinforced by the ongoing crisis, severe budget constraints and increasing unemployment that challenge present economic models, threaten citizens’ welfare and increase inequalities in European economies. The comparison of the innovation performance of EU economies indicates that after a period of convergence this process has come to a halt in 2012. Less innovative countries as a group are no longer catching-up with the best innovation performers while the innovation performance of the EU on average still marks growth, albeit weak (IUS, 2013). By observing the indicators of the innovation performance that leads to growth and economic prosperity one does not get any insight into social aspects of these developments. Eurostat Survey on Social inclusion and living conditions reveals that in 2011 approx. 24% of EU27 population (120 million) were at risk of poverty or social exclusion with the trend increasing since 2008 (Eurostat Survey, 2013). The evidence on contrasting trends in economic and social indicators could be substantiated with additional data. It seems however that the data sets mentioned suffice to illustrate the emerging dichotomy in the European economies and to call into question the inclusiveness and sustainability dimension of EU2020 Strategy. In this context the objective of the paper is to point to the gaps in understanding innovation and its implications on social dimensions of economies. Better understanding of innovation that goes beyond R&D and new techology implications is of high relevance for macro-economists when constructing models that forecast the direction and strength of impacts on different variables, as well as for public policy that uses such analyses for designing policies.
An essential margin for the reduction of greenhouse gas (GHG) emissions is the development of new emission reducing technologies and processes. It is well established that market forces alone are unlikely to provide optimal levels of R&D towards this kind of innovation, because of the combination of both positive knowledge externalities and negative environmental externalities.
Policies that primarily target the environmental externality such as carbon emissions pricing – through carbon taxing or a trading system – might not only reduce GHG emissions. By putting a price on carbon, they could also provide incentives for companies to direct R&D to clean areas. Direct empirical evidence on the underlying mechanisms is very limited. There are a few studies showing a link between energy prices and clean innovation. However these rely on aggregate data or are very specific in terms of geographic range or the sector of the economy considered. Consequently, providing empirically robust evidence on this issue is a major objective of our work. Importantly, the impact of climate policy on innovation is a first order issue to adequately model policy scenarios in integrated assessment model and similar macro models.
Because of the presence of knowledge externalities, even a global carbon price could lead to a sub-optimal innovation level in clean technologies. Policies that directly target the knowledge externality are needed. But how strong must these policies be? The answer to this question crucially depends on the degree of knowledge spillovers in clean technologies compared to other (in particular dirty) technology areas. There is a striking lack of evidence on this issue, and another objective of our current work is to provide accurate measures of knowledge spillovers in clean technologies that can be used to inform policies and be fed into macro-models.
We are currently making progress along all of these lines. In the following section we summarise current results. We try to as much as possible translate our results into simple elasticities that can easily be integrated into a macro-modelling framework. We also give a short preview on on-going work and the kind of results we expect to uncover in the coming months.