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on Technology and Industrial Dynamics |
| By: | Pianta, Mario; Coveri, Andrea; Reljic, Jelena |
| Abstract: | The Sectoral Innovation Database (SID) has been developed at the University of Urbino over the last 20 years and combines several major sources of industry-level data, shedding light on the dynamics of structural change, the nature and impact of innovation, the internationalisation of production, the evolution of the quantity and quality of employment, income distribution patterns and the role of digitalization. The database covers six major European countries – France, Germany, Italy, the Netherlands, Spain and the United Kingdom (representing 75% of EU28’s GDP) – from 1994 to 2016, considering six time periods corresponding to upswings and downswings of business cycles. The first version of the SID provides data for 21 manufacturing and 17 service sectors for two-digit NACE Rev. 1 classes. As statistical surveys have moved to the two-digit NACE Rev. 2 classification, a second version of the Sectoral Innovation Database was produced, providing data for 18 manufacturing and 23 service sectors for two-digit NACE Rev. 2 classes. Major sources of data include the Community Innovation Surveys provided by Eurostat, the OECD’s STAN database, the WIOD database, the Eurostat’s EU Labour Force Surveys, and the EU KLEMS data on digitalization. The integrated information provided by the Sectoral Innovation Database offers a comprehensive view of industries’ dynamics in Europe and allows for an in-depth investigation of key research questions related to technological change, economic performance, international production, income distribution and employment. |
| Keywords: | Innovation, Industries, Databases, Demand, Offshoring, Labour market |
| JEL: | O3 |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:106780&r=all |
| By: | Anabela Santos (European Commission - JRC); John Edwards (Policy Experimentation and Evaluation Platform); Paulo Neto |
| Abstract: | Smart Specialisation is a place-based approach to innovation policy that underpins a significant amount of EU funding. The origins of the concept lie in the transatlantic productivity gap and a concern that previous investments in Research and Innovation (R&I) had failed to deliver commercial benefits. Following more than five years of implementation, this report contributes to the evaluation of the smart specialisation approach through quantitative analysis. As part of the Stairway to Excellence project, it is one of the first to assess its impact on regional productivity, based on the case of Portugal. This is done using the country’s main instrument to support corporate Research and Development (R&D) that was launched in 2007 and adapted to accommodate smart specialisation in 2014. An analysis of project characteristics reveals that during the programming period 2014-2020, financial support to corporate R&D investment aligned with S3 priorities has been more concentrated on cooperation between regions and sectors. A higher diversification of R&D and Innovation funds across sectors, regions and beneficiaries, in comparison with 2007-2013, is also observed. As more cooperation and diversification are two important features of smart specialisation, these findings suggest improved investment choices in the programming period 2014-2020. Furthermore, after controlling for the existence of potential geographical spillover effects by applying a spatial econometric analysis, the results display a positive effect on regional productivity from the R&D and Innovation subsidies over the last two programming periods. Furthermore, a higher rate of return of RDI subsidy in the second period is also observed, which suggests that smart specialisation was able to generate an additional effect in comparison with a situation without this place-based policy. Nevertheless, we also found that – in the case of Portugal - smart specialisation has only been able to generate this additional effect in regional productivity when the R&D funding instrument is combined with other types of innovation subsidies. This finding provides additional weight to the argument for broader and more integrated smart specialization policy mixes in the new programming period. |
| Keywords: | Productivity, Innovation, Smart Specialisation Strategies, Portugal |
| JEL: | O31 R11 H71 |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc124389&r=all |
| By: | Liam Brunt (Norwegian School of Economics and Business Administration - Norwegian School of Economics and Business Administration, CEPR - Center for Economic Policy Research - CEPR); Cecilia García-Peñalosa (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique, CEPR - Center for Economic Policy Research - CEPR) |
| Abstract: | A large literature characterizes urbanisation as the result of productivity growth attracting rural workers to cities. We incorporate economic geography elements into a growth model and suggest that causation runs the other way: when rural workers move to cities, the resulting urbanisation produces technological change and productivity growth. Urban density leads to knowledge exchange and innovation, thus creating a positive feedback loop between city size and productivity that sets off sustained economic growth. The model is consistent with the fact that urbanisation rates in Western Europe, and notably in England, reached unprecedented levels by the mid-18 th century, the eve of the Industrial Revolution. |
| Keywords: | industrialization,urbanisation,innovation,long-run growth |
| Date: | 2021–01 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:halshs-03123659&r=all |
| By: | Rottner, Elisa; von Graevenitz, Kathrine |
| Abstract: | Carbon emissions from German manufacturing have increased over the past decade, while carbon intensity (emissions per Euro of gross output) has declined only slightly. We decompose changes in emissions between 2005 and 2017 into scale, composition (changes in the mix of goods produced) and technology (emission factors of production) effects. We find evidence that the production composition in the German manufacturing sector is increasingly shifting towards less carbonintensive products. However, we also find evidence to suggest that the energy intensity of production has increased. These results are largely driven by a few energy intensive sectors. |
| Keywords: | Carbon emissions,Climate Policy,Statistical Decomposition,Manufacturing |
| JEL: | D22 L60 Q41 Q48 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:21027&r=all |
| By: | Gianluca Orsatti; Francesco Quatraro; Alessandra Scandura |
| Abstract: | This paper investigates the association between region-level recombinant capabilities and the generation of green technologies (GTs), together with their interplay with the intensity of academic involvement in innovation dynamics. The analysis focuses on Italian NUTS 3 regions, over the period 1998-2009. We show that the local capacity to introduce novel combinations is positively and strongly associated to the generation of GTs, while the involvement of academic inventors in local innovation dynamics shows an interesting compensatory role when local contexts lack such capacity. |
| Keywords: | green technologies, academic inventors, recombinant novelty. |
| JEL: | O33 R11 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:cca:wpaper:617&r=all |
| By: | Nicola Cortinovis; Zhiling Wang; Hengky Kurniawan |
| Abstract: | In this paper, we explore how spillovers from multinational enterprises (MNEs) spread and impact domestic firms through different channels and at various spatial scales. Taking a firm-level approach, we test whether industrial relatedness mediates spillover effects from MNEs over and above horizontal and vertical linkages traditionally identified by the literature. Thanks to fine- grained geographical information, we further investigate the spatial reach of the spillovers and how they are associated with domestic firms’ characteristics such as absorptive capacity and technological sophistication. Our hypotheses are tested on a panel data set of Indonesian manufacturing firms census between 2002 to 2009. We find that domestic firms have higher total factor productivity when being exposed to a higher share of output from multinational firms in related industries, on top of the widely acknowledged horizontal and vertical MNE spillovers. We also show that MNE spillovers are sensitive to distance, with relatedness-mediated ones being detected between 30 and 60 km from the municipality of the MNE. Regarding heterogeneity, large firms benefit from productivity-enhancing relatedness spillovers at a wider spatial distance (up to 90km), and firms in less-advanced industries benefit from relatedness mediated effects as much as those in more advanced industries. |
| Keywords: | Multinational enterprises, spillovers, industrial relatedness, spatial effects. |
| JEL: | D24 F23 O33 R10 |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:egu:wpaper:2111&r=all |
| By: | Matteo Laffi; Ron Boschma; |
| Abstract: | The aim of the paper is to shed light on the role played by regional knowledge bases in Industry 3.0 in fostering new technologies in Industry 4.0 in European regions (NUTS3) over the period 1991-2015. We find that 4.0 technologies appear to be quite related to 3.0 technologies, with some heterogeneity among different technology fields. The paper investigates the geographical implications. We find that the probability of developing Industry 4.0 technologies is higher in regions that are specialised in Industry 3.0 technologies. However, other types of knowledge bases also sustain regional diversification in Industry 4.0 technologies. |
| Keywords: | Fourth Industrial Revolution, Industry 4.0, regional innovation, patents, knowledge space, relatedness, EU regions |
| JEL: | B52 O33 R11 |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:egu:wpaper:2112&r=all |
| By: | Joshy Easaw; Christian Grimme |
| Abstract: | We analyse the extent to which firm-level uncertainty is affected by aggregate uncertainty. Firm-level uncertainty is constructed from a large and monthly panel dataset of manufacturing firms. We find that aggregate uncertainty has a positive and robust impact on firm-level uncertainty. This effect holds across different types of domestic and international measures of aggregate uncertainty. However, the size of the impact is heterogeneous and depends on certain firm characteristics and the state of the business cycle. For example, the widely used economic policy uncertainty index matters to all firms’ uncertainty only in recessionary periods, while it is relevant over the entire business cycle only to large firms’ uncertainty. |
| Keywords: | firm-level uncertainty, aggregate uncertainty, survey data |
| JEL: | C23 E32 E01 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_8934&r=all |
| By: | Caselli, Mauro; Fracasso, Andrea; Scicchitano, Sergio; Traverso, Silvio; Tundis, Enrico |
| Abstract: | This work investigates the impact that the change in the exposure to robots had on the Italian local employment dynamics over the period 2011-2018. A novel empirical strategy focusing on a match between occupations' activities and robots' applications at a high level of disaggregation makes it possible to assess the impact of robotization on the shares of workers employed as robot operators and in occupations deemed exposed to robots. In a framework consistently centered on workers' and robots' activities, rather than on their industries of employment, the analysis reveals for the first time reinstatement effects among robot operators and heterogeneous results among exposed occupations. |
| Keywords: | Robots,Employment,Activities,Tasks,Robot applications |
| JEL: | J21 J23 J24 O33 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:glodps:802&r=all |
| By: | Bournakis, Ioannis; Papanastassiou, Marina; Papaioannou, Sotiris |
| Abstract: | We revisit the puzzle regarding the role of Multinational Enterprises (MNEs) on Total Factor Productivity (TFP) of domestic firms by drawing attention to foreign ownership structure. First, we differentiate between market share (MS) due to competition effects and knowledge agglomeration gains (AG). The former induces market pressure, due to foreign presence, and makes domestic firms to charge lower price mark-ups. Second, we investigate whether intra-industry (horizontal) and inter-industry (vertical) spillovers vary with the degree of foreign control. Using a sample of manufacturing firms from six European countries, we find that higher presence of MNEs in the domestic market makes domestic firms to charge lower mark-ups. Only majority and wholly-owned MNEs generate statistically significant horizontal spillovers. The economic size of these spillovers is low. We also detect backward spillovers from MNEs in downstream industries. However, forward spillovers from MNEs in upstream industries are negative. When we control for absorptive capacity, direct linkages with MNEs, scope of product differentiation and geographical proximity, the economic size of AG increases substantially. |
| Keywords: | MNEs, Foreign ownership, Spillovers, Market Share, Agglomeration Gains, Mark-up, Total Factor Productivity |
| JEL: | D23 D4 F14 F23 |
| Date: | 2020–05–17 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:106626&r=all |
| By: | Maryam Farboodi; Laura Veldkamp |
| Abstract: | The rise of information technology and big data analytics has given rise to "the new economy." But are its economics new? This article constructs a growth model where firms accumulate data, instead of capital. We incorporate three key features of data: 1) Data is a by-product of economic activity; 2) data is information used for prediction, and 3) uncertainty reduction enhances firm profitability. The model can explain why data-intensive goods or services, like apps, are given away for free, why many new entrants are unprofitable and why some of the biggest firms in the economy profit primarily from selling data. While our transition dynamics differ from those of traditional growth models, the long run still features diminishing returns. Just like accumulating capital, accumulating predictive data, by itself, cannot sustain long-run growth. |
| JEL: | O3 O4 |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:28427&r=all |