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on Technology and Industrial Dynamics |
| By: | Nicolo Barbieri (Department of Economics and Management, University of Ferrara, Ferrara, Italy); Alberto Marzucchi (Gran Sasso Science Institute, Social Sciences, L’Aquila, Italy); Ugo Rizzo (Department of Mathematics and Computer Science, University of Ferrara, Ferrara, Italy) |
| Abstract: | The present study explores the technological complementarities between green and non green inventions. First, we look at whether inventive activities in climate-friendly domains de pend on patenting in related technological domains that are not green. Based on patent data filed over the 1978–2014 period, we estimate a spatial autoregressive model using co-occurrence matrices to capture technological interdependencies. Our first finding highlights that the develop ment of green technologies strongly relies on advances in other green and in particular non-green technological domains, whose relevance for the green economy is usually neglected. Building on this insight, we detect the non-green complementary technologies that co-occur with green ones and assess whether environmental policies affect this particular instantiation of technologies at the country level. The results of the instrumental variable approach confirm that while envi ronmental policies spur green patenting, they do not displace the development of the non-green technological pillars upon which green inventions develop. |
| Keywords: | Green technology, patent data, environmental policy, network-dependent innovation |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:sru:ssewps:2021-08&r= |
| By: | Dughera, Stefano; Quatraro,Francesco; Ricci,Andrea; Vittori,Claudia (University of Turin) |
| Abstract: | We study the effect of temporary workers on innovation both theoretically and empirically. First, we develop a model where a representative firm chooses between different types of projects (routine vs innovative) and different types of labor contracts (temporary vs permanent). In doing so, it considers the effect of these different strategies on the workers’ incentives to invest in firm-specific skills. Our key finding is that firms offering temporary contracts are less likely to invest in innovative projects, and that this is effect is stronger in industries characterized by a “garage-business” innovation regime. Second, we test our hypotheses using firm-level data on employment composition and patent filing. Consistently with our theoretical predictions, we find that temporary workers are detrimental to innovation, and that this effect is mitigated by the concentration of patent-filing at the industry-level. |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:uto:dipeco:202115&r= |
| By: | Isabel Cavalli (Université Côte d'Azur, France; CNRS, GREDEG; Institute of Economics, Scuola Superiore Sant'Anna, Italy); Charlie Joyez (Université Côte d'Azur, France; CNRS, GREDEG) |
| Abstract: | Innovation is a dynamic process whose complexity lies in networks among heterogeneous actors, with collaboration often ending in patent co-ownership. Governments introduced many policies to redefine the role of universities in research collaboration once acknowledging their value in scientific knowledge. This paper explores how patent co-ownership evolved in France after decisive policy interventions (1999, 2006, 2007). Using French copatent data (1978-2018), we first employ Network Analysis to capture the evolution of centrality of French Universities. We then apply a Dif-in-Dif, incorporating a Propensity Score Matching (PSM), to investigate the potential causal relationship between policy interventions and the evolution of universities' centrality, contrasting with with French Public Research Organizations as well as German and Italian universities. Our results point to the increasing centrality gained by French universities in patenting co-ownership over the years and its essential role, as an innovator actor, in the French innovation system. Although the Innovation Act (1999) positively impacted their centrality, the impact of 2006-on legislation is either null or even negative, offsetting the initial trend. |
| Keywords: | Innovation dynamics, Universities, Collaborative Patents, Network centrality, treatment effect |
| JEL: | C54 D85 O32 O33 O34 O38 |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:gre:wpaper:2021-38&r= |
| By: | Carlo Cambini; Elena Grinza; Lorien Sabatino |
| Abstract: | We study the impact of ultra-fast broadband (UFB) infrastructures on the total factor productivity (TFP) and labor productivity of firms. We use unique balanced panel data for the 2013-2019 period on incorporated firms in Italy. Using the geographical location of the firms, we match firm data with municipality-level information on the diffusion of UFB, which started in 2015 in Italy. We derive consistent firm-level TFP estimates by adopting a version of the Ackerberg et al.’s (2015) method, which also accounts for firm fixed effects. We then assess the impact of UFB on productivity and deal with the endogeneity of UFB by exploiting the physical distance between each municipality and the closest backbone node. Our results show an overall positive impact of UFB on productivity. Services companies benefit the most from advanced broadband technologies, as do firms located in the North-West and South of Italy. We further decompose the impact of full-fiber networks (FTTH) from mixed copper-fiber connections (FTTC) and find that FTTH networks significantly contribute to enhancing firm productivity. Finally, by exploiting Labor Force Survey data, we provide suggestive evidence that productivity increases from UFB might be related to structural changes at the workforce level. |
| Keywords: | Ultra-fast broadband (UFB); fiber-based networks; fiber-to-the-home (FTTH) |
| JEL: | L96 D24 D22 |
| Date: | 2021–12–03 |
| URL: | http://d.repec.org/n?u=RePEc:sol:wpaper:2013/334687&r= |
| By: | Song Ma |
| Abstract: | This paper proposes a new measure of technological obsolescence using detailed patent data. Using this measure, we present two sets of results. First, firms' technological obsolescence foreshadows substantially lower growth, productivity, and reallocation of capital. This finding applies mainly for obsolescence of core innovation and embodied innovation, and it is stronger in competitive product markets. Second, in stock markets, high-obsolescence firms under-perform low-obsolescence firms by 7 percent annually. Using analyst forecast data, we show this is due to a systematic overestimation of future profits of obsolescent firms. The measure contains incremental information about firm innovation relative to measures focusing on new innovation. |
| JEL: | G1 G3 G4 O3 O4 |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:29504&r= |
| By: | Simon Bruhn (Ilmenau University of Technology, Ilmenau, Germany); Thomas Grebel (Ilmenau University of Technology, Ilmenau, Germany); Lionel Nesta (Université Côte d'Azur, France; GREDEG CNRS; OFCE, SciencesPo; SKEMA Business School) |
| Abstract: | This paper argues that the typical practice of performing growth decompositions based on log-transformed productivity values induces fallacious conclusions: using logs may lead to an inaccurate aggregate growth rate, an inaccurate description of the microsources of aggregate growth, or both. We identify the mathematical sources of this log-induced fallacy in decomposition and analytically demonstrate the questionable reliability of log results. Using firm-level data from the French manufacturing sector during the 2009-2018 period, we empirically show that the magnitude of the log-induced distortions is substantial. Depending on the definition of accurate log measures, we find that around 60-80% of four-digit industry results are prone to mismeasurement. We further find significant correlations of this mismeasurement with commonly deployed industry characteristics, indicating, among other things, that less competitive industries are more prone to log distortions. Evidently, these correlations also affect the validity of studies that investigate the role of industry characteristics in productivity growth. |
| Keywords: | productivity decomposition, growth, log approximation, geometric mean, arithmetic mean |
| JEL: | C18 L22 L25 O47 |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:gre:wpaper:2021-39&r= |
| By: | Alexandre Georgieff; Raphaela Hyee |
| Abstract: | Recent years have seen impressive advances in artificial intelligence (AI) and this has stoked renewed concern about the impact of technological progress on the labour market, including on worker displacement.This paper looks at the possible links between AI and employment in a cross-country context. It adapts the AI occupational impact measure developed by Felten, Raj and Seamans (2018[1]; 2019[2]) – an indicator measuring the degree to which occupations rely on abilities in which AI has made the most progress – and extends it to 23 OECD countries. The indicator, which allows for variations in AI exposure across occupations, as well as within occupations and across countries, is then matched to Labour Force Surveys, to analyse the relationship with employment.Over the period 2012-2019, employment grew in nearly all occupations analysed. Overall, there appears to be no clear relationship between AI exposure and employment growth. However, in occupations where computer use is high, greater exposure to AI is linked to higher employment growth. The paper also finds suggestive evidence of a negative relationship between AI exposure and growth in average hours worked among occupations where computer use is low.While further research is needed to identify the exact mechanisms driving these results, one possible explanation is that partial automation by AI increases productivity directly as well as by shifting the task composition of occupations towards higher value-added tasks. This increase in labour productivity and output counteracts the direct displacement effect of automation through AI for workers with good digital skills, who may find it easier to use AI effectively and shift to non-automatable, higher-value added tasks within their occupations. The opposite could be true for workers with poor digital skills, who may not be able to interact efficiently with AI and thus reap all potential benefits of the technology. |
| Keywords: | artificial intelligence, employment |
| JEL: | J21 J23 J24 O33 |
| Date: | 2021–12–15 |
| URL: | http://d.repec.org/n?u=RePEc:oec:elsaab:265-en&r= |
| By: | Ruchir Agarwal; Patrick Gaulé |
| Abstract: | To examine the drivers of innovation, this paper studies the global R&D effort to fight the deadliest diseases and presents four results. We find: (1) global pharmaceutical R&D activity—measured by clinical trials—typically follows the ‘law of diminishing effort’: i.e. the elasticity of R&D effort with respect to market size is about ½ in the cross-section of diseases; (2) the R&D response to COVID-19 has been a major exception to this law, with the number of COVID-19 trials being 7 to 20 times greater than that implied by its market size; (3) the aggregate short-term elasticity of science and innovation can be very large, as demonstrated by aggregate flow of clinical trials increasing by 38% in 2020, with limited crowding out of trials for non-COVID diseases; and (4) public institutions and government-led incentives were a key driver of the COVID-19 R&D effort—with public research institutions accounting for 70 percent of all COVID-19 clinical trials globally and being 10 percentage points more likely to conduct a COVID-19 trial relative to private firms. Overall, while economists are naturally in favor of market size as a driving force for innovation (i.e.“if the market size is sufficiently large then innovation will happen”), our work suggests that scaling up global innovation may require a broader perspective on the drivers of innovation—including early-stage incentives, non-monetary incentives, and public institutions. |
| Keywords: | COVID-19; Innovation; Market Size; Pharmaceutical Industry |
| Date: | 2021–02–19 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2021/048&r= |
| By: | Ruchir Agarwal; Geoff Smith; Patrick Gaulé |
| Abstract: | This paper studies the impact of U.S. immigration barriers on global knowledge production. We present four key findings. First, among Nobel Prize winners and Fields Medalists, migrants to the U.S. play a central role in the global knowledge network—representing 20-33% of the frontier knowledge producers. Second, using novel survey data and hand-curated life-histories of International Math Olympiad (IMO) medalists, we show that migrants to the U.S. are up to six times more productive than migrants to other countries—even after accounting for talent during one’s teenage years. Third, financing costs are a key factor preventing foreign talent from migrating abroad to pursue their dream careers, particularly for talent from developing countries. Fourth, certain ‘push’ incentives that reduce immigration barriers—by addressing financing constraints for top foreign talent—could increase the global scientific output of future cohorts by 42 percent. We concludeby discussing policy options for the U.S. and the global scientific community. |
| Keywords: | Immigration;Science;Talent;Universities;WP;IMO medalist;migrants to the U.S.;IMO participant;productivity regression;IMO point; Migration; Productivity; Income; Global |
| Date: | 2021–02–19 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2021/042&r= |
| By: | Sandro Mendonca; Hugo Confraria (Science Policy Research Unit, University of Sussex Business School, University of Sussex); Manuel Mira Godinho |
| Abstract: | Three decades after the publication of Zvi Griliches’ (1990) influential survey on “Patent statistics as economic indicators†, the uses and limitations of patent statistics remain a core issue in the field of innovation studies. This paper follows through Griliches’ seminal work to understand how the literature using patents as an empirical resource developed over time. How has this indicator been adopted and how has it been adapted to different research challenges? We address this question by examining the citation tree of nearly 2000 articles published in almost 400 journals found to refer to Griliches’ seminal contribution between 1990 and 2019. We combine bibliometric techniques and qualitative analysis to provide a close-up moving picture of patents as a data resource: growth and variety of usage, impact on disciplines and journals, driving institutions and geographies, major topics and research issues. We find that five main themes emerge: 1) Economic growth; 2) Geography of innovation; 3) Innovation management/performance; 4) Pat-methods; and 5) Green innovation. Shouldered by these findings, we discuss potential pathways for future patent-based research. |
| Keywords: | patents, innovation indicators, bibliometrics, survey, Zvi Griliches |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:sru:ssewps:2021-07&r= |
| By: | Chu, Angus |
| Abstract: | This paper provides a survey of studies that analyze the macroeconomic effects of intellectual property rights (IPR). The first part of this paper introduces different patent-policy instruments and reviews their effects on R&D and economic growth. This part also discusses the distortionary effects and distributional consequences of IPR protection as well as empirical evidence on the effects of patent rights. Then, the second part considers the international aspects of IPR protection. In summary, this survey draws the following conclusions from the literature. First, different patent-policy instruments have different effects on R&D and economic growth. Second, there is some empirical evidence supporting a positive relationship between IPR protection and innovation, but the evidence is stronger for developed countries than for developing countries. Third, the optimal level of IPR protection should tradeoff the social benefit of innovation against the social costs of multiple distortions and income inequality. Finally, in an open economy, achieving the globally optimal level of protection requires an international coordination (rather than the harmonization) of IPR protection. |
| Keywords: | economic growth; innovation; intellectual property rights |
| JEL: | O31 O34 O4 |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:110839&r= |
| By: | Jos\'e Alejandro Mendoza; Faustino Prieto; Jos\'e Mar\'ia Sarabia |
| Abstract: | Technology codes are assigned to each patent for classification purposes and to identify the components of its novelty. Not all the technology codes are used with the same frequency - if we study the use frequency of codes in a year, we can find predominant technologies used in many patents and technology codes not so frequent as part of a patent. In this paper, we measure that inequality in the use frequency of patent technology codes. First, we analyze the total inequality in that use frequency considering the patent applications filed under the Patent Co-operation Treaty at international phase, with the European Patent Office as designated office, in the period 1977-2018, on a yearly basis. Then, we analyze the decomposition of that inequality by grouping the technology codes by productive economic activities. We show that total inequality had an initial period of growth followed by a phase of relative stabilization, and that it tends to be persistently high. We also show that total inequality was mainly driven by inequality within productive economic activities, with a low contribution of the between-activities component. |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2111.11211&r= |
| By: | Michael Sposi; Kei-Mu Yi; Jing Zhang |
| Abstract: | We add to recent evidence on deindustrialization and document a new pattern: increasing industry polarization over time. We assess whether these patterns can be explained by a dynamic open economy model of structural change in which the two primary driving forces are sector-biased productivity growth and sectoral trade integration. We calibrate the model to the same countries used to document our patterns. We find that sector-biased productivity growth is important for deindustrialization, and sectoral trade integration is important for industry polarization through specialization. The interaction of these two driving forces is also essential. The key transmission channel is the declining relative price of manufacturing goods to services over time. |
| JEL: | F11 F43 O11 O41 |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:29483&r= |
| By: | Tahsin Saadi Sedik |
| Abstract: | COVID-19 has exacerbated concerns about the rise of the robots and other automation technologies. This paper analyzes empirically the impact of past major pandemics on robot adoption and inequality. First, we find that pandemic events accelerate robot adoption, especially when the health impact is severe and is associated with a significant economic downturn. Second, while robots may raise productivity, they could also increase inequality by displacing low-skilled workers. We find that following a pandemic, the increase in inequality over the medium term is larger for economies with higher robot density and where new robot adoption has increased more. Our results suggest that the concerns about the rise of the robots amid the COVID-19 pandemic seem justified. |
| Keywords: | Pandemics;Robots;Inequality.;WP;pandemic event;studies showing;impact of pandemic;country-industry pair level;industry breakdown |
| Date: | 2021–01–15 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2021/011&r= |
| By: | Altenburg, Tilman; Brandi, Clara; Pegels, Anna; Stamm, Andreas; Vrolijk, Kasper; Zintl, Tina |
| Abstract: | This study explores to what extent the COVID-19 crisis has been a turning point in the industrialisation process and the overall progress of countries towards sustainable development and what this implies for future inclusive and sustainable industrial development policies. The focus of the study is on latecomer economies.In the first part of this study, we show how the prospects for industrialisation are changing. The reasons are manifold, yet the following global megatrends have particularly strong effects: i) digitalisation and automation of production; ii) global economic power shifts, with enormous ramifications for trade flows and global value chains; and iii ) the greening of economies. These trends are interrelated in multiple ways and, in conjunction, shape the direction of structural change. They open up new avenues for inclusive and sustainable latecomer industrialisation - including digital technologies that reduce transaction costs for countries on the periphery that are willing to benefit from trade; the shift of labour-intensive investments from China to other latecomer economies; or the increasing demand for renewable energy and green hydrogen for which many latecomer countries offer excellent conditions. At the same time, digitalisation and increasing environmental standards raise entry barriers to markets, especially for country with weak innovation systems; likewise, automation tends to undermine latecomer countries' traditional advantages in labour-intensive industries. [...] |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:diedps:322021&r= |
| By: | Coad, Alex; Srhoj, Stjepan |
| Abstract: | The Entrepreneurial Ecosystems (EE) approach makes specific predictions regarding how EE inputs are converted into high-growth firms (HGFs) as an output. A simulation model draws out our hypothesis of regional persistence in HGF shares. Based on intuitions that EEs are persistent, we investigate whether regional HGF shares are persistent, using census data for 2 European countries taken separately (Croatia for 2004-2019, and Slovenia for 2008-2014). Overall, there is no clear persistence in regional HGF shares - regions with large HGF shares in one period are not necessarily likely to have large HGF shares in the following period. This is a puzzle for EE theory. In fact, there seems to be more persistence in industry-level HGF shares than for regional HGF shares. We formulate a 'broken clock' critique - just as a broken clock is correct twice a day, EE recommendations may sometimes be correct, but are fundamentally flawed as long as time-changing outcomes (HGF shares) are predicted using time-invariant variables (such as local universities, institutions and infrastructure). |
| Keywords: | High-Growth Firms,Persistence,Regional Persistence,Entrepreneurial Ecosystems,Clusters,Sectoral Systems of Innovation |
| JEL: | L25 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:glodps:996&r= |
| By: | Karim Bekhtiar; Benjamin Bittschi (Austrian Institute of Economic Research); Richard Sellner |
| Abstract: | In a seminal paper Graetz and Michaels (2018) find that robots increase labor productivity and TFP, lower output prices and adversely affect the employment share of low-skilled labor. We demonstrate that these effects are heavily influenced by the sample composition and argue that focusing on manufacturing and mining sectors mitigates unobserved heterogeneity and is more coherent with an identification strategy that rests on instruments that do not vary by industries. In sum, this leads to more plausible results regarding the overall economic effects of robotization, whereby the focus on robotizing industries leads to a sizable drop of the productivity effects, halving the effect size for labor productivity and insignificant price effects. The most pronounced consequences from the sample choice occur for labor market outcomes, where significant negative employment effects become insignificant and positive wage effects are reversed into the opposite. We show that controlling for demographic workforce characteristics is essential for obtaining significant labor productivity effects and leads to the negative effects of robots on wages. Additionally, investigating only robotizing sectors does not corroborate skill-biased technological change due to robotization, but rather, indicates towards labor market polarization. Finally, we document a non-monotonicity in one of the instruments, which calls for caution in the use of that instrument. |
| Keywords: | Robots, Productivity, Technological change |
| Date: | 2021–12–09 |
| URL: | http://d.repec.org/n?u=RePEc:wfo:wpaper:y:2021:i:639&r= |
| By: | Yusuf Kenan Bagir; Unal Seven |
| Abstract: | We study whether the linkage between financing and productivity growth strengthens as the severity of financial constraints increases by using firm-level administrative data from a large emerging economy. We also explore whether upstream firms’ financial constraints play a role in the linkage between finance and productivity. Using a combination of administrative databases of tax registry and firm-to-firm trade data of 896,317 Turkish firms from 2007 to 2018, employing various robustness tests and controlling for reverse causality, we find strong evidence that firms facing higher financial constraints exhibit a higher sensitivity of total factor productivity (TFP) growth to debt growth. Moreover, we show that a rise in upstream firms’ financial constraint level also leads to increased sensitivity of TFP growth to debt growth. Our results reveal important channels through which financial constraints could hinder productivity growth in Turkey. |
| Keywords: | TFP growth, Financial constraints, Debt growth |
| JEL: | D24 G30 O16 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:tcb:wpaper:2132&r= |