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on Technology and Industrial Dynamics |
| By: | Mr. JaeBin Ahn; Chan Kim; Ms. Nan Li; Andrea Manera |
| Abstract: | This paper examines the impact of Foreign Direct Investment (FDI) on knowledge diffusion by analyzing the effect of firm-level FDI activities on cross-border patent citations. We construct a novel firm-level panel dataset that combines worldwide utility patent and citations data with project-level greenfield FDI and crossborder mergers and acquisitions (M&A) data over the past two decades, covering firms across 60 countries. Applying a new local projection difference-indifferences methodology, our analysis reveals that FDI significantly enhances knowledge flows both from and to the investing firms. Citation flows between investing firms and host countries increase by up to around 10.6% to 13% in five years after the initial investment. These effects are stronger when host countries have higher innovation capacities or are technologically more similar to the investing firm. We also uncover knowledge spillovers beyond targeted firms and industries in host countries, which are particularly more pronounced for sectors closely connected in the technology space. |
| Keywords: | Greenfield FDI; Brownfield FDI; cross-border M&A; Inward FDI; Outward FDI; Knowledge spillover; Patent citation; LP-DiD |
| Date: | 2024–07–12 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2024/152 |
| By: | Findeisen, Sebastian; Dauth, Wolfgang; Schlenker, Oliver |
| Abstract: | New technologies drive productivity growth but the distribution of gains might be unequal and is mediated by labor market institutions. We study the role that organized labor plays in shielding incumbent workers from the potential negative consequences of automation. Combining German individual-level administrative records with information on plant-level robot adoption and the presence of works councils, a form of shop-floor worker representation, we find positive moderating effects of works councils on retention for incumbent workers during automation events. Separations for workers with replaceable task profiles are significantly reduced. When labor markets are tight and replacement costs are high for firms, incumbent workers become more valuable and the effects of works councils during automation events start to disappear. Older workers, who find it more challenging to reallocate to new employers, benefit the most from organized labor in terms of wages employment. Concerning mechanisms we find that robot-adopting plants with works councils employ not more but higher quality robots. They also provide more training during robot adoption and have higher productivity growth thereafter. |
| Keywords: | automation, organized labor, work councils, labor market tightness, worker re-training |
| JEL: | J20 J30 J53 O33 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:cexwps:300230 |
| By: | Karam Jo; Seula Kim |
| Abstract: | We study how friction in learning others’ technology, termed “imperfect technology spillovers, ” incentivizes firms to use different types of innovation and impacts the implications of competition through changes in innovation composition. We build an endogenous growth model in which multi-product firms enhance their products via internal innovation and enter new product markets through external innovation. When learning others’ technology takes time due to this friction, increased competitive pressure leads firms with technological advantages to intensify internal innovation to protect their markets, thereby reducing others’ external innovation. Using the U.S. administrative firm-level data, we provide regression results supporting the model predictions. Our findings highlight the importance of strategic firm innovation choices and changes in their composition in shaping the aggregate implications of competition. |
| Keywords: | competition, innovation, technology spillover, endogenous growth |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:cen:wpaper:24-40 |
| By: | Cristiana Bendetti-Fasil; Giammario Impullitti; Omar Licandro; Petr Sedlacek; Adam Hal Spencer |
| Abstract: | R&D is procyclical and a crucial driver of growth. Evidence indicates that innovation activity varies widely across firms. Is there heterogeneity in innovation cyclicality? Does innovation heterogeneity matter for business cycle propagation? We provide empirical evidence that more productive firms are less procyclical in innovation. We develop a model replicating this observation, with selection as the driver of heterogeneous innovation cyclicality. We then examine how heterogeneous innovation and growth influence business cycle propagation. Dynamics of firm entry and exit, coupled with heterogeneous cyclicality, significantly amplify TFP shock propagation. Business cycle fluctuations give substantial welfare losses, with firm heterogeneity contributing significantly. |
| Keywords: | Growth, Business Cycles, Innovation, Heterogeneous Firms |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:not:notcfc:2024/03 |
| By: | Andreas Schaefer (University of Bath); Maik T. Schneider (University of Graz) |
| Abstract: | With the 4th Industrial Revolution ahead there is huge uncertainty about the likely labour market impacts ranging from massive layoffs as a response to Automation and AI to the view that overall more jobs will be created than lost. Whatever the outcome in the end, there will be major structural change with substantial implications for individual labour income risk. We argue that precautionary savings are an ineffective protection against labour market risk arising from major technological shifts and discuss four policy instruments, 1) a private insurance scheme, 2) a universal basic income, 3) a robot tax, and 4) a governmental insurance scheme. Further, we examine whether these policy instruments are suitable to achieve high and inclusive growth. |
| Keywords: | Artificial Intelligence, Economic Growth, Endogenous Technological Change, Industrial Revolution, Robot Tax, Universal Basic Income. |
| JEL: | H20 O33 O38 |
| Date: | 2024–01 |
| URL: | https://d.repec.org/n?u=RePEc:grz:wpaper:2024-06 |
| By: | CONFRARIA Hugo (European Commission - JRC); FAKO Peter (European Commission - JRC); GAVIGAN James (European Commission - JRC); COMPANO Ramon (European Commission - JRC) |
| Abstract: | This brief provides JRC firm-level microdata-based analyses of innovation dynamics in the global automotive industry comparing EU firms to their global competitors. It takes the sector's top global players from the 2023 EU Industrial R&D Investment Scoreboard as a starting point, analyses clusters of R&D intensity, examines past financial performance and analyses participation in start-ups via corporate venturing. |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc138139 |
| By: | Pierre Cotterlaz; Arthur Guillouzouic |
| Abstract: | This paper shows that the negative effect of geographical distance on knowledge flows stems from how firms gain sources of knowledge through their existing network. We start by documenting two stylized facts. First, in aggregate, the distance elasticity of patent citations flows has remained constant since the 1980s, despite the rise of the internet. Second, at the micro level, firms disproportionately cite existing knowledge sources, and patents cited by their sources. We introduce a framework featuring the latter phenomenon, and generating a negative distance elasticity in aggregate. The model predicts Pareto-distributed innovator sizes, and citation distances increasing with innovator size. These predictions hold well empirically. We investigate changes of the underlying parameters and geographical composition effects over the period. While the distance effect should have decreased with constant country composition, the rise of East Asian economies, associated to large distance elasticities, compensated lower frictions in other countries. |
| Keywords: | Knowledge Diffusion;Innovation Networks;Spatial Frictions;Patent Citation |
| JEL: | L14 O33 R12 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:cii:cepidt:2024-08 |
| By: | Reher, Leonie; Runst, Petrik; Thomä, Jörg; Bizer, Kilian |
| Abstract: | In order to better capture non-R&D based processes related to Learning by Doing, Using and Interacting (DUI) as a basis for policy advice, this paper empirically identifies DUI mode drivers of SME innovation. For the first time, a large set of conceptually derived indicators is used in a self-conducted survey. Using lasso regression as a data-driven selection technique capable of handling such a large number of potential predictors, we find that DUI learning involves a wide range of elements beyond interaction with external actors. Moreover, our results suggest that the relevance of DUI learning for predicting SME innovation depends on both the region and the type of innovation output. SME innovation in lagging regions is strongly related to the DUI mode, which is particularly pronounced in the case of intra-firm learning processes. These results suggest that R&D capacity is not the only main driver of SME innovation, especially in lagging regions, and therefore provide an indication of how firms can compensate for unfavourable conditions in their regional innovation environment. This in turn implies going beyond innovation policy in the narrow sense to a more holistic approach that may include links with other policy areas. |
| Abstract: | Um die nicht auf formaler Forschung und Entwicklung (FuE) basierenden Prozesse im Zusammenhang mit dem handwerksnahen Innovationsmodus des "Learning by Doing, Using and Interacting (DUI)" als Grundlage für die Gestaltung innovationspolitischer Maßnahmen besser zu erfassen, werden in diesem Forschungspapier die DUI-Treiber von Innovationen in kleinen und mittleren Unternehmen (KMU) empirisch ermittelt. Erstmals wird ein umfangreiches Set konzeptionell hergeleiteter DUI-Indikatoren in einer eigenen Erhebung erhoben und ausgewertet. Unter Verwendung der Lasso-Regression als datengetriebene Selektionsmethode, die in der Lage ist, mit einer so großen Anzahl potenzieller Prädiktoren umzugehen, zeigt sich, dass DUI-Lernen in KMU eine breite Palette von Elementen umfasst, die über die Interaktion mit externen Akteuren hinausgehen. Darüber hinaus deuten unsere Ergebnisse darauf hin, dass die Relevanz des DUI-Lernens als Treiber von Innovationen in KMU sowohl von der Region als auch von der Art des Innovationsoutputs abhängt. So hängt die Innovationstätigkeit von KMU in strukturschwachen Regionen besonders stark mit dem DUI-Modus zusammen, was im Fall von unternehmensinternen Lernprozessen besonders ausgeprägt ist. Diese und andere Ergebnisse deuten darauf hin, dass die FuE-Kapazität insbesondere in strukturschwachen Regionen nicht der einzige Treiber für Innovationen in KMU ist, und geben damit einen Hinweis darauf, wie Unternehmen ungünstige Bedingungen in ihrem regionalen Innovationsumfeld zumindest teilweise kompensieren können. Dies wiederum setzt voraus, dass man über die Innovationspolitik im engeren Sinne hinausgeht und einen ganzheitlicheren Ansatz verfolgt, der auch Verbindungen zu anderen Politikbereichen wie Arbeitsmarkt oder Bildung beinhaltet. |
| Keywords: | innovation measurement, innovation indicator, modes of innovation, SME innovation, regional innovation, lagging regions, lasso regression, variable selection, group lasso, ordinal predictors |
| JEL: | C50 C81 O3 O31 R11 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:ifhwps:300235 |
| By: | Stephen Ayerst; Duc Nguyen; Diego Restuccia |
| Abstract: | We examine aggregate productivity differences across nations using cross-country firm-level data and a quantitative model of production heterogeneity with distortions featuring operation decisions (selection) and productivity-enhancing investments (technology). Empirically, less developed countries feature higher distortions and larger dispersion in firm-level productivity, mostly resulting from the higher prevalence of unproductive firms. Quantitatively, measured cross-country differences in the elasticity of distortions with respect to firm productivity generate the bulk of empirical patterns and over two-thirds of cross-country labor productivity differences. Both selection and technology channels are important. Variation in static misallocation also plays an important role, albeit smaller. |
| Keywords: | Firms, productivity, size, distortions, misallocation, selection, technology. |
| JEL: | O11 O14 O4 |
| Date: | 2024–07–18 |
| URL: | https://d.repec.org/n?u=RePEc:tor:tecipa:tecipa-779 |
| By: | Cattaneo, Maria Alejandra (Swiss Co-ordination Center for Research in Education); Gschwendt, Christian (University of Bern); Wolter, Stefan C. (University of Bern) |
| Abstract: | Advances in technology have always reshaped labor markets. Automating human labor has lead to job losses and creation but most of all, for an increasing demand for highly skilled workers. However, emerging AI innovations like ChatGPT may reduce labor demand in high skilled occupations previously considered "safe" from automation. While initial studies suggest that individuals adjust their educational and career choices to mitigate automation risk, it is unknown what people would be willing to pay for a reduced automation risk. This study quantifies this value by assessing individuals' preferences for occupations in a discrete-choice experiment with almost 6'000 participants. The results show that survey respondents are willing to accept a salary reduction equivalent to almost 20 percent of the median annual gross wage to work in an occupation with a 10 percentage point lower risk of automation. Although the preferences are quite homogeneous, there are still some significant differences in willingness to pay between groups, with men, younger people, those with higher levels of education, and those with a higher risk tolerance showing a lower willingness to pay for lower automation risk. |
| Keywords: | artificial intelligence, automation, willingness to pay, survey experiment |
| JEL: | J24 O33 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17097 |
| By: | Michal Burzynski |
| Abstract: | Automation of labor tasks is one of the most dynamic aspects of recent technological progress. This paper aims at improving our understanding of the way that automation affects labor markets, analyzing the example of European countries. The quantitative theoretical methodology proposed in this paper allows to focus on automation-induced migration of workers, occupation switching and income inequality. The key findings include that automation in the first two decades of the 21st century had a significant impact on job upgrading of native workers and generated gains in many local labor markets. Even though net migration of workers was attenuated due to convergence in incomes across European regions, mobility at occupation levels had a sizeable impact on transmitting welfare effects of automation. |
| Keywords: | automation; migration; technological progress; inequality |
| JEL: | J24 O33 R12 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:irs:cepswp:2024-04 |
| By: | Alberto Rivera-Padilla (Tulane University) |
| Abstract: | I document how the importance of structural transformation for spatial convergence of labor income varies across countries. I use microdata to show that in recent decades structural change accounts for a much larger share of income convergence in developing countries than in rich countries. Convergence in sectoral composition of employment accounts for most of the contribution of structural change. Using a quantitative general equilibrium model, I find that the increase in educational attainment has been a key determinant of income convergence in developing countries. The results of the model imply that unbalanced productivity growth in agriculture is mostly relevant for convergence in sectoral composition of employment, but not for income convergence. |
| Keywords: | structural transformation, spatial convergence, productivity, human capital |
| JEL: | E24 J24 O11 O18 R11 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:tul:wpaper:2410 |
| By: | Qichun He (China Economics and Management Academy, Central University of Finance and Economics); Xin Yang (China Economics and Management Academy, Central University of Finance and Economics); Heng-fu Zou (China Economics and Management Academy, Central University of Finance and Economics) |
| Abstract: | This paper explores the growth and welfare ects of monetary policy in a Schumpeterian vertical innovation model with automation. Money is introduced into the model via the cash-in-advance (CIA) constraints on consumption, production, automation and vertical innovation. We find that the relative strength of the cash constraints on automation and vertical innovations is crucial. If the CIA constraint is stronger (weaker) for automation, a higher nominal interest rate will lead to an increase (a decrease) in the amount of high-skilled labor allocated to vertical innovation. As a result, the automation level will decline (rise), but the vertical innovation and thereby aggregate economic growth will be faster (slower). We calibrate the model to the US economy and find a stronger cash constraint on automation. Our quantitative analysis shows that rising nominal interest rates are detrimental to automation but favorable to growth. In addition, higher nominal interest rates improve the welfare of dierent households and the aggregate welfare. As an empirical test, we find a signifficant, negative effect of the nominal interest rate on automation using cross-country panel data, consistent with our model prediction. |
| Keywords: | Monetary policy; Automation; Cash-in-advance; Schumpeterian model |
| JEL: | O42 E42 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:cuf:wpaper:640 |
| By: | ADACHI Daisuke |
| Abstract: | This paper examines the distributional impacts of the increased utilization of industrial robots, emphasizing their role in specific tasks and their international trade. The study constructs a novel dataset based on tracking shocks to the cost of acquiring robots from Japan, termed the Japan Robot Shock (JRS), and analyzes these across various occupations that have adopted robots. A general equilibrium model is developed which incorporates robot automation in a large open economy, and a model-implied optimal instrumental variable (MOIV) is constructed from the JRS to address the identification challenges posed by the correlation between automation shocks and JRS. The analysis reveals that the elasticity of substitution (EoS) between robots and labor is heterogeneous across occupations, reaching up to 3 in production and material-moving jobs, which is significantly higher than the EoS between other capital goods and labor. The findings suggest that robots significantly contributed to wage polarization in the U.S. from 1990 to 2007. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:eti:dpaper:24066 |
| By: | Martin Beraja; Wenwei Peng; David Y. Yang; Noam Yuchtman |
| Abstract: | Venture capital plays an important role in funding and shaping innovation outcomes, characterized by investors’ deep knowledge of the technology, industry, and institutions, as well as their long-running relationships with the entrepreneurship and innovation community. China, in its pursuit of global leadership in AI innovation and technology, has set up government venture capital funds so that both national and local governments act as venture capitalists. These government-led venture capital funds combine features of private venture capital with traditional government innovation policies. In this paper, we collect comprehensive data on China’s government and private venture capital funds. We draw three important contrasts between government and private VC funds: (i) government funds are spatially more dispersed than private funds; (ii) government funds invest in firms with weaker ex-ante performance signals but these firms exhibit growth rates exceeding those of firms in which private funds invest; and (iii) private VC funds follow government VC investments, especially when hometown government funds directly invest on firms with weaker ex-ante performance signals. We interpret these patterns in light of VC funds’ traditional role overcoming information frictions and China’s unique institutional environment, which includes important frictions on mobility and information. |
| JEL: | G18 G24 G28 G30 H19 O3 O38 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32701 |
| By: | Pierre-Alex Balland; Ron Boschma; ; ; |
| Abstract: | This paper proposes an evolutionary take on regional development traps. Our definition of regional traps centers around the structural inability of regions to develop new complex activities. We distinguish between several different traps. Using industry data, we follow European regions over time and provide evidence on which regions in the EU are trapped, and what kinds of traps they have fallen into. Our econometric analysis shows that being trapped has a negative impact on employment and wage growth in regions. We also find evidence that our development trap indicator explains well whether regions are stuck in a regional development trap, as defined by Iammarino et al. (2020). |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2420 |
| By: | McAdam, Peter; Meinen, Philipp; Papageorgiou, Chris; Schulte, Patrick |
| Abstract: | Using a new administrative dataset, we provide fresh micro-level evidence on firms' returns to scale (RTS). We employ a new administrative database, iBACH, which contains extensive high-quality annual balance sheet, financial, and demographic information on more than two million non-financial manufacturing, trade and service corporations for five European countries over 2008-2018. Whereas on average, we find sectoral RTS to be close to one (0.98, with a 0.74 - 1.18 range), 32 percent of firms exhibit decreasing returns, and 10 percent increasing returns to scale (IRTS). Although the RTS values have remained relatively stable, there is evidence of some tendency for them to increase over time. When we allow for imperfect competition, the RTS range tightens to 0.98 - 1.08, with a higher share of IRTS industries (15 percent) and essentially zero DRTS cases. Increasing returns are mostly a feature of manufacturing. Finally, we analyze the relationship between different industry characteristics and our RTS estimates. |
| Keywords: | Firm & sectoral production function estimation, imperfect competition, firm characteristics, Gandhi-Navarro-Rivers, iBACH database |
| JEL: | E2 D2 L1 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:bubdps:300572 |
| By: | Hendriks, Patrick; Sturm, Timo; Geis, Maximilian; Grimminger, Till; Mast, Benedikt |
| Abstract: | Research has long underscored the critical role of effective team collaboration in surpassing the limits of individual members’ capabilities. With organizations now increasingly integrating artificial intelligence (AI) as quasi-team members to enhance learning, problem-solving, and decision-making in teams, there is a pressing need to understand how to foster effective collaboration between teams and AI systems (i.e., team-AI collaboration). By adopting a design science approach and conducting nine semi-structured interviews with knowledge workers, we identify design requirements and principles for effective team-AI collaboration systems from an end-user perspective. We then develop a team-AI collaboration system within Discord (a voice, video, and text chat application) and evaluate its design through five laboratory experiments with human-AI teams. Our results show that introducing configurable roles and personalities for AI team members prompts humans to reconsider their own biases. However, human preconceptions still play a dominant role in shaping team performance. |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:dar:wpaper:146863 |