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on Technology and Industrial Dynamics |
| By: | Lukas Rosenberger; W. Walker Hanlon; Carl Hallmann |
| Abstract: | How did Britain sustain faster rates of economic growth than comparable European countries, such as France, during the Industrial Revolution? We argue that Britain possessed an important but underappreciated innovation advantage: British inventors worked in technologies that were more central within the innovation network. We offer a new approach for measuring the innovation network using patent data from Britain and France in the late-18th and early-19th century. We show that the network influenced innovation outcomes and demonstrate that British inventors worked in more central technologies within the innovation network than French inventors. Drawing on recently developed theoretical tools, and using a novel estimation strategy, we quantify the implications for technology growth rates in Britain compared to France. Our results indicate that the shape of the innovation network, and the location of British inventors within it, explains an important share of the more rapid technological change and industrial growth in Britain during the Industrial Revolution. |
| Keywords: | industrial revolution, innovation network, patents, economic growth |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11299 |
| By: | Maximiliano Machado (Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de Administración. Instituto de Economía); Carlos Bianchi (Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de Administración. Instituto de Economía) |
| Abstract: | A large body of literature has identified positive persistence effects of innovation in firms located in developed countries. However, this is not the rule in developing economies. This article adds to this topic by analysing the short- and medium-term innovation persistence in Uruguayan firms during the recent period of expansion of the innovation policies in this country. Using a panel data set from the Uruguayan Innovation Survey 2007–2018, we run parametric and nonparametric estimations of firms’ innovation persistence in manufacturing and service sectors. Our findings indicate that innovation is an uneven, even erratic, process. Contrary to most of the extant research on the topic, we find mostly negative persistence effects of outcome innovation (both product and process) in the short term and positive persistence effects of R&D and innovation activities based on external knowledge acquisition (input innovation) in the medium term. Moreover, we observe positive effects of public support on both input and outcome innovation in the short term but no effects in the medium term. We discuss timing and coordination challenges for innovation policies in Uruguay, and the applicability of our findings to developing countries as an alternative to the extended interpretation of innovation as a self-efficient process. |
| Keywords: | Innovation input, Innovation persistence, Innovation outcome, Policy mix |
| JEL: | O31 O32 L25 C01 |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:ulr:wpaper:dt-06-24 |
| By: | Brian C. Fujiy |
| Abstract: | I causally estimate local knowledge spillovers in R&D and quantify their importance when implementing R&D policies. Using a new administrative panel on German inventors, I estimate these spillovers by isolating quasi-exogenous variation from the arrival of East German inventors across West Germany after the Reunification of Germany in 1990. Increasing the number of inventors by 1% increases inventor productivity by 0.4%. I build a spatial model of innovation, and show that these spillovers are crucial when reducing migration costs for inventors or implementing R&D subsidies to promote economic activity. |
| Keywords: | inventors, research and development, innovation, agglomeration, spillovers |
| JEL: | F16 J61 O4 O31 R12 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:cen:wpaper:24-59 |
| By: | Engberg, Erik (Örebro University School of Business); Hellsten, Mark (Aarhus University); Javed, Farrukh (Lund University); Lodefalk, Magnus (Örebro University School of Business); Sabolová, Radka (Örebro University School of Business); Schroeder, Sarah (Aarhus University); Tang, Aili (Örebro University School of Business) |
| Abstract: | This paper investigates the impact of artificial intelligence (AI) on hiring and employment, using the universe of job postings published by the Swedish Public Employment Service from 2014-2022 and universal register data for Sweden. We construct a detailed measure of AI exposure according to occupational content and find that establishments exposed to AI are more likely to hire AI workers. Survey data further indicate that AI exposure aligns with greater use of AI services. Importantly, rather than displacing non-AI workers, AI exposure is positively associated with increased hiring for both AI and non-AI roles. In the absence of substantial productivity gains that might account for this increase, we interpret the positive link between AI exposure and non-AI hiring as evidence that establishments are using AI to augment existing roles and expand task capabilities, rather than to replace non-AI workers. |
| Keywords: | Artificial Intelligence; Technological Change; Automation; Labour Demand |
| JEL: | D22 J23 J24 O33 |
| Date: | 2024–11–07 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:oruesi:2024_010 |
| By: | Abbasiharofteh, Milad (University of Groningen); Kriesch, Lukas (Justus Liebig University Giessen) |
| Abstract: | A twin (a joint green and digital) transition aims to facilitate achieving the Green Deal goals. The interplay between regional capabilities and twin transition market applications remains understudied. This research utilizes Large Language Models to analyze web texts of more than 600, 000 German firms, assessing whether their products contribute to the twin transition. Our findings suggest while AI capabilities benefit the twin transition market applications, clean technological capabilities play a significant role only in highly specialized regions. To facilitate future research and informed policymaking, we provide open access to our developed dataset and AI tools (i.e., the TwinTransition Mapper). |
| Keywords: | Twin transition; TwinTransition Mapper; digital layer; technological capabilities |
| JEL: | C81 C88 O30 O31 |
| Date: | 2024–11–13 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:lucirc:2024_016 |
| By: | Marius Faber; Kemal Kilic; Gleb Kozliakov; Dalia Marin |
| Abstract: | The world economy has become more and more globalized as firms have organized production along global value chains. But more recently, globalization has stalled. This paper shows that higher uncertainty, in combination with better automation technologies, has likely contributed to that trend reversal. We show that plausibly exogenous exposure to uncertainty in developing countries leads to reshoring to high-income countries, but only if industrial robots have made this economically feasible. In contrast, we find no strong evidence of nearshoring or diversification. We address concerns about reverse causality by showing that results hold when using two alternative identification strategies. In a narrative approach, we use only locally generated spikes in uncertainty, for which the narrative around the events suggest that they are plausibly exogenous. In a small open economy approach, we restrict the sample to small developed countries that are unlikely to cause uncertainty in the developing world. Moreover, we show that results are robust to the main threats to identification related to shift-share instruments. |
| Keywords: | global value chains, uncertainty, automation, reshoring, shift-share design |
| JEL: | F14 F15 F16 J23 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11419 |
| By: | Emmens, Joseph; Hutschenreiter, Dennis; Manfredonia, Stefano; Noth, Felix; Santini, Tommaso |
| Abstract: | Does increasing common ownership influence firms' automation strategies? We develop and empirically test a theory indicating that institutional investors' common ownership drives firms that employ workers in the same local labor markets to boost automation-related innovation. First, we present a model integrating task-based production and common ownership, demonstrating that greater ownership overlap drives firms to internalize the impact of their automation decisions on the wage bills of local labor market competitors, leading to more automation and reduced employment. Second, we empirically validate the model's predictions. Based on patent texts, the geographic distribution of firms' labor forces at the establishment level, and exogenous increases in common ownership due to institutional investor mergers, we analyze the effects of rising common ownership on automation innovation within and across labor markets. Our findings reveal that firms experiencing a positive shock to common ownership with labor market rivals exhibit increased automation and decreased employment growth. Conversely, similar ownership shocks do not affect automation innovation if firms do not share local labor markets. |
| Keywords: | automation, common ownership, local labor markets, market power |
| JEL: | G23 J23 L22 O32 O33 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:iwhdps:304457 |
| By: | Gong, Huiwen (University of Stavanger); Hassink, Robert (Kiel University) |
| Abstract: | In regional studies and economic geography, interest in regional economic resilience and regional innovation policy has steadily increased in recent decades. Although these two perspectives appear to be closely related, relatively little research has elaborated on the interrelationships between them. In this chapter, we take stock of the current two key conceptual extensions of the work on regional economic resilience, i.e., 1) the simultaneous consideration of regional and value chain resilience and 2) the discussion on transformative resilience and the normative turn on regional innovation policy in economic geography and beyond. Overall, we find that the shift toward more sustainable and inclusive development is increasingly being advocated by scholars working on regional resilience or regional innovation policy, leading to increased interest in new concepts such as "transformative resilience" and "challenge-oriented/transformative regional innovation policy" in the respective research fields. However, there is relatively little evidence on how regional resilience that is transformative in nature (e.g., transformative resilience) can be fostered by the new generation of regional innovation policy and how the increasing frequency of shocks of all kinds requires new thinking in regional innovation policy. We therefore suggest four promising avenues for future research that link the hitherto largely isolated perspectives of regional resilience and regional innovation policy to explain regional economic change in the post-crisis period. |
| Keywords: | regional innovation policy; regional resilience; transformative resilience; economic geography |
| JEL: | O30 O38 R10 |
| Date: | 2024–11–12 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:lucirc:2024_015 |
| By: | Anton-Tejon, Marcos; Barge-Gil, Andrés; Albahari, Alberto |
| Abstract: | The interest in regional innovation policies has increased in recent years. Science and Technology Parks (STPs) are one of the most widespread regional innovation policies worldwide. They are considered a catalyst for regional innovation because they constitute a source of knowledge spillovers and a mechanism for knowledge transfer. The aim of this work is to evaluate the effect of the adoption of the STP policy on regional innovation performance. To this end, we build a provincial dataset for Spain covering 37 years and implement a difference-in-differences approach taking advantage of the staggered adoption of the STP policy and the fact that some provinces do not have an STP yet. The main results show that STPs increase provincial patents by 49.8% in years 6-10 after the adoption of the policy and by 79.7% in years 11-15.This result is robust to different assumptions and methodological choices. In addition, we find that the increase in patents does not come at the cost of lower patent quality, that STPs perform similarly in more or less advanced provinces, and that approximately 57% of the effect comes through STP spillovers. |
| Keywords: | Science and Technology Parks; innovation policy evaluation; regional effects; spillovers; patents; diff-in-diff |
| JEL: | O30 O31 O32 O38 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122467 |
| By: | Ali Sina Önder (University of Portsmouth); Sascha Schweitzer (Reutlingen University); Olga Tcaci (TUD Dresden University of Technology) |
| Abstract: | We estimate the impact of technological innovation on regional labor market outcomes. Our identification strategy exploits pre-reunification complementarities in innovation between East and West Germany. We employ individual-level data from the German Socio-Economic Panel to analyze labor market out- comes. Individuals’ income in West German counties with pre-reunification complementarities increased by 1.3%-1.5% on average after reunification. The effect is amplified when disentangling for different occupations: Income increases by 27%-29%, self-employment increases significantly, unemployment remains unaffected. The use of East German know-how in West German patents after reunification is driven by the migration of East German inventors to West German counties. |
| Keywords: | Economic Development; Patent Analysis; Knowledge Complementarities; Occupations; German Reunification |
| JEL: | J24 O31 O33 R11 |
| Date: | 2024–11–08 |
| URL: | https://d.repec.org/n?u=RePEc:pbs:ecofin:2024-07 |
| By: | Christoph Koenig (DEF, University of Rome "Tor Vergata"); Letizia Borgomeo (Research Department, Intesa Sanpaolo); Martina Miotto (DEM, University of Padova) |
| Abstract: | We study the impact of a government subsidy program in Italy targeted at R&D-intensive projects presented by high-tech startups in 2009. Using the score assigned by the scientific commission to each project, we employ a Regression Discontinuity Design to study how the subsidy affected successful firms’ innovation activity and performance over more than 10 years. We show that the subsidy led to substantial increases in intangible assets and had a lasting positive effect on various dimensions of firm performance. Innovation as measured by patents did not respond to the subsidy. |
| Keywords: | R&D subsidies, High-tech startups, Innovation policy, Firm performance |
| JEL: | D22 G38 L52 O31 O34 O38 |
| Date: | 2024–10–31 |
| URL: | https://d.repec.org/n?u=RePEc:rtv:ceisrp:585 |
| By: | Di Addario, Sabrina (Bank of Italy); Feng, Zhexin (University of Essex); Serafinelli, Michel (King's College London) |
| Abstract: | This paper presents direct evidence on how firms' innovation is affected by access to knowledgeable labor through co-worker network connections. We use a unique dataset that matches patent data to administrative employer - employee records from "Third Italy" - a region with many successful industrial clusters. Establishment closures displacing inventors generate supply shocks of knowledgeable labor to firms that employ the inventors' previous co-workers. We estimate event-study models where the treatment is the displacement of a "connected" inventor (i.e., a previous coworker of a current employee of the focal firm). We show that the displacement of a connected inventor significantly increases connected inventors' hiring. Moreover, the improved access to knowledgeable workers raises firms innovative activity. We provide evidence supporting the main hypothesized channel of knowledge transfer through firm-to-firm labor mobility by estimating IV specifications where we use the displacement of a connected inventor as an instrument to hire a connected inventor. Overall, estimates indicate that firms exploit displacements to recruit connected inventors and the improved capacity to employ knowledgeable labor within the network increases innovation. |
| Keywords: | social connections, firm-to-firm labor mobility, patents, establishment closure |
| JEL: | J60 O30 J23 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17398 |
| By: | Yong Suk Lee; Toshiaki Iizuka; Karen Eggleston |
| Abstract: | How do employment, tasks, and productivity change with robot adoption? Unlike manufacturing, little is known about these issues in the service sector, where robot adoption is expanding. As a first step towards filling this gap, we study Japanese nursing homes using original facility-level panel data that includes the different robots used and the tasks performed. We find that robot adoption is accompanied by an increase in employment and retention and the relationship is strongest for non-regular care workers and monitoring robots. The share of specific tasks performed by robots increases with the adoption of the respective type of robot, leading to reallocation of care worker effort to “human touch” tasks that support quality care. Robots are associated with improved quality (reduction in restraint use and pressure ulcers) and productivity. |
| JEL: | I11 J14 J23 O30 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33116 |
| By: | Frank Neffke (Center for International Development at Harvard University); Yang Li |
| Abstract: | A growing body of research documents that the size and growth of an industry in a place depends on how much related activity is found there. This fact is commonly referred to as the "principle of relatedness." However, there is no consensus on why we observe the principle of relatedness, how best to determine which industries are related or how this empirical regularity can help inform local industrial policy. We perform a structured search over tens of thousands of specifications to identify robust – in terms of out-of-sample predictions – ways to determine how well industries fit the local economies of US cities. To do so, we use data that allow us to derive relatedness from observing which industries co-occur in the portfolios of establishments, firms, cities and countries. Different portfolios yield different relatedness matrices, each of which help predict the size and growth of local industries. However, our specification search not only identifes ways to improve the performance of such predictions, but also reveals new facts about the principle of relatedness and important trade-offs between predictive performance and interpretability of relatedness patterns. We use these insights to deepen our theoretical understanding of what underlies path-dependent development in cities and expand existing policy frameworks that rely on inter-industry relatedness analysis. |
| Keywords: | Economic Complexity, Structural Transformation, Cities |
| Date: | 2023–03 |
| URL: | https://d.repec.org/n?u=RePEc:glh:wpfacu:211 |
| By: | Fabrizio Leone |
| Abstract: | Using a panel of Spanish manufacturing firms covering the 1990-2017 period, I document new evidence about affiliates of multinational enterprises (MNEs): after being acquired, they exhibit a higher propensity to use robots, which leads to a reduction in their labor share. These effects are identified using a matched event-study design, which accounts for selection into multinational ownership and robot adoption. The findings are consistent with a model of robot adoption choices by heterogeneous firms and hold even after considering other explanations for the labor share decline. The estimates imply that without MNEs, the reduction in the manufacturing labor share over the sample period would have been 8% smaller. Multinational-induced robot adoption explains about one-third of the overall impact of multinational activity on the labor share. |
| Keywords: | multinational enterprises, globalization, robots, labor share |
| JEL: | F23 F66 O33 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11396 |
| By: | Jonathan Adams; Min Fang; Zheng Liu; Yajie Wang |
| Abstract: | We document key stylized facts about the time-series trends and cross-sectional distributions of AI pricing and study its implications for firm performance, both on average and conditional on monetary policy shocks. We use the universe of online job posting data from Lightcast to measure the adoption of AI pricing. We infer that a firm is adopting AI pricing if it posts a job opening that requires AI-related skills and contains the keyword “pricing.” At the aggregate level, the share of AI-pricing jobs in all pricing jobs has increased by more than tenfold since 2010. The increase in AI-pricing jobs has been broad-based, spreading to more industries than other types of AI jobs. At the firm level, larger and more productive firms are more likely to adopt AI pricing. Moreover, firms that adopted AI pricing experienced faster growth in sales, employment, assets, and markups, and their stock returns are also more sensitive to high-frequency monetary policy surprises than non-adopters. We show that these empirical observations can be rationalized by a simple model where a monopolist firm with incomplete information about the demand function invests in AI pricing to acquire information. |
| Keywords: | artificial intelligence; firms; pricing; jobs; monetary policy; technology adoption; AI |
| JEL: | D40 E31 E52 O33 |
| Date: | 2024–11–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedfwp:99052 |