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on Technology and Industrial Dynamics |
| By: | Andrés Mauricio Gómez-Sánchez (Facultad de Ciencias Contables, Económicas y Administrativas, Universidad del Cauca, Colombia); Juan A. Máñez-Castillejo (Department of Applied Economics II, Universidad de Valencia and ERICES, Spain); Juan A. Juan A. Sanchis-Llopis (Department of Applied Economics II, Universidad de Valencia and ERICES, Spain) |
| Abstract: | This paper investigates the impact of two innovating strategies (product and process) on total factor productivity (TFP) growth and the dynamic linkages between these strategies, for Colombia. In a first stage, we explore through panel data discrete choice models whether the ex-ante more productive firms are those that introduce innovations. In a second stage, we test whether the introduction of innovations boosts productivity growth. In this second stage, we use matching techniques. In a final stage, we explore the firm’s joint decision to innovate in process and/or product through a bivariate probit model. Data from the Annual Manufacturing Survey and the Technological Development and Innovation Survey, for 2007-2016, are used for Colombian manufacturing firms. Our results suggest that the most productive firms self- select into the introduction of innovations (both process and product). Further, these innovations render positive returns in terms of productivity growth only one period forward regardless of the type of innovation. In addition, we also find a strong persistence of process and product innovation over time, and cross effects between these two strategies, as product innovations are boosted by process innovation and vice versa. |
| Keywords: | product innovation, process innovation, productivity, self-selection/returns into/from innovation |
| JEL: | O3 D24 L6 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:eec:wpaper:2311&r=tid |
| By: | Kyung Min Lee; Mee Jung Kim; J. David Brown; John S. Earle; Zhen Liu |
| Abstract: | We evaluate the contributions of immigrant entrepreneurs to innovation in the U.S. using linked survey-administrative data on 199, 000 firms with a rich set of innovation measures and other firm and owner characteristics. We find that not only are immigrants more likely than natives to own businesses, but on average their firms display more innovation activities and outcomes. Immigrant owned firms are particularly more likely to create completely new products, improve previous products, use new processes, and engage in both basic and applied R&D, and their efforts are reflected in substantially higher levels of patents and productivity. Immigrant owners are slightly less likely than natives to imitate products of others and to hire more employees. Delving into potential explanations of the immigrant-native differences, we study other characteristics of entrepreneurs, access to finance, choice of industry, immigrant self-selection, and effects of diversity. We find that the immigrant innovation advantage is robust to controlling for detailed characteristics of firms and owners, it holds in both high-tech and non-high-tech industries and, with the exception of productivity, it tends to be even stronger in firms owned by diverse immigrant-native teams and by diverse immigrants from different countries. The evidence from nearly all measures that immigrants tend to operate more innovative and productive firms, together with the higher share of business ownership by immigrants, implies large contributions to U.S. innovation and growth. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:cen:wpaper:23-56&r=tid |
| By: | Giacomo Lo Conte; Andrea Mina; Silvia Rocchetta |
| Abstract: | In this paper we explore the impact of place-based innovation policy in Europe. We focus on the effects of Smart Specialisation strategies on the labour productivity of regional economies. We design an analytical framework that takes into account the entrepreneurial discovery process through which the policy is implemented, and connect the technological relatedness of regions with their specialisation choices. We use an IV estimation approach capable of handling endogeneity problems, and apply it to an extensive dataset of 102 NUTS2 regions extracted from the European Commission Smart Specialisation Portal. The results show that Smart Specialisation strategies increase labour productivity as long as the priorities are set in sectors related to pre-existing technological capabilities, indicating the fundamental importance of path-dependency in diversification choices. The findings deepen our understanding of regional development and innovation strategies, and have relevant implications for the implementation of appropriate policy instruments. |
| Keywords: | Related diversification; Specialization; Regional policy; Innovation policy; Place-based Policies |
| JEL: | O33 R11 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:egu:wpaper:2323&r=tid |
| By: | Martin Henning; Rikard Eriksson; Petrus Garefelt; Hanna Martin; Zoltán Elekes |
| Abstract: | The local presence and composition of skills is commonly thought to have enormous implications for economic development. Yet, skills and the relations between them are notoriously difficult to pinpoint and measure. We develop a method that uses information available in Swedish job postings to measure the skill-relatedness of jobs and the skill- coherence of local economies. Our skill-relatedness measure can be assumed to be exogenous to local economic outcomes such as wages, productivity and labour mobility. We corroborate some previous research findings and show that workers tend to switch between related jobs and that local economies are on average skill-coherent. However, less coherent local economies are associated with higher average wages and productivity. Local economies where workers switch between related jobs though enjoy higher average wages. In all, this points to the benefit of local labour market clusters within more diverse regions. We conclude that job postings provide a wealth of information on the skill-foundations of local development. A job-level skill- relatedness matrix accompanies the paper. |
| Keywords: | job postings; skill-relatedness; local skill coherence; regional agglomeration; labour flows |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:egu:wpaper:2324&r=tid |
| By: | David Autor; Christina Patterson; John Van Reenen |
| Abstract: | National U.S. industrial concentration rose between 1992-2017. Simultaneously, the Herfindhahl Index of local (six-digit-NAICS by county) employment concentration fell. This divergence between national and local employment concentration is due to structural transformation. Both sales and employment concentration rose within industry-by-county cells. But activity shifted from concentrated Manufacturing towards relatively un-concentrated Services. A stronger between-sector shift in employment relative to sales explains the fall in local employment concentration. Had sectoral employment shares remained at their 1992 levels, average local employment concentration would have risen by 9% by 2017 rather than falling by 7%. |
| Keywords: | Employment concentration, sales concentration, local labor markets, structural transformation |
| JEL: | L11 L60 O31 O34 P33 R3 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:cen:wpaper:23-59&r=tid |
| By: | Osea Giuntella; Johannes König; Luca Stella |
| Abstract: | This study explores the relationship between artificial intelligence (AI) and workers’ well-being and mental health using longitudinal survey data from Germany (2000-2020). We construct a measure of individual exposure to AI technology based on the occupation in which workers in our sample were first employed and explore an event study design and a difference-in-differences approach to compare AI-exposed and non-exposed workers. Before AI became widely available, there is no evidence of differential pre-trends in workers’ well-being and concerns about their economic futures. Since 2015, however, with the increasing adoption of AI in firms across Germany, we find that AI-exposed workers have become less satisfied with their life and job and more concerned about job security and their personal economic situation. However, we find no evidence of a significant impact of AI on workers’ mental health, anxiety, or depression. |
| Keywords: | Artificial Intelligence, Future of Work, Well-being, Mental Health |
| JEL: | I10 J28 O30 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:diw:diwsop:diw_sp1194&r=tid |
| By: | Ashish Arora; Sharon Belenzon; Larisa C. Cioaca; Lia Sheer; Hansen Zhang |
| Abstract: | We study the relationships between corporate R&D and three components of public science: knowledge, human capital, and invention. We identify the relationships through firm-specific exposure to changes in federal agency R\&D budgets that are driven by the political composition of congressional appropriations subcommittees. Our results indicate that R&D by established firms, which account for more than three-quarters of business R&D, is affected by scientific knowledge produced by universities only when the latter is embodied in inventions or PhD scientists. Human capital trained by universities fosters innovation in firms. However, inventions from universities and public research institutes substitute for corporate inventions and reduce the demand for internal research by corporations, perhaps reflecting downstream competition from startups that commercialize university inventions. Moreover, abstract knowledge advances per se elicit little or no response. Our findings question the belief that public science represents a non-rival public good that feeds into corporate R&D through knowledge spillovers. |
| JEL: | O3 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31899&r=tid |
| By: | Goller, Daniel (University of Bern); Gschwendt, Christian (University of Bern); Wolter, Stefan C. (University of Bern) |
| Abstract: | In this paper, we show the causal influence of the launch of generative AI in the form of ChatGPT on the search behavior of young people for apprenticeship vacancies. There is a strong and long-lasting decline in the intensity of searches for vacancies, which suggests great uncertainty among the affected cohort. Analyses based on the classification of occupations according to tasks, type of cognitive requirements, and the expected risk of automation to date show significant differences in the extent to which specific occupations are affected. Occupations with a high proportion of cognitive tasks, with high demands on language skills, and those whose automation risk had previously been assessed by experts as lower are significantly more affected by the decline. However, no differences can be found with regard to the proportion of routine vs. non-routine tasks. |
| Keywords: | artificial intelligence, occupational choice, labor supply, technological change |
| JEL: | J24 O33 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp16638&r=tid |
| By: | Andreoni, Antonio; Anzolin, Guendalina; Labrunie, Mateus; Spinola, Danilo |
| Abstract: | This research pioneers the construction of a novel Digital Production Technology Classification (DPTC) based on the latest Harmonised Commodity Description and Coding System (HS2017) of the World Customs Organisation. The DPTC enables the identification and comprehensive analysis of 127 tradable products associated with digital production technologies (DPTs). The development of this classification offers a substantial contribution to empirical research and policy analysis. It enables an extensive exploration of international trade in DPTs, such as the identification of emerging trade networks comprising final goods, intermediate components, and instrumentation technologies and the intricate regional and geopolitical dynamics related to DPTs. In this paper, we deploy our DPTC within a network analysis methodological framework to analyse countries' engagements with DPTs through bilateral and multilateral trade. By comparing the trade networks in DPTs in 2012 and 2019, we unveil dramatic shifts in the global DPTs' network structure, different countries' roles, and their degree of centrality. Notably, our findings shed light on China's expanding role and the changing trade patterns of the USA in the digital technology realm. The analysis also brings to the fore the increasing significance of Southeast Asian countries, revealing the emergence of a regional hub within this area, characterised by dense bilateral networks in DPTs. Furthermore, our study points to the fragmented network structures in Europe and the bilateral dependencies that developed there. Being the first systematic DPTC, also deployed within a network analysis framework, we expect the classification to become an indispensable tool for researchers, policymakers, and stakeholders engaged in research on digitalisation and digital industrial policy. |
| Keywords: | Digital Production Technology (DPT); DPT Classification; Network Analysis; Bilateral Trade; Digitalisation patterns. |
| Date: | 2023–12–06 |
| URL: | http://d.repec.org/n?u=RePEc:akf:cafewp:15064&r=tid |
| By: | DIODATO Dario (European Commission - JRC); NAPOLITANO Lorenzo (European Commission - JRC); PUGLIESE Emanuele; TACCHELLA Andrea |
| Abstract: | Innovation and industrial policies in the EU is often undertaken at regional level. Policymakers that have to design regional industrial strategy need quantitative tools for guidance. Economic complexity can support policymakers especially during the early phase of policy design: patent and trade data are fed into predictive models to assess the chances of success of a strategy. The methods of economic complexity follow the driving principles of machine learning to predict the probability that a region becomes successful in a given technology or product. We present a series of quantitative tools for regions: (1) relative innovation capabilities; (2) expected diversification by sector; (3) expected diversification by product; (4) fitness of a region for a project. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc136443&r=tid |
| By: | Pascual Restrepo |
| Abstract: | This article reviews the literature on automation and its impact on labor markets, wages, factor shares, and productivity. I first introduce the task model and explain why this framework offers a compelling way to think about recent labor market trends and the effects of automation technologies. The task model clarifies that automation technologies operate by substituting capital for labor in a widening range of tasks. This substitution reduces costs, creating a positive productivity effect, but also reduces employment opportunities for workers displaced from automated tasks, creating a negative displacement effect. I survey the empirical literature and conclude that there is wide qualitative support for the implications of task models and the displacement effects of automation. I conclude by discussing shortcomings of the existing literature and avenues for future research. |
| JEL: | E24 J20 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31910&r=tid |
| By: | Ruiqi Sun; Daniel Trefler |
| Abstract: | The rise of artificial intelligence (AI) and of cross-border restrictions on data flows has created a host of new questions and related policy dilemmas. This paper addresses two questions: How is digital service trade shaped by (1) AI algorithms and (2) by the interplay between AI algorithms and cross-border restrictions on data flows? Answers lie in the palm of your hand: From London to Lagos, mobile app users trigger international transactions when they open AI-powered foreign apps. We have 2015-2020 usage data for the most popular 35, 575 mobile apps and, to quantify the AI deployed in each of these apps, we use a large language model (LLM) to link each app to each of the app developer's AI patents. (This linkage of specific products to specific patents is a methodological innovation.) Armed with data on app usage by country, with AI deployed in each app, and with an instrument for AI (a Heckscher-Ohlin cost-shifter), we answer our two questions. (1) On average, AI causally raises an app's number of foreign users by 2.67 log points or by more than 10-fold. (2) The impact of AI on foreign users is halved if the foreign users are in a country with strong restrictions on cross-border data flows. These countries are usually autocracies. We also provide a new way of measuring AI knowledge spillovers across firms and find large spillovers. Finally, our work suggests numerous ways in which LLMs such as ChatGPT can be used in other applications. |
| JEL: | F12 F13 F14 F23 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31925&r=tid |
| By: | Marco Augliera (University of Trento); Gabriella Berloffa (University of Trento); Fabio Pieri (University of Trento) |
| Abstract: | This work investigates the relationship between the numerical flexibility of a firm’s workforce and its innovative performance, taking into account the heterogeneity of firms and labor contracts. Using longitudinal data on Italian firms, we find that the share of temporary employees has a positive and significant effect on innovation for small and micro firms in low-tech and less knowledge-intensive sectors and a negative effect for medium and large firms in high-tech and knowledge-intensive sectors. These results suggest that managers and entrepreneurs may use temporary employment as an effective human resource practice to foster innovation in those firms whose technology or knowledge do not require vast and firm- specific investments. They also highlight possible unintended consequences of changes in the employment protection legislation for firms’ innovative performance. Functional flexibility (training policies) and wage flexibility (second-level wage bargaining scheme) are neither substitutes nor complements to numerical flexibility, suggesting that firms use numerical, functional, and wage flexibility in different combinations. |
| Keywords: | Numerical flexibility; labor contracts; firm innovation; industrial relations |
| JEL: | D22 L23 M54 M55 J41 |
| Date: | 2022–12–12 |
| URL: | http://d.repec.org/n?u=RePEc:csl:devewp:481&r=tid |
| By: | Detemple, Jonas; Wicht, Alexandra |
| Abstract: | The ongoing global digital transformation has significant implications for economies and societies, with potential benefits and challenges. This study addresses the critical need for a comprehensive measure of regional digitalization in Germany to better understand its impact on various aspects of life, including education, employment, and working conditions. Using confirmatory factor analysis (CFA), it introduces a multifaceted regional digitalization indicator at the administrative district level (NUTS-3) that incorporates digital infrastructure, culture, technology capacity, high-tech human capital, and digitalization-related innovativeness. The study reveals that digitalization varies significantly across regions. Urban regions tend to have higher digitalization levels, which are positively associated with economic productivity and high-skilled labor demand. Moreover, regional digitalization complements the established measure of regional automation potential, as the two are only slightly correlated, highlighting the complexity of regional disparities in the digital age. |
| Date: | 2023–12–01 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:e439g&r=tid |
| By: | Charlie Joyez (Université Côte d'Azur, CNRS, GREDEG, France); Raja Kali (University of Arkansas, Fayetteville, USA); Catherine Laffineur (Université Côte d'Azur, CNRS, GREDEG, France) |
| Abstract: | Why do labor markets in some regions recover faster from an adverse economic shock than others? We conjecture that regions with occupations offering greater redeployment opportunities to other occupations are less at risk of long-term unemployment. We examine this by creating a network of inter-occupation relatedness from worker mobility data that provides occupational transitions for a representative sample of French workers. Superimposing local occupational composition on to this "occupation space" yields a measure of occupational coherence for 304 commuting zones in France. We find that regions with stronger occupational coherence are more sensitive to a shock but recover faster. |
| Keywords: | occupational mobility, regional occupational composition, occupational coherence, economic shocks, unemployment dynamics |
| JEL: | E24 E32 J21 J24 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:gre:wpaper:2023-20&r=tid |
| By: | Eugenia Gonzalez Ehlinger; Fabian Stephany |
| Abstract: | For emerging professions, such as jobs in the field of Artificial Intelligence (AI) or sustainability (green), labour supply does not meet industry demand. In this scenario of labour shortages, our work aims to understand whether employers have started focusing on individual skills rather than on formal qualifications in their recruiting. By analysing a large time series dataset of around one million online job vacancies between 2019 and 2022 from the UK and drawing on diverse literature on technological change and labour market signalling, we provide evidence that employers have started so-called “skill-based hiring” for AI and green roles, as more flexible hiring practices allow them to increase the available talent pool. In our observation period the demand for AI roles grew twice as much as average labour demand. At the same time, the mention of university education for AI roles declined by 23%, while AI roles advertise five times as many skills as job postings on average. Our analysis also shows that university degrees no longer show an educational premium for AI roles, while for green positions the educational premium persists. In contrast, AI skills have a wage premium of 16%, similar to having a PhD (17%). Our work recommends making use of alternative skill building formats such as apprenticeships, on-the-job training, MOOCs, vocational education and training, micro-certificates, and online bootcamps to use human capital to its full potential and to tackle talent shortages. |
| Keywords: | future of work, labour markets, skills, education, AI, sustainability |
| JEL: | C55 I23 J23 J24 J31 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10817&r=tid |
| By: | Paulo Bastos (World Bank); Lisandra Flach (LMU Munich, ifo Institue); Klaus Keller (LMU Munich, Max-Planck Institute for Competition and Innovation) |
| Abstract: | We investigate the impact of product market competition on firms’ automation investments. We use a rich combination of micro-data on Portuguese exporters and exploit a novel source of variation in the degree of competition they face – a tariff liberalization between the European Union and Central and Eastern European countries in the 1990s. We find that firms facing greater competition in export markets tend to reduce investments in automation technologies. These average negative effects are driven by the least productive firms, while the most efficient exporters in industries that are more prone to automation tend to robotize in order to compete. These findings suggest that an increase in the degree of product market competition widens disparities between firms. |
| Keywords: | automation; product market competition; firm heterogeneity; trade liberalization; workers; multi-product firms; |
| JEL: | D22 F16 J23 L25 O33 |
| Date: | 2023–11–28 |
| URL: | http://d.repec.org/n?u=RePEc:rco:dpaper:467&r=tid |
| By: | KIKUCHI Shinnosuke; FUJIWARA Ippei; SHIROTA Toyoichiro |
| Abstract: | We examine the implications of automation technology in Japan since 1980, comparing different local labor markets with different degrees of automation exposure. First, we do not find that automation reduces the employment rate within demographic groups and that automation encourages workers to move from regular to non-regular employment. Second, we show that automation shifts employment from routine occupations in the manufacturing sector to service sectors, while increasing the share of establishments and sales in the manufacturing sector. Finally, we show that this shift in labor demand is attributed to younger generations and non-college-educated workers. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:eti:dpaper:23082&r=tid |
| By: | Elvira Uyarra (Manchester Institute of Innovation Research, The University of Manchester); Oishee Kundu (Y Lab, Cardiff University); Raquel Ortega-Argiles (Productivity Institute, The University of Manchester); Malcolm Harbour (Connected Places Catapult) |
| Abstract: | The use of public procurement to advance innovation but also other social, environmental and public service delivery goals has been high in the innovation policy debate in the last two decades. Drawing from the literature on the economics of innovation and innovation policy, this paper provides an overview and critique of the key debates surrounding public procurement of innovation, specifically the rationales, means and challenges associated with its use as an innovation policy tool. We note that despite strong academic interest and policy activity in this area, strategic public procurement to promote innovation is still unevenly adopted. The evidence base is also weak in terms of the methods and data to understand its impact. We argue more research is needed to quantify the outcomes of procurement interventions in different national and sectoral contexts and their integration with other innovation policy instruments. |
| Keywords: | Public procurement, Innovation, Regulation, Public policy, Evaluation |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:bdj:smioir:2023-06&r=tid |