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on Technology and Industrial Dynamics |
| By: | Daron Acemoglu; Fredric Kong; Pascual Restrepo |
| Abstract: | This chapter reviews recent advances in the task model and shows how this framework can be put to work to understand the major labor market trends of the last several decades. Production in each industry necessitates the completion of a range of tasks, which can be allocated to workers of different skill types or to capital. Factors of production have well-defined comparative advantage across tasks, which governs the pattern of substitution between skill groups. Technological change can: (1) augment a specific labor type—e.g., increase the productivity of labor in tasks it is already performing; (2) augment capital; (3) automate work by enabling capital to perform tasks previously allocated to labor; (4) create new tasks. The task model clarifies that these different types of technological changes have distinct effects on labor demand, factor shares and productivity, and their full impact depends on the pattern of substitution between different factors which arises endogenously in the task framework. We explore the implications of the task framework using reduced-form evidence, which highlights the central role of automation and new tasks in recent labor market trends. We also explain how general equilibrium effects ignored in these reduced-form approaches can be estimated structurally. |
| JEL: | J23 J31 O33 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32872 |
| By: | Damioli, Giacomo (ISER, University of Essex); Van Roy, Vincent (European Commission, Joint Research Centre); Vertesy, Daniel (European Commission, Joint Research Centre); Vivarelli, Marco (Università Cattolica del Sacro Cuore) |
| Abstract: | Artificial intelligence (AI) is emerging as a transformative innovation with the potential to drive significant economic growth and productivity gains. This study examines whether AI is initiating a technological revolution, signifying a new technological paradigm, using the perspective of evolutionary neo-Schumpeterian economics. Using a global dataset combining information on AI patenting activities and their applicants between 2000 and 2016, our analysis reveals that AI patenting has accelerated and substantially evolved in terms of its pervasiveness, with AI innovators shifting from the ICT core industries to non-ICT service industries over the investigated period. Moreover, there has been a decrease in concentration of innovation activities and a reshuffling in the innovative hierarchies, with innovative entries and young and smaller applicants driving this change. Finally, we find that AI technologies play a role in generating and accelerating further innovations (so revealing to be "enabling technologies", a distinctive feature of GPTs). All these features have characterised the emergence of major technological paradigms in the past and suggest that AI technologies may indeed generate a paradigmatic shift. |
| Keywords: | Artificial Intelligence, technological paradigm, structural change, patents |
| JEL: | O31 O33 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17183 |
| By: | Luis Bauluz; Pawel Bukowski; Mark Fransham; Annie Lee; Margarita Lopez-Forero; Filip Novokmet; Sebastien Breau; Neil Lee; Clément Malgouyres; Moritz Schularick; Gregory Verdugo |
| Abstract: | Working Paper Series no. 957. The rise of economic inequalities in advanced economies has been often linked with the growth of spatial inequalities within countries, yet there is limited comparative research that studies the relationship between national and subnational economic inequality. This paper presents the first systematic attempt to create internationally comparable evidence showing how different countries perform in terms of geographic wage inequalities. We create cross-country comparable measures of spatial wage disparities between and within similarly-defined local labour market areas (LLMAs) for Canada, France, (West) Germany, the UK and the US from the 1970s to 2010s, and assess their contribution to national inequality. By the end of the 2010s, spatial inequalities in LLMA average primary wages are similar in Canada, France, Germany and the UK; the US exhibits the highest degree of spatial inequality. Over the study period, spatial inequalities have nearly doubled in all countries, except for France where spatial inequalities have fallen back to 1970s levels, after an increase in the 1990s. Due to a concomitant increase in within-place inequality, the contribution of places in explaining national wage inequality has remained fairly constant over the 40-year study period, except in the UK where we document a significant increase. Whilst common global social, economic and technological shocks are important drivers of spatial inequality, this variation in levels and trends of spatial inequality opens the way to comparative research exploring the role of national institutions in mediating how global shocks translate into economic disparities between places. |
| Keywords: | Regional Inequality, Wage Inequality, Local Labour Markets |
| JEL: | J3 R1 R23 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:bfr:banfra:957 |
| By: | Czarnitzki, Dirk; Prüfer, Malte |
| Abstract: | This paper investigates the impact of Public Procurement of Innovation (PPI) and Research and Development (R&D) grants on firms' R&D investment using data from Belgian R&D-active firms over the past decade. Our empirical analysis robustly reveals a non-negligible crowding-out effect between the two instruments, suggesting a substitutive relationship. While each policy individually positively influences R&D investment, their combined implementation diminishes their effectiveness. These results challenge prevailing evidence and emphasize the need for a careful policy implementation, raising policymakers' awareness against a blanket increase in innovation policies without considering potential interactions. |
| Keywords: | Public procurement of innovation, Research and Development, Econometric policy evaluation, Crowding-out |
| JEL: | H57 O38 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:301866 |
| By: | Krieger, Bastian; Trottner, Fabian |
| Abstract: | We study the implications of services trade for firm innovation. Using a quasi-experimental shift-share design, we find that access to foreign knowledge-related services improves the innovativeness of domestic firms and complements their indigenously sourced R&D. To confront this evidence, we develop a theoretical model. It demonstrates outsourcing can foster firms' innovation efficiency by mitigating decreasing economies of scale in in-house innovation efforts. As a result, firms become more likely to outsource innovation efforts as they become more innovative, whereas the prevalence of offshoring depends on its associated trade costs. |
| Keywords: | International integration, Service trade, Firm innovation |
| JEL: | F14 F15 F23 O31 O32 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:301867 |
| By: | Su Jung Jee; Kerstin H\"otte; Caoimhe Ring; Robert Burrell |
| Abstract: | This study investigates the controversial role of Intellectual Property Rights (IPRs) in climate technology transfer and innovation in developing countries. Using a systematic literature review and expert interviews, we assess the role of IPRs on three sources of climate technology: (1) international technology transfer, (2) adaptive innovation, and (3) indigenous innovation. Our contributions are threefold. First, patents have limited impact in any of these channels, suggesting that current debates over IPRs may be directed towards the wrong targets. Second, trademarks and utility models provide incentives for climate innovation in the countries studied. Third, drawing from the results, we develop a framework to guide policy on how IPRs can work better in the broader context of climate and trade policies, outlining distinct mechanisms to support mitigation and adaptation. Our results indicate that market mechanisms, especially trade and demand-pull policies, should be prioritised for mitigation solutions. Adaptation differs, relying more on indigenous innovation due to local needs and low demand. Institutional mechanisms, such as finance and co-development, should be prioritised to build innovation capacities for adaptation. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.12338 |
| By: | Julia Schmidt; Graham Pilgrim; Annabelle Mourougane |
| Abstract: | This paper estimates the artificial intelligence-hiring intensity of occupations/industries (i.e. the share of job postings related to AI skills) in the United Kingdom during 2012-22. The analysis deploys a natural language processing algorithm (NLP) on online job postings, collected by Lightcast, which provides timely and detailed insights into labour demand for different professions. The key contribution of the study lies in the design of the classification rule identifying jobs as AI-related which, contrary to the existing literature, goes beyond the simple use of keywords. Moreover, the methodology allows for comparisons between data-hiring intensive jobs, defined as the share of jobs related to data production tasks, and AI-hiring intensive jobs. Estimates point to a rise in the economy-wide AI-hiring intensity in the United Kingdom over the past decade but to fairly small levels (reaching 0.6% on average over the 2017-22 period). Over time, the demand for AI-related jobs has spread outside the traditional Information, Communication and Telecommunications industries, with the Finance and Insurance industry increasingly demanding AI skills. At a regional level, the higher demand for AI-related jobs is found in London and research hubs. At the occupation level, marked changes in the demand for AI skills are also visible. Professions such as data scientist, computer scientist, hardware engineer and robotics engineer are estimated to be the most AI-hiring intense occupations in the United Kingdom. The data and methodology used allow for the exploration of cross-country estimates in the future. |
| Keywords: | AI-hiring intensity, artificial intelligence, job advertisements, natural language processing, united kingdom |
| JEL: | C80 C88 E01 J21 |
| Date: | 2024–09–05 |
| URL: | https://d.repec.org/n?u=RePEc:oec:comaaa:25-en |
| By: | De Loecker, Jan; Obermeier, Tim; Van Reenen, John |
| Abstract: | We review the existing literature on falling business dynamism and present a new analysis using comprehensive UK firm-level panel data. Since the mid-1990s, there has been a large increase in UK firm-level inequality (especially in the upper tails) of productivity, wages, markups and labour shares, similarly to the USA. We suggest a simple theoretical framework for understanding some of these trends and quantitatively analyse why, despite increasing markups, the UK labour share has not fallen as sharply as that in the USA. Finally, we suggest some policy options in response to these worrying trends, including modernizing competition rules to deal with the growth of superstar firms and strengthening worker bargaining power. |
| Keywords: | OUP deal |
| JEL: | J1 |
| Date: | 2024–07–17 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:121234 |
| By: | Lee, Neil |
| Abstract: | ‘Inclusive innovation’ has become an increasingly important subnational policy agenda. This paper reviews this agenda, critiques its current usage and presents a new framework for how the concept can be applied by city government. Efforts to shape the direction, improve participation in and share the benefits of innovation should be an important part of place-based innovation policy. Yet, inclusive innovation strategies face three related problems: neophilia, a tendency for technological fixes and the lack of local powers. The paper concludes with a framework for how the concept could be used by policymakers to link innovation with better distributional outcomes. |
| Keywords: | inclusive innovation; urban policy; inclusive growth; innovation behaviour; innovation |
| JEL: | R58 D63 O30 |
| Date: | 2023–02–24 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:117818 |
| By: | Calzada, BCO; Spinola, Danilo |
| Abstract: | Export diversification is crucial for economic development, yet many resource-rich countries have struggled to achieve significant progress in diversifying its economic structure. While the lack of capabilities is often highlighted as a primary barrier to diversification, the literature frequently underestimates the significant impact of macroeconomic conditions on diversification potential. This study seeks to bridge the gap between the capabilities literature and macroeconomic factors, particularly in the context of economies heavily dependent on extractive industries. In order to address our question, we initially introduce a novel measure of product relatedness, expanding on the framework developed by Nomaler and Verspagen (2022), and econometrically estimate its relationship with key macroeconomic variables such as international prices, exchange rates, energy and mineral dependency, and GDP per capita. The analysis spans over 5, 000 products across multiple countries from 1995 to 2019, with the objective of determining the relative significance of these factors in predicting diversification patterns and assessing how macroeconomic conditions either facilitate or impede diversification, particularly in non-extractive sectors. Product relatedness predicts diversification, especially in extractive industries where path dependence is highly pronounced. However, macroeconomic factors exert a major influence on diversification outcomes. These macroeconomic variables can either constrain or enable diversification, shaping the pathways through which industries evolve and expand their portfolios. |
| Keywords: | Export Diversification; Product Relatedness; Macroeconomic Factors; Extractive Sectors |
| Date: | 2024–09–03 |
| URL: | https://d.repec.org/n?u=RePEc:akf:cafewp:30 |