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on Technology and Industrial Dynamics |
| By: | Leonid Kogan; Dimitris Papanikolaou; Lawrence D.W. Schmidt; Bryan Seegmiller |
| Abstract: | We develop measures of labor-saving and labor-augmenting technology exposure using textual analysis of patents and job tasks. Using US administrative data, we show that both measures negatively predict earnings growth of individual incumbent workers. While labor-saving technologies predict earnings declines and higher likelihood of job loss for all workers, labor-augmenting technologies primarily predict losses for older or highly-paid workers. However, we find positive effects of labor-augmenting technologies on occupation-level employment and wage bills. A model featuring labor-saving and labor-augmenting technologies with vintage-specific human capital quantitatively matches these patterns. We extend our analysis to predict the effect of AI on earnings. |
| JEL: | E0 E01 J01 J23 J24 O3 O4 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31846&r=tid |
| By: | Francesco de Cunzo; Davide Consoli; Francois Perruchas; Angelica Sbardella |
| Abstract: | The goal of this paper is to elaborate an empirical analysis of the relationship between Critical Raw Materials (CRMs) and environmental technologies. Using text mining techniques to parse and analyse patent descriptions, we provide a thorough empirical exploration of (i) the dependence of green technologies on CRMs; (ii) the countries that lead the demand of CRMs; and (iii) the countries that are more exposed to global demand for CRMs. Framed in the context of recent policy debates on the viability of the green transition, our study points to criticalities associated to both the evolution of green technology and to the spatial network of demand and supply of CRMs. |
| Keywords: | Critical Raw Materials, Green Technologies, Text Mining |
| JEL: | O33 Q55 O13 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:egu:wpaper:2322&r=tid |
| By: | David E. Bloom; Klaus Prettner; Jamel Saadaoui; Mario Veruete |
| Abstract: | What will likely be the effect of the emergence of ChatGPT and other forms of artificial intelligence (AI) on the skill premium? To address this question, we develop a nested constant elasticity of substitution production function that distinguishes between industrial robots and AI. Industrial robots predominantly substitute for low-skill workers, whereas AI mainly helps to perform the tasks of high-skill workers. We show that AI reduces the skill premium as long as it is more substitutable for high-skill workers than low-skill workers are for high-skill workers. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.09255&r=tid |
| By: | Stojcic, Nebojsa; Matic, Matija |
| Abstract: | Global value chains (GVCs) are embraced worldwide as a gateway to technological and economic upgrading. Countries integrate into backward, value-importing linkages with the aim of accumulating technological capabilities and transitioning towards creating their own forward, value-exporting chains while capturing a greater share of the value generated within GVCs. Existing knowledge, which is largely fragmented and descriptive, points to a number of uncertainties and complexities that make this process far from linear. It remains an open question whether deepening backward linkages facilitate forward integration in GVCs. Using data from 65 countries over two decades, we demonstrate that the impact of backward integration on forward integration in GVCs varies over time and is moderated by the country's level of development, the diversity of the GVC partner network, and the innovation conditions in the home market. The research introduces a new perspective to the literature on GVC-driven upgrading. |
| Keywords: | global value chain; upgrading; backward and forward integration |
| JEL: | F14 F6 O3 |
| Date: | 2023–11–23 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:119243&r=tid |
| By: | Antonin Bergeaud; Arthur Guillouzouic |
| Abstract: | Following Bergeaud et al. (2022), we construct a new measure of proximity between industrial sectors and public research laboratories. Using this measure, we explore the underlying network of knowledge linkages between scientific fields and industrial sectors in France. We show empirically that there exists a significant negative correlation between the geographical distance between firms and laboratories and their scientific proximity, suggesting strongly localized spillovers. Moreover, we uncover some important differences by field, stronger than when using standard patent-based measures of proximity. |
| Keywords: | knowledge spillovers, technological distance, public laboratories |
| Date: | 2023–11–15 |
| URL: | http://d.repec.org/n?u=RePEc:cep:cepdps:dp1961&r=tid |
| By: | Gravina, Antonio Francesco (University of Palermo); Lanzafame, Matteo (Asian Development Bank) |
| Abstract: | This paper provides estimates of the impact of demographic change on labor productivity growth, relying on annual data over 1961-2018 for a panel of 90 advanced and emerging economies. We find that increases in both the young and old population shares have significant negative effects on labor productivity growth, working via various channels—including physical and human capital accumulation. Splitting the analysis for advanced and emerging economies shows that population aging has a greater effect on emerging economies than on advanced economies. Extending the benchmark model to include a proxy for the robotization of production, we find evidence indicating that automation reduces the negative effects of unfavorable demographic change—in particular, population aging—on labor productivity growth. |
| Keywords: | demographic change; labor productivity; robots |
| JEL: | C33 J11 O40 |
| Date: | 2023–11–09 |
| URL: | http://d.repec.org/n?u=RePEc:ris:adbewp:0701&r=tid |
| By: | Philippe Aghion; Antonin Bergeaud; Timothee Gigout; Matthieu Lequien; Marc Malitz |
| Abstract: | We examine the effect of entry by French firms into a new export market on the dynamics of their patents' citations received from that destination. Applying a difference-in-differences identification strategy with a staggered treatment design, we show that: (i) entering a new foreign market has a significant impact on the long-run flow of citations; (ii) the impact is mostly driven by the extensive margin; (iii) inventors in destination countries patent mostly in products that do not directly compete with those of the exporting firm; (iv) the spillover intensity decreases with the technological distance between the exporting firm and the destination. |
| Keywords: | international trade, spillover, innovation, patent |
| Date: | 2023–11–15 |
| URL: | http://d.repec.org/n?u=RePEc:cep:cepdps:dp1960&r=tid |
| By: | Jakub Growiec; Julia Jabłońska; Aleksandra Parteka |
| Abstract: | Output is generated through purposefully initiated physical action. Production needs energy and information, provided by respective factors: hardware (“brawn”), including physical labor and physical capital, and software (“brains”), encompassing human cognitive work and pre-programmed software, in particular artificial intelligence (AI). From first principles, hardware and software are essential and complementary in production, whereas their constituent components are mutually substitutable. This framework generalizes the neoclassical model of production with capital and labor, models with capital-skill complementarity and skill-biased technical change, and unified growth theories embracing also the pre-industrial period. Having laid out the theory, we provide an empirical quantification of hardware and software in the US, 1968-2019. We document a rising share of physical capital in hardware (mechanization) and digital software in software (automation); as a whole software has been growing systematically faster than hardware. Accumulation of digital software was a key contributor to US economic growth. |
| Keywords: | production function, technological progress, complementarity, automation, artificial intelligence |
| JEL: | O30 O40 O41 |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:sgh:kaewps:2023091&r=tid |
| By: | Arnaud Persenda (Université Côte d'Azur, France; GREDEG CNRS); Alexandre Ruiz (Université Côte d'Azur, France; GREDEG CNRS) |
| Abstract: | Green goods are products necessary to reach sustainable development targets. Since they offer many benefits, we discuss the question of why not all countries produce them. Using the economic complexity framework, we study how likely it is that a country will get a comparative advantage by producing green goods. We also study the externalities in terms of diversification prospects that arise from gaining a comparative advantage by producing and exporting green goods. For this purpose, we define a directed network in which nodes are products and links are the probability that a product catalyzes another one several years later. This network uses bilateral trade flows at the 6-digit level to assess the autocatalytic structure of product adoption, by identifying clusters of self-reinforcing products. We show that green goods are less prone to self-reinforcement compared to their non-green counterparts and offer fewer avenues for economic diversification. We also find that the impact of diversification varies across countries, suggesting that a one-size-fits-all approach to fostering the production of green goods may not be effective. |
| Keywords: | Economic complexity, Economic growth, Structural change, Networks, Autocatalytic set |
| JEL: | D85 F43 O25 O44 O50 Q50 Q56 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:gre:wpaper:2023-16&r=tid |
| By: | Daniel Goller; Christian Gschwends; Stefan C. Wolter |
| 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:iso:educat:0209&r=tid |
| By: | George Abi Younes (Ecole polytechnique federale de Lausanne); Gaetan de Rassenfosse (Ecole polytechnique federale de Lausanne) |
| Abstract: | Recognizing the increasing accessibility and importance of patent data, the paper underscores the need for standardized and transparent data analysis methods. By leveraging the BigQuery language, we illustrate the construction and relevance of commonly used patent indicators derived from Google Patents Public Datasets. The indicators range from citation counts to more advanced metrics like patent text similarity. The code is available in an open Kaggle notebook, explaining operational intricacies and potential data issues. By providing clear, adaptable queries and emphasizing transparent methodologies, this paper hopes to contribute to the standardization and accessibility of patent analysis, offering a valuable resource for researchers and practitioners alike. |
| Keywords: | BigQuery language; data transparency; patent analytics; patent data |
| JEL: | O34 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:iip:wpaper:24&r=tid |
| By: | Nikolova, Milena; Lepinteur, Anthony; Cnossen, Femke |
| Abstract: | Using survey data from 20 European countries, we construct novel worker-level indices of routine, abstract, social, and physical tasks across 20 European countries, which we combine with industry-level robotization exposure. Our conceptual framework builds on the insight that robotization simultaneously replaces, creates, and modifies workers' tasks and studies how these forces impact workers' job content. We rely on instrumental variable techniques and show that robotization reduces physically demanding activities. Yet, this reduction in manual work does not coincide with a shift to more challenging and interesting tasks. Instead, robotization makes workers' tasks more routine, while diminishing the opportunities for cognitively challenging work and human contact. The adverse impact of robotization on social tasks is particularly pronounced for highly skilled and educated workers. Our study offers a unique worker-centric viewpoint on the interplay between technology and tasks, highlighting nuances that macro-level indicators overlook. As such, it sheds light on the mechanisms underpinning the impact of robotization on labor markets. |
| Keywords: | robotization, technological change, worker-level data, tasks |
| JEL: | J01 J30 J32 J81 I30 I31 M50 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:glodps:1350&r=tid |
| By: | Einiö, Elias; Feng, Josh; Jaravel, Xavier |
| Abstract: | What are the implications of unequal access to innovation careers for the direction of innovation and inequality? Leveraging novel linked datasets in the United States and Finland, we document that innovators create products more likely to be purchased by consumers like them in terms of gender, socioeconomic status, and age. We find that a key explanatory channel is that social exposure causes a shift in the direction of innovation, independent of financial incentives. Incorporating this "social push" channel into a growth model, we estimate that unequal access to innovation careers has a large effect on cost-of-living inequality and long-run growth. |
| Keywords: | innovation, inequality, growth, innovators' socioeconomic background, Social security, taxation and inequality, O31, O41, D71, fi=Elinkeinopolitiikka|sv=Näringspolitik|en=Industrial and economic policy|, fi=Tulonjako ja eriarvoisuus|sv=Inkomstfördelning och ojämlikhet|en=Income distribution and inequality|, |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:fer:wpaper:160&r=tid |
| By: | Aghion, Philippe; Bergeaud, Antonin; Van Reenen, John |
| Abstract: | We present a framework that can be used to assess the equilibrium impact of regulation on endogenous innovation with heterogeneous firms. We implement this model using French firm-level panel data where there is a sharp increase in the burden of labor regulations on companies with 50 or more employees. Consistent with the model’s qualitative predictions, we find a sharp fall in the fraction of innovating firms just to the left of the regulatory threshold. Furthermore, we find a sharp reduction in the positive innovation response of firms to exogenous demand shocks just below the regulatory threshold. Using the structure of our model we quantitatively estimate parameters and find that the regulation reduces aggregate equilibrium innovation (and growth) by 5.7% which translates into a consumption equivalent welfare loss of at least 2.2%, approximately doubling the static losses in the existing literature. |
| Keywords: | innovation; regulation; patents; firm size |
| JEL: | O31 L11 L51 J80 L25 |
| Date: | 2023–11–01 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:120206&r=tid |
| By: | Kaichen Zhang; Ohchan Kwon; Hui Xiong |
| Abstract: | The rise of generative artificial intelligence (AI) has sparked concerns about its potential influence on unemployment and market depression. This study addresses this concern by examining the impact of generative AI on product markets. To overcome the challenge of causal inference, given the inherent limitations of conducting controlled experiments, this paper identifies an unanticipated and sudden leak of a highly proficient image-generative AI as a novel instance of a "natural experiment". This AI leak spread rapidly, significantly reducing the cost of generating anime-style images compared to other styles, creating an opportunity for comparative assessment. We collect real-world data from an artwork outsourcing platform. Surprisingly, our results show that while generative AI lowers average prices, it substantially boosts order volume and overall revenue. This counterintuitive finding suggests that generative AI confers benefits upon artists rather than detriments. The study further offers theoretical economic explanations to elucidate this unexpected phenomenon. By furnishing empirical evidence, this paper dispels the notion that generative AI might engender depression, instead underscoring its potential to foster market prosperity. These findings carry significant implications for practitioners, policymakers, and the broader AI community. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.07071&r=tid |
| By: | Thilo N. H. Albers (HU Berlin); Felix Kersting (HU Berlin); Timo Stieglitz (HU Berlin) |
| Abstract: | How does revolutionary technological change impact wealth inequality? We turn to the mother of all technological shocks–the Industrial Revolution–and analyze its role for wealth concentration both empirically and theoretically. Based on a novel dataset on wealth shares at the level of Prussian counties, we provide causal evidence on the positive effect of industrialization on the top percentile's wealth share and the inequality among top fortunes. We show that this relationship between industrialization, wealth concentration, and tail fattening is consistent with both cross-country data on national wealth distributions and with a new individual-level dataset of Prussian millionaires. We disentangle the mechanisms underlying the observed wealth concentration and tail fattening by introducing a dynamic two-sector structure into an overlapping generations model with heterogeneous returns to capital. In particular, we study the role of sector-specific scale dependence, i.e. the positive correlation of rates of return and wealth in industry, and dynastic type dependence in returns, i.e., the gradual one-directional transition of wealth-holders from the low-return traditional to the high-return industrial sector. The simulations suggest that the combination of these two features explains about half of the total increase of the top-1% share, while the other half resulted from the general increase and higher dispersion of returns induced by the emerging industrial sector. |
| Keywords: | rates of return; wealth inequality; industrialization; technology; simulation; |
| JEL: | D31 E21 N13 O14 |
| Date: | 2023–11–22 |
| URL: | http://d.repec.org/n?u=RePEc:rco:dpaper:462&r=tid |