|
on Innovation |
| By: | Laura Alfaro; Harald Fadinger; Jan Schymik; Gede Virananda |
| Abstract: | Trade and industrial policies, while primarily intended to support domestic industries, may unintentionally stimulate technological progress abroad. We document this mechanism in the case of rare earth elements (REEs) – critical inputs for manufacturing at the knowledge frontier, with low elasticity of substitution, inelastic supply, and high production and processing concentration. To assess the importance of REEs across industries, we construct an input-output table that includes disaggregated REE inputs. Using REE-related patents categorized by a large language model, trade data, and physical and chemical substitution properties of REEs, we show that the introduction of REE export restrictions by China led to a global surge in innovation and exports in REE-intensive downstream sectors outside of China. To rationalize these findings and quantify the global impact of the adverse REE supply shock, we develop a quantitative general-equilibrium model of trade and directed technological change. We also propose a structural method to estimate sectoral input substitution elasticities for REEs from patent data and find REEs to be complementary inputs. Under endogenous technologies and with complementary inputs, input-supply restrictions on REEs induce a surge in REE-enhancing innovation and lead to an expansion of REE-intensive downstream sectors. |
| Keywords: | Trade Restrictions, Industrial Policy, Global Value Chains, Rare Earths, Directed Technological Change, Input-Output Linkages, Downstream Sectors, Innovation |
| JEL: | F13 F14 F42 O33 O47 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_2025_720 |
| By: | Heng-fu Zou (IAS, Wuhan University and World Bank) |
| Abstract: | We replace Weber's "spirit of capitalism" with a constitutional-cultural framework we call the republican spirit of innovism operating within a re-public of entrepreneurs. In such an order, ordinary people repeatedly propose, test, and lawfully imitate improvements under general, impersonal rules-secure property, open entry and exit, credible contract, and freedoms of speech and association. Building on Mises(calculation and residual claimancy), Hayek (discovery and dispersed knowledge), Kirzner (alertness and equilibration), and the historical evidence assembled by Mc Closkey, Mokyr, and Phelps, we argue that modern prosperity stems less from elite R&D or capital deepening and more from creative construction by the many. We derive empirical signatures-proposal density, feedback speed, and diffusion breadth-and outline a policy agenda favoring open standards, disclosure-oriented intellectual property, contestability, and re producibility. Case evidence from Britain (since 1700), the United States (since the 1780s), and contemporary technological and biomedical sectors shows that when rules keep feedback honest and imitation lawful, total factor productivity rises persistently. |
| Keywords: | republican spirit of innovism, republic of entrepreneurs, dispersed knowledge, entrepreneurial discovery, lawful imitation, diffusion, Industrial Enlightenment, bourgeois dignity, grassroots dynamism |
| JEL: | O31 O33 L26 O43 N10 |
| Date: | 2025–11–02 |
| URL: | https://d.repec.org/n?u=RePEc:cuf:wpaper:804 |
| By: | Sang-Kyu Lee (Korea Institute for Industrial Economics and Trade) |
| Abstract: | This study is grounded on the premise that, given the transformative advances in artificial intelligence (AI) technologies occurring across the industrial landscape, AI tools should be actively implemented into the design and implementation of industrial policy. We argue that this is especially true for R&D policy, which is central to national competitiveness in science and technology, and which must consider multiple diverse variables, including the global economy, the overall industrial environment, corporate management, and technological capabilities.<p> For this study, I apply machine learning (ML)-based anomaly detection (AD) to analyze high-performing national R&D projects, and specifically assess ML-based AD that considers both input and output variables and analyzes structural patterns. Building on these analytical results, I propose firm-size-specific differentiated policy measures designed to enhance R&D performance.<p> The goal of this study is to establish a policy-decision framework that improves timeliness and precision in the operation and management of national R&D programs and, in the longer term, contributes to the realization of AI-based policy planning and operational management. |
| Keywords: | machine learning; artificial intelligence; AI; anomaly detection; DEA; SHAP; research and development; R&D; government R&D; industrial policy; South Korea |
| JEL: | I23 I28 O32 O38 |
| Date: | 2025–10–31 |
| URL: | https://d.repec.org/n?u=RePEc:ris:kieter:021804 |
| By: | Charlie Joyez (Université Côte d'Azur, CNRS, GREDEG, France) |
| Abstract: | We introduce complexity, a Stata command available on SSC that computes generalized complexity indices for specialization matrices. Originally developed for assessing economic complexity with global trade data (Hidalgo and Hausmann, 2009), these metrics have since been extended to various domains including regional development, innovation, and labor economics. The complexity command implements three core methodologies: the eigenvector method (Hausmann et al., 2011a), the Method of Reflection (Hidalgo and Hausmann, 2009), and the fitness-complexity approach (Tacchella et al., 2012). It also computes relatedness metrics such as coherence, adjacency matrices of the activity space network, and the complexity outlook as a measure of complexity potential. We describe the syntax and options, review the underlying algorithms, and provide applied examples |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:gre:wpaper:2025-50 |