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on Technology and Industrial Dynamics |
| By: | Rodríguez-Pose, Andrés; You, Zhuoying |
| Abstract: | Few studies have examined the economic consequences of deploying artificial intelligence (AI) and robotics in less-developed cities, where policies have often failed. To address this gap, we analyse a panel of 270 Chinese cities (2009–2019) using OLS, IV-2SLS, and quantile regression techniques. We find that AI and robotics significantly promote technological innovation in China, with especially pronounced implications for cities at or below the technological frontier. These technologies also enhance the returns to science and technology (S&T) investment. Its novelty lies in framing AI and robotics as policy substitutes and tools for narrowing innovation divides among Chinese cities. |
| Keywords: | AI; robotics; technological innovation; Chinese cities |
| JEL: | O31 O33 R11 R58 |
| Date: | 2026–02–05 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:137040 |
| By: | Sebastian, Raquel; Salas Rojo, Pedro; Palomino, Juan César; Rodríguez, Juan Gabriel |
| Abstract: | Technological change fuels economic growth, but its impact on wage inequality remains contested. This study presents a unified empirical framework that isolates the effects of new technologies such as automation and AI on the entire wage distribution. We develop a continuous and task-sensitive automation index and propose a distributional counterfactual-based method. Applying the approach to Spanish micro-data for 2000-2019 and instrumenting technology variables, we find automation to be a key driver of inequality: without task displacement the Gini coefficient would be 21.5% lower and significant wage shares would shift from the top 10% towards middle and bottom groups. Automation is found to barely affect the gender gap in the period studied, yet to widen the education premium. Like automation, AI exposure increases inequality, although the mechanisms to impact wages differ: automation tends to negatively impact wages in the middle of the distribution, while AI tends to increase wages at the top. Trade, offshorability, educational attainment, employment rates and mark-ups play secondary, period-specific roles. The results can inform policies on skill formation and inclusive innovation. |
| Keywords: | automation; AI; wage inequality; structural change; job tasks |
| JEL: | O33 D33 J21 J24 J31 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:137287 |
| By: | Mehic, Adrian (Research Institute of Industrial Economics (IFN)) |
| Abstract: | How are preferences for innovation formed, and what determines the long-run direction of technological change? This paper shows that early-life exposure to environmental accidents can durably reorient inventive effort decades later, even in the absence of targeted policy. I study radioactive fallout from the 1986 Chernobyl nuclear accident across Sweden, exploiting plausibly exogenous variation in local exposure driven by rainfall. Combining municipality-level fallout data with Swedish patent records from 1967 to 2021, I find that more exposed areas experienced a persistent increase in green patenting, with no change in total patenting. The effect emerges only in the early 2000s, and is driven by individuals exposed during childhood: matching inventor-level data with detailed administrative records, I show that individuals exposed to fallout during their formative years are more likely to enter the patent system as green inventors and to begin their inventive careers with green technologies, consistent with a cohort-based entry mechanism. A simple model of directed technical change with formative exposure rationalizes these findings. In addition, the paper shows that green patents originating from more exposed areas do not have a lower number of citations than other patents, suggesting that the results are not driven by low-quality innovations. |
| Keywords: | Patent; Environmental accidents |
| JEL: | D91 O31 Q53 Q55 |
| Date: | 2026–02–16 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:iuiwop:1552 |
| By: | Cagin Keskin |
| Abstract: | Horizontal expansion through an expanding product portfolio lies at the core of modern endogenous growth literature. However, evidence remains limited on how diversification across industries influences a firm's trade-off between generating social surplus and maximizing private returns. To investigate this, I categorize intangible assets by their spillovers: transferable intangibles (patents, software) generate social surplus, whereas embedded intangibles (organizational capital, brand value) primarily yield private returns. I document that diversified firms reallocate investment toward embedded intangibles, while at the same time having lower markups and productivity, as well as less competitive threats. Motivated by this evidence, I extend a canonical endogenous-growth framework to endogenize firms'allocations between transferable and embedded intangibles, allowing for both horizontal and vertical expansion. A key prediction of the model is that embedded intangibles are freely mobile across a firm's production lines; therefore, this mobility generates increasing returns to scale as the firm diversifies, which also raises entry barriers for competitors and decreases the social surplus, rather than promoting long-run growth. Thus, a shift in innovative effort ultimately sacrifices economy-wide growth for firm-level market advantages, and quantitative analysis indicates that size-dependent taxes can substantially improve welfare. |
| Keywords: | Schumpeterian growth, step-by-step innovation, intangibles, firm dynamics, span of control |
| JEL: | E22 O31 O32 O33 O34 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:cer:papers:wp811 |
| By: | Masami Imai; Koji Sakai; Michiru Sawada |
| Abstract: | Distortions in credit allocation can slow technological progress by sustaining unproductive firms and generating congestion that crowds out innovation from otherwise healthy firms. We study this mechanism using Japan’s banking crisis of the 1990s, linking firm-level borrowing data to the universe of patent applications with more than fifteen years of historical citation outcomes. Innovation declines more in technology fields facing greater credit distortion, with effects substantially larger for forward citations than for patent counts. Firm-level evidence reveals persistently low innovation by zombie firms and reduced innovation by healthy firms operating in zombie-intensive industries, consistent with congestion effects. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:tcr:wpaper:e220 |
| By: | Andrés García-Suaza (Universidad del Rosario); Carlos Sepúlveda-Rico (Universidad del Rosario); Pamela Caiza-Guamán (Universidad del Rosario) |
| Abstract: | The green transition is expected to be one of the most significant forces shaping labor markets in the incoming years. As economies shift toward cleaner technologies, green jobs will expand, while employment in high-emission sectors will either decline or move into other sectors, depending on skill transferability and policy design. In this context, the ability of workers to transition between green and non-green jobs will be crucial to ensure a just labor market adjustment. Labor transitions into and out of green jobs remain understudied, particularly in developing economies where data constraints limit empirical analysis. This paper addresses this gap, using household survey data and a synthetic panel approach to estimate the probability of labor transitions employs a skills-based green index. The results reveal a high degree of labor market persistence, explained by the role of skills in shaping mobility, and show a wage premium of 10.6% for green occupations compared to their non-green counterparts. These findings have important policy implications for ensuring a just energy transition. Given the observed rigidities in green labor mobility, targeted upskilling and reskilling programs are important to enabling non-green workers to acquire the necessary skills for green jobs. |
| Keywords: | Labor mobility; Wage inequality; Just transition; Informality |
| JEL: | J21 J24 Q52 J62 |
| Date: | 2025–11–18 |
| URL: | https://d.repec.org/n?u=RePEc:col:000092:022159 |
| By: | Armstrong, Christopher; Glaeser, Stephen; Park, Stella; Timmermans, Oscar |
| Abstract: | We study how the assignment of intellectual property rights between inventors and their employers affects innovation. Incomplete contracting theories predict that stronger employer property rights reduce the threat that employee inventors hold up their employers, thereby affecting inventor and invention outcomes. We test these predictions using a U.S. appellate court ruling that shifted the assignment of property rights from inventors to their employers. Within-employer-year analyses demonstrate that affected inventors are less likely to retain patent rights, assign patents to new employers, or leave their current employer, all consistent with reduced inventor ability to hold up their employers. Due to the reduced possibility of hold-up, affected inventors’ innovations are revealed more promptly when disclosed, draw from a broader set of prior patents, and spread more to subsequent patents. If affected inventors do leave their employer, they are more likely to relocate to unaffected states. Furthermore, employers affected by the ruling are more likely to locate their inventors in agglomeration economies and alter their innovation strategy by reallocating activity across states and expanding their innovation portfolios. Our collective evidence suggests that shifting intellectual property rights to employers affects inventor and invention outcomes by reducing the threat of employee hold-up from the employer's perspective. |
| Keywords: | corporate-innovation; disclosure; employee mobility; hold-up; incomplete contracts; employer-specific investment; corporate innovation |
| JEL: | J41 J61 O30 |
| Date: | 2026–01–08 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:130648 |
| By: | Robert J R ELLIOTT; Wenjing KUAI; Toshihiro OKUBO; Ceren OZGEN |
| Abstract: | This paper examines how international engagements shape heterogeneity in the greenness of Japanese manufacturing firms. Using a new firm-level dataset, we construct intensity-based greenness indicators distinguishing between the greenness of market facing products and the greenness of more governance-driven production processes. Our empirical results are three-fold. First, green activity is widespread across Japanese manufacturing sectors but is predominantly process-oriented, with the greenest firms concentrated in a small subset of industrial activities. Second, greenness is not linked to internationalization in general, but to firms being embedded in global value chains (GVCs), particularly in Western oriented networks, and this association is stronger for green processes. Third, we identify a vulnerability whereby product greening does not attenuate tariff induced sales losses among internationally engaged firms, and green processes do appear to amplify tariff exposure, especially for GVC participants. Overall, the results highlight that going green is multidimensional and that environmental process compliance interacts with GVC integration in shaping firms’ resilience to trade policy shocks under a trend towards further geoeconomic fragmentation. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:eti:dpaper:26018 |
| By: | Zou, Tao |
| Abstract: | In recent decades, global value chains (GVCs) have come to dominate much of world trade. Participation in GVCs is widely regarded as a key driver of development by enabling countries to climb the value‐added ladder. However, tighter governance structures within GVCs can make these benefits uncertain. This paper investigates the dynamic impact of GVC participation on economic upgrading using a semiparametric smooth coefficient model with panel data from 62 countries over 1995–2018. We uncover a novel N‐shaped nonlinear relationship between GVC participation and economic upgrading, extending beyond the linear or U‐shaped patterns found in earlier research. This relationship reveals three stages: initial learning with rising upgrading effects, an intermediate ‘upgrading trap’ with declining effects, and an advanced breakthrough stage with renewed rise. Decomposing transmission mechanisms shows that while GVC participation imposes output efficiency costs, it enhances upgrading by improving input factor productivities. Critically, forward linkage participation proves more effective than backward linkage for escaping the upgrading trap. Middle‐income countries exhibit the strongest internal input factor efficiency gains from GVC participation alongside the highest dispersion in overall outcomes. These findings offer policy insights for designing openness and industry policies tailored to a country's development stage and GVC position. |
| Keywords: | upgrading trap; nonlinearity; forward and backward linkages; global value chains |
| JEL: | L81 N0 |
| Date: | 2026–02–16 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:137389 |
| By: | Miklos Koren; Zsofia Barany; Ulrich Wohak |
| Abstract: | Generative AI is a directional technology: it excels at some task combinations and performs poorly at others. Knowledge work is also directional and endogenous: workers can satisfy their job requirements with different combinations of tasks. Studying AI adoption by knowledge workers hence requires comparing two vectors.We develop a high-dimensional model of task choice and technology adoption, with otherwise standard neoclassical assumptions. AI is adopted when its direction is aligned with what the worker values at the margin -- the worker's shadow prices, rather than with what the worker actually does -- their activity vector. This yields a cone of adoption that widens as AI capability grows; near the entry threshold, small improvements in capability translate into large expansions in the set of adopted directions. Adoption also has a structured intensive margin: a tool can be worth using but not worth using all the time, generating a region of stable hybrid production between an entry threshold and an all-in threshold. We also show how to derive shadow prices as explicit functions of observable skill and requirement vectors. The framework explains rapid adoption in aligned occupations, heterogeneous adoption elsewhere, and weak correlation with one-dimensional skill measures: the key heterogeneity is directional alignment, not skill level. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.12958 |