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on Technology and Industrial Dynamics |
| By: | Antonin Bergeaud; Ruveyda Nur Gozen; John Van Reenen |
| Abstract: | We introduce a methodology to measure cross-country trends in innovation capability - "technological trajectories" and implement this on a new rich dataset covering patents between 1836 and 2016 across multiple countries. Intuitively, trajectories are revealed by a country's sustained increases in patenting across multiple patent offices. We first describe the data patterns, showing the relative decline of the UK, and the rise first of the US and Germany, and then later of Japan and China. We then econometrically estimate trajectories on (i) the post-1902 period for France, Germany, Japan, the UK and US, and (ii) the post-1960 period for a wider sample of 40 countries. Our trajectories are strongly positively correlated with Total Factor Productivity growth, and also (but less strongly) associated with the growth of labour productivity and capital intensity. We show that future trajectories are predicted by a country’s initial levels of R&D, education and defence spending, classic drivers of innovation in modern growth theory. |
| Keywords: | patents, technical progress, economic history, innovation |
| Date: | 2026–01–22 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2146 |
| By: | Seth Gordon Benzell; Kyle R. Myers |
| Abstract: | Many experiments study the productivity effects of automation technologies such as generative algorithms. A key test in these experiments relates to inequality: does the technology increase output more for high- or low-skill workers? However, the theoretical content of this empirical test has been unclear. Here, we formalize a theory that describes the experimental effect of automation technologies on worker-level output and, therefore, inequality. Worker-level output depends on a task-level production function, and workers are heterogeneous in their task-level skills. Workers perform a task themselves or delegate it to the automation technology. The inequality effect of improved automation depends on the interaction of two factors: (i) the correlation in task-level skills across workers, and (ii) workers' skills relative to the technology's effective skill. In many cases we study, the inequality effect is non-monotonic --- as technologies improve, inequality decreases then increases. The model and descriptive statistics of skill correlations generally suggest that the diversity of automation technologies will play an important role in the evolution of inequality. |
| JEL: | D20 D31 J24 O33 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34668 |
| By: | Federico Moscatelli; Julio Raffo; Shreyas Gadgin Matha; Christian Chacua; Matté Hartog; Eduardo Hernandez Rodriguez; Muhammed A. Yildirim |
| Abstract: | In developing countries' innovation activities, limited patenting suggests structural gaps that hinder technological progress. This paper investigates whether countries can leverage their scientific and productive capabilities to realize untapped technological potential. We analyze connections between trade, science, and technology across global innovation ecosystems and introduce an indicator to assess where countries are positioned to expand their technological capabilities. Our results show that the indicator predicts technological output growth, though growth slows when countries exceed their predicted potential, indicating diminishing returns. The indicator performs better in more complex ecosystems. These findings provide valuable insights for policymakers, offering a framework to address weaknesses in innovation ecosystems and foster balanced, sustainable technological development. |
| Keywords: | Innovation capabilities, complexity metrics, innovation ecosystems, science and technology policies, industrial policy, economic development, smart specialization |
| JEL: | O25 O31 O33 O11 O14 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:wip:wpaper:90 |
| By: | Holtmann, Svea; Braun, Anna-Sophie; Cho, Jae; Koch, Reinald; Langenmayr, Dominika |
| Abstract: | We study a 2018 reform in South Korea that reduced tax credits for automation investments. This reform increased the tax cost of investing in robots and thus resembles a robot tax. Exploiting this natural experiment with industry-level data on robot installations and firm-level data from Orbis, we document a sharp decline in automation investments after the reform in industries with a large share of affected firms. At the firm level, we find that affected firms increased employment, consistent with the notion that robots replaced workers. The effects are heterogeneous: financially constrained firms cut investment overall, while unconstrained firms substituted away from robots, hired more workers, and reallocated resources toward more productive uses. For the latter group, we find improvements in various measures of investment quality, suggesting that the tax credit induced inefficient overinvestment in automation. Our evidence informs ongoing debates on robot taxation and the efficiency of tax incentives. |
| Keywords: | tax credits, automation, robot tax |
| JEL: | H25 H32 O33 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:arqudp:335022 |
| By: | Diego Sancho-Bosch (Department of Economic Analysis, Universidad Complutense de Madrid (Spain)); Elena Huergo (ICAE – Department of Economic Analysis, Universidad Complutense de Madrid (Spain)) |
| Abstract: | This paper examines how the level of public R&D subsidies and firm size jointly influence firms’ net R&D investment. Using data on Spanish manufacturing firms from 2008 to 2018, we estimate parametric and non-parametric dose–response functions after applying entropy weighting to balance covariate distributions across treatment levels. The results reveal an inverted U-shaped relationship between subsidy intensity and net R&D expenditure for small, medium-sized, and large firms, but not for very large firms, which display a negative linear pattern. We also find substantial heterogeneity in subsidy effects within both the SME and large-firm categories, and show that the public funding share of R&D expenditure at which the positive impact of subsidies peaks declines markedly with firm size. These findings suggest that support schemes should implement progressively lower maximum subsidy rates, rather than relying on only two distinct caps for SMEs and larger firms. Overall, the results underscore firm size as a critical determinant of innovation policy effectiveness and provide practical guidance for optimizing subsidy design. |
| Keywords: | R&D support, policy evaluation, dose-response, entropy balancing. |
| JEL: | L24 L25 O32 R11 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ucm:doicae:2509 |
| By: | gert Bijnens; Joep Konings; Aaron Putseys |
| Abstract: | This paper identifies a firm-level Productivity J-curve induced by intangible investments. Using novel microdata on business-to-business transactions for Belgian firms, we construct a comprehensive measure of intangible in vestment covering software, R&D, design, training, and organizational capital. Our analysis shows that returns on intangibles substantially exceed those of traditional production factors, highlighting their central role in value creation. However, because intangible expenditures are rarely capitalized and are often recorded as intermediate inputs, they are not properly accounted for in conventional measures of total factor productivity (TFP). This misclassification creates systematic mismeasurement, whereby inputs are overstated relative to output in the short run, leading to an underestimation of TFP. Exploiting the lumpy nature of intangible expenditures within a difference-in-differences event-study framework, we document that such mismeasurement results in a persistent underestimation of TFP, by about 3% over a seven-year horizon. Given average measured TFP growth of 1% annually, this represents a substantial distortion. The bias is strongest among small, young, and low-capital intensive firms, reflecting slower absorption of intangible assets. By clarifying how intangible capital and emerging technologies such as AI systematically distort measured productivity, our findings provide new empirical insights into the productivity slowdown and the role of mismeasurement in modern economies. |
| Date: | 2025–11–03 |
| URL: | https://d.repec.org/n?u=RePEc:ete:ceswps:779815 |
| By: | Sam J. Manning; Tomás Aguirre |
| Abstract: | We construct an occupation-level adaptive capacity index that measures a set of worker characteristics relevant for navigating job transitions if displaced, covering 356 occupations that represent 95.9% of the U.S. workforce. We find that AI exposure and adaptive capacity are positively correlated: many occupations highly exposed to AI contain workers with relatively strong means to manage a job transition. Of the 37.1 million workers in the top quartile of AI exposure, 26.5 million are in occupations that also have above-median adaptive capacity, leaving them comparatively well-equipped to handle job transitions if displacement occurs. At the same time, 6.1 million workers (4.2% of the workforce in our sample) work in occupations that are both highly exposed and where workers have low expected adaptive capacity. These workers are concentrated in clerical and administrative roles. Importantly, AI exposure reflects potential changes to work tasks, not inevitable displacement; only some of the changes brought on by AI will result in job loss. By distinguishing between highly exposed workers with relatively strong means to adjust and those with limited adaptive capacity, our analysis shows that exposure measures alone can obscure both areas of resilience to technological change and concentrated pockets of elevated vulnerability if displacement were to occur. |
| JEL: | J01 J20 J21 J24 J29 J63 O33 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34705 |
| By: | Ngueuleweu Tiwang Gildas |
| Abstract: | Understanding how climate and innovation policies perform during socio-technical transitions remains a central challenge in innovation studies. Empirical analyses of the relationship between economic growth and carbon emissions continue to yield conflicting results, partly because they rely on pooled models that implicitly assume stable and homogeneous dynamics. Transition theory, by contrast, emphasizes that decarbonization unfolds through heterogeneous regimes characterized by varying degrees of stability, inertia, and reconfiguration. Yet, empirical tools capable of identifying these regimes prior to policy evaluation or forecasting remain limited. This paper introduces a regime-diagnostic framework designed to condition empirical analysis on the structural state of the climate-economy system. Rather than estimating causal effects or generating forecasts directly, the framework reconstructs latent transition regimes from the time varying responsiveness of emissions to economic activity. These diagnostics are used as a pre-modeling step, allowing econometric and machine learning tools to be applied conditionally on empirically identified regimes. Using a global panel of approximately 150 countries over the period 1991-2022, we apply the framework to the emission-growth relationship. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.01545 |
| By: | Agustin Benetrix (Department of Economics, Trinity College Dublin); Hayley Pallan (The World Bank); Ugo Panizza (Geneva Graduate Institute and CEPR) |
| Abstract: | This paper reassesses the relationship between foreign direct investment (FDI) and economic growth in emerging and developing economies. Using cross-country data, it first shows that the relationship between FDI, growth, and local conditions such as financial depth and human capital is not stable over time: complementarities documented in studies based on data from the 1970s and 1980s largely disappear in more recent decades. It then builds a new dataset on sectoral FDI covering 112 emerging and developing economies over the period 1975‐2023 and documents substantial heterogeneity in the association between FDI and sectoral growth. FDI inflows are positively associated with growth in the primary sector, show no robust relationship in the secondary sector, and are negatively associated with growth in the tertiary sector. To interpret these patterns, we examine the role of global value chains (GVCs). We find that FDI is most strongly associated with growth in country‐sectors with low GVC participation, while this relationship weakens or disappears as GVC integration increases. Moreover, the growth effects of FDI depend critically on the type of GVC integration. Backward participation amplifies the positive growth effects of FDI in the primary sector but attenuates them in the secondary sector and worsens the negative effects in tertiary sector, whereas forward participation strengthens the association between FDI and growth in manufacturing. Taken together, the results suggest that the elusive aggregate relationship between FDI and growth reflects a structural transformation in how foreign investment is embedded in global production networks: in highly fragmented value chains, FDI can expand gross activity without generating commensurate domestic value-added growth. |
| Keywords: | FDI, Economic Growth, Global Value Chains |
| JEL: | F21 F23 F14 C23 F60 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:tcd:tcduee:tep0126 |
| By: | Matthieu Bordenave; Giovanna Ciaffi |
| Abstract: | This paper evaluates the macroeconomic impact of green public spending by quantifying the responses of GDP, private investment, employment, and labour productivity across 30 European countries from 1995 to 2020. Using linear and nonlinear Local Projection methods, our findings indicate that green fiscal policies can positively and persistently affect GDP and employment levels, crowding-in private investment and generating a positive impact on productivity dynamics. When distinguishing between low- and high-income countries, we observe that the multipliers on GDP and employment are higher for the latter group, although no significant gains in productivity are found. However, productivity gains, albeit small in magnitude, appear to be concentrated in low-income countries. Moreover, our results show that the impact of green investments on GDP and private investment is higher in countries with high levels of green public consumption expenditure over total green public expenditure. These findings underline the importance of tailored fiscal policies to maximize the benefits of green public expenditure across different economic contexts. |
| Keywords: | Green Public Spending, Fiscal Multipliers, Green Investment, Green Consumption, European Divide, Ecological Economics |
| JEL: | E62 Q54 Q58 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:imk:fmmpap:121-2025 |
| By: | Lorenzo Emer; Anna Gallo; Mattia Marzi; Andrea Mina; Tiziano Squartini; Andrea Vandin |
| Abstract: | Innovation emerges from complex collaboration patterns - among inventors, firms, or institutions. However, not much is known about the overall mesoscopic structure around which inventive activity self-organizes. Here, we tackle this problem by employing patent data to analyze both individual (co-inventorship) and organization (co-ownership) networks in three strategic domains (artificial intelligence, biotechnology and semiconductors). We characterize the mesoscale structure (in terms of clusters) of each domain by comparing two alternative methods: a standard baseline - modularity maximization - and one based on the minimization of the Bayesian Information Criterion, within the Stochastic Block Model and its degree-corrected variant. We find that, across sectors, inventor networks are denser and more clustered than organization ones - consistent with the presence of small recurrent teams embedded into broader institutional hierarchies - whereas organization networks have neater hierarchical role-based structures, with few bridging firms coordinating the most peripheral ones. We also find that the discovered meso-structures are connected to innovation output. In particular, Lorenz curves of forward citations show a pervasive inequality in technological influence: across sectors and methods, both inventor (especially) and organization networks consistently show high levels of concentration of citations in a few of the discovered clusters. Our results demonstrate that the baseline modularity-based method may not be capable of fully capturing the way collaborations drive the spreading of inventive impact across technological domains. This is due to the presence of local hierarchies that call for more refined tools based on Bayesian inference. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.10224 |