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Kerstin Hötte

Publications and source records attributed to Kerstin Hötte.

12 recordsLinked to original sources

Classification as Search Infrastructure: How Category Creation, Addition and Cleanup Shape Knowledge Retrieval

Classification systems shape how searchers find relevant objects. We study how revisions to classification architecture affect retrieval by changing the routes through which objects enter consideration. We theorize that creating a cross-cutting category can reduce classificatory distance and translation costs, whereas adding routes to an established system can increase route-selection and updating costs; category cleanup can restore contrast. Empirically, we exploit the 2013 creation and 2020 revision of Y02/Y04 green-technology codes in the Cooperative Patent Classification. We match treated patents to unchanged green patents within technology-cohort cells and estimate examiner- and applicant-specific effects on first-citation timing and citation volume. Category creation is associated with a 25% higher examiner first-citation hazard. In the mature system, additions are associated with 13-15% lower examiner hazards, while cleanup is associated with a 14% higher hazard and 12% higher citation volume. Effects are generally stronger for examiners, whose search task is more tightly coupled to patent classification. Timing and volume need not move together: creation and green-domain entry shift timing without detectable changes in volume, while cleanup increases both speed and recorded reach. Classification infrastructure may therefore change when relevant objects are found even when overall recorded attention is unchanged, and greater classificatory detail need not improve search efficiency.

econ.GN↗

The evolving boundary of green technology

Green patent indicators are widely used to track technological progress, assess climate and innovation policies, and identify emerging technological opportunities. Yet these indicators depend on classification systems that evolve over time and shape which inventions become visible. Drawing on a sociological perspective, we interpret patent classifications as information infrastructures and examine three mechanisms that shape the visibility of green inventions: (1) modification of category boundaries through reclassification, (2) broadening of institutional coverage through set expansion, and (3) narrowing of scope through user-defined selection criteria. Using patent families filed between 1980 and 2016, we merge the 2019 and 2023 PATSTAT releases to isolate their effects. Reclassification and set expansion increase the observed population of green patents by about 9% and 10%, respectively, while commonly used filters exclude between 42% and 93%. These effects are uneven and particularly pronounced for recent inventions, rapidly growing fields, and patents associated with Asian offices. Patents made newly visible through reclassification and set expansion are more novel and more often cited, while being slightly more specialized in narrow technological fields. Measurement choices can therefore change conclusions about the pace, direction, maturity, geography, and nature of green innovation. Observed green patent trends reflect not only inventive activity but also the evolving infrastructure through which green technology is made visible. Evaluations of policy effects, technological leadership, emerging opportunities, and progress in the green transition are therefore sensitive to classification vintages, institutional coverage, and filtering choices.

econ.GN↗

Revisiting Hötte (2025): A Companion Analysis with Extended Evidence from UK Inter-Industry Payment Data, 2017-2024

In 2025, the UK Office for National Statistics released a novel dataset of monthly inter-industry payment flows during January 2017 to November 2024 at the 5-digit SIC level (ONS, 2025a), covering $>$3.1 million UK organizations. Annual aggregates amount to 490 million transactions with an aggregate value of over £3.1 trillion in 2023. Such publicly available data are unprecedented by their granularity and timeliness, providing a rich basis for economic research and real-time policy advice. Hötte (2025) provided an empirical validation supplemented with conceptual discussions for using such bottom-up collected data in macroeconomic, industry-level, and economic network studies based on an earlier non-public and smaller version of the data. The novel data features much greater coverage, along with several methodological improvements. This paper gives an update on the earlier empirical results. It summarizes the major methodological changes of data construction, discusses key empirical observations, and their differences and consistencies relative to Hötte (2025). It concludes by discussing the implications for using payment data and outlining remaining challenges and expected future developments.

econ.GN↗

Measuring artificial intelligence: a systematic assessment and implications for governance

Governing artificial intelligence (AI) inventions is a major policy concern, yet definitions and measurement remain contested. We compare four patent-based approaches reflecting distinct understandings of AI. Using US patents (1990-2019), we assess the degree to which each approach classifies AI as a general-purpose technology (GPT) and examine patent concentration--two central policy-relevant dimensions. The approaches overlap in just 1.37% of patents, defining between 3-17% of all US patents in 2019 as AI. All approaches confirm AI's GPT characteristics, with the smallest keyword-based set exhibiting the highest growth and generality. High GPTness indicates public good characteristics, justifying public support. Across methods, AI patents concentrate among a few firms, highlighting market power and regulatory challenges. Policy implementation, thus, requires careful consideration of multiple classification methods to ensure robust, inclusive, and effective AI governance.

econ.GN↗

Mapping the disaggregated economy in real-time: Using granular payment network data to complement national accounts

In an era of rapid change, timely and disaggregated economic insights are crucial for effective policymaking. This study explores the potential of real-time payment data to complement traditional economic measurement. Using anonmysed UK business payments from 2015-2023, we analysed inter-industry financial flows at a granular 5-digit SIC level and compared them systematically with established economic indicators such as GDP and input-output tables (IOTs). Our findings show strong correlations with GDP and qualitative consistency with official IOTs, highlighting the value of the novel high-frequency data for real-time economic monitoring. We also benchmarked network statistics at the 5-digit level, showing how industry-specific payment structures align with stylised facts from the empirical economic network literature. While outlining methodological and interpretative challenges, we discuss the integration of such bottom-up data into national accounts. This work contributes to ongoing efforts to advance economic measurement and offers additional tools for tracking economic dynamics in real time.

econ.GN↗

From catch-up to frontier: The utility model as a learning device to escape the middle-income trap

The second-tier patent system, known as utility models (UMs), has been considered a tool for supporting technological learning in catching-up economies. UMs often impose lower inventive step requirements than patents, making them accessible to inventors with limited capacity. However, the mechanisms by which the UM system supports the catch-up process and the system's long-term impact in the post-catch-up period remain underexplored. We address this gap by empirically examining how and under which conditions the UM system can create synergies with catch-up strategies and influence the emerging industrial structure in the post-catch-up phase. Drawing on the literature on catch-up and intellectual property rights (IPR), we argue that catch-up strategies that prioritize imitative and adaptive learning in short-cycle technologies can be complemented by the UM system, supporting the development of indigenous capabilities to produce high-impact frontier technologies and internalize their value-added. Using South Korea as a case study, we present two key findings: First, the country's post-catch-up frontier technologies (US patents) are more impactful when they build on active learning experiences encoded in domestic UMs. Second, UM-reliant frontier technologies contribute to value internalization (local value-added relative to foreign value-added). Our findings extend the literature on the role of different types of IPR in various stages of development and inform policy discussions on how UMs can be used to support capability building in developing countries.

econ.GN↗

Making intellectual property rights work for climate technology transfer and innovation in developing countries

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.

econ.GN↗

Demand-pull, technology-push, and the direction of technological change

This paper studies the impact of Demand-pull (DP) and Technology-push (TP) on growth, innovation, and the factor bias of technological change in a two-layer network of input-output (market) and patent citation (innovation) links among 307 6-digit US manufacturing industries in 1977-2012. Two types of TP and DP are distinguished: (1) DP and TP are between-layer spillovers when market demand shocks pull innovation and innovation pushes market growth. (2) Within-layer DP arises if downstream users trigger upstream innovation and growth, while TP effects spill over from up- to downstream industries. The results support between- and within-layer TP: Innovation spillovers from upstream industries drive market growth and innovation. Within the market, upstream supply shocks stimulate growth, but this effect differs across industries. DP is not supported but shows a factor bias favoring labor, while TP comes with a shift towards non-production work. The results are strongest after the 2000s and shed light on the drivers of recent technological change and its factor bias.

econ.GN↗

Automation and Taxation

Decomposing taxes by source (labor, capital, sales), we analyze the impact of automation on tax revenues and the structure of taxation in 19 EU countries during 1995-2016. Pre-2008, robot diffusion lead to decreasing factor and tax income, and a shift from taxes on capital to goods. ICTs changed the structure of taxation from capital to labor, with decreasing employment, but increasing wages and labor income. Post-2008, we find an ICT-induced increase in capital income and services, but no effect on taxation from ICT/robots. Overall, automation goes through various phases with heterogeneous economic effects which impact the amount and structure of taxes. Whether automation erodes taxation depends on the technology and stage of diffusion, and thus concerns about public budgets might be myopic when focusing on the short-run and ignoring relevant technological trends.

econ.GN↗

Knowledge for a warmer world: a patent analysis of climate change adaptation technologies

Technologies can help strengthen the resilience of our economy against existential climate-risks. We investigate climate change adaptation technologies (CCATs) in US patents to understand (1) historical patterns and drivers of innovation; (2) scientific and technological requirements to develop and use CCATs; and (3) CCATs' potential technological synergies with mitigation. First, in contrast to mitigation, innovation in CCATs only slowly takes off, indicating a relatively low awareness of investors for solutions to cope with climate risks. Historical trends in environmental regulation, energy prices, and public support can be associated with patenting in CCATs. Second, CCATs form two main clusters: science-intensive ones in agriculture, health, and monitoring technologies; and engineering-intensive ones in coastal, water, and infrastructure technologies. Analyses of technology-specific scientific and technological knowledge bases inform directions for how to facilitate advancement, transfer and use of CCATs. Lastly, CCATs show strong technological complementarities with mitigation as more than 25% of CCATs bear mitigation benefits. While not judging about the complementarity of mitigation and adaptation in general, our results suggest how policymakers can harness these technological synergies to achieve both goals simultaneously.

econ.GN↗

Technology and jobs: A systematic literature review

Does technological change destroy or create jobs? New technologies may replace human workers, but can simultaneously create jobs if workers are needed to use these technologies or if new economic activities emerge. Furthermore, technology-driven productivity growth may increase disposable income, stimulating a demand-induced expansion of employment. To synthesize the existing knowledge on this question, we systematically review the empirical literature on the past four decades of technological change and its impact on employment, distinguishing between five broad technology categories (ICT, Robots, Innovation, TFP-style, Other). Overall, we find across studies that the labor-displacing effect of technology appears to be more than offset by compensating mechanisms that create or reinstate labor. This holds for most types of technology, suggesting that previous anxieties over widespread technology-driven unemployment lack an empirical base, at least so far. Nevertheless, low-skill, production, and manufacturing workers have been adversely affected by technological change, and effective up- and reskilling strategies should remain at the forefront of policy making along with targeted social support systems.

econ.GN↗

The rise of science in low-carbon energy technologies

Successfully combating climate change will require substantial technological improvements in Low-Carbon Energy Technologies (LCETs), but designing efficient allocation of R\&D budgets requires a better understanding of how LCETs rely on scientific knowledge. Using data covering almost all US patents and scientific articles that are cited by them over the past two centuries, we describe the evolution of knowledge bases of ten key LCETs and show how technological interdependencies have changed over time. The composition of low-carbon energy innovations shifted over time, from Hydro and Wind energy in the 19th and early 20th century, to Nuclear fission after World War II, and more recently to Solar PV and back to Wind. In recent years, Solar PV, Nuclear fusion and Biofuels (including energy from waste) have 35-65\% of their citations directed toward scientific papers, while this ratio is less than 10\% for Wind, Solar thermal, Hydro, Geothermal, and Nuclear fission. Over time, the share of patents citing science and the share of citations that are to scientific papers has been increasing for all technology types. The analysis of the scientific knowledge base of each LCET reveals three fairly separate clusters, with nuclear energy technologies, Biofuels and Waste, and all the other LCETs. Our detailed description of knowledge requirements for each LCET helps to design of targeted innovation policies.

cs.DL↗