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on Innovation |
| By: | Silvia Rocchetta; Martina Iori; Andrea Mina; Robert Gillanders |
| Abstract: | We study the effects of different types of technological diversification on the performance of regional economies. We focus on the relatedness and unconventionality of technological capabilities as drivers of GDP and employment growth. Using economic indicators from Eurostat regional statistics and patent records from the European Patent Office (EPO) PATSTAT and the OECD RegPat databases, we estimate Panel Vector Autoregression models and generate Impulse Response Functions to assess to what extent and with what persistence relatedness and unconventionality affect growth. Our findings, which have implications for place-based innovation policies, reveal that technological relatedness has short-term effects on employment growth and negative effects on GDP growth, whereas technological unconventionality has a long-lasting positive impact on GDP growth and no effect on employment growth. |
| Keywords: | Technological capabilities; Diversification; Relatedness; Unconventionality; Innovation; Regional development |
| Date: | 2023–12–23 |
| URL: | http://d.repec.org/n?u=RePEc:ssa:lemwps:2023/46&r=ino |
| By: | Gaia Dossi; Marta Morando |
| Abstract: | We study the role of political ideology for a critical group of economic agents: inventors. We document that, in "politically polarizing" fields, inventors patent innovations aligned with their political beliefs. We construct a novel dataset matching data from the US Patent Office (USPTO) with individual Voter Register data for two large US states, and with the universe of US campaign contributions data. We proxy political ideology with individual party affiliation and focus on fields where the ideological distance between Republicans and Democrats is especially large in the general population. We find that, compared to Republicans, Democrats are: i) more likely to file green patents; ii) more likely to file female-health patents, and this persists in the sub-set of male inventors; and iii) less likely to file weapon-related patents. The magnitudes are large and range from one-fourth to one-third of total patent production in these technologies. This pattern is explained by inventors sorting into firms, rather than by within-firm dynamics. Socio-economic status, geography, or differential reactions to monetary incentives cannot explain our findings. Importantly, ideological sorting persists in research organizations, suggesting that inventors may derive intrinsic utility from producing innovation aligned with their beliefs. We rationalize our findings using a stylized model of the labor market where inventors derive amenity value from producing innovation close to their political ideology. |
| Keywords: | political ideology, innovation, inventors |
| Date: | 2023–12–19 |
| URL: | http://d.repec.org/n?u=RePEc:cep:cepdps:dp1969&r=ino |
| By: | Colin Davis; Ken-ichi Hashimoto |
| Abstract: | This paper studies how national research subsidies affect productivity growth and national welfare through adjustments in the geographic location of research and development (R&D) across countries. Our two-country framework features a tension in the firm-level innovation location decision between accessing technical knowledge and sourcing low-cost high-skilled labor. With trade costs and imperfect international knowledge diffusion, the larger country has a greater share of industry and tends to host a larger share of innovation. In this setting, we find that an R&D subsidy expands the implementing country’s share of innovation and raises the rate of productivity growth. Although the non-implementing country experiences a welfare improvement, the rising cost of the policy generates a concave relationship between the R&D subsidy and the welfare of the implementing country, yielding an optimal R&D subsidy rate. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:dpr:wpaper:1226&r=ino |
| By: | Pietro Santoleri (European Commission - JRC); Emanuele Russo (Bank of Italy) |
| Abstract: | In the attempt to boost innovation, policy-makers have enacted a myriad of programs targeting innovative start-ups in recent years. Empirical evidence on these initiatives has almost exclusively focused on national-level programs, overlooking those implemented at the local level. This paper provides the first quasi-experimental evidence on the joint effects of local policies focusing on Italy, where regional governments have been very active in providing financial support to these firms. By leveraging discontinuities in program design, we adopt a local randomization approach and document a null effect of these programs over a wide range of firm-level outcomes. However, we find that securing local subsidies increases start-ups' probability to obtain additional public subsidies, which points in the direction of a vicious “Matthew effect” in subsidy allocation. Consistent with a reputation/certification mechanism, the increase in follow-on subsidies occurs for funds disbursed at the local level only, whereas no effect is detected for subsidies allocated by national or international authorities. |
| Keywords: | Regression discontinuity design, Innovation Policy, Place-based Policy, Start-ups. |
| JEL: | D22 G24 G32 L53 O31 O38 R58 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:termod:202313&r=ino |
| By: | Ferro Gustavo; Gatti Nicolás |
| Abstract: | Knowledge applied to innovation is increasingly recognized as an explanatory factor of economic growth. Innovation derives from the application of knowledge to generate new products or new processes. National Innovation Systems (NIS) performs as the formal or informal network of people within institutions, interacting to produce and apply knowledge to innovation. NIS can be understood as two subsystems: one based on scientifical and technological work, producing codified products (publications and patents), and the other centered on practical actions to diffuse, apply, and use knowledge. Our objective is to assess cost efficiency in the production of codified knowledge outputs (CKO), being our unit of analysis countries. To attain our goal, we apply a Stochastic Frontier Analysis (SFA) to estimate a cost frontier of CKO. The sample is a panel that includes 1189 observations, for 23 years (1996-2019), and 82 countries. Our main results identify determinants and patterns of efficiency and productivity, tendencies, and specifics of countries and groups of them. |
| JEL: | O12 O30 |
| Date: | 2022–11 |
| URL: | http://d.repec.org/n?u=RePEc:aep:anales:4620&r=ino |
| By: | Davide Azzolini; Nicola Doppio; Luca Mion; Iunio Quarto Russo; Alessio Tomelleri |
| Abstract: | Innovating product design is crucial for firms operating in the digital sector as it is closely linked with innovation capability and, therefore, with firm performance and productivity. In this paper, we run a randomized controlled trial to assess if participating in an open innovation initiative increases SMEs’ capability to design more competitive digital products. More specifically, the intervention aimed at increasing firms’ knowledge of the Design Sprint and their readiness to implement user-centered design techniques. 190 SMEs based in 7 different European countries took part in the field trial in spring 2021. We find that the intervention increased participants’ knowledge about user-centered design methods, although no statistically significant effects are found on participants’ intention to adopt that in their firms. This may be traced back to organizational and financial constraints typically related to the small-sized firms involved. |
| Keywords: | Open Innovation, SMEs, Randomized Controlled Trial, User Experience Design, Design Sprint |
| JEL: | D22 M31 O31 |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:fbk:wpaper:2024-01&r=ino |
| By: | Amit Goyal (University of Lausanne; Swiss Finance Institute); Sunil Wahal (Arizona State University) |
| Abstract: | We investigate the relation between inventive input (R&D), inventive output (the economic value of patents, EVP), firm-level profitability and asset growth, and stock returns. Current R&D and EVP forecast future profitability. Neither forecast future asset growth. Factor models motivated by q-theory and the dividend discount model fail to price R&D and EVP correctly, leaving large alphas on the table. But model failure is due to design specifics, not economic underpinnings: using cash-based operating profitability to measure expected profitability resurrects both models. The stock market does not appear to misprice inventive input or output. |
| Keywords: | Research & Development, Patents, Innovation, Intangibles, Profitability, Asset Pricing, Expected Returns, Accruals |
| JEL: | G11 G12 G13 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp23107&r=ino |
| By: | Jesse LaBelle; Immaculada Martinez-Zarzoso; Ana Maria Santacreu; Yoto Yotov |
| Abstract: | We build a stylized model that captures the relationships between cross-border patenting, globalization, and development. Our theory delivers a gravity equation for cross-border patents. To test the model’s predictions, we compile a new dataset that tracks patents within and between countries and industries, for 1980-2019. The econometric analysis reveals a strong, positive impact of policy and globalization on cross-border patent flows, especially from North to South. A counterfactual welfare analysis suggests that the increase in patent flows from North to South has benefited both regions, with South gaining more than North post-2000, thus lowering real income inequality in the world. |
| Keywords: | cross-border patents; gravity; policy; globalization; development |
| JEL: | F63 O14 O33 O34 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedlwp:97470&r=ino |
| By: | Michela Loi; Manuel Castriotta; Saulo Dubard-Barbosa (EM - emlyon business school); Maria Chiara Di Guardo; Alain Fayolle (EM - emlyon business school) |
| Abstract: | "Fragmentation is the main obstacle to scientific progress on entrepreneurial intention. To address this issue, we systematise the current literature with a hybrid bibliometric method that combines co-citation and bibliographic coupling analysis for the first time in entrepreneurial intention studies to show the field's knowledge base and research fronts and to examine how divergent perspectives have challenged the core knowledge of the field. We highlight three recurring dimensions of entrepreneurial intention studies: (1) personal factors, (2) social factors and (3) investigational settings. In addition to introducing new constructs, divergent perspectives have emphasised the interplay between these components and challenged the mechanisms connecting them. Based on these findings, we extend previous classifications in the literature by providing a framework that integrates divergent perspectives with the field's knowledge base, helping establish future research avenues and improving the theorising process of entrepreneurial intention." |
| Keywords: | bibliometrics, conceptual framework, divergent perspectives, entrepreneurial intention, entrepreneurship, literature review |
| Date: | 2023–09–04 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04325752&r=ino |
| By: | Stefano Bianchini; Moritz M\"uller; Pierre Pelletier |
| Abstract: | New technologies have the power to revolutionize science. It has happened in the past and is happening again with the emergence of new computational tools, such as Artificial Intelligence (AI) and Machine Learning (ML). Despite the documented impact of these technologies, there remains a significant gap in understanding the process of their adoption within the scientific community. In this paper, we draw on theories of scientific and technical human capital (STHC) to study the integration of AI in scientific research, focusing on the human capital of scientists and the external resources available within their network of collaborators and institutions. We validate our hypotheses on a large sample of publications from OpenAlex, covering all sciences from 1980 to 2020. We find that the diffusion of AI is strongly driven by social mechanisms that organize the deployment and creation of human capital that complements the technology. Our results suggest that AI is pioneered by domain scientists with a `taste for exploration' and who are embedded in a network rich of computer scientists, experienced AI scientists and early-career researchers; they also come from institutions with high citation impact and a relatively strong publication history on AI. The pattern is similar across scientific disciplines, the exception being access to high-performance computing (HPC), which is important in chemistry and the medical sciences but less so in other fields. Once AI is integrated into research, most adoption factors continue to influence its subsequent reuse. Implications for the organization and management of science in the evolving era of AI-driven discovery are discussed. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.09843&r=ino |