nep-ino New Economics Papers
on Innovation
Issue of 2023‒10‒23
seven papers chosen by
Uwe Cantner, University of Jena

  1. Monetary Policy and Innovation By Yueran Ma; Kaspar Zimmermann
  2. Environmental Policies and Innovation in Renewable Energy By Luca Bettarelli; Davide Furceri; Pietro Pizzuto; Nadia Shakoor
  3. Beyond Citations: Text-Based Metrics for Assessing Novelty and its Impact in Scientific Publications By Sam Arts; Nicola Melluso; Reinhilde Veugelers
  4. Growth and Innovation in the Modern Data Economy By Orlando Gomes; Roxana Mihet; Kumar Rishabh
  5. Technical efficiency and technology adoption in beef By Aguirre, Emilio; Garcıa Suarez, Federico; Sicilia, Gabriela
  6. SME internationalisation: Do the types of innovation matter? By Boumediene Ramdani; Fateh Belaid; Stéphane Goutte
  7. Technology and Development: An Exploration of the Data By Charles Kenny; George Yang

  1. By: Yueran Ma; Kaspar Zimmermann
    Abstract: We document that monetary policy has a substantial impact on innovation activities. After a tightening shock of 100 basis points, research and development (R&D) spending declines by about 1 to 3 percent and venture capital (VC) investment declines by about 25 percent in the following 1 to 3 years. Patenting in important technologies, as well as a patent-based aggregate innovation index, declines by up to 9 percent in the following 2 to 4 years. Based on previous estimates of the sensitivity of output to innovation activities, these magnitudes imply that output could be 1 percent lower after another 5 years. Monetary policy can influence innovation activities by changing aggregate demand and correspondingly the profitability of innovation, and by changing financial market conditions. Both channels appear relevant in the data. Our findings suggest that monetary policy may affect the productive capacity of the economy in the longer term, in addition to the well-recognized near-term effects on economic outcomes.
    JEL: E2 E5 G31 O3
    Date: 2023–09
  2. By: Luca Bettarelli; Davide Furceri; Pietro Pizzuto; Nadia Shakoor
    Abstract: This paper investigates the effect of Climate Change Policies (CCPs) on green innovation, for a sample of 40 advanced and emerging market economies and 5 economic sectors, during the period 2000-2021. Our results suggest that CCPs increase green patents, with the effect increasing gradually over time. The effect is larger for non-market-based policies—such as R&D subsidies—and technology-support instruments, in countries with greater competitiveness and during periods of stronger economic activity—that is, higher GDP growth, lower uncertainty and financial stress. The results based on a difference-in-differences approach suggest that the positive effect of stricter CCPs on green innovation is stronger in sectors with limited financial constraints.
    Keywords: green patents; climate change policy; diff-in-diff approach; innovation
    Date: 2023–09–01
  3. By: Sam Arts; Nicola Melluso; Reinhilde Veugelers
    Abstract: We use text mining to identify the origin and impact of new scientific ideas in the population of scientific papers from Microsoft Academic Graph (MAG). We validate the new techniques and their improvement over the traditional metrics based on citations. First, we collect scientific papers linked to Nobel prizes. These papers arguably introduced fundamentally new scientific ideas with a major impact on scientific progress. Second, we identify literature review papers which typically summarize prior scientific findings rather than pioneer new scientific insights. Finally, we illustrate that papers pioneering new scientific ideas are more likely to become highly cited. Our findings support the use of text mining both to measure novel scientific ideas at the time of publication and to measure the impact of these new ideas on later scientific work. Moreover, the results illustrate the significant improvement of the new text metrics over the traditional metrics based on paper citations. We provide open access to code and data for all scientific papers in MAG up to December 2020.
    Date: 2023–09
  4. By: Orlando Gomes (Lisbon Accounting and Business School); Roxana Mihet (University of Lausanne, Swiss Finance Institute and CEPR); Kumar Rishabh (University of Basel and University of Lausanne)
    Abstract: In this paper, we formulate a growth model of the data economy, highlighting data's dual role as a business optimization tool and a cybercrime target. We investigate the impact of cybercrime on firm innovation and economic growth, finding that it unequivocally leads to reduced knowledge stocks, decreased productivity, and slower overall economic growth for all firms. However, there is a silver lining: cybercrime risk prompts data-intensive companies to pursue digital innovation, enhancing productivity in other domains. We observe increased R&D, patenting, and patent diversity in response to higher cyber risk, especially among data-intensive firms. Non-data-intensive firms do not exhibit increased general innovation in response to cyber risk. Notably, in-house cybersecurity innovation sustains this cycle, while third-party cybersecurity delegation lacks the same innovation benefits.
    Keywords: Data economy, data theft, data breaches, cyber-risk, growth, artificial intelligence, innovation
    JEL: D8 O3 O4 G3 L1 L2 M1
    Date: 2023–09
  5. By: Aguirre, Emilio; Garcıa Suarez, Federico; Sicilia, Gabriela
    Abstract: Since the Second World War, the primary source of U.S. agricultural output growth has come from lifting productivity (Wang et al., 2015). Long-term investments in agricultural R&D appear as the predominate driver of those productivity gains (Alston and Pardey, 2021). Public research plays a critical role in the U.S. agricultural innovation process. From 1970 to the early 2000s, public research spending in the U.S. was nearly equal to private research spending, each amounting in 2002 to just under $6 billion (Wang et al., 2015, p. 41). However, Wang et al show that since 2002 when world commodity prices started climbing, a stark divergence between the two developed; by 2010, real public U.S. research spending fell to ~$5 billion and private research spending spiked to ~$9 billion. In the late 1990s and early 2000s, a new approach to funding U.S. innovation emerged: venture capital (VC) began to support newly-created firms to move promising inventions and business ideas from inception to commercialization (Kortum & Lerner, 2000; Arque-Castells, 2012). In agriculture, VC funding helps firms overcome high entry costs resulting from long-term research risk, spatial heterogeneity for applications, and economies of scale characteristic of many agricultural markets. In 2010, total VC investment in U.S. startups focused on farm production technologies was ~$400 million. By 2018, investment in VC-backed agricultural startups had grown to over $7 billion (Graff et al., 2020). In 2020, that investment was over $15 billion (AgFunder, 2021). Scholars hypothesize that VC investors became attracted to agriculture following the 2002 climb in commodity process, which increased farmers’ abilities to adopt new technologies and signaled to input suppliers that global demand may soon exceed supply (Fuglie, 2016). Others suggest a shift towards cleantech and biofuels in the 2000s introduced VC investors to agriculture amid an economy-wide surge in the financing of VC funds (Graff et al., 2020). It could be that the culmination of various general-purpose technologies (e.g., cloud computing, satellite imagery, vehicle automation, gene editing) opened technological opportunities in agriculture, as investors maximized economic benefits across multiple sectors of application (Olsson, 2005), including agriculture, given its historically high rates of return on research (Hurley et al, 2014). We explore the relationship between technological opportunity and the large exogenous shock in VC funding of agricultural startups. Specifically, we investigate the agricultural startup life-cycle. Within the cycle of firm birth, venture investment, and investor exit, what is the relationship between patents and firm financials? Do firms that patent have more successful financings and exits than those that patent little or not at all? In which industries/subsectors were technological opportunities pronounced? What are observed characteristics of the technological opportunity in agriculture? To investigate these issues, we began with a unique dataset of privately-held agricultural startups founded between 1977 and 2019. These unique startups were obtained from four commercial databases: Venture Source (now CB Insights), Crunchbase, Pitchbook, and CapitalIQ. Following a careful matching process, we identified 4, 681 firms from PitchBook (49.26% of the sample), 3, 399 from Capital IQ (35.77%), 1, 312 firms from Crunchbase (13.81%), and 111 from VentureSource (1.17%). From these 9, 503 firms, we narrowed to 7, 287 distinct startups founded in the United States on or after 1987. Of these agricultural startups, we matched 6, 084 to at least one establishment in the National Establishment Time Series (NETS) database, an 83.5% match rate. The NETS database is the most comprehensive source of establishment-level economic information for U.S. firms. Next, we matched the same set of agricultural startups to assignees listed in the USPTO’s pre-grant publication (PG Pub) and granted patent databases. Of those 6, 084 agricultural startups matched to economic information in NETS, we find 10% (634 startups) have one or more published patent application or grant, and 36% (2, 214 startups) have reported financing deals. Of the 634 startups with patent filing activity, 72% (458) report financing deals. We find a strong increase in the number of agricultural startups, both with and without VC investments, over the 1989-2019 period. Startups with VC grew, in terms of employment and sales, faster than startups without VC. We find substantial increases in patenting by the agricultural startups over time. Importantly, there has been great diversification of technology fields in which the startups patent, as well as of industry classifications in which startups operate, evidence of startups pursuing technological opportunity in agriculture. Among industries, we find the greatest increase of patenting by startups primarily classified in the manufacturing and professional, scientific, and technical services. Startups classified in these industries patented in Ag & Food, but also in biotech, chemicals, physics, electricity, and climate-change related new technologies. Next steps include detailing the timeline of firm birth, investment, and exit, and exploring causal and correlative relationships between patenting and VC-funded startups. REFERENCES AgFunder, 2021. AgFunder AgrifoodTech Investment Report. Available from: Alston, J., and P. Pardey, 2021. The Economics of Agricultural Innovation. In Handbook of Agricultural Economics, Eds., C. Barrett and D. Just. Vol. 5, Chapter 75, Elsevier Publishing. Arque-Castells, 2012. How Venture Capitalists Spur Invention in Spain: Evidence From Patent Trajectories. Research Policy (41): 897-912. Fuglie, 2016. The Growing Role of the Private Sector in Agricultural Research and Development World-wide. Global Food Security (10): 29-38. Graff, et al., 2020. Venture Capital and the Transformation of Private R&D for Agriculture. NBER Working Paper. Heisey and Fuglie, 2018. Public Agricultural R&D in High Income Countries: Old and New Roles in a New Funding Environment. Global Food Security (17): 92-102. Hurley, T., X. Rao, and P. Pardey, 2014. Re-Examining the Reported Rates of Return to Food and Agricultural Research and Development. American Journal of Agricultural Economics 96 (5): 1492-1504. Kortum, S., and J. Lerner, 2000. Assessing the Contribution of Venture Capital to Innovation. The RAND Journal of Economics (31): 674-692. Olsson, O., 2005. Technological opportunity and growth. Journal of Economic Growth 10: 35-57. Wang, S.L., P. Heisey, D. Schimmelpfennig, and E. Ball, 2015. Agricultural Productivity Growth in the United States: Measurement, Trends, and Drivers. Economic Research Report 189, Economic Research Service, U.S. Department of Agriculture. July.
    Keywords: Livestock Production/Industries, Research and Development/Tech Change/Emerging Technologies
    Date: 2023
  6. By: Boumediene Ramdani; Fateh Belaid (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique); Stéphane Goutte (SOURCE - SOUtenabilité et RésilienCE - UVSQ - Université de Versailles Saint-Quentin-en-Yvelines - IRD [France-Nord] - Institut de Recherche pour le Développement)
    Abstract: Existing evidence suggest that innovative Small and Medium-sized Enterprises (or SMEs) are more likely to internationalise (i.e. have a greater propensity to export) than non-innovative SMEs. However, it is not yet clear whether and to what extent different types of innovation (i.e. product, service, and process) affect SME internationalisation. To address this issue, this study uses a research model that integrates the resource and institutional perspectives and empirically test it using data from the United Kingdom (UK) Longitudinal Small Business Survey. Our results confirm that SME internationalisation is more likely to occur in firms undertaking product innovation than process and/or service innovation, and a specific configuration of resource and institutional drivers influence SME internationalisation depending on the innovation type. These results lead to major policy and managerial implications in relation to promoting SME internationalisation through different types of innovation, given the UK withdrawal from the European Union.
    Keywords: Export, Innovation, Internationalisation, SME, UK
    Date: 2023
  7. By: Charles Kenny (Center for Global Development); George Yang (Center for Global Development)
    Abstract: We present data on the global diffusion of technologies over time, updating and adding to Comin and Mestieri’s "CHAT" database. We analyze usage primarily based on per capita measures and divide technologies into the two broad categories of production and consumption. We conclude that there has been strong convergence in use of consumption technologies with somewhat slower and more partial convergence in production technologies. This reflects considerably stronger global convergence in quality of life than in income, but we note that universal convergence in use of production technologies is not required for income convergence (only that countries are approaching the technology frontier in the goods and services that they produce).
    Keywords: Technology, Diffusion, Dataset
    JEL: O33 O47 F02
    Date: 2022–05–20

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