nep-ent New Economics Papers
on Entrepreneurship
Issue of 2022‒07‒11
seven papers chosen by
Marcus Dejardin
Université de Namur

  1. Classifying Smart City Startups: The Smart City Index By M. Hermse; I. Nijland; M. Picari; Mark Sanders
  2. Knowledge-Based Information and the Effectiveness of R&D in Small Firms By Fletcher, Joshua; Howard, Eric; Link, Albert; O'Connor, Alan
  3. Public Support for Research in Artificial Intelligence: A Descriptive Study of U.S. Department of Defense SBIR Projects By Chowdhury, Farhat; Link, Albert; van Hasselt, Martijn
  4. Innovation to Keep or to Sell and Tax Incentives By Colin Davis; Laixun Zhao
  5. B2B e-commerce marketplaces and MSMES: Evidence of global value chain facilitation? By Ladrière, Maxime; Lundquist, Kathryn; Ye, Qing
  6. Causal Effects of a Tax Incentive on SME Capital Investment By HOSONO Kaoru; HOTEI Masaki; MIYAKAWA Daisuke
  7. SME Viability Assessment Methodology: Combining Altman’s Z-Score with Big Data By Dimitar Haralampiev Popov

  1. By: M. Hermse; I. Nijland; M. Picari; Mark Sanders
    Abstract: In this paper we present an index for coding new ventures, projects and firms as “smart-city†or not. The index is based on a systematic assessment of some 70+ definitions of the concept from the literature. Based on this analysis, we propose a 7-item coding scheme based on venture descriptions that are commonly available from public data sources. We identified two necessary and 5 “intensity†items and propose an algorithm that translates these items into a single smartc-city index (SCI) that expresses the degree to which an activity is contributing to smart city development in a score between 1 and 5. We then show the results of coding 759 new ventures in different datasets to illustrate that our index gives sensible results. Some 90 (11%) of these ventures could be classified as “smart city†in our sample, scoring an average of about 3.3, with significant variation around these averages that make intuitive sense. Our index can be used in a broad range of applications.
    Keywords: Urban Development, Smart City, Entrepreneurship, Innovation, DataCollection
    Date: 2021
  2. By: Fletcher, Joshua (RTI International); Howard, Eric (University of North Carolina at Greensboro, Department of Economics); Link, Albert (University of North Carolina at Greensboro, Department of Economics); O'Connor, Alan (University of North Carolina at Greensboro, Department of Economics)
    Abstract: This paper explores the impact that external sources of information have on the effectiveness of R&D in small, entrepreneurial firms. The effectiveness of R&D is measured in terms of two probabilities; the probability that a firm that received and completed a Phase I SBIR-funded research project is invited to submit a proposal for a Phase II award, and given such an invitation, the probability that a firm receives the Phase II award. Information from competitors is an important, in a statistical sense, covariate with the probability of being asked to submit a Phase II proposal whereas information from suppliers and customers in an important covariate with the probability of receiving a Phase II award.
    Keywords: Small Business Innovation Research (SBIR) program; small firms; entrepreneurial firms; R&D; knowledge sources; program evaluation;
    JEL: H43 L26 O31 O32 O38
    Date: 2022–06–07
  3. By: Chowdhury, Farhat (University of North Carolina at Greensboro, Department of Economics); Link, Albert (University of North Carolina at Greensboro, Department of Economics); van Hasselt, Martijn (University of North Carolina at Greensboro, Department of Economics)
    Abstract: We describe public support for AI research in small firms using data from U.S. Department of Defense-funded SBIR projects. Ours is the first collection of firm-level project information on publicly funded R&D investments in AI. We find that the likelihood of an SBIR funded research project being focused on AI is greater the larger the amount of the SBIR award. AI-focused research projects are associated with a 7.6 percent increase in average award amounts. We also find suggestive evidence that the likelihood of an SBIR project being AI-focused is greater in smaller-sized firms. Finally, we find that SBIR-funded AI research is more likely to occur in states with complementary university research resources.
    Keywords: Artificial intelligence; machine learning; Department of Defense; Small Business Innovation Research program; agglomeration;
    JEL: O31 O38
    Date: 2022–06–07
  4. By: Colin Davis (The Institute for the Liberal Arts, Doshisha University, JAPAN); Laixun Zhao (Research Institute for Economics & Business Administration (RIEB), Kobe University, JAPAN)
    Abstract: We study how tax policy affects economic growth through entrepreneurs' choice of commercialization mode. Introducing both heterogeneous quality jumps and a leapfrog versus sell choice into the quality-ladders model, we show that entrepreneurs use high-quality innovations to leapfrog incumbent firms and become new market leaders, but sell low quality innovations to incumbents. Tax incentives that promote leapfrogging slow the rate of innovation. A numerical analysis concludes surprisingly that corporate taxes, capital gains taxes, and subsidies to market entry all harm welfare.
    Keywords: Innovation based growth; Heterogenous quality improvements; Innovation sales; Corporate tax; Capital gains tax; Market entry subsidy
    JEL: O31 O33 O43
    Date: 2022–06
  5. By: Ladrière, Maxime; Lundquist, Kathryn; Ye, Qing
    Abstract: In theory, e-commerce marketplaces connect buyers and sellers, open trade opportunities, and reduce transaction costs thereby creating opportunities for more inclusive trade and even GVC participation, especially for micro, small and medium-sized enterprises (MSMEs). Further, there is some evidence that MSMEs are more likely to use e-commerce marketplaces than large firms given these websites reduce search frictions and transaction costs, which can be relatively more beneficial for smaller firms. This discussion paper explores non-traditional data to investigate whether e-commerce marketplaces may contribute to MSME GVC participation. By looking at the development of business-to-business (B2B) e-commerce marketplaces, the gross merchandise value (GMV) of regional e-commerce marketplaces, and MSMEs' overall participation in B2B e-commerce marketplaces, descriptive statistics are gathered that contributes to the overall discussion on this topic. This discussion paper also links B2B e-commerce marketplaces with GVC facilitation through a novel approach of cataloguing these platforms' merchandise and finds that on average, roughly one third of B2B e-commerce marketplace listings are intermediate goods.
    Keywords: Micro,Small and Medium Sized Enterprise (MSME),SME,e-commerce,marketplaces,global value chains (GVC)
    JEL: F13 L81 O30
    Date: 2022
  6. By: HOSONO Kaoru; HOTEI Masaki; MIYAKAWA Daisuke
    Abstract: We estimate the causal effects of a tax incentive for specific productivity-enhancing equipment that was introduced in 2014 for Japanese small and medium-sized enterprises. Using firm-level panel data, we obtain the following findings. First, the introduction of the tax incentive did not on average effectively increase the capital investment ratio of eligible firms, which could be due to the small number of firms using the incentives. Second, despite the first finding, the firms using the tax incentive increased their capital investment ratio and improved labor productivity more than the comparable firms did. Third, firms using the tax incentive did not increase capital intensity. Fourth, among the firms using the tax incentive, less cash-rich, smaller, and younger firms increased their capital investment ratio to a greater degree. These results show that the actual use of the tax incentive mitigates financial constraints in upgrading capital and improving labor productivity.
    Date: 2022–05
  7. By: Dimitar Haralampiev Popov (University of National and World Economy, Department of Management, Sofia, Bulgaria)
    Abstract: Due to their important place in an economy, small and medium enterprises (SMEs) viability is the focus of numerous scientific studies, European and national programs. One of the most widely used viability prediction model is Altman’s Z-score. Altman’s classical models are not suitable for all situations, though. SMEs’ large nominal number in an economy presents another challenge to researchers. One possible solution to this issue is to use data mining tools that can lead to new knowledge discovery. Data mining is the result of a natural evolution of information technology. The cross industry standard process for data mining (CRISP-DM) is a methodological framework for researching large amounts of data. This paper aims to outline the characteristics of Altman’s Z-score and CRISP-DM, and propose combining them into a methodology for predicting SMEs’ viability.
    Keywords: Altman Z-score, Data mining, CRISP-DM, SMEs, Bulgaria
    JEL: M10 P12 C38 C55
    Date: 2022–06

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