nep-sbm New Economics Papers
on Small Business Management
Issue of 2022‒04‒11
fifteen papers chosen by
João Carlos Correia Leitão
Universidade da Beira Interior

  1. Serial Entrepreneurship in China By Loren Brandt; Ruochen Dai; Gueorgui Kambourov; Kjetil Storesletten; Xiaobo Zhang
  2. Digital Entrepreneurship Indicator (DEI): An Analysis of the Case of the Greater Paris Metropolitan Area By Dorine CORNET; Jean BONNET; Sébastien BOURDIN
  3. Using big data for generating firm-level innovation indicators: A literature review By Rammer, Christian; Es-Sadki, Nordine
  4. Firm survival and gender of firm owner in times of COVID-19 Evidence from 10 European countries By Joachim Wagner
  5. Risk-Sharing and Entrepreneurship By Kilström, Matilda; Roth, Paula
  6. Government Procurement and Access to Credit: Firm Dynamics and Aggregate Implications By Julian di Giovanni; Manuel García-Santana; Priit Jeenas; Enrique Moral-Benito; Josep Pijoan-Mas
  7. Four Decades of Canadian Earnings Inequality and Dynamics across Workers and Firms By Audra Bowlus; Emilien Gouin-Bonenfant; Huju Liu; Lance Lochner; Youngmin Park
  8. Adoption of Eco and Circular Economy-Innovation in Italy: exploring different firm profiles By Massimiliano Mazzanti; Francesco Nicolli; Stefano Pareglio; Marco Quatrosi
  9. From InnoMix to University-Industry Collaboration: Fostering Exchange at Eye Level By Hille, Carsten; Morcinczyk-Meier, Daria; Schneider, Sarah; Mietzner, Dana
  10. (R)evolution in Entrepreneurial Finance? The Relationship between Cryptocurrency and Venture Capital Markets By Kirill Shakhnov; Luana Zaccaria
  11. Entrepreneurship and Regulatory Voids: The Case of Ridesharing By Deerfield, Amanda; Elert, Niklas
  12. Venture capital investments through the lens of network and functional data analysis By Christian Esposito; Marco Gortan; Lorenzo Testa; Francesca Chiaromonte; Giorgio Fagiolo; Andrea Mina; Giulio Rossetti
  13. Outcomes of Science-Industry Collaboration: Factors and Interdependencies By Uwe Cantner; Martin Kalthaus; Indira Yarullina
  14. Revisiting innovation typology: A systemic approach By Louis Knuepling; Colin Wessendorf; Stefano Basilico
  15. Do Startups Benefit from Their Investors’ Reputation? Evidence from a Randomized Field Experiment By Shai Bernstein; Kunal Mehta; Richard R. Townsend; Ting Xu

  1. By: Loren Brandt; Ruochen Dai; Gueorgui Kambourov; Kjetil Storesletten; Xiaobo Zhang
    Abstract: This paper studies entrepreneurship and the creation of new firms in China through the lens of serial entrepreneurs, i.e. entrepreneurs who establish more than one firm, and their differences with non-serial entrepreneurs. Drawing on data on the universe of all firms in China, we document key facts about serial entrepreneurship in China since the early 1990s and develop a theoretical framework to rationalize the role of endowments, ability, and capital market frictions in their behavior. We also examine the key determinants of the sectoral choice for serial entrepreneurs' second firms. Quantitatively, serial entrepreneurs are more productive, raise more capital, and operate larger firms than non-serial entrepreneurs. Moreover, serial entrepreneurs with greater liquidity and whose firms have relatively similar productivity are more likely to operate these firms concurrently rather than sequentially. We also find that less productive serial entrepreneurs are more likely to switch sectors when establishing new firms, with the choice of sector influenced by considerations of risk diversification, upstream and downstream linkages, and sectoral complementarities.
    Keywords: Serial Entrepreneurship; Entrepreneurship; Capital Distortions; Sector Choice
    JEL: D22 D24 E22 E44 L25 L26 O11 O14 O16 O40 O53 P25 R12 D21
    Date: 2022–03–23
  2. By: Dorine CORNET (Université Paris 1 Panthéon-Sorbonne, 106-112 bd de l'Hôpital, 75642 Paris Cedex 13); Jean BONNET (Normandie Univ, Unicaen, CNRS, CREM, Esplanade de la Paix, 14032 CAEN cedex 5); Sébastien BOURDIN (EM Normandie Business School, Métis Lab, 9 rue Claude Bloch, 14 000 Caen)
    Abstract: The DIGITAL ENTREPRENEURSHIP INDICATOR (DEI), which combines individual and institutional data, is designed to chart the vitality of metropolitan areas in terms of digital entrepreneurship on a suburban scale. In this study, we apply it to the case of the Greater Paris Metropolitan area. Using geographically weighted regression, we explore the spatial heterogeneity of the effect of digital entrepreneurial ecosystems on the location quotient of information and communication technology firms with fewer than 10 employees. The results highlight a positive link between the DEI and the location quotient of small ICT firms. In particular, the aspects of both ATTitudes and CAPacities (i.e., urbanization economies, Human Development Index, density of incubators, accounting and financial services, and fiber optic coverage) appear to have a significant effect on a suburban scale.
    Keywords: digital entrepreneurial ecosystem, urban area, innovation, spatial econometrics
    JEL: R12 L26 O31 P25
    Date: 2022–04
  3. By: Rammer, Christian; Es-Sadki, Nordine
    Abstract: Obtaining indicators on innovation activities of firms has been a challenge in economic research for a long time. The most frequently used indicators - R&D expenditure and patents - provide an incomplete picture as they represent inputs and throughputs in the innovation process. Output measurement of innovation has strongly been relying on survey data such as the Community Innovation Survey (CIS), but suffers from several short-comings typical to sample surveys, including incomplete coverage of the firm sector, low timeliness and limited comparability across industries and firms. The availability of big data sources has initiated new efforts to collect innovation data at the firm level. This paper discusses recent attempts of using digital big data sources on firms for generating firm-level innovation indicators, including Websites and social media. It summarises main challenges when using big data and proposes avenues for future research.
    Keywords: Big data,innovation indicators,CIS,literature review
    JEL: O30 C81
    Date: 2022
  4. By: Joachim Wagner (Leuphana Universität Lüneburg, Institut für Volkswirtschaftslehre)
    Abstract: This paper uses firm level data from the World Bank Enterprise surveys conducted in 2019 and from the COVID-19 follow-up surveys conducted in 2020 in ten European countries to investigate the link between the gender of the firm’s owner and firm survival until 2020.The estimated effect of female ownership is positive ceteris paribus after controlling for various firm characteristics that are known to be related to survival. Furthermore, the size of this estimated effect can be considered to be large on average. Having a female owner helped firms to survive.
    Keywords: Gender, female owned firms, firm survival, COVID-19, World Bank Enterprise Surveys
    JEL: D22 L20 L25 L29
    Date: 2022–03
  5. By: Kilström, Matilda (Stockholm School of Economics); Roth, Paula (Research Institute of Industrial Economics (IFN))
    Abstract: In this paper, we study the role of risk-sharing in entrepreneurship-driven innovation. Studying entrepreneurship and innovation entails modeling an occupational choice and an effort choice. Risk-sharing may increase the number of individuals who become entrepreneurs by limiting the downside risk. The effort of entrepreneurs may, however, be hampered by high risk-sharing if this limits the returns faced by successful entrepreneurs relative to unsuccessful entrepreneurs. We construct a simple theoretical model where risk-sharing may be either private or provided through the welfare state by means of taxation. We show that, in addition to the occupational and effort choice dimensions, the level of public risk-sharing also matters for the characteristics of entrepreneurs.
    Keywords: Innovation; Institutions; Growth risk-sharing; Inequality; Incentives
    JEL: D64 E02 O30 O33 O43 O47
    Date: 2021–02–16
  6. By: Julian di Giovanni; Manuel García-Santana; Priit Jeenas; Enrique Moral-Benito; Josep Pijoan-Mas
    Abstract: We provide a framework to study how different allocation systems of public procurement contracts affect firm dynamics and long-run macroeconomic outcomes. We start by using a newly created panel data set of administrative data that merges Spanish credit register loan data, quasi-census firm-level data, and public procurement projects to study firm selection into procurement and the effects of procurement on credit growth and firm growth. We show evidence consistent with the hypotheses that there is selection of large firms into procurement, that procurement contracts provide useful collateral for firms more so than sales to the private sector and that procurement contracts facilitate firm growth beyond the contract duration. We next build a model of firm dynamics with both asset-based and earnings-based borrowing constraints and a government that buys goods and services from private sector firms. We use the calibrated model to quantify the long-run macroeconomic consequences of alternative procurement allocation systems. We find that granting procurement contracts to small firms, either by directly targeting them or by slicing large contracts into smaller ones, helps these firms grow and overcome financial constraints in the long run. However, we also find that reducing the average size of contracts or making it less likely for large firms to access them removes saving incentives for large firms, whose negative effects on capital accumulation can overcome the expansionary consequences for small firms and hence generate a drop in aggregate output.
    Keywords: government procurement; financial frictions; capital accumulation; aggregate productivity
    JEL: E22 E23 E62 G32
    Date: 2022–02–01
  7. By: Audra Bowlus (University of Western Ontario); Emilien Gouin-Bonenfant (Columbia University); Huju Liu (Statistics Canada); Lance Lochner (University of Western Ontario); Youngmin Park (Bank of Canada)
    Abstract: This paper studies the evolution of individual earnings inequality and dynamics in Canada from 1983 to 2016 using tax files and administrative records. Linking these individuals to their employers (and rich administrative records on firms) beginning in 2001, it also documents the relationship between the earnings dynamics of workers and the size and growth of their employers. It highlights three main patterns over this period: First, with a few exceptions (sharp increase in top 1% and declining gender gap), Canada has experienced relatively modest changes in overall earnings inequality, volatility, and mobility between 1983 and 2016. Second, there is considerable variability in earnings inequality and volatility over the business cycle. Third, the earnings dynamics of individuals are strongly related to the size and employment growth of their employers.
    Date: 2021
  8. By: Massimiliano Mazzanti (University of Ferrara); Francesco Nicolli (University of Ferrara); Stefano Pareglio (Università Cattolica del Sacro Cuore); Marco Quatrosi (University of Ferrara)
    Abstract: Applying clustering techniques, this paper identifies homogeneous groups of enterprises within the heterogeneous landscape of the italian manufacturing tissue. The algorithm will be fed with data from a survey on a cross-section of SMEs in 2019. The set of questions span from economic and financial performances to innovation adoption (product, process, organization), to circular economy implementation and environmental protection. Clustering has been chosen to identify groups of firms with respect to multiple and diverse characteristics without any preexisting hypothesis on a possible relationship among the variables. Results will group profiles of enterprises considering the information on multi-dimensional aspects of a firm. Indeed, the overarching aim of this work is to single out common characteristics among the diverse landscape of enterprises within the manufacturing sector. This will in turn support (local and national) policy makers in better designing and targeting an appropriate set of policy instruments with respect to the relevant areas (i.e., circular economy, environmental protection, eco-innovation) of the ecological/sustainability transition. If the one-size-fits-all has not been proved a viable approach in policy making, a more targeted intervention at policy level tackling the consistent heterogeneity of the manufacturing tissue might improve the effectiveness of (sectorial) policies.
    Keywords: Circular Economy, Sustainable production, Environmental Innovation, Cluster analysis, Firm profile
    JEL: O30 O44 O55
    Date: 2022–02
  9. By: Hille, Carsten; Morcinczyk-Meier, Daria; Schneider, Sarah; Mietzner, Dana
    Abstract: In this paper, we address a specific tool-InnoMix-that is implemented to overcome the lack of university-industry interaction in a selected region facing structural change with its corresponding impact on the economy and society. InnoMix is facilitated and implemented by university-based transfer scouts who act as mediators and translators between the players of the regional innovation system. These transfer scouts are part of the Innovation Hub 13, in which the region's partners and stakeholders, infrastructures and competencies are systematically networked with each other to set new impulses for knowledge and technology transfer. These new impulses are brought into the region through new transfer approaches ranging from people and tools to infrastructure. InnoMix can be considered to be a highly interactive tool to overcome the weak, direct interaction between researchers and potential corporate partners in the region to foster strong collaboration between academia and industry. InnoMix especially aims to strengthen interdisciplinary exchange to shed light on cross-disciplinary perspectives. For that reason, transfer scouts focusing on transfer activities related to the life sciences, digitalisation and lightweight construction are involved in the implementation of InnoMix. Based on 11 InnoMix running since 2019, we provide insights into the planning and preparation phase of InnoMix and the selection of relevant topics and requirements for matching participants. Furthermore, we clearly indicate which formats of InnoMix work best and in which way university-industry interactions could be curated after InnoMix is implemented.
    Keywords: collaboration,transfer scouts,knowledge and technology transfer (KTT),innovation,innovation hub,networking/matchmaking
    Date: 2021
  10. By: Kirill Shakhnov (University of Surrey); Luana Zaccaria (EIEF)
    Abstract: We propose a model of entrepreneurial finance where start-ups raise capital via Initial Coin Offering (ICO) or traditional funding methods such as Venture Capital (VC). While token sales allow startups to leverage network effects, VC's value-adding services enhance product quality. We show that, even when projects have large potential network effects, ICOs may not be optimal if entrepreneurial ability is low. Moreover, despite the potential complementarity between network effects and value-adding services, entrepreneurs combine VC and ICO funding only in highly efficient VC markets and for projects with high network effects. Using data on funding rounds of blockchain startups, we empirically validate the main results of the model.
    Date: 2022
  11. By: Deerfield, Amanda (Economics Department); Elert, Niklas (Research Institute of Industrial Economics (IFN))
    Abstract: Formal institutions, e.g., regulations, are considered crucial determinants of entrepreneurship, but what enables regulatory change when there is a regulatory void, meaning entrepreneurship clashes with existing regulations? Drawing on public choice theory, we hypothesize that regulatory freedom facilitates the introduction of legislation to fill such voids. We test this hypothesis using unique data documenting the time for ridesharing to become legalized at the state level across the United States following its local (and often illegal) rollout. Results suggest states with greater regulatory freedom passed ridesharing legislation quicker, highlighting an underappreciated way that extant regulatory freedom facilitates the accommodation of entrepreneurship.
    Keywords: Entrepreneurship; Innovation; Regulation; Institutional change; Institutional voids; Institutional entrepreneurship; Sharing economy; Economic freedom; Survival analysis
    JEL: C21 O31 R49
    Date: 2022–03–24
  12. By: Christian Esposito; Marco Gortan; Lorenzo Testa; Francesca Chiaromonte; Giorgio Fagiolo; Andrea Mina; Giulio Rossetti
    Abstract: In this paper we characterize the performance of venture capital- backed firms based on their ability to attract investment. The aim of the study is to identify relevant predictors of success built from the network structure of firms' and investors' relations. Focusing on deal-level data for the health sector, we first create a bipartite network among firms and investors, and then apply functional data analysis (FDA) to derive progressively more refined indicators of success captured by a binary, a scalar and a functional outcome. More specifically, we use different network centrality measures to capture the role of early investments for the success of the firm. Our results, which are robust to different specifications, suggest that success has a strong positive association with centrality measures of the firm and of its large in- vestors, and a weaker but still detectable association with centrality measures of small investors and features describing firms as knowl- edge bridges. Finally, based on our analyses, success is not associated with firms' and investors' spreading power (harmonic centrality), nor with the tightness of investors' community (clustering coefficient) and spreading ability (VoteRank).
    Keywords: Network analysis; functional data analysis; venture capital; investment trajectory.
    Date: 2022–02–28
  13. By: Uwe Cantner (Friedrich Schiller University Jena, Department of Economics, and University of Southern Denmark, Department of Marketing and Management); Martin Kalthaus (Friedrich Schiller University Jena, Department of Economics); Indira Yarullina (Friedrich Schiller University Jena, Department of Economics)
    Abstract: Science-industry collaboration is one of the major channels for transferring new scientific ideas into economic applications. Whereas the factors leading to collaboration are reasonably well understood, the determinants of the outcomes generated by such collaboration are unknown. This paper fills this gap by a new conceptualisation of collaboration outcomes and proposes factors that influence the generation of outcomes. We distinguish three different types of outcomes, namely scientific ones, commercialisable ones, and follow-up cooperation. We argue that scientific factors influence the generation of scientific outcomes, and economic factors the generation of commercialisable outcomes; interaction factors are proposed to influence the emergence of follow-up cooperation. We further propose that these outcomes depend on each other and hence are co-generated. We test our propositions with survey data from scientists in the German state of Thuringia. We asked scientists about characteristics of a particular collaboration and its outcomes. Multivariate probit estimations show that scientific factors positively related to scientific outcomes, and interaction factors are relevant for the follow-up cooperation. However, for economic factors, we find mixed evidence for their relation to commercialisable outcomes. As to outcome interdependence, we only find support for scientific outcomes to be co-generated with each of the other two types. Our results provide implications for policymakers and science managers on how to design funding policies and their evaluation.
    Keywords: Technology Transfer, Science-Industry collaboration, Scientific outcome, Commercialisable outcome, Follow-up cooperation, Mulitvariate probit
    JEL: I23 O31 O32
    Date: 2022–03–04
  14. By: Louis Knuepling (Institute of Economic and Cultural Geography, Leibniz University Hannover); Colin Wessendorf (Centre for Regional and Innovation Economics, University of Bremen); Stefano Basilico (Chair of Microeconomics, Friedrich-Schiller-Universität Jena & Faculty of Economics and Business Studies, University of Bremen)
    Abstract: Innovation studies use labels such as radical or disruptive to qualify innovation according to different concepts. Within the literature, these labels are frequently used interchangeably due to overlaps in their characteristics. These various definitions present challenges when the labels are operationalized in empirical studies. Based on a quantitative analysis of the most common innovation labels' definitions in 532 scientific papers, we find that novelty and impact, predominantly used for empirical operationalization, differentiate only between ordinary and more exceptional innovations. Based on our findings, a differentiation between the impact’s target and the consideration of positive versus negative effects enables better distinction between labels for more 'exceptional' innovations. We extend the existing literature and enable a more precise definition of (single) innovations by providing a novel, more nuanced description of innovations' different characteristics and a further distinction of their effects. Thereby, the relevant decisive aspects will be communicated more accurately.
    Keywords: radical, incremental, disruptive, breakthrough, innovation typology
    JEL: O31 O32 O33
    Date: 2022–03–02
  15. By: Shai Bernstein; Kunal Mehta; Richard R. Townsend; Ting Xu
    Abstract: We analyze a field experiment conducted on AngelList Talent, a large online search platform for startup jobs. In the experiment, AngelList randomly informed job seekers of whether a startup was funded by a top-tier investor and/or was funded recently. We find that the same startup receives significantly more interest when information about top-tier investors is provided. Information about recent funding has no effect. The effect of top-tier investors is not driven by low-quality candidates and is stronger for earlier-stage startups. The results show that venture capitalists can add value passively, simply by attaching their names to startups.
    JEL: C93 G24 J22 J24 L26
    Date: 2022–03

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