Keywords
Startups
Space-time agglomeration
How to Cite
Abstract
In this paper we analize the spatio-temporal emergence and aglomeration dynamics of technology-based startups that ocurred in Florianópolis, São José and Palhoça (SC) during 2000-2020 period. To study this phenomenon, a parametric approach based on the spatial K-function and the space-time K-function (Arbia, Espa e Giuliani, 2021) is suggested, considering a geo-referenced startups observed over the period considered. We find that there is a strong concentration of these startups in a short distance, whose center is the Celta incubator (Alfa Technology Park), of the Certi Foundation, created within the scope of the Federal University of Santa Catarina and at a greater distance. A statistically significant time-space interaction was also found, especially in 5-year cycles. The results reinforce the importance of Marshallian externalities that operate at the microgeographic scale (district or neighborhood) as well as the Jacobian ones, which operate at the macrogeographic scale (city).
References
ADLER, P. et al. The city and high-tech startups: the spatial organization of Schumpeterian entrepreneurship. Cities, London, v. 87, p. 121-130, 2019.
ANTONELLI, C. The economics of localized technological change and industrial dynamics. Dordrecht: Springer Science & Business Media, 2012.
ANTONELLI, C. The knowledge growth regime: a Schumpeterian approach. Cham: Springer, 2019.
ARBIA, G. Spatial data configuration in statistical analysis of regional economic and related problems. Dordrecht: Springer Science & Business Media, 2012.
ARBIA, G. et al. Spatio-temporal clustering in the pharmaceutical and medical device manufacturing industry: a geographical micro-level analysis. Regional Science and Urban Economics, Amsterdam, v. 49, p. 298-304, 2014.
ARBIA, G. et al. On the spatial determinants of firm growth: a microlevel analysis of the Italian SMEs. In: COLOMBO, S. (Org.). Spatial economics volume II. Cham: Palgrave Macmillan, 2021. p. 89-120.
ARBIA, G.; ESPA, G.; GIULIANI, D. Spatial microeconometrics. New York: Routledge, 2021.
ARRUDA, C.; NOGUEIRA, V. S.; COSTA, V. The Brazilian entrepreneurial ecosystem of startups: an analysis of entrepreneurship determinants in Brazil as seen from the OECD pillars. Journal of Entrepreneurship and Innovation Management, Istanbul, v. 2, n. 3, p. 17-57, 2013.
BADDELEY, A. et al. On tests of spatial pattern based on simulation envelopes. Ecological Monographs, Durham, v. 84, n. 3, p. 477-489, 2014.
BATHELT, H.; MALMBERG, A.; MASKELL, P. Clusters and knowledge: local buzz, global pipelines and the process of knowledge creation. Progress in Human Geography, London, v. 28, n. 1, p. 31-56, 2004. BEAUDRY, C.; SCHIFFAUEROVA, A. Who’s right, Marshall or Jacobs? The localization versus urbanization debate. Research Policy, Amsterdam, v. 38, n. 2, p. 318-337, 2009.
BESAG, J. E. Contribution to the discussion of the paper by Ripley (1977). Journal of the Royal Statistical Society. Series B. Methodological, London, v. 39, p. 193-195, 1977.
BOSCHMA, R. Proximity and innovation: a critical assessment. Regional Studies, Oxfordshire, v. 39, n. 1, p. 61-74, 2005.
BOSCHMA, R.; FRENKEN, K. The emerging empirics of evolutionary economic geography. Journal of Economic Geography, Oxford, v. 11, n. 2, p. 295-307, 2011.
CAINELLI, G.; GANAU, R.; JIANG, Y. Detecting space-time agglomeration processes over the Great Recession using firm-level micro-geographic data. Journal of Geographical Systems, Berlin, v. 22, n. 4, p. 419-445, 2020.
CLARK, G.; FELDMAN, M.; GERTLER, M. Economic geography: transition and growth. In: CLARK, G.; FELDMAN, M.; GERTLER, M. (Org.). Oxford handbook of economic geography. Oxford: Oxford University Press, 2000. p. 3-17.
DOSI, G.; NELSON, R. R. Technical change and industrial dynamics as evolutionary processes. In: HALL, B. H.; ROSENBERG, N. (Org.). Handbook of the economics of innovation. Amsterdam: Elsevier, 2010. p. 51-127. v. 1.
ELLISON, G.; GLAESER, E. L. Geographic concentration in US manufacturing industries: a dartboard approach. Journal of Political Economy, Chicago, v. 105, n. 5, p. 889-927, 1997.
ESCOLA NACIONAL DE ADMINISTRAÇÃO PÚBLICA – ENAP. ENDEAVOR. Índice de cidades empreendedoras. Brasília, 2021.
Disponível em: <https://repositorio.enap.gov.br/bitstream/1/6097/1/relatorio_ICE_2020.pdf>. Acesso em: 2 jul. 2021.
ESPA, G. et al. Measuring industrial agglomeration with inhomogeneous K-function: the case of ICT firms in Milan (Italy). Trento: University of Trento, 2010. p. 1-11. (Discussion Paper, 14).
FELDMAN, M. P.; KOGLER, D. F. Stylized facts in the geography of innovation. In: BRONWYN, H.; ROSENBERG, N. (Org.). Handbook of the economics of innovation. North Holland: Elsevier, 2010. p. 381-410. v. 1.
FELDMAN, M.; AUDRETSCH, D. Innovation in cities: science-based diversity, specialization and localized competition. European Economic Review, White Plains, v. 43, n. 2, p. 409-429, 1999.
GABRIEL, E. Estimating second-order characteristics of inhomogeneous spatio-temporal point processes. Methodology and Computing in Applied Probability, Boston, v. 16, n. 2, p. 411-431, 2014.
GABRIEL, E.; DIGGLE, P. J. Second‐order analysis of inhomogeneous spatio‐temporal point process data. Statistica Neerlandica, Netherlands, v. 63, n. 1, p. 43-51, 2009.
GLAESER, E. et al. Growth of cities. Journal of Political Economy, Chicago, n. 100, p. 1126-1152, 1992.
INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATÍSTICA – IBGE. Base Cartográfica Contínua do Brasil, 1:250.000 – BC250: versão
Rio de Janeiro: IBGE/DGC, 2017.
JACOBS, J. The economy of cities. New York: Random House, 1969.
KRUGMAN, P. R. Geography and trade. Cambridge: MIT Press, 1991.
KUBRUSLY, L.; SABOIA, J. Poverty and spatial deconcentration in Brazilian manufacturing and mining industry. European Union: Nopoor, 2017.
MAINE, E. M.; SHAPIRO, D. M.; VINING, A. R. The role of clustering in the growth of new technology-based firms. Small Business Economics, Dordrecht, v. 34, n. 2, p. 127-146, 2010.
MASKELL, P. Towards a knowledge-based theory of the geographical cluster. In: MARTIN, R. (Org.). Economy. London: Routledge, 2017. p. 377-399.
MUSTAR, P. et al. Conceptualising the heterogeneity of research-based spin-offs: a multi-dimensional taxonomy. Research Policy, Amsterdam, v. 35, n. 2, p. 289-308, 2006.
PEREZ, C. Technological revolutions and techno-economic paradigms. Cambridge Journal of Economics, London, v. 34, n. 1, p. 185-202, 2010.
RIPLEY, B. D. Modelling spatial patterns. Journal of the Royal Statistical Society. Series A (General), London, v. 1, n. 39, p. 172-212, 1977.
SCHUMPETER, J. A. Capitalism, socialism, and democracy. London: Allen and Unwin, 1934.
SKALA, A.; SKALA, B. Digital startups in transition economies. Cham: Springer International Publishing, 2019.
STILL, K. Accelerating research innovation by adopting the lean startup paradigm. Technology Innovation Management Review, Ottawa, v. 7, n. 5, p. 32-43, 2017.
TER WAL, A.; BOSCHMA, R. Co-evolution of firms, industries and networks in space. Regional Studies, Oxfordshire, v. 45, n. 7, p. 919-933, 2011.
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