Palavras-chave
Startups
Aglomeração espaço-temporal
Como Citar
Dados de financiamento
Resumo
Este trabalho analisa a emergência e dinâmica de aglomeração espaço-temporal de startups de base tecnológica que ocorre em Florianópolis, São José e Palhoça (SC) durante o período 2000-2020. Para estudar este fenômeno sugere-se uma abordagem paramétrica baseada na função K espacial e na função K espaço-temporal não homogênea (Arbia, Espa e Giuliani, 2021), considerando uma base de dados georreferenciada de startups criadas no período considerado. Encontramos que existe uma forte concentração destes empreendimentos em uma pequena distância, cujo centro é a incubadora Celta (Parque Tecnológico Alfa), da Fundação CERTI, criada no âmbito da Universidade Federal de Santa Catarina e também em uma distância maior. Encontrou-se também uma interação tempo-espaço estatisticamente significativa, especialmente em ciclos de 10 anos. Os resultados reforçam a importância das externalidades marshallianas que operam na escala microgeográfica (distrito ou bairro) como as jacobianas, que operam na escala macrogeográfica (cidade).
Referências
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.
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
Copyright (c) 2022 Revista Brasileira de Inovação
Downloads
