Skip to main content
SpringerLink
Log in

Location patterns of service activities in large metropolitan areas: the Case of São Paulo

  • Original Paper
  • Published:
The Annals of Regional Science Aims and scope Submit manuscript

Abstract

We present a set of detailed evidence about the location patterns of service activities in the largest and most important Brazilian metropolitan region, the São Paulo Metropolitan Region (SPMR). Different from previous analysis of this big urban agglomeration, our results are obtained using a unique dataset of geocoded firms and a distance-based measure of firms’ location, thus not susceptible to the modifiable areal unit problem (MAUP). We find that around 89% of 3-digit service sectors present significant defined location patterns and, based on maximum distances where significant location patterns are observed, identify spatial location of clusters of some activities. Our results also indicate that firms’ activities of FIRE (finance and real estate), IT-related services, and high human capital-based services present the highest probability of location at shorter distance from each other. The tendency for location at shorter distances between firms engaged in these activities contrasts with the more decentralized patterns observed for firms involved in retail and urban infrastructure services. Additional results indicate that both the location patterns of activities and the degree of proximity or agglomeration of firms are positively associated with human capital, the degree of product differentiation, and the degree of inter-sector dependence between activities.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. The municipality is the local administrative unit in Brazil. It is akin to a county, except with a single mayor and municipal council. Municipalities range from lightly populated rural ones with one or two small towns to heavily populated urban ones that are part of greater metropolitan regions. There are no unincorporated areas in Brazil.

  2. More details available at: http://www.seade.gov.br/.

  3. More details available at: https://www.emplasa.sp.gov.br.

  4. Previous evidence for Brazil shows that the externalities generated by locally diversified and specialized economic environments are important to explain the spatial configuration of productive activity (see Silva (2007); Silva and Silveira Neto (2009); Fochezatto and Valentini (2010); Rocha et al. (2013) and Almeida et al. (2017)).

  5. We use distance between municipal centroids.

  6. The formal jobs include formally registered workers, employers, and autonomous workers that contribute to any social security system.

  7. Although there is a newer version of CNAE released in 2007 (CNAE 2.0), there are no significant differences for the sectors analyzed in this survey. More details on the correspondence between the versions can be found at: https://concla.ibge.gov.br/classificacoes/correspondencias/atividades-economicas.html.

  8. Following Silverman (2018), the ideal bandwidth for the Gaussian kernel function is \(1.06sn ^{- 0.2}\) where s is the standard deviation of n(n-1) bilateral distances.

  9. Klier and McMillen (2008), for example, used zip codes with no plants as possible counterfactual of randomly located industries. Note, however, that this kind of alternative does not avoid using, for example, manufacturing locations or residential lots as possible counterfactuals for service establishments.

  10. Following Duranton and Overman (2005), robustness tests with 2,000 and 10,000 simulations are also performed.

  11. Unlike Duranton and Overman (2005), Behrens and Bougna (2015) and Aleksandrova et al. (2019) analyzed large countries with large territorial extension, so it is important to define an adequate range. For Canada, Behrens and Bougna (2015) used 800 kilometers and for Russia, Aleksandrova et al. (2019) used 1,000 kilometers. For more details on the computational implementation, see Aleksandrova et al. (2019).

  12. When a sector shows peaks of concentration, it is possible that other points of the curve will fall below the lower confidence bound as a form of compensation. This happens because the values are normalized to sum to 1; this does not imply dispersion.

  13. For every meter, we have an estimate; so we work with 52,900 observations of the index for each sector.

  14. Although some of these activities can be classified as creative sectors (Méndez-Ortega and Arauzo-Carod 2019), they are often based on higher levels of human capital.

  15. Using the index of Ellison and Glaeser (1997) for the USA, Kolko (2010) found that service sectors are also heavily concentrated.

  16. As a specific example, consider the case of firms of Non-specialized retailing-CNAE 521 we have discussed above.

  17. Considering the 5 km column of Table 5, the value of Spearman’s rank correlation coefficient is 0.5173, while Kendall’s rank correlation coefficients have values of 0.3864 and 0.3886 for \(\tau _a\) and \(\tau _b\), respectively.

References

  • Abel JR, Deitz R (2015) Agglomeration and job matching among college graduates. Reg Sci Urban Econ 51:14–24

    Article  Google Scholar 

  • Accetturo A, Di Giacinto V, Micucci G, Pagnini M (2018) Geography, productivity, and trade: does selection explain why some locations are more productive than others? J Reg Sci 58(5):949–979

    Article  Google Scholar 

  • Acemoglu D, Angrist J (2000) How large are human-capital externalities? evidence from compulsory schooling laws. NBER Macroecon Annu 15:9–59

    Article  Google Scholar 

  • Aleksandrova E, Behrens K, Kuznetsova M (2019) Manufacturing (co) agglomeration in a transition country: evidence from Russia. J Reg Sci 60(1):88–128

    Article  Google Scholar 

  • Almeida ET, Rocha RM (2018) Labor pooling as an agglomeration factor: evidence from the Brazilian Northeast in the 2002–2014 period. Economia 19(2):236–250

    Article  Google Scholar 

  • Almeida ET, Rocha RM, Gomes SMFPO (2017) Economias de aglomeração e o crescimento das indústrias intensivas em tecnologia: evidências para o Nordeste no período 2002–2014. Revista Brasileira de Estudos Regionais e Urbanos 11(4):467–494

    Google Scholar 

  • Andersson F, Burgess S, Lane JI (2007) Cities, matching and the productivity gains of agglomeration. J Urban Econ 61(1):112–128

    Article  Google Scholar 

  • Arzaghi M, Henderson JV (2008) Networking off madison avenue. Rev Econ Stud 75(4):1011–1038

    Article  Google Scholar 

  • Baer W (2002) Economia Brasileira. NBL Editora

  • Barlet M, Briant A, Crusson L (2008) Concentration géographique dans l’industrie manufacturière et dans les services en France: une approche par un indicateur en continu. Documents de Travail de la DESE-Working Papers of the DESE

  • Behrens K, Bougna T (2015) An anatomy of the geographical concentration of Canadian manufacturing industries. Reg Sci Urban Econ 51:47–69

    Article  Google Scholar 

  • Behrens K, Duranton G, Robert-Nicoud F (2014) Productive cities: sorting, selection, and agglomeration. J Polit Econ 122(3):507–553

    Article  Google Scholar 

  • Billings SB, Johnson EB (2012) A non-parametric test for industrial specialization. J Urban Econ 71(3):312–331

    Article  Google Scholar 

  • Billings SB, Johnson EB (2016) Agglomeration within an urban area. J Urban Econ 91:13–25

    Article  Google Scholar 

  • Chauvin JP, Glaeser E, Ma Y, Tobio K (2017) What is different about urbanization in rich and poor countries? cities in Brazil, China, India and the United States. J Urban Econ 98:17–49

    Article  Google Scholar 

  • Ciccone A, Hall RE (1996) Productivity and the density of economic activity. Am Econ Rev 86(1):54–70

    Google Scholar 

  • Clark GL (2002) London in the european financial services industry: locational advantage and product complementarities. J Econ Geogr 2(4):433–453

    Article  Google Scholar 

  • Combes PP, Duranton G, Gobillon L, Puga D, Roux S (2012) The productivity advantages of large cities: distinguishing agglomeration from firm selection. Econometrica 80(6):2543–2594

    Article  Google Scholar 

  • Costa MAC (2013) Caracterização e quadros de análise comparativa da governança metropolitana no Brasil: arranjos institucionais de gestão metropolitana (componente 1): região metropolitana de São Paulo RMSP

  • Di Addario S (2011) Job search in thick markets. J Urban Econ 69(3):303–318

    Article  Google Scholar 

  • Dingel JI, Miscio A, Davis DR (2019) Cities, lights, and skills in developing economies. National Bureau of Economic Research, Tech rep

  • Domingues EP, Ruiz RM, Moro S, Lemos MB (2006) Organização territorial dos serviços no Brasil: polarização com frágil dispersão. IPEA, Estrutura e dinâmica do setor de serviços no Brasil Brasília

  • Duranton G (2016) Determinants of city growth in Colombia. Pap Reg Sci 95(1):101–131

    Article  Google Scholar 

  • Duranton G, Overman HG (2005) Testing for localization using micro-geographic data. Rev Econ Stud 72(4):1077–1106

    Article  Google Scholar 

  • Duranton G, Puga D (2004) Micro-foundations of urban agglomeration economies. In: Handbook of regional and urban economics, vol 4, Elsevier, pp 2063–2117

  • Duranton G, Puga D (2014) The growth of cities. In: Handbook of economic growth, vol 2, Elsevier, pp 781–853

  • Ellison G, Glaeser EL (1997) Geographic concentration in US manufacturing industries: a dartboard approach. J Polit Econ 105(5):889–927

    Article  Google Scholar 

  • Fochezatto A, Valentini PJ (2010) Economias de aglomeração e crescimento econômico regional: um estudo aplicado ao Rio Grande do Sul usando um modelo econométrico com dados de painel. Revista Economia 11(4):243–266

    Google Scholar 

  • Fu S (2007) Smart café cities: testing human capital externalities in the Boston metropolitan area. J Urban Econ 61(1):86–111

    Article  Google Scholar 

  • Fujita M, Thisse JF (2013) Economics of agglomeration: cities, industrial location, and globalization. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Gaubert C (2018) Firm sorting and agglomeration. Am Econ Rev 108(11):3117–53

    Article  Google Scholar 

  • Glaeser EL, Mare DC (2001) Cities and skills. J Labor Econ 19(2):316–342

    Article  Google Scholar 

  • Greenstone M, Hornbeck R, Moretti E (2010) Identifying agglomeration spillovers: evidence from winners and losers of large plant openings. J Polit Econ 118(3):536–598

    Article  Google Scholar 

  • Hellerstein JK, Kutzbach MJ, Neumark D (2014) Do labor market networks have an important spatial dimension? J Urban Econ 79:39–58

    Article  Google Scholar 

  • Hotelling H (1929) Stability in competition. Econ J 39(153):41–57

    Article  Google Scholar 

  • Inoue H, Nakajima K, Saito YU (2019) Localization of collaborations in knowledge creation. Ann Reg Sci 62(1):119–140

    Article  Google Scholar 

  • Klier T, McMillen DP (2008) Evolving agglomeration in the us auto supplier industry. J Reg Sci 48(1):245–267

    Article  Google Scholar 

  • Koh HJ, Riedel N (2014) Assessing the localization pattern of German manufacturing and service industries: a distance-based approach. Reg Stud 48(5):823–843

    Article  Google Scholar 

  • Kolko J (2010) Urbanization, agglomeration, and coagglomeration of service industries. In: Agglomeration economics, university of Chicago Press, pp 151–180

  • Konishi H (2005) Concentration of competing retail stores. J Urban Econ 58(3):488–512

    Article  Google Scholar 

  • Krugman PR (1991) Geography and trade. MIT Press, Cambridge

    Google Scholar 

  • Lautert V, Araújo NCMd (2007) Concentração industrial no Brasil no período 1996–2001: uma análise por meio do índice de Ellison e Glaeser (1994). Economia Aplicada 11(3):347–368

    Article  Google Scholar 

  • Leslie TF, HUallachain BO, (2006) Polycentric phoenix. Econ Geogr 82(2):167–192

  • Lorenzen M, Mudambi R (2013) Clusters, connectivity and catch-up: bollywood and Bangalore in the global economy. J Econ Geogr 13(3):501–534

    Article  Google Scholar 

  • Lychagin S, Pinkse J, Slade ME, Reenen JV (2016) Spillovers in space: does geography matter? J Ind Econ 64(2):295–335

    Google Scholar 

  • Maciente A (2013) The determinants of agglomeration in brazil: input-output, labor and knowledge externalities. PhD thesis, University of Illinois at Urbana-Champaign

  • Marcon E, Puech F (2009) Measures of the geographic concentration of industries: improving distance-based methods. J Econ Geogr 10(5):745–762

    Article  Google Scholar 

  • Martin R, Sunley P (2003) Deconstructing clusters: chaotic concept or policy panacea? J Econ Geogr 3(1):5–35

    Article  Google Scholar 

  • Méndez-Ortega C, Arauzo-Carod JM (2019) Do software and video game firms share location patterns across cities? evidence from barcelona, lyon and hamburg. The Annals of Regional Science pp 1–26

  • Moretti E (2004) Estimating the external return to higher education: evidence from cross-sectional and longitudinal data. J Econom 120(1–2):175–212

    Article  Google Scholar 

  • Nakajima K, Saito YU, Uesugi I (2012) Measuring economic localization: evidence from Japanese firm-level data. J Japn Int Econ 26(2):201–220

    Article  Google Scholar 

  • Overman HG, Puga D (2010) Labor pooling as a source of agglomeration: an empirical investigation. In: Agglomeration economics, University of Chicago Press, pp 133–150

  • Puga D (2010) The magnitude and causes of agglomeration economies. J Reg Sci 50(1):203–219

    Article  Google Scholar 

  • Resende M, Wyllie R (2005) Aglomeração industrial no Brasil: um estudo empírico. Estudos Econômicos (São Paulo) 35(3):433–460

    Article  Google Scholar 

  • Rocha RdM, Bezerra FM, de Mesquita CS (2013) Uma análise dos fatores de aglomeração da indústria de transformação brasileira. Revista EconomiA

  • Rocha RdM, Araújo JES, de Almeida ETd (2019) As indústrias da tranformação são concentradas geograficamente? um teste empírico para o Brasil (2002-2014). Nova Economia

  • Silva MVB (2007) Silveira Neto RdM. Crescimento do emprego industrial no Brasil e geografia econômica, Evidências para o perıodo pós-real. Revista EconomiA

  • Silva MVBd, Silveira Neto RdM (2009) Dinâmica da concentração da atividade industrial no brasil entre 1994 e 2004: uma análise a partir de economias de aglomeração e da nova geografia econômica. Economia Aplicada 13(2):299–331

    Article  Google Scholar 

  • Silva RLPd, Silveira Neto RdM, Rocha R (2019) Localization patterns within urban areas: evidence from Brazil. Area Development and Policy pp 1–20

  • Silveira Neto RdM (2005) Concentração industrial regional, especialização geográfica e geografia econômica: evidências para o Brasil no período 1950–2000. Revista Econômica do Nordeste, Fortaleza 36(2):189–208

  • Silveira Neto RdM, Duarte G, Páez A (2015) Gender and commuting time in São Paulo metropolitan region. Urban Stud 52(2):298–313

    Article  Google Scholar 

  • Silverman BW (2018) Density estimation for statistics and data analysis. Routledge, London

    Book  Google Scholar 

  • Thisse JF (2018) Human capital and agglomeration economies in urban development. Dev Econ 56(2):117–139

    Article  Google Scholar 

  • Vitali S, Napoletano M, Fagiolo G (2013) Spatial localization in manufacturing: a cross-country analysis. Reg Stud 47(9):1534–1554

    Article  Google Scholar 

  • Wheeler CH (2008) Local market scale and the pattern of job changes among young men. Reg Sci Urban Econ 38(2):101–118

    Article  Google Scholar 

  • Zhu X, Liu Y, He M, Luo D, Wu Y (2019) Entrepreneurship and industrial clusters: evidence from China industrial census. Small Bus Econ 52(3):595–616

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge and are grateful for financial support by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). Constructive comments from the editors and from the anonymous referees are greatly appreciated.

Author information

Authors and Affiliations

  1. Department of Economics, Federal University of Pernambuco - UFPE, Recife, Pernambuco, Brazil

    Edilberto Tiago de Almeida, Raul da Mota Silveira Neto, Jaime Macedo de Brito Bastos & Rubens Lopes Pereira da Silva

Authors
  1. Edilberto Tiago de Almeida
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Raul da Mota Silveira Neto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jaime Macedo de Brito Bastos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rubens Lopes Pereira da Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Edilberto Tiago de Almeida.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

de Almeida, E.T., da Mota Silveira Neto, R., de Brito Bastos, J.M. et al. Location patterns of service activities in large metropolitan areas: the Case of São Paulo. Ann Reg Sci 67, 451–481 (2021). https://doi.org/10.1007/s00168-021-01054-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00168-021-01054-1

JEL Classification

Search

Navigation