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Cluster development policy, SME’s performance, and spillovers: evidence from Brazil

Abstract

This paper studies the impact of the Brazilian Arranjos Productivos Locais (APL) policy, a cluster development policy, on small and medium enterprises’ (SMEs) performance. Using firm-level data on SMEs for the years 2002–2009, this paper combines fixed effects with reweighting methods to estimate both the direct and the indirect causal effects of participating in the APL policy on employment growth, value of total exports, and likelihood of exporting. Our results show that APL policy generates a positive direct impact on the three outcomes of interest. They also show evidence of short-term negative spillovers effects on employment in the first year after the policy implementation and positive spillovers on export outcomes in the medium and long term. Thus, our findings highlight the importance of accounting for the timing and gestation periods of the effects on firm performances when assessing the impact of clusters policies.

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Notes

  1. Because of Marshall’s seminal work, this phenomenon is often referred to as Marshallian externalities. In more generic terms, the literature has also referred to the concept of industry-specific local externalities (ISLE). Henderson et al. (1995) refer to these types of industry-specific externalities that arise from regional agglomeration as “localization externalities”, in particular, when firms operate in related sectors and are closely located.

  2. Given the confidentiality of the data, the estimations were conducted following the Instituto de Pesquisa Econômica Aplicada’s (IPEA) microdata policy, which implies working in situ under the supervision of its staff and with blinded access to sensible information.

  3. The simplest definition of an industry cluster is derived from the work of Porter (1990), who defines clusters as “a geographic concentration of competing and cooperating companies, suppliers, service providers, and associated institutions”.

  4. Other papers presenting evidence of agglomeration economies include Ellison and Glaeser (1999), Hanson (2001), Dumais et al. (2002), Rosenthal and Strange (2001, 2003), Rodriguez-Clare (2005, 2007), Combes et al. (2008, 2010), Rosenthal and Strange (2010), Li et al. (2012), and Rizov et al. (2012).

  5. Recent studies present evidence of the effect of clustering on the growth of new technology-based firms (Maine et al. 2010), the survival and performance of new firms (Wemberg and Lindqvist 2010), and how firm growth is influenced by the strength of the industrial cluster in which the firm is located (Beaudry and Swann 2009).

  6. For a review on coordination problems in development, see Hoff (2000). On clusters and coordination failures, see also Rodriguez-Clare (2005).

  7. As Anderson et al. (2004) pointed out, thorough evaluations of specific cluster initiatives and cluster actions are in fact few and have been developed only in few countries. Few solid attempts have been made to assess whether first-best results are obtained, go beyond efficiency in use of given resources to encompass economic results, or take into account interactions and synergies in the performance of different actors. Further, most evaluations of cluster policies pursued still focus on single tools, which fits poorly with the systemic notion of cluster policy.

  8. The ICP was initiated by the Japanese Ministry of Economy, Trade and Industry in 2001 and aimed at developing regional industries and included both direct R&D support and indirect networking/coordination support.

  9. The econometric analysis presented in Maffioli (2005) confirms a strong correlation between PROFO firms’ innovativeness and industrial cooperation, proving the existence of an interactive learning process among participant firms. Using sociometric data to refine the analysis of the impact of the program on the network multiplier results show that participant firms increase their productivity and that this improvement is strongly correlated with firm centrality and network density, which are the two variables best representing the structure and function of the network multiplier and that are affected by PROFO.

  10. For instance, Santos et al. (2002a, b), Cassiolato et al. (2003), Machado (2003), Hoffman (2004), La Rovere et al. (2004), Lastres and Cassiolato (2005), Mytelka and Farinelli (2005), La Rovere and Shibata (2007), and Souza Filho and Martins (2013).

  11. As defined in the Termo de Referencia para Politica Nacional de Apoio ao Desenvolvimiento de arranjos productivos locais (2004).

  12. SEBRAE’s budget comes from contributions of 0.3–0.6 % of Brazilian corporations’ payrolls. Resources are collected by the Brazilian Social Security Institute (INSS) and transferred to SEBRAE.

  13. See Clerides et al. (1998), Bernard and Jensen (1999), Aw et al. (2000), Bernard et al. (2003) and Bernard and Jensen (2004). Furthermore, Melitz (2003)’s model showed how the exposure to trade induces only the more productive firms to export while simultaneously forcing the least productive firms to exit reallocating market shares (and profits) toward the more productive firms and contributing to an aggregate productivity increase.

  14. The cost of entering into new markets often consists of knowledge related to the assessment of the market demand, product standards, distribution channels, regulatory environment etc. (Melitz 2003).

  15. On the role that public policy can play in fostering coordination among exporters see also Bernard and Jensen (2004).

  16. For instance, firms that share the geographical location with participating firms may indirectly benefit from higher foreign direct investment in the region attracted by cluster firms (De Propris and Driffield 2006). Bronzini and Piselli (2009) consider geographical spillovers assuming that factors enhancing productivity in one region can also affect the productivity in the neighboring regions. Bottazzi and Peri (2003) use geographical proximity as a channel for R&D spillovers.

  17. Similar to the firm identifier, the municipality identifier was re-codified by IPEA to preserve the confidentiality of the data. Thus, it is not possible to link the APL (or firms) to real municipalities.

  18. We will refer to an industry with a positive number of treated firms within a municipality as a “treated industry-municipality” and to the municipalities with absence of treated firms as “non-treated municipalities”.

  19. See Bertrand et al. (2004) for a formal discussion on differences-in-differences estimates.

  20. A similar approach is followed by Moretti (2004) to measure human capital spillovers in manufacturing in the US.

  21. The Herfindahl index was created by industry-municipality-year using level of employment. For a full discussion on measures of concentration see Hay and Morris (1987).

  22. For additional discussion regarding pre-treatment trends please refer to Dehejia and Wahba (1999), Blundell and Costas Dias (2000) and Imbens et al. (2001).

  23. Heckman et al. (1997, 1998) point out this source of bias.

  24. We use the Stata package called ebalance introduced by Hainmueller and Xu (2011). For implementation issues see also Hainmueller (2012).

  25. The RAIS is an annual survey including socio-economic information of firms in Brazil. It is an administrative record of the labor force profile which is mandatory in Brazil for all firms in all sectors.

  26. Using the reweighting method will only keep firms who were observed in both pre-treatment years, i.e. 2002–2003.

  27. Several industries presented only one observation in the 2007 RAIS and were therefore excluded due to confidentiality issues. Other industries such as paper products, metal products, medical instruments and chemical products industries were also excluded since they had a negligible number of APL participating firms.

  28. Both for employment and for total exports, the series will be expressed in natural logarithms. For the outcome log of exports we assign the value of 0 when firms have 0 exports to avoid excluding non-exporting firms from the sample, which could bias the results by affecting the composition of the treatment and control groups (see Angrist and Pischke, 2008).

  29. The large direct effect on exports could be partially due to the fact that we are not excluding non-exporting firms and therefore the average of exports before the program was implemented is relatively low (U$S 21,744) compared with the one that only considers exporting firms (US$ 914,738).

  30. Since the assessment of heterogeneous effects inevitably implies statistical power problems—i.e. the sub-sample of beneficiaries for each interaction term could be rather small—we follow the standard rule-of-thumb of considering interactions for which at least twenty beneficiaries are available. We make an exception in the case of the heterogeneity by size, because the sample can only be divided in small and medium firms.

  31. \( Cs_{i } \_2002 \) is omitted in Eq. (2) because of perfect collinearity.

  32. Additional evidence of the validity of this assumption is also provided by the graphs and tables in appendix 1 and 2, which show that treated and the reweighted comparison groups are very similar both in levels and trends of observed characteristics in the pre-treatment period.

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Acknowledgments

We are grateful to Patrick Franco Alves, Conner Mullaly, and Rodolfo Stucchi for useful discussions and comments on this project. We would also like to thank SEBRAE and two anonymous referees for their suggestions and comments. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the Inter-American Development Bank. The usual disclaimer applies. Senior authorship is not assigned.

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Correspondence to Cesar M. Rodriguez.

Appendices

Appendix 1: Mean outcomes over time

Appendix 2: Mean comparison tests (before and after reweighting)—2003

See Tables 14, 15, 16.

Table 14 Direct versus control group
Table 15 Indirect versus control group
Table 16 Direct versus indirect

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Figal Garone, L., Maffioli, A., de Negri, J.A. et al. Cluster development policy, SME’s performance, and spillovers: evidence from Brazil. Small Bus Econ 44, 925–948 (2015). https://doi.org/10.1007/s11187-014-9620-2

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Keywords

  • Cluster policies
  • SMEs
  • Brazil
  • Impact evaluation
  • Spillovers
  • Panel data
  • Fixed-effects method

JEL Classifications

  • C23
  • D22
  • H43
  • L25
  • L26
  • O12
  • O54
  • R10