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Abstract
Cravo T. A., Becker B. and Gourlay A. Regional growth and SMEs in Brazil: a spatial panel approach, Regional Studies. This paper examines economic growth for a panel of 508 Brazilian micro-regions for the period 1980–2004, using spatial econometrics and paying particular attention to the importance of small and medium-sized enterprises (SMEs). The findings indicate the presence of spatial dependence in the process of economic growth and the existence of two spatial regimes in Brazil. The human capital level of the whole population is an important growth determinant, but does not generate positive spillovers. Furthermore, human capital embodied in SMEs is more important than the size of this sector for regional growth and SME activity generates positive spatial spillovers.
Cravo T. A., Becker B. and Gourlay A. 巴西的区域成长与中小型企业：空间面板方法，区域研究。本文运用空间计量经济学，检视自 1980 年至 2004 年间，巴西五百零八个微型区域组的经济成长，并特别关注中小型企业 (SMES) 的重要性。研究结果显示，巴西经济成长的过程具有空间依赖，并存在着两个空间体制。总人口的人力资本水平，是重要的成长决定因素，但却未能产生正向的外溢此外，中小型企业所体现的人力资本，较该?部门的规模而言，对区域成长更为重要，且中小型企业活动产生了正向的空间外溢。
Cravo T. A., Becker B. et Gourlay A. La croissance régionale et les Pme au Brésil: une approche spatiale de panel, Regional Studies. Cet article examine la croissance économique pour ce qui est d'un panel de 508 micro-régions au Brésil pendant la période allant de 1980 à 2004, employant l’économétrie spatiale et prêtant une attention particulière à l'importance des Petites et moyennes entreprises (Pme). Les résultats indiquent la présence d'une dépendance spatiale dans le processus de croissance économique et l'existence de deux régimes spatiaux au Brésil. Le niveau du capital humain de la population globale est un déterminant important de la croissance, mais ne produit pas de retombées positives. Qui plus est, le capital humain incarné dans les Pme s'avère plus important que ne l'est la taille de ce secteur pour la croissance régionale et l'activité des Pme génère des retombées spatiales positives.
Cravo T. A., Becker B. und Gourlay A. Regionales Wachstum und KMU in Brasilien: ein räumlicher Panel-Ansatz, Regional Studies. In diesem Beitrag untersuchen wir das Wirtschaftswachstum eines Panels von 508 brasilianischen Mikroregionen für den Zeitraum von 1980 bis 2004 mit Hilfe von räumlicher Ökonometrie und unter besonderer Berücksichtigung der Bedeutung von kleinen und mittelständischen Unternehmen (KMU). Die Ergebnisse verdeutlichen die Präsenz einer räumlichen Dependenz im Prozess des Wirtschaftswachstums sowie die Existenz von zwei räumlichen Regime in Brasilien. Der Umfang des Humankapitals in der Gesamtbevölkerung ist ein wichtiger Wachstumsdeterminant, erzeugt aber keine positiven Spillover-Effekte. Darüber hinaus ist das von KMU verkörperte Humankapital für das regionale Wachstum wichtiger als die Gröβe dieses Sektors, und die Aktivität der KMU erzeugt positive räumliche Spillover-Effekte.
Cravo T. A., Becker B. y Gourlay A. Crecimiento regional y pymes en Brasil: un planteamiento de panel espacial, Regional Studies. Mediante factores econométricos espaciales y prestando especial atención a la importancia de las pequeñas y medianas empresas (pymes), en este artículo analizamos el crecimiento económico para un panel de 508 micro-regiones brasileñas para el periodo entre 1980 y 2004. Los resultados indican la presencia de dependencia espacial en el proceso del crecimiento económico y la existencia de dos regímenes espaciales en Brasil. El nivel de capital humano en toda la población es un importante determinante del crecimiento, pero no genera efectos secundarios positivos. Además, el capital humano personificado en las pymes es más importante que el tamaño de este sector para el crecimiento regional y la actividad de las pymes genera efectos secundarios espaciales positivos.
Acknowledgements
The authors thank two anonymous referees for their valuable comments and suggestions that helped to improve the paper. The first author would like to thank the Brazilian Ministry of Labour for providing access to the RAIS database.
Notes
1. For the same period the support of the International Finance Corporation (IFC) of the World Bank Group directed to SMEs amounted to US$25 billion between 2006 and 2012.
2. The study of Beck et al. (Citation2005) is based on a broad cross-section of 45 countries and includes countries with different levels of development.
3. This literature uses two types of proxies for entrepreneurship activities. The start-up rates used by Audretsch and Keilbach (Citation2004) is a dynamic measure of entrepreneurship, and the stock of SMEs' employment used by Beck et al. (Citation2005) is a static measure. These measures represent different dimensions of entrepreneurship but are related concepts. Higher small businesses start-up rates will lead to a higher share of workers in the SME sector, while a higher mortality rate will adjust the size of this sector in the opposite direction (Cravo et al., Citation2012).
4. See the second section for a detailed conceptual discussion of the mechanisms through which spatial spillovers may affect growth.
5. This paper also considers spatial spillovers stemming from factors such as GDP per capita income, human capital and population growth, which are extensively documented in theoretical and empirical papers in the economic growth literature (e.g. Rey and Montouri, Citation1999; Ertur and Koch, Citation2007; Mohl and Hagen, Citation2010).
6. The country's government recognizes the importance of this sector, with the budget of the Brazilian Small Business Support Service (SEBRAE) amounting to approximately US$1.6 billion in 2012, for instance. SEBRAE is not a government agency, but approximately 75% of its budget comes from taxes collected by the Brazilian federal government.
7. Clusters tend to occur in a spatially delimited area, as argued by Altenburg and Meyer-Stamer (Citation1999), but might span across the borders of several regions (e.g. Carroll et al., Citation2008).
8. This cultural characteristic can increase entrepreneurship in a region and thus competition, which might impact the efficiency of the regional economy and generate growth.
9. The authors present results, based on data for the United States, indicating that the direct effect of human capital (via absorptive capacity) on entrepreneurship is much stronger than its indirect effect via new knowledge. This implies that human capital contributes to entrepreneurship primarily by building entrepreneurial absorptive capacity.
10. Institutional constraints impede SMEs in reaching their full potential and do not stimulate productive entrepreneurship that could contribute to reinforce competition and increase economic efficiency that leads to economic growth.
11. Poschke (Citation2013), based on a cross-section of countries, argues that the concept of necessity entrepreneurship is related to entrepreneurs who have lower education, run smaller firms and expect their firms to grow less. Still, Poschke presents results that suggest that by necessity entrepreneurs are likely to stay in the market and concludes that policies intended to promote small firms because of their expected contribution to growth have to be implemented with particular care in the context of necessity entrepreneurship.
12. In a ranking of 32 countries, Brazil is at the bottom and presents the worst ratio of opportunity to necessity entrepreneurship (Acs et al., Citation2008).
13. The same level of entrepreneurship might lead to a different impact on growth if the ‘U’-shaped relationship between entrepreneurship and development is true.
14. Analogous to time-series models where lagged values of the dependent variable are often included to account for missing explanatory variables, a similar motivation can be used for spatial lags of the dependent variable in cases where spatial dependence is likely to occur (Abreu et al., Citation2005).
15. For instance, LeSage and Fischer (Citation2008) point out that data on physical capital are not available and omitted in most regional growth regressions.
16. See also Fischer (Citation2011) who suggests a similar model that leads to a spatial Durbin model (SDM) as the econometric specification.
17. In the SAR model, the spatial lag is an average of neighbouring values and leads to a connectivity relation where the covariance of the error term between two regions is not zero.
18. The log-likelihood functions for spatial panel data and the demeaning process to remove time and space (individual) fixed effects are detailed in Elhorst (Citation2010a).
19. Resende (Citation2011) discusses MAUP for regional growth regressions in Brazil and shows that spillover effects change with the geographical scale of analysis.
20. The data for the variable School are available only in a ten-year time span interval from 1980 to 2000 (based on census data), and data points in between these years were constructed by interpolation. This variable was lagged by five years because the year 2000 was the most recent period available. The population growth variable, ln(n + d + g), is adjusted for depreciation (d) and technological growth (g), under the usual assumption that d + g equals 0.05.
21. Every year establishments are obliged to report all required information to the Ministry of Labour. In 2004, RAIS covered 31.5 million workers (97% of formal employment according to the Ministry of Labour).
22. See in Appendix A for further details.
23. According to the Ministry of Labour, RAIS data present inaccurate information for small municipalities (see also Saboia, Citation2000). This is an additional reason for the use of micro-regions as this more aggregated territorial unit reduces the bias from inaccurate data for small municipalities.
24. The sample loses 50 micro-regions located in poor and isolated areas that did not present data at all data points, most located in the Amazon region. In 2004, this subset of micro-regions encompassed only 2.3% of the Brazilian population and so it is reasonable to assume that their omission does not generate a serious bias.
25. A queen spatial weight matrix defines neighbours as locations that share a border or vertex. The first spatial lag considers only the immediate neighbours; the second spatial lag order considers neighbours of neighbours, and so on for further spatial lags.
26. The authors would like to thank Professor Eduardo Haddad from USP for providing the road distances for each pair of Brazilian micro-regions. The inverse-squared distance spatial weight is also row-standardized.
27. The Moran's I statistic is given by the following expression:
where Z is the vector of a given variable in deviation from its mean; and W is the spatial weight matrix.
28. This index is expressed as follows:
where variables are defined as in Moran's I.
29. Significant high–high values are only found in the Southern regions (Southeast, South and Centre-West), while significant low–low values are found in the Northern regions (Northeast and North), with the exception of three regions on the border between the two clusters, which are located in the north of the Minas Gerais state. Including these regions in the richer Southern regions is justified because Minas Gerais as a whole is a rich state. This follows Ramajo et al. (Citation2008) who grouped Italian poor regions together with the core of Italian regions because Italy as a whole is a rich country. Alternative LISA maps based on simpler binary spatial weights are reported in Appendix B and confirm the pattern observed in . The Southern region has 301 micro-regions and the Northern 207 micro-regions.
30. See Anselin et al. (Citation1996) for more details about the LM tests. As an alternative, the inverse-squared distance standardized by its largest eigenvalue is used to comply with the stationary requirement without losing quantitative information on distances (Anselin, Citation1988, pp. 23–24; Elhorst, Citation2010a, pp. 3–4). Estimation results based on this matrix are presented in in Appendix D and provide similar qualitative results.
31. It is important to note that causality is not being implied since the spatial panel estimators used in the regressions cannot treat endogeneity related to the independent variables. Nevertheless, the estimator used in this paper accounts for the endogeneity coming from the spatial lag of the dependent variable (Elhorst, Citation2010a). For a discussion of the causality identification problems in spatial models, see Gibbons and Overman (Citation2010).
32. This means that regions with the same parameters can have different growth rates if their neighbours have different levels of GDP per capita, possibly due to different physical capital endowments. Thus, this result can also be interpreted as the spatial externalities stemming from physical capital. As noted by López-Bazo et al. (Citation2004), an alternative interpretation to this lies in the fact that some of the spillovers could have been caused by pecuniary externalities present in models of new economic geography (e.g. Krugman, Citation1991).
33. This reasoning is in line with the argument linked to New Economic Geography models: that more skilled labour is an important mobile factor that constitutes a centripetal force towards geographical concentration (e.g. Krugman, Citation1999).
34. In line with the results provided in this paper, Bruce et al. (Citation2009, tab. Citation3) suggest that for the US states a larger number of small business establishments is not related to higher rates of economic growth (the coefficient is negative, as in this paper, however is not significant). On the other hand, they also found that the number of small firm establishments in neighbouring states have positive effects on growth.
35. Importantly, the externalities captured in the general context of SMEs does not identify to which extent the more specific spillovers generated by joint actions in clusters of SMEs described by Schmitz (Citation1995) influenced the results. Thus, disentangling the general SME spillovers from the more specific spillovers generated by clusters of SMEs remains a challenge for future research.
36. The results of the SDM model might be affected by multicollinearity due to the use of spatial lags of all variables in the model, especially the two proxies of human capital used. in Appendix C provides the correlations among the variables considered in the regressions and shows high cross-correlations among lnSCHOOL, lnSMEH, lnSCHOOL*W and lnSMEH*W. As a robustness test, these variables were dropped and the results reported in in Appendix D (columns 1–4) are in line with the baseline regressions shown in . The regression coefficient signs are always consistent with the baseline results and the level of significance is similar most times. The panel data used in this paper comprise a large number of regions, which helps to reduce collinearity problems.
37. The results reported in the main text are based on the manufacturing sector, as the data for the SME sector used in this paper are for the formal sector of the economy and manufacturing is characterized by higher rates of formality. This limitation was also a shortcoming identified by Beck et al. (Citation2005), who argue that it would be useful to have information on SME employment beyond manufacturing. In order to consider this limitation, alternative results for an extended SME sector considering manufacturing, commerce and services (SMER) are included in in Appendix D (column 5), as in Cravo et al. (Citation2012). The estimations for a broader SME sector provide similar qualitative results when compared with the baseline regressions shown in .
38. Considering two groups of contiguous regions separately implicitly assumes that there are barriers preventing spatial spillovers from one cluster to another in order to observe better structural differences within each cluster. For instance, Acemoglu et al. (Citation2001) provide evidence that GDP per capita and the quality of institutions are positively related, and Naritomi et al. (Citation2009) document that income per capita is positively related with institutional development in Brazil. Thus, spatial regimes might be related to different institutional regimes where the cluster of poorer regions has a worse institutional quality.
39. The channels through which technology spills over across regions in Brazil is an important route for future research. Nevertheless, the smaller distances between micro-regions in the Southern region might facilitate interregional spillovers generated from technological interdependence. The average area of the micro-regions of the Southern group is 11 714 km2 and for the Northern group is 17 956 km2.
40. Alternative results using the eigenvalue standardized inverse-squared distance weight matrix are presented in in Appendix D and provide the same qualitative results, the coefficients that are significant always show the same sign. Also, the robustness results show that SMEH has the same positive sign for the Southern regions, but in this case suggests that this association is significant.
41. The relationship between the SME sector variable and economic growth discussed in the fifth section is based on data from 1980 to 2004 as explained in Appendix A. However, a robustness test removing cross-sections of the data to test this relationship for other time periods provided results that are either in line with the main results of this paper or do not provide any evidence against these results (see in Appendix D). The main results of this paper are preferred as they use all observations, which is likely to increase variability among the variables and the efficiency of the estimation.
42. The existence of a better common infrastructure could also be important to facilitate demonstration effects, as it will be less costly to travel across neighbouring regions. This is an interesting topic that could be explored in future research to study how infrastructure affects entrepreneurial demonstration effects and spillovers.