Industrial scope of agglomeration economies in Brazil

Abstract

The tendency towards urbanization in the emerging world accompanied by the constant pursuit for higher productivity prompts an urge for studies aiming at understanding agglomeration economies. In the context of Brazil, a country with extremely high regional disparities, exploring this issue is important not only for private stakeholders, but also for public policy practitioners. In the framework of static agglomeration effects, we investigate the industrial scope of agglomeration economies in Brazil. On the basis of identified registration data covering the whole formal labor market in three distinct years (2004, 2008 and 2012), we estimate separate models for the logarithm of the hourly individual wage for five broad economic sectors (S1-Manufacturing low-tech, S2-Manufacturing medium-tech, S3-Manufacturing high-tech, S4-Services less-knowledge, and S5-Services high-knowledge). Different estimation strategies are considered in a two-stage mode: with and without individual fixed effects in the first stage, and with and without instrumental variables for population density in the second stage. The main results indicate that there is not a unique optimal local industrial mix to foster productivity in different technological sectors. Comparing possible theoretical approaches (MAR, Jacobs, Porter) related to combinations of diversity, specialization and competition, we find that for S5 only diversity is significant (and positive), suggesting that a Jacobs' perspective is rather adequate. For S1, S2 and S4, the MAR framework seems more adequate to explain the underlying patterns. In the case of S3, there are elements from both Marshall's and Jacobs' perspectives. These results seem to be robust to different specifications and estimation strategies. Finally, the urbanization economies coefficient appears to be positive and significant for all sectors, ranging from 0.0511 to 0.0940 in different specifications, under the simplest estimation (OLS in the first and the second stages). Ordering these effects between the sectors from the highest to the lowest, we find the following sequence: S3, S1, S5, S4 and S2. This can be considered as evidence that high-tech and low-tech manufacturing sectors benefit more from the urban or metropolitan scale in Brazil, followed by services associated with higher knowledge intensity.

Effects of dynamic and spatial externalities on local growth: Evidence from Brazil

“…Beaudry and Schiffauerova () emphasize that, in most cases, the models result in negative or non–significant effects of specialization externalities. For the Brazilian economy, Catela et al () and Barufi et al () also identified a positive effect for Brazil. Graham (), Kemeny and Storper () and Caragliu et al () found a positive effect of specialization on local growth for Great Britain, the United States and European regions, while Mameli et al () identified positive effects of specialization for industry at three‐digits of aggregation for Italy, although it found a negative effect at two‐digits.…”

“…Robust standard errors in parentheses. Barufi et al (2016). However, Mameli et al (2014) found a negative result for two-digit and a positive one for three-digit aggregation level.…”

“…These externalities reflect the result of complex interactions among different sources, hence obtaining controls for all these effects is a challenge (Rosenthal & Strange, 2004). Most models to identify agglomeration effects therefore use one or two of the key aspects of scope, for example, Glaeser et al (1992), Henderson et al (1995), Henderson (1997Henderson ( , 2003, Combes (2000), Ciccone (2002), Combes, Magnac, and Robin (2004), Rosenthal and Strange (2003), Blien, Suedekum, and Wolf (2006), Paci and Usai (2008), Graham (2009), Marrocu, Paci, and Usai (2012), Mameli, Faggian, and McCann (2014), Kemeny and Storper (2015), Caragliu, de Dominicis, and Groot (2016), Camagni, Capello, and Caragliu (2016) and Barufi, Haddad, and Nijkamp (2016), with the exception of Dauth (2013) who added all three effects.…”

Related variety and employment growth: a spatial dynamic model for Brazilian microregions

“…Most of the previous studies showed positive effects for Brazilian regions concerning diversification (Badia & Figueiredo, ; Silva & Silveira Neto, ; Fochezatto & Valentini, ; Catela et al, ). Barufi et al () found this for high technological sectors, while Araújo et al () sees positive effects from diversification from neighbouring regions. What can be seen here is that in fact the positive effects from diversification emanate when sectors are diversified, but related with each other.…”

“…Thus, the hypothesis of a region's economic performance depending on the ones around it is considered. These spatial externalities were also controlled by Dauth (2013) and Barufi et al (2016). We use the spatial lag of the X (SLX) model as it is the most flexible among all other spatial models because it is easily interpretable in terms of signal, magnitude and significance level (Vega & Elhorst, 2015).…”