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Industrial scope of agglomeration economies in Brazil

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Abstract

The tendency towards urbanisation in the emerging world accompanied by the constant pursuit of higher productivity has prompted many studies which aim to understand 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 which cover the whole formal labour market in three particular 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 model: 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 no unique optimal local industrial mix to foster productivity in different technological sectors. Comparing possible theoretical approaches (Marshall–Arrow–Romer, Jacobs, Porter) related to combinations of diversity, specialisation and competition, we find that for S5 only diversity is significant (and positive), suggesting that Jacobs’s perspective is adequate. However, for S1, S2 and S4, the MAR framework seems better able to explain the underlying patterns. In the case of S3, there are elements from both Marshall’s and Jacobs’s perspective. These results seem to be robust to different specifications and estimation strategies. Finally, the urbanisation 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.

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Notes

  1. Considering the Brazilian Gini Index of 2012 and the most recent information for other countries, available at https://www.cia.gov/library/publications/the-world-factbook/rankorder/2172rank.html, accessed on 27/06/2014.

  2. Based on data from the Demographic Census of 2010, IBGE (2013).

  3. With information from the Municipal GDP, provided by IBGE.

  4. See Appendix 4.

  5. Regions are labour market areas (REGIC areas), which aggregate the municipalities in each region (482 regions containing all 5565 municipalities), as defined by the Brazilian Institute of Geography and Statistics for 2007 (IBGE 2013), taking into account all daily commuting and transportation connections among the municipalities.

  6. These last three variables were generated and kindly provided by Naritomi et al. (2012). In the 1940s, the Brazilian economy was much less industrialised and productivity differentials were more related to agriculture. Therefore, population density in that decade is expected to be exogenous in relation to wages in the twenty-first century. Concerning distance to the coast, the colonisation process in Brazil was mostly concentrated on the coast, and a simple analysis of the distribution of cities in Brazil shows that there is still a high population concentration in that part of the country. Finally, the main economic activities developed during the colonial period helped to define the location of many urban centres, but are not necessarily related to the determinants of productivity nowadays.

  7. In the case when the combination of region–sector–year is considered for all industries simultaneously, the limit of Stata MP and R in a computer with RAM of 64G is reached.

  8. The authors are grateful to FIPE (The Economic Research Foundation Institute), Prof. Helio Zylberstajn and Eduardo Zylberstajn for allowing access to the database. All confidentiality requirements were respected.

  9. Total jobs include public sector and military, formal and informal employees, self-employment, entrepreneurs and unpaid work. Therefore, the percentages discussed here refer to the representativeness of RAIS in the labour market.

  10. Information obtained from the National Household Sample Survey (PNAD), from the Brazilian Institute of Geography and Statistics (IBGE).

  11. The original database contains individual information for each contract, meaning that individuals can appear more than once if they work for more than one firm. A few steps were taken in order to select the desired observations (ending up with only one contract per worker). Firstly, a filter selected only active contracts in December of each year for individuals working for private companies in permanent jobs. Then, contracts with missing individual IDs, wages equal to zero, or with less than 20 h weekly were excluded. The next steps for individuals with multiple contracts included: keeping those with five or less contracts; dropping those with a different gender in each contract; and keeping the contracts with the highest number of weekly hours and with the oldest hiring date. Finally, for the remaining cases of multiple contracts, only one of them was randomly selected, leading to one contract per individual for the whole database. After all these procedures, and keeping only the manufacturing and service sectors, the database size ranged from 8.9 to 12.6 million between 2004 and 2012 (the increase is related to the expansion of the formal sector previously mentioned). Only those individuals who were observed in each period (2004 and 2008; 2008 and 2012; and 2004, 2008 and 2012—depending on the model considered) were kept, with the same gender and birth date in every year. Finally, following a common practice in the related literature, the analysis was conducted for men who were 18–56 years old in 2004.

  12. These samples are representative for the following characteristics: age group (less than 30 years old, 30–45 years old, 46 or more); population size (less than 100 thousand, 100 thousand to less than 500 thousand, 500 thousand to less than 1 million, 1 million or more); firm size (up to 4 employees, 5 to 9, 10 to 19, 20 to 49, 50 to 99, 100 to 249, 250 to 499, 500 to 999, 1000 employees or more); macro-region; educational level (illiterate, incomplete primary school, complete primary school to incomplete high school, complete high school to incomplete college, college degree or more); and technological sector (see Appendix 1).

  13. Within the same technological sector.

  14. It is important to note that the spatial wage can have negative values, as it is a deviation of the omitted coefficient of the combination of REGIC area–industry–year.

  15. The map in the centre refers to the spatial wage for the model with a pooled OLS estimation in the first stage, while the map on the right is related to the spatial wage obtained from the estimation with individual fixed effects in the first stage.

  16. This classification is based on the one proposed by Eurostat: http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:High-tech (last accessed on 15/01/2915).

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Acknowledgments

This contribution of Ana Maria Bonomi Barufi had the financial support of the Brazilian government through the National Council for Scientific and Technological Development (CNPq) - Process 229708/2013-7 and the Coordination for Improvement of Higher Education Personnel (CAPES) - Process BEX 2501/14-7. Eduardo Haddad acknowledges financial support from CNPq and FAPESP; he also thanks Princeton University and Rutgers University for their hospitality.

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Authors and Affiliations

  1. Department of Economics, University of São Paulo, Av. Prof. Luciano Gualberto, 908, São Paulo, SP, 05508-010, Brazil

    Ana Maria Bonomi Barufi & Eduardo Amaral Haddad

  2. Department of Spatial Economics, VU University Amsterdam, De Boelelaan 1105, 1081 HV, Amsterdam, Netherlands

    Ana Maria Bonomi Barufi & Peter Nijkamp

  3. Faculty of Geography and Earth Sciences, A. Mickiewicz University, Poznan, Poland

    Peter Nijkamp

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  1. Ana Maria Bonomi Barufi
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  2. Eduardo Amaral Haddad
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  3. Peter Nijkamp
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Correspondence to Ana Maria Bonomi Barufi.

Appendices

Appendix 1: Aggregation of manufacturing and service industries

Two-digit industries were aggregated in three manufacturing and two service sectors, based on technological and knowledge intensity (Table 6).Footnote 16

Table 6 Aggregations of manufacturing and service industries according to technological knowledge intensity.
figure f
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Appendix 2: Definition of variables and data sources

See Table 7.

Table 7 Definition and description of the main variables considered in the model.
figure g
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Appendix 3: Indicators of the industrial scope of agglomeration economies

See Table 8.

Table 8 Indicators and measures of the industrial scope of agglomeration economies.
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Appendix 4: Skills

Maciente (2013) has developed a matching among the occupational classification in Brazil and the profile of skills based on ONET (Occupational Information Network). Using that, the author finds a weighting system for each ability required for the job, based on the worker qualification and the job complexity. Then, with a factor analysis it is possible to calculate the skill requirement of each occupation, with the classification presented in Table 9.

Table 9 Description of the factors defined by Maciente (2013) which aggregates the skill requirements of each occupation in Brazil.
figure i
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Appendix 5: Comparison of the full sample and the individuals who provide variation for the spatial wage calculation when individual fixed effects are included

See Table 10.

Table 10 Comparison of selected descriptive statistics of the full sample and the individuals with panel variation, 2012 (for the database 2004–2008–2012).
figure j
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Appendix 6

See Table 11.

Table 11 Results for the first-stage regressions, with and without individual fixed effects.
figure k
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Appendix 7: Comparison of 2004–2008 and 2008–2012

See Table 12.

Table 12 Second-stage regression results for the spatial wage, with instrumental variables for ln(density) and OLS estimation in the first stage, 2004–2008 and 2008–2012.
figure l
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Barufi, A.M.B., Haddad, E.A. & Nijkamp, P. Industrial scope of agglomeration economies in Brazil. Ann Reg Sci 56, 707–755 (2016). https://doi.org/10.1007/s00168-016-0768-3

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