The aim of this study is to evaluate the impacts of this expansion on the income of people in the state’s districts and towns. Beginning with a breakdown of the main determinants of per capita income, a spatial dynamic panel model is proposed. The proportion of adults in the municipal population, the labour force utilization rate, and the average labour income were used as control variables. Furthermore, to isolate the impacts of the expansion of the sugarcane sector on per capita gross domestic product (GDP), the share of farming in municipal areas, the share of agriculture within farming in general, the share of sugarcane farming within agriculture, and a dummy for districts and towns with an operational plant were included in the model. The series cover the 645 districts and towns of S？o Paulo State from 2000 to 2008. The results of the system generalized method of moments (system-GMM) showed a positive relationship of spatial and temporal dependence in the real per capita GDP. And the estimated direct and indirect effects indicate that the expansion of the sugarcane sector had a positive impact on per capita GDP, both in towns where the expansion took place and in their neighbouring towns. 1. Introduction Over the past ten years, there has been a significant growth in sugarcane production in S？o Paulo State, increasing from almost 2.5 million hectares in 2000 to approximately 4.5 million hectares in 2008 [1]. From 2005 to 2008 alone, the area of harvested sugar cane in the state increased by over 1.8 million hectares, with 53.0% substituting pasture land and 46.7% substituting other crops [2]. This expansion was accompanied by increased capacity of preexisting plants and/or distilleries and also more new units throughout the state. According to a primary data survey for this study, the number of towns with an operational industrial plant and/or distillery increased from 109 in 2000 to 143 in 2008. But this rapid expansion of the sugarcane sector has raised a number of questions concerning its economic, social, and environmental impacts. From a socioeconomic viewpoint, one of the most important aspects to be dealt with is the effect of this growth on income (using the consumer theory, Deaton and Muellbauer [3] show that the level and distribution of per capita income enable an evaluation of social welfare). In 2008, the GDP of S？o Paulo State surpassed one trillion reais (or the equivalent to 546 billion dollars), accumulating a real growth of 13.7% in comparison with 2000. At the same time, the number of people living in the state rose by almost
References
[1]
Instituto Brasileiro de Geografia e Estatística—IBGE, Banco de dados agregados: sistema IBGE de recupera？？o automática—SIDRA, http://www.sidra.ibge.gov.br/.
[2]
A. M. Nassar, B. F. T. Rudorff, L. B. Antoniazzi, D. A. de Aguiar, M. R. P. Bacchi, and M. Adami, “Prospects of the sugarcane expansion in Brazil: impacts on direct and indirect land use changes,” in Contributions To Climate Change Mitigation and the Environment, P. Zuurbier and J. van der Vooren, Eds., pp. 63–93, Wageningen Academic Publishers, Wageningen, The Netherlands, 2008.
[3]
A. Deaton and J. Muellbauer, Economics and Consumer Behavior, Cambridge University Press, New York, NY, USA, 2009.
[4]
R. Silva, “Setor sucroalcooleiro no Estado de S？o Paulo: mensurando impactos sócio-econ？micos,” in Workshop do Observat？rio do Setor Sucroalcooleiro, FEA-RP, Ribeir？o Preto, Brazil, 2008.
[5]
A. Walter, P. Dolzan, O. Quilodrán et al., “A sustainability analysis of the Brazilian ethanol,” in Biocombustível Brasileiro: Uma Análise de Sustentabilidade, UNICAMP, Campinas, Brazil, 2008.
[6]
G. Spavorek, A. Barreto, G. Berndes, S. Martins, and R. Maule, “Environmental, land-use and economic implications of Brazilian sugarcane expansion 1996–2006,” Mitigation and Adaptation Strategies For Global Change, vol. 14, no. 3, pp. 285–298, 2009.
[7]
A. Deuss, “Did sugarcane expansion lead to GDP growth in S？o Paulo state? An analysis at municipal level using propensity score-based estimators,” 2009, https://editorialexpress.com/cgi-bin/conference/download.cgi?db_name=res_phd_2010&paper_id=174.
[8]
J. G. de Oliveira, Indicadores Sócio-Econ？micos em Estados Produtores de Cana-de-A？úcar: Análise Comparativa Entre Municípios [Doutorado em Planejamento de Sistemas Energéticos], Faculdade de Engenharia Mecanica, Universidade Estadual de Campinas, Campinas, Brazil, 2011.
[9]
A. L. S. Chagas, R. Toneto Jr., and C. R. Azzoni, “The expansion of sugar cane cultivation and its impacts on municipal revenues,” in Energy, Bio Fuels and Development: Comparing Brazil and the United States, E. Amann, W. Baer, and D. V. Coes, Eds., Routledge, New York, NY, USA, 2010.
[10]
A. L. S. Chagas, R. Toneto Jr., and C. R. Azzoni, “Avaliando os impactos da produ？？o de cana-de-a？úcar sobre indicadores sociais de regi？es produtoras por meio de propensity score matching espacial,” in Congresso da Sociedade Brasileira de Economia, Administra？？o e Sociologia Rural, vol. 48, SOBERB, Brasilia, Brazil, 2010.
[11]
A. L. S. Chagas, R. Toneto Jr., and C. R. Azzoni, “Sugar cane, land use and regional development,” Texto para Discuss？o 009/2011, Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil, 2011, Programa de Pós-Gradua？？o em Economia Aplicada.
[12]
R. P. de Barros, M. de Carvalho, S. Franco, and R. Mendon？a, Acesso ao trabalho e produtividade no Brasil: implica？？es para crescimento, pobreza e desigualdade, IPEA, Rio de Janeiro, Brazil, 2004.
[13]
Funda？？o Sistema Estadual de Análise de Dados—SEADE. Informa？？es dos municípios paulistas, http://www.seade.gov.br/produtos/imp/.
[14]
Instituto de Pesquisa Econ？mica Aplicada—IPEA, Ipeadata, http://www.ipeadata.gov.br/.
[15]
Instituto de Economia Agrícola—IEA. Banco de dados: área e produ？？o, http://www.iea.sp.gov.br/out/index.php.
[16]
M. Kukenova and J. A. Monteiro, “Spatial dynamic panel model and system GMM: a Monte Carlo investigation,” MPRA Paper 14319, Munich Personal RePEc Archive, 2009.
[17]
R. K. Pace and R. Barry, “Sparse spatial autoregressions,” Statistics and Probability Letters, vol. 33, no. 3, pp. 291–297, 1997.
[18]
J. P. Elhorst, “Dynamic spatial panels: Models, methods, and inferences,” Journal of Geographical Systems, vol. 14, no. 1, pp. 5–28, 2012.
[19]
J. P. Elhorst, “Dynamic models in space and time,” Geographical Analysis, vol. 33, no. 2, pp. 119–140, 2001.
[20]
O. Parent and J. P. LeSage, “A space-time filter for panel data models containing random effects,” Computational Statistics and Data Analysis, vol. 55, no. 1, pp. 475–490, 2011.
[21]
N. Debarsy, C. Ertur, and J. P. LeSage, “Interpreting dynamic space-time panel data models,” Statistical Methodology, vol. 9, no. 1-2, pp. 158–171, 2012.
[22]
M. Arellano and S. R. Bond, “Some tests specification for panel data: Monte Carlo evidence and application to employment equations,” Review of Economic Studies, vol. 58, pp. 277–297, 1991.
[23]
L. F. Satolo, Impactos socioecon？micos da expans？o do setor sucroenergético: uma análise espacial dinamica sobre o bem-estar social no Estado de S？o Paulo (2000–2008) [Doutorado em Economia Aplicada], Escola Superior de Agricultura “Luiz de Queiroz”, Universidade de S？o Paulo, Piracicaba, Brazil, 2012.
