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A Spatial Propensity Score Matching Evaluation of the Social Impacts of Sugarcane Growing on Municipalities in Brazil

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  • André Luis Squarize Chagas
  • Rudinei Toneto
  • Carlos Roberto Azzoni

Abstract

The expansion of sugarcane growing in Brazil, spurred particularly by increased demand for ethanol, has triggered the need to evaluate the economic, social, and environmental impacts of this process, both on the country as a whole and on the growing regions. Even though the balance of costs and benefits is positive from an overall standpoint, this may not be so in specific producing regions, due to negative externalities. The objective of this paper is to estimate the effect of growing sugarcane on the human development index (HDI) and its sub-indices in cane producing regions. In the literature on matching effects, this is interpreted as the effect of the treatment on the treated. Location effects are controlled by spatial econometric techniques, giving rise to the spatial propensity score matching model. The authors analyze 424 minimum comparable areas (MCAs) in the treatment group, compared with 907 MCAs in the control group. The results suggest that the presence of sugarcane growing in these areas is not relevant to determine their social conditions, whether for better or worse. It is thus likely that public policies, especially those focused directly on improving education, health, and income generation/distribution, have much more noticeable effects on the municipal HDI.

Suggested Citation

  • André Luis Squarize Chagas & Rudinei Toneto & Carlos Roberto Azzoni, 2012. "A Spatial Propensity Score Matching Evaluation of the Social Impacts of Sugarcane Growing on Municipalities in Brazil," International Regional Science Review, , vol. 35(1), pages 48-69, January.
    • Handle: RePEc:sae:inrsre:v:35:y:2012:i:1:p:48-69
    DOI: 10.1177/0160017611400069
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    References listed on IDEAS

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    1. Arata, Linda & Hauschild, Sofia & Sckokai, Paolo, 2018. "Economic and social impact of grape growing in Northeastern Brazil," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 6(3), May.
    2. Marusca De Castris & Guido Pellegrini, 2015. "Neighborhood Effects On The Propensity Score Matching," Working Papers 0515, CREI Università degli Studi Roma Tre, revised 2015.
    3. Andre Chagas & Carlos Azzoni & Alexandre Almeida, 2015. "A Spatial Difference-in-Difference Analysis to Measure the Sugarcane Producing Impact in Respiratory Health," ERSA conference papers ersa15p511, European Regional Science Association.
    4. Eduardo A. Haddad & Jesús P. Mena-Chalco, Otavio J. G. Sidone, 2015. "Scholarly Collaboration in Regional Science in Developing Countries: The Case of the Brazilian REAL Network," Working Papers, Department of Economics 2015_12, University of São Paulo (FEA-USP).
    5. Antonio Pastorelli Rodrigues, Thiago & Ledi Gonçalves, Solange & Squarize Chagas, André, 2019. "Wind power and the labor market in the Brazilian Northeast: a spatial propensity score matching approach," Revista Brasileira de Estudos Regionais e Urbanos, Associação Brasileira de Estudos Regionais e Urbanos (ABER), vol. 13(3), pages 357-378, March.
    6. Gilio, Leandro & Azanha Ferraz Dias de Moraes, Márcia, 2016. "Sugarcane industry's socioeconomic impact in São Paulo, Brazil: A spatial dynamic panel approach," Energy Economics, Elsevier, vol. 58(C), pages 27-37.
    7. Arata, Linda & Hauschild, Sofia & Sckokai, Paolo, 2017. "Socio-economic impact of grape growing in North-eastern Brazil," 2017 Sixth AIEAA Conference, June 15-16, Piacenza, Italy 261264, Italian Association of Agricultural and Applied Economics (AIEAA).
    8. Andre Chagas & Alexandre Almeida, 2014. "The impact of change in MSEs? regulation in municipalities in Sao Paulo state, Brazil," ERSA conference papers ersa14p1460, European Regional Science Association.
    9. Marynia Kolak & Luc Anselin, 2020. "A Spatial Perspective on the Econometrics of Program Evaluation," International Regional Science Review, , vol. 43(1-2), pages 128-153, January.
    10. Demidova, Olga, 2021. "Methods of spatial econometrics and evaluation of government programs effectiveness," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 64, pages 107-134.
    11. Eduardo A. Haddad & Jesús P. Mena-Chalco & Otávio J. G. Sidone, 2017. "Scholarly Collaboration in Regional Science in Developing Countries," International Regional Science Review, , vol. 40(5), pages 500-529, September.
    12. Wei Yang & Jorie Knook, 2021. "Spatial evaluation of the impact of a climate change participatory extension programme on the uptake of soil management practices," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 65(3), pages 539-565, July.
    13. Timo Mitze & Alfredo R. Paloyo & Björn Alecke, 2015. "Is There a Purchase Limit on Regional Growth? A Quasi-experimental Evaluation of Investment Grants Using Matching Techniques," International Regional Science Review, , vol. 38(4), pages 388-412, October.
    14. Chagas, André L.S. & Azzoni, Carlos R. & Almeida, Alexandre N., 2016. "A spatial difference-in-differences analysis of the impact of sugarcane production on respiratory diseases," Regional Science and Urban Economics, Elsevier, vol. 59(C), pages 24-36.
    15. Nenci, Silvia & Vurchio, Davide, 2023. "Modeling country-sectoral spillovers in generalized propensity score matching: An empirical test on trade data," Economic Modelling, Elsevier, vol. 124(C).

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