IDEAS home Printed from https://ideas.repec.org/a/sae/inrsre/v35y2012i1p48-69.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0160017611400069
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0160017611400069?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    2. Hardle, Wolfgang & Linton, Oliver, 1986. "Applied nonparametric methods," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 38, pages 2295-2339, Elsevier.
    3. Florax, Raymond J. G. M. & Folmer, Hendrik & Rey, Sergio J., 2003. "Specification searches in spatial econometrics: the relevance of Hendry's methodology," Regional Science and Urban Economics, Elsevier, vol. 33(5), pages 557-579, September.
    4. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    5. DiPrete, Thomas A. & Gangl, Markus, 2004. "Assessing bias in the estimation of causal effects: Rosenbaum bounds on matching estimators and instrumental variables estimation with imperfect instruments," Discussion Papers, Research Unit: Labor Market Policy and Employment SP I 2004-101, WZB Berlin Social Science Center.
    6. James J. Heckman & Hidehiko Ichimura & Petra Todd, 1998. "Matching As An Econometric Evaluation Estimator," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(2), pages 261-294.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mason, Nicole M. & Wineman, Ayala & Kirimi, Lilian & Mather, David, 2016. "The Effects of Kenya’s ‘Smarter’ Input Subsidy Program on Smallholder Behavior and Incomes: Do Different Quasi-Experimental Approaches Lead to the Same Conclusions?," Food Security Collaborative Working Papers 232090, Michigan State University, Department of Agricultural, Food, and Resource Economics.
    2. Nicole M. Mason & Ayala Wineman & Lilian Kirimi & David Mather, 2017. "The Effects of Kenya's ‘Smarter’ Input Subsidy Programme on Smallholder Behaviour and Incomes: Do Different Quasi-experimental Approaches Lead to the Same Conclusions?," Journal of Agricultural Economics, Wiley Blackwell, vol. 68(1), pages 45-69, February.
    3. Ichimura, Hidehiko & Todd, Petra E., 2007. "Implementing Nonparametric and Semiparametric Estimators," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 74, Elsevier.
    4. K. Sudhir & Debabrata Talukdar, 2015. "The "Peter Pan Syndrome" in Emerging Markets: The Productivity-Transparency Tradeoff in IT Adoption," Cowles Foundation Discussion Papers 1980, Cowles Foundation for Research in Economics, Yale University.
    5. Sakos, Grayson & Cerulli, Giovanni & Garbero, Alessandra, 2021. "Beyond the ATE: Idiosyncratic Effect Estimation to Uncover Distributional Impacts Results from 17 Impact Evaluations," 2021 Annual Meeting, August 1-3, Austin, Texas 314017, Agricultural and Applied Economics Association.
    6. Mirella Damiani & Fabrizio Pompei & Andrea Ricci, 2020. "Opting Out, Collective Contracts and Labour Flexibility: Firm‐Level Evidence for The Italian Case," British Journal of Industrial Relations, London School of Economics, vol. 58(3), pages 558-586, September.
    7. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    8. López-Iturriaga, Félix J. & Santana Martín, Domingo Javier, 2019. "The payout policy of politically connected firms: Tunnelling or reputation?," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    9. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, February.
    10. Naureen Fatema & Shahriar Kibriya, 2018. "Givers of great dinners know few enemies: The impact of household food sufficiency and food sharing on low intensity interhousehold and community conflict in Eastern Democratic Republic of Congo," HiCN Working Papers 267, Households in Conflict Network.
    11. Anindya Ghose & Vilma Todri, 2015. "Towards a Digital Attribution Model: Measuring the Impact of Display Advertising on Online Consumer Behavior," Working Papers 15-15, NET Institute.
    12. Chad D. Meyerhoefer & Muzhe Yang, 2011. "The Relationship between Food Assistance and Health: A Review of the Literature and Empirical Strategies for Identifying Program Effects," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 33(3), pages 304-344.
    13. Liebenehm, S. & Affognon, H. & Waibel, H., 2010. "Assessing the impact of agricultural research on cattle farmers’ knowledge about African animal trypanosomosis: an application of the propensity score matching approach," Proceedings “Schriften der Gesellschaft für Wirtschafts- und Sozialwissenschaften des Landbaues e.V.”, German Association of Agricultural Economists (GEWISOLA), vol. 45, March.
    14. Petra E. Todd & Kenneth I. Wolpin, 2008. "Ex Ante Evaluation of Social Programs," Annals of Economics and Statistics, GENES, issue 91-92, pages 263-291.
    15. Ha Trong Nguyen & Luke B Connelly, 2017. "Cost-sharing in health insurance and its impact in a developing country: evidence from a quasi-natural experiment," Bankwest Curtin Economics Centre Working Paper series WP1702, Bankwest Curtin Economics Centre (BCEC), Curtin Business School.
    16. Giulia Bettin & Isabella Giorgetti & Stefano Staffolani, 2024. "The impact of Covid-19 lockdown on the gender gap in the Italian labour market," Review of Economics of the Household, Springer, vol. 22(1), pages 1-33, March.
    17. Markus Gangl & Thomas A. DiPrete, 2004. "Kausalanalyse durch Matchingverfahren," Discussion Papers of DIW Berlin 401, DIW Berlin, German Institute for Economic Research.
    18. Jason Abrevaya & Yu-Chin Hsu & Robert P. Lieli, 2015. "Estimating Conditional Average Treatment Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(4), pages 485-505, October.
    19. Liebenehm, Sabine & Affognon, Hippolyte & Waibel, Hermann, 2009. "Impact assessment of agricultural research in West Africa: an application of the propensity score matching methodology," 2009 Conference, August 16-22, 2009, Beijing, China 50829, International Association of Agricultural Economists.
    20. K. Sudhir & Debabrata Talukdar, 2015. "The “Peter Pan Syndrome” in Emerging Markets: The Productivity-Transparency Trade-off in IT Adoption," Marketing Science, INFORMS, vol. 34(4), pages 500-521, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:inrsre:v:35:y:2012:i:1:p:48-69. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.