IDEAS home Printed from https://ideas.repec.org/p/ias/cpaper/18-wp583.html
   My bibliography  Save this paper

The Distributional Impact of Climate Change in Brazilian Agriculture: A Ricardian Quantile Analysis with Census Data

Author

Abstract

The economic impact of global warming varies across firms because of differences in climate, technology, and adaptive capacity. Aggregate estimates of the average effect of warming are thus insufficient to model climate change vulnerability in developing countries. In this study, I measure the distributional effect of climate change in Brazilian agriculture by estimating the quantile and interquantile regressions of land value on climate, using agricultural census data for 490,000 commercial farms. The effect of a 1°C rise in average temperature on land values ranges from -5% for the most productive farmers located in the colder South region to -34% for the least productive farmers located in the warmer North region. The impact is most severe in the extreme 0.01 quantile of the land value distribution. The productivity inequality between farms in the extremes of the distribution of land values may double with marginal warming.

Suggested Citation

  • Guilherme DePaula, 2018. "The Distributional Impact of Climate Change in Brazilian Agriculture: A Ricardian Quantile Analysis with Census Data," Center for Agricultural and Rural Development (CARD) Publications 18-wp583, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    • Handle: RePEc:ias:cpaper:18-wp583
    as

    Download full text from publisher

    File URL: https://www.card.iastate.edu/products/publications/pdf/18wp583.pdf
    File Function: Full Text
    Download Restriction: no
    File URL: https://www.card.iastate.edu/products/publications/synopsis/?p=1278
    File Function: Online Synopsis
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Emanuele Massetti & Robert Mendelsohn, 2011. "Estimating Ricardian Models With Panel Data," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 2(04), pages 301-319.
    2. Anthony C. Fisher & W. Michael Hanemann & Michael J. Roberts & Wolfram Schlenker, 2012. "The Economic Impacts of Climate Change: Evidence from Agricultural Output and Random Fluctuations in Weather: Comment," American Economic Review, American Economic Association, vol. 102(7), pages 3749-3760, December.
    3. Carlo Fezzi & Ian Bateman, 2015. "The Impact of Climate Change on Agriculture: Nonlinear Effects and Aggregation Bias in Ricardian Models of Farmland Values," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 2(1), pages 57-92.
    4. Griliches, Zvi & Hausman, Jerry A., 1986. "Errors in variables in panel data," Journal of Econometrics, Elsevier, vol. 31(1), pages 93-118, February.
    5. Mendelsohn, Robert & Nordhaus, William D & Shaw, Daigee, 1994. "The Impact of Global Warming on Agriculture: A Ricardian Analysis," American Economic Review, American Economic Association, vol. 84(4), pages 753-771, September.
    6. Wolfram Schlenker & W. Michael Hanemann & Anthony C. Fisher, 2005. "Will U.S. Agriculture Really Benefit from Global Warming? Accounting for Irrigation in the Hedonic Approach," American Economic Review, American Economic Association, vol. 95(1), pages 395-406, March.
    7. Ortiz-Bobea, Ariel, 2016. "The Economic Impacts of Climate Change on Agriculture: Accounting for Time-invariant Unobservables in the Hedonic Approach," Working Papers 250035, Cornell University, Department of Applied Economics and Management.
    8. Olivier Deschênes & Michael Greenstone, 2007. "The Economic Impacts of Climate Change: Evidence from Agricultural Output and Random Fluctuations in Weather," American Economic Review, American Economic Association, vol. 97(1), pages 354-385, March.
    9. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    10. Buchinsky, Moshe, 1994. "Changes in the U.S. Wage Structure 1963-1987: Application of Quantile Regression," Econometrica, Econometric Society, vol. 62(2), pages 405-458, March.
    11. Ortiz-­Bobea, Ariel, 2013. "Understanding Temperature and Moisture Interactions in the Economics of Climate Change Impacts and Adaptation on Agriculture," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150435, Agricultural and Applied Economics Association.
    12. Victor Chernozhukov & Christian Hansen, 2005. "An IV Model of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 73(1), pages 245-261, January.
    13. Joshua Angrist & Victor Chernozhukov & Iván Fernández-Val, 2006. "Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure," Econometrica, Econometric Society, vol. 74(2), pages 539-563, March.
    14. Victor Chernozhukov & Christian Hansen, 2004. "The Effects of 401(K) Participation on the Wealth Distribution: An Instrumental Quantile Regression Analysis," The Review of Economics and Statistics, MIT Press, vol. 86(3), pages 735-751, August.
    15. Mendelsohn, Robert & Dinar, Ariel & Williams, Larry, 2006. "The distributional impact of climate change on rich and poor countries," Environment and Development Economics, Cambridge University Press, vol. 11(2), pages 159-178, April.
    Full references (including those not matched with items on IDEAS)

    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. DePaula, Guilherme, 2020. "The distributional effect of climate change on agriculture: Evidence from a Ricardian quantile analysis of Brazilian census data," Journal of Environmental Economics and Management, Elsevier, vol. 104(C).
    2. Steven Passel & Emanuele Massetti & Robert Mendelsohn, 2017. "A Ricardian Analysis of the Impact of Climate Change on European Agriculture," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 67(4), pages 725-760, August.
    3. Emanuele Massetti & Steven Van Passel & Camila Apablaza, 2018. "Is Western European Agriculture Resilient to High Temperatures?," CESifo Working Paper Series 7286, CESifo.
    4. Arellano Gonzalez, Jesus, 2018. "Estimating climate change damages in data scarce and non-competitive settings: a novel version of the Ricardian approach with an application to Mexico," 2018 Annual Meeting, August 5-7, Washington, D.C. 274010, Agricultural and Applied Economics Association.
    5. Ariel Ortiz‐Bobea, 2020. "The Role of Nonfarm Influences in Ricardian Estimates of Climate Change Impacts on US Agriculture," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(3), pages 934-959, May.
    6. Ariel Ortiz-Bobea, 2021. "Climate, Agriculture and Food," Papers 2105.12044, arXiv.org.
    7. Meyer, Kevin Michael, 2017. "Three essays on environmental and resource economics," ISU General Staff Papers 201701010800006585, Iowa State University, Department of Economics.
    8. Kaixing Huang, 2015. "The Economic Impacts of Global Warming on Agriculture: the Role of Adaptation," School of Economics and Public Policy Working Papers 2015-20, University of Adelaide, School of Economics and Public Policy.
    9. Dale T. Manning & Christopher Goemans & Alexander Maas, 2017. "Producer Responses to Surface Water Availability and Implications for Climate Change Adaptation," Land Economics, University of Wisconsin Press, vol. 93(4), pages 631-653.
    10. Bareille, François & Chakir, Raja, 2023. "The impact of climate change on agriculture: A repeat-Ricardian analysis," Journal of Environmental Economics and Management, Elsevier, vol. 119(C).
    11. Huang, K., 2018. "How Large is the Potential Economic Benefit of Agricultural Adaptation to Climate Change?," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277238, International Association of Agricultural Economists.
    12. Jianhong E. Mu & Benjamin M. Sleeter & John T. Abatzoglou & John M. Antle, 2017. "Climate impacts on agricultural land use in the USA: the role of socio-economic scenarios," Climatic Change, Springer, vol. 144(2), pages 329-345, September.
    13. Kaixing Huang & Nicholas Sim, 2021. "Adaptation May Reduce Climate Damage in Agriculture by Two Thirds," Journal of Agricultural Economics, Wiley Blackwell, vol. 72(1), pages 47-71, February.
    14. Tao Xiang & Tariq H. Malik & Jack W. Hou & Jiliang Ma, 2022. "The Impact of Climate Change on Agricultural Total Factor Productivity: A Cross-Country Panel Data Analysis, 1961–2013," Agriculture, MDPI, vol. 12(12), pages 1-20, December.
    15. Frederick Quaye & Denis Nadolnyak & Valentina Hartarska, 2018. "Climate Change Impacts on Farmland Values in the Southeast United States," Sustainability, MDPI, vol. 10(10), pages 1-16, September.
    16. Chau Trinh Nguyen & Frank Scrimgeour, 2022. "Measuring the impact of climate change on agriculture in Vietnam: A panel Ricardian analysis," Agricultural Economics, International Association of Agricultural Economists, vol. 53(1), pages 37-51, January.
    17. B. James Deaton & Chad Lawley, 2022. "A survey of literature examining farmland prices: A Canadian focus," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 70(2), pages 95-121, June.
    18. Mintewab Bezabih & Salvatore Di Falco & Alemu Mekonnen, 2014. "Is it the climate or the weather? Differential economic impacts of climatic factors in Ethiopia," GRI Working Papers 148, Grantham Research Institute on Climate Change and the Environment.
    19. Abdul Quddoos & Klaus Salhofer & Ulrich B. Morawetz, 2023. "Utilising farm‐level panel data to estimate climate change impacts and adaptation potentials," Journal of Agricultural Economics, Wiley Blackwell, vol. 74(1), pages 75-99, February.
    20. Victor Chernozhukov & Iván Fernández‐Val & Blaise Melly, 2013. "Inference on Counterfactual Distributions," Econometrica, Econometric Society, vol. 81(6), pages 2205-2268, November.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:ias:cpaper:18-wp583. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/caiasus.html .

    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.