{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:36:50Z","timestamp":1791333410626,"version":"4.3.1"},"reference-count":36,"publisher":"The Econometric Society","issue":"4","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["QE"],"abstract":"<jats:p>We study potential impacts of future climate change on U.S. agricultural productivity using county\u2010level yield and weather data from 1950 to 2015. To account for adaptation of production to different weather conditions, it is crucial to allow for both spatial and temporal variation in the production process mapping weather to crop yields. We present a new panel data estimation technique, called mean observation OLS (MO\u2010OLS) that allows for spatial and temporal heterogeneity in all regression parameters (intercepts and slopes). Both forms of heterogeneity are important: We find strong evidence that production function parameters adapt to local climate, and also that sensitivity of yield to high temperature declined from 1950\u201389. We use our estimates to project corn yields to 2100 using 19 climate models and three greenhouse gas emission scenarios. We predict unmitigated climate change will greatly reduce yield. Our mean prediction (over climate models) is that adaptation alone can mitigate 36% of the damage, while emissions reductions consistent with the Paris targets would mitigate 76%.<\/jats:p>","DOI":"10.3982\/qe1319","type":"journal-article","created":{"date-parts":[[2020,11,17]],"date-time":"2020-11-17T14:28:03Z","timestamp":1605623283000},"page":"1391-1429","source":"Crossref","is-referenced-by-count":44,"title":["Climate change and U.S. agriculture: Accounting for multidimensional slope heterogeneity in panel data"],"prefix":"10.3982","volume":"11","author":[{"given":"Michael","family":"Keane","sequence":"first","affiliation":[{"name":"Department of Economics, University of New South Wales"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timothy","family":"Neal","sequence":"additional","affiliation":[{"name":"Department of Economics, University of New South Wales"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2359","published-online":{"date-parts":[[2020]]},"reference":[{"issue":"1","key":"quan200109-bib-0001","doi-asserted-by":"crossref","DOI":"10.3390\/agronomy9010013","volume":"9","author":"Alsajri","year":"2019","journal-title":"Agronomy"},{"issue":"4","key":"quan200109-bib-0002","doi-asserted-by":"crossref","first-page":"821","DOI":"10.2307\/2951764","volume":"61","author":"Andrews","year":"1993","journal-title":"Econometrica"},{"issue":"5","key":"quan200109-bib-0003","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1257\/aer.p20151031","volume":"105","author":"Annan","year":"2015","journal-title":"American Economic Review"},{"issue":"1","key":"quan200109-bib-0004","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1057\/s41308-019-00102-3","volume":"68","author":"Auerbach","year":"2020","journal-title":"IMF Economic Review"},{"issue":"2","key":"quan200109-bib-0005","first-page":"1","volume":"4","author":"Auerbach","year":"2012","journal-title":"American Economic Journal: Economic Policy"},{"issue":"2","key":"quan200109-bib-0006","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1093\/reep\/ret016","volume":"7","author":"Auffhammer","year":"2013","journal-title":"Review of Environmental Economics and Policy"},{"key":"quan200109-bib-0007","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/978-3-642-42039-9_5","volume-title":"Advances in Non-Linear Economic Modeling","author":"Benati","year":"2014"},{"key":"quan200109-bib-0008","unstructured":"Brekke, L., B. L. Thrasher, E. P. Maurer, and T. Pruitt (2013), \u201cDownscaled CMIP3 and CMIP5 climate projections: Release of downscaled CMIP5 climate projections, comparison with preceding information, and summary of user needs.\u201d U.S. Department of the Interior, Bureau of Reclamation, Technical Service Center, Denver, CO. Available at http:\/\/gdo-dcp.ucllnl.org\/downscaled_cmip_projections\/techmemo\/downscaled_climate.pdf."},{"issue":"3","key":"quan200109-bib-0009","first-page":"106","volume":"8","author":"Burke","year":"2016","journal-title":"American Economic Journal: Economic Policy"},{"key":"quan200109-bib-0010","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1038\/nclimate1585","volume":"3","author":"Butler","year":"2013","journal-title":"Nature Climate Change"},{"issue":"1","key":"quan200109-bib-0011","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0304-4076(98)00084-0","volume":"92","author":"Conley","year":"1999","journal-title":"Journal of Econometrics"},{"issue":"3","key":"quan200109-bib-0012","first-page":"66","volume":"4","author":"Dell","year":"2012","journal-title":"American Economic Journal: Macroeconomics"},{"issue":"4","key":"quan200109-bib-0013","first-page":"152","volume":"3","author":"Desch\u00eanes","year":"2011","journal-title":"American Economic Journal: Applied Economics"},{"issue":"4","key":"quan200109-bib-0014","doi-asserted-by":"crossref","first-page":"387","DOI":"10.2307\/1907330","volume":"1","author":"Frisch","year":"1933","journal-title":"Econometrica"},{"key":"quan200109-bib-0015","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/978-3-540-75892-1_6","volume-title":"The Econometrics of Panel Data","author":"Hsiao","year":"2008"},{"key":"quan200109-bib-0016","first-page":"365","volume-title":"Statistical Inference in Dynamic Economic Models","author":"Hurwicz","year":"1950"},{"issue":"1","key":"quan200109-bib-0017","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1257\/jel.20181207","volume":"58","author":"Johnson","year":"2020","journal-title":"Journal of Economic Literature"},{"key":"quan200109-bib-0018","doi-asserted-by":"crossref","unstructured":"Keane, M. P. and T. Neal (2020a), \u201cComparing deep neural network and econometric approaches to predicting the impact of climate change on agricultural yield.\u201d UNSW Research Working Paper 2020-02. Available at https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3521260.","DOI":"10.2139\/ssrn.3521260"},{"key":"quan200109-bib-0019","doi-asserted-by":"crossref","unstructured":"Keane, M. and T. Neal (2020b), \u201cSupplement to \u2018Climate change and U.S. agriculture: Accounting for multidimensional slope heterogeneity in panel data\u2019.\u201d Quantitative Economics Supplemental Material, 11, https:\/\/doi.org\/10.3982\/QE1319.","DOI":"10.3982\/QE1319"},{"issue":"8","key":"quan200109-bib-0020","first-page":"1823","volume":"68","author":"Li","year":"2016","journal-title":"Journal of Experimental Botany"},{"issue":"12","key":"quan200109-bib-0021","doi-asserted-by":"crossref","first-page":"E2285","DOI":"10.1073\/pnas.1615922114","volume":"114","author":"Liang","year":"2017","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"quan200109-bib-0022","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1038\/nclimate1043","volume":"1","author":"Lobell","year":"2011","journal-title":"Nature climate change"},{"issue":"3","key":"quan200109-bib-0023","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1088\/1748-9326\/3\/3\/034007","volume":"3","author":"Lobell","year":"2008","journal-title":"Environmental Research Letters"},{"key":"quan200109-bib-0024","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1038\/nclimate1832","volume":"3","author":"Lobell","year":"2013","journal-title":"Nature Climate Change"},{"issue":"6183","key":"quan200109-bib-0025","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1126\/science.1251423","volume":"344","author":"Lobell","year":"2014","journal-title":"Science"},{"key":"quan200109-bib-0026","doi-asserted-by":"crossref","unstructured":"Malcolm, S., E. Marshall, M. Aillery, P. Heisey, M. Livingston, and K. Day-Rubenstein (2012), \u201cAgricultural adaptation to a changing climate: Economic and environmental implications vary by U.S. region.\u201d Technical Report 136, United States Department of Agriculture.","DOI":"10.2139\/ssrn.2112045"},{"key":"quan200109-bib-0027","unstructured":"Marshall, E., M. Aillery, S. Malcolm, and R. Williams (2015), \u201cClimate change, water scarcity, and adaptation in the U.S. fieldcrop sector.\u201d Technical Report 201, United States Department of Agriculture."},{"issue":"12","key":"quan200109-bib-0028","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.aat4343","volume":"4","author":"Ortiz-Bobea","year":"2018","journal-title":"Science Advances"},{"key":"quan200109-bib-0029","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/0304-4076(94)01644-F","volume":"68","author":"Pesaran","year":"1995","journal-title":"Journal of Econometrics"},{"key":"quan200109-bib-0030","first-page":"485","volume-title":"Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change","author":"Porter","year":"2014"},{"key":"quan200109-bib-0031","volume-title":"The Design and Implementation of U.S. Climate Policy","author":"Roberts","year":"2012"},{"issue":"37","key":"quan200109-bib-0032","doi-asserted-by":"crossref","first-page":"15594","DOI":"10.1073\/pnas.0906865106","volume":"106","author":"Schlenker","year":"2009","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"quan200109-bib-0033","doi-asserted-by":"crossref","unstructured":"Tannura, M. A., S. H. Irwin, and D. L. Good (2008), \u201cWeather, technology, and corn and soybean yields in the U.S. corn belt.\u201d Technical Report 01, Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign.","DOI":"10.2139\/ssrn.1147803"},{"key":"quan200109-bib-0034","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1175\/1520-0493(1920)48<439:MIITEO>2.0.CO;2","volume":"48","author":"Wallace","year":"1920","journal-title":"Monthly Weather Review"},{"issue":"1","key":"quan200109-bib-0035","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1007\/s13253-011-0067-5","volume":"17","author":"Wang","year":"2012","journal-title":"Journal of Agricultural, Biological, and Environmental Statistics"},{"key":"quan200109-bib-0036","unstructured":"Westcott, P. C. and M. Jewison (2013), \u201cWeather effects on expected corn and soybean yields.\u201d Technical Report FDS-13g-01, United States Department of Agriculture. Available at https:\/\/www.ers.usda.gov\/webdocs\/outlooks\/36651\/39297_fds-13g-01.pdf?v=8715.6."}],"container-title":["Quantitative Economics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.3982\/QE1319","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,17]],"date-time":"2020-11-17T14:35:05Z","timestamp":1605623705000},"score":1,"resource":{"primary":{"URL":"http:\/\/qeconomics.org\/ojs\/index.php\/qe\/article\/view\/QE1319"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":36,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020]]}},"alternative-id":["10.3982\/QE1319"],"URL":"https:\/\/doi.org\/10.3982\/qe1319","relation":{},"ISSN":["1759-7323"],"issn-type":[{"value":"1759-7323","type":"print"}],"subject":[],"published":{"date-parts":[[2020]]}}}