Crop Supply Dynamics and the Illusion of Partial Adjustment

We use field-level data to estimate the response of corn and soybean acreage to price shocks. Our sample contains more than 8 million observations derived from satellite imagery and includes every cultivated field in Iowa, Illinois, and Indiana. We estimate that aggregate crop acreage responds more to price shocks in the short run than in the long run, and we show theoretically how the benefits of crop rotation generate this response pattern. In essence, farmers who change crops due to a price shock have an incentive to switch back to the previous crop to capture the benefits of crop rotation. Our result contradicts the long-held belief that agricultural supply responds gradually to price shocks through partial adjustment. We would not have obtained this result had we used county-level panel data. Standard econometric methods applied to county-level data produce estimates consistent with partial adjustment. We show that this apparent partial adjustment is illusory, and we demonstrate how it arises from the fact that fields in the same county are more similar to each other than to fields in other counties. This result underscores the importance of using models with appropriate micro-foundations and cautions against inferring micro-level rigidities from inertia in aggregate panel data. Our preferred estimate of the own-price long-run elasticity of corn acreage is 0.29, and the cross-price elasticity is −0.22. The corresponding elasticities for soybean acreage are 0.26 and −0.33. Our estimated short-run elasticities are 37% larger than their long-run counterparts.

[1]  David Zilberman,et al.  The Welfare Economics of Price Supports in U.S. Agriculture , 1986 .

[2]  Hélène Rey,et al.  PPP Strikes Back: Aggregation and the Real Exchange Rate , 2002 .

[3]  Bruce A. Babcock,et al.  Agricultural Land Elasticities in the United States and Brazil , 2011 .

[4]  Clive W. J. Granger Implications of Aggregation with Common Factors , 1987 .

[5]  M. Piketty,et al.  Towards a better estimation of agricultural supply elasticity: the case of soya beans in Brazil , 2012 .

[6]  Cheng Hsiao,et al.  Analysis of Panels and Limited Dependent Variable Models , 1999 .

[7]  Paul Scott Dynamic Discrete Choice Estimation of Agricultural Land Use , 2014 .

[8]  Futures Prices in Supply Analysis Reconsidered , 2013 .

[9]  Arthur Lewbel,et al.  Aggregation and Simple Dynamics , 1994 .

[10]  A. Bernard,et al.  Why Some Firms Export , 2001, Review of Economics and Statistics.

[11]  Cheng Hsiao,et al.  Bayes Estimation of Short-run Coefficients in Dynamic Panel Data Models , 1998 .

[12]  David Roodman,et al.  A Note on the Theme of Too Many Instruments , 2008 .

[13]  Carlos M. Carvalho,et al.  Aggregation and the PPP Puzzle in a Sticky-Price Model , 2008 .

[14]  M. Holt,et al.  Economic Behavior under Uncertainty: A Joint Analysis of Risk Preferences and Technology , 1996 .

[15]  Marc Nerlove,et al.  The Dynamics of Supply: Estimation of Farmers' Response to Price. , 1960 .

[16]  H. Askari,et al.  Estimating Agricultural Supply Response with the Nerlove Model: A Survey , 1977 .

[17]  P. Zaffaroni Contemporaneous aggregation of linear dynamic models in large economies , 2004 .

[18]  Zhengwei Yang,et al.  Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program , 2011 .

[19]  P. Trivedi Distributed Lags, Aggregation and Compounding: Some Econometric Implications , 1985 .

[20]  W. Schlenker,et al.  Identifying Supply and Demand Elasticities of Agricultural Commodities: Implications for the Us Ethanol Mandate , 2010 .

[21]  Albert K. W. Yeung,et al.  Concepts And Techniques Of Geographic Information Systems , 2002 .

[22]  W. Huffman,et al.  Dynamic Corn Supply Functions: A Model with Explicit Optimization , 1988 .

[23]  Erik Lichtenberg,et al.  Agriculture and the environment. , 2000 .

[24]  Yue Zhang,et al.  Optimal Sequential Plantings of Corn and Soybeans Under Price Uncertainty , 2015 .

[25]  John Rust Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher , 1987 .

[26]  David Hennessy,et al.  On Monoculture and the Structure of Crop Rotations , 2006 .

[27]  Jacinto F. Fabiosa,et al.  Use of U.S. Croplands for Biofuels Increases Greenhouse Gases Through Emissions from Land-Use Change , 2008, Science.

[28]  Z. Eckstein A Rational Expectations Model of Agricultural Supply , 1984, Journal of Political Economy.

[29]  M. Pesaran,et al.  Estimating Long-Run Relationships From Dynamic Heterogeneous Panels , 1995 .

[30]  C. Granger Long memory relationships and the aggregation of dynamic models , 1980 .

[31]  P. Zaffaroni,et al.  Can aggregation explain the persistence of inflation , 2009 .

[32]  Christopher J. Kucharik,et al.  Corn-based ethanol production compromises goal of reducing nitrogen export by the Mississippi River , 2008, Proceedings of the National Academy of Sciences.

[33]  Donald Robertson,et al.  Some strange properties of panel data estimators , 1992 .