Why should a cattle rancher in Texas care about a severe drought hitting the Corn Belt states? Although genuine sympathy could be part of the answer, the main reason might be less charitable: cattle farmers in Texas are major buyers of corn from the Midwest. A drought in the Corn Belt would increase the corn price and, in turn, increase the cattle farmers’ cost of production. Indeed, many Texas cattle farmers lost a fortune because of a severe drought in the Midwest in 2012 (Ray, 2012). In the long run, changes in the climate and in its volatility are expected to lead to changes in the types of locally produced goods and commodities, in the places where current goods are produced, and in the magnitude and directionality of the trade flows, including the supply chain linkages. This short essay focuses on the trade flows.
Interstate trade of commodities, agricultural goods, and prepared foodstuffs is large. According to the Commodity Flow Survey (CFS) from the U.S. Census Bureau, agricultural products represented $1,222,497 million in 2012, i.e. 9 percent of all interstate trade. Although U.S. states trade these products to various extents (California, Illinois, Ohio are the largest exporters while Texas, Illinois and Pennsylvania are the largest importers), 75 percent of the states export at least 46 percent of their locally produced goods to other states and abroad. They also import at least 43 percent of their local consumption from other states.
Traditional international trade theory would say that these figures are to be expected. Indeed, the Hecksher-Ohlin model (Feenstra, 2004), predicts that different factor endowments and free factor mobility are at the origin of regional comparative advantages, leading to regional specialization in the good that intensively uses the most abundant local factor. For instance, sun-bathed Florida produces more oranges than any other state in the nation. In the case of agriculture, the differences in factor endowments emerge from the diversity in soil conditions and climate characteristics present across the nation.
Furthermore, trade is also driven by the consumers’ “taste for variety” highlighted in the Dixit-Stiglitz (1977) model of monopolistic competition whereby locally- and non-locally made products may be similar but still perceived as different. Apples are an example. While nearly 70 percent of the apples produced nationwide come from the state of Washington, many other places, including Illinois, produce their own apples and sell them alongside imports from Washington.
In spite of these well-established theoretical models and of the overwhelming empirical evidence, the econometric literature focusing on the impact of the climate – and of its change – on the U.S. agriculture has completely ignored the role of trade (e.g. Schlenker et al., 2005, 2006; Mendelsohn et al., 1994; Deschênes and Greenstone, 2007). Local production is assumed to depend on a set of local variables only as if all of it were to be consumed locally.
From a statistical point of view, the omitted variable bias due to ignoring interregional dependence leads to biased and inefficient estimates, hence to erroneous conclusions on the role of climate on agriculture. Since the large majority of the above contributions use these estimates in conjunction with projected future climate conditions to predict future agricultural conditions, it is likely that such predictions are biased as well. Some recent contributions have attempted to control for interregional dependence (e.g. Polsky, 2004; Lippert et al., 2009; Dall’erba and Dominguez, 2015) but dependence is based on geographical proximity only and is justified by the social network of farmers and similarities in nearby climate, soil and water access characteristics.
Yet, trade which is a much larger driver of interregional dependence does not necessarily take place across nearby regions. For instance, the three major import sources for Texas are California, Iowa, and Nebraska (CFS data). Furthermore, a geography-based definition of “partners” or “neighbors” leads to symmetric interactions (the distance from i to j is the same as from j to i) that do not change over time. On the other hand, the actual interstate trade flows of agricultural goods and commodities are hardly symmetric (Wyoming imports more from California than the other way around), they are dynamic, and they clearly report the magnitude and directionality of the flows.
Taking stock of the importance of local and non-local demand for a state’s agricultural output, ongoing work by Chen et al. (2016) demonstrates how a farmer’s profits depend not only on the conditions experienced locally (climate, soil, production process, demand), but also on the conditions experienced within the trade partners. The agricultural specialization that each farmer chooses is based on its own and others’ comparative advantages; and the difference thereof is at the core of the interstate trade they observe. Once derived, their theoretical model leads to a so-called spatial cross-regressive model (Vega and Elhorst, 2015) which can then be estimated in a panel setting across 48 U.S. continental states over the last four Agriculture Censuses (1997, 2002, 2007 and 2012).
Results confirm the authors’ expectations that agricultural output in one location is a function of local characteristics and of characteristics of their trade partners, especially their partner’s climate. Trade acts as a substitute for the lack of non-local climate conditions and satisfies the local consumers’ demand for variety. Under new climate conditions that will disrupt the current regional system of comparative advantage, producers will be obliged to modify their production process, their production choices and to export to new partners. Trade will therefore be used more increasingly as an adaptation mechanism to climate change (Reilly and Hohmann, 1993; Taigas, Frisvold and Kuhn, 1997; Julia and Duchin, 2007). As a result, we anticipate that defining strategies to proactively adapt the current food supply chain will capture the attention of scholars, farmers and stakeholders alike in the decades to come.
Anselin L., Bera A., Florax R.J.G.M., Yoon M. (1996) Simple Diagnostic Tests for Spatial Dependence, Regional Science and Urban Economics, 26, 77–104.
Chen Z., Dall’erba S. and Fang F. (2016) The Ricardian model of climate change impact meets the Ricardian model of interregional trade: theory and evidence, REAL working paper.
Dixit, Avinash K.; Stiglitz, Joseph E. (1977). “Monopolistic competition and optimum product diversity”. The American Economic Review. 67, 3, 297–308.
Mendelsohn R., Nordhaus W.D. and Shaw D. (1994) The Impact of Global Warming on Agriculture: A Ricardian Analysis, The American Economic Review, 84, 4, 753-771.
Dall’erba S. and Dominguez F. (2015) The Impact of Climate Change on Agriculture in the South-West United States: the Ricardian Approach Revisited, Spatial Economic Analysis, 10, 4, 1-19.
Deschênes O. and Greenstone M. (2007) The Economic Impacts of Climate Change: Evidence from Agricultural Output and Random Fluctuations in Weather, The American Economic Review, 97, 1, 354–385.
Feenstra, Robert C. (2004). “The Heckscher–Ohlin Model”. Advanced International Trade: Theory and Evidence. Princeton: Princeton University Press. pp. 31–63
Lippert C., Krimly T., Aurbacher J. (2009) A Ricardian Analysis of the Impact of Climate Change on Agriculture in Germany, Climatic Change, 97, 593–610.
Polsky C. (2004) Putting Space and Time in Ricardian Climate Change Impact Studies: The Case of Agriculture in the U.S. Great Plains, Annals of the Association of American Geographers, 94, 3, 549-564.
Schlenker W., Hanemann W. M. and Fisher A.C. (2005) Will U.S. Agriculture Really Benefit from Global Warming? Accounting for Irrigation in the Hedonic Approach, The American Economic Review, 95, 1, 395 – 406.
Schlenker W., Hanemann W.M. and Fisher A.C. (2006) The Impact of Global Warming on U.S. Agriculture: an Econometric Analysis of Optimal Growing Conditions, Review of Economics and Statistics, 88, 1, 113–125.
Tsigas, M., G. Friswold, and B. Kuhn. 1997. Global Climate Change and Agriculture, Global Trade Analysis: Modeling and Applications. Cambridge: Cambridge Univ. Press.
Ray J., (2012, Aug. 22th) Midwest Drought Means Less Corn For Texas Cattle, Retrieved from http://dfw.cbslocal.com/2012/08/22/midwest-drought-means-less-corn-for-texas-cattle/
Reilly, J., & Hohmann, N. (1993). Climate change and agriculture: the role of international trade. The American Economic Review, 83, 2, 306-312.
Vega S.H. and Elhorst J.P. (2013) On Spatial Econometric Models, Spillover Effects, and W, Working Paper.