the corn price surge: impacts on rural mexico

World Development Vol. 39, No. 10, pp. 1878–1887, 2011� 2011 Elsevier Ltd. All rights reserved.

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The Corn Price Surge: Impacts on Rural Mexico

GEORGE A. DYERThe James Hutton Institute, Aberdeen, UK

and

J. EDWARD TAYLOR *

University of California at Davis, USA

Summary. — We use an agent-based, general-equilibrium model to explore the impacts of world corn-price increases on land use andincome in rural Mexico. In the model, interactions among heterogeneous agents within a local context shape both macro and microeco-nomic outcomes. Results suggest that subsistence activities allowed agriculture to absorb the shock, limiting the benefits of higher pricesfor the population while keeping deforestation pressures in check. An estimated 5.7% corn-area expansion by 2008 and wide variationacross regions corresponds well with ex-post reports. Agricultural growth led to 0.02% and 3.9% increases in real income for rural house-holds and absentee landholders, respectively.� 2011 Elsevier Ltd. All rights reserved.

Key words — food, land use, maize, Latin America, Mexico, biofuels

* We would like to thank Mateusz Filipski and Peri Fletcher for their

valuable comments and the William and Flora Hewlett Foundation

and Inter-American Development Bank for providing support for this

research. Taylor is a member of the Giannini Foundation of Agricul-

tural Economics. Final revision accepted: March 3, 2011.

1. INTRODUCTION

According to the Food and Agriculture Organization, overa billion people were undernourished in 2009, more than atany time since 1970 (FAO, 2009). Access to food decreasedfor many due to a combination of low incomes, unemploy-ment, and high food prices, which began rising in 2003 afterdecades of decline (Abbott, Hurt, & Tyner, 2009; FAO,2009; OECD-FAO, 2010). Wheat, rice, and coarse cerealprices reached record highs during the global food crisis of2008, peaking at between 60% and 80% above their levels ofthe previous decade (OECD-FAO, 2010). Among the factorsdriving this price surge were changes in world production/uti-lization ratios that decimated inventories, the low value of thedollar, and growth of the biofuels industry (Abbott et al.,2009; FAO, 2009; Hochman, Rajagopal, & Zilberman,2010). Poor households’ ability to cope with the adverse termsof trade was reduced further during the global economic crisisin late 2008, but food prices declined rapidly in the wake ofthis second shock (Abbott et al., 2009; FAO, 2009). Some de-gree of normalcy returned to international food markets; how-ever, crop prices remain unpredictable due to various factors,including high growth rates in some low and middle incomecountries, national policies, and international energy prices(Abbott et al., 2009; OECD-FAO, 2010). Average crop pricesare expected to remain well above their levels prior to thesurge; evidence on long-term price volatility necessarily re-mains inconclusive, but short term volatility has increasedsince the surge (Abbott et al., 2009; OECD-FAO, 2010).

There is a widespread awareness that these changes will affectincome and food security, but how remains uncertain. It is clearthat price volatility increases the risks for vulnerable house-holds, but it is not entirely evident which groups benefit or sufferfrom particular commodity-price changes (FAO, 2009; WorldBank, 2007). Received wisdom is that high prices benefit surplusfarmers, while low prices benefit the urban poor. But this is onlya partial picture. Our appreciation of the processes by whichchanges in international commodity prices influence the welfareof different economic agents remains limited. More generally,our understanding of how the macro and micro scales connect

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within economic systems is still lacking. Little is known abouthow macro forces seep into micro scales, or how micro re-sponses integrate to shape macroeconomic outcomes. Gen-eral-equilibrium analyses reveal important economy-widefeedbacks associated with crop-price changes, but they com-monly ignore the behavior of individual economic agents, theinteractions among them, and the ramifications on these agents’wellbeing (Polasky, Ganesh-Kumar, McDonald, Panda, &Robinson, 2008; Taylor, Dyer, & Yunez-Naude, 2005). Aggre-gate models thus yield limited insight into processes and out-comes at the level of households.

Commodity price transmission from international to domes-tic markets differs widely across regions (OECD-FAO, 2010).Exchange rate fluctuations, trade restrictions, infrastructuredevelopment, domestic policies, and price supports determineprices observed by individual producers and consumers. Pricevariation also depends on household characteristics(De Janvry, Fafchamps, & Sadoulet, 1991). Analyses of thedistributive effects of price changes, therefore, must accountfor household heterogeneity in price transmission and decisionmaking. The analytical challenge is to reconcile this heteroge-neity with explanations of macroeconomic behavior as a cohe-sive response to a single set of price signals. This paperpresents a modeling approach that takes into account the het-erogeneous transmission of international shocks to individualagents. It describes how agents’ individual responses integrateback onto larger scales to shape aggregate supply responsesand land-use patterns. The approach is built upon an agent-based, general equilibrium model of rural Mexico, used hereto explore the impacts of the corn price surge of 2007. Ourfindings highlight the role of local market conditions andinteractions among individual agents, and they offer insights

http://dx.doi.org/10.1016/j.worlddev.2011.04.032

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THE CORN PRICE SURGE: IMPACTS ON RURAL MEXICO 1879

into the potential implications on the distribution of costs andbenefits and land use.

(a) Crop prices and supply response in Mexico

Analysts anticipated that the demand for feedstocks from arapidly expanding biofuel industry would generate considerableincreases in crop prices in some regions by 2020 (Elobeid & Hart,2007; Hazell & Pachauri, 2006; Searchinger et al., 2008). Risingfood consumption demands in high-growth developing econo-mies, particularly China, added to price pressures. Mexicowas expected to bear some of the most adverse impacts of pricesincreases, particularly for corn (Elobeid & Hart, 2007). Corn is astaple in Mexico; per capita consumption is one of the highestworldwide, and the country is the second largest importer ofUS corn. Imports represent a considerable share of domesticconsumption. Moreover, a weak dollar cannot buffer producersor consumers in Mexico from price increases, as in other coun-tries, because the peso is closely tied to the dollar. Due to a com-bination of factors, world crop-price increases becamewidespread much sooner than expected (Abbott et al., 2009;FAO, 2009; Hochman et al., 2010). Mexico was among the firstcountries affected by the price surge, thus becoming the posterchild of the ensuing food crisis (Runge & Senauer, 2007; vonBraun, 2007).

Aggregate models commonly predict a strong corn-supply re-sponse in Mexico and substantial land-use change in response tointernational price shocks (Brooks, Dyer, & Taylor, 2008;Searchinger et al., 2008; Westhoff & Thompson, 2007). Butthe response of the Mexican corn sector to the recent shockwas surprisingly modest. Interactions within the remarkablyheterogeneous corn sector are part of the reason (Dyer, 2008).

Domestic policies are said to interact through global mar-kets, influencing trade outcomes (Hertel, Rose, & Tol, 2009).A strong interaction of EU and US biofuel mandates is ex-pected to amplify impacts on agricultural rents, resulting ina marked expansion of cropland within these two blocks aswell as in third countries like Mexico (Gurgel, Reilley, & Palt-sev, 2007; Hertel et al., 2009; Searchinger et al., 2008). Interac-tions among individual producers also can shape supplyresponses, but most analyses are too aggregate to accountfor these interactions and the feedbacks that they generate.In aggregate models, all agricultural producers within an econ-omy are subsumed by a single representative agent that reallo-cates production factors across crops and land uses (Elobeid &Hart, 2007; Gurgel et al., 2007; Hertel et al., 2009; Searchingeret al., 2008). Aggregation is largely innocuous under the stan-dard neoclassical representation of the economy with perfectmarkets, which most models assume (Gurgel et al., 2007).Aggregate models were developed in industrialized economies,where relatively homogeneous agents respond to similar setsof signals in well-developed markets using standard technolo-gies. Interactions among similar agents (e.g., farms) are con-sidered inconsequential and sidelined in favor of simpleassumptions concerning price determination and market re-sponses. In developing areas, in contrast, the abundance ofmarket imperfections and striking heterogeneity across pro-ducers create circumstances in which outcomes can dependon interactions among individual economic actors (Bonabeau,2002). Our results highlight the cost of ignoring those interac-tions when addressing the impacts of international crop prices.

0.01980 1985 1990 1995 2000 2005

(

Consumer Producer Imports

Figure 1. Corn prices in Mexico (1980–2008). Source: SIAP (2008),

Banco de Mexico (2008).

2. MODELING FRAMEWORK

Our analysis is based on a static, agent-based, general equi-librium model of the Mexican rural economy. The model pre-

sented here is an expanded and revised version of a previousmodel designed to analyze the liberalization of agriculturaltrade and policy, with an emphasis on land-use change (Dyer,Ceron, Lopez-Feldman, Moreno, & Yunez, 2007). The origi-nal model was used in early 2007 to anticipate what the conse-quences of the then incipient corn-price surge might be forMexican farmers. Although consumer prices had risen consid-erably, producer prices had not (Figure 1). In fact, farmers hadreceived record low prices for the previous fall’s harvest (Dyeret al., 2007). The revised version of the model was run in late2007 using producer prices reported for that year (Dyer, 2008).A sharp price increase had been documented, but data on pro-ducer responses were still unavailable; the model offered anestimate of what those responses might be. Aggregate govern-ment statistics of corn-area changes in 2008 are now public,and the model’s aggregate estimates compare favorably withthem (see below).

This baseline simulation reveals interesting insights into howprice shocks translate imperfectly into land-use changes.Although estimates for a given year are of no particular inter-est ex post, they provide validation for the model, enabling usto analyze the impacts of price surges on disaggregate out-comes (for which data are not available) with greater confi-dence. Our interest in this paper is to analyze themicroeconomic aspects of the aggregate (macroeconomic) pat-terns known so far. Aggregate statistics are available, buthousehold level data are scarce. It would be difficult, in anycase, to attribute changes in household-level income in ruralMexico to the corn price surge. Not surprisingly, few in-depthanalyses of the surge’s implications for rural wellbeing andland use are available. The results from our structural model-ing suggest what these implications were, while highlightingthe complexity of the processes that determine household-levelimpacts of crop price changes.

(a) Model and data

General equilibrium models (CGE) describe the functioningof an entire economy defined by the determination of pricesand output based on supply and demand equilibrium. CGEmodels can represent the global economy or a household econ-omy where output and shadow prices of subsistence activitiesare determined jointly. The model used here nests multiplemodels of individual rural households and absentee producers

1880 WORLD DEVELOPMENT

into one of five regional models, which in turn are nested intoa model of all rural Mexico linked to the world economythrough trade and migration. This three-level modelingapproach provides much greater detail and flexibility thanaggregate CGE models, allowing us to incorporate differencesin prices, production technologies and market participationacross households and regions that are vastly heterogeneousin terms of microeconomic behavior. For instance, a house-hold that does not participate in product markets may sellits labor to other households or employ wage labor in subsis-tence crop production using a technology that is distinct fromthat of other households. Each household also has distinct de-mands for market goods, and its valuation of the subsistencecrop is represented by a household-specific shadow price(Dyer, 2008; Dyer, Taylor, & Boucher, 2006; Taylor et al.,2005).

(i) First level (household) equations and constraintsIn a first stage, rural households in each region are grouped

into four types: (a) landless households, (b) small-holders(<2 ha), (c) medium-holders (2-5 ha) and (d) large-holders(>5 ha). This typology works well to describe the socioeco-nomic landscape of rural Mexico (Taylor & Dyer, 2009). Eachtype is modeled and calibrated independently (see Taylor etal., 2005). The basic household model is similar to the modeldescribed by Singh, Squire, and Strauss (1986) when all goodsand variable inputs are tradable; it resembles the model of deJanvry et al. (1991) when one or more markets are missing. Asin those models, households are assumed to maximize theirutility of consumption, Uh ¼ UhðX h

i Þ; where X hi is household

h’s demand for good i = 1,. . ., I. The household is subject toseveral constraints, including a self-sufficiency constraint thatsets consumption equal to production for subsistence house-holds. The constraints include:

(1) A cash income constraint: Y h ¼P

iP iX hi ; where Yh is

the household’s income, the sum of net output, factor pay-ments, remittances and exogenous income, and Pi is theprice of good i.(2) Production Technology Constraints: Qh

i ¼ QiðFDhi ; V

hi Þ;

where Qhi is the household’s output of good i, FDh

i is a vec-tor of f factor inputs whose elements are FDh

i;f (f = labor,

land and capital); and V hi represents intermediate inputs.

Production is represented by Cobb-Douglas productionfunctions, where exponents are equal to measured factorshares in value added. We adopt the usual assumption inagricultural-household models that capital is fixed. Land,however, is not fixed, within the limits imposed by theimperfect transformability of land from one use to another(see below). Following the economy-wide modeling tradi-tion, constant input–output ratios are assumed for other(intermediate) inputs. Unlike aggregate CGE models, how-ever, these input–output ratios vary widely across house-hold groups and regions, reflecting constraints (e.g.,liquidity on small farms) and other considerations limitinginput (e.g., fertilizer) demands.(3) Endowments: �T f ¼

PiFDh

i;f þ FShf þMIGh

f ; where FDhi;f

denotes the household’s demand for factor f in productionactivity i; FSh

f is the household’s net supply of factor f toregional markets; MIGh

f is its net supply outside the region(for example, labor migration); and T f is the household’stotal factor endowment.(4) Remittance Functions: Rh

f ¼ /hf ðMIGh

f Þ:

The first-order conditions for utility maximization include(a) marginal value product equals price for all f factors:

P i@Qi=@FDhi;f ¼ wf ;i; where wf ;i, a factor’s price in activity i,

equals either wf , regional prices for factors that are mobileacross production activities and households (in our simula-tions, these are labor and land), or xf ;i, shadow price of fac-tors fixed by activity and household (capital); (b) marginalremittances (the marginal value product of migration for thehousehold) equal the regional wage @Rh

f =@MIGhf ¼ wf ; and

(c) marginal utility equals the marginal utility cost of con-sumption: Uh

i ¼ khP i; where kh is the marginal utility ofincome. Cash income constraints are binding:Y h �

PiP iX h

i ¼ 0. In subsistence households, demand cannotexceed supply for non-traded goods: Qh

i ¼ X hiþ

V hi ; i 2 nontradables. Unobserved household-specific “shadow

prices,” together with supply and demand, adjust to insurethat the subsistence constraints are met. These first-order con-ditions yield consumption-demand functions of the form:X h

i ¼ X hi ðP i; Y hÞ, which are represented here by a linear expen-

diture system (LES) with no minimum required quantities.The parameters in the demand equations are set equal tomeasured budget shares for each household and good.

(ii) Second level (regional) integrationIn a second stage, household types are integrated with

absentee landholders in each region. This yields five regionalmodels. Absentee (i.e., non-rural) landholders are individualand corporate producers or landowners based outside ruralareas but that nonetheless participate in rural markets; theyaccount for most of the corn grown in Mexico. Wages andland rents are determined at this stage because transactioncosts across regions create rate disparities. General-equilib-rium conditions include equilibrium in every region’s factormarkets:

PhðFSh

f �P

iFDhi;f Þ ¼ 0. A second condition implies

material-balance equations for every good i and all householdsh in each region: MSi ¼ Qh

i �P

hðX hi þ V h

i Þ, where MSi is netregional marketed surplus. The regional trade balance is im-plied by this together with the other equations in the model:P

iP iMSi þP

hRhf ¼ 0.

Given the importance of factor markets in our analysis, themodel was modified to incorporate explicit supplies of landand migrant labor. Remittance elasticities were estimatedeconometrically using survey data and controlling for migra-tion selectivity (Taylor & Dyer, 2009). Elasticities vary acrosshousehold groups and regions, reflecting differences in accessto foreign and domestic labor markets. The supply of landacross uses was restricted through a nested constant elasticityof transformation (CET) function to reflect the sluggishness ofland-use change (Ahammad & Mi, 2005; Hertel et al., 2009;OECD, 2005). 1 The function was calibrated using survey data(see below) and econometric estimates available in the litera-ture (Abler, 2001; OECD, 2005; Yunez-Naude, 2006). 2

Abler (2001) sets the own-price elasticity of land supply forindividual crops in the North American region at 0.4 (with arange of 0.2–0.6), which implies an elasticity of transformationamong crops (r3) equal to �0.58. A more recent study uses aprice elasticity of 0.145 for coarse grains in Mexico, which im-plies r3 = �0.21 (OECD, 2005). 3 We estimate r3 based onAbler’s (2001) own-price elasticity and our estimates of the va-lue share of corn land in Mexico, adjusting for differences inthis share and in land-use transition probabilities across re-gions (Dyer, 2008). Since the probability of transitioning outof corn is several times higher in the northwest than in thesoutheast (Yunez-Naude et al., 2006), we set the own-priceelasticity of land supply in these regions at opposite ends ofthe range proposed by Abler. Our estimates of r3 for differentregions fall between �0.35 and �0.75, which is well within therange of previous country-wide studies. Abler (2001) also re-

Table 1. Estimated percent changes in corn area in 2008

Government estimatesa Simulation estimatesb

Mexico 3.5 5.7

Region

Southeast �5.1 1.6Center 1.8 1.7West-center 9.9 9.0Northwest 12.3 15.8Northeast 13.9 14.4

a Source: SIAP (2008).b Simulation estimates correspond well with ex post government estimatesin every region but the southeast, where corn area reportedly decreaseddue to a 20% drop in the state of Chiapas. We suspect that this reflects anerror in the federal government’s reporting. In 2008, corn output reachedrecord highs in Chiapas (SIAP). The state government reportedly sup-ported “around 800,000 ha” of corn, compared to the 671,617 ha total incorn reported by the federal government (Gobierno de Chiapas, Comu-nicado 3621, October 12th, 2008).


ports a value of 0.1 for the elasticity of supply of cropland inthe North American region (with a range of 0.0–0.2), implyingthat the elasticity of transformation between agriculture andpasture in Mexico is low: r2 = �0.14. This is reasonable innorthern Mexico, where livestock grazes in arid lands whileagriculture is largely irrigated, but it probably underestimatesthe elasticity in the rainfed southeast, where land-use transi-tion probabilities between agriculture and pasture are consid-erably higher than in the rest of the country. We use Abler’sown-price elasticity and our estimates of the value share ofcropland to estimate r2 in every region but the southeast,where we assume that the elasticity of transformation betweenagriculture and pasture is the same as that among crops; thatis, r 2 = r3 = �0.35. 4 Given that there are no estimates of theelasticity of transformation of land between agriculture andforests (r1) in Mexico, we stick to a two-tiered CET functionrather than imposing on it an arbitrary value. 5 This impliesthat we do not model changes in forest cover explicitly but relyon estimates of rents at the forest margin to assess changes inland-use pressure.

(iii) Third level (economy-wide) constraintsIn a final stage, the five regional models are integrated into a

model of rural Mexico. Given the integration of North Amer-ican agricultural markets, agricultural prices are exogenous tothe model, but material-balance equations imply that the sumof surpluses of good i for all regions r be equal to net exports:P

rMSri ¼ NX i. Similar conditions apply for migration and fac-

tor balances.Our data on non-rural agriculture (i.e., the activities of

absentee or non-rural producers) were derived from theMexican government’s Agricultural Information System(SIAP) after adjusting for the share of production pertainingto rural producers (Dyer, 2008). Data on non-rural technolo-gies were derived from input–output matrixes for differentregions (Yunez-Naude et al., 2006). The 2003 Mexico NationalRural Household Survey (Encuesta Nacional a Hogares Rur-ales de Mexico or ENHRUM) was the source of data for ruralagents. The survey’s sampling frame provides a statisticallyreliable characterization of Mexico’s population living in ruralareas in 2002, that is, in communities with fewer than 2,500inhabitants. The survey contains detailed data on assets,endowments, productive activities and market participationfor 1765 households. Net incomes for households in the sam-ple were estimated based on all household activities and otherincome sources, including agricultural and non-agriculturalactivities; on- and off-farm wage labor; migration; and publictransfers. The sum of income from these sources equals house-hold total net income. Summary statistics for all householdgroups are found in Dyer (2008) and Taylor and Dyer (2009).

(b) Scenario simulation

Our interest is in simulating the effect of corn-price increasesexperienced by Mexican producers before the start of the 2008cycle. SIAP reports substantial variation in producer pricesacross regions, but survey data show equally significant varia-tion across producers in every region (Dyer, 2008). Some de-gree of parsimony nevertheless is necessary when modelingcountry-wide responses to an international-price shock. De-spite regional differences, producer prices have moved in thesame general direction across Mexico, but consumer priceshave exhibited a surprisingly different trend ( Figure 1). Inthe fall of 2007, average producer corn prices were up 23%from a record low a year before, while consumer prices in-creased 5% during the same period (Banco de Mexico, 2008;

SIAP, 2008). Therefore, we focus on price changes as experi-enced by the following groups: commercial (i.e., surplus)producers linked to commodity markets, commercial produc-ers selling directly to consumers, and subsistence farmers.

According to ENHRUM survey data, around 75% of sur-plus corn growers in west-central, northeast and northwestMexico sell corn at or below average producer prices (Dyer,2008). This group is tied to international commodity markets.In contrast, 75% of surplus farmers in the central and south-east regions sell directly to consumers and receive prices upto 100% higher (Dyer, 2008). Accordingly, we assume thatcommercial producers in west-central and northern Mexicoexperienced a 23% increase in producer prices, while commer-cial producers in the rest of the country experienced only the5% increase in consumer prices. Finally, subsistence producersin every region respond largely to household-specific shadowprices (Arslan & Taylor, 2009).

3. IMPACT OF THE PRICE SURGE ON OUTPUT,LAND-USE AND WELLBEING

According to our simulations, corn land expanded 5.7%across Mexico in 2008; however, regional responses variedwidely. Corn land expanded almost 16% in northwest Mexicobut only 1.6% in the southeast (Table 1). Simulation resultsdraw light on the mechanisms responsible for regional differ-ences.

Results show that the price surge created surplus rents (i.e.,profits) for corn farmers, generating a disparity across sectorsthat drew production factors into corn. As farmers convertedland into corn and the sector expanded, rents diminished incorn while increasing in other crops (i.e., factor reallocation al-lowed surplus rents to dissipate); but some disparities persisteddue to short-run restrictions on land conversion and differ-ences in land quality. Rent disparities also surfaced withinthe corn sector across regions: rents in corn increased fivetimes more in the northeast and northwest than in centralMexico (Table 2).

Regional disparities are closely tied to changes in prices butalso wages, which affect land rents indirectly. Since it is theshort-run that we model, wages are driven up by the rising de-mand for labor in corn but not influenced by changes in pro-ductivity, which are visible only in the long run. Estimatedwage increases ranging from 2.0% in west-central Mexico to

Table 2. Percentage change in rents and wages in 2008

Rents Wages

Corna Other cropsa Cropland rental ratesb Livestock Opportunity costs of forest landc

Southeast 6.5 0.0 3.0 0.2 2.0 0.2Center 5.7 �0.1 0.4 0.1 0.3 0.1West-center 23.4 �0.1 5.7 0.9 5.2 2.0Northwest 27.8 0.0 8.4 0.6 7.2 1.9Northeast 30.8 0.1 1.1 0.0 0.4 0.0

a Corn, other crops and livestock rents are surplus rents (i.e., profits) partly appropriable by producers.b Rental rates for cropland are equal to the composite cropland rents in the model.c The opportunity costs of land at the forest margin are composite rents for all agricultural land, that is, cropland plus pastures.


negligible in the northeast reflect, among other factors, the sizeof the corn sector relative to the local economy and workers’access to distant labor markets via migration. As wages in-creased, land rents decreased, and profits fell, partly offsettingthe shock’s direct effect on rents (Table 2). As a result, rents inother labor-intensive sectors experienced only minute changesafter the price surge. Significant corn rents drove up the rentalrate for cropland nevertheless. This drew pasture into cultiva-tion in every region, pushing up rents in the livestock sector,and eventually leading to an increase in the composite rentfor agricultural land, which ultimately reflects the opportunitycost of land at the forest margin (Table 2).

Thus far we have referred only to aggregate responses to theprice surge, but these were actually derived by aggregatingindividual responses in the model. Individual responses variedwidely across and within regions, reflecting not only differ-ences among producers but also market interactions betweendifferent producer groups as they adjusted to the price surge.We aggregated individual producers into five groups in eachregion—four rural household groups and absentee landown-ers/producers, whom we call non-rural landholders (see Sec-tion 2). The latter include Mexico’s largest commercialproducers. Each group’s response to the price surge reflectsits participation in corn markets or, in the case of subsistenceproducers, its lack of participation (Table 3). Commercial pro-ducers in every region responded directly to the surge, reallo-cating land and labor within their own farms. In contrast,subsistence growers did not experience a direct change in theircrop’s value, since they are guided by household-specific sha-dow prices (Arslan & Taylor, 2009; de Janvry et al., 1991;Dyer, 2008). Nevertheless, no one was entirely isolated fromthe price shock, inasmuch as local rents and wages increasedas commercial producers demanded more land and labor.Not everyone was equally affected. Wages and rents raisedproduction costs for large-holders, dampening their supply re-sponse. They also raised the opportunity costs of family landand labor for small-holders, increasing the cost of corn forhome consumption. Individual supply responses in the modeldiffered because each producer responded to a particular com-bination of price, rent, and wage changes based on farm-spe-cific production technologies. Taken together, these disparatemicro responses resulted in a muted aggregate supply responseto simulated price increases (Table 3).

We can unravel the relationship between individual andaggregate behavior further by focusing on factor use and rentsin each region. In west-central Mexico, for instance, individualproducer groups adjusted their output by as much as 26%(landless, renter farmers), yet aggregate regional output grewby only 6.4% (Table 4). Since every group observed a differentset of price signals, rent disparities arose not only across sec-tors within the region but also across producers, with impor-

tant ramifications for the land rental market. In order tocapture surplus rents, absentee landholders, who account formost corn production, raised cropland rental rates consider-ably. But they also moved land out of the rental market togrow corn, at which they are more efficient than their ruraltenants. While use of corn land on non-rural farms expanded21.4%, it declined 14% on rural farms. Agricultural laborfollowed land from rural to non-rural farms (Table 4). Someof these workers were responding to an increase in wages; oth-ers were released from subsistence households, where the de-mand for family labor in subsistence production fell. Takingadvantage of relatively stable consumer prices, rural house-holds in the model increasingly opted for purchasing corn inlocal markets and working for a wage, rather than growingcorn for home consumption. As a result of the surge, inwest-central Mexico subsistence corn production (mostly bysmall and medium-holders) fell 23%, while commercial cornagriculture (mostly by rural large-holders and non-rural pro-ducers) grew 17% while employing additional cropland and la-bor.

West-central Mexico occupies an intermediate position inrural Mexico in more than a geographical sense. It combinescharacteristics of agricultural sectors in the rest of the country,where either commercial or subsistence farms tend to domi-nate the landscape. In the Northwest, the simulation suggeststhat widespread mechanization, high yields, and access tomarkets allowed most farms to boost corn surpluses in re-sponse to a 27.8% increase in corn rents (Table 2). Corn pro-duction spread as much on rural as on non-rural farms (Table4), increasing total corn land by 15.8% (Table 1) and croplandrental rates by 8.4% (Table 2). In contrast, corn area expandedonly one tenth as much in southeast and central Mexico,where subsistence households ceded use of rented corn landto non-rural producers in response to a 3% increase in rentalrates (Tables 2 and 4). Subsistence households across Mexicooften face cash constraints and are very sensitive to cost in-creases.

In sum, simulation results suggest that supply responsesacross Mexico were shaped by local feedback mechanisms(that is, general equilibrium effects) that also determined thedistribution of production factors and value added acrossfarms, with implications on land use and income. Results sug-gest that the net value (i.e., value added) of Mexican agricul-ture might have grown 3% after increases in corn prices andoutput (Table 3); however, most value was created on non-rur-al farms, given the reallocation of production factors. If non-rural producers owned all factors of production, they mighthave appropriated this value fully; however, few farmers areself-sufficient in both land and labor. As a result, corn rentswere distributed across groups as economy-wide adjustmentstransformed them into profits for producers, higher wages

Table 4. Percentage response to corn price changes in different regions of Mexico in 2008

(a)Region(b + c)

(b)Non-ruralproducers

(c)Rural

households(d–g)

Rural households

(d)Landless

(e)Small-holders

(<2 ha)

(f)Medium-holders (2–5 ha)

(g)Large-holders (>5 ha)

West-central

Corn production 6.4 16.4 �6.2 �26.4 �15.6 �20.6 20.0Corn labor 0.3 6.1 �4.9 �7.9 �6.1 �4.9 1.8Corn land 9.0 21.4 �14.0 �40.4 �31.7 �35.7 37.3Real income 6.3 0.5 0.0 �0.5 �0.6 3.3

Northwest

Corn production 13.4 12.3 16.7 13.8 29.2 15.8 16.8Corn labor 0.1 4.6 �4.1 �3.5 �2.3 33.4 �4.4Corn land 15.8 15.7 15.9 11.5 36.8 16.0 16.1Real income 9.2 2.1 0.7 0.9 10.4 3.5

Southeast

Corn production 0.4 4.7 �0.5 � �4.1 �2.7 4.8Corn labor 0.1 0.9 �0.1 �0.4 �1.3 �1.1 1.2Corn land 1.6 5.2 �0.8 � �10.0 �8.6 8.1Real income 1.0 �0.6 0.0 �1.1 �0.5 �0.9

Central

Corn production 0.7 5.5 �2.3 – �2.4 �4.6 2.4Corn labor 0.0 0.2 �0.3 �0.1 �0.3 �0.4 �0.1Corn land 1.7 6.8 �5.2 0.0 �7.7 �9.8 3.8Real income 0.6 �0.5 �0.1 �0.6 �0.8 �0.4

Northeast

Corn production 14.1 10.3 18.5 �13.5 25.6 28.9 17.2Corn labor 0.1 0.1 0.0 �0.1 1.0 7.3 0.0Corn land 14.4 11.0 27.1 �33.9 51.0 91.9 26.6Real income 0.3 0.1 0.0 1.3 3.5 0.3

Table 3. Percentage response to corn price changes in Mexico in 2008a

(a)Mexico(b + c)

(b)Non-ruralproducers

(c)Rural

households(d–g)

Rural households

(d)Landless

(e)Small-holders

(<2 ha)

(f)Medium-holders (2–5 ha)

(g)Large-holders (>5 ha)

Production

Corn 5.8 12.2 0.0 �25.0 �5.0 �5.5 11.9Other crops �1.3 �0.5 �2.7 �6.0 �0.3 �0.4 �4.7Livestock �0.8 �0.6 �1.5 �2.1 �1.3 �1.1 �1.1Non-ag �1.4 �2.0 �1.9 – �0.9

Ag value added 3.0 3.9 0.9 �6.7 �2.5 �1.0 3.8

Factor use

Labor 0.1 1.9 �1.4 �1.7 �1.6 �0.9 �0.5Corn land 5.7 11.4 �2.2 �15.0 �8.1 �6.8 18.0Other cropland �1.5 �0.5 �4.0 �5.9 �0.3 �0.3 �5.4All cropland 0.2 1.8 �3.6 �13.2 �6.0 �2.0 �2.0Pasture �1.0 �0.8 �1.6 �1.5 �2.5 �2.9 �1.1

Income

Nominal income 3.9 0.5 0.1 0.0 0.1 2.1Real income 3.9 0.02 0.0 �0.8 �0.6 1.0

a Aggregate, nationwide responses to corn-price changes (a), are disaggregated into responses by absentee producers (b) and rural households (c), which inturn are disaggregated based across households (d–g) based on landholdings (see Section 2).


for workers, and land rents for landowners (Tables 2 and 3).Preexisting factor endowments determined how much of thisvalue reached each segment of the population. Some value

spilled from non-rural farms into rural areas as wage income,but a similar amount flowed back out in the form of rentalpayments. Since rental rates in our simulation increased up


to 8.4% but wages rose no more than 2%, the greatest benefitsaccrued to landowners, particularly in northeast Mexico,where wages increases were marginal (Table 2). In their roleas surplus corn growers and landowners, non-rural landhold-ers in the model were able to appropriate most of the benefitscreated by the price surge. Their income increased 3.9%nationwide and up to 9.2% in the northwest (Table 4). In con-trast, nominal rural incomes increased only 0.5%.

Impacts spread beyond producer groups, affecting consum-ers as well as non-agricultural employers who faced higherwages. As consumers of corn, most rural households experi-enced price increases that made rural income gains vanish inreal terms (Table 3). But income changes varied considerablyacross regions and household groups (Table 4). Corn profitscontributed disproportionately to large-holders’ income,which rose 1% in real terms. Significant increases in wage in-come for other groups barely offset losses due to the contrac-tion of subsistence activities. As a result, our results suggest,real income changes for subsistence households were negligibleor slightly negative.

4. RECONCILING MICRO AND MACRO

As noted above, our ex ante estimate of a 5.7% expansionwith marked differences across regions compares favorablywith ex post government estimates for the same period (Table1), which suggests that a disaggregated modeling approach canbe as reliable as aggregate models while offering distinctadvantages (see also Brooks et al., 2008). One of these is thepossibility of reconciling micro and macroeconomic explana-tions of aggregate processes. This is possible because, in adisaggregated model, macroeconomic behavior is construedas a result of individual decision-making. An additionaladvantage of a disaggregated approach is the ability to iden-tify winners and losers at a fine scale. Arguably, the result isa more thorough understanding of economic processes and amore suitable basis for policy design and analysis.

In state-of-the-art macro models, the supply of land is mod-eled in increasing detail, but decision-making remains highlyaggregated: a country’s agricultural sector still responds as ablock to market and policy shocks (Elobeid & Hart, 2007;Gurgel et al., 2007; Hertel et al., 2009). Aggregate modelshighlight the reallocation of production factors across cropsand land uses, which helps dissipate price shocks and rent dis-parities. However, supply responses also involve the realloca-tion of land and labor across farms. Such reallocation shouldbe common wherever price signals at the farm level (and henceindividual supply responses) depend on highly individualizedtransaction costs (de Janvry et al., 1991; Key, Sadoulet, &de Janvry, 2000). In much of Mexico, large commercial farmscoexist with subsistence households, each responding dis-tinctly to price shocks (Dyer et al., 2006). Our results suggestthat subsistence activities not only allow peasants to weathermarket shocks but also endow the entire agricultural sectorwith an ability to absorb these shocks. 6 We should expectaggregate supply responses to be relatively inelastic when idi-osyncratic responses are idiosyncratic rather than uniform. Inthis case, however, the capacity to absorb shocks is not due toindividual responses but to market exchanges between hetero-geneous agents. As factors flow in and out of subsistence pro-duction, regulated by local (endogenous) rents and wages, theagricultural sector is able to adjust to shocks with minorchanges in total factor demand. In our model, a vigorous com-mercial supply response in central and southeast Mexico, forinstance, led to an unremarkable expansion of corn land, as

subsistence production contracted (Table 4). In contrast,factor markets could not absorb commodity price shockswherever relatively homogeneous producers had few incen-tives to exchange land and labor, as in the northeast andnorthwest regions. Some macro models account for subsis-tence production by netting out the share of land dedicatedto this activity, but they consistently overestimate supply re-sponses by failing to account for such interactions. Aggre-gate-model supply-response estimates for west-centralMexico exceed ours by as much as 200% (Brooks et al., 2008).

Significant economic interactions are not limited to strik-ingly different actors, such as commercial and subsistencefarmers. Indirect interactions might arise between any twogroups responding to different sets of signals, even amongcommercial farmers. In Mexico, corn growers selling directlyto consumers in local markets respond differently from pro-ducers linked to the international market, because consumerand producer prices are not co-integrated (Motamed, Foster,& Tyner, 2008). Nevertheless, responses by one group affectthe other through local factor markets. Interactions could alsooccur among agents selling in the same markets; e.g., risingland and labor costs after a crop-price increase might forcefarmers without access to credit to reduce output while othersexpand it. Interactions among agents also need not be local;they can occur across regions, as between participants andnon-participants in Mexico’s deficiency payment program.Despite claims that the program is not distortionary, owingto its limited coverage and dissociation from market prices,supply responses from program participants in northern Mex-ico have depressed prices faced by non-participants in thesouth and central regions (ca. 5%), dampening the agriculturalsector’s aggregate supply response (Sumner & Balagtas, 2007).

In contrast to the macro-level interaction between the USand EU biofuel mandates (Hertel et al., 2009), every interac-tion described above can buffer rather than amplify responsesto market and policy shocks. In combination with local mar-ket structure, these interactions also result in dissimilar out-comes across localities for any particular shock. Everyoutcome is associated with a particular redistribution of pro-ductive factors across economic agents. Costs and benefits alsoare redistributed. Gains and losses might depend on an agent’sparticular endowment of land, labor, and capital, as much ason variables shared with others, such as rent and wagechanges. The mobility of labor helps dissipate shocks intoother economic sectors and across borders despite regionalwage disparities. Thus, wages tend to react less than rentalrates to local shocks (Table 2). Restrictions on land mobilitylimit price transmission across regions and crops, creating sig-nificant rents for local landowners and surplus corn growers.

Our results suggest that corn-price increases in Mexicomight have brought about a transfer of land and labor intonon-rural commercial agriculture, while rural households’ out-put contracted. As labor was drawn into non-rural agriculture,to be sure, there might have been an influx of wage incomeinto the rural economy, but commercial integration wouldhave drawn this income back out into the urban economy,reducing the local multiplier. At the same time, higher wagescould hinder rural off-farm activities, which contracted slightlyin our simulation. Hence, higher corn prices actually couldhave a negative income multiplier effect on some rural areas,particularly in west-central Mexico (Dyer, 2008).

Crop-price impacts on rural employment are similarly com-plex. According to our analysis, employment increased onlymarginally in rural Mexico after the price surge (Table 3). Inthe water-constrained northwest, where cropland area is fixedby irrigation in the short term, agricultural employment could


grow only through conversion to labor-intensive crops. Incontrast, conversion of pastures to corn might have raised to-tal agricultural labor demand in west-central Mexico, andmost value added after the price surge would have consistedof wages. However, in our model, the 8.2% increase in wageincome translated into a <1% increase in total income, for itcame largely at the expense of losses in the value of on- andoff-farm family labor and migrant remittances (Table 4).Where there was already full employment and wages did notincrease significantly, the surge would have resulted largelyin the transfer of labor use from one sector into another withno effect on income. As rural migrants recognize, the well-being of working families depends on higher wage rates.Employment growth would not raise wage rates where localemployment opportunities could draw migrants back (or sim-ply deter migration by new labor-force entrants).

Rural income and welfare effects are similarly ambiguous.Although some rural corn growers in the model expandedtheir output, most rents accrued not to them but to non-rurallandowners, except in the northeast. Since the vast majority(87%) of Mexican rural households are subsistence producersor produce no corn, few real benefits from the price surgewould have reached rural areas either directly or indirectly, ex-cept in the northwest where commercial agriculture predomi-nates. In the southeast and central regions, where poverty isgreatest, subsistence farmers might have been relatively unaf-fected by price increases, as they were unaffected by persistentdecreases in previous years (Dyer et al., 2006). However, ruralincomes might have decreased slightly in real terms, becausemost rural areas are net buyers of corn (Dyer, 2008).

(a) Limitations and caveats

There are inherent tradeoffs between simplicity and realismin simulation models. Assumptions make simulation modelstractable, transparent, and feasible given data limitations,but they may compromise the validity of simulation results.On the other hand, too much structure can increase the sensi-tivity of the model to parameter and specification errors. Theeconometric estimation of model parameters from microhousehold data lends confidence to our model’s structure. Inconventional general-equilibrium models, out-of-sample pre-diction and sensitivity analysis are the two main tools to vali-date simulation models. Our model’s success at recreating landuse in the baseline year is important in establishing the model’svalidity for other purposes. An important assumption con-cerns the specification of production functions. Cobb-Douglasproduction functions are tractable yet allow for substitutabil-ity among factor inputs; this explains their popularity ineconometrics and simulation models. Simulation results aregenerally robust to the specification of functional forms inas-much as (i) scenarios involve marginal changes in exogenousvariables, and (ii) the model is always estimated at the samepoint given by survey data. Nonlinearities around these pointsgenerally have to be unrealistically large in order to qualita-tively alter simulation results (Taylor, Yunez-Naude, &Hampton, 1999). Moreover, despite the linearity of individualresponses, aggregate supply and demand responses are highlynonlinear, shaped by the combination of all households’ pro-duction and consumption parameters and the endogenousprice of the composite agricultural good.

Intermediate-input demands are assumed to be fixed perunit of output, and the model makes the short-run assumptionthat capital (but not land) is fixed in the production functions.With constant returns to scale, a fixed input is necessary forprofit maximization. Capital inputs, while fixed, vary across

households and regions, reflecting household-specific capitalconstraints and adding realism to our simulations. However,if there is significant substitutability between intermediate in-puts and factors, our model could understate supply responseseven in the short run. This is not the case for seed; however, itcould be a problem for other intermediate inputs, for example,fertilizer. In this respect, our multi-agent model offers anadvantage, because although the input–output relationshipsare fixed for specific households, they vary substantially acrosshouseholds. Well documented liquidity and other constraintslimit the use of fertilizer and other purchased inputs on smallfarms, as reflected in input–output coefficients. At the otherextreme, large commercial corn producers, particularly innorthern Mexico, apply fertilizer intensively, and attain highyields. This lowers their output responsiveness to additionalfertilizer inputs.

There are various estimates of yield elasticities for the US,but we are unaware of any estimates for Mexico (see alsoAbler, 2001). Estimates for the US vary widely but, accordingto Lobell, Cassman, and Field (2009), expert opinions of short-run elasticities tend to favor lower values. Apparently, one ofthe reasons for low yield elasticities in the US is that individualfarmers do not show a pronounced management response tohigher crop prices (Lobell et al., 2009). Despite this, regionalyields could respond to prices if farmers with higher fertilizerrates become more profitable and expand their operations atthe expense of less competitive farmers with lower crop yields.This is precisely what happens in our model. Since differentproducers exhibit different input intensities, aggregate input–output ratios change in response to corn prices through shiftingland use across farms. As a result, regional corn yields increaseslightly, as we might expect based on the US literature.

5. CONCLUSIONS

Our results cast a new light on the impact of corn trade oneconomic welfare, land use, and the risk of deforestation inMexico. Fifteen years ago it was feared that the removal ofprice supports for grains after NAFTA and an ensuing declineof the Mexican corn sector would force unemployed peasantsinto a subsistence economy, impinging on the forest margin(Barbier & Burgess, 1996). A decade later, it was suggestedthat price decreases ultimately would force subsistence pro-ducers out of rural areas, promoting reforestation (Aide &Grau, 2004). Our model describes how subsistence productionmight allow farmers and the entire agricultural sector to ab-sorb corn-price shocks.

Simulation results suggest that the impact of the recentprice surge on both rural incomes and land-use changemight have been overestimated. Subsistence farmers mighthave had few reasons to expand into marginal lands in re-sponse to international commodity price increases, particu-larly in southeast Mexico where deforestation has beenhighest in the past (Dyer, 2008). Imperfect price transmis-sion, subsistence demands and increased labor costs couldlimit the surge’s impact on land rents in this region, keepingdeforestation pressures in check. The opportunity costs ofland might have risen most in the better integrated north-west; however, it is the scarcity of water, not forests, thatdefines the agricultural frontier in this region and keepsland-use changes in check. In sum, changes in world cornprices observed in late 2007 could have had widely variedeffects across rural Mexico, but it seems unlikely that theyhad a significant impact on rural incomes or the forestmargin.


NOTES

1. In the CET function, the sensitivity of land allocation to disparities inrelative returns to land across uses is determined by the elasticity oftransformation (r), known as the CET parameter. Since allocation is moresensitive to disparities in returns across similar uses, studies often groupland into use categories via a nested CET function with three parameters(Ahammad & Mi, 2005). These represent the elasticity of transformationof land across crops (r3), between crops and pasture (r2), and betweenagriculture and forest (r1). Since land is more fungible across similar uses,it generally is the case that r3 > r2 > r1.

2. CET land supply functions often are calibrated by imposing astandard value for the CET parameter. Alternatively, this value iscalculated using estimates of the own-price elasticity of land supply (Hertelet al., 2009; OECD, 2005). The CET parameter is equal to this elasticityfor a given use weighted by the value share of land in other uses in thesame nest, so the parameter represents the elasticity’s upper bound

(OECD 2005): for example, the elasticity of transformation across crops isr3 = �ei/(1 � li), where ei and li are the own-price elasticity of crop i andthe share of cropland in i.

3. In contrast, the standard GTAP model commonly assumes a uniformvalue of �1.0 for all crops and regions, while recent versions of the modeladopt values between �0.8 and �0.9 for different agro-ecological zones inLatin America (Hertel et al., 2009).

4. OECD (2005) sets the price elasticity at 0.281 for Mexico, whichimplies r2 = �0.39.

5. Estimates of r1 in the US are few and inconsistent, ranging between�0.25 and �1.5 (Hertel et al., 2009).

6. Historically, corn area in Mexico has been significantly less volatilewherever subsistence is prominent (Dyer, 2008).

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the corn price surge: impacts on rural mexico

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