This work is distributed as a Discussion Paper by the

STANFORD INSTITUTE FOR ECONOMIC POLICY RESEARCH

SIEPR Discussion Paper No. 07-39

Brazilian Ethanol: A Gift or Threat to the Environment

and Regional Development?

By Sriniketh Nagavarapu Stanford University

January 2008

Stanford Institute for Economic Policy Research Stanford University Stanford, CA 94305

(650) 725-1874 The Stanford Institute for Economic Policy Research at Stanford University supports research bearing on economic and public policy issues. The SIEPR Discussion Paper Series reports on research and policy analysis conducted by researchers affiliated with the Institute. Working papers in this series reflect the views of the authors and not necessarily those of the Stanford Institute for Economic Policy Research or Stanford University.

Brazilian Ethanol: A Gift or Threat to the Environment

and Regional Development? ∗

Sriniketh Nagavarapu

Department of Economics

Stanford University

ssn@stanford.edu

(Job Market Paper)

January 2, 2008

Abstract

The Brazilian government has been pushing for changes to the United States’ extensive barri-ers to ethanol imports. However, removing these barriers would have uncertain consequences forthe environment and regional development in Brazil. Regarding the environment, the expansionin sugarcane production required to produce more ethanol could lead to greater deforestation.In terms of regional development, greater sugarcane production could be a boon to the poorer,rural parts of Brazil; but at the same time, wealthier regions of Brazil could expand productionmore rapidly, actually reinforcing regional inequality. This paper addresses these two issuesby developing and estimating a general equilibrium model of regional agricultural and labormarkets. The model allows for labor supply responses on both an extensive margin (choice ofsector and region of work) and an intensive margin (choice of hours). Heterogeneity in landquality underlies land use decisions. I estimate the model using data covering the last 20 years,including rich household survey data, region-level data on production and land use, and dataon the prices of key goods. I then use the estimates to simulate the general equilibrium effectsof a change in US import barriers. I find that the threat to forests is minimal. The greatestexpansion in sugarcane cultivation occurs in Sao Paulo, away from the most environmentallysensitive areas. Concerning the regional development issue, I find that the ethanol expansionleads to a significant rise in expected wages in all regions, with some of the largest proportionalincreases in very poor regions. Still, the gap in expected wages between wealthier and poorerregions does not close in general. Most of the earnings gains accrue to workers who were alreadyworking in sugarcane prior to the policy change. In part because of this, within many regionswage inequality actually increases somewhat.

∗I owe a large debt to Thomas MaCurdy, John Pencavel, and Luigi Pistaferri for their advice and support dur-

ing both college and graduate school. I am equally grateful to Aprajit Mahajan and Jay Bhattacharya for their

encouragement and guidance over the past years. In addition, I benefited greatly from conversations with Orazio

Attanasio, Nick Bloom, Colleen Manchester, Caroline Hoxby, Giacomo De Giorgi, Lovell Jarvis, Seema Jayachandran,

Bryan Keating, Soohyung Lee, Naercio Menezes-Filho, Pedro Miranda, Jon Meer, Marc Muendler, Kevin Mumford,

Alejandro Ponce-Rodriguez, Felix Reichling, Andres Santos, Gopi Shah Goda, Frank Wolak, Joanne Yoong, atten-

dees of Stanford’s Applications Seminar, and members of Stanford’s Labor Reading Group. For their immense help

with acquiring data used here, I thank Steven Helfand, Frank McIntyre, Marcia Moraes, and the staff at Fundacao

Getulio Vargas. I appreciate the financial support provided by SIEPR for data acquisition and by the Taube/SIEPR

dissertation fellowship. Remaining errors are, of course, my own.

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1 Introduction

Removing the United States’ barriers to ethanol imports could profoundly affect the world’s envi-ronment and the economies of those developing countries able to produce ethanol. Nowhere is thismore true than Brazil. The country’s natural endowments are conducive to growing sugarcane,which can be transferred into ethanol more efficiently than corn or other materials. Moreover,Brazil has vast potential for expanding sugarcane cultivation further. It is no wonder that theBrazilian government has been pushing hard to eliminate the United States’ barriers to Brazilianethanol.1

Nevertheless, more open markets for Brazilian ethanol generate uncertain implications for boththe environment and regional development in Brazil. On both scores, there are important trade-offs. Regarding the environment, ethanol is a renewable, cleaner-burning alternative to petroleum.On the other hand, the increase in sugarcane cultivation required to produce more ethanol couldfurther diminish the Amazon Rainforest and other environmentally sensitive areas. Concerningregional development, expanding sugarcane production promises to alleviate poverty among low-skilled workers, especially in the poorest region of Brazil, where the crop is historically important.But wealthier areas like Sao Paulo may expand production more readily, actually reinforcing re-gional inequality. Therefore, opening up greater export opportunities for Brazilian ethanol carriesboth benefits and dangers for the environment and regional development.

These concerns about what would happen if there were greater opportunities to export ethanolto the US lead to two policy questions:

• To what extent would increasing sugarcane cultivation lead to greater deforestation?

• Would wage inequality within and between regions increase or decrease, and by how much?

This paper addresses these questions by developing and estimating an empirical model of re-gional agricultural and labor markets. To address the relevant issues, this model is general equilib-rium in nature, linking agricultural markets into the rest of the Brazilian economy. When simulatingthe effect of opening up the international market for ethanol, I find that the threat of deforestationis minimal. The greatest growth in sugarcane production takes place in Sao Paulo, where the totalshare of land used for agriculture increases between 1.9 and 3.9 percentage points, depending on theassumptions about world ethanol prices in the simulation. This is not worrisome since this regionis away from the most environmentally sensitive areas. A portion of the Northeast also exhibits astrong expansion of sugarcane cultivation, leading to an increase in total agricultural land of be-tween 1.3 and 2.6 percentage points. The proximity of this region to parts of the Atlantic Rainforestmay be a cause for concern. Nevertheless, the simulations predict that the Center-West portion ofthe country - which is the source of greatest anxiety because it includes forests of the Amazon andlarge tracts of the Cerrado savannas - would exhibit negligible growth in agricultural area. Whatgrowth there is in sugarcane cultivation comes mostly at the expense of other agricultural land.

In terms of regional development, I find that average wages increase in all regions due to strongincreases in sugarcane wages. In proportional terms, this increase is more pronounced in parts of the

1For instance, see “Our Biofuels Partnership”, by President Luiz Inacio Lula da Silva, in the March 30, 2007

edition of the Washington Post (page A17).

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poor Northeast than in the wealthier South and Southeast of the country. In the simulation withmore conservative assumptions, the regions in the Northeast experience increases in the expectedwage of between 4.8% and 7.4%. In absolute terms, the increase in expected wages varies acrossregions, with some wealthier regions actually seeing higher increases than the poorer areas. Butthe change is similar enough across regions that inter-regional wage inequality - interpreted asdifferences in expected wages across regions - changes very little. Moreover, wage inequality actuallyincreases within many regions. This is because the undesirable nature of sugarcane work causesvery few workers to enter the sector. Instead, earnings gains accrue mainly to workers who beganin sugarcane, and who increase their hours supplied in response to greater wages. As sugarcanewages rise, low-skilled workers in other parts of the economy do not see a commensurate rise.

In coming to these conclusions, I rely on a general equilibrium model because, a priori, onecannot say whether a partial equilibrium framework is sufficient to address these issues. Fundamen-tally, the impact of ethanol changes on land use and regional development depends on the elasticityof sugarcane supply in the various regions. As sugarcane prices increase in a region, more land willbe devoted to sugarcane and more labor will be used in sugarcane production. But precisely howmuch more land and how much more labor may depend critically on the interactions between thesugarcane sector and the rest of the economy. And these interactions also determine how changesin the sugarcane sector affect other sectors.

The model developed here takes these interactions into account. The model is static in nature.The economy includes one intermediate good (sugarcane) and four final goods (ethanol, sugar, anon-sugarcane agricultural good, and a composite consumption good). Farms use land and laboras inputs to produce sugarcane and the other agricultural good. Ethanol and sugar producers usecapital and sugarcane as inputs, while producers of the composite consumption good use labor andcapital. Therefore, the activities of all producers are linked through their competition over theinputs of land, labor, capital, and sugarcane. Together, these producers generate the demand forinputs. Regarding the supply of inputs, individuals in the model make decisions involving labor,land, and capital. Heterogeneity in preferences for region and sector and heterogeneity in levelof non-labor income underlie individual choices about where to work, in which sector to work,and how many hours to work. Heterogeneous land quality determines whether land is used insugarcane or other agriculture, or is kept in a non-agricultural use. Finally, although the modelis static, a savings-like motive causes individuals to hold some capital while renting the remainderout to producers.

I estimate parameters of the model using data covering the 1981-2005 period. In some instances,necessity or convenience dictates that I calibrate a subset of the parameters using these data. Thedata come from a variety of sources, including cross-sectional household survey data, state-levelinformation on production and land use, and trends in sugarcane and other prices. I rely on thehousehold survey data to estimate the parameters governing individuals’ choices of where to workand how many hours to work. I use the data on production, land use, and prices - as well as data onaggregate labor hours derived from the household survey - to estimate parameters of the productionfunctions and features of the land heterogeneity distributions. I then use these parameters toperform simulation exercises in which I assess the consequences of changes in US policies regarding

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ethanol imports. I represent the change in US policy in a simple way, as making internationaldemand for Brazilian ethanol perfectly elastic at a price higher than the initial equilibrium price. Iinclude results from two simulations, where one sets the world ethanol price at 5% above the initialequilibrium price, and the other sets the world price at 10% above the initial price. These exercisesyield the conclusions summarized above - namely, opening up ethanol export opportunities will notquicken the pace of deforestation and will have mixed effects on regional development and wageinequality.

This paper is most closely related in substance to work by Moraes (2007) and Barros, de V. Cav-alcanti, Dias, and Magalhaes (2005) that is in progress. Moraes (2007) analyzes the pass-throughof international sugarcane prices to workers’ wages, while Barros, de V. Cavalcanti, Dias, and Mag-alhaes (2005) take a broader approach, hoping to estimate the impact of sugar trade policy changeson Brazil. In addition, a recent paper by Elobeid and Tokgoz (2006) addresses the response ofBrazilian ethanol production to potential changes in U.S. trade policies. My approach differs fromthese authors’ approaches, as I model labor supply and land usage decisions explicitly and accountfor equilibrium linkages. Unlike Elobeid and Tokgoz (2006), my focus is not on the details of domes-tic and international ethanol policies, but on the changes caused by a more favorable environmentfor sugarcane production.2

This paper complements the existing literature describing the effects of trade reform on labormarket outcomes, inequality, and poverty.3 Most studies in this area rely on variation in manufac-turing tariff declines across industries and over time to identify the consequences for outcomes ofinterest. Pavcnik, Blom, Goldberg, and Schady (2004) and Arbache, Dickerson, and Green (2004)use this approach to examine changes in Brazil. This paper complements the existing literature byexamining rural labor markets. In doing so, I focus in particular on regional differences. This focusrelates to the work of Topalova (2005), who addresses the regional impacts of changes in Indiantrade policies. Though the scope of this paper is narrower than Topalova’s, I hope to delineate andquantify the underlying mechanisms that generate regionally disparate consequences. Importantwork by Porto (2003a, 2003b) assesses the first-order welfare consequences of trade reforms in ageneral equilibrium context. In order to focus my contribution on behavioral responses, I do notattempt to incorporate micro-level consumption data here, as Porto does.

Finally, this paper is related to the literature on migration. An essential component of themodel involves location decisions by workers. Timmins (2005) proposes a novel application of IOmethods to analyze location decisions in Brazil and assess the importance of both monetary andnon-monetary factors in these decisions. I analyze the location decision in a less detailed mannerthan Timmins, but allow hours and sector of work to be chosen at the same time as location. Allthree choices result from the comparison of indirect utilities derived from a simple utility function.Other work has emphasized the importance of local networks in preventing migration. A notable

2Studies of Brazil such as de Souza Ferreira Filho and Horridge (2005) and Bussolo, Lay, and van der Mensbrugghe

(2004) have used computational general equilibrium models as an alternative approach to modeling trade policies in

a detailed manner. For an interesting blend of the CGE approach with use of household survey data, see for example

Ravallion (2004).3Winters, McCulloch, and McKay (2004) and Goldberg and Pavcnik (2004) are helpful summaries of the micro

literature on trade reform.

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example is Munshi and Rosenzweig (2005). Here, I estimate parameters that are common to allindividuals and that indicate preferences for specific regions and sectors. These parameters mayindicate the presence of local amenities or networks that all individuals can benefit from. Moreover,I allow for unobservable individual heterogeneity around these common parameters; differencesamong individuals in access to local networks, family ties, or enjoyment of local amenities could besome possible components of this heterogeneity.

The next section of the paper provides an overview of sugarcane production in Brazil. Itillustrates the areas where expanding sugarcane production could affect forest land and regionaldevelopment. A project of this type requires data from a large array of sources, and Section 3describes these sources in detail. Section 4 develops the general equilibrium model. It begins withan overview of the main elements, and then includes separate sub-sections describing the specificforms of utility functions and production functions. I describe the data before the model becausecertain aspects of the data motivate choices in the model. Section 5 begins by presenting descriptivestatistics to familiarize readers with the basic patterns in the data. I then go on to estimate theparameters of the model. Section 6 describes the simulations and discusses the results. The lastsection summarizes these results and points to potentially fruitful areas for future work.

2 Sugarcane Production in Brazil

Several aspects of the sugarcane industry are essential to the analysis below. To start with, sug-arcane production in Brazil has a strong regional dimension. Understanding the geography ofproduction is therefore crucial to approaching the issues of deforestation and regional developmentthat are of interest here. Here, I first discuss the geographic relationship between production areas,forest spaces, and regional poverty. I then briefly discuss particulars of the sugarcane industry inBrazil.

2.1 The Regional Dimension of Sugarcane Markets

Sugarcane has been a crucial part of Brazilian agriculture for hundreds of years. Traditionally,the Northeast portion of the country was the most important sugarcane-growing region. However,the Center-South region of the country has many natural advantages for sugarcane cultivation.The majority of production had shifted to this region by the 1950s, especially to the flat landsof the Southeast region, which provide more opportunities for mechanical harvesting than in theNortheast, where labor is more important to production.4

Figure 1 shows a map of Brazil, with the darker areas indicating a higher density of sugarcanecultivation (area cultivated per unit of land). Although this map dates from 1993, the essentialpicture has remained unchanged since then. Figure 2 is a political map of Brazil showing stateboundaries. As can be seen by comparing the two figures, Sao Paulo and Minas Gerais in theSoutheast of Brazil are two of the largest producers.5 The high concentration of production in the

4Nunberg (1986) provides a detailed description of this shift over time, as well as an analysis of Brazilian sugarcane

policies before the 1980s. The discussion below draws on this study for information on this time period.5The role of Rio de Janeiro in sugarcane cultivation has fallen since 1993.

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Northeast occurs primarily in Pernambuco and Alagoas. Production has grown in other states inrecent times, but remains relatively low.

The areas of primary environmental concern are the Amazon Rainforest, the Cerrado, and theAtlantic Rainforest.6 The Brazilian portion of the Amazon covers the states of Roraima, Amazonas,Para, Rondonia, Acre, and Amapa. In addition, it extends into Mato Grosso, Tocantins, andMaranhao. Unfortunately, due to data limitations discussed below, the analysis here must confineitself to portions of the Amazon in the latter three states. This is not a large limitation, given theextremely limited amount of agricultural production in the former six states. The original area ofthe savannas known as the Cerrado lie primarily in Mato Grosso, Mato Grosso do Sul, Tocantins,and Goias, though the area also covers the edges of neighboring states. Large swathes of thisarea are already under agricultural use (to a large extent, pasture), and there is concern aboutfurther expansion. Finally, the Atlantic Rainforest exists today only as a thin strip along Brazil’seastern coast, running from Rio Grande do Norte down through Sao Paulo. Sugarcane productionis currently uncommon in the Center-West portion of the country (Mato Grosso, Mato Grosso doSul, and Goias), but could pose a significant threat if it grows dramatically.7 Expansion in otherportions of the country, such as the Northeast, could potentially affect the Atlantic Rainforest.

Finally, it is useful to note how the regional wealth distribution is related to sugarcane pro-duction. As stated previously, the Northeast portion of the country is the poorest. Agricultureis a relatively more important portion of the economy in the Northeast, where wages tend to bequite low as described below. In contrast, the South and Southeast are the wealthiest portions ofBrazil, and there is a higher degree of urbanization, especially in Sao Paulo and Rio de Janeiro.The Northern portion of Brazil consists of the Amazon, and the six states composing this area havebeen discussed above. The Center-West has experienced much growth and in-migration in recentdecades, and has become a key agricultural producer for the country. In this area, the pluralityof agricultural land is used for pasture. Agricultural producers in this region tend to be wealthierand to operate on a large scale.

This provides the regional context in which Brazilian policy makers push for an expansion insugarcane and ethanol. The importance of sugarcane to the agricultural economies in the poorNortheast suggest that an expansion in cultivation could be a boon to the region. The large openareas of the Center-West hold the promise of large expansions in the future, as well.

2.2 The Sugarcane Industry

Beyond this regional concentration, there are three key aspects of the sugarcane industry that arerelevant to the analysis: first, on the supply side, there are a relatively small number of producers;second, demand for sugarcane comes almost completely from ethanol and sugar mills; third, thegovernment intervened strongly in the sugarcane, sugar, and ethanol industries until the mid-1990s.On the first count, the sugarcane industry consists of two basic types of producers: the independent

6The Pantanal, in Mato Grosso and Mato Grosso do Sul, is another ecologically rich area, but the threats to these

wetlands do not appear to come from agricultural expansion.7This is especially true given that such small portions of the Cerrado are legally protected by the government.

For the Amazon, some protection exists, but the degree of enforcement is unclear.

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cane growers (fornecedores) and the millers (usineiros). The large-scale growers of sugarcane areoften the millers themselves, which is a natural arrangement owing to the fact that harvestedsugarcane must be transported to factories very quickly.8 Despite this, there are a large number ofindependent growers, as many as 60,000 by some measures. This ensures a degree of competitionin the sugarcane industry. The model proposed below treats the relationship between sugarcaneproducers and sugar/ethanol mills as a competitive one. A more detailed model would allow forthe feature that many mills grow their own sugarcane and rely on outside sugarcane as a residual.Nevertheless, the large number of small producers suggests that the framework used here is at leasta reasonable first approximation.

On the second point, demand for sugarcane comes from ethanol and sugar producers. Sugarcaneis essentially an intermediate good, and will be treated as such in the model below. In some cases,sugar and ethanol are produced in distinct facilities by distinct producers; in others, a singleproducer can produce both goods. Independent distilleries were popular for a time, but currentlythere are a large number of mills that can produce both sugar and ethanol, according to the relativeprices of these goods.9 In total, there are more than 300 distilleries capable of producing ethanolin Brazil, with new projects appearing at a rapid pace.

Finally, the Brazilian government has played a large role in the sugarcane sector since the 1930s.The Instituto do Acucar e do Alcool (IAA) operated a system of quotas and price controls forsugarcane, sugar, and ethanol over the next 60 years. Importantly for sugarcane, IAA productionquotas were not traditional caps on production. Rather, they specified the amount of sugarcanethat mills had to purchase from independent growers. Over the 1980s, quotas for sugar productionworked to limit the production of sugarcane by larger enterprises as well. However, the increasingemphasis on alcohol production, which was not constrained by quotas in the same way, provided agrowing outlet for “excess” sugarcane.10 In addition to quotas, the IAA set prices. Over the 1980s,the agency reduced sugarcane prices dramatically, which is reflected in the price data discussedbelow. Still, they remained high enough to ensure production quotas were filled.11

As time went on, the system of quotas and administered prices came under increasing pressure.In 1990, the IAA was abolished and sugar exports were privatized. In 1995, sugar quotas wereended, and from 1997-1999, prices for sugar, ethanol, and sugarcane were liberalized. Since 1999,sugarcane prices have been determined in essentially a free market.12

8For details on the preparation and milling of harvested cane, the website of the Sao Paulo cooperative COPER-

SUCAR is an especially useful source (www.copersucar.com.br).9There is a limit to this flexibility, however. Most of these dual activity mills can move their ethanol production

share between 45% and 55%. For simplicity in the model, I abstract from these details about mills and distilleries.

This would be a useful area for future research, but would require more detailed data.10Ethanol quotas took the form of levels of domestic production that a mill/distillery had to meet before exporting.

Ethanol policy in the early 1990s is described in detail by Borrell, Bianco, and Bale (1994).11Wong, Sturgiss, and Borrell (1989) note this explicitly, but the picture provided in Nunberg (1986) is also

consistent with this point. The reason for the government-imposed decline in sugarcane prices relates to movements

in petroleum prices at the time.12For a brief listing of major events in government policy, see notes by I.C. Macedo of Unicamp on the website for

Cane Resources Network of South Africa, www.carensa.net/Brazil.htm. For greater detail, see Barros and Moraes

(2002) and Lopes and Lopes (1998).

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This government intervention potentially has implications for the analysis. The model assumesconstant returns to scale production functions in sugarcane, sugar, and ethanol, with all producersexhibiting price-taking, cost-minimizing behavior. Under this assumption, quotas will not posea problem. For instance, the sugarcane quotas do not affect farms’ or mills’ decision-makingbecause no one sugarcane producer has an advantage over another, and all are indifferent aboutthe level of production. The fact that prices were administered does not pose a large problemunder this framework either. Estimation of production-side parameters uses the implied zero-profit conditions; assuming there are no barriers to entry, and that the price received by sugarcaneproducers is the same as the price paid by mills/distilleries, using the zero-profit equations willyield correct estimates even if prices are under government control. Problems arise if we departfrom this admittedly simplistic framework, for instance if decreasing returns to scale in any ofthese production activities exist. However, addressing such concerns is beyond the scope of thispaper. My purpose is to estimate a tractable model in order to obtain an initial understandingof how sugarcane production in Brazil’s regional markets - and land and labor allocations - wouldrespond to changes in ethanol demand. Given this objective, the treatment below appears to be areasonable initial approximation.

At the same time, the pattern of government intervention over time lends credence to themodel’s implicit assumption of complete foresight. If all changes in sugarcane prices were completelyunexpected, then incorporating uncertainty into the model would be a first-order concern. In reality,though, farmers knew in the early 1980s that the government planned to reduce sugarcane pricesmarkedly for some time; and in the early 1990s, farmers knew that prices would rebound as thesugar and ethanol markets were gradually liberalized.

3 Data Sources

A topic of this nature requires data from a large array of government and non-government sources.Below, I describe the main sources of the information used in the analysis. As will be clear,particular characteristics of the data dictate important choices during the process of developingthe model. For this reason, I introduce the data before discussing the model. Details about theconstruction of variables appear in Appendix A.

I rely primarily on three sources of data: first, the Pesquisa Nacional por Amostra de Domicilios(PNAD); second, the Producao Agricola Municipal (PAM) and other data on land allocation; andthird, data on output prices from Fundacao Getulio Vargas (FGV, the Getulio Vargas Foundation).To begin with, the PNAD is a cross-sectional representative household survey with a variety ofemployment information. On average, the PNAD surveys roughly 100,000 households each year,which corresponds to more than 300,000 people. The only region of the country not representedfully in the PNAD sample is the North Census region, which includes states with Amazon landand which is substantially less inhabited than the rest of the country.13 This will prevent me from

13The rural parts of six states in the North region - Rondonia, Acre, Amazonas, Roraima, Para, and Amapa - are

the portions that the PNAD did not represent through 2003. In the 2004 and 2005 data, this deficiency has been

remedied, so that the PNAD is now nationally representative.

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addressing the sugarcane markets in the Northern portion of the country, but the PNAD doescontain data on parts of the country that contain Amazon forest land, such as Mato Grosso. I willuse PNAD data from 1981 through 2005 for all but three years.14

The PNAD has information on several aspects of agricultural employment. From 1981 through1990, I have the following information for each member of a household: whether or not the memberis employed (with September being the reference month); if she is employed, then her monthly in-come and usual weekly work hours from her primary job; if she is not employed, then the position,occupation, and sector of her last job; the sector of her primary job, with sector being narrowlydefined enough to allow for the identification of specific crops; and whether the individual is hiredlabor, a non-remunerated family worker, self-employed, or an employer. The advantage of suchlarge samples is that there are a considerable number of survey respondents working in any par-ticular crop. The PNAD was re-designed in 1992 and three useful elements were added. First,the 1992-2002 surveys have information on the total area devoted to the enterprise for those whoare self-employed or employers. Unfortunately, for agricultural enterprises, this does not translatedirectly into the amount of area devoted to a particular crop. Second, the later surveys give basicinformation (occupation, sector, informality status) on a currently employed person’s secondaryjob, and on her previous job if the person was in the previous job for a majority of the previousyear. Third, the re-designed questionnaire asks about the time spent in the current state of resi-dence and the identity of the prior state of residence. I do not use these new elements directly inthe analysis, but do use them to investigate the validity of certain assumptions in the model.

The PAM data provide annual information on total production and total area harvested formajor crops in each state, over the relevant time period. As in the case of the PNAD, the Brazilianstatistical institute IBGE collects these data. Data are available for more than 30 crops in the1990-2005 time period, and the most important crops are covered in the 1981-1989 period.15

The analysis below requires an estimate of the total amount of land devoted to agriculture ina particular state, by year. As noted above, the rearing of livestock is one of the most importantagricultural activities. Because the PAM data do not cover land for pasture (or land used for smallercrops, wood products, etc.), I turn to other sources to estimate total agricultural land by year. Forthis purpose, I use agricultural census data from 1980, 1985, and 1995, which contain information onpasture and all other agricultural land. I also use data on cattle herd sizes from Producao PecuariaMunicipal (PPM), data constructed by the government to analyze livestock. Using these data andthe PAM data, I estimate simple regression equations that are used to project total agriculturalarea in the years of my analysis period. The projections rely on the PAM and PPM data in theyears between agricultural censuses being good predictors of total non-pasture land. I scale thepredictions to ensure that the total land devoted to agriculture in Brazil matches the annual totals

14In 1991 and 2000, the PNAD was not conducted because those were national demographic census years. In 1994,

the PNAD was not conducted due to other constraints. Although I have the national census data for 1991 and 2000,

these were conducted at different times of year from the PNAD, making comparisons difficult in the agricultural

sector.15The quality of the data varies over time, generally being better near agricultural census years. I obtained the

1990-2005 data from SIDRA, an online database operated by the Brazilian government. The earlier PAM data come

from IPEA, an institute affiliated with the government, with a website at www.ipeadata.com.br.

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provided in data from the Food and Agriculture Organization of the United Nations.Finally, the price data I use come from the FGV, a foundation that compiles a large number

of price indexes for Brazil. In particular, the FGV has monthly time series on prices received fordetailed agricultural product categories, by state. These data are collected using surveys of farmsin the relevant geographical regions. 16 In the model, I will also need an output price that corre-sponds to all agricultural output besides sugarcane. For this purpose, I use an FGV-constructedagricultural price index for Brazil and information on the share of non-sugarcane agricultural pro-duction to construct a non-sugarcane agricultural output price. For ethanol and sugar prices, I relyon FGV price indexes. I deflate all prices using the national consumer price index (IPCA) obtainedfrom the OECD.

In addition to these central components, I also use national and regional income accounts datafrom IBGE, exchange rate information from the central bank of Brazil, and data on imports andexports from Brazil’s Secretaria de Comercio Exterior (SECEX). Details regarding the constructionof all key variables appear in the appendix.

4 Model

I cover the key aspects of the model first, and then provide details about production functions,utility functions, and equilibrium conditions in sub-sections later. The economy contains sevenregions. I choose to group states together into regions based primarily on geographical proximity,but also consider anecdotal evidence about the integration of labor markets. The states composingthese regions are: Parana, Santa Catarina, and Rio Grande do Sul (region 1); Sao Paulo (region 2);Minas Gerais, Rio de Janeiro, and Espirito Santo (region 3); Bahia and Sergipe (region 4); MatoGrosso, Mato Grosso do Sul, Goias, Tocantins, and the Federal District (region 5); Pernambuco,Alagoas, Paraiba, and Rio Grande do Norte (region 6); and Maranhao, Piaui, and Ceara (region7).17 In the analysis, I do not include the remaining states of Brazil, which all come from thesparsely populated North Census region of Brazil. This is because, as described above, the PNADdoes not contain information on the rural parts of these states for most years.18 For later reference,Table 5 displays the states composing each region. The second column provides the “shorthandreference” that I use to refer to each region in future tables. The table also shows how each of myregions come together to form the official “Census regions”. To avoid confusion, whenever I usethe term “region” in the paper, I am referring to my definition of the seven regions and not the

16Of course, different farms will face different prices for the same product, based on heterogeneity of product quality,

market access, etc. Ideally, this should be taken into account in the analysis, but this would necessitate farm-level

survey data on prices or additional distributional assumptions.17In determining the number of regions, I strike a balance between, on the one hand, using so few regions that

important heterogeneity within regions is neglected and, on the other hand, using so many regions that the PNAD

does not contain a reasonably large number of sugarcane workers in some regions. I sometimes refer to region 5 as

the “Center-West”, even though Tocantins is technically a part of the North census region, and not the Center-West.

I do so because Tocantins was once connected to Goias.18In the empirical analysis, I implicitly assume that the North portion of the country does not contribute anything

to the economy. In reality, these states contribute around 5% of GDP currently, though the figure has grown to this

level from smaller levels.

10

Census definition. The seven regions constitute the seven regional markets in the economy. Wagesand land prices differ across these markets, but I assume that all other prices are national.

The model is static in nature. In each region, firms produce one of five goods: sugarcane,another agricultural good (which I will call “other agriculture”), ethanol, sugar, and a compositeconsumption good.19 Sugarcane is an intermediate good that is used by ethanol producers or sugarproducers. Ethanol, sugar, the other agricultural good, and the composite consumption good are allfinal goods, either consumed by individuals in the Brazilian economy or exported. The agriculturalgood and the composite good can be exported or imported freely. However, ethanol and sugar faceinternational barriers in a manner to be made clear below.

For tractability, production in all sectors is characterized by constant returns to scale technolo-gies. In the two agricultural sectors, labor and effective land units serve as inputs. An hour oflabor is equally productive regardless of who it comes from. However, the same is not true of aunit of land: instead, an effective unit of land is equally productive regardless of where it comesfrom. The notion of “effective land units” is exactly analogous to a Roy model framework for laborsupply decisions, in which a single person can provide a different number of effective labor units indifferent sectors because of comparative advantage.20 For an example of how effective land unitsdiffer from actual land units (hectares, say), consider two plots of land, A and B. Plot A is 1 hectareand plot B is 0.5 hectares. However, plot B is located close to a sugar mill and is well-irrigated,while plot A consists of steeply sloped land that makes sugarcane harvesting difficult. Then plot Bis much more conducive to growing sugarcane than plot A. Even though plot B is smaller in size,it may represent more effective land units than plot A.

Capital does not enter agricultural production, nor do inputs such as fertilizer. This choicerelates to data constraints, since information on capital usage and other inputs in agricultureis only available during agricultural census years. For this reason, differences in land or laborproductivity parameters across regions will in part reflect differences in usage of other inputs.

Production of ethanol and sugar takes capital and sugarcane as inputs. Because of the wayindustries are defined in the PNAD, it is not possible to clearly identify those who work in theethanol and sugar industries prior to 2004. Owing to this limitation, I omit labor from the ethanoland sugar production functions. Finally, production of the composite consumption good takescapital, high-skilled labor, and low-skilled labor as inputs. High-skilled labor is defined as laborcoming from people with more than four years of education; low-skilled labor is labor coming frompeople with four or fewer years of education.

This completes the description of firms’ production. Next, I turn to individuals.21 Individualshave preferences defined over leisure, the composite consumption good, ethanol, sugar, the otheragricultural good, and capital. Having individuals value holding capital is unconventional. However,in a static setting it is important to make an assumption of this sort. Otherwise, individuals wouldconsume all their income and the national income accounts would be grossly violated. I treatcapital analogously to labor; each individual has a capital endowment and chooses to rent some of

19One can think of this good as including an amalgam of goods and services that do not include the other products.20For a classic discussion, see for instance Heckman and Sedlacek (1985).21Because this is a general equilibrium model, the distinction between individuals and firms is not meaningful; I

use the distinction only as a method of framing the components of the model in a straightforward way.

11

it out and hold the remainder.22 For convenience, we can think of this as individuals holding goodsfor future consumption.

Individuals consume out of their non-labor income and labor income. Their potential non-laborincome is the value of their capital endowment, plus revenues received from land rented out toagricultural production, plus net transfers from the government. The capital rental rate determineshow much of the capital endowment the individual rents out and how much the individual holdsfor future consumption. The prices of effective land units and the quality of the individual’s landin different uses determine the specific use that the individual rents her land to, and the returnthat she obtains on this renting activity.

Individuals’ labor income is the result of three choices: a choice of which region to work in;a choice of which sector to work in within that region; and a choice of how many hours to work.These three choices happen simultaneously. Moreover, the choice to work any hours at all is takenas exogenous; the unemployed are not modeled here.23 In choosing which sector and region to workin, workers take into account the wages in every sector and region, as well as their heterogeneouspreferences for each sector and region. Wages in a sector/region are common across all workers, withone exception: high-skilled workers see a different non-agricultural wage than low-skilled workers.In practice, I use the median wage in a sector/region as the wage for that sector/region.

The government plays only a limited role in the economy. Individuals receive a lump sum nettransfer/tax from the government, and the government spends its net intake on the compositeconsumption good, the other agricultural good, ethanol, and sugar, in the same proportions as theindividuals do.

My strategy will be to estimate (or, if necessary, calibrate) the parameters of the model byrelying on sources of data that are consistent with one another. In particular, I avoid using outsideparameter estimates from other studies. I then bring together the production functions and laborsupply functions to simulate a baseline equilibrium under the current scenario of export barriers inethanol. Finally, I simulate alternative settings in which these barriers are removed to assess theconsequences on the outcomes of interest.

In the sub-sections below, I discuss each of the model’s components separately in order to clearlydelineate assumptions about utility functions, non-labor income, land supply, and production func-tions. I briefly discuss key assumptions in the relevant sections. In each sub-section, I will typicallyuse the subscript j to denote the choice of sector; j can take values from 1 to 3, with 1 referringto sugarcane, 2 referring to other agriculture, and 3 referring to non-agriculture. I will use thesubscript r to denote the region. Sub-section 5.4 describes the assumptions about the internationalmarket before and after a change in US ethanol policy. The final sub-section sets out the conditionsfor equilibrium in the product, labor, and land markets.

22This is the approach taken in a recent paper by Bovenberg, Goulder, and Jacobsen (2006), which uses a one-period

general equilibrium model to assess the consequences of particular environmental policies.23One can think of the unemployed as consuming out of goods purchased by the government, or of the employed

consuming on behalf of the unemployed. Future work should consider the extensive margin of the labor participation

decision, but this feature of the model substantially simplifies the simulations.

12

4.1 Individual Utility Maximization

In period t, individual i chooses a market r, a sector j within that market, hours of work, andconsumption of a variety of goods. The decision over these items happens simultaneously, in astatic setting. There is no uncertainty. The utility function takes a Cobb-Douglas form. Whenchoosing a utility function for the purposes considered here, the ease of aggregation is an importantcharacteristic. This choice of utility function satisfies the conditions necessary for exact linearaggregation, described succinctly in Deaton and Muellbauer (1983) and Blundell and MaCurdy(2007). As seen below, this makes the simulation more tractable.24 The individual’s maximizationproblem is given by:

max`it,cit,sit,mit

∑r

∑j

1(sit = j,mit = r)[βln(`it) + (1− β)ln(cit) + prj + ηirjt]

subject to

wmitsit`it + potyoit + patyat + petyeit + pstysit + rtkit = wmitsitT + rtK̄it + πit − τit

cit = (yeit)γe(ysit)γs(yait)γa(yoit)γo(kit)γk

The sector choice is denoted by sit and the region choice, by mit. The additive term prj

gives preferences for a particular region-sector combination that have a component that is commonacross individuals, as well as a component that can depend on observable characteristics Zit (suchas gender). Heterogeneous preferences for a region-sector combination arise in ηirjt. Meanwhile,yo,ya,ye,ys, and k are the amount of the composite good, the other agricultural good, ethanol,sugar, and capital that the individual consumes (or holds, in the case of capital). The price ofgood x is given by pxt, with rt denoting the capital rental rate. Leisure is denoted by `it. Thewage that the individual faces in the chosen market-sector is given by wmitsit , and this wage iscommon across all individuals. The share parameters β, γo, γe, γs, γa, and γk are common acrossindividuals, with the five γ’s summing to one. The time endowment is given by T , and the capitalendowment is given by K̄it; importantly, the time endowment is common across individuals butthe capital endowment is not.25 Land rents received appear as πit and net taxes are given by τit.I discuss land rents further in the next sub-section.

Define Mit = rtK̄it + πit − τit. This represents the individual’s potential non-labor income. Afundamental challenge in modeling labor supply in the setting here is that this non-labor incomeis unobservable. This is not simply because we do not observe K̄it. In a survey of this sort, we donot observe an individual’s share of household income. Moreover, assumptions about what shareeach individual gets are not sufficient to produce a satisfactory result because of concerns aboutunder-reporting of income in agricultural households in the PNAD.

24In preliminary estimation, I considered other preferences with the Gorman polar form, such as Stone-Geary

preferences. But, even though models were theoretically identified, variation in the data did not allow for identification

in practice.25I set T = 168, the number of total hours in a week.

13

In order to deal with the unobservable nature of income, it is preferable to use as flexible anapproach as possible. At the same time, it must remain tractable for use in simulations. Assumepotential non-labor income is given by Mit = eθi+µit , where both θi and µit represent individualheterogeneity. Here, θi can be thought of as a permanent component of individual heterogeneity,while µit is a transitory component.26 I assume a flexible distribution for Mit with the followingassumptions about the unobservables. All the θi, µit, and ηirjt errors are mutually independent,and µit and ηirjt are also independent of all covariates Zit. Furthermore, I assume the following:

µit ∼ N(0, σ2)

ηirjt ∼ Type I Extreme V alue

In the case of θi, I assume a discrete distribution with a finite number of points of support.This follows the non-parametric approach of Heckman and Singer (1984). I allow the distributionover these points of support to differ depending on the year under consideration, and to also differbased on whether the individual is high-skilled or low-skilled.

I discuss the specifics of estimation in the following section, but it will be useful to set outexpressions for the optimal hours choice and associated indirect utility function in each sector-market combination. Given a choice of r and j, these are:

hirjt = (1− β)T − βMit

wrjt(1)

Virjt = ln[w−βrjt (Mit) + w1−βrjt T ] + ln[Wt] + prjt + ηirjt (2)

where Wt is a function of the share parameters and national-level prices.Finally, to illustrate how the estimates of the utility function and non-labor income distribution

will be used in the simulations, I set out an expression for aggregate labor supply to a particularregion/sector. Some notation is necessary first. Let f(x) be the probability density function ofthe random vector x, and F (x) denote the corresponding cumulative distribution function. LetNt(z) be the total number of workers in period t with demographic vector Zit = z, and similarlylet Nrjt(z) be the total number of workers in region-sector combination (r, j) with this vector ofcharacteristics. Based on the expressions above, we can write period t aggregate labor supply inregion-sector combination (r, j), for workers with Zit = z, as:

LSrjt(z) = Nrjt(z)∫θ

∫µh(µ, θ)f(µ, θ|z, i ∈ (r, j))dµdθ

=Nrjt(z)

Pr(i ∈ (r, j)|z)

∫θ

∫µh(µ, θ)Pr(i ∈ (r, j)|µ, θ, z)f(µ, θ)dµdθ

= Nt(z)∫θ

∫µ[(1− β)T − β

wrjt(eθ+µ)]

eV∗rjt(µ,θ)∑

p

∑k e

V ∗pkt(µ,θ)dF (µ)dF (θ)

26This is an admittedly simplistic approach to the complicated problems of unobservable income and within-family

income sharing. Another way of approaching the latter issue would be with a bargaining model within families.

However, this introduces additional complexity to the model here and also carries the basic problem that a bargaining

model cannot be taken to the data without several further assumptions.

14

where Virjt ≡ V ∗rjt(µ, θ)+ ηirjt. An increase in labor supply to (r, j) from Zit = z workers can come

from an increase in the exogenous level of these workers, an increase in the share of workers in (r, j)conditional on µ and θ, or an increase in hours among these workers. Finally, total labor supply tomarket-sector (r, j) is given by LSrjt =

∑z L

Srjt(z).

A discussion of the fundamental assumptions here is warranted. First, I assume that all workerswork only in one sector; consequently, I use hours data only from the primary sector of employment.While this may not be a credible assumption in many developing countries, this assumption isreasonable in the case of Brazil. Table 2 displays the percentage of agricultural workers who reportworking hours in a second job in the PNAD reference period, where the sample is broken down bythe reported position in the primary job (hired labor, self-employed, or employer; non-remuneratedworkers are not included here). For each category, the proportion with a second job oscillates overtime, with an overall tendency to increase. Still, by 2002, the reported share for workers is onlyabout 5%, and less than 15% for employers.

A second important assumption concerns taking the number of workers as exogenous, as well asomitting the number of workers in the six states of the North. This will not pose a problem for theconsistency of estimates, as long as we assume that the unobservables determining income have theabove distributions, conditional on choosing to work in the non-Northern parts of Brazil.27 Givendata limitations, it would be difficult to incorporate the North region. However, a more completemodel that could be pursued in the future would allow for a choice on the extensive margin as well.Such a model would yield different predictions in the simulations, as increasing wages could drawmore people into the labor force (or increasing incomes through rents could drive workers out of thelabor force). Nonetheless, the simulations used here are still informative. A look at changes in laborforce participation in Brazil over the 1992-2005 period shows that changes in participation rateshappen within a relatively narrow band. In this period, the highest participation rate was 66.7%,while the lowest was 63.7%. The same is true when participation is broken down by gender, since asubstantial portion of the rise in female labor force participation in Brazil happened prior to 1992.The lowest female participation rate in the period was 48.4%, and the highest was 52.7%. Thissuggests that if the number of workers in 2004 is used in the simulations, the simulations will yielda reasonable first-order approximation to the changes in labor supply even without considering theextensive margin.

As a final note, a potential complication in this model concerns those who declare themselvesas employers, self-employed, or non-remunerated family workers. These individuals are treatedanalogously to wage laborers. Employers and the self-employed work in their chosen sector for thesame wage as other hired laborers in that sector; profits from their enterprises are treated as non-labor income. Non-remunerated family workers are also treated as hired laborers. In particular,a producer (employer or self-employed) is indifferent as to whether a unit of labor comes from ahousehold member or someone outside of the household. At the same time, a member of a firm-

27When making this statement, I have in mind a model in which an additional region (i.e., the North) and an

additional sector (i.e., unemployment) are added to the current model, and independent Type I errors indicate

heterogeneity in preferences over the new set of regions and new set of sectors. Because of the IIA property, this

does not cause much of a complication. Taking unemployment into account in the simulations would be non-trivial,

however.

15

operating household is indifferent between working inside the household and outside the household;in particular, there are no institutional impediments to working outside the household. Essentially,this is an assumption that labor markets are complete and there are not efficiency gains to hiring afamily worker over an outside worker (because of lower monitoring costs, for instance). Under theseconditions, the model here is related to a traditional household maximization problem in which“separation” between the household-operated firm and consumption decisions allows for a two-stage maximization process in which firm profits are maximized independently of other householdcharacteristics. 28

The appropriateness of this assumption depends in part on the amount of frictions in the labormarket. The significant hiring costs in Brazil suggest that frictions will be present in the outsidelabor market, though the fact that informality is fairly common indicates that these frictionsmay not always be potent. Fixed costs to working outside the household could also undermineseparation. Table 3, based on the PNAD data for households with someone working in agriculture,suggests that a fair number of agricultural households overcome whatever cost there is. Each rowof the table gives the percentage of households in the delineated group, where the first row refersto households in which a respondent declares himself as self-employed and no household memberswork outside the household, the second row consists of households in which all members work ashired laborers, the third row refers to households with a mix of self-employed and hired laborers,and the fourth row refers to households with employers. The remaining households have memberswho all declare themselves non-remunerated workers. Between 12% and 14% of households havebeen mixed in the years shown, suggesting that even if fixed costs to work or other frictions arehigh, they are not insurmountable.

4.2 Agricultural Production

Individuals and the government supply the land they own to agricultural production or non-agricultural use. The decision of how to use the land is assumed to be completely separable from anindividual’s other decisions about work hours, sector, and region. Land owners have some amountof land, and merely want to obtain as high a profit from it as possible. The framework presentedhere does not incorporate rental markets. Given the problems with land rental markets in Brazil,this is an advantage. Table 1, from Valdes and Mistiaen (2003), shows the small percentage of landoperators who rent their land, in many areas less than 10%.

Land is of heterogeneous quality, so a given hectare of land may garner a higher return insugarcane than in another sector, for instance. It may be possible that the highest value use isnot in sugarcane or other agriculture, but in the third use of non-agriculture. An individual whoemploys land in this third use is assumed to receive a transfer from the government in exchange.Such an assumption is necessary to assure coherence of the model, but also corresponds to realityin those cases in which the government subsidizes forest preservation by private landowners, rentsland for city use, etc.29 In addition, the government may own land. For the purposes of the

28Although the setup here differs from the more conventional household maximization problem, the basic principles

carry over to this case. Bardhan and Udry (1999) provide a compact discussion of the assumption of “separation” in

the household problem in Chapter 2 of their book. Useful references to empirical work on the subject appear there.29It is important to note that landowners sometimes hold land purely for speculative or savings purposes, and do

16

model, I treat this as an artificial distinction. The government acts like other individuals and usesland in the production of sugarcane or other agriculture if the value of these activities exceedsthe threshold value of the third, non-agricultural use.30 Any rents received are passed along toindividuals in lump-sum fashion. This ensures that all profits received by agricultural producersaccrue to individuals’ non-labor income.

To be specific about how heterogeneous land is allocated, consider a parcel of land. It is char-acterized by a vector of unobservables (u1, u2, u3) that help determine land quality. In particular,the effective units of the parcel in use j are given by eλjrt+ujrt , where λjrt is a region-time-specificindicator of land quality that is common across all parcels in region r. The profit from the twoagricultural endeavors - sugarcane (j = 1) and other agriculture (j = 2) - are compared to theprofit from preserving the land in its non-agricultural use. Taking the vector of heterogeneity(u1rt, u2rt, u3rt) as given, the maximum possible profit from using the parcel of land in either agri-cultural use comes from solving the following problem:

maxs,Lsrt πrt(u1rt, u2rt) =2∑j=1

1(s = j)[pjrtyjrt(Ljrt;ujrt)− wjrtLjrt]

where s denotes the choice of agricultural sector, Ljrt indicates the amount of annual labor hoursused on the parcel of land, and yjrt gives the amount of annual output on the parcel. The outputprice and wage in the region are given by pjrt and wjrt. For simplicity, I assume that outputprices for sugarcane and the other agricultural good do not vary by region. The profit-maximizingchoice of sector, output, and labor are functions of prices, wages, and the effectiveness of the parcelunder consideration in production. If the optimum exceeds the value of the outside option, whichis governed by u3rt, then the parcel is put into the appropriate agricultural use. Otherwise, theparcel goes to non-agricultural use. That is, if π∗jrt(ujrt) indexes the optimal profits in use j, forj = 1, 2, and π∗3rt indexes the value of the outside option j = 3, then the parcel is used for j if andonly if:

π∗jrt > π∗krt for k 6= j

To make further progress, I need to specify the production functions, make distributional as-sumptions on the vector u, and make several normalizations. To begin with, assume the productionfunction for sugarcane is given by the CES function:31

not use it in agriculture for this reason. Assuncao argues convincingly that savings during times of uncertainty can

be a significant motive for a large number of landowners in Brazil. See, e.g., Assuncao (2006). de Rezende (2002)

and Helfand and de Rezende (2004) also point to the role of the macro economy in determining the evolution of land

prices over time, as investors responded to changing risk structures. Such motives cannot be taken into account in

the necessarily simplified model here.30In “using” the land, it is possible that the government simply rents it to individuals, who then put the land into

its highest value use.31Alternatively, one could rely on the simpler Leontief structure y1rt(L1rt; u1rt) = z1rtmin

�eλ1rt+u1rt , α1rtL1rt

.

Results relying on the simpler specification are not presented here, but are available upon request. Qualitatively, the

results are extremely similar.

17

y1rt(L1rt;u1rt) = z1rt((1− α1rt)(eλ1rt+u1rt)ρ1 + α1rtLρ11rt)

1/ρ1 (3)

where z1rt = eφ1rt is a scale or TFP term. Here, z1rt cannot be disentangled from α1rt and theland quality parameter entering through effective land units. I discuss this further in the section onestimation. The optimal product supply and labor demand for the parcel, as well as the optimalprofits, are given by:

y1rt(u1rt) = z1rt(1− α1rt)1

ρ1 (w1rt)1

1−ρ1

[(w1rt)

ρ11−ρ1 − (α1rt)

11−ρ1 (p1tz1rt)

ρ11−ρ1

]−1ρ1 eλ1rt+u1rt

L1rt(u1rt) = (p1tz1rtα1rt)1

1−ρ1 (1− α1rt)1

ρ1

[(w1rt)

ρ11−ρ1 − (α1rt)

11−ρ1 (p1tz1rt)

ρ11−ρ1

]−1ρ1 eλ1rt+u1rt

π∗1rt(u1rt) = p1tw1rtz1rt(1− α1rt)1

ρ1

[(w1rt)

ρ11−ρ1 − (α1rt)

11−ρ1 (p1tz1rt)

ρ11−ρ1

] ρ1−1ρ1 eλ1rt+u1rt

In contrast to sugarcane, the production function for other agriculture takes a Cobb-Douglasform:

y2rt(u2rt) = z2rtLα2rt2rt (eλ2rt+u2rt)1−α2rt (4)

where L2rt is the amount of labor hours used in the year, λ2rt+u2rt is the amount of effective landunits provided by the given parcel, α2rt is the usual share parameter, and z2rt is a scale or TFPterm. As discussed in the section on estimation, one cannot separately identify z2rt and the landquality parameter λ2rt. The optimal labor demand, product supply, and profits for the parcel aregiven by:

y2rt(u2rt) = (z2rt)1

1−α2rt (p2rtα2rt

w2rt)

α2rt1−α2rt eλ2rt+u2rt

L2rt(u2rt) = (p2rtz2rtα2rt

w2rt)

11−α2rt eλ2rt+u2rt

π∗2rt(u2rt) = (p2rtz2rt)1

1−α2rt (α2rt

w2rt)

α2rt1−α2rt (1− α2rt)eλ2rt+u2rt

Next, we need distributional assumptions for the distribution of land quality. Assume thatujrt = 1

2ηjrt for j=1,2,3, and that the ηjrt are independent and distributed as Type I ExtremeValue. That is, the probability density function of ujrt is given by 2e−2ujrte−e

−2ujrt and the ujrtare mutually independent.

Using these assumptions, I integrate over the parcels of land allocated to each use in orderto form the aggregate amount of land, product supply, and labor demand for each use. Beforedoing so, however, it is necessary to add one additional element to the model. As is clear fromthe set-up described thus far, the model imposes a tight relationship between shares of land andproduction. The production parameters govern both the share of land and production in each ofthe two activities, sugarcane and other agriculture. This imposes a situation of over-identification.Based on estimates of preliminary versions of the model, this results in an extremely poor fit, as wemight expect given the uncertainty about production levels when land decisions are made. Sincethe model here is static, it is impossible to introduce this uncertainty. Instead, we need anothervehicle to loosen the relationship between share of land and production.

18

For this purpose, introduce the vector (κ1rt, κ2rt, κ3rt). Let the perceived return of allocatinga parcel to use j be eκjrtπ∗jrt(ujrt). This new parameter can be interpreted in two ways. First,we can view it as optimization error; that is, people allocating land incorrectly perceive the returnthey can get for it. This accords with the argument about uncertainty given above. Second, wecan view it as frictions in the agricultural output market; those who farm land lose a portion of theprofits due to frictions. This does not pose a problem for the framework here as long as agriculturalprofits resulting from land allocation decisions accrue to some individual in the economy.32

By integrating over all the parcels in region r, one can then derive the share of land devotedto producing each good (Sjrt), total effective units of land in each good (Ajrt), total labor demandin each sector (LDjrt), and total supply of each good (Y S

jrt). The complete derivation appears in theappendix. For convenience, define πcjrt such that π∗jrt = πcjrte

λjrt+κjrt . Let cjrt = ln(πcjrt)+λjrt+κjrtand let A∗rt be the total amount of land in region r at time t. Then, dropping the (r, t) subscripts,for k, p 6= j:

Sj = Pr(cj + uj > ck + uk, cj + uj > cp + up)

=e2cj∑3i=1 e

2ci

Total effective land units Ajrt for j = 1, 2 are given by:

Aj = SjA∗E(eλj+uj |cj + uj > ck + uk, cj + uj > cp + up)

= A∗eλj√πSj

From this expression for total effective land units, and from the expressions above for theproduction and labor demand on each parcel, it follows immediately that:

LD1rt = (p1tz1rtα1rt)1

1−ρ1 (1− α1rt)1

ρ1

[(w1rt)

ρ11−ρ1 − (α1rt)

11−ρ1 (p1tz1rt)

ρ11−ρ1

]−1ρ1 A∗eλ1rt

√πS1rt(5)

LD2rt = (p2rtz2rtα2rt

w2rt)

11−α2rtA∗eλ2rt

√πS2rt (6)

Y S1rt = z1rt(1− α1rt)

1ρ1 (w1rt)

11−ρ1

[(w1rt)

ρ11−ρ1 − (α1rt)

11−ρ1 (p1tz1rt)

ρ11−ρ1

]−1ρ1 A∗eλ1rt

√πS1rt (7)

Y S2rt = (z2rt)

11−α2rt (

p2rtα2rt

w2rt)

α2rt1−α2rtA∗eλ2rt

√πS2rt (8)

Finally, I make several normalizations. I leave the details of these normalizations to the followingsection on estimation and identification.

There are two key assumptions here. First, there are no costs to adjustment of production.An ideal data set would provide information on farms over time, allowing a researcher to trackthe process of adjustment. Unfortunately, not only is there no panel data on farms in Brazil,

32An alternative to making these assumptions would be to model the land use decision more flexibly, perhaps

including ownership and rental decisions in a dynamic framework. However, this involves complicated, long-term

considerations that would be difficult to address credibly with the cross-sectional data here.

19

but also it is difficult to identify particular sugarcane producers in the available PNAD data. Asnoted previously, sugarcane is characterized by relatively few suppliers, with the market dominatedby especially large suppliers. This makes picking up sugarcane farms in representative householdsurveys extremely difficult. The problem is exacerbated when one wishes to focus on particularregions, especially areas such as Sao Paulo, where the concentration among sugarcane suppliersis especially pronounced. Therefore, when one sees the proportion of sugarcane farms evolve overtime in a particular state, much of this could be due to sampling noise. Table 4 shows the smallproportions of farms (i.e., self-declared employers or self-employed people in agriculture) that arein sugarcane by state, for a few key states. Therefore, any approach to adjustment costs wouldrequire looking at aggregates instead of individual farms. This is a potentially valuable topic forfuture research, but this is beyond the scope of this paper.

Finally, I assume that changes in international ethanol policies do not induce endogenouschanges in agricultural technologies. In particular, productivity in the agricultural sectors - and, aswe will see shortly, in the non-agricultural sectors - cannot be affected by firm composition effectsresulting from changes to the market environment. Hay (2001) and Muendler (2002) find that inBrazilian manufacturing, productivity tended to improve as firms faced increased competition dueto changes in the tariff structure. As export opportunities in ethanol improve, it is possible that acomposition effect could change productivities in various sectors as well.

4.3 Production Technologies in Ethanol, Sugar, and the Composite Good

The production of all three non-agricultural goods is characterized by constant returns to scale.Firms minimize costs in a static setting, leading to the usual zero-profit conditions in equilibrium.

As mentioned previously, ethanol and sugar take only capital and sugarcane as inputs. For bothgoods, I use Leontief production functions that are common across all regions:

Ysrt = min {αstY1srt, βsKsrt} (9)

Yert = min {αetY1ert, βeKert} (10)

where Y1jrt and Kjrt are the amounts of sugarcane and capital demanded by producers of goodj, where j = s for sugar and j = e for ethanol. The αst and αet are allowed to vary over time ina specific way delineated below, in the section on estimation. Every local market faces the sameprice of sugar and ethanol, pst and pet.33

The fixed-coefficients assumption is a reasonable starting point for analysis. In the production ofsugar and ethanol, substitution possibilities between sugarcane and other inputs (whether capitalor labor) are extremely limited; a certain amount of sugarcane is necessary to produce a givenamount of output, and a producer cannot economize on sugarcane by using more of other inputs.The Leontief structure leads to the equilibrium condition that will be used for estimation:

33Clearly, this is not the case in reality. Certain regional subsidies still exist, as well as the differing sugarcane

seasons that dictate where ethanol must be purchased from in different parts of the year. Both facts lead to some

price disparities across regions that the simple model here does not take into account. While there are some data on

region-specific prices (see http://cepea.esalq.usp.br/), these data do not use consistent definitions across regions and

are available, in any case, only for 2001 onwards for the Northeast.

20

Y1srt + Y1ert =Ysrtαst

+Yertαet

(11)

The composite good takes capital, high-skilled labor, and low-skilled labor as inputs. Theproduction function takes a Cobb-Douglas form, with the share parameters potentially differingacross regions:

Yort = zortLβhrhrtL

βlrlrtK

βkrrt (12)

where as usual βkr = 1 − βhr − βlr. The zort is a scale term that includes region-year shocks toproduction that are not directly observed, but can be backed out as a residual once the β’s andinput quantities are known.

4.4 The International Market

Since the focus of the paper is not the international market for ethanol or other products, I modelthe interaction between Brazil and the rest of the world in a very simple way. To begin with, allfinal goods - the composite good, the other agricultural good, ethanol, and sugar - can be exportedand imported. Individuals and firms in Brazil take prices of the composite good and the otheragricultural good as exogenous; that is, these prices are determined outside of Brazil. After theearly 1990s, this is a clearly tenable assumption in Brazil. Of course, the composite good in partrepresents services that are non-tradeable. In terms of manufacturing goods and agricultural goods,however, trade barriers fell markedly in the early 1990s. In the case of agriculture, Helfand (2003)illustrates how domestic agricultural prices move more closely with international prices after thereforms. Even though Brazil’s interaction with the international market is not my focus, assumingthat all goods are tradeable has important implications for the conclusions. In the current set-up,Brazilian consumers can freely substitute imported composite commodity goods for domesticallyproduced composite goods, so capital can move easily out of composite good production. If themodel were to take into account non-tradeables, capital could not move as much into ethanolproduction in response to greater opportunities for ethanol exports (assuming that non-tradeableproduction uses capital). My ongoing work addresses this issue. Despite this deficiency, the modelhere is still a useful first look at the questions at hand.

I assume trade barriers for sugar and ethanol that take a specific form. For ethanol, the USis the largest potential market for Brazilian output. Currently, the US protects its own ethanolproducers with a $0.54 per gallon duty on imports and a 2.5% ad valorem tariff. In the case ofsugar, barriers are also very high in the US and European Union (though Brazil is able to exporta large amount of sugar to other nations).

To avoid modeling US ethanol and sugar demand, or demand in other countries, I take astylized approach to modeling the current, baseline setting and the alternative setting in which theUS removes barriers to ethanol imports. I represent the current policy situation as one in whichBrazil faces downward-sloping domestic demand for ethanol and sugar, but also faces L-shapedinternational demand: International demand for both sugar and ethanol is perfectly inelastic atlow quantities, and perfectly elastic once the price gets low enough to overcome the effect of barriers

21

to trade. This price level at which international demand becomes elastic is assumed to be lowerthan the equilibrium price level before policy changes are made. In the baseline simulation, then,both ethanol and sugar prices in Brazil are determined by Brazilian demand.

Removing US import duties on ethanol is assumed to increase the price at which Brazilianethanol becomes competitive with US ethanol. The perfectly elastic portion of international demandmoves upwards, to a point higher than the price in the baseline equilibrium. That is, in thealternative simulation of no barriers, the ethanol price is set internationally and ethanol quantitiesare determined by total (Brazilian plus international) demand. In the simulations, I consider twocases: in the first, ethanol demand becomes elastic at a price 5% greater than the price in thebaseline scenario; in the second, ethanol demand becomes elastic at a price 10% greater than thebaseline. Finally, in order to close the model, I assume that the sum of net exports of the compositegood, other agricultural good, ethanol and sugar is zero.

4.5 Equilibrium

The model involves the following equilibrium conditions. Define Y Xjt to be net exports of good j in

year t. Let Y Djt be the total amount of good j consumed inside Brazil in period t. This is the sum

of demand by individuals in all regions and demand by the government. Let kDt denote demand forcapital by individuals. Additionally, Y D

1ert denotes the demand for sugarcane by ethanol producers inregion r, while Y D

1srt denotes the demand by sugar producers. Capital demand by firms and supplyare represented by KD and KS , with subscripts on capital demands expressing the destination ofthe capital. Let Y S

jrt be the amount of good j produced in region r at time t. Labor and landsupplies and demands are denoted analogously. Let Sjrt be the share of land in use j in regionr. Subscript “2” refers to other agriculture, though the price of other agriculture appears as pat.Finally, represent potential non-labor income of individual i residing in region r with Mirt. Thenequilibrium is given by the following relationships:

Y D1ert + Y D

1srt = Y S1rt∀r

LDjrt = LSjrt forj = 1, 2, 3,∀r

S1rt + S2rt + S3rt = 1

Y Dot + Y X

ot =∑r

Y Sort

Y Det + Y X

et =∑r

Y Sert

Y Dst + Y X

st =∑r

Y Ssrt

Y D2t + Y X

2t =∑r

Y S2rt∑

r

(KDort +KD

ert +KDsrt) = KS

t

potYXot + petY

Xet + pstY

Xst + patY

X2t = 0

potYDot + petY

Det + pstY

Dst + patY

D2t + rtk

Dt =

∑r

∑i

(wirtLirt +Mirt)

22

Non-labor income includes net transfers from the government. The government budget is bal-anced. I fix the level of government spending so that it does not change from the baseline settingto the simulated environments.

5 Estimation

I begin by illustrating basic patterns in the raw data and presenting descriptive statistics. In thesecond sub-section, I discuss the estimation procedure and estimation results.

5.1 Descriptive Patterns

The goal of this paper is to predict how the different regions of Brazil would respond to opportunitiesto expand ethanol production, with a focus on the consequences for land use and workers. Towardthis end, before estimating the model it is useful to first examine patterns in the raw data.

Figure 3 illustrates movements in sugarcane prices in Sao Paulo, the region containing MatoGrosso (denoted CW), and the region containing Pernambuco (denoted NE1). The price of sugar-cane moves in similar ways over the long run in various states. In the early portion of the studyperiod, this is the result of price setting by the federal government; however, prices exhibit similartrends throughout the latter half of the 1990s as well. There is some regional heterogeneity aroundthe common trend. Most notably, a significant decline in sugarcane prices occurred in Sao Pauloduring the late 1990s.34 Moreover, producers in the Northeast appear to see systematically higherprices than other parts of the country. This is likely the result of two related causes: first, sugarcaneis a locally marketed good and producers in the Northeast face higher costs of production; second,the government provides the Northeast with a small sugarcane subsidy to help offset higher costs.35

I abstract from these regional differences in sugarcane prices in the model below for the sake ofsimplicity, but this is a potentially important issue to consider in the future.

One immediately wonders how agricultural producers’ land allocations changed as sugarcaneprices moved in this way. Figure 4 shows how the area cultivated with sugarcane changed inthe same three regions over this time period. Of the three regions, the greatest absolute growthoccurred in Sao Paulo, though the Mato Grosso region exhibited very strong proportional growth.In Sao Paulo, growth proceeded at a moderate, though subdued pace, even during the time of thegreatest price declines. As we will see, this could be related to strong growth in labor productivityduring this period, allowing producers to buttress themselves against adverse price changes.

Table 6 shows land allocations and production quantities in 2004 for all seven regions usedin the analysis. The regions containing Bahia and Maranhao have negligible production relativeto the other regions, in both sugarcane and other agriculture. This is not simply due to lower

34This could be due in part to the introduction of new price negotiation mechanisms in the industry around this

time.35Unfortunately, time series data on the size of the government subsidies are not available, to my knowledge. The

Northeast region gets other benefits as well. For example, Brazil’s sugar export quota to the US has been assigned

to the Northeast in the interest of regional development.

23

population sizes; as seen below, there are important productivity differences at work.36 Exceptfor the sugarcane-growing states of Pernambuco and Alagoas (in the “NE1” region in the previousfigure), the Center-West and South-Southeast portions of the country serve as the agriculturalpowerhouses, in sugarcane and otherwise.

Understanding differences in hours and wages between sectors and across regions is equallyimportant to the analysis. Table 7 shows median hours of work and total number of workers ineach sector/region combination. The tendency of the Northeast region to have more employment inagriculture is clear. The significance of the sugarcane sector in the Northeast region with Pernam-buco is equally apparent. Hours of work tend to be higher among sugarcane workers; all else equal,the model interprets this as these workers having lower non-labor income than other workers. Afinal important detail in the table involves the small number of sugarcane workers in some sectors,especially the region containing Mato Grosso. The Mato Grosso region has high levels of productiongiven its level of labor. Partly, this is due to higher labor productivity. In part, however, this isdue to a measurement problem in the data, since the PNAD is only able to pick up a small numberof sugarcane workers in the region. 37

Table 8 shows median wages in the sugarcane sector, other agriculture, and non-agriculture in2004. All wages are in 2000 Reais, and the Real/US Dollar exchange rate was approximately 1.84Reais per US Dollar at the time. The non-agricultural wage is given for both low-skilled (fouryears or less of education) and high-skilled workers (more than four years). One point emergesimmediately. Wages in sugarcane tend to be higher than those in other agriculture, and sometimesthose in low-skilled non-agriculture. In Mincer-type regressions not shown here, I examine the roleof crop-specific wage premia for agricultural workers. I regress the logarithm of wages on controlsfor year, age, education, literacy, and gender, as well as on dummy variables for major agriculturalactivities (e.g., sugarcane, coffee, cocoa, livestock, etc.). I find large wage premia in sugarcane.38

In addition, the table demonstrates the large wage differences across regions in all three sectors.Figure 5 looks at the particular case of Sao Paulo and graphs median wages over time. In recent

years, the wages of sugarcane workers have increased relative to non-agricultural wages. This, incombination with the fact that the proportion of sugarcane workers has remained fairly constant,suggests that this period may have been characterized by increasing demand for labor in sugarcanebut decreasing supply (that is, a rightward shift in labor demand and a leftward shift in laborsupply to the sector).39

Interestingly, as sugarcane prices fell markedly through the 1980s, wages in sugarcane did not36This is not to say that these regions are not very productive in particular crops besides sugarcane. For instance,

cocoa is an important product in Bahia.37Looking at just one year of data is particularly troublesome, because it is difficult to know how much of the

small number of labor hours is real and how much is not. The model uses multiple years of data to average over the

sampling variation, but can do little to address the fundamental problem of small sample sizes.38This finding is not driven by the fact that the PNAD takes place during the sugarcane harvest. Using data from

the 2000 Census, which takes place at a different time period, I find the same feature.39A similar figure for Pernambuco suggests a slightly different story. Here, sugarcane wages also increased relative

to non-agricultural wages, but this came at the same time as a similar increase in the proportion of the workforce

in sugarcane. This is consistent with an increase in labor demand in the sugarcane sector, without a strongly

countervailing shift in labor supply.

24

fall sharply relative to other wages. There is some isolated evidence in the figure for Sao Paulothat sugarcane wages fell slightly relative to other agricultural wages. Still, the relative stabilityof sugarcane wages during this time is surprising. It suggests that the sugarcane labor market wasreasonably integrated with the remainder of the labor market or that productivity improvementshelped support sugarcane wages (or both).

Finally, to illustrate the scale of ethanol and sugar production, I include Table 9. This tableshows the quantities of sugarcane, ethanol, and sugar produced in each region in 2004, as well asthe implied values of ethanol and sugar sales. High ethanol prices in 2004 ensure that the valueof ethanol production out paces that of sugar production in even those regions that traditionallyfocus more on sugar (such as the Northeast region containing Pernambuco).40

5.2 Estimation Procedure, Estimation Results, and Calibrated Parameters

The estimation strategy involves three steps: first, use the survey data on wages, hours, and sectoralallocations to estimate the parameters governing individuals’ preferences; second, use the data onaggregate production, land usage, prices, and wages to estimate the agricultural production functionand land quality parameters; third, use aggregate production data to estimate a key ethanol andsugar production function parameter.

5.2.1 Individual Preferences

First, consider the estimation of labor supply. For estimation, I use an 8% random sample ofthe PNAD data for the years 1992-2005. This yields approximately 121,000 observations. Thelikelihood function is:

L =∏t

Nt∏i=1

Lit(hirjt, sit = (r, j)|Zit) (13)

where hirjt is the hours choice and sit is the region-sector choice, with sit taking a value (r, j), forr = 1, ..., 7 and j = 1, 2, 3. I assume that the distribution of θ can be approximated by a discretedistribution over a finite number of points on the real interval.41 Denote these points as (θ0, θ1, ...),and define πm|z = Pr(θ = θm|Zit) for non-negative integers m. Then, given the functional formassumptions from above, we have

Lit(hirjt, sit = (r, j)|Zit) = P ((r, j)|hirjt, Zit)∑m

πm|zg(hirjt|Zit, θim)

40This table also reveals a limitation of the analysis below, as regional differences in the ratio of ethanol to sugar

produced suggest possible differences in production structures across regions. The model assumes, in contrast, that

production structures are identical across regions. This is a deficiency that could be addressed in the future.41This method has been widely used in the labor literature, with just two notable examples being Mroz (1999) and

Eckstein and Wolpin (1999).

25

where, with f(µ) denoting the probability density function of µ, leaving the conditioning on Zit

implicit, and dropping several of the subscripts, we have:

P ((r, j)|h) =eprjw1−β(T − h)∑

p

∑k e

ppk

{w−βpktwrjt[T (1− β)− h] + βw1−β

pkt T} (14)

g(h|θm) =f

(ln[(1− β)T − h]− [θm + ln( β

wrjt)]

)(1− β)T − h

(15)

The demographic characteristics Zit include three dummy variables indicating whether or nota worker is female, whether or not the worker is older than 40, and whether or not the worker hasmore than 4 years of education.

I estimate three sets of parameters: β; parameters governing the income distribution, i.e. θmand πm for all m, as well as σ; and the sector preference shifters, allowing preferences for sector tovary by gender, age, and education. I allow the preference shifters prj to take the additive formp0rj + Zitp

1j , where p1

j is a vector of parameters. That is, I estimate a base level of preferences forevery region-sector combination, and allow deviations from this base level according to how muchpeople with particular demographic characteristics favor a sector. The omitted region-sector isnon-agriculture in Sao Paulo. Weights on the points of support for income vary by year to capturethe fact that the non-labor income distribution - which is a function of capital and land rental rates- varies by year. Finally, weights over the discrete factors differ depending on whether a worker ishigh-skilled or low-skilled. Given that very little weight is placed on the third point of support, Ichoose not to use four points of support or more.42

In addition to estimating these parameters, I match the remaining share parameters in the utilityfunction (governing preferences for the composite good, ethanol, sugar, the other agricultural good,and capital) to 2004 data. In particular, I use the national income accounts in combination withthe net export condition and government budget balance to construct the total amount of eachgood demanded as a percentage of the sum of labor income and potential non-labor income. I treatinvestment and inventories in the income accounts as capital accumulation by individuals.43

I discuss the estimates and matched shares only briefly, since these estimates are not of intereston their own, but rather as an element of the simulations that follow. Table 10 displays the shareparameters from the utility function. Here, β is estimated in the maximum likelihood procedureabove, and its standard error appears in parentheses. All the share parameters - not including β -sum to one. The table shows that a very small share of income is spent on ethanol and sugar, andaround 10% is spent on agricultural goods.

Next, Table 11 provides estimates of the parameters governing the income distribution. Theestimation procedure allows the weights on the discrete factors to vary depending on the year.The weights (i.e., π’s) given here correspond to the estimates for 2004, and differ depending on

42Formal tests for the numbers of points of support are not available, though authors use various criteria as

approximate tests. In particular, in what would seem to be the most natural test, a standard likelihood ratio test

statistic is not asymptotically chi-squared. Estimating a previous version of the model with three points of support

did not alter results very much from two points of support.43The government does not hold capital, but is assumed to spend its net in-take on goods in the same proportions

as individuals.

26

whether someone is high-skilled or low-skilled. To be clear, π1 refers to the weight placed on thediscrete factor θ1. For both skill levels, only weights on the first two points of support are shown;the weight on the third point of support is small. The estimates of the income distribution showthat low-skilled workers are more likely to be a middle type for non-labor income (i.e., have theirpermanent component of non-labor income given by θ1). The lower income of low-skilled workerswill ensure that they tend to work more than high-skilled workers in the simulations, at any givenwage.

Implied non-labor income is high relative to labor income. However, this should not be sur-prising. A substantial number of Brazilians are self-employed or are employers. All profits fromthese enterprises enter the model as non-labor income, so that any self-employed person or em-ployer would be expected to have substantially higher non-labor income than wage income. Whencomparing total predicted income in a year (wage income plus potential non-labor income) to to-tal consumption and investment by families in the 2004 national income accounts, I find that thepredicted total is only slightly more than total consumption and investment.44

Finally, Table 12 shows estimates of the preference shifters. I do not present odds ratios forthe sake of brevity. The first section of the table gives estimates of p0

rj (i.e., the base utility froma sector, with the relevant parameter set to 0 for Sao Paulo non-agriculture). Within this section,the estimates are divided by region. Controlling for wage differences, it is immediately clear that,regardless of the region, sugarcane and other agriculture are undesirable to work in relative to non-agriculture. The next section of the table shows deviations from these base values for particulardemographic groups in sugarcane and agriculture (with non-agriculture serving as the normalizingchoice). The estimate for low-skilled workers in sugarcane shows that these workers tend to findworking in sugarcane less distasteful than their higher educated counterparts. Moreover, womenand older workers have a tendency to find sugarcane work distasteful on the whole.

5.2.2 Production Functions: Sugarcane and Other Agriculture

With a few additional assumptions and normalizations, equation system 5-8 yields equations conve-nient for estimation. First, it is necessary to consider identification issues. As noted above, it is notpossible to separately identify z1rt(= eφ1rt), α1rt, and λ1rt.45 Since the level of α1rt is arbitrary, Iset it to 1

2 . This implicitly assumes a commonality in the production process that forces differencesacross regions in production and labor choices to come through λ1rt and z1rt (land quality andTFP, respectively). Turning to the production of the other agricultural good, z2rt clearly cannotbe separately identified from λ2rt. I set z2rt equal to 1 for all regions and time periods, recognizingthat λ2rt then captures differences in both land quality and total factor productivity.

Under these assumptions, observing agricultural production levels helps to identify λ1rt andλ2rt. However, there is no way to disentangle κ3rt and λ3rt. Therefore, I set κ3rt = 0. It is alsoimpossible to disentangle λ3rt, κ1rt, and κ2rt. Consequently, I simply set κ1rt = 0. Thus, κ2rt

captures the relative tendency to put land into non-sugarcane agricultural use, regardless of the44For the purposes of computing the share parameters, I take the small excess out of government expenditures.45To see this, consider a vector (ρ1, z1, α1, λ1) that satisfies the system for a particular region and year. If z̃1 =

(α1α̃1

)1/ρ1z1 and eλ̃1 = ( α̃1α1

1−α11−α̃1

)1/ρ1eλ1 for some α̃1 in (0, 1), then the vector (ρ1, z̃1, α̃1, λ̃1) also satisfies the system.

27

level of profits received directly by producers.After dealing with these identification issues, one needs to parameterize λjrt for j = 1, 2, φ1rt

and α2rt. Assume that for each j, λjrt takes the following form:

λjrt = λ0jr + λ1

jrt+ νjrt for j = 1, 2

where νjrt is a mean-zero shock to land quality that is common to all parcels of land in the givenregion. Setting the moments of these land shocks to zero will identify the parameters. Set φ1rt =φ0

1r +φ11rt+φ2

1r(t−1991) ·1(t ≥ 1992) and α2rt = Φ(α02r +α1

2rt+α22r(t−1991) ·1(t ≥ 1992)), where

Φ(.) is the standard normal cumulative distribution function. This particular parameterization ispurely a mechanism to ensure that the share parameter α2rt remains between 0 and 1.46

Finally, measurement error plays an important role in estimation, as follows. The informationon a respondent’s industry and hours of work relates to one week in September. To construct totallabor hours in sugarcane and other agriculture, I simply multiply the total hours in the PNAD forworkers in these sectors by 52. This measure may have a great deal of error associated with it.47

Another measurement issue is equally important. The Brazilian government altered the definitionof work between the pre- and post-1992 PNAD surveys. Before 1992, subsistence workers or non-remunerated workers who worked fewer than 15 hours per week were not counted as working in thesurvey. This is not likely to affect the measurement of labor hours in sugarcane because sugarcaneworkers are predominantly hired laborers. However, this has a substantial effect in the case of otheragricultural production.

To take these issues into consideration, assume first that measured labor hours in sugarcaneare given by Lm1rt = L1rte

ζ1rt , where ζ1rt is independent, mean-zero measurement error and L1rt isthe true value of labor hours. In regards to other agriculture, assume that after 1991, measuredlabor hours are Lm2rt = L2rte

ζ2rt , with these items having analogous interpretations to the case ofsugarcane. Prior to 1991, I introduce an additional factor ψ2 to capture the effect of the changein definitions, so that Lm2rt = L2rte

ζ2rt−ψ2 . I assume ψ2 is the same across all regions and all timeperiods t such that t < 1992.

These assumptions and some algebraic manipulation of equation system 5-8 yield the followingequations for sugarcane:

log(Y S

1rt

A∗rt(πS1rt)1/2) = φ1rt + λ1rt −

1ρ1log

[2− (

p1teφ1rt

2w1rt)ρ1/(1−ρ1)

]+ ν1rt

log(Y S

1rt

LmD1rt

) =1

1− ρ1log(

2w1rt

p1t)− ρ1

1− ρ1φ1rt − ζ1rt

46In estimation, I actually measure time from 1980, so that t = 1 corresponds to 1981. The post-1992 increment

to the time trend still starts from a value of 1 in 1992.47If “52” is the incorrect factor to multiply weekly hours by, then in general all the estimates shown here will be

affected, and the approach taken below is not an adequate remedy. This warrants a sensitivity analysis to determine

how this choice affects the conclusions here; I do not undertake such an analysis here.

28

And for other agriculture:

log(Y S

2rt

A∗rt(πS2rt)1/2) = λ2rt +

α2rt

1− α2rtlog(

p2tα2rt

w2rt) + ν2rt

log(w2rtL

mD2rt

p2tY S2rt

) = log(α2rt)− ψ21(t < 1992) + ζ2rt

Here, φ1rt,α2rt, and λjrt for j = 1, 2 take the form given above. Sjrt is the share of land in use j inregion r at time t. In what follows, I allow φ0

1r to shift by an amount common to all regions priorto 1992. This pre-1992 shift is given by φ0

92.I begin by estimating the sugarcane equations jointly.48 Each observation is a region-year

combination, and I use data from 1981 through 2005. Instruments are necessary: prices and wagesin sugarcane are determined in equilibrium, and are therefore functions of the ν1rt errors. Therefore,non-linear least squares applied to the equation with the νirt shocks will yield inconsistent estimatesof the parameters. Sugarcane prices and wages do not serve as appropriate instruments in the secondequation either; this is because the measurement error contains the true value of labor hours byconstruction, and this true value is determined jointly with prices and wages. As instruments, Iuse region dummies, time trends (both baseline and the post-1992 increment), a pre-1992 dummyvariable, region-time interactions, and two excluded instruments. These excluded instruments arethe log of petroleum prices from the Nigeria port and the log of the total number of low-skilledworkers in the Brazilian population. Petroleum prices are determined internationally, and henceplausibly exogenous. The total number of low-skilled workers is assumed to be exogenous in themodel, since schooling decisions are not modeled. To the extent that schooling decisions respondto wages over time, this is not an appropriate assumption. I plan to experiment with alternativeinstruments in the future.

The results of estimating the two sugarcane equations jointly using GMM are presented in Table13. For each parameter, the first column shows the estimate, the second gives the standard error,and the third provides the t-statistic. The remaining columns give the 95% confidence interval.The standard errors are robust to heteroscedasticity. In each case where region-specific parametersare estimated, the Parana region serves as the base case, and the region-specific estimates are theregional deviations from this estimate.49

Three important points emerge from the estimation results. First, sugarcane production ischaracterized by a low degree of substitution between labor hours and land quality. The estimateof ρ1 implies an elasticity of substitution ( 1

1−ρ1 ) that is statistically significantly different from 1,which implies that a Cobb-Douglas specification is rejected. On the other hand, at conventionalsignificance levels, the null of the elasticity of substitution being zero cannot be rejected. Thisshould not be surprising. The most intensive use of labor in sugarcane is in the task of harvesting;

48I could also estimate all four equations simultaneously to further improve asymptotic efficiency of the estimates.

Due to the differences in the available observations for the sugarcane and other agriculture equations, this would

require minor modifications to the standard errors to take into account the unbalanced nature of the panel.49The usual test of over-identifying restrictions rejects the null hypothesis. This could indicate that the excluded

instruments are not valid. However, this could also point to problems with the specification. This issue deserves

closer examination in the future.

29

there is little room to expand output through increased labor hours if there is not more to harvest,and there can only be more to harvest if more effective units of land are used.

Second, Sao Paulo and the Mato Grosso region (the Center-West) exhibit high base levelsof labor productivity and strong growth in productivity as well. To see this, note that laborproductivity is given by −ρ1

1−ρ1 eφ1rt , so that all differences in labor productivity across regions come

through the φ1 parameters shown in the table. The third important point is that the Pernambucoregion makes up for weak labor productivity with a high base level of land quality. The coefficientindicating the trend in land quality in this region is negative, but it is small and statisticallyindistinguishable from zero. The Mato Grosso region has poor land quality in sugarcane, while SaoPaulo enjoys both high land quality and high labor productivity.

Next, I estimate the two equations for other agriculture jointly, using GMM. Unfortunately,data on total agricultural output by region are not available for the 1981-1984 period or for 2005.Therefore, this estimation relies on 119 region-year observations instead of the larger number usedfor sugarcane. Parana again serves as the base case in all situations where parameters are region-specific. Examination of the equations reveals that using region dummies, time trends (the baselinetrend and the post-92 increment), and region-time interactions as instruments is sufficient to identifyall the parameters. This makes outside instruments unnecessary.

The results appear in Table 14, where the layout is analogous to Table 13. Again, the standarderrors are constructed using a heteroscedasticity-consistent weighting matrix. There are severalnotable aspects of the results. First, the value of ψ2 captures the effect of the definitional changein the survey data. The estimate implies that the pre-1992 survey fails to capture approximatelyone-third of labor hours.50 Failing to take this into account would severely bias the remainder ofthe estimates. Second, judging from the α0

2r estimates, labor is substantially more important toproduction in the Northeast than in the remainder of the country. The estimates on the trend termssuggest that there is no real sign of convergence in this respect, either. Third, land quality alsotends to be lower than average in the Northeast of the country. Since this term in reality capturesboth land quality and total factor productivity, it is difficult to pick out the practical sources ofthis result, as it may come, for example, from differences in mechanization as well as differences insoil quality or weather.

Finally, the simulations will require values for λ3rt and κ2rt. These can be backed out using theestimates above. Manipulating the ratio of non-agricultural land to sugarcane land gives λ3rt as afunction of the land shares, profits in sugarcane, λ1rt, and ν1rt. Therefore, this relationship pinsdown λ3rt. I then calculate κ2rt using the relationship

κ2rt =√S2rt

S3rt

eλ3rt

(p2rt)1

1−α2rt (α2rtw2rt

)α2rt

1−α2rt (1− α2rt)eλ2rt

This provides values for all the agricultural parameters for all years. In the simulations, Ibenchmark the baseline simulation to the 2004 data. Therefore, I use the values only for this year.Table 15 shows the values of all the relevant parameters for each region in 2004.

50This is plausible, though somewhat high. The percentage of subsistence workers in Brazilian agriculture tends

to be around 20% of the total agricultural workforce.

30

5.2.3 Production Functions: Non-Agricultural Goods

Finally, I turn to the estimation of the production parameters in non-agricultural production. Inregards to sugar and ethanol production, there are two crucial data limitations. First, the amountof sugarcane used specifically to produce ethanol or sugar is not available. Instead, available dataprovide the total amount of sugarcane crushed in a region, the amount of ethanol produced, and theamount of sugar produced. I must rely on the structure imposed above to produce the estimates.

Second, these quantities - crushed cane and resulting ethanol/sugar amounts - are provided forthe local harvest year, which differs from the calendar year. I impute a measure of the ethanol andsugar quantities for calendar year t by taking a weighted average of the surrounding harvest years,where the weights differ between the Northeast region and the rest of the country.51 Below, I usedata on harvested sugarcane, rather than crushed sugarcane, in order to produce the estimates.There are discrepancies between the total amount of sugarcane crushed in a region and the totalamount produced, with the total amount produced being larger in every case besides one region-year observation. This should be expected: Sugarcane will be lost during transportation and initialprocessing, and a small amount of sugarcane is used for purposes other than ethanol and sugar.52

Here, I assume that all sugarcane goes into the production of either ethanol or sugar. Since theestimates rely on data on total sugarcane produced, rather than total crushed, the parametersreflect errors in this assumption as well as frictions that lead to lost sugarcane before crushing.

In addition to the assumptions above, I impose a proportionality assumption to ease estimation:assume that for all years t, δαet = αst, for δ equal to some positive constant. This assumption isplausible; shocks that make ethanol producers more productive will often make sugar producersmore productive, since in many cases these are the same producers. Assume further that αst =αs + εst, where εst is a mean zero shock to production. Let Y m

xrt denote the measured value ofYxrt, with Y m

xrt = Yxrt + ηxrt for x = s, e. Here, ζxrt is mean zero measurement error, distributedindependently of all shocks to production in the economy. Then equation 11 becomes:

Y1rt =Y msrt

αst+ δ

Y mert

αst− ζsrtαst

− δζertαst

This leads to two possible equations for use in estimation. The first merely rearranges the equi-librium condition, while the second works with national aggregates rather than regional quantities:

Y msrt

Y1rt− αs + δ

Y mert

Y1rt= εt +

ζsrtY1rt

+ δζertY1rt

(16)∑r Y

msrt∑

r Y1rt− αs + δ

∑r Y

mert∑

r Y1rt= εt +

∑r ζsrt∑r Y1rt

+ δ

∑r ζert∑r Y1rt

(17)

Either equation must be estimated with instruments, because the measured ratios are correlatedwith the measurement errors, by construction. Since there are only two parameters to estimate, Iuse a constant and one outside instrument in estimation. The excluded instrument is the logarithm

51The harvest year runs from May to April in the Center-South, and from September to August in the Northeast.

In recent years, the harvest period has been shifting somewhat in response to changing patterns in demand.52In a recent year, for example, approximately 10% of harvested sugarcane went to a use other than ethanol and

sugar. Such uses include chemical-based products, plastics, etc.

31

of petroleum prices at the Texas port. This appears to be a reasonable instrument. Petroleumprices move closely with ethanol prices, and there is a strong positive correlation between sugarprices and petroleum prices as well (though weaker than with ethanol). The prospects of sugar,ethanol, and sugarcane are closely related to petroleum prices. Table 16 shows results from thefirst-stage regressions using both the aggregate and the region-by-region data. These are regressionsof the ratio of ethanol to total sugarcane on the log or level of the petroleum price and a constant.In all four cases shown in the table, the instrument enters in a statistically significant manner at the5% level.53 In addition, petroleum prices - determined by the international market - are plausiblyindependent of the production shock and measurement error.

Table 17 provides the results from estimating equations 16 and 17 using either the level or logof petroleum prices as an instrument. The left panel shows the results corresponding to equation17, and the right panel shows those corresponding to equation 16. Robust standard errors appearin brackets below the estimates. The difference between the OLS and IV results is consistent withthe presence of conventional attenuation bias due to classical measurement error. Regardless ofwhether the log or level of petroleum price is used as an instrument, and regardless of whether weconsider the aggregate or regional model, the results are similar.

In deciding which estimates to rely on for the simulations, it is important to consider a dataquality issue. In total, the available data on ethanol and sugar production have 77 observations,with 11 observations over the 1995-2005 period for each region. Unfortunately, the quality of thedata appears mixed. For two regions in particular - the Bahia and Maranhao regions - the share oftotal harvested cane that is measured as crushed is less than 0.5, which appears too low. To testthe sensitivity of results to dropping observations with a low crushed cane share, I experiment withvarious thresholds. The reported results for the regressions in Tables 16 and 17 use data from allregions with a crushed cane share above 0.6. The results for the region-level regressions are sensitiveto changing this threshold, with a threshold of 0.5 yielding an estimate of approximately -2.3 for δ,for example. However, the aggregate regressions are substantially less sensitive to changes in thethreshold.

For this reason, I rely on the estimates of δ and αs from the aggregate regressions. In thesimulation, I use the estimates in which the log of the petroleum price serves as the instrument.This is because the resulting value of δ is in accordance with the effectiveness of sugarcane in sugarand ethanol production that is implied by the sugarcane pricing scheme used in Sao Paulo state.54

Finally, the simulations require values for εt, βst, and βet. I use the IV estimates to calculateresiduals for each region-year combination. Relying on those observations with a crushed caneshare above 0.6, I then regress these residuals on a full set of year effects, common to all regions.According to the structure of the model, these year effects correspond to εt. In benchmarking theethanol and sugar production functions to the 2004 data, I use the zero profit conditions to back

53The negative sign is somewhat surprising, since higher petroleum prices would be thought to favor ethanol at the

expense of sugar. But given the ties of sugar prices to ethanol prices, this sign should not be alarming.54Burnquist (1999) provides a succinct explanation of this pricing scheme, which came into force in 1999. The

scheme relies on the stipulation of conversion factors for total cane sugars recovered (Acucar Total Recuperavel, or

ATR) into sugar and ethanol. The implied value of δ is between 1.66 and 1.72, depending on whether one is dealing

with hydrous or anhydrous ethanol.

32

out values for βst and βet.The last piece of the production side to consider involves the supply of the composite consump-

tion good. Production of this good is characterized by a Cobb-Douglas function taking capital,high-skilled labor, and low-skilled labor as inputs. As noted above, the share parameters are al-lowed to differ across regions. The simplest approach to obtaining values of these parameters is tomatch them with the proportion of high-skilled labor wage bills and low-skilled labor wage bills intotal value of production in each region. This is the approach I take, using 2004 data to computethe high-skilled and low-skilled labor shares for each region. I do not report these shares here, butuse them in the simulations; the shares can be provided upon request. The most notable featureof these shares is that high-skilled labor and capital play a larger role in wealthier regions, as wewould expect.

6 Simulations of Alternative Scenarios

The parameter estimates have some intrinsic interest, but the primary goal of this paper is to usethese estimates to simulate the effect of changes in international ethanol policies on Brazil. Inorder to do so, I use the parameter estimates to form the system of nonlinear equations impliedby the equilibrium conditions.55 The equilibrium vector of prices and quantities is the vector thatsolves this system.56 I first solve the nonlinear system in the baseline case, where internationaldemand for sugar and ethanol is treated as inelastic and prices of sugar and ethanol are determineddomestically. Then I simulate two alternative scenarios. In the first, international demand forethanol becomes perfectly elastic at a world price that is 5% higher than the price in the baseline.In the second, demand again becomes elastic, but now at a world price that is 10% higher than thebaseline price.

There are simple analytical expressions for most components of the system of nonlinear equi-librium equations. The one exception involves the labor supply equations, which introduce twoimportant issues. First, equation 3 shows that the labor supply to a particular sector and regionwill involve integrating against the density of non-labor income. I numerically approximate this in-tegral using Gauss-Hermite quadrature with 10 nodes.57 The second issue involves the allocation ofchanges in non-labor income among individuals. As prices and quantities in the economy shift, thisinduces a shift in non-labor income. This change in non-labor income affects not only individuals’choice of hours of work, but also potentially affects their choice of sector and region. The difficultyis that the framework here is silent about the sources of individuals’ non-labor income.58 In view of

55Preliminary simulations of the baseline scenario showed implausibly large values of sugarcane wages in the

Center-West region. This could be related to statistical noise involving the extremely small number of sugarcane

workers appearing in this region in the random sample used for estimation. Accordingly, for the sugarcane preference

parameter in this region, I use the upper end of the 95% confidence interval for the parameter estimate.56In practice, after substituting out various endogenous quantities, I am left with 74 equations and 73 unknown

prices and quantities. I leave in the “extra” equation as a check to ensure that the model economy is closed.57Judd (1998) is an excellent reference for numerical integration techniques. This book includes a brief example

comparing the accuracy of approximations with different numbers of nodes. I have not yet explored how sensitive

the results here are to using a larger number of nodes.58In reality, if returns to land rented to sugarcane production increase, this gain accrues to those who own land

33

this, I use a simple allocation rule. I treat a change in non-labor income as a rightward or leftwardshift in the entire distribution of non-labor income, where the size of the shift is such that theresulting integral over non-labor income matches the non-labor income total in the economy. Thisprocedure scales individuals’ incomes up or down in proportion to their initial non-labor income.Therefore, as is intuitively desirable, an increase in the returns to capital or the returns to landresults in a larger absolute gain in non-labor income for those with larger initial non-labor income.

I begin by comparing prices and wages in the baseline simulation with actual 2004 data. Ifthe baseline values are close to the actual values, we may have more faith in the model and in thepolicy experiments. Table 18 compares aggregate production quantities and prices in the actualdata and baseline simulation. In the left panel, we see that the quantities of goods produced arequite close to one another. Slightly more ethanol and sugar are produced in the actual data thanin the baseline simulation, while the baseline involves somewhat more sugarcane production. Theprices in the right panel of the table are also comparable.

Table 19 takes a disaggregated perspective and examines land shares and wages in the actualdata and baseline simulation. In the panel with land share information, columns 1, 3, and 5 sumto 1, while columns 2, 4, and 6 also sum to 1 (subject to roundoff error). We see that 11.9% ofSao Paulo’s land is devoted to sugarcane cultivation in actual 2004 data. The baseline simulationyields a sugarcane land share of 13.1% for Sao Paulo, slightly larger. The “Other” columns showthe corresponding totals for the share of non-agricultural land. In regions such as Sao Paulo, arelatively large amount of non-agricultural land will be used for urban purposes, while in regionssuch as that containing Mato Grosso, more of this non-agricultural land will be essentially unused.The values in the “Other” columns are fairly close to one another in four regions. In the Bahia,Pernambuco, and Maranhao regions, the differences are larger. Still, the values are close enoughto provide confidence in the approach here.

Turning to wages, columns one through four in the “Wages” panel show actual and baselinewages for sugarcane and other agriculture. The largest discrepancies occur in Pernambuco andMaranhao, as baseline wages move higher to draw enough labor hours into the sectors. The nextset of columns in the panel show wages for non-agricultural high-skilled workers and non-agriculturallow-skilled workers. Again, there are discrepancies, but the overall pattern of results is reassuring.For instance, the ordering of the regions in magnitude of low-skilled non-agricultural wages is thesame in the actual and baseline results.

Given that the baseline simulation presents a fairly accurate picture of the actual data, I nextturn to comparing the baseline quantities and prices with those in the simulated environments.59

I start with Table 20. This table compares the baseline results with those in the two simulations.Hereafter, in the tables I refer to the simulation of the 5% price increase as “Sim 5%” and callthe simulation of the 10% increase “Sim 10%”. Regardless of whether the price increases 5% or10% when demand becomes elastic, the table shows that sugarcane and ethanol production increasesubstantially. Although it is not shown here, Brazilian consumption of ethanol falls slightly with theprice increase. Thus, all of the increase in ethanol production is directed toward ethanol exports.

that is relatively good for sugarcane use. Here, it is not possible to tell who owns what type of land.59The baseline simulation performs poorly, however, in regards to the total number of sugarcane workers per region

and average hours per worker. I discuss this below.

34

This results in a substantial jump in net exports of ethanol. In the baseline, ethanol exports are2.37 million cubic meters, or 626.35 million gallons. In the 5% experiment, this total jumps toabout 2.38 billion gallons. To get an idea of the size of this number, note that ethanol productioncapacity in the US in 2005 was approximately 4.3 billion gallons (see Tokgoz and Elobeid (2006)).The 10% experiment suggests an even larger jump in exports.60

The large rise in sugarcane production predicted in the policy experiments is driven by largechanges in land use. Table 21 examines these changes in each region. The results suggest thatthe threat of increased deforestation is minimal. The three panels of the table provide the shareof land devoted to each of the three uses. Within each panel, the columns present the results forthe baseline, the 5% experiment, and the 10% experiment. Each row of the table shows the resultsfor a particular region. For instance, 13.1% of land is devoted to sugarcane in Sao Paulo in thebaseline, while this number increases to 18.1% in the 5% experiment and increases to 23.3% in the10% experiment. The expansion of sugarcane cultivation in Sao Paulo comes at the expense of landfor other agriculture and land for non-agricultural uses, though the effect is larger in the case ofother agriculture, as one might expect in such a highly urbanized region.

The table suggests that changes in Sao Paulo drive the large sugarcane expansions in the policyexperiments. Land does move into sugarcane production in all the regions, but the changes are notas striking. In most of the other regions, the consequences for non-agricultural land are minimal.This is especially comforting in the Mato Grosso region, which includes a portion of the AmazonRainforest and relatively untouched portions of the Cerrado. The one exception to this rule isthe Pernambuco region, which is historically the most important sugarcane-producing region inthe Northeast of Brazil. This region exhibits strong growth in the proportion of land devoted tosugarcane. The policy experiments predict a small fall in non-agricultural land in this region, whichcould add some pressure on the Atlantic Rainforest, which has already been diminished to a narrowstrip of land over time.

The remainder of the tables speak to the regional development question set forth in the intro-duction. To begin with, Table 22 illustrates the predicted changes in hourly wages in sugarcane,other agriculture, high-skilled non-agriculture and low-skilled non-agriculture. In moving from thebaseline to the simulated environments, the wage increases are concentrated in the sugarcane sector.The largest wage increase in absolute terms occurs in the Mato Grosso region, in the case of the 5%experiment. In a few cases, sugarcane wages under a simulated experiment actually surpass high-skilled non-agricultural wages. This result is unsettling, and suggests potential avenues to improvethe model. Still, one should not discount this possibility out of hand. Importantly, sugarcane wagesbeing higher than high-skilled non-agricultural wages does not imply that sugarcane workers havemore total income than high-skilled non-agricultural workers. This is because a simple comparisonof wages does not take into account hours of work and, more critically, the high-skilled receivesubstantially higher non-labor income.

For all regions, there are not dramatic wage spillovers into other agriculture. Theoretically, the60This predicted increase in net exports is much larger than that predicted in Elobeid and Tokgoz (2006). The

discrepancy is likely due to my allowance for large influxes of land into sugarcane and, more importantly, my stylized

approach to modeling the fall in trade barriers. A more complete approach to reducing trade barriers would be

desirable, but the basic patterns in the results here still carry important insights.

35

effect of expanding sugarcane production on the other agricultural sector is ambiguous. On the onehand, rising wages in sugarcane will draw workers away from other agriculture. This puts upwardpressure on wages in other agriculture. Moreover, as other agriculture loses land to sugarcane,the production process will substitute labor input to the extent possible. On the other hand, asland moves into sugarcane and out of other agriculture, total production in other agriculture willfall, possibly pulling labor demand down with it. This would tend to put downward pressure onwages. The results show that the former effects tend to dominate, but only slightly. In the otheragricultural sector, wages increase very slightly in both policy experiments. The only region wherethis is not true is in Sao Paulo, where the decline in land for other agriculture is the most dramatic.Finally, wages fall slightly in non-agriculture, regardless of the level of skill. This effect is relatedto the movement of capital out of the production of the composite good. The upward pressure onwages induced by rising wages in sugarcane is not enough to counteract this effect; this suggeststhat the sugarcane labor markets are not closely linked to the non-agricultural labor markets, whichis not surprising given the region-sector preferences estimated above.

Tables 23 and 24 add richer detail to the picture provided in Table 22. These tables reveal theexact manner of adjustment in sugarcane labor markets. Essentially, the large increases in sugarcaneproduction are supported by marked increases in average hours of work among sugarcane workers,rather than a large influx of workers into sugarcane. Table 23 illustrates the picture for annualhours of work in each sector. The layout of the table is exactly analogous to the previous tables,while the entries are now millions of annual labor hours in a sector and region. It is immediatelyclear from the table that annual labor hours in sugarcane increase sharply in going from the baselineto either simulated policy environment. In contrast, the proportional declines in labor hours forother agriculture and non-agriculture are much smaller in magnitude.

Such an increase in labor hours can result from two sources: an increase in the total number ofsugarcane workers; or an increase in average hours among sugarcane workers. Table 24 indicatesthat the change in average hours is the driving force behind the jump in total sugarcane hours.This table shows that the number of sugarcane workers increases by only a small amount from thebaseline in each of the policy experiments. Looking at the 5% experiment, we see that the numberof sugarcane workers in Sao Paulo increases from approximately 125,000 in the baseline to about129,000 in the simulated alternative. The absolute increase in number of workers is in fact largestfor Sao Paulo and the Pernambuco region, with the latter region seeing a rise of around 5,000workers. The implied increase in average hours among workers is therefore large. In the 10% case,it is large enough to suggest that an alternative labor supply function - which allows the elasticityof hours to fall more as total hours worked increases - may be worthwhile exploring in the future.There is also a more general, related issue involving work hours: In the baseline for some regions,each sugarcane worker appears to work more hours on average than in reality, as the baseline hasfewer sugarcane workers in all in these regions. A comparison of Table 24 and Table 7 shows that,in fact, the model’s baseline shows significantly fewer workers in sugarcane than there are in realityin Pernambuco, for instance. In other cases, this effect goes in the opposite direction; for instance,the baseline simulation shows double the number of sugarcane workers in Parana that one sees inthe actual data. Regardless, the results here provide an important insight: the low desirability

36

of work in sugarcane ensures that earnings gains in sugarcane are concentrated on those alreadyworking in sugarcane in the baseline.

These tables carry information that is useful for thinking about the consequences of a policychange on regional development and inequality. However, Table 25 speaks to the introduction’sregional development questions most directly. This table is structured analogously to the previoustables, except now the first panel shows the expected wage for all workers (over all sectors), andthe second panel shows the variance in wages for all workers. Whether we consider the 5% or 10%experiment, the three largest proportional changes in expected wages from the baseline occur inthe Mato Grosso, Bahia, and Pernambuco regions, with the latter two regions in the Northeast ofBrazil. In the 5% experiment, the proportional change in expected wages (not shown in the table)is between 4.77% and 7.43% in the Northeast region (Bahia, Pernambuco, and Maranhao), whileit varies from 3.05% to 5.54% in the South-Southeast regions. However, only Bahia shows strongerabsolute growth in expected wages than Sao Paulo, Mato Grosso, and Minas Gerais. In this sense,inter-regional wage inequality - the gap in expected wages between regions - is not affected much,and in some cases actually worsens slighly.

Finally, in every region but the Parana region, within-region inequality actually increases. Wesee this in the second panel of Table 25, which shows the variance of wages under the baselinesituation and the policy experiments. In all but the first region, the variance is lower in the baselinethan in the simulated outcomes. The patterns we have seen thus far provide an explanation forthis. The lowest wages in each region are usually earned by workers in other agriculture. Thesewages do not increase much at all after the policy change, very few workers move into sugarcane,and the initially higher sugarcane wages increase substantially. In this case, it is no surprise thatthe variance of wages increases. The same argument does not hold for the Parana region, sincesugarcane wages start out lower than other agricultural wages in the baseline.

7 Conclusion

Interest in ethanol has increased in developed and developing countries alike. In industrializedcountries, ethanol could help ween people away from the petroleum that carries environmental andnational security risks. In middle-income and developing countries, ethanol could be a boon tothe rural poor. As Brazilian President Lula da Silva mentioned in his Washington Post editorialon ethanol: “This is a recipe for increasing incomes, creating jobs and alleviating poverty amongthe many developing countries where biomass crops are abundant.” This makes a crucial point.To be sure, if the US reduces trade barriers to ethanol imports, and if Brazil and the US succeedin starting a thriving international market in ethanol, this would primarily benefit Brazil in theshort-term. But in the long-term, this could affect many other countries in Latin America, Africa,and Asia, wherever biomass can be converted efficiently into energy.

And yet there is cause for concern in Brazil, and other countries may face similar concerns in thefuture. Two primary concerns involve the consequences of expanding sugarcane production on theenvironment and regional development. On the first point, environmentalists worry that increasingthe incentive to produce sugarcane could quicken the pace of deforestation in the Amazon, Cerrado,

37

and Atlantic Rainforest. The threat is greatest in the Center-West region of the country, whichcontains a portion of the Amazon, as well as relatively untouched parts of the Cerrado. On thesecond point, expanding sugarcane will certainly alleviate poverty among some low-skilled workers.This could be especially helpful to the poor Northeast region of Brazil, where sugarcane is a commoncrop. However, if wealthier regions expand cultivation more quickly, this could actually exacerbateregional inequality.

In this paper, I examine the consequences of relaxing US barriers to Brazilian ethanol fordeforestation and regional development. Regarding the issue of deforestation I find the following:

• The share of land devoted to non-agricultural purposes in the Center-West changes by muchless than a percentage point, even in the simulation in which world ethanol prices increaseby 10% over the baseline level. Correspondingly, the threat to the Amazon and the Cerradoappears minimal.

• The greatest expansion in sugarcane production - and the largest decline in share of land de-voted to non-agriculture - occurs in Sao Paulo, away from the most environmentally sensitiveareas.

• The primary threat of deforestation occurs in the area of the Northeast containing the statesof Pernambuco and Alagoas, where sugarcane production picks up strongly in response tochanges in ethanol export opportunities. This area contains a strip of the Atlantic Rainforest.

The analysis also has implications for the regional development issues discussed here. Specifi-cally, I find:

• The expansion of ethanol exports leads to a large rise in sugarcane wages in all regions ofthe country. The spillover effect on wages in other agriculture is generally positive, but quitesmall.

• Regions with the lowest expected wages before the policy change experience large absoluteincreases in expected wages; but the magnitude of wage increases are larger in some wealthierregions and, regardless, are quite similar in magnitude across all regions. Consequently, thegap in expected wages among regions does not close in general.

• In all regions, wage gains from the sugarcane expansion accrue primarily to workers who wereinitially in sugarcane before the expansion. Because of the undesirable nature of sugarcanework, very few workers move into sugarcane to take advantage of the higher wages there.

• Generally speaking, the variance of wages within each region grows due to the ethanol ex-pansion.

These results, taken together, suggest that expanding ethanol production could alleviate poverty,but that these benefits will be experienced primarily by those workers who are in sugarcane tobegin with. This is not, therefore, a broad-based strategy to alleviate rural poverty.

In crafting the model used to produce these conclusions, I have made many simplifications.These simplifications are of two types. The first type consists of those assumptions that can be

38

relaxed even with the limited data available, without introducing onerous technical issues. Forinstance, I intend to explore the effects of an alternative estimation strategy. Instead of estimatingpieces of the model in separate steps, I could use simulation-based methods to estimate all pa-rameters jointly within an equilibrium. Moving to such methods adds computational complexity,but offers some advantages as well. For example, it could free me from the necessity of assumingproduction functions that lead to estimating equations that are linear in the error term. Thisconstraint is binding as long as I rely on GMM methods.

The second type of simplification consists of those assumptions that cannot be reasonablyrelaxed in view of data constraints. Examining the issues behind these assumptions in greaterdetail would, however, lead to promising areas for future research. I have in mind three issues inparticular. First, I abstract from capital usage in agriculture. In the framework above, differencesin capital usage are captured purely by productivity differences. While one could make assumptionson production functions that allow for a role for capital in the current setup, these assumptionswould be highly stringent. To make headway on this issue, having farm-level data on capital usagewould be ideal. In the coming years, Brazil will release results from a new agricultural census, andthis could prove to be a useful source.

Second, the model developed here does not allow for adjustment costs and dynamic decision-making. Authors such as Carrington (1996) and Kline (2007) have examined adjustment costsby firms, while Lee and Wolpin (2006) examine multi-period decision-making by individuals in ageneral equilibrium model. In my case, this could be important when thinking about farmers’ cropchoices. Farmers face uncertainty about prices when making planting decisions, and they also facecosts of adjusting to other crops when relative prices change. However, the aggregate productionand land use data available in Brazil do not provide a good opportunity to examine these issues, atleast without assumptions much stronger than the ones here. To understand these issues in greaterdetail, it may be necessary to rely on panel data from other countries.61

This adjustment issue is connected with a third area for future work. The model assumesidentical production technologies across regions for ethanol and sugar. This is a reasonable firstapproximation. In reality, however, ethanol production capacity is much higher in the South-Southeast portions of Brazil than in the Northeast. In the long-term, production capacity canequalize across regions, and my model essentially focuses on this case. In the short-term, though, anethanol boom could have very different regional implications purely because of capacity constraints.Analyzing these types of issues in detail would require much more data on the details of the ethanoland sugar industries.62

While there are opportunities to investigate and relax the assumptions here, the results inthis paper still provide insight into issues that stimulate debate in Brazil, and that will becomeimportant for other middle-income and developing countries in the future.

61One possibility is the ICRISAT data on villages in India, but it is not clear how farmers’ decision-making and

technologies have changed in the intervening years since those data were collected. More recent panel data on farms

exist in Kenya, and this is another possibility.62Another interesting issue related to production capacity involves the relationship between ethanol/sugar producers

and independent sugarcane producers. If there is a very small number of mills or distilleries in a region, they may

exert some monopsony power.

39

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42

A Figures and Tables

Figure 1: Intensity of Sugarcane Cultivation, 1993

Figure 2: Brazil Political Map (Source: www.mapsofworld.com)

43

1015

2025

3035

Pric

e (2

000

Rea

is)

1980 1985 1990 1995 2000 2005Year

Sao Paulo CWNE 1

Figure 3: Regional Sugarcane Prices, 1981-2005

010

0020

0030

00H

ecta

res

(Tho

usan

ds)

1980 1985 1990 1995 2000 2005Year

Sao Paulo CWNE 1

Figure 4: Total Hectares of Harvested Cane, 1981-2005

44

01

23

4H

ourly

Wag

e (2

000

Rea

is)

1980 1985 1990 1995 2000 2005Year

Sugarcane Other Ag.Non−Ag.Low Non−Ag. High

Sao Paulo

Figure 5: Median Hourly Wages, 1981-2005

45

Table 1: Share of Number of Farms by Land Tenure

Tenure Status 1975 1980 1985 1995Owners 64.06 65.72 64.60 74.16Renters 11.41 11.36 9.91 5.51

Sharecroppers 5.98 6.18 7.65 5.70Squatters 18.55 16.76 17.84 14.61

Source: Valdes and Mistiaen (2003), page 119.

Table 2: Low Propensity to Work in Two Industries

% With Second IndustryYear Hired Labor Self-Emp Employer1981 2.18 4.95 9.041984 2.63 6.27 10.421987 3.69 8.36 12.351990 3.52 6.36 8.971995 4.45 11.87 14.151998 5.60 15.27 13.062002 4.90 11.31 14.51

Table 3: Household Earning Types

1982 1987 1992 1997 2002% of Hholds

Selfemp 42.0 38.2 39.6 41.7 41.0Hired 38.8 40.7 38.8 38.3 39.4

Selfemp/Hired 12.4 14.2 13.6 13.0 13.1Employer 6.7 6.8 7.4 6.5 6.0

Other 0.1 0.1 0.6 0.5 0.5

46

Table 4: Share of Total Firms in Sugarcane

State 1985 1989 1993 1997 2001 2005Pernambuco 0.015 0.006 0.003 0.001 0.009 0.008

Alagoas 0.028 0.020 0.002 0.022 0.016 0.014Sao Paulo 0.004 0.002 0.002 0.002 0.002 0.001

Mato Grosso 0.001 0.000 0.000 0.000 0.003 0.000

Table 5: Composition of Regions

Region Number Shorthand Reference States Census Region1 Parana Parana South

Santa CatarinaRio Grande do Sul

2 Sao Paulo Sao Paulo Southeast3 Minas Minas Gerais Southeast

Rio de JaneiroEspirito Santo

4 Bahia Bahia NortheastSergipe

5 Mato Grosso Mato Grosso Center-WestMato Grosso do Sul

GoiasTocantins

Federal District6 Pernambuco Pernambuco Northeast

AlagoasParaiba

Rio Grande do Norte7 Maranhao Maranhao Northeast

PiauiCeara

Note: The Federal District and Tocantins are not officially part of the Center-West Census region.

47

Table 6: Land Usage and Production, 2004

Land (Hectares) ProductionRegion Sugarcane Other Ag. Other Cane Other Ag.Parana 0.448 41.659 14.234 34.272 48941.965

Sao Paulo 2.952 13.516 8.350 239.528 29391.924Minas 0.566 36.761 30.314 37.065 20412.211Bahia 0.111 24.839 33.674 6.640 8094.818

Mato Grosso 0.517 101.248 86.609 38.046 31069.863Pernambuco 0.956 8.790 13.831 54.921 6516.194Maranhao 0.070 14.175 58.649 3.939 6353.036

Note: Cane production is in millions of metric tons; other agriculture production is in millions of2000 Reais.

Table 7: Median Hours and Number of Workers by Sector, 2004

Cane Other Ag. Non Ag. Low Non Ag. HighRegion Hours Number Hours Number Hours Number Hours NumberParana 48 14279 40 2747055 44 2110992 44 7247432

Sao Paulo 48 170583 44 798436 44 3292601 40 11647878Minas 45 45616 40 2225972 44 3273391 42 9728064Bahia 48 24269 40 2173301 40 1174989 40 2469945

Mato Grosso 48 18139 45 1126343 44 1204985 44 3380433Pernambuco 46 218763 36 1672384 40 1495943 40 2752258Maranhao 40 30810 35 2464627 42 1341852 42 2588688

Table 8: Median Hourly Wages in 2004 (2000 Reais)

Region Cane Other Ag. Low-Skill Non-Ag. High-Skill Non-Ag.Parana 1.015 1.055 1.476 2.029

Sao Paulo 1.483 1.181 1.623 2.367Minas 0.933 0.933 1.217 1.826Bahia 1.172 0.812 0.974 1.461

Mato Grosso 1.353 1.202 1.217 1.856Pernambuco 0.879 0.649 0.947 1.407Maranhao 0.568 0.609 0.879 1.217

48

Table 9: Cane, Ethanol, and Sugar Production

Ethanol SugarRegion Cane Quantity Quantity Value Quantity ValueParana 28.772 1.197 1463.889 1.806 694.658

Sao Paulo 218.963 8.889 10874.504 15.993 6152.400Minas 29.419 1.128 1379.780 1.995 767.405Bahia 3.712 0.119 145.073 0.242 93.162

Mato Grosso 37.922 2.014 2463.769 1.754 674.878Pernambuco 52.987 1.481 1812.443 4.229 1626.897Maranhao 1.695 0.114 138.906 0.019 7.123

Note: Ethanol quantity is in millions of cubic meters. Sugar quantity and sugarcane quantitiesare in millions of metric tons. Both values are in millions of 2000 Reais.

Table 10: Share Parameters of the Utility Function

Share Share of Labor and Non-Labor IncomeLeisure (β) Composite (γo) Ag (γa) Ethanol (γe) Sugar (γs) Capital (γk)

0.4139 0.6643 0.0995 0.0112 0.0029 0.2221(0.0004)

Note: All parameters except β are calculated to match shares in 2004. In contrast, β is estimatedalong with the region-sector preference shifters and non-labor income distribution. The standard

error appears below in parentheses.

Table 11: Parameters of the Non-Labor Income Distribution

95% Confid Int.Parameter Estimate S.E. Lower Upperπ1 High 0.071 0.005 0.061 0.080π2 High 0.928 0.005 0.919 0.938π1 Low 0.896 0.009 0.878 0.914π2 Low 0.102 0.009 0.085 0.120θ1 4.772 0.005 4.763 4.782θ2 5.602 0.003 5.595 5.608θ3 1.106 0.222 0.670 1.542σ 0.382 0.002 0.378 0.386

Observations 121523

Note: “π1 High” refers to the probability placed on the point of support θ1 for high-skilledworkers, while “π2 High” refers to the weight on θ2 for these workers. The “Low” parameters are

defined analogously. Here, σ gives the common variance of the underlying normal randomvariables.

49

Table 12: Estimates of Labor Supply Function

95% Confid IntParameter Estimate Standard Error Lower Upper

Pref for Reg/Sec:Parana:

Cane -7.017 0.155 -7.321 -6.713Oth Ag -2.569 0.028 -2.623 -2.515Non Ag -0.441 0.011 -0.464 -0.419

Sao Paulo:Cane -5.653 0.128 -5.904 -5.402

Oth Ag -3.736 0.040 -3.813 -3.658Minas Gerais:

Cane -6.726 0.137 -6.995 -6.457Oth Ag -2.724 0.026 -2.776 -2.673Non Ag -0.071 0.011 -0.092 -0.050

Bahia, Sergipe:Cane -7.706 0.186 -8.071 -7.342

Oth Ag -2.858 0.026 -2.909 -2.807Non Ag -1.300 0.014 -1.327 -1.272

Mato Grosso:Cane -8.181 0.188 -8.550 -7.812

Oth Ag -3.463 0.028 -3.518 -3.408Non Ag -1.175 0.012 -1.199 -1.151

Pernambuco:Cane -5.336 0.103 -5.538 -5.134

Oth Ag -3.147 0.028 -3.201 -3.093Non Ag -1.090 0.014 -1.117 -1.064

Maranhao:Cane -7.577 0.186 -7.941 -7.213

Oth Ag -2.725 0.026 -2.776 -2.674Non Ag -1.247 0.015 -1.276 -1.217

Dem Pref. for Sec:Sugarcane:

Female -1.415 0.103 -1.618 -1.212≤ 40 0.649 0.074 0.503 0.795

Low Ed 2.276 0.092 2.095 2.456Other Agric:

Female -0.251 0.017 -0.285 -0.218≤ 40 -0.048 0.017 -0.081 -0.015

Low Ed 2.171 0.019 2.133 2.208Observations 121523

50

Table 13: Estimates of Sugarcane Production Parameters

Standard 95% Confid. Int.Parameter Estimate Error t-stat Lower Upper

Common ParametersSub. Param (1/(1-ρ1)) 0.301 0.191 1.573 -0.074 0.675Pre-1992 TFP (φ0

92) 0.147 0.093 1.586 -0.035 0.329TFP (φ0

1r)Base Case -1.469 0.454 -3.238 -2.359 -0.580Sao Paulo -0.139 0.251 -0.555 -0.631 0.353

Minas -0.843 0.357 -2.358 -1.543 -0.142Bahia -1.270 0.445 -2.855 -2.141 -0.398

Mato Grosso -0.099 0.359 -0.275 -0.801 0.604Pernambuco -1.794 0.604 -2.969 -2.978 -0.610Maranhao -1.169 0.416 -2.807 -1.985 -0.353

TFP Trend (φ11r)

Base Case 0.008 0.034 0.244 -0.059 0.076Sao Paulo 0.081 0.041 2.000 0.002 0.160

Minas 0.016 0.039 0.411 -0.060 0.092Bahia 0.040 0.050 0.798 -0.058 0.139

Mato Grosso 0.116 0.060 1.921 -0.002 0.234Pernambuco 0.055 0.038 1.423 -0.021 0.130Maranhao 0.037 0.051 0.715 -0.064 0.138

Post-92 Trend (φ21r)

Base Case 0.114 0.061 1.855 -0.006 0.234Sao Paulo -0.139 0.072 -1.942 -0.279 0.001

Minas -0.036 0.070 -0.518 -0.173 0.101Bahia -0.137 0.088 -1.555 -0.309 0.036

Mato Grosso -0.184 0.093 -1.983 -0.366 -0.002Pernambuco -0.144 0.067 -2.134 -0.275 -0.012Maranhao -0.218 0.093 -2.327 -0.401 -0.034

Land Quality (λ01r)

Base Case 1.962 0.654 3.001 0.681 3.244Sao Paulo 1.418 0.287 4.947 0.856 1.980

Minas 1.230 0.491 2.503 0.267 2.193Bahia 0.402 0.449 0.897 -0.477 1.282

Mato Grosso -1.239 0.267 -4.642 -1.762 -0.716Pernambuco 2.893 0.804 3.599 1.317 4.469Maranhao -0.339 0.389 -0.872 -1.101 0.423

Land Trend (λ11r)

Base Case -0.035 0.020 -1.754 -0.073 0.004Sao Paulo -0.010 0.021 -0.498 -0.052 0.031

Minas -0.025 0.023 -1.120 -0.070 0.019Bahia 0.026 0.025 1.011 -0.024 0.075

Mato Grosso 0.003 0.024 0.144 -0.044 0.051Pernambuco -0.009 0.022 -0.414 -0.052 0.034Maranhao 0.087 0.042 2.065 0.004 0.170

Observations 154

51

Table 14: Estimates of Other Agriculture Production Parameters

Standard 95% Confid. Int.Parameter Estimate Error t-stat Lower Upper

Common Parameterψ2 0.398 0.072 5.543 0.258 0.539

Labor Share (α02r)

Base Case -0.588 0.151 -3.891 -0.884 -0.292Sao Paulo -0.112 0.242 -0.462 -0.587 0.363

Minas 0.019 0.159 0.118 -0.294 0.331Bahia 0.516 0.298 1.733 -0.068 1.100

Mato Grosso -0.036 0.162 -0.220 -0.353 0.282Pernambuco 0.339 0.190 1.784 -0.033 0.711Maranhao 1.208 0.281 4.295 0.657 1.760

Share Trend (α12r)

Base Case 0.010 0.017 0.593 -0.023 0.043Sao Paulo -0.021 0.027 -0.785 -0.074 0.032

Minas 0.001 0.022 0.063 -0.043 0.045Bahia 0.013 0.037 0.367 -0.058 0.085

Mato Grosso 0.000 0.020 -0.011 -0.040 0.040Pernambuco -0.011 0.023 -0.485 -0.056 0.034Maranhao -0.044 0.034 -1.289 -0.112 0.023

Post-92 Trend (α22r)

Base Case -0.073 0.023 -3.245 -0.118 -0.029Sao Paulo 0.024 0.034 0.710 -0.042 0.090

Minas 0.026 0.032 0.804 -0.037 0.089Baha 0.002 0.050 0.034 -0.097 0.100

Mato Grosso -0.004 0.028 -0.156 -0.060 0.051Pernambuco 0.050 0.031 1.597 -0.011 0.111Maranhao 0.066 0.045 1.469 -0.022 0.154

Land Quality (λ02r)

Base Case -1.107 0.351 -3.154 -1.795 -0.419Sao Paulo 1.050 0.352 2.982 0.360 1.741

Minas -0.389 0.410 -0.949 -1.193 0.415Bahia -5.536 1.360 -4.071 -8.202 -2.871

Mato Grosso -1.817 0.388 -4.679 -2.578 -1.056Pernambuco -1.964 0.600 -3.275 -3.140 -0.789Maranhao -10.834 2.405 -4.505 -15.547 -6.121

Land Trend (λ12r)

Base Case 0.095 0.016 5.956 0.064 0.126Sao Paulo -0.009 0.017 -0.525 -0.042 0.024

Minas -0.044 0.018 -2.445 -0.079 -0.009Bahia 0.080 0.064 1.262 -0.044 0.205

Mato Grosso 0.012 0.017 0.668 -0.022 0.045Pernambuco -0.004 0.028 -0.130 -0.059 0.051Maranhao 0.306 0.123 2.492 0.065 0.547

Observations 119

52

Table 15: Production Function and Land Parameters in Agriculture, 2004

Production Param. Land ParametersRegion ρ1 z1 α2 exp(λ1) exp(λ2) exp(λ3) exp(κ2)Parana -2.33 1.23 0.10 2.41 3.38 384.67 1.28

Sao Paulo 1.23 0.05 10.03 6.32 456.83 0.68Minas 0.49 0.18 5.55 0.88 404.38 2.35Bahia 0.15 0.33 9.01 0.13 287.48 3.08

Mato Grosso 1.63 0.08 1.03 0.81 498.65 4.62Pernambuco 0.12 0.28 51.71 0.41 282.70 1.23Maranhao 0.05 0.38 18.34 0.06 265.35 1.90

Note: z1 = eφ1rt refers to the TFP parameter in sugarcane production, while α2 is the share oftotal wage payments in total output value for other agriculture. Here, λ1, λ2, and λ3 govern “landquality” in each of the three uses, sugarcane, other agriculture, and non-agriculture. Finally, κ2 isthe factor indexing the propensity to use land in other agriculture, regardless of profit differences.

Table 16: Ethanol and Sugar Production: First-Stage Results

Aggregate Region-by-regionVariable Total-Level Total-Log Reg-Level Reg-Log

Petrol. Price -0.00012** -0.00011*[0.00003] [0.00004]

Log(Petrol. Price) -0.00653** -0.00592**[0.00143] [0.00214]

Constant 0.04565** 0.06403** 0.04353** 0.06027**[0.00205] [0.00559] [0.00275] [0.00839]

Observations 11 11 56 56R-squared 0.61 0.7 0.11 0.12

Note: Dependent variable is the ratio of total ethanol production to total sugarcane production.Standard errors in brackets. * significant at 5%; ** significant at 1%

Table 17: Estimates of Ethanol and Sugar Production Parameters

Aggregate Region-by-regionParameter OLS IV-Level IV-Log OLS IV-Level IV-Log

δ 1.249 2.035 1.879 0.568 1.978 1.775[0.451] [0.540] [0.476] [0.166] [0.779] [0.657]

αs 0.102 0.132 0.126 0.070 0.122 0.115[0.018] [0.022] [0.019] [0.006] [0.029] [0.025]

Observations 11 11 11 56 56 56R-squared 0.46 0.18

Note: Standard errors in brackets.

53

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60.

330

0.31

10.

291

Min

as0.

008

0.01

20.

017

0.53

20.

530

0.52

70.

459

0.45

80.

456

Bah

ia0.

002

0.00

30.

004

0.38

20.

381

0.38

00.

616

0.61

60.

615

Mat

oG

ross

o0.

003

0.00

40.

006

0.53

90.

538

0.53

70.

458

0.45

80.

457

Per

nam

buco

0.03

30.

052

0.07

20.

313

0.30

70.

300

0.65

40.

641

0.62

8M

aran

hao

0.00

00.

000

0.00

10.

132

0.13

10.

131

0.86

80.

868

0.86

8

55

Tab

le22

:W

ages

Und

erth

eB

asel

ine

and

Sim

ulat

edSc

enar

ios

(200

0R

eais

)

Suga

rcan

eO

ther

Agr

icul

ture

Non

-Ag

Hig

h-Sk

illed

Non

-Ag

Low

-Ski

lled

Reg

ion

Bas

eSi

m5%

Sim

10%

Bas

eSi

m5%

Sim

10%

Bas

eSi

m5%

Sim

10%

Bas

eSi

m5%

Sim

10%

Par

ana

1.06

21.

172

1.30

01.

071

1.07

31.

074

2.14

32.

141

2.13

91.

200

1.19

91.

198

Sao

Pau

lo1.

389

1.60

71.

839

1.09

51.

088

1.08

12.

490

2.48

72.

484

1.35

01.

349

1.34

7M

inas

1.24

21.

433

1.65

21.

035

1.03

71.

039

1.90

71.

904

1.90

11.

081

1.07

91.

077

Bah

ia1.

305

1.53

71.

793

0.96

70.

969

0.97

11.

520

1.51

81.

517

0.92

20.

919

0.91

6M

ato

Gro

sso

1.48

11.

767

2.09

61.

163

1.16

51.

167

1.91

21.

911

1.91

01.

110

1.10

91.

108

Per

nam

buco

1.16

01.

339

1.53

50.

933

0.93

40.

935

1.45

51.

454

1.45

30.

903

0.90

00.

897

Mar

anha

o0.

947

1.07

31.

215

0.88

50.

887

0.88

91.

309

1.30

81.

308

0.74

50.

743

0.74

1

Tab

le23

:A

nnua

lLab

orH

ours

Und

erth

eB

asel

ine

and

Sim

ulat

edSc

enar

ios

(Mill

ions

)

Suga

rcan

eO

ther

Agr

icul

ture

Non

-Ag

Low

-Ski

lled

Non

-Ag

Hig

h-Sk

illed

Reg

ion

Bas

eSi

m5%

Sim

10%

Bas

eSi

m5%

Sim

10%

Bas

eSi

m5%

Sim

10%

Bas

eSi

m5%

Sim

10%

Par

ana

6176

9144

2444

0743

8860

5060

1859

8616

089

1600

415

918

Sao

Pau

lo39

647

254

014

5514

2313

8710

755

1070

810

660

3013

730

009

2988

0M

inas

114

141

166

3454

3440

3424

7605

7548

7491

1938

519

231

1908

1B

ahia

4658

6824

1224

0223

9116

7516

5216

2932

0331

5231

03M

ato

Gro

sso

5060

7021

8121

7521

6926

1926

0525

9064

5564

1763

78Per

nam

buco

403

513

613

1561

1544

1525

1969

1941

1914

3294

3241

3187

Mar

anha

o24

3646

1797

1788

1778

892

866

838

1392

1347

1301

56

Table 24: Number of Cane Workers in Baseline and Simulated Scenarios

Low-Skilled High-SkilledRegion Baseline Sim 5% Sim 10% Baseline Sim 5% Sim 10%Parana 30315 30833 31450 9696 9709 9745

Sao Paulo 1.25E+05 1.29E+05 1.34E+05 38227 38688 39298Minas 41731 43030 44548 12998 13100 13267Bahia 15819 16420 17096 4886 4942 5024

Mato Grosso 14685 15367 16152 4441 4521 4633Pernambuco 1.65E+05 1.70E+05 1.76E+05 52100 52387 52902Maranhao 17020 17338 17719 5550 5542 5553

Table 25: Expectation and Variance of Wages Under Baseline and Simulated Scenarios

Expected Wage Variance of WageRegion Baseline Sim 5% Sim 10% Baseline Sim 5% Sim 10%Parana 1.192 1.228 1.271 0.087 0.079 0.077

Sao Paulo 1.378 1.450 1.530 0.131 0.143 0.182Minas 1.189 1.255 1.333 0.055 0.069 0.106Bahia 1.117 1.200 1.296 0.042 0.085 0.161

Mato Grosso 1.320 1.423 1.548 0.057 0.106 0.212Pernambuco 1.045 1.107 1.178 0.027 0.049 0.089Maranhao 0.906 0.949 0.999 0.021 0.028 0.044

57

B Construction of Variables

Below, I discuss the methods used to construct the key variables utilized in the analysis. Thedescription is divided up by the type of variable under consideration. The programs used toconstruct the variables and perform the estimation can be provided upon request.

Prices

• Price of Sugarcane: I take the price of sugarcane directly from the FGV-provided prices,which cover 17 states at one point or another during the period of study. FGV provides priceson a month-by-month basis. I use only the September price, since the PNAD questions referto this month. In a case in which a region includes more than one state with a sugarcane priceprovided, I use the average of the prices of all the states in that region. In a case in which nostates within a region have a sugarcane price, I use the sugarcane price of the closest regiongeographically. In the discussion of descriptive patterns, I illustrate regional differences insugarcane prices within any given year. For the estimation and simulations, I assume onesugarcane price exists for all of Brazil. For this purpose, I use the median price of sugarcaneacross the regions.

• Price of Sugar: The FGV provides a price index for refined sugar. In order to make theunit-less index refer to real quantities of sugar, I set the 2000 price equal to the price of apound of sugar on the international market (described below). I then use values of the indexto compute the implied prices in other periods. I deflate the resulting series of prices usingthe IPCA to form real prices.

• Price of Ethanol: As with the case of refined sugar, the FGV provides a price index forethanol. I benchmark this index using the implied price of a cubic meter of exported ethanolin a particular year. I obtained this price using data from SECEX.

• Price of Other Agricultural Good: The FGV provides a price index for all of agricul-ture. Using sugarcane prices and data on the share of agricultural production resulting fromsugarcane (from IBGE sources), I calculate a price index for “other agriculture.” Since sucha price has no natural units, I simply set the 2000 value to 100 Reais. This defines a “unit”of non-sugarcane agricultural production.

• Sectoral Hourly Wages: To construct the hourly wage, I multiply reported monthly earn-ings by 7

30 , and then divide the result by reported hours of work. In the analysis, I use theunconditional median hourly wage in the sugarcane sector and other agricultural sector in aparticular region as the regional wage for those sectors. In the case of the non-agriculturalsector, the skilled wage is the median wage, conditional on having more than a primary levelof schooling (4 years). The unskilled wage is the median wage, conditional on having fouryears of education or less. In calculating the wage, I use only the earnings of hired workersin the relevant sector. In particular, I do not use self-employment earnings.

Land Use

58

• Land Cultivated for Sugarcane: The Producao Agricola Municipal (PAM) data provideinformation on the number of acres on which sugarcane was harvested in a given year. It isimportant to note that the PAM data refers to the calendar year, rather than the sugarcaneharvesting year (which differs depending on the region of the country).

• Land Cultivated for Other Agricultural Uses: I impute the total amount of land usedfor other agricultural purposes due to data limitations. In the period extending to 1990, Ionly have PAM data on the acreage for the ten major crops. After 1992, the available PAMdata include a much larger range of temporary and permanent crops. I use this information incombination with data from the agricultural censuses of 1980, 1985, and 1995, which providesmore complete information. In particular, I use a scale factor defined by the ratio of thePAM acreage to the agricultural census acreage in a census year to estimate total acreagesfor non-census years. The agricultural censuses also provide information on total land usedfor pasture. To interpolate pasture values for a region between census years, I first run aregression on time trends and total cattle raised in a region in a year (available in PPMdata), allowing for region-specific intercepts. Not surprisingly, the resulting standard errorsare large, but as the purpose is only to obtain reasonable predictions, I use the regressionequation to predict values for pasture land for each non-census year in each region. I thensum the values for crop land and pasture land across all states, and compare it to the FAOdata on total agricultural land in Brazil. I proportionally scale all the state land values toensure that the sum equals the FAO value. Further details are available upon request.

• Non-Agricultural Land: Non-agricultural land in a region is simply the residual resultingfrom subtracting sugarcane and other agricultural land from the total land area of the region.Data on total land area is obtained from the Brazilian government. A given region maybecome slightly larger or smaller over time due to changes in method of measurement, changesin state boundaries, etc. Because of the difficulty in knowing the sources of this variation, Isimply use the given land area for each year.

Production Quantities

• Sugarcane: The PAM data provide the total amount of sugarcane produced by state eachyear.

• Other Agriculture: I use regional income accounts to find annual information on agricul-tural production over the 1985-2004 period. I then define “other” agricultural production asthe difference between the total production and the value of sugarcane produced.

• Sugar and Ethanol: I obtain data on the amount of sugar and ethanol produced in eachstate and year from the Brazilian government. These data span the 1995-2005 time period.Unfortunately, these data are for harvesting years, rather than calendar years. Using informa-tion on the months of harvesting years for each region, I proportionally allocate harvest-yeartotals to calendar years. Information on the amount of sugarcane actually crushed by millsand distilleries comes from the same source, and is dealt with in the same way.

59

Characteristics of Individuals

• Sector and Region of Work: Respondents in the PNAD report the sector of their primaryoccupation and - in the post-1991 versions of the PNAD - their secondary occupation. Iallocate a respondent to a sector if her primary occupation is in that sector. For region ofwork, there are a limited number of cases in which the survey respondent was not present inthe current state as of the last week of September (the reference month). In these cases, Isimply drop the respondent. In all other cases, the respondent works in the regional marketin which she is living at the time of the PNAD interview.

• Hours of Work: Respondents report their hours of work in a “usual week” for their primaryoccupation and - in the post-1991 versions of the PNAD - their secondary occupation. Forhours of work, I use only the hours worked in the primary occupation.

Other Variables

• World Sugar Prices: I use the price of a pound of sugar from the New York market, asprovided by the FGV. To translate this into Reais from US dollars, I use contemporaneousexchange rate data from the Central Bank of Brazil.

• Petroleum Prices: The FGV provides historical series of the price of a barrel of oil in USdollars at various points of origin. I use the prices for the Texas and Nigeria points of origin,relying on the Central Bank exchange rate series to convert these into Reais.

60

C Derivation of Land Shares and Total Effective Land Units

In the main text, I state expressions for the fraction of land used for a particular purpose, as wellas the total effective land units used for that purpose. Here, I provide a more complete derivationof those expressions. Beginning with the land shares, and using the same notation as used in themain text:

Sj = Pr(cj + uj > ck + uk, cj + uj > cp + up)

=∫ ∞

−∞

∫ cj−ck+uj

−∞

∫ cj−cp+uj

−∞8e−2uj−2uk−2upe−e

−2uj−e−2uk−e−2updupdukduj

=∫ ∞

−∞

∫ cj−ck+uj

−∞4e−e

−2(cj−cp+uj)

e−2uj−2uke−e−2uj−e−2ukdukduj

=∫ ∞

−∞2e−e

−2(cj−cp+uj)

e−e−2(cj−ck+uj)

e−2uje−e−2uj

duj

=e2cj∑3i=1 e

2ci

Total effective land units in use j, j=1, 2 can be derived as follows:

Aj = SjA∗E(eλj+uj |cj + uj > ck + uk, cj + uj > cp + up)

= A∗∫ ∞

−∞

∫ cj−ck+uj

−∞

∫ cj−cp+uj

−∞8eλj+uje−2uj−2uk−2upe−e

−2uj−e−2uk−e−2updupdukduj

= A∗∫ ∞

−∞

∫ cj−ck+uj

−∞4eλj+uje−e

−2(cj−cp+uj)

e−2uj−2uke−e−2uj−e−2ukdukduj

= A∗∫ ∞

−∞2eλj+uje−e

−2(cj−cp+uj)

e−e−2(cj−ck+uj)

e−2uje−e−2uj

duj

= 2A∗eλj

∫ ∞

−∞e−uje−e

−2uj (1+e2(cj−cp)+e2(cj−ck))duj

= A∗eλj√πSj

where the last equality follows from substitution and use of the known expression for the integralof a normal random variable. The expressions for aggregate supply and aggregate labor use followimmediately.

61