World Development Vol. 40, No. 4, pp. 850–864, 2012� 2011 Elsevier Ltd. All rights reserved

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www.elsevier.com/locate/worlddevdoi:10.1016/j.worlddev.2011.09.002

Welfare Outcomes and the Advance of the Deforestation Frontier

in the Brazilian Amazon

DANIELLE CELENTANOIMAZON (Amazon Institute of People and the Environment), Belem, Brazil

ERIN SILLSNorth Carolina State University (NCSU), Raleigh, USA

IMAZON, Belem – PA, Brazil

MARCIO SALES and ADALBERTO VERISSIMO *

IMAZON, Belem – PA, Brazil

Summary. — Frontier expansion in the Brazilian Amazon is often described as “boom-bust” development. We critically assess this char-acterization by mapping and estimating statistical models of welfare as a function of deforestation at the municipal level. After control-ling for potential confounding variables and spatial autocorrelation, estimation results are consistent with a frontier “boom” generatedby exploitation of natural resources, followed by a “bust” during which forests continue to fall but there is no compensating gain inwelfare. However, average per capita welfare increases again with deforestation at very high levels. This second turning point in averagewelfare, along with the strong bivariate correlation between deforestation and municipal GDP/km2, may encourage local leaders toequate deforestation with development. This confirms the need for international incentive payments for global public goods, such asbiodiversity and carbon sequestration, that are provided by the Amazon forest.� 2011 Elsevier Ltd. All rights reserved.

Key words — Brazil, Amazon, tropical deforestation, welfare indicators, frontier development

* This research was supported by the Gordon and Betty Moore Founda-

tion, the International Programs Office of the USDA Forest Service, and

the Fundacion Avina. We are grateful to Eugenio Arima, Paulo Barreto,

Subhrendu Pattanayak, Guillaume Rousseau, Rodney Salomao, Denis

Valle, Daniel Santos, Carlos Souza Jr., Marco Lentini, and Anthony A-

nderson for their collaboration and input to the analyses and concepts

during the preparation of this paper. Comments from four anonymous

reviewers also helped us significantly improve the manuscript. Final rev-ision accepted: August 12, 2011.

1. INTRODUCTION

Brazil has the world’s largest remaining area of dense trop-ical forest, representing both a classic frontier for economicdevelopment and a vitally important source of global environ-mental services. More than 760 thousand km2, almost 19% ofthe Amazon forest in Brazil, has been converted to other landuses—primarily farming and ranching (INPE, 2010; Pereira,Santos, Vedoveto, Guimaraes, & Verıssimo, 2010). The defor-estation frontier was galvanized in the 1960s and 1970s by gov-ernment road construction, colonization projects, andagricultural subsidies (Mahar, 1989). Today, deforestation islargely driven by private investors seeking to maximize profits,especially by supplying global and national markets fortimber, soybeans, and beef (Arima, Barreto, & Brito, 2005;Barona, Ramankutty, Hyman, & Coomes, 2010; DeFries,Rudel, Uriarte, & Hansen, 2010; Lentini, Pereira, Celentano,& Pereira, 2005; Nepstad, Stickler, & Almeida, 2006). The log-ging industry plays a critical role in opening new areas bybuilding unofficial new roads (Brandao, Souza, Ribeiro, &Sales, 2007; Perz et al., 2008; Verıssimo, Cochrane, Souza, &Salomao, 2002). The government also supports frontier expan-sion by investing in infrastructure, facilitating credit for agri-culture, and recognizing and supporting new settlements ofsmall farmers in forested areas (Barreto, Pinto, Brito, &Hayashi, 2008; Fearnside, 2005; Pacheco, 2009). Since 1990,deforestation has averaged 17.5 thousand km2 per year(INPE, 2010), making forest loss the largest source of carbondioxide emissions in Brazil (MCT, 2004). 1

Despite the timber resources and new agricultural land thathave been tapped through this frontier development process,poverty and other socio-economic problems remain more se-

850

vere in the Amazon than in most of Brazil, and the region isfar from achieving the Millennium Development Goals(Celentano & Verıssimo, 2007a). For example, the povertyrate in the Amazon remained constant at 45% between 1990and 2005, even while it fell from 42% to 31% in Brazil as awhole. Extreme poverty declined in the Amazon, but only athalf the rate as in the rest of Brazil (Celentano & Verıssimo,2007a). Schneider, Arima, Verıssimo, Barreto, and Souza(2002), Celentano and Verıssimo (2007b) and Rodrigueset al. (2009) suggest that the current frontier development par-adigm in the Amazon perpetuates a boom-bust economic pat-tern, with profits from logging first generating income andjobs, followed by a severe collapse as forest resources and soilfertility are depleted.

This begs the question of why regional and local govern-ment leaders would perpetuate such a model of development.One explanation is that the short-term appeal of the boomoutweighs the long-term costs of the bust. The opposite expla-nation is also possible: development of the frontier may even-tually lead to improved socio-economic conditions, in a longertime frame than typically considered in the literature on the

WELFARE OUTCOMES AND THE ADVANCE OF THE DEFORESTATION FRONTIER 851

boom-bust scenario. Yet a third explanation is that local lead-ers judge welfare differently, for example, focusing on munici-pal GDP (which translates directly into tax revenues), ratherthan income per capita or the poverty rate.

Another possibility is that the boom-bust pattern is an al-most inevitable feature of frontier development (Barbier,2005). This is consistent with the concept of the “resourcecurse” (Bulte, Damania, & Deacon, 2005; Ross, 2001). Coun-tries rich in natural resources—especially mineral resources—are said to suffer from this curse due to the volatility of pricesand hence income from natural resources, decline in the com-petitiveness of other economic activities, corruption, and weakinstitutions engendered by dependence on natural resources,and reduced incentives to invest in human capital (Gylfason,2001). When the resources run out, countries are not able totransition smoothly to other economic activities, and thus aneconomic bust follows the natural resource boom.

In the case of renewable resources such as the Amazon forest,economic exhaustion is not inevitable, because the resource canbe managed (Boltz, Carter, Holmes, & Pereira, 2001; Pearce,Putz, & Vanclay, 2003; Rice, Sugal, Ratay, & Fonseca, 1998;Uhl et al., 1997; Verıssimo, Barreto, Mattos, Tarifa, & Uhl,1992; Vincent, 1992). However, there has been limited uptakeof best management practices for harvesting timber—let alonelong-term sustainable forest management—due to a variety offactors including lack of credit, uncertain land tenure, and com-petition from illegal logging (Applegate, Putz, & Snook, 2004;Bacha, 2003; Barbier, 1995; Putz, Dykstra, & Heinrich, 2000;Verıssimo et al. , 2002). In practice, the Amazon frontier hasbeen characterized by highly profitable ’mining’ of timber re-sources (Barreto, Amaral, Vidal, & Uhl, 1998; Holmes et al.,2002), followed by conversion of forest into crops and pasture.This still leaves open the possibility of sustained long-term eco-nomic growth and improved standards of living based on theagricultural 2, industrial, and service sectors. On the other hand,poor governance, corruption (as recent working papers by Brol-lo, Nannicini, Perotti, and Tabellini (2010) and Caselli andMichaels (2009) have found to be associated with windfall in-come in Brazilian municipalities), and failure to invest timberprofits locally and productively also leave open the possibilitythat the frontier boom will be followed by a post-deforestationbust.

In this paper, we first describe the history and status of for-est resources and the deforestation frontier in the BrazilianAmazon. To assess evidence of a boom-bust pattern, we cate-gorize and map Amazonian municipalities into three “defores-tation frontier zones” based on their history of deforestation(cumulative deforestation through 2000), and we verify thatthese zones reflect stages in the frontier development processby comparing their current deforestation rates and demo-graphics. We also categorize and map municipalities in threewelfare zones, defined separately for GDP per square kilome-ter, the human development index (HDI), income per capita,

and the poverty rate. 3 This allows us to test for evidence ofboom-bust patterns through cross-tabulation of welfare anddeforestation categories. Next, we estimate and graph regres-sion models relating the welfare measures to historical cumu-lative deforestation, again testing for a boom-bust pattern(i.e., an inverted-U quadratic relationship) versus steadilyincreasing welfare with deforestation (i.e., a linear relation-ship) or a boom-bust and recovery (i.e., an N-shaped cubicrelationship, with two turning points). We compare the cubic,quadratic, and linear specifications based on both overallmodel fit (as represented by adjusted R2) and statistical signif-icance of the higher order terms. 4

While the maps and statistical models provide useful sum-maries of the observed patterns in current welfare and histor-ical deforestation, they cannot be taken as evidence ofcausality because they do not control for potential confound-ing factors that may be driving both deforestation and welfare.Thus, next, we add controls for biophysical, demographic, andaccess factors that have been identified as key constraints anddrivers of Amazonian development and deforestation(Chomitz & Thomas, 2001; Chomitz & Thomas, 2003;Laurance et al., 2001, 2002; Nepstad et al., 2001; Pfaff, 1999;Schneider et al. , 2002; Walker, 2004). We only include pre-determined variables that represent conditions prior to 2000(e.g., population in 1991), are time invariant (e.g., agriculturalpotential), or that change relatively slowly over time (e.g.,mining centers recorded in 2002). We further control for spa-tial autocorrelation through a spatial autoregressive model(incorporating spatial lag and error), and again compare mod-el specifications in linear, quadratic, and cubic forms.

Last, we explore the possibility that the welfare measures re-flect characteristics of a mobile frontier population, ratherthan just conditions in municipalities at different stages offrontier development. We, therefore, provide a final set ofmaps and statistics on migration patterns across the region,and discuss whether welfare is being imported (and exported)as the frontier develops.

2. METHODS

(a) Study area

We consider all municipalities in the Brazilian Amazonwhose original vegetation cover was at least 50% Amazonrainforest, according to IBGE’s maps of vegetation (IBGE,1997) and municipal boundaries (IBGE, 2001). Our studyarea, which has a different shape than either the legalAmazon or the Amazonian biome (Figure 1), includes 3.7million km2 in 408 municipalities (74% of the legally definedBrazilian Amazon). These municipalities vary in size from103 km2 to 160 thousand km2, with a mean of 9.3 thou-sand km2 (median = 3.7 thousand km2, SD = 17.1 thou-sand km2) (IBGE, 2000a). In our maps, the existence ofvery large municipalities can give the appearance of homog-enous areas of welfare that in fact simply reflect municipalboundaries. However, the municipality is the smallest unitat which data on welfare (in 2000) and its covariates (in1991) are available for the entire region.

(b) Data

The variables used in this paper as well as definitions,sources, and descriptive statistics are presented in Table 1.Municipalities as defined in 2000 are our mapping unit as wellas the unit of statistical analysis. The regression analyses use399 observations, due to lack of data for four municipalitiesand the exclusion of four state capitals: Manaus (1.4 millioninhabitants in 2000), Belem in including Ananindeua (1.7 mil-lion), Porto Velho (335 thousand), and Rio Branco (253 thou-sand). 5 Between the years 1991 and 2000, 143 newmunicipalities were created in the study area by municipaldivision. Thus, for 1991, we use data from UNDP (2003),which recalculated the 1991 census data in terms of the 2000municipal boundaries using census sectors (the smallest unitfor which census data are geographically identified).

Figure 1. Study area in the Brazilian Amazon (municipalities originally more than 50% forested). Sources of data: IBGE (1997, 2001).

852 WORLD DEVELOPMENT

(c) Analyses

(i) Spatial analysesWe first map deforestation and welfare across Amazonian

municipalities. Adapting the methodology of Celentano andVerıssimo (2007b), we sort municipalities into three categoriesbased on cumulative deforestation and each of the welfare cat-egories. We then test whether the apparent frontier boom seenin the maps is statistically significant, checking if welfare levelsare highest among municipalities in the active frontier, and ifdeforestation is most rapid among municipalities in the highestwelfare zones.

To calculate cumulative deforestation through 2000, we useArcGIS 9.0 to process land cover data generated from LAND-SAT images by Inpe (the Brazilian Space Agency). We correctan error in Inpe’s land cover map, which makes some munic-ipalities obscured by cloud cover in 2000 appear forested eventhough they were among the first colonized in the Amazon andare well known to have been deforested before 2000. 6 Thus,we include some information from 2001 and 2002 images clas-sified by Inpe in our maps. We exclude protected areas createdprior to 2000 (ISA, 2005) from the calculation of percent ofthe municipality deforested, since deforestation is legally pro-hibited in these areas and, therefore, tends to occur at reducedrates as compared to nonprotected areas (Adeney, Christen-sen, & Pimm, 2009; Soares-Filho et al., 2010).

To categorize municipalities according to their stage ofdeforestation, we apply the natural breaks classification meth-od of statistical mapping, using Jenks’ optimization algorithm(Jenks, 1967 as cited in Mitchel, 1999). This algorithm calcu-lates groupings of data values (e.g., cumulative deforestationin each municipality) to minimize total error, equal to thesum of absolute deviations around each of the class medians.We partition municipalities into three categories, as suggestedby previous cluster analysis of Amazonian municipalities(Celentano & Verıssimo, 2007b) and as the minimum neces-sary to explore whether there is a boom-bust pattern. To verifythat this method sorts municipalities according to their stage

in frontier development, we compare annual deforestationand demographic characteristics across categories. Finally,we apply ANOVA and the Tukey test to welfare measures (de-scribed next) across the three deforestation frontier zonesidentified.

To assess welfare, we use three common indicators of thepopulation’s welfare: the Human Development Index (HDI),income per capita, and the poverty rate (percent of populationin extreme poverty). We also consider the total income in themunicipality normalized by area: GDP per km2. We apply thenatural breaks classification method to each of these measuresto identify and map three categories of municipalities. 7 Final-ly, we test mean cumulative (total) and incremental (annual)deforestation across the welfare zones with ANOVA andTukey tests.

(ii) Bivariate analysesWhile partitioning the municipalities into three categories is

convenient for exploring and illustrating boom-bust patterns,it does not make full use of the available data. By regressingcontinuous measures of welfare on a continuous measure ofdeforestation, we can test for functional form. We comparethe quadratic form suggested by the boom-bust hypothesisto linear and cubic functional forms by examining both overallexplanatory power (adjusted R2) and the statistical signifi-cance (5% level) of the coefficients on the higher order terms(b2 and b3) in the following equation:

w ¼ aþ b1d þ b2d2 þ b3d3 þ �where w is a welfare measure, and d is cumulative deforesta-tion (ranging from 0.0 to 1.0). Consistent with the maps, wefind that a linear functional form is best for GDP/km2, withno gains in fit from adding quadratic or cubic terms. The pov-erty rate is best represented as a quadratic function of defores-tation, with a turning point equal to �b1/2b2. HDI andincome per capita are cubic functions of deforestation, withturning points calculated by setting the first derivative equalto zero and applying the quadratic formula. We illustrate these

Table 1. Variables used in the analyses (sources, definitions, and descriptive statistics)

Variable Source Definition Mean (SD)

Cumulative deforestation INPE (2010) % of municipality deforested through 2000 mapped withLANDSAT images (scale 1:100,000)

35 (34)

Incremental deforestation INPE (2010) Square kilometers of deforestation August 2000 to July 2001mapped with LANDSAT images (scale 1:100,000)

49.7 (118.8)

Income per capita UNDP (2003) Per capita GDP (US$/year) adjusted to local cost of living in 2000using purchasing power parity index (Taylor, 2002)a

809.87 (422.82)

GDP per km2 IPEA (2010) Municipal GDP (US$/year) in 2000 per km2 of municipal territory(excluding water bodies)a

21,340 (148,438)

HDI UNDP (2003) Quality of life index based on education (literacy rates andeducational attainment), per capita income and life expectancy in2000 (UNDP, 1990)

0.658 (0.064)

Poverty rate UNDP (2003) % of individuals with per capita income less than US$1/day(extreme poverty) in 2000

36.09 (15.56)

Agriculture potential IBGE (2002) % of municipality with medium to high agricultural potential asdetermined by soil fertility, physical and morphologicalcharacteristics, topography and limiting factors

15 (28)

Amazon River IBGE (2003) Amazon River passes through or within 50 km of municipality(binary variable)

12% of municipalities b

Distance IPEA (2000) Distance (in 100 km) in a straight line between the municipalgovernment seat and its state capital

0.37 (0.26)

Migrant population IBGE (2000b) % of 2000 population born outside the Legal Amazon 18 (21)Mining IBGE (2003) Presence of mining activities in 2002 (binary variable) 16% of municipalities b

Paved Roads IBGE (2003) Presence of paved roads in or within 10km of the municipality in2002 (binary variable)

40% of municipalities b

Population UNDP (2003) Total inhabitants in 1991 by municipality 19,271 (25,773)Total inhabitants in 2000 by municipality 24,166 (32,742)

Population growth UNDP (2003) % change in population during 1991–2000 15 (42)Population density UNDP (2003) Total inhabitants in 2000 per km2 of municipal territory (excluding

water bodies)18.0 (109.8)

Total inhabitants in 1991 per km2 of municipal territory (excludingwater bodies)

10.3 (26.5)

Rain Thomas (2003) Annual rainfall (m) 2.1 (0.36)Rural population UNDP (2003) % of population living in rural areas in 1991 61 (25)

% of population living in rural areas in 2000 52 (20)a Value converted to US dollars at 2000 exchange rate of US$1 = R$1.82.b Refer to Figure 2.

WELFARE OUTCOMES AND THE ADVANCE OF THE DEFORESTATION FRONTIER 853

estimation results graphically, as well as presenting the esti-mated model coefficients.

(iii) Multivariate analysesFor each municipality, we compile data on likely confound-

ers that could drive both welfare and deforestation, selectingexogenous variables that represent the biophysical environ-ment (rainfall and agricultural potential); mining resources(indicator for mining activity); historical occupation(population density and rural population in 1991); and accessconditions (distance to state capital, and indicators for munic-ipalities located with 10 km of paved roads, and 50 km of theAmazon River).

In addition to these observable factors, welfare, and defor-estation patterns across municipalities may also be driven byunobserved spatially correlated factors. This is confirmed byvariograms of the residuals from the first set of multivariateOLS regression models. To explore and control for spatialcorrelation of deforestation, potentially driven by theseunobserved factors, we estimate Spatially AutoregressiveModels (SAR) of welfare as function of deforestation andour set of observed covariates. We use the package spded(Bivand, 2010) in the statistical software R (R DevelopmentCore Team, 2010), testing for spatial lag and error.Specifically, we estimate the following model by maximumlikelihood:

w ¼ aþ q�S�wþ b1d þ b2d2 þ b3d3 þ ccþ �where q is the spatial autoregressive lag parameter, S is aninverse distance spatial weights matrix, 8 c is the vector ofpotential confounders included in the model, and � is the errorterm, which is itself a function of the spatial weights matrixand a spatial autoregressive parameter.

We employ the Lagrange multiplier tests (including ro-bust tests) for spatial lag and error. Based on these tests,we present results for a SAR model with spatial lag forGDP/km2 and SAR models with spatial lag and error forthe other welfare measures. We choose whether to specifydeforestation in linear, quadratic, or cubic form based onthe AIC and the statistical significance (5% level) of thecoefficients on the higher order terms, and we calculateturning points as described above. To provide a generalsense of the explanatory power of our final models, wereport Nagelkerke’s pseudo-R2.

Because historical data on migration patterns are notavailable at the municipal level prior to 2000, we do notinclude migration in the vector c, but we do examine thebivariate distribution of welfare and percent of the popula-tion who are migrants from different regions of Brazil, toassess the possibility that the “boom” is a product ofhuman capital and wealth “imported” by colonists whomigrate to the Amazon.

Figure 2. State capitals, transportation corridors (paved roads and main branches of the Amazon River), agricultural land (% of municipality in crops or

pasture in 1995), major mines, logging centers (production centers processing >100,000 m3 timber/year in 2004). Sources of data: IBGE (1996, 2003) and

Lentini et al. (2005).

854 WORLD DEVELOPMENT

3. RESULTS

(a) Overview of study area

The Amazon is far from homogenous in environmental, eco-nomic, and social terms, as confirmed by the large standarddeviations reported in Table 1. Annual rainfall varies from1,200 to 3,600 mm, with gradients from the south-east border(the driest) to the west and the Atlantic coast (the most hu-mid). This variation in precipitation combined with differencesin geology, soils, and topography results in different foresttypes, timber availability, mineral deposits, and agriculturalpotential (IBGE, 2002, 2003; Pires & Prance, 1985). Figure 2shows the distribution of logging centers, farmlands, andmines. Mineral resources (especially iron, bauxite, and gold)are concentrated in relatively few municipalities (16%) scat-tered throughout the region. Likewise, only 19% of the munic-ipalities in the study area were responsible for processing morethan 90% of the Amazonian roundwood produced in 2004(Lentini et al., 2005). The percent of municipal land in agricul-tural use (34% on average) varies widely (SD = 34) from 0% insome municipalities north of the Amazon river to 100% insome municipalities in the southeast (Figure 2).

(b) Deforestation frontier

By 2000, 405.5 thousand km2, or 11% of the study area, hadbeen deforested (Figure 3). In 2000–01, deforestation contin-ued apace, with annual incremental deforestation of14.7 thousand km2 (0.4%).

More than two-thirds of Amazon deforestation has takenplace within 50 km of major paved roads, resulting in theso-called “arc of deforestation” across the southern BrazilianAmazon (Chomitz & Thomas, 2001; Laurance et al., 2002;Nepstad et al., 2001). There were two major roads inaugurated

in the early 1960s: the BR-010 connecting Brasılia (the na-tional capital) to Belem (the capital of Para) in the east, andthe BR-364 connecting Cuiaba (the capital of Mato Grosso)to Porto Velho (the capital of Rondonia) in the west. Beforethese roads, rivers were the most important transportationcorridors, with the exception of a railroad in the far easternBragantina region, which was extensively deforested and con-verted to agriculture to supply the city of Belem by the early1900s (Vieira, Salomao, Rosa, Nepstad, & Roma, 1996). Asshown in Figure 3, the areas with the greatest cumulativedeforestation correspond to this railroad and the first majorroads in the east and the west. In the early 1970s, the Trans-amazonica/BR-230, which runs east to west across the statesof Para and Amazonas, and the BR-163, which runs southto north from Cuiaba (Mato Gross) to Santarem (Para), wereconstructed but not paved. The impact of these official roads ismagnified by the associated network of more than 300,000 kmof unofficial logging tracks (Brandao et al., 2007; Perz et al.,2008). In recent years, the most active deforestation frontier(with the highest annual deforestation rates) has been in thecentral Amazon, associated with soybean cultivation alongthe BR-163 (Fearnside, 2007). This area still had a moderatelevel of cumulative deforestation in 2000, as shown in Figures3 and 4.

The picture of frontier development that emerges from thehistory of roads in the region is replicated by our statisticalmapping of Amazonian municipalities into three frontierszones (deforested post-frontier, active deforestation frontier,and still forested pre-frontier) based on cumulative defores-tation through 2000 (Figure 4). From 0% to 100% of eachmunicipality had been deforested by 2000, with the meanof 35% reflecting the relatively large number of small munic-ipalities in deforested areas. By construction, cumulativedeforestation is significantly different across the three zones(Table 2). If the zone with intermediate levels of cumulative

Figure 3. Deforestation through August 2000. Source of data: INPE (2010).

Figure 4. Amazon frontier zones (deforested, active and forested) based on

natural breaks applied to cumulative deforestation through 2000

WELFARE OUTCOMES AND THE ADVANCE OF THE DEFORESTATION FRONTIER 855

deforestation (from 28% to 66%) is in fact the active fron-tier, it should also have significantly higher levels of on-going deforestation. Incremental deforestation in 2000–01varied from 0.01 to 1,668 km2 per municipality, with amean of 49.7 km2 (Table 1). Similar areas were deforestedper municipality in 2000–01 in the zone that remains themost forested (31 km2 per year per municipality on average)and the zone that is the most deforested (25.9 km2 per yearper municipality on average). In the intermediate zone, an-nual deforestation is significantly higher, at 99.7 km2 permunicipality on average. This supports our interpretationof the three zones as a cross-sectional representation ofthe history of deforestation: cumulative deforestation startsin a low steady state (pre-frontier), then increases rapidly(active frontier), and stabilizes at a high steady state

(post-frontier), while annual deforestation starts low, spikes,and then falls off.

(c) Demographic facts

The total population of the study area was 9.7 millioninhabitants in 2000 (excluding 3.7 million in the four state cap-itals), reflecting 25% growth since 1991. Even excluding thestate capitals, the percentage of the population living in ruralareas decreased from 61% to 52% between 1991 and 2000.Eighteen percent of the population in 2000 was born outsidethe Amazon region. The population density of the entire studyarea was 2.6 inhabitants/km2 in 2000 (compared to 2.0 inabi-tants/km2 in 1991), but this varied widely across municipali-ties, which had a mean density of 18 and standard deviationof 110 inhabitants/km2. Higher population density is foundcloser to capitals and roads.

Comparing frontier zones (Figure 4 and Table 2), the totalpopulation growth between 1991 and 2000 was higher in theactive (49%) than in the forested (29%) and deforested (20%)zones. In 2000, almost 30% of the population of the activefrontier was born outside of the Amazon, while migrants rep-resented 20% of the population in the deforested and 11% inthe forested zones. In other ways, the demography of thedeforested zone is distinct from the other two zones. Forexample, this zone has the most municipalities (16%) that lostpopulation during 1991–2000, and is the only zone with anaverage loss in municipal population (�6%). Both total andmunicipal average population density in 2000 were much high-er in the deforested zone (14.9 inhabitants/km2) than in the ac-tive (4.4) and forested (1.3) zones.

(d) Welfare indicators

Municipal GDP is likely to be of key interest to local gov-ernment leaders. To account for the very different sizes ofmunicipalities, we consider GDP per km2. Previous analyses

Table 2. Average demographic and welfare indicators for municipalities by frontier zone (Figure 4). Different letters indicate statistically different meansaccording to a Tukey test (p < 0.05)

Indicators Frontier zones (mean ± SD)

Deforested Active Forested F p

Number of municipalities 146 99 155Total area of zone (103 km2) 228 610 2,879Municipal cumulative deforestation (%) through 2000 88 ± 10 c 47 ± 12 b 8 ± 8 a 2390.8 <0.0001Total annual deforestation in 2000–01 in zone (km2) 2,172 8,576 3,968Municipal annual deforestation in 2000–01 (km2) 25.87 ± 45 a 99.72 ± 194 b 31.49 ± 63 a 11.57 <0.0001Total population of zone 2000 (million inhabitants) 3.4 2.7 3.6Municipal population 2000 (thousand inhabitants) 23.2 ± 28.3 26.8 ± 44.2 23.4 ± 27.8 0.43 0.6496Total population growth 1991–2000 (%) 20 49 29Municipal population growth 1991–2000 (%) -6 ± 26 a 12 ± 36 b 28 ± 38 c 24.09 <0.0001Total population density (inhabitants/km2) 14.9 4.4 1.3Municipal population density (inhabitants/km2) 26.1 ± 63.5 b 6.8 ± 6.7 a 4.3 ± 14.3 a 12.95 <0.0001Municipal migrants from other states in 2000 (%) 20 ± 20 b 29 ± 21 c 11 ± 18 a 24.61 <0.0001

Municipal GDP per km2 (US$/year) in 2000 26,199 ± 58,679 37,274 ± 286,474 6,584 ± 34,992 1.42 0.2436Municipal HDI in 2000 0.65 ± 0.06 a 0.69 ± 0.06 b 0.65 ± 0.07 a 5.35 0.0051Municipal income per capita (US$/year) in 2000 757 ± 351 a 989 ± 486 b 759 ± 445 a 11.04 <0.0001Municipal poverty rate (% in extreme poverty) in 2000 36 ± 14 b 29 ± 15 a 40 ± 16 c 14.07 <0.0001

856 WORLD DEVELOPMENT

have focused on the Human Development Index (HDI), whichcombines data on education, income, and lifespan to representhuman welfare (cf. Celentano & Verıssimo, 2007b; Rodrigueset al., 2009). The average HDI of municipalities in the studyarea was 0.658 in 2000, as compared to 0.766 in Brazil as awhole. We also separately consider average per capita income,which is one component of HDI. Finally, we examine the pov-erty rate, or percent of a municipality’s population living in ex-treme poverty, defined as per capita income below US$1/day.See Table 1 for descriptive statistics.

We use these indicators to characterize welfare in the fron-tier zones (Figure 4). As shown in Table 2, the population’saverage welfare (HDI, income per capita) is significantly high-er and the poverty rate significantly lower in the active fron-tier, as compared to the zones that are already deforested orstill forested. HDI and income are not statistically differentin the forested and the deforested zone, pre- and post- frontierdevelopment. The poverty rate is higher in the pre- than in thepost- frontier. The density of income (GDP/km2) is highest in

Figure 5. Welfare zones based on Human Development Index in 2000. Data

from UNDP (2003).

Figure 6. Welfare zones based on per capita income in 2000, using natural

breaks method. Data from UNDP (2003).

the active frontier, although because of its variability, it is notstatistically different across zones.

Another perspective is gained by defining and mappingmunicipalities with low, medium, and high levels of welfare(Figures 5–8). These maps show that all welfare indicatorsare strongly spatially determined. Income/km2 (Figure 8) ishigher in the older and active frontiers and near cities, illus-trating why local leaders often favor advancing the deforesta-tion frontier. In contrast, when considering more typicalindicators of the average welfare of a population (incomeper capita, poverty rate, and HDI), most municipalities inthe lowest welfare category (shown in light gray) are in the ex-treme east (older frontiers) and northwest (conserved forest).Most municipalities in the highest welfare category (black)are located in the center and southwest (the new frontiers), un-less they have mineral resources or large cities. This pattern isconfirmed by comparing incremental deforestation across thethree zones (Table 3). There are no significant differences inincremental deforestation across the three categories of

Figure 8. Welfare zones based on Municipal GDP per km2. Data from

IPEA (2010).

Figure 7. Welfare zones based on percent in extreme poverty in 2000. Data

from UNDP (2003).

WELFARE OUTCOMES AND THE ADVANCE OF THE DEFORESTATION FRONTIER 857

income/km2, but municipalities with the highest level of aver-age welfare and lowest poverty rates also consistently have thehighest level of annual deforestation in 2000–01.

(e) Bivariate analyses

Bivariate ordinary least squares analysis suggests that GDP/km2 has a linear relationship with cumulative deforestation(Figure 9). 9 This model explains only 4% of the variation inGDP/km2, but there are no significant gains from adding qua-dratic or cubic terms. In contrast, linear models of HDI andincome per capita as a function of deforestation have essen-tially no explanatory power. The relationship between thesewelfare measures and deforestation only becomes apparentwith the addition of quadratic terms. The best fit models arecubic, but the second turning point is beyond the range ofthe data, resulting in an inverted-U shaped relationship consis-tent with the boom-bust hypothesis (Figures 10–12). In thecase of the poverty rate, the best fit model is quadratic. Insum, municipalities that are either less deforested or moredeforested tend to have lower HDI, lower income and higher

poverty, reflecting a general boom-bust pattern. Total incomeper unit area, however, increases steadily with deforestation.And in all cases, deforestation explains <20% of the variationin the welfare measure.

(f) Regression models

There are many factors other than deforestation that influ-ence welfare outcomes. If these other factors are also relatedto deforestation, they could be the underlying reason for theobserved patterns in Figures 9–12. To rule this out, we addvariables representing key factors identified in previous stud-ies, including: (1) soil quality and precipitation patterns (Cho-mitz & Thomas, 2001, 2003; Schneider et al., 2002; Lauranceet al., 2002); (2) mineral resources (Laurance et al., 2001);(3) access by roads (Nepstad et al., 2001; Pfaff, 1999) and riv-ers to central places that offer markets and public services; and(4) percent of the population that is rural and overall popula-tion density, since in a labor scarce region, population itself isa key resource for development (Binswanger & McIntire,1987). There may also be unobserved spatially correlated fac-tors that confound the relationship between deforestation andwelfare. We, therefore, construct a spatial weights matrixbased on the inverse distance between municipalities and esti-mate spatial autoregressive (SAR) model via maximum likeli-hood. The results of LM tests for spatial lag and error arepresented in the last rows of Table 4. The coefficient estimatesin Table 4 are from a spatial lag model of GDP/km2, and SARmodels with spatial lag and error for the other welfare mea-sures. 10

After controlling for covariates, we find that GDP/km2 hasa quadratic relationship with deforestation, with a turningpoint at 34%. That is, GDP/km2 increases with deforestationuntil (approximately) 34% of a municipality is deforested,and past that point, GDP/km2 falls with deforestation, all elseequal. The other statistically and substantively important vari-ables are agricultural potential, population density, and accessto paved roads—all of which make sense as important driversof municipal GDP. The pseudo-R2 of 74% indicates that themodel has good explanatory power.

The other welfare indicators have cubic relationships withdeforestation, with turning points at 35% and 84% in the caseof HDI, 30% and 82% in the case of income per capita, and40% and 90% in the case of poverty rate. Unlike the bivariateestimation results, both turning points fall within the range ofthe data for average welfare, suggesting a N-shaped relation-ship and an eventual recovery of average welfare at high levelsof deforestation, all else equal. The poverty rate starts increas-ing at 40% deforestation and only starts to fall again at 90%deforestation.

There are several other covariates that influence the povertyrate and average welfare. Access to paved roads is consistentlyimportant: if a paved road passes through or within 10 km ofa municipality, income per capita and HDI (as well as GDP/km2) are higher and the poverty rate is lower, all else equal(including deforestation). Demographic history is also consis-tently important, with a denser and more urban population in1991 leading to higher welfare in 2000. The fraction of thepopulation that is rural is particularly important for povertyrates, with a large and strongly significant coefficient. Severalother variables have statistically significant effects only onsome of the outcomes. Agricultural potential, reflecting soilquality, and terrain, has the expected effect on HDI and pov-erty rate, while mines have the expected effect on HDI and in-come per capita. Some variables that are strongly spatiallydetermined, such as precipitation, influence average welfare

Table 3. Cumulative and annual deforestation across zones defined by different welfare indicators. Different letters indicate statistically different meansaccording to a Tukey test (p < 0.05)

Welfare indicators Low Medium High ANOVA

F p

GDP/km2 N 273 91 21GDP/km2 3723.41 a 15779.67 b 38214.40 c 1051.51 <0.0001Cumulative deforestation (%) 35 a 70 b 82 c 57.14 <0.0001Annual deforestation (km) 55.02 37.00 25.83 0.79 0.4567

HDI N 87 165 148HDI 0.56 a 0.65 b 0.72 c 993.39 <0.0001Cumulative deforestation (%) 37 42 40 0.43 0.6526Annual deforestation (km) 15.4 a 31.27 a 96.15 b 21.95 <0.0001

Poverty rate N 115 154 131Poverty 17.19 a 34.87 b 53.88 c 1130.92 <0.0001Cumulative deforestation (%) 43 43 35 2.3 0.102Annual deforestation (km) 111.10 c 38.37 a 15.61 a 28.70 <0.0001

Income per capita N 209 136 55Income 493.61 b 966.7 a 1661.86 c 1122.5 <0.0001Cumulative deforestation (%) 39 45 33 2.83 0.0603Annual deforestation (km) 16.48 a 61.43 b 162.39 c 48.99 <0.0001

Figure 9. Scatter plot (in black) with predicted value from linear regression (in red) for municipal GDP/km2 and cumulative deforestation. (For

interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

858 WORLD DEVELOPMENT

in multivariate OLS models (not reported here) but not in theSAR models. It is notable that even after controlling for all ofthese factors, as well as unobserved spatial correlation, defor-estation still has a strong relationship with all of the welfareindicators. This provides much stronger empirical evidenceof a boom-bust pattern of frontier development than previousanalyses that considered only bivariate relationships.

One critical question about interpretation of the models isthe degree to which the welfare measures reflect the experienceof particular places (municipalities) as opposed to the welfareof a mobile frontier population that moves from one place toanother. In the 2000 data, there is a high correlation betweenpercent immigrants and welfare (Pearson q = +0.59 withHDI, +0.77 with income per capita, �0.73 with poverty rate,all p < 0.001; and Pearson q = +0.12 with GDP/km2,p = 0.021). 11 This is reflected in Figure 13, which categorizesmunicipalities into three zones based on percent migrants. Themunicipalities with the highest percent migrants are concen-

trated in the west. These include the deforested municipalitieswith the highest welfare levels, suggesting that “importation”of initially high levels of welfare may help mitigate (or post-pone) the bust.

The origin of migrants may also matter. As compared to theforested (11%) and deforested (20%) frontiers, the municipal-ities of the active frontier have a higher percentage (29%) ofimmigrants from the economically privileged South andSoutheast regions of Brazil. 12 This suggests that the apparentboom in the active frontier may be at least partially due to“importation” of education, health, and wealth by immigrantsfrom outside the Amazon. Thus, even the temporary benefitsfrom deforestation may be illusory. On the other hand, thegreater percentage of migrants from southern Brazil, withtheir higher levels of human capital, in the current active fron-tier also suggests at least the possibility that these municipali-ties will follow a different trajectory than the municipalities inthe deforested zone. Much of the deforested zone was

Figure 10. Scatter plot (in black) with predicted value from linear regression (in red) for municipal HDI and cumulative deforestation. (For interpretation of

the references to color in this figure legend, the reader is referred to the web version of this article.)

Figure 11. Scatter plot (in black) with predicted value from linear regression (in red) for per capita income (US$/per capita) and cumulative deforestation.

(For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

WELFARE OUTCOMES AND THE ADVANCE OF THE DEFORESTATION FRONTIER 859

originally settled by immigrants from the Northeast, who to-day make up 55% of immigrants in the zone. The Northeasthas the lowest income per capita and HDI and highest povertyamong Brazilian regions.

4. DISCUSSION AND CONCLUSION

The Brazilian Amazon is the quintessential case of rapid andextensive change in ecosystems to meet human demands. Theenvironmental costs of Amazonian deforestation are the sub-ject of much attention (Gullison et al., 2007; Horton,Colarullo, Bateman, & Peres, 2003; Marengo, 2006; Torras,2000), but the economic benefits are equally important tounderstand in order to design efficient and effective poli-cies—such as international payments for ecosystem services(Hall, 2008). We model welfare as a function of cumulativedeforestation at the municipal level to evaluate the benefitsof deforestation. Our best models, controlling for key poten-tial confounders and for spatial autocorrelation, show a boombust pattern in all welfare measures, largely confirming the

hypothesis posed by Schneider et al. (2002), Celentano andVerıssimo (2007b), and Rodrigues et al. (2009) and consistentwith so-called resource curse.

We posed the question of why regional and local govern-ment leaders would perpetuate such a model of development.The boom-bust in average welfare and the poverty rate is alsoapparent in simple cross-tabs across frontier and welfarezones, suggesting that it is a very clear pattern that shouldbe apparent to local leaders without requiring any evidencefrom statistical models. GDP/km2, on the other hand, appearsto increase with deforestation, until we control for other vari-ables and spatial autocorrelation. The multivariate SAR mod-els also reveal a more complex story about average welfare,suggesting that it decreases after a third of the municipalityis deforested, but then increases again once the municipalityis more than 82% deforested (which reflects the status of nearly100 municipalities in our study area). These different patternsmay contribute to different perceptions of the development po-tential of deforestation. More fundamentally, different timehorizons could lead to different perceptions. From a short-run perspective—and certainly from the perspective of

Table 4. Estimation results for SAR models of welfare measures as a function of cumulative deforestation and covariates (N = 399)

GDP/km2 (lag) a HDI (lag + error) Income per capita

(lag + error) aPoverty rate(lag + error)

Coeff. p-Value Coeff. p-Value Coeff. p-Value Coeff. p-Value

Intercept �2.876 0.764 0.094 0.905 0.592 0.898 �16.270 0.857Deforestation 24.843 0.077 0.316 0.000 1.442 0.000 �49.456 0.001Defor 2 �36.380 0.006 �0.644 0.000 �3.295 0.001 89.295 0.013Defor 3 0.363 0.000 1.959 0.002 �45.681 0.051Rain �0.734 0.842 �0.011 0.167 �0.093 0.088 2.709 0.173Agric potential 7.168 0.075 0.026 0.001 0.066 0.227 �4.203 0.040Mining �0.540 0.860 0.014 0.010 0.070 0.069 �2.336 0.100Pop density 1.371 0.000 0.000 0.002 0.001 0.052 �0.051 0.012Rural pop �4.959 0.288 �0.082 0.000 �0.480 0.000 16.479 0.000Distance 0.162 0.735 �0.003 0.002 �0.002 0.819 0.534 0.038Paved roads 4.551 0.063 0.010 0.020 0.097 0.002 �4.142 0.000Amazon River 0.347 0.930 0.006 0.458 �0.085 0.114 2.432 0.240LM test for spatial lag 2.345 0.071 577.105 0.000 598.75 0.000 469.563 0.000LM test for error 2.345 0.126 504.457 0.000 536.596 0.000 395.542 0.000Robust LM test for lag 1.454 0.228 100.038 0.000 100.083 0.000 90.44 0.000Robust LM test for error 0.542 0.461 27.39 0.000 37.929 0.000 16.419 0.000AIC 3616 �1430.7 109.26 2992.4Pseudo-R2 74% 64% 62% 60%

a GDP/km2 is in $1000s to facilitate presentation of estimation results.

Figure 12. Scatter plot (in black) with predicted value from linear regression (in red) for poverty (%) and cumulative deforestation. (For interpretation of the

references to color in this figure legend, the reader is referred to the web version of this article.)

860 WORLD DEVELOPMENT

agribusiness, ranchers, or the logging industry—deforestationbrings an economic boom, with not only higher average in-come, but also lower poverty rates, and higher levels of“human development” taking into account education and lon-gevity. From a slightly longer-run perspective, the costs be-come apparent, but in the very long-run, near completedeforestation also appears to benefit the local population.

As with any statistical model, our analyses require simplifi-cations of the real world. Data on welfare, deforestation, andcovariates are only available at the municipal level, whichaggregates large areas in the Amazon. Again due to data avail-ability, we model cross-sectional variation in welfare (in 2000),using its relationship with cumulative deforestation as a proxyfor temporal patterns in welfare as the deforestation frontierproceeds. Our models of average welfare suggest similar pat-terns across indicators, but of course these indicators are re-lated by construction, with income per capita one of the

factors in HDI. Both are likely to under-state welfare levelsin the forested zone, where there is greater dependence on sub-sistence production and greater access to free natural re-sources to support subsistence. This would tend to over-statethe boom in the active deforestation frontier. The boom couldalso be over-stated insofar as it reflects “imported” welfare, orthe higher education, better health, and greater wealth of mi-grants from southern Brazil. If we expanded our definition ofwelfare to consider factors such as violence and forced labor,the situation in both the active frontier and the deforestedzone again would look far less favorable (Celentano & Verıs-simo, 2007b). On the flip side, our local perspective may notfully capture the benefits of deforestation to Brazil, to theextent that profits from timber, ranching, and soybean cultiva-tion are “exported” and invested elsewhere in the country.

We do address another potential problem with bivariate anal-yses of deforestation and welfare: the possibility of omitted

Figure 13. Percent of immigrants from Brazilian states outside of the

Amazon in 2000.

WELFARE OUTCOMES AND THE ADVANCE OF THE DEFORESTATION FRONTIER 861

underlying factors that drive both deforestation and welfare. Inorder for these omitted factors to explain the observed relation-ship, some combination of them must have a monotonic rela-tionship with deforestation and a quadratic or cubicrelationship with welfare. Factors that increase both deforesta-tion and welfare over their entire range could not be responsiblefor the relationships that we observe. Therefore, it is perhapsnot surprising that we find the boom-bust relationship to be ro-bust to inclusion of biophysical, access, and historical demo-graphic characteristics, as well as spatial autocorrelation.

As our maps show, there are some notable exceptions to theboom-bust pattern. These include municipalities with mineralresources, where the boom depends on mining rather than theprogression of the deforestation frontier. In the southwest ofthe study area, there are some regions with higher quality soils,favorable precipitation patterns, and relatively secure tenurewhere both deforestation and welfare levels are high. In asense, these are the exceptions that make the rule, and theysuggest some of the key underlying reasons for the generalboom-bust pattern.

The bust that follows the deforestation frontier in the Ama-zon is reminiscent of the “resource curse” that has beenblamed on the suppression of other productive sectors, cor-ruption and weak institutions, and lack of investment in hu-man capital (Auty, 1993; Bulte et al., 2005; Gylfason, 2001;Sachs & Warner, 1995). The “curse” is most consistently ob-served in countries that are highly dependent on mineral re-sources. The parallel in the Amazon is “forest mining,” in

which harvest of the high value timber from mature forest gen-erates temporarily high profits and employment but leaves be-hind a less productive forest and appears not to generatesufficient human or other productive capital to sustain the lo-cal economy (Lentini, Verıssimo, & Pereira, 2005; Verıssimoet al., 2002; cf. Ross, 2001 for similar failure to manage forestsustainably in Southeast Asia). This phenomenon is under-pinned by biophysical factors such as the slow growth ratesof high value trees and the poor soils for agriculture that covermuch of the region (Alfaiai, Ribeiro, Nobre, Luizao, & Lui-zao, 2004; Arima et al., 2005). Political corruption to capturewindfall frontier profits (cf. Brollo et al., 2010; Caselli &Michaels, 2009) and weak institutions for managing forestand land tenure (Barreto et al., 2008) contribute to the prob-lem. High discount rates and a general lack of “far-sighted-ness” in both the private and public realms have beenidentified as another key stumbling block to sustainabledevelopment (Ascher, 2006). This short-sightedness may beparticularly acute among recent migrants who do not have astrong sense of place, attachment, or long-term commitmentto a region.

From the global environmental perspective, the boom-bustpattern means that incentives for forest conservation mustovercome the short-term appeal of the economic boom gener-ated by deforestation. However, the N-shaped patterns inaverage welfare and the poverty rate that emerge once we con-trol for other factors also suggest that incentives must be suf-ficient to address a fundamental long-term trade-off betweenconservation and local development. The incentives must sub-stantially and permanently increase local returns to sustain-able management or conservation of the forest. From theregional development perspective, the challenge is to createsustained benefits—whether from standing forest or fromdeforested areas. Policies currently under discussion that ad-dress both of these perspectives include fiscal reforms thatwould determine transfers to local government based on forestarea as well as population (Manifesto, 2008), establishment ofnational forests, and a system of local revenue sharing from le-gal logging activities (Verıssimo et al., 2002), and finally pay-ments for ecosystem services, such as avoiding carbonemissions from deforestation (Hall, 2008; Duchelle et al.,2010) or payments for sequestering carbon in restored forestsas part of a forest transition in the deforested zone (Perz &Skole, 2003; Rudel, Bates, & Machinguiashi, 2002). The de-sign of new global institutions for REDD+ (reduced emissionsfrom deforestation and forest degradation and sustainable for-est management) must combat the appeal of both short-termeconomic booms and possible long-term welfare gains fromdevelopment paths built on deforestation.

NOTES

1. Deforestation averaged 16.5 thousand km2 per year from 1990 to2000, the time period considered in this paper. From 2001 to 2008,deforestation averaged 18.9 km2 per year. The deforestation rate fell everyyear from 2005 to 2010, generating debate over the degree to which thiswas due to government actions versus reduced demand for exportsassociated with the global economic downturn.

2. There is a well-established body of research demonstrating thetechnical feasibility of sustained agricultural production beyond the initialfertility pulse obtained by burning the forest (Fearnside 1985; Scatenaet al. 1996; Serrao, Nepstad, & Walker 1996; Serrao & Toledo 1990;Smith, Serrao, Alvim, & Falesi 1995).

3. We estimate models but do not report results here two other measures:number of people in extreme poverty per km2, and the Gini coefficient.Both increase linearly with deforestation (in a bivariate sense). However,the number of poor per km2 mostly reflects urbanization; populationdensity and percent rural provide almost all of the explanatory power inmultivariate models. On the other hand, our models of the Gini coefficient(bivariate and multivariate OLS and SAR) have very limited explanatorypower. For example, consider the multivariate OLS models: the R-squaredfor the model of the Gini coefficient is just 13%, compared to R-squaredfor other models ranging from 33% for an OLS model of income percapita to 74% for the OLS model of GDP/KM2.

862 WORLD DEVELOPMENT

4. In general, more flexible functional forms fit the data better. Thus, wecheck whether adding the cubic term improves the AIC in SAR modelsusing inverse distance and contiguity weights, improves the adjusted R-squared in the OLS model, and results in statistical significance of thecoefficient on the cubic term at the 5% level, and joint statisticalsignificance of the coefficients on the quadratic and cubic terms at the5% level. Our modeling approach parallels the empirical literature on theenvironmental Kuznets curve (EKC), which typically tests for N-shapedvs. inverted U-shaped curves. However, note two fundamental differences:(a) we are considering gross deforestation (not forest recovery), and (b) weare modeling welfare as a function of historical deforestation, rather thanthe inverse or simultaneous relationship posited in the EKC literature.

5. These capitals have favored access to public services and formalemployment, and therefore are excluded from the statistical analyses asoutliers, although they are included in the maps. The other Amazonianstate capitals are in areas that originally had less than 50% forest coverand therefore are excluded from both the analyses and maps (see Figure2). The four municipalities with no welfare data were all created after 2000in the state of Mato Grosso: Colniza, Rondolondia, Nova Santa Helena,and Bom Jesus do Araguaia. In the welfare maps, they are represented atthe same level of welfare as the municipalities from which they wereextracted. In the deforestation maps, they are represented at their owndeforestation rates.

6. Specifically, these areas correspond to LANDSAT scenes 222/062,221,062, 221/061 and 220/062 in the northeastern Amazon.

7. In the case of GDP/km2, we first defined municipalities more than onestandard deviation above the mean as part of the highest category, andthen applied natural breaks to the remaining municipalities. This results ina more informative map but does not affect findings from the statisticaltests. For all of the welfare measures, we excluded the five municipalitiesrepresenting state capitals from the natural breaks analysis, but weincluded them in the maps.

8. The inverse distance matrix is based on Euclidean distance betweenmunicipal centroids. To test the robustness of estimation results to ourchoice of spatial weights matrix, we also estimated all models with acontiguity matrix, in which municipalities are defined as neighbors if theircentroids are “close” to one another. We defined close as less than 283 kmapart in order to ensure that all municipalities have at least two“neighbors.”

9. In the bivariate and multivariate regressions, GDP/km2 is in $1000s tofacilitate presentation of estimation results.

10. We also estimate OLS models and SAR models with the contiguityspatial weights matrix. The preferred functional form and the signs andstatistical significance of the deforestation variables are the same acrossmodels, with the exception of the models of the poverty rate. In the SARmodel using a contiguity-based spatial weights matrix (not reported here),deforestation is not a significant predictor of poverty rates. The signs andstatistical significance of the coefficients on the other covariates in thisunreported SAR model of poverty are generally consistent with thereported model, except that distance is not statistically significant.

11. There is also significant positive correlation between percent immi-grants and incremental deforestation (Pearson q = +0.43). Migration isnot included in the regression models because 1991 data are not availableat the municipal level.

12. In 2000, HDI in the South (0.81) and Southeast (0.79) were thehighest among Brazilian regions (UNDP, 2003). Site-specific surveys aswell as anecdotal evidence suggest that migrants from the South andSoutheast of Brazil arrive in the Amazon with higher levels of wealth,education and health, essentially importing welfare.

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