Soybeans, Land Use and Poverty in the Brazilian Amazon

Eustaquio Reis, IPEADiana Weinhold, LSE

Preliminary Work in Progress Comments Welcome

January 2008

“The cost to the country of producing soybeans includes biodiversity loss when natural ecosystems are converted to soybeans, severe impacts of some of the transportation systems, soil erosion, health and environmental effects of agricultural chemicals, explusion of the population that formerly inhabited the areas used for soybeans, lack of production of food for local consumption because cropland used for subsistence agriculture is taken over by soybeans, and the opportunity cost of government funds devoted to susidising soybeans not being used for education, health and investment in activities that generate more employment than does mechanised cultivation of soy.”

- Philip Fearnside (2001)

Keeping tropical forests standing will not be easy. The problem is that they are perched on some of the world’s largest remaining tracts of soil suitable for crop and pastureland expansion.

… the demand for agricultural commodities ... must come down…. If Americans face the connections between diet and the planet by eating less meat they could provide a rare act of leadership in slowing global warming.”

- Daniel Nepstad (IHT, Nov 25-26, 2006)

“The expansion of soybeans acreage does not cause difficulties for environmental policy, particularly in respect to the Amazon Forest. … BR-163 should be paved as quickly as possible. This will have the effect of reducing transportation costs and, additionally will facilitate planting of soybeans in the vicinity of the highway, increasing the efficacy of environmental preservation initiatives.”

- Brandao, Castro de Rezende, and Costa Marques (2005)

“The real road to riches in regards to expanding soybean production in the Legal Amazon lies through improving the existing road and rail network and in maximizing the advantage offered by shipping exports via the Amazon River itself. Government officials, agribusiness executives, and producers alike recognize this, and are collectively working to ensure this happens.”

- FAS/USDA (2003)

Explosive expansion of soybean cultivation in Legal Amazonia

Year Soybean Area(hectares)

Cattle Herd(head)

1990 5800 999861991 4493 1088271992 5259 1094851993 6209 1169681994 7363 1210001995 8102 1275421996 6623 1240231997 7635 1292111998 9656 1364591999 9550 1410882000 10588 1501932001 11342 1609182002 13884 1759912003 16101 1911882004 19457 .

Causes:

• EMBRAPA, National Agricultural Research Agency

“There are in fact few natural limits to the future expansion of grain and oilseed production which cannot be overcome by astute planning, research, and adequate investment capital.

… Over the past 30 years average soybean yields have increased approximately 130 percent, while seed quality is as high as any produced in the world including in the United States. Brazilian crop researchers have succeeded in breeding high-yield soybeans for every climate regime in the country, including tropical varieties for the equatorial lowlands.

This means soybeans can be grown anywhere in the country where soil physical properties are adequate, without any climatic limitations whatsoever.”

- FAS/USDA (2003)

Soybean yield in Legal Amazonia and in the rest of Brazil, 1975-2004 (ton/ha)

1,0

1,5

2,0

2,5

3,0

3,5

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Fonte: IBGE - PAM

ton/

ha

Legal Amazonia Rest of Brazil

Soybean yield in AML and the restof Brazil, 1975-2004 (ton/ha)

Causes (cont.):

• Moderfrota programme in 2000 significantly increased credit for agricultural machinery.

• Improved infrastructure

• International soybean and beef prices/demand

Year Soybean Area (k hectares)

%change in domestic

soya price (time of

marketing) 1999 9550 9.9 2000 10588 7.6 2001 11342 -7.4 2002 13884 15.4 2003 16101 42.8 2004 19457 26.3 • international soybean prices fell through 1998-

2001, but a devaluation of the Real in 1999 propped up domestic prices and kept agriculture profitable. Since 2003 international prices have increased as well, yielding windfall profits to soybean producers.

Soybean: harvested area as % of area, 1995

Soybean: harvested area as % of area, 2000

Soybean: harvested area as % of municipal area, 2004

Intensive mechanized agriculture in the Brazilian Amazon grew by >3.6 million hectares (ha) during 2001–2004.

Whether this cropland expansion resulted from intensified use of land previously cleared for cattle ranching or new deforestation has not been quantified and has major implications for future deforestation dynamics, carbon fluxes, forest fragmentation, and other ecosystem services. (Morten et. al. 2006)

For now, what we can say something about .... Soybeans and Poverty

Some Hypotheses:

• Because of mechanization and scale economies, soybean cultivation favours large producers at the expense of small farmers and hence increases inequality.

• The high capital intensity of soybean production also increases unemployment, thus increasing poverty.

“The rise of soybeans displaced 11 agricultural workers for every one finding employment in the new production system” - Zockun, 1980, quoted in Fearnside 2001

“In the 1970’s, 2.5 million people left rural aras in Parana; in the same period, the number of farms declined by 109,000 in Parana and 300,000 in Rio Grande do Sul.” - Kaimowitz&Smith 2001, quoted in Fearnside, 2001

Soybeans and Poverty: Alternative Hypotheses:

i.e. EMBRAPA, Mueller and Bustamante (2002):

• Soybeans generate considerable wealth which can help development.

• New industries spring up in soybean areas that generate employment such as soybean crushing, soybean oil plants, and transport activities.

• Associated improved infrastructure (including significantly lowered transport costs) could have significant welfare enhancing effects.

DATA: • Unit of analysis is the AML municipio. Actual municipio boundaries change occassionally, so these are constructed to be comparable over time.

• DESMAT data from IPEA: Hundreds of variables of ecological, economic and agricultural conditions collected for the years 1975, 1980, 1985 and 1995 for all Brazil.

• Next full agricultural census 2007

• Income and poverty data every 5 year from 1975-2000

• Agricultural survey data of crop areas and cattle herd annually from 1974 to 2004

Table 3: Poverty, Income and Soya, All Brazil, 2000

Dep Var RuralPoverty

Rate

UrbanPoverty

Rate

Log Ruralmedian HH

income

Log Urbanmedian HH

income

Log(RuralGDP)

Log(UrbanGDP)

constant 0.698(19.4***)

0.747(22***)

188(9.87***)

196(7.41***)

5.53(17***)

5.73(41.9***)

Larea 0.0169(6.14***)

0.0224(10.4***)

-4.33(-3.38***)

-6.61(-4.08***)

0.287(9.3***)

-0.0249(-2.06**)

Lat 0.0189(12.2***)

0.019(13***)

-6.5(-8.79***)

-8.8(-10.8***)

-0.0161(-1.57)

-0.0391(-6.93***)

Lpopr 0.0226(6.05***)

-0.00155(-.77)

-9.34(-4.85***)

-2.59(-1.1)

0.57(9.2***)

0.0653(5.8***)

Lpopu -0.0242(-11.2***)

-0.028(-20.5***)

9.77(8.38***)

24.6(18***)

0.0331(1.08)

1.01(115***)

Soy -0.0413(-2.76***)

-0.0379(-3.31***)

0.826(.0845)

1.5(.205)

0.291(3.58***)

0.0631(.915)

Lsoyar -0.00024(-.125)

0.000644(.439)

3.79(2.83***)

0.897(.945)

0.0758(7.91***)

0.00378(.422)

Nobs 3618 3659 3618 3659 3659 3659Rsq 0.7532 0.7813 0.4982 0.6469 0.6655 0.9047NOTE: All regressions include State dummies (not shown). Robust t-statistics in parenthese.

Poverty, Income and Soya, All Brazil, 2000

Dep Var: Change inRural

PovertyRate

Change inUrban

PovertyRate

Growth ofRural

median HHincome

Growth ofUrban

median HHincome

Growth ofRural GDP

Growth ofUrban GDP

Constant 0.644(16.9***)

0.575(14.7***)

1.86(14.5***)

1.36(15.9***)

0.206(.628)

2.99(22.1***)

PovrL1 -0.972(-141***)

PovuL1 -0.862(-41.2***)

Lmincrl1 -0.364(-15.3***)

Lmincul1 -0.286(-15.5***)

LGDPrL1 -0.0989(-2.41**)

LGDPuL1 -0.323(-13.6***)

Larea 0.0165(5.96***)

0.0191(9.37***)

-0.0179(-2.75***)

-0.00948(-2.19**)

0.056(2.33**)

0.0121(1.41)

Lat 0.0186(12.1***)

0.0179(13***)

-0.0347(-9.15***)

-0.021(-7.65***)

0.0023(.32)

-0.00163(-.331)

lpoprL1 0.0223(5.43***)

-0.000725(-.38)

-0.0178(-1.88*)

-0.00196(-.494)

0.14(3.79***)

0.0447(5.31***)

lpopuL1 -0.0207(-9.03***)

-0.0182(-10.6***)

0.0106(1.91*)

0.0278(7.86***)

-0.0425(-2.01**)

0.243(8.69***)

soy -0.0281(-3.25***)

-0.0238(-3.67***)

0.0363(1.69*)

0.00576(.429)

0.172(4.82***)

0.022(.737)

lsoyarL1 -0.00259(-2.35**)

-0.000833(-.975)

0.01(3.39***)

0.00233(1.41)

0.0132(2.64***)

0.00722(1.81*)

Nobs 3614 3659 3614 3659 3603 3652Rsq 0.9243 0.8575 0.2940 0.2042 0.3072 0.3417NOTE: All regressions include State dummies (not shown). Robust t-statistics in parenthese.

Poverty, Income and Soya, Spatial Neighborhood Effects, 2000

Dep Var: Change inRural

PovertyRate

Change inUrban

PovertyRate

Growth ofRural

median HHincome

Growth ofUrban

median HHincome

Growth ofRural GDP

Growth ofUrban GDP

Constant .786(18.6***)

.727(16***)

1.6(12***)

1.14(12.4***)

-.0949(-.27)

2.52(19.1***)

PovrL1 -.993(-148***)

PovuL1 -.882(-43***)

Lmincrl1 -.376(-13.3***)

Lmincul1 -.277(-12.7***)

LGDPrL1 -.0658(-1.48)

LGDPuL1 -.35(-13.2***)

Larea .0179(5.77***)

.0166(7.21***)

-.0204(-2.77***)

-.00781(-1.56)

.0332(1.2)

.0103(1.08)

Lat .0227(13***)

.0214(12.9***)

-.0389(-9.02***)

-.0224(-6.66***)

.00985(1.1)

.000746(.135)

LpoprL1 .0114(2.45**)

-.00177(-.811)

-.00934(-.852)

-.0017(-.392)

.137(3.32***)

.0452(5.11***)

LpopuL1 -.0163(-6.53***)

-.0173(-9.37***)

.00626(1.02)

.0237(5.65***)

-.0427(-1.87*)

.271(8.83***)

SP_soyar -.718(-1.59)

-.497(-1.56)

-.467(-.312)

-.899(-1.63)

4.01(2.43**)

-4.49(-3.19***)

Nobs 2748 2793 2748 2793 2751 2789Rsq 0.9382 0.8691 0.3111 0.1932 0.2806 0.3855NOTE: All regressions include State dummies (not shown). Robust t-statistics in parentheses.

Poverty, Income and Soya, Legal Amazonia 2000

Dep Var RuralPoverty

Rate

UrbanPoverty

Rate

Log Ruralmedian HH

income

Log Urbanmedian HH

income

Log(RuralGDP)

Log(UrbanGDP)

Constant .612(8.89***)

.515(10.2***)

149(3.92***)

97.4( 2.95***)

4.48(13.4***)

4.94(18.7***)

Larea .00465(.592)

.0093(1.7*)

-1.01(-.292)

2.59(.63)

.221(5.9***)

.00986(.25)

Lat .0178(5.12***)

.0142(4.25***)

-7.63(-4.62***)

-2.65( -1.43)

-.121(-5.9***)

-.0139(-.969)

Lpopr .0262(2.12**)

.0126(1.78*)

-15.1(-1.93*)

-10.4(-1.91*)

.468(8.91***)

.143(2.7***)

Lpopu -.0209( -3.85***)

-.0376(-8.71***)

10.2(1.79*)

21.8( 6.78***)

.229(5.57***)

.939(28.4***)

Soy .0702(2.75***)

.0353(1.54)

-70(-3.21***)

-37.6(-3.02***)

-.388(-2.71***)

-.222(-1.95*)

Lsoyar -.0115(-3.48***)

-.00789(-2.77***)

12.8(3.77***)

7.13(4.09***)

.0881(4.48***)

.0406(2.72***)

Nobs 439 439 439 439 439 439Rsq 0.7740 0.7846 0.4648 0.5793 0.8110 0.9229NOTE: All regressions include State dummies (not shown). Robust t-statistics in parenthese.

Poverty, Income and Soya, Legal Amazonia 2000

Dep Var: Change inRural

PovertyRate

Change inRural

PovertyRate

Change inUrban

PovertyRate

Growth ofRural

median HHincome

Growth ofUrban

median HHincome

Growth ofRural GDP

Growth ofUrban GDP

Constant .596(8.46***)

.577(8.16***)

.495(9.02***)

.317(.846)

1.84(7.02***)

-.153(-.371)

2.68(9.32***)

PovrL1 -.971(-55.5***)

PovuL1 -.972( -55.1***)

-.965(-40.4***)

Lmincrl1 -.233(-3.23***)

Lmincul1 -.372(-7.06***)

LGDPrL1 -.287(-5.74***)

LGDPuL1 -.472(-8.24***)

Larea .00452(.624)

.00568(.783)

.00861(1.57)

-.0012(-.0706)

-.000247(-.0176)

.136(4.4***)

-.0199(-.853)

Lat .0188(5.5***)

.0178(5.3***)

.0147(4.45***)

-.0177(-2.14**)

-.0115(-1.66*)

-.0612(-3.47***)

.0049(.424)

LpoprL1 .0245(1.89*)

.0236(1.84*)

.0116(1.49)

-.0504(-1.56)

-.0493(-2.22**)

.121(2.61***)

.204(5.12***)

LpopuL1 -.0197(-2.77***)

-.0184(-2.61***)

-.0371(-7.81***)

.0259(1.3)

.0705(5.15***)

.0841(2.75***)

.347(5.54***)

Soy .0107(.594)

.0815(2.86***)

-.0134(-.944)

.0144(.335)

-.0115(-.309)

.0811(1.3)

-.0015(-.0237)

Gsoyar -.0157(-2.77***)

LsoyarL1 -.00445(-1.85*)

-.0125(-3.73***)

-.00127(-.703)

.018(2.61***)

.0107(2.49**)

.0128(1.37)

.0233(2.73***)

Nobs 439 439 439 439 439 435 438Rsq 0.9234 .9247 0.8643 0.4292 0.2555 0.5343 0.4314NOTE: All regressions include State dummies(not shown). Robust t-statistics in parenthese.

Poverty, Income and Soya, All Brazil 1980-1995

Dep Var Change inRural

PovertyRate

Change inUrban

PovertyRate

GrowthRural

medianHH

income

GrowthUrban

medianHH

income

Growth ofRural GDP

Growth ofUrbanGDP

constant 1.07(9.75***)

0.866(24.1***)

1.14(10.2***)

0.973(16.2***)

1.8(8.26***)

1.39(11.2***)

PovrL1 -1.14(-138***)

PovuL1 -0.867(-55.2***)

Lmincrl1 -0.2(-24***)

Lmincul1 -0.195(-28.3***)

LGDPrL1 -0.265(-12.6***)

LGDPuL1 -0.207(-28.4***)

Larea 0.00939(2.32**)

0.0153(6.4***)

-0.00775(-2.47**)

-0.00252(-1.09)

-0.00153(-.166)

0.0351(6.13***)

Lat 0.00963(5.8***)

0.0129(11.4***)

-0.013(-9***)

-0.00839(-7.36***)

-0.0158(-5.46***)

-0.0136(-5.39***)

LestabL1 -0.0226(-4.81***)

-0.0019(-.587)

0.00719(1.71*)

-0.0112(-3.93***)

0.2(8.83***)

-0.0249(-3.89***)

LpoprL1 0.0753(14.3***)

0.0135(7.47***)

-0.0454(-10.6***)

-0.0099(-5.22***)

0.0985(6.56***)

0.0058(1.37)

LpopuL1 -0.0695(-29.7***)

-0.0483(-33.4***)

0.0267(13.2***)

0.0249(18.1***)

0.000585(.0669)

0.213(25.2***)

LpasplaL1 0.00682(3.24***)

-0.00341(-1.77*)

0.00788(4.59***)

0.00908(7.23***)

-0.0255(-5.04***)

-0.00263(-.956)

Soy -0.0279(-3.14***)

-0.0196(-3.77***)

0.0138(1.67*)

0.00819(1.49)

0.0475(3.58***)

0.0473(3.68***)

LsoyarL1 -0.00209(-1.75*)

0.0000116(.0166)

0.0012(1.02)

0.000669(.9)

0.00906(4.58***)

-0.00328(-1.82*)

Nobs 14433 14579 14433 14579 17881 18110Rsq 0.5716 0.4585 0.3588 0.3280 0.3952 0.3227NOTE: All regressions include State and Period dummies (not shown).Robust t-statistics in parentheses.

Poverty and Soya, controlling for GDP

Dep Var RuralPovertyRate, All

Brazil1980-1995

RuralPovertyRate, All

Brazil2000

RuralPoverty

Rate,Amazonia

2000constant 1.71

(14.4***)1.27

(28.2***)1.19

(12.1***)Larea 0.00869

(2.33**)0.025

(9.55***).0158

(2.14**)Lat 0.000611

(.426)0.0155

(11.1***).011

(3.24***)Lestab -0.0106

(-1.99**).

Lpopr 0.0636(12***)

0.0493(12.4***)

.0588(4.75***)

Lpopu 0.0382(8.44***)

0.039(8.17***)

.0588(4.01***)

Lpaspla 0.0123(5.74***)

.

LGDPrur -0.0368(-9.45***)

-0.0397(-13.3***)

-.0471(-4.54***)

LGDPurb -0.0761(-19.1***)

-0.0609(-13***)

-.0733(-5.38***)

Soy -0.0256(-2.44**)

-0.0294(-2.19**)

.0357(1.53)

Lsoyar -0.000459(-.314)

0.00347(2.03**)

-.0044(-1.45)

Nobs 14278 3618 439Rsq 0.5864 0.7862 0.8107NOTE: All regressions include State and Perioddummies (not shown). Robust t-statistics in parentheses.

Conclusions and Discussion

• Astonishingly fast rate of change

• Hard data is piecemeal, incomplete

• Theory is ambiguous

• polarized opinions with anecdotal, heuristic evidence that can support a broad range of conclusions

Our Evidence: Poverty and Soybeans

• Previous studies: minimal

• Our study: up to 2000 only, so again we are limited in what we can say about the impact of the recent explosive growth of soybeans. However we can examine what happened in the past to give us some hints.

• We find no robust evidence that soybean cultivation increased poverty in the past.

Discussion (cont.)

• There is some evidence that soybean cultivation in the Amazon has initially been established in areas with higher poverty rates and lower GDP, but then as soybean cultivation increases, poverty decreases.

• Once we control for this “fixed effect”, soybeans have a beneficial effect on poverty and median rural household incomes (as well as GDP).

• We find no evidence that soybean production pushes poverty into neighboring municipios without soybeans. However we cannot rule out longer-range migratory movements.

• We did not find any evidence that soybean production systematically decreased rural population (not shown), so it may be that the accounts from the 70’s quoted in Fearnside (2001) were specific to certain times and places with very mobile, migrant populations in the early years of Amazonian settlement.

• We find some evidence that soybean cultivation has mixed but potentially beneficial impact on poverty independent of its positive impact on overall GDP in all of Brazil.

• In the Amazon, soybean’s impact on poverty may primarily operate via its impact on aggregate (mostly urban) GDP. This suggests primarily indirect channels of causation.

• Out-of-sample forecasting causality tests (not shown) suggest causality does in fact go from soybean cultivation to poverty reduction, not the other way around.

Thus overall we find preliminary evidence to suggest that soybean cultivation can be an economically very powerful tool for development.

• If it turns out that soybean production is in fact a serious threat to the Amazon forest, international agreements on deforestation abatement will have to take into account the large opportunity cost borne by Brazil by not clearing more land.