The Effect of Malaria on Settlement and Land Use: Empirical Evidence from the Brazilian Amazon Shufang Zhang, Marcia Caldas de Castro, and David Canning.

The Effect of Malaria on Settlement and Land Use:

Empirical Evidence from the Brazilian Amazon

Shufang Zhang, Marcia Caldas de Castro, and David Canning

Harvard School of Public HealthMay, 2010

Malaria and Development• Malaria burden:

• Potentially large effects on income levels• Mechanisms

• Health care costs, prevention and treatment• Labor productivity • Childhood exposure and cognitive development• Educational Attainment and adult productivity• Population pressure

• Avoidance

Effect of Malaria on Land Use

• Land use • Avoidance can reduce the burden of

malaria but had costs• Settlement, choice of crop

• Evidence of Effect of Malaria on Crop Choice• Paraguay, Conly (1975)• Kenya, Wang'ombe and Mwabu (1993)• Vietnam, Laxminarayan (2004)

Machadinho Resettlement Project

• Resettlement project in Brazil in the 1980s• Land plots allocated to settlersOur Questions • What is the impact of malaria on

settlement? • What is the impact of malaria on land use?

Machadinho Resettlement Project

Machadinho

Site Photo (source: M. Castro)

Legend

Plots

Urban

^ South_Entry

Suburban Center

Machado

River

Road and Type

Newtype

Collector Road

Access Road

Penetration Road

Forest Reserve

Data Source:Center for Regional Development and Planning Federal Univeristy of Minas Gerais, Brazil

Machadinho Resettlement Area

Settlement Infrastructure• Infrastructure constructed between 1982 and

1984, 1200 km2 total area.• Plots were laid out based on topographic

features—steam at the back, with front access to a road.

• Urban and sub urban areas designated• Roads and laid out in advance (3 classes).

Placed to avoid flooding in rainy season.• Forest reserves maintained - right of use to

indigenous rubber tappers.

Allocation of plots • Designated for landless small farmers• 1740 plots, each about 45 hectares Settlement

oversubscribed.• Settlers randomly allocated to plots July/

August 1984. • Settler gets right to use plot –lapses if plot not

farmed.• In theory no trade in plots allowed – in

practice some swaps and trades carried out.

House built in 1985, all made of palm thatch.

House built in 1985. The roof is made of plastic, and thesealing of the whole house is precarious.

House built in 1985. There is no door closing the house.

Source: M. Castro

Settlers in Machadinho in 1985

Main urban area of Machadinho in 1985

Main urban area of Machadinho in 2001

Farming

• Slash and Burn– Cut down vegetation, wait to dry, and burn

• Typically poor soil quality – Burn fertilizes soil– Soil quality declines with use

• Main crops– rubber, coffee, cocoa

Malaria Ecology• Malaria

• Both Plasmodium falciparum and Plasmodium vivax

• River is preferred mosquito habitat• Anopheles Darlingi • Forest fringe for sun/shade• Stagnant water when river falls• Mosquito range up to 7km

Malaria and People

• New settlers lacked natural immunity and knowledge and were very susceptible to malaria

• Indigenous rubber trappers were asymptomatic but were a natural reservoir for malaria parasites

• Frontier pattern of malaria • Clearing forest increases malaria initially – more

fringe - full clearance removes fringe. • Malaria rate peaked in 1986, de Castro et al (2006)

Machadinho Land Use Literature: mainly case studies

• Malaria can lead to settlers abandoning a plot, Martine (1990).

• Many settlers live in town to avoid malaria and for job opportunities, Sawyer (1993).

• High levels of malaria and poor soil quality led to many failures among farmer-settlers in the long run and the emergence of large cattle ranges. de Castro and Singer (2005).

Data• Household Surveys

– For plots occupied and lived on – Malaria, self reported symptoms, episodes per

month/person– Demographic and socioeconomic indicators– Data for 1986 and 1987 are used in the study

• Map of Settlement Area– Plot geography, distances

• Satellite images – Area of plot cleared, crop cover, water cover.

Satellite Images• Remote sensing data on land use

– Acreage and percentage of plot deforested – Acreage and percentage of plot cropped– Data available for year 1985 and 1986

• Remote Sensing Data

Satellite images: M. Castro (PNAS, 2006)

Variables • Measured variables with ArcGIS

– Distance to river, Distance to nearest urban or suburban center, Distance to south entry, Distance to nearest stream, Nearest road type, Adjacent to river or forest reserve, Plot area

• Survey data– Malaria rate, Household characteristics: education, age

structure, number of people live on the plot, number of planters, number of chainsaws.

• Satellite images– Water cover, area cleared, area cropped

Summary Statistics: PlotsTable 1: Summary Statistics

Plot Characteristics Obs MeanStd. Dev. Min Max

Distance to the Machadinho river (=7km if over 7km) 1734 4.75 2.36 0.2 7

Plot within 200 meters of the Machadinho river 1734 0.08 0.27 0 1

Plot within 200 meters of the forest reserve 1734 0.25 0.44 0 1

Distance to the nearest urban or suburban centers (km) 1734 4.38 2.04 0.3 11.6

Nearest road is major road (1=yes; 0=no) 1734 0.13 0.34 0 1

Nearest road is sub-major road (1=yes; 0=no) 1734 0.35 0.48 0 1

Distance to the nearest stream (km) 1734 0.46 0.26 0 1.58

Soil quality index 1670 0.16 0.03 0.07 0.44

Plot area (hectare) 1734 45.41 10.36 16.1 124.1

Distance to the south entry (km) 1734 15.20 7.80 0.44 29.48

Summary Statistics: householdsTable 1: Summary Statistics

Household Characteristics by Plot, 1986 and 1987 Obs Mean St.dev. Min Max

Plot is occupied (1=yes; 0=no) 3468 0.37 0.48 0 1

Self-reported malaria rate 1286 0.28 0.26 0 1

Household head's education 1281 1.66 1.91 0 7

Household head wife's education 1083 1.59 1.79 0 7

Proportion of people on the plot younger than 5 1279 0.13 0.17 0 1

Proportion of people on the plot between 5 and 15 1279 0.26 0.23 0 1

Proportion of people on the plot over 65 on a plot 1279 0.01 0.09 0 1

Total number of people living on the plot 1299 5.17 3.01 1 18

Number of chainsaws 1299 0.53 0.55 0 3

Number of planters 1299 0.50 0.54 0 2

Summary Statistics: Land Use

Land Use by Plot, 1985 and 1986 obs meanSt.

dev. min max

Water area (fixed) 1734 0.05 0.05 0 0.33

Proportion of land deforested 3468 0.10 0.09 0 0.69

Proportion of land cropped 3468 0.01 0.02 0 0.27

Malaria Prevalence 1986

Malaria Prevalence 1987

Percentage deforested in 1985 Percentage cropped in 1985

Clearance and Cropping 1985

Percentage

0-5%

5-10%

10-20%

20-30%

30-100%

Percentage deforested in 1986 Percentage cropped in 1986

Clearance and Cropping 1986

Percentage

0-5%

5-10%

10-20%

20-30%

30-100%

The Impact of Malaria on Land Occupancy

Simultaneous EquationStructural Model

1, 0it itY if y Plot Occupied if

m: malaria ratey:latent variable for occupancyx: plot variablesr: is distance to river

h:household variabless: distance to south entryd: time dummiesi: plot/household* Identification

*x i r i hit it tt t itim hx d vr

( | , , , ,* )it it i i it tm it ix i s i h t t ity E m x r h d v hx s d u

Removing Household Characteristics

• Household characteristics go into the error term – valid because of randomization of plot allocation

Note that error also includes difference in conditional expectation of malaria with and without household characteristics but this isuncorrelated with plot characteristics

'x i r ii t it tt t im vx r d

( | , , , ) 'it it i i t ix i s i t tm t ity E m x r d vx s d u

Correlation Matrix Between Plot Fixed Characteristics and Household Characteristics

Distance to river

Adjacency to river

Adjacency to forest

reserve

Distance to nearest

urban/suburban center

Nearest road is

major road

Nearest road is sub major

road

Distance to the nearest

stream

Soil quality index

Plot areaPlot water

area percentage

Distance to south entry

Household head's education 0.0086 0.0371 0.0113 0.0632 0.007 -0.0142 -0.0311 0.0124 0.0523 -0.0468 -0.0335

Household head wife's education

0.0281 0.0245 -0.0209 0.0474 -0.0156 -0.0232 -0.0347 -0.0108 0.0475 -0.044 0.0006

Proportion of people on the plot younger than 5

0.0999 -0.0449 0.0283 0.0108 0.0025 0.0424 -0.011 -0.0385 0.0287 0.0183 0.0137

Proportion of people on the plot between 5 and 15

0.0093 0.0269 0.0066 -0.0108 -0.0224 0.0375 0.0605 -0.0513 -0.0315 0.0017 -0.0088

Proportion of people on the plot over 65 on a plot

-0.0858 0.1100* 0.0195 -0.0141 -0.0176 -0.0176 -0.0197 -0.0331 0.0074 0.0402 0.0462

Total number of people living on the plot

0.0306 0.0302 0.0178 -0.0741 -0.0429 0.1184* 0.0988 -0.0498 -0.0269 -0.0331 -0.0277

Number of chainsaws a plot has

0.0225 0.015 -0.0263 -0.0185 0.0033 0.0597 0.0296 -0.0033 0.0419 -0.0268 0.0323

Number of planters a plot has

-0.0435 0.0382 -0.0028 -0.0432 0.0442 0.0278 0.0625 0.026 -0.0439 -0.0925 -0.0086

Reduced Form• Substituting for expected malaria

x i i itrit t tx r dm

() ()( )x m x i i s imi t m t t ir tt x ry s d u

( )m rm

r

Plot Occupied 1, 0it itY if y

• Standard Heckman selection model

Identification• We need an “instrument” for malaria in the

settlement equation. Identifying assumption is that distance to river (up to 7 km) is correlated with malaria exposure but does not directly affect settlement. We correct for being adjacent to the river.

• We need an “instrument” that affects settlement but not malaria. We use distance to the south entry and connection with the outside world.

Legend

Plots

Urban

^ South_Entry

Suburban Center

Machado

River

Road and Type

Newtype

Collector Road

Access Road

Penetration Road

Forest Reserve

Data Source:Center for Regional Development and Planning Federal Univeristy of Minas Gerais, Brazil

Machadinho Resettlement Area

Distance to River as

IV for malaria

South Entry

Heckman Selection ModelDependent variable Plot

Occupancy Malaria

Rate

Independent variables (2) (1)

Distance to river 0.0156**(0.00501)

-0.0431***(0.00429)

Nearest road is collector (best) road

0.157***(0.0337)

-0.0973***(0.0211)

Distance to the south entry (km)

-0.00462***(0.00128)

Year 87 0.114***(0.0134)

-0.0598***(0.0133)

rho -0.105(0.064)

Wald test of independent equations

2.71

No of observations 3333

Malaria Rate: Reported vs Predicted 0

24

68

Den

sity

0 .2 .4 .6 .8 1self reported malaria rate at plot level

Self-reported malaria rate

02

46

8D

ensi

ty0 .2 .4 .6 .8 1

E(mrate|Zg>0)

Predicted malaria rate conditional on occupancy

Malaria: reported vs predicted

Obs. mean S.D Min Max

Self-reported malaria rate for occupied plots

1286 0.282 0.261 0 1

Predicted malaria conditional on occupancy for occupied plots

1246 0.281 0.099 0.082 0.517

Predicted malaria unconditional on occupancy for all plots

3340 0.326 0.107 0.096 0.582

Self-reported malaria rate in 1986 Predicted malaria rate in 1986

Malaria Rate

0-10%

11-25%

25-100%

Self-reported vs. Predicted Malaria Rate: 1986

Self-reported malaria rate in 1987 Predicted malaria rate in 1987

Malaria Rate

0-10%

11-25%

25-100%

Self-reported vs. Predicted Malaria Rate: 1987

Result: The Impact of Malaria on Land Occupancy

• Point estimates of the effect of malaria on occupancy (t stat 2.98)

• Effect on probability of land occupancy of going from no malaria to 0.326 malaria rate (area average) is 0.12

• No malaria would have raised settlement fraction of plots from 0.37 to 0.49

( ) 0.0150.36

0.043m r

mr

Malaria and Land Use

• Similar structural model• Simpler because land use observed for each

plot – still problem of finding expected malaria

• Tobit Model – Many plots have zero clearance/cropping– Left censored data

Dependent variable Percentage Deforested Percentage Cropped

Independent variables (3) (4)

Distance to Machadinho river -0.00140(0.000716)

-0.000517**(0.000166)

Adjacent to river -0.0129*(0.00567)

-0.00316**(0.00121)

Adjacent to forest reserve -0.0114**(0.00361)

-0.00283***(0.000725)

Nearest road is collector (best) road 0.0524***(0.00583)

0.00687***(0.00118)

Nearest road is access (2nd best) road 0.00892**(0.00341)

0.00129(0.000711)

Distance to nearest stream (km) 0.0143*(0.00665)

0.00345**(0.00133)

plot area (hectare) -0.000645***(0.000143)

-0.0000899**(0.0000303)

Distance to the south entry (km) -0.000696***(0.000200)

-0.000313***(0.0000404)

Year 86 0.0659***(0.00153)

0.0187***(0.000554)

No of observations 3340 3340

Table 4 The Impact of Malaria on Land Use: Tobit Model

The Impact of Malariaon Land Use

• No effect of the river on clearance• Land close to the river is more likely to be

cropped • Plot occupancy uncorrelated with clearance• Occupied plots less likely to be cropped• Plots adjacent to major road more likely to be

cleared and cropped

Land Use by Occupancy 1986

Obs. mean S.D Min Max

Proportionplot cleared

Plot occupied 825

0.135 0.093 0 0.620

Plot not occupied 1190 0.124

0.092 0.001 0.092

Proportion plot cropped

Plot occupied 825 0.017

0.023 0 0.235

Plot not occupied 1190 0.025

0.029 0 0.269

Malaria and Land Use

• Malaria deters settlement and living on a plot• However, people may live in town and clear a

plot in the same way as an occupied plot• Cropping is more prevalent in high malaria /

non-occupied plots– Less malaria among farmers– More access to work in town– More money for capital and seeds

Conclusions

• Malaria deters settlers from living on plots • Land use, clearance and cropping, is not

deterred – commuting to work on the plot is possible

• Pattern may be particular to this settlement area in Brazil– good roads and transport

• Occupancy different from land use