The Effect of Malaria on Settlement and Land Use: Empirical Evidence from the Brazilian Amazon

1. 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 Health May, 2010

2. 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

3. 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)

4. Machadinho Resettlement Project • Resettlement project in Brazil in the 1980s • Land plots allocated to settlers Our Questions • What is the impact of malaria on settlement? • What is the impact of malaria on land use?

5. Machadinho Resettlement Project Machadinho Site Photo (source: M. Castro)

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

7. 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.

8. 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.

9. House built in 1985, all made of palm thatch. House built in 1985. The roof is made of plastic, and the sealing of the whole house is precarious.

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

11. Settlers in Machadinho in 1985 Source: M. Castro

12. Main urban area of Machadinho in 1985 Main urban area of Machadinho in 2001

13. 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

14. Malaria Ecology Malaria Both Plasmodiumfalciparum and Plasmodiumvivax River is preferred mosquito habitat Anopheles Darlingi Forest fringe for sun/shade Stagnant water when river falls Mosquito range up to 7km

15. 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)

16. 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).

17. 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.

18. 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)

19. 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

20. Summary Statistics: Plots

21. Summary Statistics: households

22. Summary Statistics: Land Use

23. Malaria Prevalence 1986

24. Malaria Prevalence 1987

25. Clearance and Cropping 1985 Percentage deforested in 1985 Percentage cropped in 1985

26. Clearance and Cropping 1986 Percentage deforested in 1986 Percentage cropped in 1986

27. The Impact of Malaria on Land Occupancy

28. Simultaneous EquationStructural Model Plot Occupied if m: malaria rate y:latent variable for occupancy x: plot variables r: is distance to river h:household variables s: distance to south entry d: time dummies i: plot/household * Identification

29. 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 is uncorrelated with plot characteristics

30. Correlation Matrix Between Plot Fixed Characteristics and Household Characteristics

31. Reduced Form • Substituting for expected malaria Plot Occupied • Standard Heckman selection model

32. 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.

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

34. Distance to River as IV for malaria

35. South Entry

36. Heckman Selection Model

37. Malaria Rate: Reported vs Predicted Predicted malaria rate conditional on occupancy Self-reported malaria rate

38. Malaria: reported vs predicted

39. Self-reported vs. Predicted Malaria Rate: 1986 Self-reported malaria rate in 1986 Predicted malaria rate in 1986

40. Self-reported vs. Predicted Malaria Rate: 1987 Self-reported malaria rate in 1987 Predicted malaria rate in 1987

41. 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

42. 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

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

44. 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

45. Land Use by Occupancy 1986

46. 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

47. 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