Empirical Results and Applications to Early Warning

Marc Levy

CIESIN

Earth Institute, Columbia University

Malanding Jaiteh

Christian Webersik

Cait Thorkelson

Jan Hagiwara

Create a spatial time series conflict databasePrimkey Location Begin End Lat Lon

Radius

318001998 Eritrea – Ethiopia 1998 2000 15 39 300311001995 Ecuador – Peru 1995 1995 -3.5 -78.5 50

232001975 Cambodia – Vietnam 1975 1977 11 106 200

197011977 Cambodia - Thailand 1977 1978 14 102.5 200

grid sftgcode year outbr1 outbr2 outbr311919 CAN 1979 0 0 011919 CAN 1980 0 0 011919 CAN 1981 0 0 011919 CAN 1982 0 0 011919 CAN 1983 0 0 011919 CAN 1984 0 0 0

Convert PRIO data to grid

Demographic Data

CIESIN, Gridded Population of the World, 350,000 census input units

2.5’ lat x lat (ca. 21km2@ equator)

Hypotheses linking water availability and civil conflict

Background Level Effects Variability Effects

Absolute(stand alone)

effects

H1: Regions with low levels of baseline water availability are more prone to conflict than other regions

H3: Regions with significant departures from normal available water will become more prone to conflict than other regions.

Difference(contrast)

effects

H2: Contiguous or near-contiguous regions that exhibit significant disparities in baseline levels of water availability are more prone to conflict than other regions

H4: Deviations from baseline water availability that result in significant disparities across regions will experience more conflict than other regions.

|--------------------------Resource Reliability -------------------------|

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Model 1 Model 2 Model 3

Variables Coefficient Coefficient Coefficient

Infant Mortality (CY) .6066*** .555** .5627**Trade Openness (CY) -.0056 -.0061 -.0065Polity -5 to 7 (CY) .5972 .6185 .6194Population (natural log) (G) 1.544*** 1.370** 1.403**Square of population (G) -.0753** -.0661** -.0683**Rainfall Deviations (GY) -.0433*** -.045***

Average Surface Freshwater per capita 1979-2000 (G)

-.0002

Logistic regression. RHS variables lagged 1 year. Stata Robust Error With Country as Cluster ID. Original Data Resolution: CY=country-year; G=Grid; GY=Grid-year

• Drought helps predict high-intensity conflicts only

• Low and medium intensity not preceded by droughts

• More consistent with incentive hypothesis, not capacity

1 2 3 4 5 6 7 8 9 10

Deciles of Rainfall Deviation(1=below normal; 10=above normal)

0.05

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Prob

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Conditional Probability of High-IntensityOutbreak, by Rainfall Deviation Decile,given ongoing Low or Medium Intensity Conflict

Rainfall Deviations in Nepal 1979 - 2002

-15

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1979 1984 1989 1994 1999

Year

Sum

of m

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Non-conflict Zone .Conflict Zone .

Average annual runoff (mm/square meter)

Partial regression plot, runoff versus conflict-years

Conflict years (high-level), by basin

What are the hydrologic characteristics of high-conflict river basins?

- High runoff associated with fewer years of high-level conflict

- High variability in runoff associated with more years of low/medium level conflict

Putting Knowledge to UseStructural Risk Assessments

•Political Instability Task Force

•UMd Conflict Ledger

Dynamic Analyses• Intergovernmental Authority on Development CEWARN*

• WANEP*• International Conflict Group CrisisWatch

• OSCE High Commissioner for National Minorities

• SwissPeace FAST Early Warning Mechanism

• Control Risk Group• UNDP SEE Early Warning*

Consultative Processes• EU Check List for Root Causes of Conflict

• World Bank Country Policy and Institutional Assessment (CPIA)

• Fund for Peace CAST

• Many efforts to provide early warning for conflict• Very little application of relevant background

climate or current weather data • We know enough about the relevance of water

to start paying attention systematically• We do not need to assume simplistic causal

determinism (as we avoid doing so in all early warning efforts)

• Here is a “proof concept” (not a finished product)

Map_ID Country Situation

AF001 Côte d’Ivoire New Forces vs govt

AF002 Central African Republic Rebels vs govt

AF002 Central African Republic UFDR vs govt

AF003 Western Sahara Frente Polisario vs Morocco

AF004 Uganda LRA vs govt

AF005 Somaliland (Somalia) UIC vs govt

AF006 Somalia UIC vs transitional govt, Ethiopian army

AF007 DR Congo Rebels vs govt,UN

AF007 DR Congo FDLR vs UN

AF007 DR Congo Mayi-Mayi groups vs civilians

AF008 Chad FUCD, RUFD, others vs govt

AF009 Ethiopia/Eritrea Border dispute

AF010 Ethiopia Somali Islamists vs govt

AF011 Nigeria (Delta region) MEND vs govt

AF012 Angola FLEC-FAC vs govt

AF013 Sudan (N/S Darfur) NRF vs govt

AF014 Sudan (South) LRA vs UPDF

AF015 Sudan (eastern) Eastern Front rebels vs govt

AF016 Nigeria Intercommunal violence

AF017 Guinea Militia groups vs govt

AF018 Burundi War btwn PALIPEHUTU-FNL, CNDD-FDD and govt

AF019 Algeria GSPC, others vs govt

AF020 Senegal MFDC vs. govt

AF021 Egypt Govt vs political groups

AF022 Zimbabwe MDC vs govt

AF023 Rwanda FDLR vs govt

AF024 Mali, Algeria, Niger, Chad, Western Sahara GSPC vs DAC (Tuaregs), others

Crisis Group’s November 2006 watch list was georeferenced

Map data were taken from various sources.

Precision and timeliness varies

Conflict Hotspots with Significantly Below-Normal 12-month Rainfall

---------------------------------------------------------------

Côte d’Ivoire

Sudan (South)

Guinea

Bangladesh

Haiti

India (Nagaland, Manipur)

Merits of Tracking Conflict Hotspots Spatially

• Permits examination of background climate and near-real-time weather patterns

• Permits use of long-range weather forecast information• Permits consideration of other natural hazard risks

(landslides, floods, pests, disease) that may influence conflict dynamics

• Permits explicit consideration of interaction between conflict and other high-priority problems (public health, poverty)

• Permits consideration of conflict geography (terrain, “dangerous neighborhoods,” critical pathways and buffers)

• Permits examination of non-linear climate impacts

Summary

• Initial search for water/conflict linkages mixture of speculation, half-truths, real insights

• Advances in data collection and spatial analytic tools make it possible to move ahead

• Empirical record shows strong relationship between rainfall shortfalls and conflict risk

• Such knowledge can be put to practical use

End

Fig. 5.

Mean runoff per basin area 1975-2000.Source: GRDC

Runoff per basinmm/yr/km^2

0.00

0.01

0.02 - 0.03

0.04 - 0.05

0.06 - 0.09

0.10 - 0.12

0.13 - 0.16

0.17 - 0.22

0.23 - 0.41

0.42 - 0.59

Variable Description Low Level

Conflict Events with 25 to 1000 battle deaths

Conflict

High Level Conflict Events with > 1000 battle deaths Regression Model #

0 1 2 3 4 5 6 7*Runoff Mean runoff per basin area X X X X X X X

Temporal Variance

Standard deviation of yearly runoff normalized by mean X X X X X X X

Hydrology Spatial

Variance Mean of standard deviation of grid runoff

weighted by area X X X X X X X

Area Land area within each basin X X X X X X X X

Poverty Infant mortality rate weighted by population, deaths/live births X X X X X

Population density 1990 population per basin area X X X

Population Growth Rate

LN((2000 population/ 1990 population) /10 yrs)*100 X

Forest Forested land area per basin area X X X

Controls

Mountain Mountainous land area per basin area X X X

Outbreak results weak

• Significant variables for high-level outbreak– IMR– Spatial variance in runoff– Size of basin (a control)

Total Conflict Years as Dependent Variable

Model Number 0 1 2 3 4 5 6 7Area 0.000*** 0.000*** 0.000** 0.000*** 0.000*** 0.000* 0.000*** 0.000****Runoff -2.856 -4.836 -4.748 -5.262 -0.760 -3.014Temporal Variance 0.435 1.075** 1.301** 1.121*** 0.506 0.649Spatial Variance 0.606*** 0.809*** 0.906*** 0.821*** 0.473** 0.699*** 0.603***Poverty 0.068*** 0.072*** 0.080*** 0.076*** 0.070***Population 0.450 0.277Growth Rate -0.731Forest 1.189 -0.879* -1.242***Mountain -2.442 0.591 0.698*Constant 5.270*** 3.778*** -2.676 -4.786* -2.706 -26.734 -4.709 -3.465*Adjusted R square 0.142 0.260 0.386 0.386 0.410 0.274 0.406 0.414

Low Level Confict

Model Number 0 1 2 3 4 5 6 7Area 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000***Runoff -14.396** -16.427*** -16.408*** -16.204** -12.499** -17.557*** -18.387***Temporal Variance -0.042 0.614 0.663 0.590 -0.025 1.106Spatial Variance -0.155 0.054 0.075 0.048 -0.230 0.322Poverty 0.070** 0.071** 0.064*** 0.074*** 0.073***Population 0.098 -0.414Growth Rate 0.382Forest 0.824 1.040Mountain -2.610 1.578*** 1.465***Constant 5.057*** -1.221*** 0.804 0.346 0.820 -14.108 5.957 4.077**Adjusted R square 0.249 0.282 0.353 0.345 0.349 0.277 0.405 0.406

High Level Confict

Fig. 7.

Partial regression plot of high intensity conflict versus runoff level model 6.

-0.2 -0.1 0.0 0.1 0.2 0.3 0.4

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R Sq Linear = 0.114

Fig. 7.

Partial regression plot of high intensity conflict versus runoff level model 6.

-0.2 -0.1 0.0 0.1 0.2 0.3 0.4

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-5

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R Sq Linear = 0.114

• Get the scales right• Let politicians shape discourse on values

and priorities; let science shape search for causal connections

• When a causal hypothesis is treated as a value, everyone suffers

Illustration: Senegal• C. 400 individual battles

located in time and space

2 4 6 8 10

Dry ...........WASP deciles ........Wet

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Mea

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Other tests

• No significant relationship to low or medium intensity outbreaks

• No cumulative effect detected• Other lag specifications not significant

Illustration: Nepal 2002 Outbreak

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n=decile of rainfall deviation measure

Drought (3 consecutive overlapping 3-month seasons with rainfall at least 50% below normal)

Not poor Somewhat poor Moderately poor

Poor Extremely poor

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0 1 2 3 4 5 6 7 8 9 1011 121314 15 19

Drought frequency 1980-2000

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0 1 2 3 4 5 6 7 8 9 10 11 121314 1519

Drought frequency 1980-2000