Empirical Results and Applications to Early Warning · Empirical Results and Applications to Early...
Transcript of Empirical Results and Applications to Early Warning · Empirical Results and Applications to Early...
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
0.10
0.15
0.20
Prob
abili
ty o
f Hig
h-In
tens
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utbr
eak
Conditional Probability of High-IntensityOutbreak, by Rainfall Deviation Decile,given ongoing Low or Medium Intensity Conflict
Rainfall Deviations in Nepal 1979 - 2002
-15
-10
-5
0
5
10
15
20
25
30
1979 1984 1989 1994 1999
Year
Sum
of m
onth
ly d
evia
tions
-15
-10
-5
0
5
10
15
20
25
30
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
-10
-5
0
5
10
15
20
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
-10
-5
0
5
10
15
20
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
0.00
0.20
0.40
0.60
Mea
n nu
mbe
r of c
onfli
ct e
vent
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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
1
21
4
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
10.00
20.00
30.00
40.00
50.00
% o
f pop
ulat
ion
0 1 2 3 4 5 6 7 8 9 1011 121314 15 19
Drought frequency 1980-2000
10.00
20.00
30.00
40.00
50.00
% o
f pop
ulat
ion
0 1 2 3 4 5 6 7 8 9 10 11 121314 1519
Drought frequency 1980-2000