the drivers of maritime piracy fragility, deprivation, and loss of strength gradient brandon prins...
TRANSCRIPT
The Drivers of Maritime PiracyFragility, Deprivation, and Loss of Strength Gradient
Brandon PrinsUniversity of Tennessee
&
Ursula DaxeckerUniversity of Amsterdam
ASAM 1985-2013
Heat Map of Piracy Incidents
Overall Objectives of Research Project
Build a theoretical model of maritime piracy Existing research concentrates on state fragility and economic deprivation as drivers of piracy We theorize that the effects of both factors are conditioned by distance (loss of strength gradient, which is defined as
the ability of governments to enforce order over distance) Our project will explore distance from several different angles
Geographic Economic Cultural
Operationalize loss of strength gradient We need measures of critical factors affecting maritime piracy (fragility, deprivation, distance)
Geo-code all piracy incidents
Reconcile the various datasets that currently exist on maritime piracy
Build Database on Pirate Organizations in 4 or 5 countries
Use theoretical model to build country-level & sub-national (for several countries) risk indices
Forecast piracy events at the country and sub-country levels of analysis
Build a web-based portal to access data and map piracy incidents
How Research Maps into MINERVA Topic
MINERVA Topic 3 Subtopic D: Theories of Power & Deterrence: Beyond Conventional Deterrence
Our research provides new thinking on the drivers of maritime piracy drawing on Ken Boulding’s pivotal work on loss of strength gradient. LSG has been applied (in a limited way) to insurgency, but we think the concept also has leverage in explaining maritime piracy, location of piracy, and positioning of pirate organizations
Our theoretical model connects both opportunity and the threat of punishment (deterrence) to maritime piracy.
We model strategic behavior on the part of pirates and governments
Research also has implications for Topic 4: Emerging Topics in Conflict and Security
How Research Advances Current Theory
LSG, or the interactive relationship between distance and standard correlates of maritime piracy, provides leverage in explaining piracy and advances current theory
Our research will extend micro-level analyses of piracy beyond Somalia
We reconcile various databases on piracy and test our theoretical model on different data sources
We use new modeling tools that incorporate binomial distributions, event count estimators, and spatial statistics to better understand piracy.
Apply new approaches to forecasting events that should aid in establishing a valid risk index for maritime piracy
Build several databases that will be available to researchers GPI – Global Piracy Incidents Database MPO – Mapping Pirate Organizations Database MPELD – Maritime Piracy Event Location Database
Research in Progress
Initial/Preliminary Work by PIs Forthcoming manuscripts in Journal of Conflict Resolution, Foreign Policy Analysis, and SAIS Review Preliminary LSG paper will be presented at special (invite only) workshop on forecasting methods at ISA meeting in
Toronto in March 2014
Panel Proposal for EPSA 2014 Title: Theoretical and Empirical Advances in the Study of Maritime Piracy We have paper that examines the relationship between state fragility and the distance to piracy incidents in
territorial waters. We expect piracy to occur closer to a country’s power center as state fragility increases.
Future Work Effect of piracy on trade flows Connections between insurgency and piracy Disaggregate piracy incidents by month and examine in several countries, such as Nigeria, Indonesia, Malaysia, etc. Use hierarchical modeling tools to explore drivers of maritime piracy. Examine youth bulges in piracy-prone countries Acquire shipping data to get a better sense of whether maritime traffic in and out of a country’s ports is related to
piracy. Currently our regional trade measure is significantly related to piracy, but a shipping data would provide a better measure of opportunity.
Preliminary Analyses of Maritime Piracy
Following slides begin analyses of: Distance Micro-level analyses of piracy Beginnings of hierarchical model of piracy Building country and within country databases Building MPO database LSG and piracy
Aggregate Data on Piracy
Distance to Piracy from Capital Cities
Piracy Data Source: IMB
Distance (Kilometers)
Strong States 674.63
Weaker States 480.54
Failed States 425.46
Least Corrupt (Top 3rd) 846.56
Partially Corrupt (middle 3rd) 563.15
Most Corrupt (Bottom 3rd) 427.24
We see that as state strength increases, piracy moves farther away from capital cities.The same relationship occurs with a measure of government corruption.
Correlates of Maritime Piracy
State Weakness Economic Deprivation
Extractive Capacity – Distance Interaction
Figures show that the effect of state weakness on piracy increases with increasing distance between capital and coastline
Weak states cannot project power over territory effectively and so pirates strategically locate themselves outside of a government’s political reach
Accuracy of Predicted Risk Index 2013
True Piracy Risk 2013 Predicted Piracy Risk 2013
We our structural loss-of-strength-gradient model to forecast piracy in 2013. The model tends to over-predict more than under-predict and generally captures the most at-risk countries. The table on the next slide shows the cases we correct predict and the ones that we miss. Grey boxes show correct predictions.
Prediction Accuracy for High-and Moderate Risk Countries, Extractive
Capacity 2013 Model Prediction
Low RiskModel Prediction
Moderate RiskModel Prediction
High RiskTRUE HIGH RISK COUNTRIES
None Colombia Nigeria Peru Somalia Ivory Coast Egypt Togo India Malaysia Bangladesh Indonesia
TRUE MODERATE RISK COUNTRIES
Gabon Dominican Republic Guinea Guyana Tanzania Ecuador Mozambique Brazil Philippines Mauritania Sierra Leone Ghana Congo Kenya Morocco
Global Piracy and PredictionsCountry 2014
Risk Predictio
n
3 Year MATrue Risk
Philippines High High
India High High
Madagascar High Low
Indonesia High High
Yemen High Low*
Dem Congo High Moderate
Haiti High Moderate
Nigeria High High
Malaysia Moderate High
Peru Moderate Moderate
The figure below shows true global piracy counts by year (spikes) and our model prediction (dashed line). Our model predicts 248 piracy incidents in 2014. Currently IMB reports approximately 45.
Armed Conflict in Somalia and Proximity to Pirate Hubs
The map shows geo-coded piracy data and geo-coded armed conflict data
There may be a connection between armed insurgency and maritime piracy.
We have looked and the temporal relationship between armed conflict and piracy and find that piracy does appear to increase in the year after armed conflicts
Piracy may help fund insurgent movements in some countries
Disaggregating Piracy: Nigeria
Has substantial piracy
Comparison with Somalia
Sub-Saharan Africa DOD Project
Explaining & Predicting Nigerian Piracy
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Indonesia 94 79 50 43 28 15 40 46 81 106
Somalia 2 35 10 31 19 80 139 160 49 7
Nigeria 28 16 12 42 40 29 19 10 27 31
Bangladesh 17 21 47 15 12 17 23 10 11 12
India 15 15 5 11 10 12 5 6 8 14
Malaysia 9 3 10 9 10 16 18 16 12 9
Philippines 4 0 6 6 7 1 5 5 3 3
Peru 5 6 9 6 5 13 10 2 3 4
Brazil 7 2 7 4 1 5 9 3 1 1
Piracy Data Source: IMB
Nigerian Piracy, 1985-2013
Piracy Data Source: ASAM
Monthly Piracy - Somalia & Nigeria
Piracy Data Source: IMB
Weather in Greater Gulf of Aden Northeast Monsoon, December to March. Transition season, April and May. Southwest Monsoon, June to September. Transition season, October and November.
Poisson Model of Monthly Nigerian Piracy, 1995-2009
Poisson Model1995-2010
L.Piracy .140***(.043)
L.Price of Crude Oil (ln) .835***(.138)
L.Price of Sugar (ln) -3.19***1.10)
Monthly SCAD Incidents .045***(.014)
Dummy for Summer Months
-.359**(.168)
Constant -2.40***(.389)
Predicted vs. Actual Piracy Incidents
In-Sample - 1995-2010
Predicted vs. Actual Piracy Incidents
Out of Sample - 2011-2013
The EndQuestions? Comments?