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?