INFORM
Index for Risk Management
Concept andMethodology
Version 2017
Marin-Ferrer, M.
Vernaccini, L.
Poljansek, K.

2017

EUR 28655 EN

This publication is a Science for Policy report by the Joint Research Centre (JRC), the European Commission’s
science and knowledge service. It aims to provide evidence-based scientific support to the European
policymaking process. The scientific output expressed does not imply a policy position of the European
Commission. Neither the European Commission nor any person acting on behalf of the Commission is
responsible for the use that might be made of this publication.
Contact information
Name: Montserrat Marin-Ferrer
Address: Via Fermi, 2479 – Ispra (VA), Italy
Email: montserrat.marin-ferrer@ec.europa.eu
Tel. +39 0332785810
JRC Science Hub
https://ec.europa.eu/jrc

JRC106949
EUR 28655 EN

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Title Index for Risk Management INFORM Concept and Methodology Report — Version 2017.
Abstract
This is the forth report of the revised INFORM’s concept and methodology. INFORM is a composite indicator that
identifies countries at risk of humanitarian crisis and disaster that would overwhelm national response capacity.
The INFORM index supports a proactive crisis and disaster management framework.
Printed in Italy

2

Contents
Acknowledgements ................................................................................................ 4
Executive summary ............................................................................................... 5
1 Introduction ...................................................................................................... 8
2 Key concepts of the INFORM index..................................................................... 10
2.1 Objective of INFORM .................................................................................. 10
2.2 Risk concept ............................................................................................. 10
2.3 INFORM model .......................................................................................... 11
3 Changes in INFORM 2017 ................................................................................. 13
3.1 Overview .................................................................................................. 13
3.2 New exposure layer ................................................................................... 13
3.3 Improvement in the processing of coastal hazards ......................................... 13
3.4 Global Conflict Risk Index ........................................................................... 14
3.5 New indicator: Maternal mortality rate ......................................................... 15
3.6 Lack of Reliability Index .............................................................................. 16
3.6.1 Methodology ..................................................................................... 16
4 Component and core indicator selection ............................................................. 18
4.1 Introduction .............................................................................................. 18
4.2 Dimension: Hazard & exposure .................................................................... 18
4.2.1 Overview .......................................................................................... 18
4.2.1.1 Hazard & exposure: Categories ...................................................... 18
4.2.2 Category: Natural hazard .................................................................... 19
4.2.2.1 Definition .................................................................................... 19
4.2.2.2 Natural hazards: components ........................................................ 22
4.2.2.3 Component: Earthquake ............................................................... 24
4.2.2.4 Component: Tsunami ................................................................... 24
4.2.2.5 Component: Flood........................................................................ 24
4.2.2.6 Component: Tropical cyclone ......................................................... 25
4.2.2.7 Component: Drought .................................................................... 25
4.2.3 Category: Human hazard .................................................................... 26
4.2.3.1 Definition .................................................................................... 26
4.2.3.2 Human hazard: Components ......................................................... 26
4.2.3.3 Component: Conflict intensity ........................................................ 27
4.2.3.4 Component: Projected risk of conflict ............................................. 29
4.3 Dimension: Vulnerability ............................................................................. 30
4.3.1 Overview .......................................................................................... 30
4.3.2 Vulnerability: Categories ..................................................................... 30i

4.3.3 Category: Socio-economic vulnerability ................................................ 31
4.3.3.1 Definition .................................................................................... 31
4.3.3.2 Socio-economic vulnerability: Components ..................................... 32
4.3.3.3 Component: Development & deprivation ......................................... 33
4.3.3.4 Component: Inequality ................................................................. 33
4.3.3.5 Component: Aid dependency ......................................................... 34
4.3.4 Category: Vulnerable groups ............................................................... 34
4.3.4.1 Definition .................................................................................... 34
4.3.4.2 Vulnerable groups: Components .................................................... 36
4.3.4.3 Component: Uprooted people ........................................................ 38
4.3.4.4 Component: Other vulnerability groups/Health condition .................. 38
4.3.4.5 Component: Other vulnerability groups/Children under 5 .................. 38
4.3.4.6 Component: Other vulnerability groups/Recent shocks ..................... 38
4.3.4.7 Component: Other vulnerability groups/Food security ...................... 39
4.4 Dimension: Lack of coping capacity .............................................................. 40
4.4.1 Overview .......................................................................................... 40
4.4.2 Lack of coping capacity: categories ...................................................... 40
4.4.3 Category: Institutional ....................................................................... 41
4.4.4 Definition .......................................................................................... 41
4.4.4.1 Component: Disaster Risk Reduction .............................................. 42
4.4.4.2 Component: Governance .............................................................. 42
4.4.5 Category: Infrastructure ..................................................................... 43
4.4.5.1 Definition .................................................................................... 43
4.4.5.2 Component: Communication ......................................................... 44
4.4.5.3 Component: Physical infrastructure ................................................ 44
4.4.5.4 Component: Access to health system ............................................. 44
5 Limitations and constraints of INFORM ............................................................... 45
5.1 Methodological limitations ........................................................................... 45
5.2 Data limitations ......................................................................................... 45
5.3 Ranking of countries .................................................................................. 47
6 Building the INFORM model .............................................................................. 48
6.1 Imputation of missing values ...................................................................... 48
6.2 Transformations ........................................................................................ 48
6.3 Normalisation ............................................................................................ 48
6.4 Aggregation .............................................................................................. 50
7 Interpretation of the INFORM index score ........................................................... 52
7.1 Cluster analysis ......................................................................................... 52
7.2 Fixed 5 risk classes .................................................................................... 52ii

7.3 Trends...................................................................................................... 54
References ......................................................................................................... 56
List of abbreviations and definitions ....................................................................... 60
List of boxes ....................................................................................................... 62
List of figures ...................................................................................................... 63
List of tables ....................................................................................................... 64
Annexes ............................................................................................................. 65
Annex 1. Key statistical metrics ......................................................................... 65
Annex 2. Core indicators................................................................................... 69
Annex 3. INFORM 2017 results — countries by alphabetic order ............................ 70
Annex 4. INFORM 2017 results — countries by rank ............................................. 76
Annex 5. Maps of the indexes............................................................................ 82iii

Acknowledgements
The authors would like to acknowledge the contributions of all partners in INFORM. The
various dimensions and categories as well as all the following development of INFORM
are based on expert input from individuals during the workshops and during the editing
process. In particular, we would like to thank Andrew Thow (OCHA) for his professional
and personal contribution.
At the JRC, we are particularly grateful for the contributions of Daniele Ehrlich for his
contribution in coastal hazards, Thomas Kemper, Martino Pesaresi, Sergio Freire forfacilitating the access to the GHSL Population layer latest release, Winfried Ottoy forcontributing with the GCRI data, and Ainara Casajus Valles for her valuable support
during the evaluation process.
This work has been financially supported by the European Commission DG for European
Civil Protection and Humanitarian Aid Operations, DG Joint Research Centre and the
UK Department for International Development.
Authors
Montserrat Marin Ferrer, Luca Vernaccini, Karmen Poljansek.

4

Executive summary
This is the fourth report of the revised INFORM’s concept and methodology. INFORM is acomposite indicator that identifies countries at risk of humanitarian crisis and disasterthat would overwhelm national response capacity. The INFORM index supports a
proactive crisis and disaster management framework. The INFORM initiative began in2012 as a convergence of interests of UN agencies, donors, NGOs and research
institutions to establish a common evidence base for global humanitarian risk analysis.
The INFORM model is based on risk concepts published in scientific literature andenvisages three dimensions of risk: Hazards & exposure, Vulnerability, and Lack of
coping capacity. The INFORM model is split into different levels to provide a quick
overview of the underlying factors leading to humanitarian risk and builds up the pictureof risk by 54 core indicators.
Any changes in the INFORM methodology are always applied to at least the five previous
years of data to preserve the consistency of the trend analysis.
Policy context
The Index for Risk Management INFORM is a composite indicator developed by JRC as atool for understanding the risk of humanitarian crisis and disasters. INFORM provides thescientific support to the following EU policy initiatives:•

Communication from the Commission to the Council and the European
Parliament — EU strategy for supporting disaster risk reduction in developing
countries. [1]•Council Regulation (EC) No 1257/96 of 20 June 1996 concerning humanitarianaid. [2]•Council Conclusions of 30 November 2009 on a Community framework on
disaster prevention within the EU. [3]•Communication from the Commission to the European Parliament, the Council,
the European Economic and Social Committee and the Committee of theRegions — A Community approach on the prevention of natural and manmade disasters. [4]•Commission Staff working Paper of 21 October 2010 — Risk Assessment and
Mapping Guidelines for Disaster Management. [5]•Communication from the Commission of 22 November 2010 — The EU Internal
Security Strategy in Action: five steps towards a more secure Europe,
CoM(2010)673. [6]•TFEU (2012), C 326, 26.10.2012, Treaty on the functioning of the European
Union (consolidated version 2012). [7]•Communication from the Commission of 16 April 2013 — An EU Strategy on
adaptation to climate change, CoM(2013) 216 final. [8]•Regulation (EU) No 1290/2013 of the European Parliament and of the Councilof 11 December 2013 laying down the rules for participation and disseminationin ‘Horizon 2020 — the Framework Programme for Research and Innovation(2014-2020)’ and repealing Regulation (EC) No 1906/2006. [9]•Decision No 1313/2013/EU of the European Parliament and of the Council of
17 December 2013 on a Union Civil Protection Mechanism Text with EEA
relevance. [10]•Communication from the Commission of 8 April 2014 — The post 2015 Hyogo
Framework for Action: Managing risks to achieve resilience. [11]5•Council conclusions on the post 2015 Hyogo Framework for Action: Managing
risks to achieve resilience (9884/14, May 2014). [12]•Council conclusions on Risk Management Capability — Adoption (June 2014,
13375/14 and June 2014). [13]•Communication from the Commission of 2 September 2015 — Towards theWorld Humanitarian Summit: A global partnership for principled and effective
humanitarian action. [14]INFORM is a joint initiative of the European Commission and the Inter-Agency Standing
Committee Task Team (IASC) for Preparedness and Resilience, in partnership with many
UN Agencies, donors, NGOs, and Member States. INFORM is also intended to support
global policy processes, including:•

The Sendai framework for development and disaster risk reduction adopted in
March 2016;•

The 17th Sustainable Development Goals adopted in UN Summit in September
2015;

•

The 2016 World Humanitarian Summit;•

The 2017 Global Platform for Disaster Risk Reduction;•

The resilience ‘agenda’, around which many organisations are focusing their
humanitarian and development work.

Key conclusions
Most humanitarian crises can be predicted to some extent, and while they cannot always
be prevented, the suffering they cause can often be greatly reduced. Understanding crisis
risk — the probability of a crisis occurring and its likely impact — is a fundamental step in
reducing and managing the risk. Risk analysis is used to identify the people and places
most at risk and, therefore, reduce and manage the threat. When all those involved incrisis prevention, preparedness and response — including governments, humanitarianand development agencies and donors — have a common understanding of risk, they canwork more effectively together.
INFORM has been developed to improve the common evidence basis for risk analysis so
all governments, development agencies, disaster risk reduction actors and organisations
can work together. INFORM is the first global, open-source, continuously updated,transparent and reliable tool for understanding risk of humanitarian crises and disasters.
It covers 191 countries. All the results and data used are freely available and the INFORM
partnership includes many data source organisations. Methodology is completelytransparent and is based on scientific concepts and methods.
The INFORM index supports a proactive crisis and disaster management framework. It
will be helpful for an objective allocation of resources for disaster management as well as
for coordinated actions focused on anticipating, mitigating, and preparing forhumanitarian emergencies. Many organisations are already using INFORM.

6

Main findings

Figure 1: INFORM summarises the multitudeof factors contributing to the risk forhumanitarian crises and disasters into a single
index.

Figure 2: INFORM 2017 Risk index map
shows risk of humanitarian crises anddisaster across the globe.

As a composite indicator, INFORM:•

ranks countries according to the likelihood of needed international assistancein the near future;•creates a risk profile for every country which shows the level of the individualcomponents of risk;•

allows for trend analysis because the results of INFORM are available for atleast 5 consecutive years.

Related and future JRC work
The JRC is actively involved in the continuous development of INFORM, which includes
methodological improvements for more accurate risk assessment as well as introductionof new and better (more complete, representative and/or accurate) data when it
becomes available. The main model is expected to remain stable to guarantee thecomparability over the years and trend analysis. INFORM can also be used to measurerisk at the sub-national level. The JRC and INFORM partners are working with regionaland national counterparts to develop region- and country-specific versions of INFORM.
Quick guide
The Index for Risk Management — INFORM — is a way to understand and measure therisk of a humanitarian crisis. INFORM is a composite indicator, developed by the JRC andendorsed by INFORM partners, combining 54 indicators into three dimensions of risk:
Hazards (events that could occur) and exposure to them, Vulnerability (the susceptibilityof communities to those hazards) and the Lack of coping capacity (lack of resources that
can alleviate the impact). The index’s results are published twice a year. They give an
overall risk score out of 10 for each country, and for each of the dimensions, categories,
and components of risk (see Figure 1). The purpose of INFORM is to provide an open,transparent, consensus-based methodology for analysing crisis risk at global, regional or
national level.

71 Introduction2015 was a crucial year for disaster risk management. Three international frameworks ofthe post-2015 development agenda were signed with clear targets to reduce disasters
and humanitarian suffering for the world’s population. In the Sendai Framework for
Disaster Risk Reduction (1), adopted in March 2015, national governments engage
themselves to prevent new and reduce existing disaster risk by reducing hazard exposureand vulnerability and increase preparedness and resilience. In September 2015, 17
Sustainable Development Goals were adopted in a UN Summit and disaster risk isprominent in three of them: end poverty, build resilient cities, and combat climatechange. In December, the UN Framework on Climate Change aims to achieve a legallybinding agreement in the Paris Climate Conference. All frameworks emphasise the role ofscience and objective data to monitor progress in risk reduction, and INFORM has been
widely promoted at the world conferences.
In 2016, the first World Humanitarian Summit (2) set a new agenda to keep humanitarian
action fit for the future. Humanitarian and development stakeholders increasingly
recognise the need to transition from a reactive humanitarian crisis response model to aproactive crisis and disaster management framework. Such a framework must be built ona sound understanding of the drivers of humanitarian risk so that actors can work from acommon understanding of priorities in order to target their resources in a coordinatedand effective manner. This has been the guiding principle in the development of INFORM.
INFORM is a partnership of a group of UN agencies, donors, NGOs and research
institutions to develop a comprehensive and flexible, widely accepted, open andcontinuously updated, transparent and evidence-based multi-hazard humanitarian risk
index with global coverage and regional/subnational scale and seasonal variation. The
group is engaged in incorporating the risk index in internal decision-making processes
and to demonstrate the added value of doing so to other interested organisations. Some
examples are the following:•

INFORM is successfully integrated in the internal processes of the European
Commission’s Humanitarian and Civil Protection Office (ECHO). After atransition period, ECHO adopted fully the INFORM index (3) and is promoting
its use with partners.

•

OCHA, the Office for Coordination of Humanitarian Affairs, adopted the
INFORM index. INFORM is used to support decisions and prioritisation of
OCHA’s preparedness activities as well as being one of the inputs into theprocess for deciding on funding allocation from pooled funds.

•

The UK Department for International Development (DFID) is transitioning its
risk assessment to incorporate INFORM, while it is also promoting INFORMinspired thematic risk assessment pilots (e.g. during the Ebola crisis).

•

The World Food Programme (WFP) is using INFORM to support decisions onprioritisation of emergency preparedness and resilience activities and as abasis for deciding which countries should be the focus for further research andanalysis for the purpose of early warning.

•

Unicef has customised INFORM based on consultations with regional
emergency advisors to be used to prioritise technical and financial support aswell as to assign lighter emergency preparedness standards to low-riskcountries.

•

The US Department of State and the US Agency for International Development(USAID) are promoting the use of INFORM-inspired risk analyses to inform
annual strategy and budget decision-making.

(1) http://www.preventionweb.net/files/43291_sendaiframeworkfordrren.pdf(2) https://www.worldhumanitariansummit.org(3) http://ec.europa.eu/echo/what/humanitarian-aid/needs-assessments_en8During 2016, INFORM Subnational continues to be rolled out by local lead organisations,with projects underway in Latin America and the Caribbean, southern Africa and central
Asia regions, as well as in Guatemala, Honduras and Jordan. From 2017, INFORM
partners will be further supporting INFORM Subnational development through an‘Acceleration Programme’, which will result in improved guidance, training for INFORM
Subnational developers and users, as well as directly supporting five additional national
projects.
INFORM is a composite indicator designed to support decisions about prevention,
preparedness and response. The INFORM initiative started in a workshop in October 2012
organised at the Joint Research Centre of the European Commission (JRC). The JRC isthe main scientific partner in the INFORM process, and has led the bottom-up process ofbuilding a consensus-based new methodology, taking into account the requirements ofparticipating institutions as well as limitations of data availability. INFORM has annual
partner conferences where strategic developments are discussed, and frequent
teleconferences of the core group and/or thematic groups to discuss implementation of
methodological improvements and changes.
Over the last year, some improvements have been implemented into the INFORM
methodology:•

The data layer used to calculate the exposure to natural hazard components
has been updated from LandScan© to the new open-source Global Human
Settlements Layer GHSL Population Grid produced by the European
Commission JRC. The GHSL Population Grid fits all of INFORM’s requirements,
being global coverage, open source, transparent methodology and highresolution for the subnational models.

•The enhanced spatial resolution of the GHSL Global Population Grid comparedto the previous exposure layer, allowed the improvement of the estimation of
the population exposed to coastal hazards like Tsunami, Cyclon wind andSurge. Furthermore, the processing of the Storm surge hazard data wasrevised.

•The ‘Global Conflict Risk Index’, which is used in the ‘Projected Conflict
Risk’ component of INFORM, has been significantly improved.

•‘Maternal mortality ratio’ has been added as a new indicator in the ‘Accessto health system' component of the ‘Lack of coping capacity’ dimension.

•

In addition, a new measure of reliability has been added, which is now
displayed for every country. This has been introduced to increase transparency
in the quality (completeness, representativeness, recentness and/or accuracy)of data used to calculate INFORM’s score, while still ensuring that as many
countries as possible are included. It is presented as a Lack of Reliability
Index on a scale from 0-10 and takes into account missing data, out-of-date
data, and conflict status. Countries with lower Lack of Reliability Index scores
have risk scores that are based on data that are more reliable.

The scope of this publication is to describe the methodology of the global INFORM index
in detail. It can be considered as the third version of the methodology, greatly improvedby feedback of real use by participating organisations, suggestions of new partners, andavailability of new science and data. INFORM will keep evolving when new science anddata becomes available, but the main concepts, dimensions and indicators are expected
to remain stable to allow for comparability over the years and trends analysis.
For more information and updated versions of this document, please refer to the INFORM
website: http://www.inform-index.org

9

2 Key concepts of the INFORM index
2.1 Objective of INFORM
The INFORM index is a way to simplify a lot of information about crisis risk so it can beeasily used for decision-making. It is a composite indicator that identifies ‘countries atrisk from humanitarian emergencies and disasters that could overwhelm current national
response capacity, and therefore lead to a need for international assistance’.
INFORM’s methodology is designed to answer several questions.
●

Which countries are at risk of crisis that will require humanitarian assistance in
response to disasters?

●

What are the underlying factors that could lead to crisis?

●

How does the risk change with time?

The objective of INFORM is to answer those questions with a relatively simple frameworkfor quantifying humanitarian crisis risk, which is based on risk concepts published inscientific literature.

2.2 Risk concept
In scientific literature there are many different views on how to systematically address
disaster risk reflected in various analytical concepts and models of diverse complexity[1]. Those which are the most relevant to INFORM methodology, were presented alreadyin [16], [17], [18]. They range from the basic conceptual framework of disaster risk
community to identifying risk as the interaction of hazard, exposure, vulnerability andcapacity measures, [19], [20], to a complex framework for studying the interface andreciprocal interactions that link humans to nature known as coupled human-environment
system [21]. Among these the features of the pressure and release model (PAR model)[22] and Cardona’s holistic perspective of vulnerability and risk [23] have had thestrongest influence on development of the INFORM model. PAR model understands thedisaster as the interaction between socio-economic pressures as the underlying factors ofvulnerability and physical exposure to the hazardous event. While Cardona’s
understanding of vulnerability takes into account three aspects, that of physical exposure
and physical vulnerability, the fragility of the socio-economic system (prevalent aspects
of individual self-protection) and lack of resilience to cope and recover (aspect ofcollective self-protection). Such perception of the vulnerability has been adopted also in
the 2009 UNISDR terminology [24].
Essentially, INFORM risk concept envisages three dimensions of risk:●

Hazards & exposure (events that could occur and exposure to them);●

Vulnerability (the susceptibility of communities to those hazards);●

Lack of coping capacity (lack of resources available that can alleviate theimpact).

They are conceptualised in a counterbalancing relationship: the risk of what (natural andhuman hazard), and the risk to what (population).
The INFORM model balances two major forces (Figure 3) as identified in PAR model: thehazard & exposure dimension on one side, and the vulnerability and the lack of coping
capacity dimensions on the other side. Hazard-dependent factors are treated in thehazard & exposure dimension, while hazard-independent factors are divided among two
dimensions: the vulnerability dimension that considers the strength of the individuals andhouseholds relative to a crisis situation, and the lack of coping capacity dimension thatconsiders factors of institutional strength.
The INFORM risk concept considers all three aspects of Cardona’s vulnerability. The
aspects of physical exposure and physical vulnerability are integrated in the hazard &10

exposure dimension, the aspect of fragility of the socio-economic system becomes
INFORM’s vulnerability dimension while lack of resilience to cope and recover is treatedunder the lack of coping capacity dimension. The split of vulnerability in threecomponents is particularly useful for tracking the results of disaster reduction strategies
over time. Disaster risk reduction activities are often localised and address particularcommunity-level vulnerabilities and institutional capacities.

Figure 3: The risk concept of INFORM

The INFORM score is calculated with a multiplicative equation where each of thedimensions is treated equally:
1

11

𝑅𝑖𝑠𝑘 = 𝐻𝑎𝑧𝑎𝑟𝑑&𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒 3 × 𝑉𝑢𝑙𝑛𝑒𝑟𝑎𝑏𝑖𝑙𝑖𝑡𝑦 3 × 𝐿𝑎𝑐𝑘𝑜𝑓𝑐𝑜𝑝𝑖𝑛𝑔 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 3

Equation 1

In this form INFORM’s score is more susceptible to Vulnerability and Lack of coping
capacity, the internal forces of risk that can be most influenced by the DRR activities.
Equation 1 resembles the risk equation, where all the factors come with the probabilistic
notion. However, composite indicator methodology can afford to use proxies whenever
the probabilistic presentation of the concept’s dimension is not available.
For the sake of more straightforward communication, higher values in INFORM refer toworse conditions. High values in Vulnerability and Lack of coping capacity lead to worse
outcomes in the presence of high values of Hazard & exposure. The risk equals zero if
one of the three dimensions is zero. Theoretically, in case of tropical cyclones there is norisk if there is no likelihood of a tropical cyclone occurring or/and the hazard zone is not
populated or/and if the population is not vulnerable (e.g. all people have a high level of
education and live in a high level of health and livelihood condition plus they can afford
houses built to a high level of wind security) or/and if the resilience of the country tocope and recover is ideal. In practice, only some indicators in the Hazard & exposure canbe zero, when the hazard is truly zero.

2.3 INFORM model
INFORM model is a multilayer structure (Table 1) that builds up a score of risk bybringing together 54 (4) different indicators. They measure three dimensions of INFORM
risk concept.

(4) In the INFORM 2017 release a new indicator, the Maternal mortality rate, was introduced.

11Each dimension is made up from two risk categories. Categories cannot be fully
captured by an individual indicator. They have been chosen to reflect the interest ofusers of INFORM. For example, UNISDR may follow the institutional category index in the
coping capacity dimension while Unicef and WFP may be more interested in the categoryof vulnerable groups in the vulnerability dimension. Underlying factors that contribute to
the ranking results can be sought down through the levels depending on how narrowly
the users intend to target their interventions.
Table 1. INFORM model

INFORM
Lack of coping capacity

Access to health system

Physical infrastructure

Communication

Institutional Infrastructure
Governance

Vulnerable
groupsOther vulnerable groups

SocioEconomic

Uprooted people

Projected conflict intensity

Current conflict intensity

Components

Human

Aid dependency (25%)

NaturalInequality (25%)

Categories

Vulnerability

Development deprivation (50%)

Hazard & exposure

Earthquake
TsunamiFlood
Tropical cyclone
Drought

Dimensions

DRR

Risk

Categories comprise a number of components. Components are carefully chosen sets ofindicators that capture a specific topic of the component, for example earthquake,
inequality, or governance. The components of INFORM have been chosen to fulfil the‘3 Rs’ criteria: relevant, representative, and robust.
Indicators are the individual datasets that make up INFORM, for example the number ofpeople exposed to earthquake of a certain magnitude, the Gender Inequality Index or
government effectiveness. Indicators may be composite indices themselves. The source
data of indicator is pre-processed (Chapter 6) before it is used in INFORM. Indicators
have been chosen if they are open source and continuous, provide consistent global
coverage and are potentially scalable from national to local level, from yearly to seasonal(monthly) scale.
The data used in INFORM comes from international organisations and academic institutes
and is considered to be the most reliable available. INFORM works directly with source
organisations to ensure quality and appropriate use of the source data in INFORM.
All levels of the INFORM model (from dimensions to indicators) are made available.
Therefore, users can explore risk at different levels of detail and according to their
specific needs and interest. The source data that makes up INFORM is also madeavailable.

123 Changes in INFORM 2017
3.1 Overview
Since last year, we have made some changes to the INFORM methodology. The exposure
layer used to calculate the Natural hazard components has been updated fromLandScan © to the new Global Human Settlements Layer Population Grid produced by
the European Commission JRC. The Global Conflict Risk Index, which is used in the‘Projected Conflict Risk’ component of INFORM, has been significantly improved.
‘Maternal mortality ratio’ has been added as a new indicator in the ‘Access to healthsystem’ component of the ‘Lack of coping capacity’ dimension. In addition, we haveadded a new measure of reliability, which is now displayed for every country. This has
been introduced to increase transparency on the quality of data used to calculate
INFORM, while still ensuring we include as many countries as possible. It is presented asa Lack of Reliability Index on a scale from 0-10 and takes into account missing data, outof-date data, and conflict status. Countries with lower Lack of Reliability Index scores
have risk scores that are based on more reliable data.

3.2 New exposure layer
From the 3rd release of the index in 2017, the Global Human Settlement Layer (GHSL)
Global Population Grid [25] became the exposure layer in the INFORM model. The GHSL
Global Population Grid is already used as exposure layer in most of the INFORM
subnational models, specifically in Lebanon, Guatemala, the Sahel, the Great Horn ofAfrica, central Asia and the Caucasus, Latin America and the Caribbean. The GHSL Global
Population Grid fits all of INFORM’s requirements, being global coverage, open source,transparent methodology and high resolution for the subnational models. Anotheradvantage of the GHSL Global Population Grid is the link with physical footprint ofbuilding, mapping of the resident people. This is very important for assessing the
population exposed to natural hazards such as earthquakes and floods, where most of
the causalities are caused by the destruction of the buildings. The INFORM Index is
published yearly, and therefore a regular update of the GHSL Global Population Grid isneeded in order to monitor the changes in exposure to natural hazards.

3.3 Improvement in the processing of coastal hazards
The enhanced spatial resolution of the GHSL Global Population Grid compared to theprevious exposure layer, allowed the improvement of the estimation of the population
exposed to coastal hazards like Tsunami, Cyclone’s wind and Surge.
Furthermore, the processing of the Storm surge hazard data for better estimation of thehazards zone was revised (section 4.2.2.1).
The global coastal inundation model used herein uses four variables: 1. the sea-levelsurge dataset (5), 2. the SRTM 90 m Digital Elevation Database (SRTM-90) [26] derived
elevation classes, 3. the SRTM-90 derived continental shoreline, and 4. the GHLS
population density datasets at 250 m.
The SRTM-90 derived elevation classes were produced by recoding the continuous
elevation values of SRTM-90 in the four elevation classes that include: 1. land areas
below sea level, 2. land areas between sea level and 3 metres; 3. land areas included
between 3 and 10 metres; and 4. land area above 10 m in elevation.
The continental coastline was also extracted from the SRTM-90 datasets. SRTM-90 codes
the ocean as no-data. The entire land masses of SRTM-90 were recoded as a binary fileand the edge of the land masses were transformed into a vector that defines the(5) Storm Surge hazard, GAR 2015, UNISDR: http://risk.preventionweb.net/capraviewer/download/SSWorld_g15179.zip

13continental shoreline. GHSL population density datasets deliver population estimates at
250 m × 250 m global grids.
For coastal flood modelling the sea level surge datasets were processed based on thefollowing assumptions:

The potential wave height calculated offshore remains unchanged when it hits
land;



The inundation unfolds based on the height of the wave and the friction createdby the land cover.

These assumptions are justified considering the uncertainties in the wave calculations,the precision of the SRTM-90 digital elevation model and the recoding in the four
elevation classes derived, and the global scope of this exercise.
The sea level surge datasets include points regularly distanced along the edges of thecontinents. The attributes of the points record the potential wave height at different
return periods. To make this dataset suitable for an inundation model, the wave height
points were translated to the nearest shoreline based on least distance operation. The
points were crossed with the elevation classes. The elevation class that matches thesurge height was used in the modelling. The penetration of the inundation wave wasmodelled based on an empirical equation (6) [27], [28], [29]. The operation defines theinundation area. That inundation area was then crossed with the GHSL population density
to produce a potential exposed population to sea level surge.
The processing included standardisation of the four datasets to a common reference layer
and the recoding of the potential inundation areas to the 250 m used in GHSL.

3.4 Global Conflict Risk Index
For the Human hazard category, we profit from a more elaborated model of the Global
Conflict Risk Index (GCRI) that offers the Projected Risk of Conflict for each individual
country. The GCRI is a quantitative model developed by the JRC that uses structural
indicators to determine the probability of conflict within the coming years. Compared toearlier versions, the GCRI now includes 24 variables such as the country’s conflicthistory, regime type, and ethnic compilation as well as other socioeconomic, political,
geographic, and security variables that contribute to the outbreak of civil war (7). Conflictintensity levels as used in the GCRI are provided by the Heidelberg Institute forInternational Conflict Research (HIIK). As the GCRI as well as the HIIK are purely data
driven and composed of broadly accepted quantitative factors that add up to acomprehensive reflection of risk for and consequences of armed conflict, it allows us tocomplement our risk assessment with a man-made variable and contributes adequately
to the overall predictive abilities of the model.
The best-performing model for subnational and national power was selected to determine
the risk for each dimension of conflict. We selected a model that includes a country set of
all states with more than 500 000 inhabitants that makes use of an interaction between
the regime type, the GDP/capita in the country, and the income inequality as determinedby the SWIID dataset (8). This model is run without EU Member States to even enhance
its performance.
The model gives us predictions for the projected risk of conflict for both violent andhighly violent conflicts. Trained on conflicts in both dimensions since 1989 and applied to(6) The storm surge intrusion distance is computed using equation (2) developed by Freeman and Le Mehaute(1964) and used in the Multipurpose Cyclone Shelter Programme (1992).
(7) The complete methodology of the GCRI is available via http://conflictrisk.jrc.ec.europa.eu.
(8) The Standardised World Income Inequality Dataset uses a custom missing-data algorithm to standardise
inequality data from sources such as the United Nations. It covers 174 countries for as many years as
possible since the 1960s to the present along with estimates of uncertainty in these statistics. Moreinformation on the SWIID is provided in Solt, Frederick. 2009. ‘Standardizing the World Income Inequality
Database’, Social Science Quarterly 90(2):231-242.

14the most recent data available, the GCRI has a true positive rate of 79 % for conflicts
over national power and 74 % for subnational conflict.
A number of important features are added with the latest version of the GCRI. The
construction of the dataset is now made more transparent and reproducible by the use of
R scripts rather than the previous Excel-based approach. The use of machine imputation
for missing data has been replaced with a system where data is taken from either theclosest known historical data, from regional averages, or from similar countries. The
imputation is now included in the data construction phase, and so the data is now asingle, complete dataset ready for statistical analysis.
The previous model validation work was repeated using the new data to verify thatearlier findings still hold true. The old code was then cleaned of superfluous material, andthe remaining code was adapted and simplified to make it easier to use. The code thatcomes with the current version is designed as a tool to produce the GCRI rather than totest multiple models.

3.5 New indicator: Maternal mortality rate
The majority (61 %) of maternal deaths occur in the 35 countries currently affected by ahumanitarian crisis or fragile conditions. This equates to an estimated ratio of 417
maternal deaths per 100 000 live births, which is 1.9 times higher than the global
estimated ratio of 216 [27].
Maternal mortality (MM) is significantly correlated with disaster mortality, and istherefore a key indicator of vulnerability and adaptive capacity [20] as well as ofpredicting the severity of impact [32]. Also, a study of maternal mortality in Sub-SaharanAfrica showed significantly higher levels in countries that had a recent conflict relative tothose that had not [33].
Poor maternal health outcomes are often linked to three delays: (1) delay in decision toseek care (due to low status of women, lack of knowledge of complications and risk
factors, financial implications); (2) delay in reaching care (due to distance to reachhealth facilities, availability and cost of transport, poor roads and infrastructure,geographical terrain; and (3) delay in receiving adequate health care (due to poor
facilities and lack of medical supplies, inadequate referral system, lack of qualified
medical staff). Emergency and humanitarian situations exacerbate the impact of each of
the three delays leading to higher risks of maternal morbidity and mortality. Maternalmortality is thus one of the best integrated indicators of both the status of women, andthe strength of the health system, including accessibility and capacity of services linked
to the presence and functionality of basic infrastructure such as roads, health facilities,
etc.
Unlike other emergency health responses like child mortality, maternal death is also auniquely valuable indicator of overall population vulnerability given the fact that this‘emergency’ event is actually anticipated and therefore can, and should be, planned for if
services and roads allow. It is a sufficiently dangerous and rapid event once underwaythat it provides much information on the overall capacity of a household and/or
community to plan, coordinate, and mobilise available resources to save not only one life,
but two lives — hence it says much about adaptive capacity in communities. For thesereasons, maternal mortality is referred to as a ‘system-level’ indicator [34], withinterventions to address it that cut across sectors and risk reduction measures.
Other health system level indicators in INFORM — vaccine coverage, physician density,
private health care expenditure, health of children under five and communicable diseases— do not capture the capacity of communities or health systems to respond to
emergency health needs, let alone emergencies that have known and anticipated risks.
Maternal mortality offers a valuable indicator of structural vulnerability, and the coping
capacity of a country with regard to crisis, in ways that have not yet been captured in
INFORM Index.

15

‘Maternal mortality rate’ has been added as a new indicator in the ‘Access to healthsystem’ component of the ‘Lack of coping capacity’ dimension.

3.6 Lack of Reliability Index
A measure of reliability is now displayed for each country. It is presented as a Lack of
Reliability Index on a scale from 0-10 and takes into account missing data, out-of-date
data, and conflict status. Countries with lower Lack of Reliability Index scores have risk
scores that are based on more reliable data.
The Lack of Reliability Index has been added to increase transparency about the qualityof data used to calculate INFORM, while still ensuring we include all the INFORM
countries.
In some cases, for example due to an ongoing conflict, the most recent data available
does not accurately reflect the current situation. Rather than excluding these countries
from the analysis, or impute missing data, INFORM partners decided to add a measure of
reliability so that users will be aware of such cases.

3.6.1 Methodology
The INFORM Lack of Reliability Index includes three dimensions, 1. missing data, 2. outof-date data and 3. conflict status.
1. The first dimension considers how many original indicators were available forcalculating the INFORM index for each country. It uses as indicator the number ofmissing values, which includes also estimated values not present in the original data
source. For instance, the HDI value derived from the GDP per capita is considered as
missing value for the calculation of the Lack of Reliability Index.
2. The second dimension looks to how recent are the data used for a particular country.
INFORM methodology allows to use older data as proxy where updated data are notavailable. The metrics used for the Lack of Reliability Index is the average of the total
number of the years older than the reference year for each indicator.
The last dimension takes into account if a country is in conflict. Normally collection ofdata in country affected by conflict is very challenging and therefore their reliability ispoor. We define a country in conflict if the Conflict Barometer of the Heidelberg Institutefor International Conflict Research (HIIK) sets a conflict intensity 4 o 5 (highly violent
conflict). The first two dimensions are normalised between 0 and 10 as an INFORM
indicator, while the conflict dimension counts as an aggravating factor of 30 %.

16

Figure 4. INFORM Lack of Reliability Index methodology.

17

4 Component and core indicator selection
4.1 Introduction
A composite indicator is typically a compromise between a data-driven and a user-driven
model. There are always some components that existing data cannot describe, especially
if the demands for quality of data are very high.
When selecting the indicators, the possible scalability in geographical and temporal scaleis always considered as an important property for the future development of the INFORM
index.
The following chapters present the component selection for each dimension and explain
the aggregation rules within different levels of the INFORM model.

4.2 Dimension: Hazard & exposure
4.2.1 Overview
The Hazard & exposure dimension reflects the probability of physical exposure associatedwith specific hazards. There is no risk if there is no physical exposure, no matter how
severe the hazardous event is. Therefore, the hazard and exposure dimensions are
merged into Hazard & exposure dimension. As such it represents the load that thecommunity has to deal with when exposed to a hazardous event.
Box 1. Variations in the subnational models: Hazard & exposure— An INFORM subnational model uses the same risk assessment methodology anddevelopment process, but is adapted to regional or national level (9).
— Regarding the Natural hazards dimension, there was a large use of local hazard maps
(Lebanon, Colombia, East Africa), a suggestion to include epidemics as natural
hazard, in particularly Ebola outbreak (Sahel), an inclusion of land degradation, foodsecurity (Sahel), forest fire (Lebanon), landslides (Colombia, central Asia andCaucasus) as natural hazards.
— In the Human hazards dimension, a large benefit of regional/local data should benoticed (ACLED (10) in Africa, local sources in Lebanon and Colombia).

4.2.1.1 Hazard & exposure: Categories
The dimension comprises two categories: Natural hazards and Human hazards,
aggregated with the geometric mean, where both indexes carry equal weight within thedimension.

(9) INFORM Guidance Note 2016 (http://www.inform-index.org).
(10) http://www.acleddata.com

18

Figure 5. Graphical presentation of the Hazard & exposure dimension

4.2.2 Category: Natural hazard
4.2.2.1 Definition
According to the CRED EM-DAT database [22] the death toll of natural hazards during
1900-1999 is caused in 86.9 % of cases by famines, 12.9% by floods, earthquakes andstorms, and less than 0.2 % by volcanic eruptions, landslides and wildfires. On the other
hand the rapid onset hazards with a more limited geographic extent, sometimes labelled
as extensive disasters, seldom exceed entry criteria (11) of the EM-DAT database. Fromthat point of view their presence in the database is incomplete and the cumulative death
toll is higher, while a single event rarely causes a humanitarian crisis.
Rapid-onset hazards, i.e. earthquakes, tsunamis, tropical cyclones and floods, are dealt
with differently than slow-onset hazards, i.e. droughts. Indicators for each component of
rapid-onset hazards are based on the physical exposure to the hazard.
The metric for the natural hazard risk used in INFORM is the annual average
exposed population (AAEP) or, when hazard maps for different return periods are notavailable, annual exposed population (AEP).
(11) Hazardous events have to fulfil at least one of the following criteria, in order to be included in the database(http://www.emdat.be/frequently-asked-questions):
10 or more people reported killed;
100 people reported affected;

Declaration of a state of emergency;Call for international assistance.

19

They are defined in Chapter 3.2 of the INFORM Concept and Methodology Report 2016[18]. They are closely related to the definition of the hazard zones.
The hazard zone does not contain information on internal variability of intensity. The
population is either in the hazard zone or outside, the people are either exposed or not,respectively. The exposure of the population is thus a binary value, rather than a degreeof exposure.
Furthermore, in the case of earthquakes and cyclone winds, the available hazard maps
provide information on different intensity level zones. The hazard zones where minimum
intensity is set to low intensities inherit also the hazard zones with high intensities but
their more detrimental impact is not visible with a simple overlay of the population map.
It would be high intensity events that would more likely cause humanitarian crises.
To overcome this shortcoming of the hazard zone definition the areas of high intensities
within the hazard zone of low intensities were extracted. Their presence was introduced
into the model as a parallel indicator at the sub-component level where AAEP was basedon the hazard zone with the higher minimum intensity level. We took the advantage ofthe composite indicator methodology and considered the areas of high intensities as
another type of event with the same probability of occurrence. Such indicator pushes up
the countries exposed to extreme events as well as pull down those countries where highintensity events are not very likely to happen and/or are spatially very limited. The final
hazard component indicator is a geometric average of the normalised AAEP gained fromtwo hazard zones of two distinct levels of minimum intensity, i.e. a low as well as
extreme one. A high hazard component indicator is the result of high values in bothlevels of intensities. While low values of the indicator for high intensities will decreasehigh values of the indicator for low intensities and indirectly suggest that despite the highnumber of people exposed the share of affected people is expected to be comparatively
smaller.
There are different intensity scales for different hazard types, e.g. Modified Mercalli
Intensity (MMI) scale for earthquakes and Saffir-Simpson (SS) hurricane scale for
cyclone wind. For each hazard type we chose intensity levels equivalent to two distinct
damage levels:
Light/moderate potential damage for resistant/vulnerable buildings, respectively; andModerate/heavy potential damage for resistant/vulnerable buildings, respectively.

In the case of the earthquakes MMI VI and VIII, while in the case of the cyclone winds SS
1 and 3 fit the chosen damage levels description (Table 3).
Table 2. Minimum intensity/magnitude levels used for different type of hazards and data source

Hazardtype

Intensity levels

Earthquake

Modified Mercalli Intensity
scale VI and VIII

TsunamiInundated area

Flood

Inundated area

Cyclone
wind
Storm
surge

Saffir-Simpson category 1
and 3

Drought

SourceGSHAP Seismic hazard intensity map(475-return period, 10 % probability ofexceedance in 50-year of exposure )Tsunami Hazard (Run up) RP 500 years(GAR 2015)
Flood hazard map 25, 50, 100, 500, 1 000
years RP (GAR 2015)
Cyclone wind hazard map 50, 100, 250,
500, 1 000 years RP (GAR 2015)
Storm surge hazard map 10, 25, 50, 100,
250 years RP (GAR 2015)

Inundated area
Impact (affected people) andfrequency of drought
disasters
Agricultural drought: 30 % of

20

EM-DAT database for the period 1990now
Map of annual agricultural drought based

cropland in stress for morethan 10 dayson remote sensing (ASI, FAO 2014)

Table 3. Intensity scale levels vs. damage level

Hazardtype

Intensity
levelsDamage level

Earthquake

Modified
Mercalliscale VI

Perceived shaking: strong
Resistant structures: light damage
Vulnerable structures: moderate damage
Perceived shaking: severe
Resistant structures: moderate/heavy
damageVulnerable structures: heavy damage
Wind speed: 119-153 km/h
Very dangerous winds will produce
some damage: Well-constructed frame
homes could have damage to roof, shingles,
vinyl siding and gutters. Large branches oftrees will snap and shallowly rooted trees
may be toppled. Extensive damage to powerlines and poles likely will result in power
outages that could last a few to several
days.
Wind speed: 178-208 km/h
Devastating damage will occur: Well-built
framed homes may incur major damage or
removal of roof decking and gable ends.
Many trees will be snapped or uprooted,
blocking numerous roads. Electricity andwater will be unavailable for several days toweeks after the storm passes

Modified
Mercalliscale VIII

Cyclonewind

SaffirSimpson
category 1

SaffirSimpson
category 3

Reference(12) http://pubs.usgs.gov/fs/2010/3036/pdf/FS10-3036.pdf(13) http://www.nhc.noaa.gov/aboutsshws.php

21

USGS(PAGER) (12)

USGS(PAGER)NOAA (13)NOAA

Box 2. Absolute vs. relative physical exposure — correction in favour of small countries

There are two ways to consider population exposed to natural hazards. The absolutevalue of people exposed will favour more populated countries while the value of
population exposed relative to the total population will reverse the problem and favour
less populated hazard-prone countries, especially small islands where the entire
population may be affected by a single cyclone. To enable a proper comparison between
countries, in INFORM the subcomponent indicator is calculated both ways and thenaggregated using an arithmetic average.
At the level of core indicators (Table 5) the datasets are rescaled into a range of 0 to 10
in combination with a min–max normalisation. Since distribution of the absolute value of
exposed people is extremely skewed, the log transformation is applied (Chapter 6).
4.2.2.2 Natural hazards: components
The Natural hazard category includes five components aggregated with a geometricaverage (Table 5):●

Earthquake,●

Tsunami,●Flood,●

Tropical cyclone (Cyclone wind and Storm surge),●

Drought (Historical impact and Agricultural drought probability).
Table 4. Indicators of the Natural hazard category

Component

Earthquake

TsunamiIndicator

SourceMIN–MAX

Physical exposure to MMI VI
earthquake (absolute)

GSHAP

Log(10)Log(10E5)-

Physical exposure to MMI VI
earthquake (relative)

GSHAP; GHSLPOP

0 %-0.2 %-Physical exposure to MMI VIII
earthquake (absolute)

GSHAP

Log(10)Log(10E4)-

Physical exposure to MMI VIII
earthquake (relative)

GSHAP; GHSLPOP

0 %-0.1 %-Physical exposure to tsunamis(absolute)

UNISDR GAR
2015

Log(10E-2)Log(10E3)-

Physical exposure to tsunamis(relative)

UNISDR GAR
2015; GHSLPOP

Log(10E-9)Log(10E-4.5)-

Physical exposure to flood (absolute)

UNISDR GAR
2015

Log(100)Log(10E6)

32/191

Physical exposure to flood (relative)

UNISDR GAR
2015; GHSLPOP

0 %-1 %

32/191

Flood

22

No of
missingvaluesTropical
cycloneDrought

Physical exposure to SS-1 tropical
cyclone (absolute)

UNISDR GAR
2015

Log(100)Log(10E6)-

Physical exposure to SS-1 tropical
cyclone (relative)

UNISDR GAR
2015; GHSLPOP

0 %-2 %-

Physical exposure to SS-3 tropical
cyclone (absolute)

UNISDR GAR
2015

Log(1)Log(10E6)-

Physical exposure to SS-3 tropical
cyclone (relative)

UNISDR GAR
2015; GHSLPOP

0 %-0.5 %-Physical exposure to Storm surges(absolute)

UNISDR GAR
2015

Log(10E-2)Log(10E3)-

Physical exposure to Storm surge(relative)

UNISDR GAR
2015: GHSLPOP

Log(10E-9)Log(10E-4)-

Agriculture drought probability

FAO

0-0.3

21/191

People affected by droughts(absolute)

EM-DAT, CRED

Log(10)Log(10E5)-

People affected by droughts (relative)

EM-DAT, CRED

0 %-3 %-

Frequency of droughts events

EM-DAT, CRED

0-0.3

-

Table 5. Aggregation of the Natural hazards category

Natural hazard

Category

GEOMETRIC AVERAGEGEOMETRIC GEOMETRICAVERAGEAVERAGE

Tropical Cyclone

Drought

GEOMETRIC AVERAGE

ARITHMETIC AVERAGE

Cyclone Abs Cyclone Rel

Absolute

CW

Abs

Rel

GEOMETRIC AVERAGEGEOMETRIC AVERAGE

ARITHMETICAVERAGEHistorical
Impact
ARITH.AVG

CS Log(relative)

Relative

Log(absolute)Log(relative)

Log(absolute)

EQ MMI VIII Relative

EQ MMI VI Relative

EQ MMI VIII Log(abs)Core indicator

EQ MMI VI Log(abs)

GEOMETRIC GEOMETRIAVERAGE C AVERAGECWCW SS3 Relative

Rel

GEOMETRICAVERAGECW SS1 Relative

Abs

GEOMETRICAVERAGECS Log(abs)EQ

CW SS3 Log(abs)EQ

CW SS1 Log(abs)

Aggregation

Drought
impactProbability of agricultural drought

GEOMETRIC AVERAGE

Flood

Frequency

TsunamiRelative

Earthquake

Log(abs)

Component

Absolute value of physical exposure (AAEP)

Relative value of physical exposure (AAEP per capita). AAEP isnormalised with the country’s population.

Relative

Scalability: Approach used enables geographical and temporal scalability of physicalexposure. Hazard zones and population distribution maps allow extraction of subnational
indicators as well as adaptation to mid-term and long-term variability when applying

23

El Niño scenarios or observed trends in climate changes, and incorporating seasonality of
weather-related hazard events.
4.2.2.3 Component: Earthquake
Earthquakes can be one of the most destructive natural hazards. The unpredictability ofthe seismic event can cause several fatalities in areas with high physical vulnerability ofthe buildings (2010 Haiti, 2015 Nepal).
Data source: As described in the 2016 INFORM Concept and Methodology Report [18] in
INFORM GSHAP probabilistic hazard map is used. It is available only for 475-year return
period, which only enables the derivation of the exposed population for that particular
return period.
Technical explanation to derive hazard zone: GSHAP seismic hazard map showsdifferent intensity levels of earthquake presented in terms of PGA. In INFORM a derived
product based on GSHAP dataset was used, converted to Modified Mercalli Intensity(MMI) using the methodology developed by Wald et al. [35]. This product was compiled
by CIESIN (Columbia University) for the Global Assessment Report on Risk Reduction(GAR 2009-2011) (14).
Two hazard zones for each country were extracted using two different minimum intensity
levels, i.e. MMI VI and MMI VIII (Table 3). The choice of the minimum intensities is
simply based on two distinct damage levels. This is a way to overcome the hazard zone
definition that ignores the internal variability of the hazard intensity and it takes
advantage of the composite indicator methodology. We consider a hazard zone with ahigher minimum intensity as another event and aggregate the metric derived with thegeometric average into earthquake component (Table 5).
4.2.2.4 Component: Tsunami
As earthquakes, tsunamis can be very destructive. Even if the frequency of the events isvery low, the humanitarian impact of the most intensive tsunamis is huge (2004 Indian
Ocean, 2011 Japan).
Data source: GAR 2015 [36] provides tsunami hazard map for only one return period,
i.e. 500-year RP. The score of the Tsunami component is based on the exposed
population for 500-year RP only.
Technical explanation to derive hazard zone: The GAR Tsunami hazard map displays
binary information on the probable inundated areas. Those areas represent the hazard
zones.
4.2.2.5 Component: Flood
Floods are often predictable natural hazards, which can encompass incredibly large
areas, causing a very large impact on population (2010 Pakistan).
Data source: Several sources for global probabilistic flood hazard maps are available:●

GloFAS-JRC global flood hazard maps, developed by the Joint Research Centre ofEuropean Commission [37]. The maps are based on long-term hydrologicalsimulations of the Global Flood Awareness System (GloFAS), coupled withhydrodynamic modelling at 1 km resolution.

●

The Aqueduct Global Flood Maps (15) are based on the Glofris (16) model recently
developed by research institute Deltares [38]. It uses hydrological data from 1960
through 1999 for generating flood inundations for nine return periods, from 2-year
flood to 1 000-year flood. They are publicly available.

(14) http://preview.grid.unep.ch/index.php?preview=data&events=earthquakes&evcat=5&lang=eng(15) http://www.wri.org/resources/data-sets/aqueduct-global-flood-risk-maps(16) Global Flood Risk with IMAGE Scenarios.

24●

GAR 2015 flood hazard maps for 25, 50, 100, 200, 500, and 1 000-year RP [39].
The hazard maps are developed at 1 km × 1 km resolution. GAR 2015 flood maps
do not take into account flood defences. They are publicly available.

They produce different results and consequently different implications for risk. There isongoing study on comparison of different flood models at the University of Bristol, which
will provide more information on benefits and drawbacks of each. At the moment, theGAR 2015 [36] flood hazard maps are used in INFORM 2016. The score of the Floodcomponent is based on the AAEP risk metrics.
Technical explanation to derive hazard zone: The GAR 2015 flood hazard map
displays probable inundated areas related to a specific return period. Inundated areas are
not broken down into different water height levels, so only binary information is provided
for each point, which is positive if the location is inundated and negative if not. The
hazard zones are derived from all inundated areas for each return period.
4.2.2.6 Component: Tropical cyclone
Tropical cyclones (including hurricanes and typhoons) are some of the most damaging
events. They occur in yearly cycles and affect coastal population through high windspeeds (destroying dwellings and infrastructure), storm surge and associated floods(destroying crops) and heavy rainfall sometimes causing riverine floods and landslides.
The tropical cyclone component is an aggregation with arithmetic average of the physicalexposure for cyclone wind and cyclone surge.
Data source: GAR 2015 [36] provides cyclone wind intensity maps for 50, 100, 250,
500, 1 000 years RP. The score of the Cyclone wind component is based on AAEP risk
metrics.
Technical explanation to derive hazard zone: GAR 2015 cyclone wind hazard maps
display different intensity levels of cyclone wind presented in terms of Saffir-Simpson
Hurricane Scale (Category 1-5). Therefore two hazard zones for each country wereextracted for the same return period using two different minimum intensity levels, i.e.
SS1 and SS3 (Table 3).
Data source: GAR 2015 provides Storm surge hazard maps for 10, 25, 50, 100,
250 years RP. The score of the Storm surge component is based on AAEP risk metrics.
Technical explanation to derive hazard zone: GAR 2015 Storm surge hazard maps
are expressed in points along the coast representing the expected storm surge level. In
order to derive the hazard zone, first the point layer was converted into a raster. Thenfor each pixel the information of surge level was compared with the terrain elevation (17).
The pixels where the expected surge level is higher or equal than the DEM, define thehazard zone.
4.2.2.7 Component: Drought
Drought is a complex process to model because of the inherent spatial and temporal
uncertainty. In general terms, a drought can be understood as a deficiency inprecipitation that severely affects a certain region, environment, industry, or people.
According to the FAO, droughts are ‘the world’s most destructive natural hazard’ with‘devastating impacts on food security and food production’. The frequency as well as
intensity of droughts has increased in the past 20 years due to climate change and it isexpected that this trend will intensify in the future.
In INFORM, the impact of drought is measured by a combination of two factors: 1. therisk for drought, calculated as the probability for an agricultural drought (which may or
may not result in a drought disaster through reduced food production) and 2. thepopulation affected by droughts in recent years (materialised risk).

(17) SRTM digital elevation model.

25

For the first factor, we define an agricultural drought as a dry period in a certain region,in which at least 30 % of the crop area was in stress for more than 10 days. This ismeasured using the Agriculture Stress Index (ASI) (18), which is an index based on the
integration of the Vegetation Health Index (VHI) in two dimensions that are critical in theassessment of a drought event in agriculture: temporal and spatial [40]. The first step of
the ASI calculation is a temporal averaging of the VHI, assessing the intensity andduration of dry periods occurring during the crop cycle at pixel level. The second step
determines the spatial extent of drought events by calculating the percentage of pixels inarable areas with a VHI value below 35 %.
We consider a country in drought in a particular year if the ASI index indicates drought in
one or more crop seasons. While the drought probability is based on the country’s
frequency of droughts within the last 30 years.
The second factor, historical drought impact, considers the number of affected people per
year (both absolute and relative to the country’s population size) based on historicalevents in EM-DAT database for the last 25 years, which is the period when reporting isassumed to be consistent. To emphasise drought-prone countries with frequent andextensive drought (as well as to compensate for uncertainty associated with unique,intensive drought events), we combine the average annual drought-affected people withthe frequency of drought events in an arithmetic average.
The calculation of drought risk has several limitations, which have to be taken into
account. First of all, the ASI model does not consider the impact of drought on
pastoralism. Second, due to the coarse resolution of ASI, countries smaller than
1 000 km2 are not considered. Lastly, the applicability of historical impact data is limited,as people ‘affected’ by drought are not consistently defined over events (in EM-DAT).
Note that food security is a component under the Vulnerable group category in
Vulnerability dimension (Chapter 4.3.4). Food insecurity increases the population’svulnerability to new shocks.
Scalability: A useful feature of the ASI index is the geographical and temporal
scalability, i.e. calculation of subnational indexes with seasonal component based on thehistorical archive of remote sensing data.

4.2.3 Category: Human hazard
4.2.3.1 Definition
Human-made hazards are either technological (e.g. industrial accidents withenvironmental impact) or sociological in nature. The latter encompass such divergent
phenomena as civil wars, high-intensity crime, civil unrest as well as terrorism. Especiallyarmed internal conflict yields catastrophic results for populations and economies and isalmost always accompanied by humanitarian risk on a larger scale, caused by thebreakdown of supply lines, absent harvests, refugee flows as well as an overall
deterioration of health services.
4.2.3.2 Human hazard: Components
INFORM includes two quantitative variables on man-made disaster that complement theHazard & exposure section with the dimension of violent conflict and the consequences
generated by it, such as large refugee flows and overall destruction of infrastructure.

(18) It is developed by FAO’s Global Information and Early Warning System (GIEWS) and the Climate, Energy
and Tenure Division.

26

Table 6. Indicators of the Human hazard category
Component

Conflict intensity

Projected risk of
conflict

Indicator

SourceMIN–MAXNo ofmissingvaluesNational power conflicts

Conflict Barometer, HIIK

4-5-

Subnational power conflicts

Conflict Barometer, HIIK

4-5-

Probability for violent
conflict

Global Conflict Risk Index,
JRC

0-0.95-

Probability for highly violent
conflict

Global Conflict Risk Index,
JRC

Log(0.01)Log(0.95)-

Scalability: Subnational and monthly updates could be supported by the Conflict
Barometer but they are not yet available. Data exist, at the moment, only for scientificpurposes. The GCRI is planned to be updated in semi-annual intervals.

Table 7: Aggregation of Human hazard category

Human hazard

Category

MAXIMUM

GEOMETRICAVERAGE

Conflict Barometer (Subnational)Coreindicator

Global Conflict Risk Index (Probability forviolent conflict)

MAXIMUM

Aggregation

Global Conflict Risk Index (Probability forhighly violent conflict, log)

Projected
conflict
intensity

Conflict Barometer (National Power)

Component

Current
conflict
intensity

4.2.3.3 Component: Conflict intensity
INFORM takes into account the current intensity of conflict in a country or — in casethere is currently no conflict — an estimate of future conflict probability. To determine
the current intensity of a conflict, we use data by the annual Conflict Barometer [41]of the Heidelberg Institute for International Conflict Research (HIIK) (19).

(19) The HIIK approach distinguishes a total of five intensity levels, subdivided into non-violent conflicts(Disputes and Non-violent Crises) and violent conflicts (Violent Crises, Limited Wars, and Wars).
The overall intensity is determined by the number of casualties and refugees caused by conflict, as well as
by the number of personnel involved, the weapons that were used, and the destruction that was caused.

27

Table 8. Adaption of conflict intensity

Type of conflict

HIIK intensity

Non-violent
conflict

1 (dispute)

Violent conflict

3 (violent crisis)

Highly violent
conflict

4 (limited war)

2 (non-violent crisis)

5 (war)

INFORM conflictintensity
0-5
5-8
9-10

The HIIK defines conflict as a dynamic process made up of a sequence of interlocking
conflict episodes. The conflict intensity is determined by two criteria: Instruments on theuse of force (use of weapons and use of personnel) and the consequences of the use offorce (casualties, refugees, and demolition). Its values (Table 8) range from 1 (dispute)to 5 (war).
For our purpose, we cluster the conflicts observed by the HIIK into three different
groups:●Conflicts over national power in a country (National power);●

Over intrastate items apart from national power such as secession (Subnational);●

Interstate conflicts (20).

We clearly distinguish conflicts over national power from those over subnational items, as
they have different causes and drivers that attributes to onset, duration, and escalationof violence.
Table 9. Conflict items, groups, and intensity

HIIK conflict item

National power

INFORM conflictgroupsHIIK intensity
level

INFORM conflictintensity

5 (war)10

4 (limited war)8

5 (war)94 (limited war)

7

Violent conflict withlower intensity

3 (violent crisis)

Not consideredInterstate-

Not considered

National power

Secession
Autonomy
Subnational
predominance
AnyInternational power
Territory

Subnational

The basic data is provided by the HIIK’s annual Conflict Barometer which includes information about morethan 400 political conflicts in the world (see http://hiik.de/en/konfliktbarometer/).
(20) In our model, we only take into consideration the two intrastate dimensions of conflict. This has several
reasons: First of all, scientific evidence shows that interstate conflict has become a rather rare
phenomenon since the end of the Cold War. Besides, if military confrontations between states occur, they
are mostly restricted to remote border regions and tend not to last longer than several weeks or evendays, whereby they do not affect the civilian population as much as intrastate conflicts.

28

In INFORM we consider conflicts over national power to have a graver impact on
population, supplies, and long-term development than those over subnational items. Firstof all, they constrain the overall national production and supply lines and are mostly
fought with heavier weapons and more personnel and turn more people into refugees
than conflicts over e.g. secession. Second, wars over government usually affect large
parts of national territory and often have the tendency of involving foreign powers.
Subnational conflicts are mostly restricted to certain regions of a country and only affectregional production and security. We therefore transfer the HIIK data on conflict intensity
into a modified intensity scale: Conflicts with HIIK intensity 5 receive an INFORM
intensity of 10 if the object is National power, and 9 if the object is Subnational.
Analogously, conflicts with HIIK intensity 4 (limited wars) are attributed values of 8
(National power) and 7 (Subnational).
4.2.3.4 Component: Projected risk of conflict
If a country does not experience highly violent conflict in the year of observation,
INFORM estimates instead the Projected Risk of Conflict using the Global Conflict Risk
Index (GCRI). The GCRI [42] is a quantitative model developed by the JRC that uses
structural indicators to determine a given country’s risk for conflict. It uses 26
quantitative variables including, among others, a country’s regime type, its conflicthistory as well as other socioeconomic, political, geographic and security variables that
contribute to the outbreak of civil war. Intensity levels as used in the GCRI are thereby
also provided by the HIIK. INFORM uses the GCRI assessment of the risk for violent
conflict within the next 4 years. The risk for either violent conflict (VC) or highly violent
conflict (HVC) is calculated using the geometric average of the probability for either typeof conflict, with a log transformation of the HVC. A probability of 95 % is thereby
equivalent to a risk level of 7.

Figure 6. Transformation of GCRI probability of conflicts to INFORM score

The total risk score for the Human hazard category is then calculated by using the
maximum score of either the actual conflict intensity or the projected intensity. As the
GCRI as well as the HIIK are purely data-driven and composed of broadly accepted
quantitative factors that add up to a comprehensive reflection of risk for andconsequences of armed conflict, it allows us to complement our risk assessment with aman-made variable and contributes adequately to the overall predictive abilities of themodel.

29

4.3 Dimension: Vulnerability
4.3.1 Overview
The main focus of humanitarian organisations is people, which is the element at risk
contemplated in the INFORM composite index. The impact of disasters on people in termsof number of people killed, injured, and made homeless is predominantly felt indeveloping countries while the economic costs of disasters are concentrated in theindustrialised world. The Vulnerability dimension addresses the intrinsic predispositions ofan exposed population to be affected, or to be susceptible to the damaging effects of ahazard, even though the assessment is made through hazard-independent indicators. So,
the Vulnerability dimension represents economic, political and social characteristics of thecommunity that can be destabilised in case of a hazardous event. Physical vulnerability,
which is a hazard dependent characteristic, is dealt with separately in the Hazard &exposure dimension.
Box 3. Variations in the subnational models: Vulnerability

An INFORM Subnational model uses the same risk assessment methodology anddevelopment process, but is adapted to regional or national level (21).
Relevant additions were the inclusion of remittances (Sahel), food security (Cadre
Harmonisé in Sahel), and malnutrition (Sahel).

4.3.2 Vulnerability: Categories
There are two categories aggregated through the geometric average: Socio-economic
vulnerability and vulnerable groups. The indicators used in each category are different intime variability and the social groups considered in each category are the target ofdifferent humanitarian organisations. If the socio-economic vulnerability category refers
more to the demography of a country in general, the vulnerable group category capturessocial groups with limited access to social and health care systems.

(21) INFORM Guidance Note 2016 (http://www.inform-index.org).

30

Figure 7. Graphical presentation of the vulnerability dimension

4.3.3 Category: Socio-economic vulnerability
4.3.3.1 Definition
The question is what makes a population vulnerable when faced by a hazardous event. In
most cases vulnerability has a negative relationship with the provision of basic needs. In
such cases vulnerability is closely related to the level of self-protection mechanisms.
Therefore, the Socio-economic vulnerability category tries to measure the (in)ability of
individuals or households to afford safe and resilient livelihood conditions and well-being.
These in turn dictate whether people can live in safe houses and locations as well as
maintain an adequate health in terms of nutrition and preventive medicine to be resistant
to increased health risk and reduced food intake in the case of disasters. Socio-economic
vulnerability depends only in part on adequate income. Other deficiencies can be31

corrected with adequate development level that strengthens those cultural processes,
which raise level of awareness and knowledge.
4.3.3.2 Socio-economic vulnerability: Components
INFORM describes population performance with the weighted arithmetic average of threecomponents (Table 11).
Table 10. Indicators of the socio-economic vulnerability category
Component

Development &deprivation

Inequality

Aid dependency

Indicator

Source

MIN–
MAX

No of
missingvaluesHuman Development Index

Human Development Report,
UNDP

0.3-0.95

6/191

Multidimensional Poverty
Index

Human Development Report,
UNDP

0.05-0.5

100/191

GINI index

World Bank

25-65

56/191

Gender Inequality
Distribution

Human Development Report,
UNDP

0-0.7539/191

Total ODA in the last
2 years per capita

OECD

Global Humanitarian
Funding per capita

Financial Tracking System,
UNOCHANet ODA Received inpercentage of GDP

World Bank

0-500
0 %15 %-

Scalability: All core indicators (Table 10) of Socio-economic vulnerability are publishedannually. The data for indicators of Development & deprivation, and Inequality
component are available on subnational level, while the unit of analysis for the indicatorsof the aid dependency component is country.

32

Table 11. Aggregation of the Socio-Economic vulnerability category

Socio-Economic vulnerability

Category

ARITHMETIC AVERAGE 50/25/25

Aggregation

GEOMETRIC AVERAGE

ARITHMETIC AVERAGE

ARITHMETIC AVERAGE

Total ODA in the last
2 years per capita
Total Humanitarian
Funding in the last
2 years per capita

SUM

Net ODA Received (% of GNI)

Aid dependency

Public Aidper capita

Inequality

Gender Inequality Distribution

Development &deprivation

GINI index

25 %Multidimensional Poverty Index

Core indicator

25 %Human Development Index

Component

50 %4.3.3.3 Component: Development & deprivation
The Development & deprivation component describes how a population is doing on
average. It comprises two well recognised composite indices by UNDP: the Human
Development Index (HDI) and the Multidimensional Poverty Index (MPI). The Human
Development Index covers both social and economic development and combines factorsof life expectancy, educational attainment, and income. While the Multidimensional
Poverty Index identifies overlapping deprivations at the household level across the same
three dimensions as the Human Development Index (living standards, health, andeducation), it also includes the average number of poor people and deprivations, withwhich poor households contend. Even though dealing with similar dimensions, there is no
double counting. If HDI measures capabilities in the corresponding dimension, MPI
reflects the prevalence of multidimensional deprivation and its intensity in terms of how
many deprivations people experience at the same time. However both indexes have atransparent methodology [43] with a justified choice of indicators and should beconsidered as a whole. This component is weighted 50 % to fairly convey thecontribution of both aspects, development as well as deprivation.
4.3.3.4 Component: Inequality
The Inequality component introduces the dispersion of conditions within population
presented in Development & deprivation component with two proxy measures: the Gini
index by the World Bank and Gender Inequality Index by UNDP. The Gini index (named
after Italian statistician and sociologist Corrado Gini) measures how evenly distributed
resident’s income is among a country’s population while the Gender Inequality Index
exposes differences in the distribution of achievements between men and women.
Income inequalities are linked to and can reinforce other inequalities such as educationand health inequality [44]. There is a relationship between high inequality and weakgrowth in developing countries, where a large part of population is trapped in poverty.
Furthermore the data show [43] that countries with unequal distribution of human33

development within the nation also experience high inequality between women and men.
So, the Inequality and Development & deprivation components together help point out
how the average person is doing and overcome the assumption that if the whole is
growing, everyone must be doing better.
4.3.3.5 Component: Aid dependency
With the Aid dependency component, the methodology points out the countries thatlack sustainability in development growth due to economic instability and humanitariancrisis. It is comprised of two indicators: Public aid per capita and net official development
assistance (ODA) received in percentage of gross national income (GNI) by the WorldBank.
Public aid per capita is obtained as a sum of total official development assistance in thelast 2 years per capita published by OECD and Global Humanitarian Funding per capita
published by UN OCHA.
Official development assistance (22) has the promotion of economic development andwelfare as its main objective. The effects of the economic instability are the main sourceof growth regression [44] because it decreases the ability of governments to predictbudget revenue and thus expenditure, but also has an impact on income in dependent
households. And once progress on human development is reversed, the damage canhave multiplier effects and be lasting. For instance, deteriorating health and education
today can lead to higher mortality rates tomorrow. Lower investments can hamper future
progress in sanitation and water supply. The presence of fewer children in school canlead to lower completion rates in later years. And household incomes that fall far below
the poverty line can delay escapes from poverty.
In a very simplistic view, the poorest regions on the world receive the highest volume ofdevelopment aid relative to other regions [44]. These are the countries of sub-SaharanAfrica and other least developed countries based on HDI ranking. So, development aid
flows can cause developing countries to maintain government spending.
Parallel to the Aid dependency component other aspects of economic dependency wereconsidered as well, such as Export dependency (the ratio of the international trade toGDP), Export concentration (a degree to which a country’s export is concentrated on asmall number of products or a small number of trading partners) and Personal
remittances received (in % of GDP). They would address economic vulnerability in acountry as a risk to have its development hampered by financial shocks triggered bydifferent events on the foreign markets. Finally, they were not adopted due to a weakcausal link with the humanitarian risk.

4.3.4 Category: Vulnerable groups
4.3.4.1 Definition
The Vulnerable group category refers to the population within a country that has specificcharacteristics that make it at a higher risk of needing humanitarian assistance thanothers or being excluded from financial and social services. In a crisis situation suchgroups would need extra assistance, which appeals for additional measures, i.e. extra
capacity, as a part of the emergency phase of disaster management.
Why are certain groups of people more vulnerable than others? At a conceptual level two
fundamental reasons of increased vulnerability can be identified:●

Intrinsic due to internal qualities of individual themselves:Special disabilities;

Disease; and(22) http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm

34


●

Limitations imposed by stages of human life.

Extrinsic as a result of external circumstances:

Social: ethnic, religious minorities, indigenous peoples;Political: people affected by conflicts; refugees and IDPs;



Environmental: people recently exposed to frequent natural hazardous
events or living in areas difficult to access, like mountainous regions orextremely rural areas.

It is often the case that a particular vulnerable group is prone to several weaknesses as
one characteristic of increased vulnerability develops circumstances for another one totake place. Those specific characteristics bear also a higher risk than others for a need ofhumanitarian assistance in the crisis situation.
For example, a study of rural communities in North Eastern India [45] shows thatfrequent exposure to floods is associated with long-term malnutrition of children underfive. The underlying cause is the adverse impacts of flooding on crop productivity. Crop
yield variation is one of the leading mechanisms to limited access to food. In suchsituations children are the first to suffer because of their greater sensitivity to certain
exposure and dependence on care givers.
The vulnerable groups are a weak part of the society also in highly developed countries.
The Kobe earthquake of M 7.2 in 1997 revealed [22] a particularly vulnerable minority of
Korean-Japanese workers and foreign illegal and legal workers. They were subjected to
official neglect and economic deprivation. Within the most severely affected wards ofKobe City there were 130 000 foreign and migrant workers. Most were paid low wages insmall businesses that were damaged or destroyed by the earthquake, which made theirrecovery even more difficult. However, they failed to surface in official reporting bygovernment as well as in most NGO reports.
Furthermore, children, the elderly and women in general are a more vulnerable part ofthe society. Their presence is a demographic characteristic of the country (and in case ofgender not even country specific), which is why we do not consider them as a special
vulnerable group. The aim is to address special issues related to them. Children
underweight indicator considers the group of children that are in a weak health condition,
while together with child mortality it reflects also efficiency of the country’s healthsystem and food access problems. Gender inequality is taken into account under theInequality component in the socioeconomic vulnerability. Regarding older people, they
are also affected by inadequate health service and lack of protection, issues common toolder ages. Declining health as well as social (e.g. isolation) and economic
marginalisation makes them even more vulnerable in disasters and conflicts [46].
Physical or mental impairment impede the ability to evacuate or specific health problems
need adequate health care and medicines or isolation due to forgotten responsibilities ofrelatives and community results in poor nutritional status and poor livelihood conditions
in general. Globally, the proportion of older people is increasing faster than any othergroup but the number of old people alone or old-age dependency ratio alone is notreflecting their weaknesses. Namely, old-age dependency ratio is higher in higher income
countries but their basic insurance providing basic health care and old age pension
makes their situation better. Altogether it is the matter of the Lack of coping capacity
dimension, partially related with the quality of the social and health system, but mainly it
is about strategies to protect older people during emergencies, which are notmomentarily directly covered by any available indicators.
However, effective monitoring and related indicators exist only for some of the identified
vulnerable groups.

35

4.3.4.2 Vulnerable groups: Components
The Vulnerable group category (Table 13) is split in two: Uprooted people and Other
vulnerable groups. Uprooted people are effectively weighted more because they are
not a part of the society or the social system, are only partially supported by thecommunity and often trigger the humanitarian intervention.
Table 12. Indicators of the Vulnerable groups category
Component/

Indicator

SourceMIN–MAX

Number of refugees, returned refugees,
internally displaced persons (absolute)

UNHCR, IDMC

Log(1,000)Log(1 000 000)-

Number of refugees, returned refugees,
internally displaced persons (relative)

UNCHR, IDMC,World Bank

0.005 %-10 %-Prevalence of HIV-AIDS above 15 yearsWHO

0 %-5 %

34/191

Tuberculosis prevalence

WHO

0-550

2/191

Malaria mortality rate

WHO

0-120

90/191Children underweight

Unicef, WHO

0 %-45 %60/191Child mortality

Unicef, WHO

0-130

1/191

Relative number of affected population by
natural disasters in the last three years

EM-DAT,
CRED

0 %-10 %-Prevalence of undernourishment

FAO

5 %-35 %29/191

Average dietary energy supply adequacy

FAO

75 %-150 %

31/191

Domestic Food Price Level Index

FAO

1-2.5

45/191

Domestic Food Price Volatility Index

FAO

0-20

54/191Sub-component

UprootedpeopleOther
Vulnerable
groups/Healthconditions
Other
Vulnerable
groups/Childrenunder 5
Other
Vulnerable
groups/Recent
shocksOther
Vulnerable
groups/Foodsecurity36

No ofMissingvaluesTable 13. Aggregation of the Vulnerable groups category

Vulnerable groups

Category

GEOMETRIC AVERAGE

Component Uprooted
peopleOther vulnerable groups

ARITHMETICAVERAGEGEOMETRIC AVERAGE

Availability

Access

ARITHMETICAVERAGE
80/20

Domestic Food Price Volatility
Index

80 % 20 %Domestic Food Price Index

Prevalence of undernourishment

Relative number of affected population by natural
disasters in the last 3 years

Child mortality

Children underweight

Malaria mortality rate

Tuberculosis prevalencePrevalence of HIV–AIDS (> 15 years)

Number of IDPs

Number of returned refugees

Number of refugees

ARITHMETIC AVERAGE
Utilisation

SUM

Core
Indicator

Food security

ARITHMETICAVERAGE

Relative

Aggregation

Log(absolute)

ARITHMETICAVERAGEChildren Recent
under 5 shocks

Average dietary energy supply adequacy

Healthconditions

Absolute Absolute value of uprooted people
Relative Uprooted people relative to total population

The Vulnerable groups category should be always fed with the most recent data available(e.g. uprooted people, people affected by recent shocks …).
Scalability: The indicators for the Uprooted people component are foreseen to be
updated as soon as data are available on subnational scale. The indicators of the Health
Conditions and the Children under-5 sub-component are updated annually and could be
potentially provided sub-nationally if the data would exist. The data for the Recent shock
sub-component are limited to national scale and provided every 3 months. In case of
Food Security indicators the data are available annually on national scale but otheroptions considered in Box 4, not available at the moment globally, would allow
geographical and temporal disaggregation.

37

4.3.4.3 Component: Uprooted people
The total number of uprooted people is the sum of the highest figures from the selectedsources for each uprooted group. The Uprooted people component is the arithmetic
average of the absolute and relative value of uprooted people. The absolute value ispresented using the log transformation while the uprooted people relative to the total
population are transformed into indicator using the GNA criteria and then normalised into
range from 0 to 10 (Table 14).
Table 14. Transformation criteria for the relative value of uprooted people% of total population

Level ofvulnerability

Uprooted people(relative
subcomponent)> 10 %

High

10.0> 3 % AND < 10 %> 1 % AND < 3 %8.3
Medium

6.7> 0.5 % AND < 1 %> 0.1 % AND < 0.5 %

5.0
Low

3.3

> 0.005 % AND < 0.1 %< 0.005 %1.7
No vulnerability

0.0

4.3.4.4 Component: Other vulnerability groups/Health condition
A Health condition subcomponent refers to people in a weak health conditions. It is
calculated as the arithmetic average of the indicators for three deadly infectious diseases,
AIDS, tuberculosis and malaria, which are considered as pandemics of low- and middleincome countries. The combat against these three diseases is one of the 2015 Millennium
Development Goals (23) and the Sustainable Development Goals (24). Similarly, theGlobal Fund (25) is an international financing institution that fights AIDS, tuberculosis andmalaria.
4.3.4.5 Component: Other vulnerability groups/Children under 5
A Children under-5 subcomponent captures the health condition of children. It is
referred to with two indicators, malnutrition and mortality of children under 5. Children
Underweight extracts the group of children that are in a weak health condition mainly
due to hunger. Child mortality shows general health condition of the children and isclosely linked to maternal health since more than one third of children deaths occur
within the first month of life and to how well the country tackles major childhood
diseases (e.g. proper nutrition, vaccinations, monitoring system, family care practice,
health system access, sanitation and water resources). Therefore, decrease ofunderweight children and the child deaths are one of the MDG by 2015 as well.
4.3.4.6 Component: Other vulnerability groups/Recent shocks
Recent shocks subcomponent accounts for increased vulnerability during the recovery
period after a disaster and considers people affected by natural disasters in the past
3 years. The affected people from the most recent year are considered fully while
affected people from the previous years are scaled down with the factor 0.5 and 0.25 forthe second and third year, respectively, assuming that recovery decreases vulnerability
progressively. This way the smoothness of the INFORM index in time series is assured.
(23) https://mdgs.un.org/unsd/mdg/Default.aspx(24) https://sustainabledevelopment.un.org/sdgs(25) http://www.theglobalfund.org/en/

38

4.3.4.7 Component: Other vulnerability groups/Food security
The FAO definition of food security is: ‘A situation that exists when all people, at all
times, have physical, social and economic access to sufficient, safe and nutritious foodthat meets their dietary needs and food preferences for an active and healthy life’ (26).
For our model, we therefore suggest that the Food security subcomponent is dependent
on Food access, Food availability, and Food utilisation. This concept serves as a set ofproxy measures for the number of people lacking secure access to food. Leaning on
definitions provided by the Integrated Phased Food Security Classification (IPC), we
determine Food availability on whether food is actually or potentially physically present
regarding production, wild foods, food reserves, markets, and transportation. Foodaccess assesses whether or not households have sufficient access to that food, taking
into account physical (distance, infrastructure), financial (purchasing power) and social(ethnicity, religion, political affiliation, etc.) aspects. Finally, Food utilisation covers thequestion as to whether or not households are sufficiently utilising food in terms of foodpreferences, preparation, feeding practices, storage and access to improved water
sources.
The combination of lack of food, lack of means to actually make it available, and lacking
quality of food may lead to famine and hunger for poor populations. Therefore, the threecomponents are aggregated with an arithmetic average. All components are thearithmetic average of the raw indicators. In the Food access component more weight isgiven to the price index (absolute) versus price volatility, 80 % versus 20 %,respectively. For example, there are some situations of countries with high but stableprices that seem better off than countries with average prices and average volatility.
Box 4. Other options for food insecurity sub-component

For the Food security sub-component some other options were considered, which seem
more adequate but their coverage was too sparse:— The IPC (Integrated Food Security Phase Classification) classifies the severity of foodsecurity and humanitarian situations into five phases based on a widely accepted setof indicators. The phase classification describes the current situation for a given area,
while also communicating the likelihood and severity of further deterioration of thesituation.
— The FEWSNet (27) methodology used by a famine early warning systems network. It
uses scenarios to forecast the most likely outcomes based on continuous monitoringof weather, climate, agriculture, production, prices, trade, and other factors,
considered together with an understanding of local livelihoods.
These options may be integrated in the INFORM methodology in the future, when data
coverage increases.

(26) http://www.fao.org/3/a-i4030e.pdf, p. 50. The complementary definition for food insecurity is: ‘A situationthat exists when people lack secure access to sufficient amounts of safe and nutritious food for normal
growth and development and an active and healthy life. It may be caused by the unavailability of food,insufficient purchasing power, inappropriate distribution or inadequate use of food at the household level.
Food insecurity, poor conditions of health and sanitation and inappropriate care and feeding practices are
the major causes of poor nutritional status. Food insecurity may be chronic, seasonal or transitory’ (ibid.).
(27) http://www.fews.net

39

4.4 Dimension: Lack of coping capacity
4.4.1 Overview
For the Lack of coping capacity dimension, the question is, which issues the government
has addressed to increase the resilience of the society and how successful theirimplementation is. The Lack of coping capacity dimension measures the ability of acountry to cope with disasters in terms of formal, organised activities and the effort ofthe country’s government as well as the existing infrastructure, which contribute to the
reduction of disaster risk.
Box 5. Variations in the subnational models: Lack of coping capacity

An INFORM Subnational model uses the same risk assessment methodology anddevelopment process, but is adapted to regional or national level (28).
The most important changes in Lack of coping capacity dimension include the addition of
Financial and economic system (East Africa), International investments in risk reduction(Sahel, East Africa).

4.4.2 Lack of coping capacity: categories
It is aggregated by a geometric mean of two categories: Institutional and Infrastructure.
The difference between the categories is in the stages of the disaster management cyclethat they are focusing on. If the Institutional category covers the existence of DRR
programmes, which address mostly mitigation and preparedness/early warning phase,then the Infrastructure category measures the capacity for emergency response andrecovery.

(28) INFORM Guidance Note 2016 (http://www.inform-index.org).

40

Figure 8. Graphical presentation of the Lack of coping capacity dimension

4.4.3 Category: Institutional
4.4.4 Definition
The Institutional category quantifies the government’s priorities and institutional basis forthe implementation of DRR activities. It is calculated as an arithmetic average of two
components, Disaster risk reduction and Governance (Table 16), in order toincorporate the effectiveness of the governments’ effort for building resilience across all
sectors of society.

41

Table 15. Indicators of the Institutional category
Component

Indicator

SourceMIN–MAXNo ofmissingvaluesDisaster risk
reduction

Hyogo Framework for Action selfassessment reports

UNISDR

1-5

40/191Government effectivenessWorld Bank– 2.5-2.5-

Corruption Perception Index

Transparency
International

0-100

17/191Governance

Table 16. Aggregation of Institutional category

Institutional

Category

ARITHMETIC AVERAGE

GovernanceCorruption
Perception
Index

ARITHMETIC AVERAGE

Government
effectivenessCore indicator

Disaster risk reduction
Hyogo Framework
for Action Scores

Component

Scalability: For all indicators of the Institutional category only annual updates on anational scale are possible.
4.4.4.1 Component: Disaster Risk Reduction
The indicator for the Disaster Risk Reduction activity in the country comes from thescore of Hyogo Framework for Action self-assessment reports of the countries. The
Hyogo Framework for Action [47] covers the following topics:
1. Ensure that disaster risk reduction is a national and a local priority with a strong
institutional basis for implementation.
2. Identify, assess and monitor disaster risks and enhance early warning.
3. Use knowledge, innovation and education to build a culture of safety andresilience at all levels.
4. Reduce the underlying risk factors.
5. Strengthen disaster preparedness for effective response at all levels.
Self-evaluation has a risk of being perceived as a process of presenting inflated grades
and being unreliable.
4.4.4.2 Component: Governance
The subjectivity of HFA Scores is counterweighted by arithmetical average with external
indicators of Governance component, i.e. the Government Effectiveness andCorruption Perception Index.

42

The Government Effectiveness (29) captures perceptions of the quality of public
services, the quality of the civil service and the degree of its independence from politicalpressures, the quality of policy formulation and implementation, and the credibility of thegovernment’s commitment to such policies [48] while the Corruption Perception
Index adds another perspective, that is the level of misuse of political power for private
benefit, which is not directly considered in the construction of the Government
Effectiveness even though interrelated.

4.4.5 Category: Infrastructure
4.4.5.1 Definition
Communication networks, physical infrastructure and accessible health systems aretreated as essential parts of the infrastructure needed during emergency response,
focusing on the early warning phase, and carrying through response and recovery. Since
all parts of the infrastructure should be operational to a certain level, the aggregation
process uses the arithmetic average of indicators describing accessibility as well as theredundancy of the concerned system that are two crucial characteristics in a crisis
situation.
Table 17. Indicators of the Infrastructure category
Component

Communication

Physicalinfrastructure

Access to healthsystemIndicator

SourceMIN–MAXNo ofmissingvaluesAccess to electricity

World Bank(30)2-(100)2

1/191

Internet users

World Bank

0 %-100 %1/191

Mobile cellular subscriptions

World Bank

5-200

-

Adult literacy rate

Unesco0 %-100 %42/191

Roads density

OpenStreetMap

1-100

-

Access to improved water source

WHO/Unicef

50 %-100 %2/191

Access to improved sanitation facilities

WHO/Unicef

10 %-100 %3/191

Physicians density

WHO

0-4

19/191

Health expenditure per capita

WHO50-3 000

6/191

Measles immunisation coverageWHO

60 %-99 %

1/191(29) http://info.worldbank.org/governance/wgi/index.aspx#doc

43

Table 18. Aggregation of the Infrastructure category

Infrastructure

Category

ARITHMETIC AVERAGE

Maternal mortality rate

Measles immunisation
coverage

Health expenditure percapitaPhysicians density

Access to improved
sanitation facilities

ARITHMETIC AVERAGE

Access to improved water
source

ARITHMETIC AVERAGE

Roads density

ARITHMETIC AVERAGE

Adult literacy rate

Access to health system

Mobile cellular subscriptions

PhysicalinfrastructureInternet users

Core indicator

Communication

Access to electricity

Component

Scalability: Health expenditure per capita has a unit of analysis locked to country while
all the other indicators could be potentially developed on subnational scale if the data
would exist. Regarding the temporal scalability only annual updates are expected.
4.4.5.2 Component: Communication
The Communication component aims at measuring the efficiency of dissemination ofearly warnings through a communication network as well as coordination of preparedness
and emergency activities. It is dependent on the dispersion of the communication
infrastructure as well as the literacy and education level of the recipients.
4.4.5.3 Component: Physical infrastructure
Physical infrastructure component is the arithmetic average of different proxymeasures. We mainly try to assess the accessibility as well as the redundancy of thelifeline systems, which are crucial in a crisis situation, i.e. roads, water and sanitation
systems.
4.4.5.4 Component: Access to health system
Access to health system component is the arithmetic average of different proxymeasures. We mainly try to assess the accessibility as well as the redundancy of thedifferent assets of the existing health systems.

44

5 Limitations and constraints of INFORM
There are certain areas of the three dimensions of INFORM that are not covered or
covered only partially. The main constraints are related to limitations of the methodology
and incomplete data availability.

5.1 Methodological limitations
Composite indicator. The composite indicators are simplification of reality. The simple‘big picture’ results which composite indicators show may invite politicians to draw
simplistic policy conclusions. Composite indicators should be used in combination with the
sub-indicators to draw sophisticated policy conclusions (30).
Interactions among dimensions are not considered. For example, the measures ofdisaster risk reduction in the Lack of coping capacity dimension might reduce theexposure data in the Hazard & exposure dimension. The methodology is not able tointroduce such interactions in a quantitative manner.
The usage of proxies limits the ‘representativeness’. Certain phenomena that wereaddressed as important for the humanitarian risk assessment cannot be measured
exactly in the way we want or adequate indicators are not available. In such situations,proxy measures are used which measure something that is close enough to reflect similar
behaviour and can provide relative differences among the countries for ranking purposes.
The proper representativeness of phenomena is limited to the presence of causes,
consequences, measurable parts of the process or even accompanying processes. For
example, the malaria mortality rate is a proxy used to rank countries by the prevalenceof malaria as the latter data are deemed unreliable.

5.2 Data limitations
Extensive hazardous events and sudden onset hazardous events with a more
limited geographic extent such as landslides, forest fires and volcanoes, are notincluded. One reason is lack of data availability while the other is their lower relevancein terms of causing humanitarian crises. According to the CRED EM-DAT database [22]the death toll of natural hazards during 1900-1999 is less than 0.2 % due to volcanic
eruptions, landslides and wildfires. On the other hand, the rapid onset hazards with amore limited geographic extent seldom exceed entry criteria of the EM-DAT database.
From that point of view their presence in the database is incomplete and the cumulative
death toll is higher, as one event rarely causes humanitarian crises.
Biological hazards (i.e. epidemics/large-scale epidemics/pandemics) are notincluded. They can have a large impact not only on mortality and morbidity but also on
travel and trade as well as socioeconomic effects. To consider their potential threat, thedata on probability of re-emerging diseases with certain level of impact are needed andare not so easily available. WHO and JRC are working to include an infectious disease
outbreaks component in the natural hazard category of INFORM.
Technological hazards are not included. Technological hazards originate fromtechnological or industrial accidents that may arise as a result of an intentional plan
(terrorist attack), a random process (human error), natural hazardous event (Natech), or
the lack of maintenance or ageing processes. The likelihood of such events is partiallyrelated to the presence of critical assets (uranium tailings, UXO, nuclear power plants,
chemical plants) in the country and partially to the probability of occurrence of triggering
event. The list of critical assets (uranium tailings, UXO, nuclear power plants, chemicalplants) by country is therefore not enough to define the country’s risk. To consider theconsequences, data with a certain level of impact are needed, for example in terms ofphysical exposure, and each critical asset should come together with impact area notconstrained by country borders. These data are currently not available.
(30) https://composite-indicators.jrc.ec.europa.eu/?q=10-step-guide/step-1-theoretical-framework

45

Lower reliability of disaster risk reduction component. The disaster risk reduction
component is based on the scores of Hyogo Framework for Action self-assessment
reports of which the reliability is unknown. Self-assessment reports cover almost 80 % of
the countries. But it is not a stand-alone indicator and its trustfulness is estimated withthe governance component. However, at the time being there are no other internationalframeworks for assessing the capacity to cope with humanitarian crises that would fit thescope so well [49]. Sendai Framework [47] for development and disaster risk reduction
aims to provide new indicators to monitor global targets. As soon as they become
available they will replace the HFA scores within the DRR component.
Missing data can distort the real value of the composite indicator. The presence of
missing data cannot be completely avoided. The goal of the composite indicator is toaggregate the different aspects of the humanitarian risk. Whenever certain values are
missing, the aggregation process fails as a tool to compensate a deficit in one dimension/category/components by surplus in another. In the case of poor coverage, we
introduced, whenever available, more than one proxy measure for the same component
to complement each other.
Table 19. Countries with more than 20 % of missing values in INFORM 2017

Country

Missing
total)

values

Liechtenstein

22 (43 %)Nauru

15 (29 %)Tuvalu15 (29 %)Marshall Islands

14 (27 %)Dominica

13 (25 %)Saint Kitts and Nevis

13 (25 %)Grenada12 (24 %)Korea DPR

12 (24 %)Antigua and Barbuda

11 (22 %)Eritrea

10 (20 %)Kiribati

10 (20 %)Micronesia

10 (20 %)Palau

10 (20 %)Somalia

10 (20 %)(%of

In INFORM 2017 36 countries have all data, while 14 countries have more than 20 % ofmissing values (Table 19).
Countries in conflict. In countries facing internal conflicts (i.e. Syria, Iraq, Libya), thereliability of the data (when available) is normally weak. Therefor the resulting INFORM
score for those countries has to be taken with caution.
Limitations in the sensitivity of indicators and data updates affect the
responsiveness of the INFORM index. Some indicators in the INFORM index aredesigned to reflect the real-time situation but there are time constraints that should be
kept in mind. Firstly, there is a time lag between a situation changing and the indicatorreflecting this change and, secondly, the indicators are usually issued with delays
because they need to go through a validation process.
Natural hazard category is static. The probability of natural hazard doesn’t change inshort-medium period, while the population movement is more dynamic. Urbanisation,46

economical and conflict-induced migration are processes that can change the population
distribution dramatically. Moreover, vulnerable population is likely to move to the morehazard risk areas. The exposure data currently used in INFORM cannot capture thedescribed dynamics, but some potential alternatives exist and they will be taken into
consideration for the next realises.

5.3 Ranking of countries
The composite indicator is a simplified view of the reality and the user should be aware of
its limitations. Understanding humanitarian risk is a complex problem which can bereferred to as a multidimensional phenomenon. The role of the theoretical framework is
to specify single dimensions and their interrelations as well as to provide the basis forindicator selection. The ranking value of the index is the result of the methodology thatdefines the mathematical combination of individual indicators. Therefore not risk, but risk
as described by the methodology of the composite indicator could be managed.
Furthermore, the INFORM index conveys only the information measured by indicators.
Indicators have to be compliant with the selection criteria (Chapter 4.1) and the choice issometimes more data-driven than user-driven. Different types of indicators are used:●

Direct measures (e.g. number of uprooted people) which have a strong influenceon the score;●

Proxy measures (e.g. Gini index can be a proxy for inequality in education,
livelihood, health conditions) which serve mainly for ranking;●composite indicators (e.g. HDI, MPI …) that can be a combination of both.

The INFORM index can provide different types of results. One is the ranking of countries.
This builds a relationship among the countries in terms of ‘a certain country is ranked
higher or lower than the other’. The other is the score of the countries which can be used
for following trends in time series. The higher the presence of the direct measures overproxies, the larger is the relevance of the scores. For more qualitative assessment thecountries can be grouped into five classes of very low, low, medium, high and very highrisk of humanitarian crises. Furthermore, the same results can be gained in the level ofdimensions and categories (Chapter 7).

47

6 Building the INFORM model
Before the construction of the composite indicator and sub-indices, all raw data values of
the core indicators are pre-processed. A pre-processed indicator is referred to as anindex.
Pre-processing may include:●

Imputation of missing values;●

Transformation into non-dimensional scales, e.g. utilising percentages, per capita
or density functions;●

Log transformation;●

Re-scaling into range 0-10 in combination with min–max normalisation:
Outliers identification;

Setting min and max values;

Inversion of values for the clear communication of the results: thehigher the worse through all the dimensions, categories andcomponents.

For each core indicator, the pre-processing steps are described in a separate
document (31).

6.1 Imputation of missing values
If data for some countries are not available for a given year, a systematic imputation of
missing values is using the data from the most recent year available in 5 years span.
In the case of the missing data due to the weak coverage, the approach is to introduce
more than one indicator for the same component to complement each other.

6.2 Transformations
Transformations are applied whenever it can be justified to change the absolutedifferences among the countries.
The log transformation is used to reduce the positive skewness of data. Such datasets
include those where the indicator is based on a people count with certain conditions. The
log scale gives more weight to the differences between the countries with lower values
and less weight to the countries with higher values of indicators. Log transformations
take into account not only the absolute difference between two countries similar in
performance but also the proportion of the gap compared to the real value of theindicator. The same gap on the lower side of the range is more important than being on
the upper side of the rank. Therefore, transformed data more clearly differentiate thesmall differences at all ranges of performance and improve the interpretation ofdifferences between the countries on opposite ends of ranking.

6.3 Normalisation
Re-scaling normalises indicators to have an identical range of 0.0-10.0 with the notionthat higher is worse. The normalised indicators have been rounded to the first decimal
place. As outliers often cause min and max values to be very different from the bulk of
the values in the dataset rescaling with predefined min and max values is applied(Equation 2).
Identification of outliers and setting min and max values. Fixed min and max
values for each indicator dataset are preferred in order to:(31) Data Factsheet (http://www.inform-index.org/Results/Global).

48

●preserve the rescaling factor and make the transformation stable through the timeseries;●exclude the distortion effect of outliers on indicator’s set;●consider the nature of the topic reflected which predefines the reasonable min andmax values (e.g. expert opinion).

𝑗𝑥𝑖,𝑛𝑜𝑟𝑚 =𝑗𝑗𝑥𝑖 − 𝑥𝑖,𝑚𝑖𝑛
× 10
𝑥𝑖,𝑚𝑎𝑥 − 𝑥𝑖,𝑚𝑖𝑛𝑥𝑖− 𝑑𝑎𝑡𝑎 𝑝𝑜𝑖𝑛𝑡 𝑓𝑜𝑟 𝑡ℎ𝑒 𝑗 − 𝑡ℎ 𝑐𝑜𝑢𝑛𝑡𝑟𝑦 𝑓𝑟𝑜𝑚 𝑖 − 𝑡ℎ 𝑖𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟 ′ 𝑠 𝑑𝑎𝑡𝑎𝑠𝑒𝑡

𝑥𝑖,𝑚𝑖𝑛− 𝑚𝑖𝑛 𝑣𝑎𝑙𝑢𝑒 𝑓𝑜𝑟 𝑖 − 𝑡ℎ 𝑖𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟 ′ 𝑠 𝑑𝑎𝑡𝑎𝑠𝑒𝑡

𝑥𝑖,𝑚𝑎𝑥− 𝑚𝑎𝑥 𝑣𝑎𝑙𝑢𝑒 𝑓𝑜𝑟 𝑖 − 𝑡ℎ 𝑖𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟 ′ 𝑠 𝑑𝑎𝑡𝑎𝑠𝑒𝑡

Equation 2

𝑗𝑥𝑖,𝑛𝑜𝑟𝑚 − 𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 𝑑𝑎𝑡𝑎 𝑝𝑜𝑖𝑛𝑡 𝑜𝑟 𝑡ℎ𝑒 𝑗 − 𝑡ℎ 𝑐𝑜𝑢𝑛𝑡𝑟𝑦 𝑓𝑟𝑜𝑚 𝑖 − 𝑡ℎ 𝑖𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟 ′ 𝑠 𝑑𝑎𝑡𝑎𝑠𝑒𝑡

An outlier is a data point that is distinctly separate from the rest of the data. Outliers areindicative of heavy-tailed distribution, a mixture of two distributions, or errors. In thefirst two cases they indicate that the distribution has high kurtosis and skewness or may
be two distinct sub-populations, then one should be very cautious in using tools or
intuitions that assume a normal distribution. In the case of errors one wishes to discardthem or use statistics that are robust to outliers. There are many techniques to identify
outliers, but in INFORM index a combination of the following two has been used:●

Box plot [44] based on interquartile range (IQR) where the lowest datum is still
within 1.5 IQR of the lower quartile, and the highest datum is still within 1.5 IQRof the upper quartile and the rest of the data are treated as outliers. Thisapproach focuses on the range containing 50 % of the countries and then extends
that range independently from the distribution. So the number of data points thatexceeds the limits varies. For right-skewed distributions the boxplot typicallylabels too many large outliers and too few small outliers.

●

Anomalous values of skewness and kurtosis: the min and max values for whichskewness is lower than 2 AND kurtosis is lower than 3.5 (32). Skewness andkurtosis are calculated iteratively for the whole dataset without the obvious
outliers, until pre-set conditions are met. The minimum and maximum data pointof the remaining dataset are taken as min and max.

The two technics were used to find the indicative min and max values based on datafrom 2008-2013. They were adjusted to cover expected changes (beyond 2013) overtime based on expert opinion. The min and max values will be re-evaluate periodically,e.g. every 5 years.
Inversion. The methodology defines in what way single indicator affects the composite
indicator. In the model all values are presented with the notion that higher is worse. So,
whenever higher values of the indicator would contribute to a lower INFORM index, thefollowing inversion of already rescaled dataset, is executed:(32) Saisana, M., 13th JRC Annual Training on Composite Indicators & Scoreboards, 12-13/11/2015, Ispra (IT),Joint Research Centre.

49

𝑗

𝑗

𝑥𝑖,𝑛𝑜𝑟𝑚,𝑖𝑛𝑣 = 10 − 𝑥𝑖,𝑛𝑜𝑟𝑚

𝑗𝑥𝑖,𝑛𝑜𝑟𝑚

Equation 3− 𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑠𝑒𝑑 𝑑𝑎𝑡𝑎 𝑝𝑜𝑖𝑛𝑡 𝑓𝑜𝑟 𝑡ℎ𝑒 𝑗 − 𝑡ℎ 𝑐𝑜𝑢𝑛𝑡𝑟𝑦 𝑓𝑟𝑜𝑚 𝑖 − 𝑡ℎ 𝑖𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟 ′ 𝑠 𝑑𝑎𝑡𝑎𝑠𝑒𝑡

𝑗𝑥𝑖,𝑛𝑜𝑟𝑚,𝑖𝑛𝑣 − 𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑠𝑒𝑑 𝑑𝑎𝑡𝑎 𝑓𝑜𝑟 𝑡ℎ𝑒 𝑗 − 𝑡ℎ 𝑐𝑜𝑢𝑛𝑡𝑟𝑦 𝑓𝑟𝑜𝑚 𝑖 − 𝑡ℎ 𝑖𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟 ′ 𝑠 𝑑𝑎𝑡𝑎𝑠𝑒𝑡 𝑖𝑛𝑣𝑒𝑟𝑠𝑒𝑑

6.4 Aggregation
Different aggregation rules are possible. Which one to choose depends on themethodology [52] which defines how the information from indicators should contribute to
the composite indicator. Aggregation rules can be defined using mathematical operations
such as:●Minimum: the best indicator only●

Maximum: the worst indicator only●

Arithmetic average●

Geometric average

The INFORM methodology implements the arithmetic and geometric average.
Aggregation rules are applied to indexes at each level in order to progress through thelevels in a hierarchical bottom-up way, i.e. starting at indicator level and going one by
one through the component level, the category level, to the dimension level. The finalscore of the INFORM index is calculated with the risk equation (Equation 1) in
Chapter 2.2.
In arithmetic and geometric aggregations weighting can be applied to control thecontribution of each indicator to the overall composite and should be justified by thetheoretical framework. Practically, weights express a desired trade-off between
indicators.
Box 6. Arithmetic vs. geometric average

For ranking purposes, aggregation is a tool to compensate a deficit in one dimension bysurplus in another. With arithmetic average compensation is constant while withgeometric average compensation is lower and rewards more the indicators with higherscores (33). For a country with high and low scores, an equal improvement for low scores
will have a much greater effect on the aggregation score than an equal improvement in
the high score. So, the country should focus in those sectors with the lowest score if it
wants to improve its position in ranking in case of the geometric aggregation.

(33) The geometric average is always smaller or equal than the arithmetic average. To use that characteristic ofgeometric average, i.e. to reward more those countries with high scores, the following procedure wasapplied [16]:1. Inversion of index following the notion higher the better.
2. Rescaling of index into the range [1,10].
3. Calculation of geometric average.
4. Rescaling the score back into the range [0,10].
5. Inversion of the score with the notion that higher is worse.

50

To provide an understanding of the implication of using either average, let us consider
the Hazard & exposure dimension which is aggregated by two categories with equal
weights, Natural and Human hazard. For example, we consider Ethiopia and Nigeria.
These two countries have almost equal arithmetic average in those two categories.
However, arithmetic average implies that in order to have a high score in the Hazard &exposure dimension, then both the Natural and the Human hazard category have to behigh. Instead, the use of a geometric average implies that it is enough for a country tohave a high score either on the Natural or on the Human hazard category, in order forthe country to have a high Hazard & exposure score. As a high exposure in at least oneof the hazard categories already put the country at high risk of exposure to hazards, it ismore logical to use geometric average.

51

7 Interpretation of the INFORM index score
The INFORM index is scored between 0.0 and 10.0. The low values of the index represent
a positive performance, and the high values of the index represent a negative
performance in terms of managing humanitarian risk. The notion that higher is the worse
is consistently applied also at dimension, category and component level.
For the better perception of risk, the countries were sorted into different clusters basedon their score/scores from the past 5 years. The scope of the fixed threshold obtained
from the clustering analysis on the 5 years of historical data is:●more meaningful trend analysis;●

more solid perception of risk classes among users.

The previous quartile approach in INFORM 2015 to define four classes of risk has many
drawbacks:●

Constant number of countries within the classes;●

Change of the class doesn’t imply improvement in score;●

The transitions among classes based on the performance was not allowed.

In this section, we describe the new approach introduced in INFORM 2016 to cluster
countries, and provide a guide to interpretation of the scores.

7.1 Cluster analysis
Clustering is finding groups in a dataset by some natural criterion of similarity (34). This isalso the common objective of many different cluster algorithm (35). In INFORM thehierarchical clustering model was used. Hierarchical clustering models also called
connectivity based clustering were among the earliest techniques developed. They arebased on the idea that the objects are more related to nearby objects than those further
away. Clusters are thus developed based on distance between objects in the data space.
The idea is to build a binary tree of the data that successively merges similar groups.
Each level of the resulting tree is a segmentation of data. There is no single partitioning
provided, but rather a hierarchy of clusters which expand or decrease in number solely
based on distance measure and linkage criterion without the need to know the number ofclusters in advance.
In the case of INFORM, Ward’s minimum variance criterion was applied. This is an
agglomerative hierarchical clustering procedure (bottom up approach) where the criterion
for choosing the pair of clusters to merge at each step is based on minimum increase intotal within-cluster variance after merging. At the initial step, all clusters are singletons(clusters containing a single object). To apply a recursive algorithm with this criterion,
the initial distance between individual objects must be squared Euclidean distance. Thiswas implemented using hcluster function in R.

7.2 Fixed 5 risk classes
It was decided to fix the threshold for five classes (Table 20) within each category (6),each dimension (3) and the final INFORM risk score (1) based on all data for the last
5 years (2012-2016). 10 datasets were assembled with five values for each of 191
countries. Data within each dataset are grouped into five classes. Dealing with
1-dimensional data the output of the cluster analysis for each dataset are five intervals(34) Estivill-Castro, V. (20 June 2002), ‘Why so many clustering algorithms — A Position Paper’, ACM SIGKDD
Explorations Newsletter 4 (1): 65-75 (doi:10.1145/568574.568575).
(35) Clustering algorithms can be categorised based on their cluster model. Some typical cluster models arehierarchy, centroid, distribution and density clustering. There are possibly over 100 published clustering
algorithms. The most appropriate clustering model for a particular problem often needs to be chosen
experimentally, unless there is a mathematical reason to prefer one cluster model over another.

52

with defined thresholds. The thresholds were rounded generally at the first decimal while
for the final risk score the thresholds were rounded to 0.5. Next, in the category of
Human hazard the thresholds for the very high and high risk of conflict were adjusted tocorrespond to the meaning of the initial definitions of conflict risk score (Table 8). It is
foreseen to keep the same thresholds for at least 5 years.
There were two reasons to pass from four classes in the previous releases to five classes(Table 20):●

To have a balanced scale of risk assessment;●

To have manageable size of the classes.

The whole purpose of risk classification in the form of a hierarchical scale is tosystematically identify risk in a consistent manner. Risk classes give greater ability tomonitor, control and even manage risk because it helps to identify the root causes of riskin better way. It might be that a similarity-based approach to risk classification isinherently ambiguous. As it is open to more than one interpretation we believe it makes
sense for each organisation to describe the risk classes according to the goals of theorganisation and the risk management decision that they face. In most cases the users
will find it useful to look at the classes within specific dimension or even categories (Table
21 and Table 22).
Table 20. The labels of the five risk classes

Level
1
2
3
4
5

Number of countries

Risk class(INFORM 2017)
14
35
50
59
33

Very highHigh
MediumLow
Very low53

Table 21. Fixed thresholds at the level ofdimensions

Table 22. Fixed thresholds at the level ofcategories

CLASSES THRESHOLDS IN INFORM

CLASSES THRESHOLDS IN INFORM
MAX

MIN

6.5

very high

10.0

6.9

6.4

5.0

high

6.8

4.7

medium

4.9

3.5

medium

4.6

2.8

low

3.4

2.0

low

2.7

1.3

very low

1.9

0.0

very low

1.2

0.0

very high

10.0

6.1

very high

10.0

9.0

high

6.0

4.1

high

8

7

medium

4.0

2.7

medium

6.9

3.1

MIN

very high

10

high

Category

HUMAN

MAX

NATURAL

CLASS

CLASS

2.6

1.5

low

3.0

1.0

1.4

0.0

very low

0.9

0.0

very high

10.0

6.4

very high

10.0

7.1

high

6.3

4.8

medium

4.7

3.3

low

3.2

2.0

SOCIOECONOMIC

lowvery low

high

7.0

5.4

medium

5.3

3.5

low

3.4

1.8

1.9

0.0

very low

1.7

0.0

10.0

7.4

very high

10.0

6.3

high

7.3

6.0

high

6.2

4.4

medium

5.9

4.7

medium

4.3

2.9

low

4.6

3.2

low

2.8

1.6

very low

3.1

0.0

very low

1.5

0.0

10.0

7.3

high

7.2

6.0

medium

5.9

4.9

INSTITUTIONAL

VULNERABLEGROUPS

very low
very highvery high

low

4.8

3.3

very low

3.2

0.0

INFRASTRUCTURE

LACK OFCOPINGCAPACITY

VULNERABILITY

HAZARD &EXPOSURE

RISK

Dimensionvery high

10.0

7.4

high

7.3

5.4

medium

5.3

3.5

low

3.4

2.1

very low

2.0

0.0

7.3 Trends
Due to fixed threshold the countries can in the future change the class based on theirperformance. As there are many global proactive initiatives to improve the disaster risk
management at a national level most probably the number of the countries within thevery high/high risk cluster will be decreasing. On the other hand increased exposure andincreased hazard probability due to climate change would increase the number ofcountries in the very high/high risk cluster.
INFORM results are always available for at least 5 years, making it easy to analyse risk
trends (36). The historical results are back calculated using the same methodology anddata source of the published release.
(36) http://www.inform-index.org/Results/Global

54

Figure 9. Dynamics of the classes sizes through last 5 years

Limitation of the trend data. The natural hazard category is kept constant over the
5 years. In order to have continuous data for the all 5 years, the most recent values havebeen used to cover missing values for the previous years (e.g. in the HFA some countries
started reporting very recently, and there are cases where exist data for 2013 but not forprevious year).

55

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59

List of abbreviations and definitions
ACAPS Assessment Capacities Project
ASI

Agricultural Stress Index

CRED

Centre for Research on the Epidemiology of Disasters

DFID

United Kingdom government — Department for International Development

DRR

disaster risk reduction

ECHO

European Commission — Directorate-General for European Civil Protection and
Humanitarian Aid Operations

FAO

Food and Agriculture Organization of the United Nations

FCI

Forgotten Crisis Index

GAR

Global Assessment Report

GCRI

Global Conflict Risk Index

GDPgross domestic product

GFM

Global Focus Model

GNA

global needs assessment

GNI

gross national income

GSHAP Global Seismic Hazard Assessment Program
HDI

Human Development Index

HFA

Hyogo Framework for Action

HIIK

Heidelberg Institute for International Conflict Research

IDMC

Internal Displacement Monitoring Centre

IDP

internally displaced persons

IOM

International Organization for Migration

IQR

interquartile range

MDG

Millennium Development Goals

MMI

Modified Mercalli Intensity Scale (I-XII)MPIMultidimensional Poverty Index

Natech Natural Hazard Triggering Technological Disasters
NOAA

National Oceanic and Atmospheric Administration, United States

OCHA

Office for the Coordination of Humanitarian Affairs

ODA

Official Development AssistanceOECD

Organisation for Economic Co-operation and Development

PAGER Prompt Assessment of Global Earthquakes for Response
PPP

purchasing power parity

RP

return period

SDG

sustainable development goals

SFDRR Sendai Framework for Disaster Risk ReductionSS

Saffir-Simpson Hurricane Scale (Category 1-5)

UNEP

United Nations Environment Programme

60

UNHCR United Nations refugee agency
Unicef

United Nations Children’s Fund

UNISDR United Nations Office for Disaster Risk Reduction
UNODC United Nations Office on Drugs and Crime
UXO

Unexploded ordnance (i.e. explosive weapons, e.g. bombs, bullets, shells,
grenades, land mines, naval mines, etc.)

WFP

United Nations World Food Programme

WHO

World Health Organization

WRI

World Risk Index

61

List of boxes
Box 1. Variations in the subnational models: Hazard & exposure ...............................18
Box 2. Absolute vs. relative physical exposure — correction in favour of small countries..........................................................................................................................22
Box 3. Variations in the subnational models: Vulnerability ........................................30
Box 4. Other options for food insecurity sub-component ...........................................39
Box 5. Variations in the subnational models: Lack of coping capacity .........................40
Box 6. Arithmetic vs. geometric average .................................................................50

62

List of figures
Figure 1: INFORM summarises the multitude of factors contributing to the risk forhumanitarian crises and disasters into a single index. ................................................ 7
Figure 2: INFORM 2017 Risk index map shows risk of humanitarian crises and disaster
across the globe. ................................................................................................... 7
Figure 3: The risk concept of INFORM ....................................................................11
Figure 4. INFORM Lack of Reliability Index methodology. .........................................17
Figure 5. Graphical presentation of the Hazard & exposure dimension ........................19
Figure 6. Transformation of GCRI probability of conflicts to INFORM score ..................29
Figure 7. Graphical presentation of the vulnerability dimension .................................31
Figure 8. Graphical presentation of the Lack of coping capacity dimension ..................41
Figure 9. Dynamics of the classes sizes through last 5 years.....................................55

63

List of tables
Table 1. INFORM model ........................................................................................12
Table 2. Minimum intensity/magnitude levels used for different type of hazards and data
source .................................................................................................................20
Table 3. Intensity scale levels vs. damage level .......................................................21
Table 4. Indicators of the Natural hazard category ...................................................22
Table 5. Aggregation of the Natural hazards category ..............................................23
Table 6. Indicators of the Human hazard category ...................................................27
Table 7: Aggregation of Human hazard category .....................................................27
Table 8. Adaption of conflict intensity .....................................................................28
Table 9. Conflict items, groups, and intensity ..........................................................28
Table 10. Indicators of the socio-economic vulnerability category ..............................32
Table 11. Aggregation of the Socio-Economic vulnerability category ..........................33
Table 12. Indicators of the Vulnerable groups category ............................................36
Table 13. Aggregation of the Vulnerable groups category .........................................37
Table 14. Transformation criteria for the relative value of uprooted people .................38
Table 15. Indicators of the Institutional category .....................................................42
Table 16. Aggregation of Institutional category .......................................................42
Table 17. Indicators of the Infrastructure category ..................................................43
Table 18. Aggregation of the Infrastructure category ...............................................44
Table 19. Countries with more than 20 % of missing values in INFORM 2017 .............46
Table 20. The labels of the five risk classes ............................................................53
Table 21. Fixed thresholds at the level of dimensions ...............................................54
Table 22. Fixed thresholds at the level of categories ................................................54
Table 23. Correlation matrix ()..............................................................................65
Table 24. Statistical influences of the INFORM categories within dimensions ...............66
Table 25. Statistical influences of the underlying components ...................................67

64

RISK

COPING CAPACITY

VULNERABILITY

HAZARD

Infrastructure

Access to health care

Physical infrastructure

Communication

Institutional

Governance

DRR

Vulnerable Groups

Other Vulnerable Groups

Food Security

Recent Shocks

Children U5

Health Conditions

Uprooted people

Social-Economics Vulnerability

Aid Dependency

Inequality

Development & Deprivation

Human

Current Highly violent conflicts

Conflict Probability

NaturalDroughts

Tropical cyclones

Tsunamis

FloodsEarthquakes-

Earthquakes 1.00 0.32 0.30 0.18 0.25 0.73 0.27 0.09 0.24 -0.23 -0.09 -0.21 -0.24 0.26 -0.35 -0.18 0.08 -0.09 -0.16 0.15 -0.14 0.07 -0.04 -0.20 -0.05 -0.17 -0.15 0.48 -0.04 -0.11 0.17
Floods 0.32 1.00 -0.01 0.03 0.39 0.56 0.58 0.30 0.54 0.19 0.15 -0.27 0.06 0.40 0.12 0.31 0.03 0.26 0.19 0.38 -0.06 0.34 0.22 0.20 0.39 0.22 0.30 0.64 0.25 0.29 0.50
Tsunamis 0.30 -0.01 1.00 0.45 -0.01 0.61 0.01 -0.01 0.00 -0.06 0.08 0.06 0.01 0.02 -0.17 -0.10 0.22 -0.07 0.00 0.03 -0.08 0.05 -0.01 -0.01 -0.05 0.01 -0.02 0.26 0.03 -0.02 0.11
Tropical cyclones 0.18 0.03 0.45 1.00 -0.09 0.50 -0.01 -0.06 -0.03 -0.05 0.13 -0.05 -0.03 -0.19 -0.14 -0.05 0.21 0.03 0.04 -0.14 -0.08 0.00 -0.05 -0.01 -0.06 -0.02 -0.03 0.19 -0.10 -0.04 0.02
Droughts 0.25 0.39 -0.01 -0.09 1.00 0.48 0.44 0.21 0.43 0.28 0.29 0.05 0.25 0.28 0.17 0.31 0.12 0.22 0.25 0.34 0.07 0.37 0.28 0.27 0.42 0.22 0.34 0.51 0.33 0.34 0.48
Natural 0.73 0.56 0.61 0.50 0.48 1.00 0.42 0.19 0.38 0.04 0.18 -0.12 0.02 0.25 -0.15 0.09 0.22 0.12 0.10 0.25 -0.08 0.29 0.15 0.07 0.20 0.10 0.14 0.71 0.16 0.16 0.43
Conflict Probability 0.27 0.58 0.01 -0.01 0.44 0.42 1.00 0.63 0.97 0.46 0.39 0.02 0.37 0.64 0.28 0.51 0.10 0.40 0.39 0.69 0.19 0.59 0.47 0.44 0.56 0.47 0.52 0.89 0.62 0.55 0.82
Current Highly violent conflicts 0.09 0.30 -0.01 -0.06 0.21 0.19 0.63 1.00 0.77 0.31 0.23 0.13 0.29 0.57 0.12 0.40 0.01 0.24 0.23 0.59 0.16 0.38 0.35 0.27 0.32 0.34 0.33 0.70 0.52 0.37 0.63
Human 0.24 0.54 0.00 -0.03 0.43 0.38 0.97 0.77 1.00 0.45 0.38 0.06 0.38 0.66 0.25 0.52 0.09 0.39 0.37 0.71 0.19 0.58 0.48 0.43 0.54 0.47 0.51 0.91 0.63 0.54 0.84
Development & Deprivation -0.23 0.19 -0.06 -0.05 0.28 0.04 0.46 0.31 0.45 1.00 0.62 0.52 0.93 0.20 0.68 0.89 0.30 0.75 0.81 0.51 0.40 0.71 0.66 0.93 0.80 0.84 0.91 0.36 0.81 0.87 0.75
Inequality -0.09 0.15 0.08 0.13 0.29 0.18 0.39 0.23 0.38 0.62 1.00 0.35 0.73 0.13 0.52 0.55 0.27 0.57 0.60 0.36 0.45 0.59 0.58 0.59 0.64 0.69 0.67 0.35 0.60 0.68 0.62
Aid Dependency -0.21 -0.27 0.06 -0.05 0.05 -0.12 0.02 0.13 0.06 0.52 0.35 1.00 0.76 0.02 0.31 0.37 0.28 0.41 0.47 0.27 0.45 0.43 0.47 0.57 0.29 0.55 0.49 0.00 0.60 0.52 0.38
Social-Economics Vulnerability -0.24 0.06 0.01 -0.03 0.25 0.02 0.37 0.29 0.38 0.93 0.73 0.76 1.00 0.16 0.64 0.79 0.34 0.73 0.79 0.49 0.51 0.72 0.70 0.90 0.72 0.86 0.87 0.30 0.84 0.86 0.73
Uprooted people 0.26 0.40 0.02 -0.19 0.28 0.25 0.64 0.57 0.66 0.20 0.13 0.02 0.16 1.00 0.06 0.25 -0.02 0.17 0.12 0.88 0.04 0.21 0.17 0.20 0.25 0.21 0.24 0.60 0.63 0.23 0.61
Health Conditions -0.35 0.12 -0.17 -0.14 0.17 -0.15 0.28 0.12 0.25 0.68 0.52 0.31 0.64 0.06 1.00 0.67 0.13 0.57 0.72 0.36 0.30 0.46 0.45 0.71 0.66 0.68 0.72 0.12 0.56 0.66 0.48
Children U5 -0.18 0.31 -0.10 -0.05 0.31 0.09 0.51 0.40 0.52 0.89 0.55 0.37 0.79 0.25 0.67 1.00 0.23 0.71 0.80 0.54 0.41 0.68 0.62 0.88 0.85 0.84 0.91 0.44 0.75 0.85 0.76
Recent Shocks 0.08 0.03 0.22 0.21 0.12 0.22 0.10 0.01 0.09 0.30 0.27 0.28 0.34 -0.02 0.13 0.23 1.00 0.25 0.65 0.31 0.14 0.27 0.25 0.32 0.20 0.24 0.27 0.14 0.37 0.28 0.30
Food Security -0.09 0.26 -0.07 0.03 0.22 0.12 0.40 0.24 0.39 0.75 0.57 0.41 0.73 0.17 0.57 0.71 0.25 1.00 0.79 0.47 0.42 0.63 0.61 0.76 0.67 0.71 0.76 0.34 0.67 0.75 0.66
Other Vulnerable Groups -0.16 0.19 0.00 0.04 0.25 0.10 0.39 0.23 0.37 0.81 0.60 0.47 0.79 0.12 0.72 0.80 0.65 0.79 1.00 0.55 0.40 0.65 0.61 0.84 0.73 0.77 0.82 0.32 0.75 0.79 0.69
Vulnerable Groups 0.15 0.38 0.03 -0.14 0.34 0.25 0.69 0.59 0.71 0.51 0.36 0.27 0.49 0.88 0.36 0.54 0.31 0.47 0.55 1.00 0.24 0.48 0.44 0.53 0.52 0.50 0.55 0.64 0.88 0.55 0.82
DRR -0.14 -0.06 -0.08 -0.08 0.07 -0.08 0.19 0.16 0.19 0.40 0.45 0.45 0.51 0.04 0.30 0.41 0.14 0.42 0.40 0.24 1.00 0.57 0.88 0.44 0.41 0.51 0.48 0.09 0.43 0.70 0.43
Governance 0.07 0.34 0.05 0.00 0.37 0.29 0.59 0.38 0.58 0.71 0.59 0.43 0.72 0.21 0.46 0.68 0.27 0.63 0.65 0.48 0.57 1.00 0.91 0.69 0.69 0.74 0.75 0.56 0.68 0.89 0.80
Institutional -0.04 0.22 -0.01 -0.05 0.28 0.15 0.47 0.35 0.48 0.66 0.58 0.47 0.70 0.17 0.45 0.62 0.25 0.61 0.61 0.44 0.88 0.91 1.00 0.65 0.64 0.69 0.70 0.43 0.65 0.90 0.72
Communication -0.20 0.20 -0.01 -0.01 0.27 0.07 0.44 0.27 0.43 0.93 0.59 0.57 0.90 0.20 0.71 0.88 0.32 0.76 0.84 0.53 0.44 0.69 0.65 1.00 0.83 0.86 0.95 0.35 0.81 0.88 0.75
Physical infrastructure -0.05 0.39 -0.05 -0.06 0.42 0.20 0.56 0.32 0.54 0.80 0.64 0.29 0.72 0.25 0.66 0.85 0.20 0.67 0.73 0.52 0.41 0.69 0.64 0.83 1.00 0.80 0.94 0.49 0.69 0.87 0.78
Access to health care -0.17 0.22 0.01 -0.02 0.22 0.10 0.47 0.34 0.47 0.84 0.69 0.55 0.86 0.21 0.68 0.84 0.24 0.71 0.77 0.50 0.51 0.74 0.69 0.86 0.80 1.00 0.94 0.40 0.77 0.90 0.77
Infrastructure -0.15 0.30 -0.02 -0.03 0.34 0.14 0.52 0.33 0.51 0.91 0.67 0.49 0.87 0.24 0.72 0.91 0.27 0.76 0.82 0.55 0.48 0.75 0.70 0.95 0.94 0.94 1.00 0.45 0.80 0.94 0.81
HAZARD 0.48 0.64 0.26 0.19 0.51 0.71 0.89 0.70 0.91 0.36 0.35 0.00 0.30 0.60 0.12 0.44 0.14 0.34 0.32 0.64 0.09 0.56 0.43 0.35 0.49 0.40 0.45 1.00 0.54 0.48 0.82
VULNERABILITY -0.04 0.25 0.03 -0.10 0.33 0.16 0.62 0.52 0.63 0.81 0.60 0.60 0.84 0.63 0.56 0.75 0.37 0.67 0.75 0.88 0.43 0.68 0.65 0.81 0.69 0.77 0.80 0.54 1.00 0.80 0.89
COPING CAPACITY -0.11 0.29 -0.02 -0.04 0.34 0.16 0.55 0.37 0.54 0.87 0.68 0.52 0.86 0.23 0.66 0.85 0.28 0.75 0.79 0.55 0.70 0.89 0.90 0.88 0.87 0.90 0.94 0.48 0.80 1.00 0.84
RISK 0.17 0.50 0.11 0.02 0.48 0.43 0.82 0.63 0.84 0.75 0.62 0.38 0.73 0.61 0.48 0.76 0.30 0.66 0.69 0.82 0.43 0.80 0.72 0.75 0.78 0.77 0.81 0.82 0.89 0.84 1.00

Annexes

Annex 1. Key statistical metrics
Table 23. Correlation matrix (37)(37) Element i,j equals to the Pearson’s correlation coefficient between the ith row and the jth column variable.

65

Table 24. Statistical influences of the INFORM categories within dimensions

Lack ofcopingcapacity

INFORM

CC2

Norm

CC2

Norm

Institutional 50 %

0.81

0.48

Infrastructure 50 %

0.89

0.52

Hazard & exposure 33 %

0.63

0.32

Vulnerability 33 %

0.73

0.37

Lack of coping capacity 33%

0.62

0.31

Hazard &exposure

Vulnerability

CC2

Norm

CC2

Norm

Natural 50 % 0.51

0.40

Human 50 % 0.76

0.60

Socio-economic 50 %

0.69

0.49

Vulnerable groups 50 %

0.71

0.51

CC — Pearson’s correlation coefficient
Norm — Normalised influence

Correlation analysis reveals bivariate (i.e. pairwise) Pearson’s correlation coefficients
between the indexes (i.e. variables), positioned in the same level or different levels of
the composite index structure (Table 23). A lack of correlation among the sub-indices ofthe same component/category/dimension, that is the indices within the same level, is a
useful property. It indicates that they are measuring different ‘statistical dimensions’ indata. The less they are correlated the more variables are needed to explain the samelevel of the variance. The covariance of indices may be further investigated via factor
analysis (38). How many ‘factors’ should be retained in the composite index without losing
too much information can be decided by, among others, variance explained criteria [52].
Usually the rule is to keep enough factors to account for 90 % of the variation. This is theway to reduce the number of variables by finding dominant ones within the full set.
A square of a Pearson’s correlation coefficient between the sub-indices and one-level-up
aggregate index (component/category/dimension) can measure the influence of subindex on the aggregate index due to correlation [53]. The relative differences among
those correlations explain the influence of a given sub-index for the aggregate index. In
weighted arithmetic or geometric average (including the case of equal weights), nominal
weights are defined by the methodology. However the relative influence of indices for theaggregated index depends on their distribution after normalisation as well as theircorrelation structure. So, it can be the case that the nominal weighting scheme of thecomposite index does not reflect the statistical importance of individual indices within the
structure. In that case is good practice to adjust the weighting scheme.

(38) An extended statistical audit will be performed in 2017 by JRC, and will be published separately.

66

CC2

Norm

CC2

CC2

Norm

Norm

CC2

Norm

Infrastructure

Institutional

Vulnerable
GroupsSocioeconomic
Vulnerability

Human

NaturalTable 25. Statistical influences of the underlying components

CC2

Norm

CC2

Norm

Earthquakes 20 % 0.53 0.32
Tsunamis 20 % 0.21 0.13
Floods 20 % 0.55 0.33
Tropical cyclones 20 % 0.27 0.16
Droughts 20 % 0.12 0.07
Current highly violent
conflict 50 %0.74 0.46

Conflict probability
50 %0.87 0.54

Development &deprivation 50 %0.86 0.43

Inequality 25 %0.56 0.28

Aid dependency 25 %0.60 0.30

Uprooted people 50 %0.84 0.71

Other vulnerable
groups 50 %0.34 0.29

DRR 50 %0.76 0.48

Governance 50 %0.82 0.52

Communication 33 %0.87 0.33

Physical infrastructure
33 %0.87 0.33

Access to health care
33 %0.86 0.33

CC — Pearson’s correlation coefficient
Norm — Normalised influence

The results of the correlation analysis are shown in Table 24 and Table 25. Similar
Pearson’s correlation coefficients (always squared) of the categories within the same
dimension justifies the equal weighting imposed in the INFORM methodology (Table 24).
The higher influence of the Vulnerability dimension and the Lack of coping capacity
dimension compared to the Hazard exposure dimension is appreciated in order toincrease the sensitivity of the composite index to the indicator that can be mostinfluenced by DRR activities. So, dimensions and categories of the composite index are
well structured and balanced.
For the lower levels (Table 25) results suggest that all underlying components contributein a similar way to the variation of the aggregated score of the next level. Within theSocio-Economic vulnerability category, the Development & deprivation component has astronger influence as intended through a double nominal weight. Within the Human

67

hazard category, the normalised influences of the Current highly violent conflict andConflict probability are well balanced. So, the overall index is well-structured andbalanced in the underlying components.
The results of the correlation analysis are time-dependent and will change with updated
datasets.

68Annex 2. Core indicators
Name of core indicator

118

Physical exposure to earthquake MMI VI (absolute)
Physical exposure to earthquake MMI VI (relative)
Physical exposure to earthquake MMI VIII (absolute)
Physical exposure to earthquake MMI VIII (relative)
Physical exposure to tsunamis (absolute)
Physical exposure to tsunamis (relative)
Physical exposure to flood (absolute)
Physical exposure to flood (relative)
Physical exposure to surge from tropical cyclone (absolute)
Physical exposure to surge from tropical cyclone (relative)
Physical exposure to tropical cyclone of SS 1 (absolute)
Physical exposure to tropical cyclone of SS 1 (relative)
Physical exposure to tropical cyclone of SS 3 (absolute)
Physical exposure to tropical cyclone of SS 3 (relative)
People affected by droughts (absolute)
People affected by droughts (relative)
Frequency of Drought events
Agriculture Drought probability

19

GCRI Violent Internal Conflict probability

20

GCRI High Violent Internal Conflict probability

21

Current National Power Conflict Intensity

22

Current Subnational Conflict Intensity

23

Human Development Index
Multidimensional Poverty Index
Gender Inequality Index
Gini Coefficient
Public Aid per capita
Net ODA Received (% of GNI)

4
5
6
7
8
9
10
11
12
13
14
15
16
17

24
25
26
27
28

Earthquake

TsunamiFlood

NaturalTropical Cyclone

Drought

Projected Conflict Risk

Human
Current Conflicts Intensity
Poverty & Development
Inequality
Aid Dependency

31

Total Persons of Concern (absolute)
Total Persons of Concern (relative)
Children Underweight

32

Child Mortality

33

Prevalence of HIV-AIDS above 15years
Tuberculosis prevalence
Malaria mortality rate

Other Vulnerable Groups
Health Conditions

Relative number of affected population by natural disasters in thelast three years

Other Vulnerable Groups
Recent Shocks
Other Vulnerable Groups
Food Security

40

Prevalence of undernourishment
Average dietary supply adequacy
Domestic Food Price Level Index
Domestic Food Price Volatility Index

41

Hyogo Framework for Action

DRR implementation

42

Government effectivenessCorruption Perception Index

Governance

29
30

34
35
36
37
38
39

43
44
45
46
47
48
49
50
51
52
53

Socio-Economic
Vulnerability

Uprooted people
Other Vulnerable Groups
Children under-5

Access to electricity (% of population)
Internet Users (per 100 people)
Mobile cellular subscriptions (per 100 people)
Adult literacy rate
Road density (km of road per 100 sq. km of land area)
Access to Improved water source (% of pop with access)Access to Improved sanitation facilities (% of pop with access)Physicians density
Health expenditure per capita
Measles immunization coverage

69

Vulnerable
GroupsInstitutional

Communication

Physical Connectivity

Access to health system

Vulnerability

3

Infrastructure

Lack of Coping Capacity

2

Position in the INFORM model

Hazard & Exposure

N.

ISO3

Hazard & Exposure

Social-Economics Vulnerability

Vulnerable Groups

Vulnerability

Institutional

Infrastructure

Lack of Coping Capacity

INFORM 2017

RANK

Lack of Reliability Index

Afghanistan

AFG

6.2 10.0 8.8

6.6

7.5

7.1

7.3

8.1

7.7

7.8

4

3.9

Albania

ALB

5.8

0.1

3.5

2.0

0.7

1.4

5.8

2.7

4.4

2.8 120 2.2

Algeria

DZA

4.1

6.7

5.5

3.1

3.4

3.3

4.9

4.6

4.8

4.4

62

2.6

Angola

AGO

2.1

5.4

3.9

4.4

4.4

4.4

6.6

7.4

7.0

4.9

50

3.6

Antigua and Barbuda

ATG

2.7

0.6

1.7

1.8

0.9

1.4

5.3

2.0

3.8

2.1 145 4.2

Argentina

ARG

3.5

2.4

3.0

1.6

1.1

1.4

5.0

2.2

3.7

2.5 135 3.3

Armenia

ARM

4.4

2.4

3.5

2.1

3.7

2.9

6.7

2.4

4.9

3.7

Australia

AUS

6.0

0.1

3.6

0.6

2.6

1.7

2.2

1.9

2.1

2.3 141 2.9

Austria

AUT

2.4

0.0

1.3

0.8

3.5

2.3

2.1

1.0

1.6

1.7 164 2.1

Azerbaijan

AZE

4.5

5.5

5.0

1.4

6.5

4.4

6.4

2.6

4.8

4.7

Bahamas

BHS

3.6

0.0

2.0

2.3

0.9

1.6

3.3

2.5

2.9

2.1 145 3.2

Bahrain

BHR

0.1

2.5

1.4

1.8

0.9

1.4

4.1

1.2

2.8

1.8 161 3.3

Bangladesh

BGD

8.3

6.5

7.5

3.7

5.6

4.7

5.0

5.9

5.5

5.8

Barbados

BRB

2.5

0.0

1.3

1.9

0.5

1.2

2.7

2.1

2.4

1.6 170 2.7

Belarus

BLR

2.3

2.0

2.2

1.0

1.3

1.2

4.6

1.4

3.2

2.0 153 3.3

Belgium

BEL

1.6

5.1

3.5

0.6

2.7

1.7

2.3

0.7

1.5

2.1 145 2.2

Belize

BLZ

5.2

0.1

3.0

3.0

1.5

2.3

6.3

4.0

5.3

3.3 105 2.4

Benin

BEN

1.5

3.9

2.8

5.8

2.0

4.2

5.9

7.8

7.0

4.4

Bhutan

BTN

3.2

0.1

1.8

4.2

1.2

2.8

4.3

5.1

4.7

2.9 114 2.1

Bolivia

BOL

3.8

5.4

4.6

3.4

1.7

2.6

6.0

4.8

5.4

4.0

80

2.2

Bosnia and Herzegovina

BIH

4.2

3.4

3.8

2.3

5.3

4.0

6.1

2.4

4.5

4.1

74

2.5

Botswana

BWA

2.7

0.1

1.5

4.1

2.9

3.5

4.9

4.6

4.8

2.9 114 2.0

Brazil

BRA

3.7

5.5

4.7

2.5

1.6

2.1

5.1

3.0

4.1

3.4 100 1.7

Brunei Darussalam

BRN

2.3

0.1

1.3

1.0

0.6

0.8

4.7

4.2

4.5

1.7 164 4.1

Bulgaria

BGR

3.4

1.6

2.5

2.0

2.6

2.3

4.3

1.7

3.1

2.6 131 2.8

Burkina Faso

BFA

2.8

5.3

4.2

7.3

4.9

6.2

4.7

7.4

6.2

5.4

32

2.4

Burundi

BDI

3.0

8.0

6.1

6.9

5.8

6.4

6.1

6.7

6.4

6.3

16

3.9

Cabo Verde

CPV

1.9

0.1

1.0

6.1

1.0

4.0

4.1

4.0

4.1

2.5 135 2.0

Cambodia

KHM

5.4

4.2

4.8

4.1

1.7

3.0

7.0

6.0

6.5

4.5

59

2.2

Cameroon

CMR

2.3

9.0

6.8

4.9

6.5

5.8

4.8

6.9

6.0

6.2

18

2.7

Canada

CAN

4.9

0.6

3.0

0.8

3.5

2.3

2.2

2.3

2.3

2.5 135 2.9

Central African Republic

CAF

1.7 10.0 7.9

8.6

8.5

8.6

8.2

9.1

8.7

8.4

Human

COUNTRY

NaturalAnnex 3. INFORM 2017 results — countries by alphabetic order

70

91

58

24

62

3

2.1

2.6

1.3

1.8

4.0

COUNTRY

ISO3NaturalHuman

Hazard & Exposure

Social-Economics Vulnerability

Vulnerable Groups

Vulnerability

Institutional

Infrastructure

Lack of Coping Capacity

INFORM 2017

RANK

Lack of Reliability Index

Chad

TCD

3.7

9.0

7.2

6.9

7.5

7.2

7.9

9.6

8.9

7.7

5

2.6

Chile

CHL

6.6

1.3

4.5

2.3

1.5

1.9

3.1

2.6

2.9

2.9 114 1.8

China

CHN

7.9

5.5

6.9

1.8

3.6

2.7

3.9

3.5

3.7

4.1

74

2.3

Colombia

COL

6.5

7.0

6.8

2.7

7.8

5.8

4.4

3.7

4.1

5.4

32

2.2

Comoros

COM

2.6

0.6

1.7

5.8

2.6

4.4

7.9

5.8

7.0

3.7

91

4.1

Congo

COG

2.5

5.5

4.2

3.9

5.5

4.8

7.5

7.1

7.3

5.3

37

1.8

Congo DR

COD

3.3

8.0

6.2

6.3

7.6

7.0

7.8

8.1

8.0

7.0

8

2.3

Costa Rica

CRI

6.3

0.1

3.8

2.8

1.7

2.3

3.0

2.6

2.8

2.9 114 1.8

Côte d’Ivoire

CIV

2.0

6.0

4.3

5.5

5.8

5.7

7.3

7.6

7.5

5.7

Croatia

HRV

5.0

0.7

3.1

1.5

0.9

1.2

4.4

1.4

3.0

2.2 142 2.2

Cuba

CUB

5.6

1.1

3.7

2.7

0.2

1.5

3.9

2.2

3.1

2.6 131 3.0

Cyprus

CYP

3.0

0.6

1.9

1.3

6.6

4.5

3.3

1.6

2.5

2.8 120 2.3

Czech Republic

CZE

2.1

0.1

1.2

0.8

1.4

1.1

3.1

1.0

2.1

1.4 175 2.0

Denmark

DNK

1.0

0.0

0.5

0.4

2.9

1.7

2.0

0.8

1.4

1.1 184 2.0

Djibouti

DJI

4.9

3.2

4.1

5.6

5.8

5.7

6.2

6.7

6.5

5.3

Dominica

DMA

3.6

0.0

2.0

4.1

3.2

3.7

4.6

2.9

3.8

3.0 113 5.6

Dominican Republic

DOM

5.7

2.6

4.3

2.6

1.3

2.0

5.5

3.6

4.6

3.4 100 0.6

Ecuador

ECU

6.8

1.9

4.8

2.4

4.5

3.5

4.7

3.8

4.3

4.2

69

0.7

Egypt

EGY

5.5

7.0

6.3

2.5

4.1

3.3

5.4

3.5

4.5

4.5

59

1.8

El Salvador

SLV

6.1

7.0

6.6

3.7

6.2

5.1

5.4

3.4

4.5

5.3

37

2.8

Equatorial Guinea

GNQ

1.5

4.5

3.1

3.8

1.9

2.9

8.0

6.3

7.2

4.0

80

3.2

Eritrea

ERI

4.1

4.3

4.2

5.9

3.5

4.8

8.2

7.5

7.9

5.4

32

3.3

Estonia

EST

0.9

0.1

0.5

1.2

1.0

1.1

3.0

1.0

2.1

1.0 186 2.2

Ethiopia

ETH

4.3

6.6

5.6

6.5

6.9

6.7

4.6

8.4

6.9

6.4

Fiji

FJI

3.8

0.8

2.4

3.7

3.3

3.5

2.9

4.4

3.7

3.1 108 3.2

Finland

FIN

0.1

0.0

0.1

0.7

2.3

1.5

1.6

0.9

1.3

0.6 189 2.2

France

FRA

3.8

1.5

2.7

0.9

3.9

2.5

2.8

1.1

2.0

2.4 139 1.9

Gabon

GAB

1.9

5.0

3.6

3.0

2.6

2.8

6.6

5.4

6.0

3.9

83

1.8

Gambia

GMB

2.2

1.1

1.7

6.3

3.5

5.1

4.9

6.0

5.5

3.6

95

1.9

Georgia

GEO

4.5

2.8

3.7

2.9

5.9

4.6

4.6

2.0

3.4

3.9

83

2.4

Germany

DEU

2.2

0.1

1.2

0.5

4.0

2.4

2.2

0.7

1.5

1.6 170 2.8

Ghana

GHA

2.4

2.6

2.5

4.0

3.0

3.5

4.4

6.1

5.3

3.6

Greece

GRC

4.7

2.9

3.9

1.5

2.8

2.2

3.6

1.0

2.4

2.7 126 2.1

71

26

37

15

95

1.8

3.3

1.9

2.1

Natural

Human

Hazard & Exposure

Social-Economics Vulnerability

Vulnerable Groups

Vulnerability

Institutional

Infrastructure

Lack of Coping Capacity

INFORM 2017

GRD

0.5

0.1

0.3

3.1

0.5

1.9

5.0

2.4

3.8

1.3 179 3.7

Guatemala

GTM

6.9

4.8

6.0

4.4

5.4

4.9

6.2

5.2

5.7

5.5

31

1.8

Guinea

GIN

2.4

4.2

3.4

5.7

3.8

4.8

6.3

8.4

7.5

5.0

45

1.9

Guinea-Bissau

GNB

2.4

2.2

2.3

7.2

4.5

6.0

8.0

7.6

7.8

4.8

54

2.6

Guyana

GUY

3.1

0.4

1.8

4.2

3.6

3.9

6.3

4.6

5.5

3.4 100 2.6

Haiti

HTI

5.6

5.2

5.4

6.6

6.7

6.7

7.7

7.3

7.5

6.5

14

1.9

Honduras

HND

5.8

3.9

4.9

4.0

5.2

4.6

6.0

4.3

5.2

4.9

50

1.3

Hungary

HUN

3.6

1.8

2.7

1.5

1.7

1.6

2.9

1.1

2.0

2.1 145 1.9

Iceland

ISL

1.3

0.0

0.7

0.6

0.9

0.8

2.1

1.7

1.9

1.0 186 2.6

India

IND

7.9

6.7

7.3

3.9

6.6

5.4

3.8

5.6

4.8

5.7

26

2.7

Indonesia

IDN

7.8

6.6

7.2

2.3

2.3

2.3

4.5

5.1

4.8

4.3

66

1.3

Iran

IRN

7.2

5.3

6.3

2.7

5.5

4.2

5.5

3.7

4.7

5.0

45

2.5

Iraq

IRQ

5.5

9.0

7.7

2.8

8.0

6.0

8.2

5.3

7.0

6.9

10

2.2

Ireland

IRL

2.0

0.0

1.0

0.7

1.7

1.2

2.2

1.3

1.8

1.3 179 1.9Israel

ISR

4.3

4.5

4.4

1.2

2.9

2.1

3.3

1.1

2.3

2.8 120 2.5

Italy

ITA

5.0

1.3

3.4

1.0

3.3

2.2

3.7

0.9

2.4

2.6 131 1.7

Jamaica

JAM

3.7

1.1

2.5

2.5

0.9

1.7

4.3

3.5

3.9

2.5 135 3.1

Japan

JPN

8.3

0.6

5.7

0.9

0.8

0.9

2.0

1.0

1.5

2.0 153 3.3

Jordan

JOR

3.9

1.5

2.8

3.8

7.7

6.1

5.4

2.6

4.1

4.1

Kazakhstan

KAZ

4.3

1.5

3.0

1.2

0.4

0.8

5.0

2.4

3.8

2.1 145 2.7

Kenya

KEN

4.9

7.0

6.1

4.9

6.7

5.9

5.3

7.3

6.4

6.1

19

3.0

Kiribati

KIR

3.0

0.1

1.7

6.1

2.7

4.6

6.2

5.8

6.0

3.6

95

4.3

Korea DPR

PRK

4.8

5.3

5.1

5.0

5.0

5.0

8.8

3.4

6.9

5.6

30

4.0

Korea Republic of

KOR

5.2

1.8

3.7

0.8

0.4

0.6

2.5

1.3

1.9

1.6 170 2.8

Kuwait

KWT

2.3

0.4

1.4

2.4

0.9

1.7

5.2

1.4

3.5

2.0 153 2.0

Kyrgyzstan

KGZ

5.9

3.2

4.7

3.0

1.0

2.1

5.4

3.4

4.5

3.5

98

1.6

Lao PDR

LAO

4.7

2.9

3.9

4.2

2.0

3.2

6.4

6.0

6.2

4.3

66

1.7

Latvia

LVA

2.3

0.2

1.3

1.6

0.9

1.3

3.8

1.6

2.8

1.7 164 2.3

Lebanon

LBN

3.7

7.0

5.6

4.1

8.3

6.7

5.6

2.3

4.1

5.4

32

4.4

Lesotho

LSO

1.9

2.4

2.2

5.5

4.9

5.2

7.1

6.2

6.7

4.2

69

2.0

Liberia

LBR

3.0

2.2

2.6

7.7

4.6

6.4

7.0

8.6

7.9

5.1

43

2.8

Libya

LBY

4.6 10.0 8.4

1.6

6.0

4.1

8.4

3.9

6.7

6.1

19

5.6

Liechtenstein

LIE

1.3

0.5

1.4

1.0

1.6

0.9

1.3

1.1 184 4.1

0.5

0.9

72

74

Lack of Reliability Index

ISO3

GrenadaRANKCOUNTRY

2.3

Natural

Human

Hazard & Exposure

Social-Economics Vulnerability

Vulnerable Groups

Vulnerability

Institutional

Infrastructure

Lack of Coping Capacity

INFORM 2017

LTU

1.8

0.0

0.9

1.4

1.0

1.2

3.5

1.1

2.4

1.4 175 2.3

Luxembourg

LUX

0.4

0.0

0.2

0.9

1.3

1.1

1.8

0.6

1.2

0.6 189 2.2

Madagascar

MDG

5.9

1.4

4.0

5.3

2.7

4.1

6.1

8.6

7.6

5.0

45

3.2

Malawi

MWI

3.7

1.5

2.7

6.8

5.7

6.3

5.4

7.2

6.4

4.8

54

1.9

Malaysia

MYS

4.8

3.6

4.2

2.3

3.7

3.0

3.3

2.9

3.1

3.4 100 3.1

Maldives

MDV

3.1

0.1

1.7

2.1

0.7

1.4

5.8

1.5

4.0

2.1 145 4.3

Mali

MLI

3.3

7.0

5.4

6.8

5.2

6.1

5.9

7.5

6.8

6.1

Malta

MLT

2.1

0.0

1.1

1.6

2.8

2.2

3.7

0.8

2.4

1.8 161 2.4

Marshall Islands

MHL

2.1

1.1

1.6

7.3

2.0

5.2

7.7

5.2

6.6

3.8

88

5.3

Mauritania

MRT

5.1

5.2

5.2

5.2

4.9

5.1

5.9

7.9

7.0

5.7

26

2.6

Mauritius

MUS

3.4

0.1

1.9

2.9

0.6

1.8

3.5

2.0

2.8

2.1 145 1.6

Mexico

MEX

7.0

9.0

8.2

2.2

3.9

3.1

5.4

3.3

4.4

4.8

54

1.4

Micronesia

FSM

3.0

0.1

1.7

6.5

3.9

5.3

6.0

5.3

5.7

3.7

91

4.5

Moldova Republic of

MDA

3.9

0.4

2.3

2.5

1.3

1.9

6.3

2.5

4.7

2.7 126 1.8

Mongolia

MNG

3.6

3.6

3.6

2.6

3.6

3.1

5.6

4.5

5.1

3.8

Montenegro

MNE

4.0

0.2

2.3

1.9

1.7

1.8

4.6

2.0

3.4

2.4 139 2.4

Morocco

MAR

4.9

4.1

4.5

3.3

1.9

2.6

5.8

4.1

5.0

3.9

83

2.8

Mozambique

MOZ

5.9

4.6

5.3

7.0

4.7

6.0

4.4

8.2

6.7

6.0

22

2.3

Myanmar

MMR

8.0

7.0

7.5

5.0

6.9

6.0

7.4

5.7

6.6

6.7

12

3.4

Namibia

NAM

4.1

0.4

2.4

4.5

3.5

4.0

4.6

5.9

5.3

3.7

91

2.7

Nauru

NRU

1.4

0.2

0.8

5.6

3.1

4.5

7.2

4.3

5.9

2.8 120 6.4

Nepal

NPL

5.5

5.0

5.3

4.1

5.9

5.1

6.2

5.5

5.9

5.4

Netherlands

NLD

1.9

0.0

1.0

0.4

3.5

2.1

1.5

0.9

1.2

1.4 175 2.2

New Zealand

NZL

5.2

0.0

3.0

0.8

1.0

0.9

1.9

2.1

2.0

1.8 161 3.1

Nicaragua

NIC

6.6

3.1

5.1

3.6

1.5

2.6

5.9

4.8

5.4

4.2

69

2.5

Niger

NER

4.2

9.0

7.3

7.4

6.5

7.0

6.0

8.9

7.7

7.3

7

1.7

Nigeria

NGA

2.8

9.0

6.9

4.2

6.6

5.5

5.1

7.7

6.6

6.3

16

2.6

Norway

NOR

0.2

0.0

0.1

0.2

3.5

2.0

1.9

1.3

1.6

0.7 188 1.8

Oman

OMN

6.2

0.4

3.9

2.1

0.9

1.5

5.0

2.6

3.9

2.8 120 2.2

Pakistan

PAK

7.2 10.0 9.0

3.9

6.7

5.5

5.4

6.0

5.7

6.6

Palau

PLW

3.1

0.1

1.7

4.5

0.8

2.9

6.1

3.4

4.9

2.9 114 5.0

Palestine

PSE

3.2

4.4

3.8

4.3

7.7

6.3

6.0

2.7

4.6

4.8

Panama

PAN

4.9

0.1

2.8

2.9

2.9

2.9

4.8

3.3

4.1

3.2 106 1.9

73

19

88

32

13

54

Lack of Reliability Index

ISO3

Lithuania

RANK

COUNTRY

2.6

2.0

1.7

1.8

3.8

COUNTRY

ISO3NaturalHuman

Hazard & Exposure

Social-Economics Vulnerability

Vulnerable Groups

Vulnerability

Institutional

Infrastructure

Lack of Coping Capacity

INFORM 2017

RANK

Lack of Reliability Index

Papua New Guinea

PNG

5.3

3.7

4.5

5.7

5.6

5.7

6.8

8.4

7.7

5.8

24

3.2

Paraguay

PRY

1.9

2.5

2.2

3.7

0.9

2.4

5.4

3.6

4.6

2.9 114 1.6

Peru

PER

7.0

2.5

5.2

2.3

3.3

2.8

4.8

4.3

4.6

4.1

74

1.0

Philippines

PHL

8.4

9.0

8.7

2.6

4.1

3.4

4.6

3.6

4.1

4.9

50

2.0

Poland

POL

2.4

0.4

1.5

1.3

1.9

1.6

4.0

1.3

2.8

1.9 159 1.6

Portugal

PRT

3.6

0.0

2.0

1.5

0.7

1.1

3.0

0.9

2.0

1.6 170 2.1

Qatar

QAT

1.0

2.9

2.0

2.5

0.7

1.6

3.9

0.4

2.3

1.9 159 2.7

Romania

ROU

4.7

1.5

3.3

1.8

1.3

1.6

4.6

2.3

3.5

2.6 131 1.9

Russian Federation

RUS

6.3

5.7

6.0

2.1

4.1

3.2

6.2

2.2

4.5

4.4

62

2.8

Rwanda

RWA

3.2

6.2

4.9

6.5

5.5

6.0

3.9

6.2

5.2

5.3

37

1.9

Saint Kitts and Nevis

KNA

1.7

0.0

0.9

5.3

0.5

3.3

4.6

2.3

3.5

2.2 142 4.4Saint Lucia

LCA

1.8

0.3

1.1

1.9

0.5

1.2

4.6

2.7

3.7

1.7 164 3.4Saint Vincent and the
Grenadines

VCT

1.0

0.6

0.8

2.7

0.6

1.7

4.1

3.0

3.6

1.7 164 3.6

Samoa

WSM

2.7

0.1

1.5

5.5

0.4

3.4

4.6

3.6

4.1

2.8 120 3.3

Sao Tome and Principe

STP

0.1

0.1

0.1

4.8

1.4

3.3

6.3

4.7

5.6

1.2 182 3.3

Saudi Arabia

SAU

2.6 10.0 8.0

1.8

0.4

1.1

4.7

2.1

3.5

3.1 108 3.5

Senegal

SEN

4.3

4.6

4.5

5.5

3.9

4.8

5.2

6.7

6.0

5.1

43

0.9

Serbia

SRB

4.6

4.8

4.7

1.6

5.9

4.1

5.2

2.3

3.9

4.2

69

1.7

Seychelles

SYC

2.5

0.0

1.3

3.3

0.9

2.2

4.4

2.6

3.6

2.2 142 3.9

Sierra Leone

SLE

2.3

4.6

3.5

7.4

3.3

5.7

5.4

8.5

7.2

5.2

Singapore

SGP

0.1

0.1

0.1

0.6

0.3

0.5

1.2

0.9

1.1

0.4 191 3.3

Slovakia

SVK

3.4

0.1

1.9

1.1

0.9

1.0

3.8

1.1

2.6

1.7 164 1.8

Slovenia

SVN

3.8

0.0

2.1

0.6

0.9

0.8

2.2

1.2

1.7

1.4 175 1.7

Solomon Islands

SLB

5.5

0.8

3.5

7.2

1.9

5.1

6.9

6.8

6.9

5.0

45

4.6

SomaliaSOM

6.8 10.0 8.9

9.6

9.2

9.4

9.6

8.6

9.2

9.2

1

7.1

South Africa

ZAF

4.4

4.5

3.3

4.7

4.0

4.5

4.2

4.4

4.3

66

1.5

South Sudan

SSD

3.7 10.0 8.2

9.4

8.5

9.0

8.9

9.4

9.2

8.8

2

4.6

Spain

ESP

4.5

2.8

3.7

1.1

1.4

1.3

2.9

0.7

1.9

2.1 145 1.6

Sri Lanka

LKA

4.9

2.3

3.7

2.7

4.4

3.6

4.6

3.6

4.1

3.8

88

1.6

Sudan

SDN

4.7

9.0

7.4

4.8

7.9

6.6

6.7

7.3

7.0

7.0

8

3.9

Suriname

SUR

3.5

0.1

2.0

2.8

0.8

1.9

5.9

3.8

4.9

2.7 126 2.3

Swaziland

SWZ

2.0

1.8

1.9

4.5

3.3

3.9

5.2

5.8

5.5

3.4 100 2.8

Sweden

SWE

1.1

0.0

0.6

0.5

4.4

2.7

1.9

0.9

1.4

1.3 179 1.9

4.6

74

42

1.7

Natural

Human

Hazard & Exposure

Social-Economics Vulnerability

Vulnerable Groups

Vulnerability

Institutional

Infrastructure

Lack of Coping Capacity

INFORM 2017

CHE

1.9

0.1

1.0

0.4

3.6

2.1

1.0

0.6

0.8

1.2 182 1.9

Syria

SYR

5.1 10.0 8.5

5.3

7.9

6.8

6.4

4.6

5.6

6.9

10

6.5

Tajikistan

TJK

6.1

5.7

5.9

2.7

2.9

2.8

5.8

4.1

5.0

4.4

62

2.3

Tanzania

TZA

4.6

5.3

5.0

5.3

5.8

5.6

5.1

7.5

6.5

5.7

26

1.4

Thailand

THA

6.4

4.3

5.4

2.1

3.8

3.0

5.0

2.9

4.0

4.0

80

2.2

The former Yugoslav
Republic of Macedonia

MKD

3.6

1.8

2.7

2.4

1.5

2.0

4.6

2.4

3.6

2.7 126 2.5

Timor-Leste

TLS

3.6

0.2

2.1

4.9

5.0

5.0

6.8

7.2

7.0

4.2

69

4.2

Togo

TGO

1.6

2.2

1.9

5.1

4.0

4.6

8.2

7.6

7.9

4.1

74

1.2

Tonga

TON

2.2

0.1

1.2

5.8

0.9

3.7

5.5

3.6

4.6

2.7 126 4.0

Trinidad and Tobago

TTO

2.3

0.2

1.3

2.0

1.3

1.7

4.9

1.8

3.5

2.0 153 3.3

Tunisia

TUN

4.5

4.3

4.4

2.1

0.7

1.4

6.1

3.3

4.9

3.1 108 2.3

Turkey

TUR

6.0

9.0

7.8

2.8

6.6

5.0

3.6

2.7

3.2

5.0

Turkmenistan

TKM

4.6

1.1

3.0

2.7

0.9

1.8

7.5

4.6

6.3

3.2 106 4.9

Tuvalu

TUV

2.6

1.0

1.8

7.3

3.7

5.8

6.9

4.3

5.8

3.9

83

5.5

Uganda

UGA

3.4

6.2

5.0

5.7

6.3

6.0

6.7

7.1

6.9

5.9

23

1.8

Ukraine

UKR

3.2

9.0

7.0

1.6

6.5

4.5

6.6

2.4

4.8

5.3

37

1.9

United Arab Emirates

ARE

6.0

0.0

3.6

1.7

0.7

1.2

2.3

1.5

1.9

2.0 153 2.9

United Kingdom

GBR

2.1

2.4

2.3

0.9

3.3

2.2

2.0

0.9

1.5

2.0 153 1.8

United States of America

USA

6.9

4.3

5.8

1.2

3.3

2.3

2.7

1.6

2.2

3.1 108 2.5

Uruguay

URY

1.3

0.0

0.7

2.4

1.1

1.8

3.7

1.8

2.8

1.5 174 1.9

Uzbekistan

UZB

6.1

5.0

5.6

1.9

0.7

1.3

4.9

3.3

4.1

3.1 108 5.1

Vanuatu

VUT

4.2

0.0

2.3

5.0

3.2

4.2

5.8

6.4

6.1

3.9

83

3.5

Venezuela

VEN

5.9

5.8

5.9

2.9

4.1

3.5

5.2

3.4

4.4

4.5

59

2.5

Viet Nam

VNM

7.2

2.8

5.4

2.6

0.9

1.8

5.1

3.4

4.3

3.5

98

1.8

Yemen

YEM

3.7 10.0 8.2

5.1

8.2

6.9

8.3

7.3

7.8

7.6

6

3.3

Zambia

ZMB

2.4

1.8

2.1

5.2

5.3

5.3

4.8

7.1

6.1

4.1

74

2.3

Zimbabwe

ZWE

4.7

4.1

4.4

4.8

4.5

4.7

5.2

6.4

5.8

4.9

50

1.6

75

45

Lack of Reliability Index

ISO3

Switzerland

RANK

COUNTRY

1.6

ISO3

Hazard & Exposure

Social-Economics Vulnerability

Vulnerable Groups

Vulnerability

Institutional

Infrastructure

Lack of Coping Capacity

INFORM 2017

RANK

Lack of Reliability Index

SomaliaSOM

6.8 10.0 8.9

9.6

9.2

9.4

9.6

8.6

9.2

9.2

1

7.1

South Sudan

SSD

3.7 10.0 8.2

9.4

8.5

9.0

8.9

9.4

9.2

8.8

2

4.6

Central African Republic

CAF

1.7 10.0 7.9

8.6

8.5

8.6

8.2

9.1

8.7

8.4

3

4.0

Afghanistan

AFG

6.2 10.0 8.8

6.6

7.5

7.1

7.3

8.1

7.7

7.8

4

3.9

Chad

TCD

3.7

7.2

6.9

7.5

7.2

7.9

9.6

8.9

7.7

5

2.6

Yemen

YEM

3.7 10.0 8.2

5.1

8.2

6.9

8.3

7.3

7.8

7.6

6

3.3

Niger

NER

4.2

9.0

7.3

7.4

6.5

7.0

6.0

8.9

7.7

7.3

7

1.7

Congo DR

COD

3.3

8.0

6.2

6.3

7.6

7.0

7.8

8.1

8.0

7.0

8

2.3

Sudan

SDN

4.7

9.0

7.4

4.8

7.9

6.6

6.7

7.3

7.0

7.0

8

3.9

Iraq

IRQ

5.5

9.0

7.7

2.8

8.0

6.0

8.2

5.3

7.0

6.9

10

2.2

Syria

SYR

5.1 10.0 8.5

5.3

7.9

6.8

6.4

4.6

5.6

6.9

10

6.5

Myanmar

MMR

8.0

7.5

5.0

6.9

6.0

7.4

5.7

6.6

6.7

12

3.4

Pakistan

PAK

7.2 10.0 9.0

3.9

6.7

5.5

5.4

6.0

5.7

6.6

13

1.8

Haiti

HTI

5.6

5.2

5.4

6.6

6.7

6.7

7.7

7.3

7.5

6.5

14

1.9

Ethiopia

ETH

4.3

6.6

5.6

6.5

6.9

6.7

4.6

8.4

6.9

6.4

15

1.9

Burundi

BDI

3.0

8.0

6.1

6.9

5.8

6.4

6.1

6.7

6.4

6.3

16

3.9

Nigeria

NGA

2.8

9.0

6.9

4.2

6.6

5.5

5.1

7.7

6.6

6.3

16

2.6

Cameroon

CMR

2.3

9.0

6.8

4.9

6.5

5.8

4.8

6.9

6.0

6.2

18

2.7

Kenya

KEN

4.9

7.0

6.1

4.9

6.7

5.9

5.3

7.3

6.4

6.1

19

3.0

Libya

LBY

4.6 10.0 8.4

1.6

6.0

4.1

8.4

3.9

6.7

6.1

19

5.6

Mali

MLI

3.3

7.0

5.4

6.8

5.2

6.1

5.9

7.5

6.8

6.1

19

2.6

Mozambique

MOZ

5.9

4.6

5.3

7.0

4.7

6.0

4.4

8.2

6.7

6.0

22

2.3

Uganda

UGA

3.4

6.2

5.0

5.7

6.3

6.0

6.7

7.1

6.9

5.9

23

1.8

Bangladesh

BGD

8.3

6.5

7.5

3.7

5.6

4.7

5.0

5.9

5.5

5.8

24

1.3

Papua New Guinea

PNG

5.3

3.7

4.5

5.7

5.6

5.7

6.8

8.4

7.7

5.8

24

3.2

Côte d'Ivoire

CIV

2.0

6.0

4.3

5.5

5.8

5.7

7.3

7.6

7.5

5.7

26

1.8

India

IND

7.9

6.7

7.3

3.9

6.6

5.4

3.8

5.6

4.8

5.7

26

2.7

Mauritania

MRT

5.1

5.2

5.2

5.2

4.9

5.1

5.9

7.9

7.0

5.7

26

2.6

Tanzania

TZA

4.6

5.3

5.0

5.3

5.8

5.6

5.1

7.5

6.5

5.7

26

1.4

Korea DPR

PRK

4.8

5.3

5.1

5.0

5.0

5.0

8.8

3.4

6.9

5.6

30

4.0

Guatemala

GTM

6.9

4.8

6.0

4.4

5.4

4.9

6.2

5.2

5.7

5.5

31

1.8

Burkina Faso

BFA

2.8

5.3

4.2

7.3

4.9

6.2

4.7

7.4

6.2

5.4

32

2.4

Human

COUNTRY

NaturalAnnex 4. INFORM 2017 results — countries by rank

9.0

7.0

76

COUNTRY

ISO3NaturalHuman

Hazard & Exposure

Social-Economics Vulnerability

Vulnerable Groups

Vulnerability

Institutional

Infrastructure

Lack of Coping Capacity

INFORM 2017

RANK

Lack of Reliability Index

Colombia

COL

6.5

7.0

6.8

2.7

7.8

5.8

4.4

3.7

4.1

5.4

32

2.2

Eritrea

ERI

4.1

4.3

4.2

5.9

3.5

4.8

8.2

7.5

7.9

5.4

32

3.3

Lebanon

LBN

3.7

7.0

5.6

4.1

8.3

6.7

5.6

2.3

4.1

5.4

32

4.4

Nepal

NPL

5.5

5.0

5.3

4.1

5.9

5.1

6.2

5.5

5.9

5.4

32

1.7

Congo

COG

2.5

5.5

4.2

3.9

5.5

4.8

7.5

7.1

7.3

5.3

37

1.8

Djibouti

DJI

4.9

3.2

4.1

5.6

5.8

5.7

6.2

6.7

6.5

5.3

37

3.3

El Salvador

SLV

6.1

7.0

6.6

3.7

6.2

5.1

5.4

3.4

4.5

5.3

37

2.8

Rwanda

RWA

3.2

6.2

4.9

6.5

5.5

6.0

3.9

6.2

5.2

5.3

37

1.9

Ukraine

UKR

3.2

9.0

7.0

1.6

6.5

4.5

6.6

2.4

4.8

5.3

37

1.9

Sierra Leone

SLE

2.3

4.6

3.5

7.4

3.3

5.7

5.4

8.5

7.2

5.2

42

1.7

Liberia

LBR

3.0

2.2

2.6

7.7

4.6

6.4

7.0

8.6

7.9

5.1

43

2.8

Senegal

SEN

4.3

4.6

4.5

5.5

3.9

4.8

5.2

6.7

6.0

5.1

43

0.9

Guinea

GIN

2.4

4.2

3.4

5.7

3.8

4.8

6.3

8.4

7.5

5.0

45

1.9

Iran

IRN

7.2

5.3

6.3

2.7

5.5

4.2

5.5

3.7

4.7

5.0

45

2.5

Madagascar

MDG

5.9

1.4

4.0

5.3

2.7

4.1

6.1

8.6

7.6

5.0

45

3.2

Solomon Islands

SLB

5.5

0.8

3.5

7.2

1.9

5.1

6.9

6.8

6.9

5.0

45

4.6

Turkey

TUR

6.0

9.0

7.8

2.8

6.6

5.0

3.6

2.7

3.2

5.0

45

1.6

Angola

AGO

2.1

5.4

3.9

4.4

4.4

4.4

6.6

7.4

7.0

4.9

50

3.6

Honduras

HND

5.8

3.9

4.9

4.0

5.2

4.6

6.0

4.3

5.2

4.9

50

1.3

Philippines

PHL

8.4

9.0

8.7

2.6

4.1

3.4

4.6

3.6

4.1

4.9

50

2.0

Zimbabwe

ZWE

4.7

4.1

4.4

4.8

4.5

4.7

5.2

6.4

5.8

4.9

50

1.6

Guinea-Bissau

GNB

2.4

2.2

2.3

7.2

4.5

6.0

8.0

7.6

7.8

4.8

54

2.6

Malawi

MWI

3.7

1.5

2.7

6.8

5.7

6.3

5.4

7.2

6.4

4.8

54

1.9

Mexico

MEX

7.0

9.0

8.2

2.2

3.9

3.1

5.4

3.3

4.4

4.8

54

1.4

Palestine

PSE

3.2

4.4

3.8

4.3

7.7

6.3

6.0

2.7

4.6

4.8

54

3.8

Azerbaijan

AZE

4.5

5.5

5.0

1.4

6.5

4.4

6.4

2.6

4.8

4.7

58

2.6

Cambodia

KHM

5.4

4.2

4.8

4.1

1.7

3.0

7.0

6.0

6.5

4.5

59

2.2

Egypt

EGY

5.5

7.0

6.3

2.5

4.1

3.3

5.4

3.5

4.5

4.5

59

1.8

Venezuela

VEN

5.9

5.8

5.9

2.9

4.1

3.5

5.2

3.4

4.4

4.5

59

2.5

Algeria

DZA

4.1

6.7

5.5

3.1

3.4

3.3

4.9

4.6

4.8

4.4

62

2.6

Benin

BEN

1.5

3.9

2.8

5.8

2.0

4.2

5.9

7.8

7.0

4.4

62

1.8

Russian Federation

RUS

6.3

5.7

6.0

2.1

4.1

3.2

6.2

2.2

4.5

4.4

62

2.8

Tajikistan

TJK

6.1

5.7

5.9

2.7

2.9

2.8

5.8

4.1

5.0

4.4

62

2.3

77

COUNTRY

ISO3NaturalHuman

Hazard & Exposure

Social-Economics Vulnerability

Vulnerable Groups

Vulnerability

Institutional

Infrastructure

Lack of Coping Capacity

INFORM 2017

RANK

Lack of Reliability Index

Indonesia

IDN

7.8

6.6

7.2

2.3

2.3

2.3

4.5

5.1

4.8

4.3

66

1.3

Lao PDR

LAO

4.7

2.9

3.9

4.2

2.0

3.2

6.4

6.0

6.2

4.3

66

1.7

South Africa

ZAF

4.4

4.6

4.5

3.3

4.7

4.0

4.5

4.2

4.4

4.3

66

1.5

Ecuador

ECU

6.8

1.9

4.8

2.4

4.5

3.5

4.7

3.8

4.3

4.2

69

0.7

Lesotho

LSO

1.9

2.4

2.2

5.5

4.9

5.2

7.1

6.2

6.7

4.2

69

2.0

Nicaragua

NIC

6.6

3.1

5.1

3.6

1.5

2.6

5.9

4.8

5.4

4.2

69

2.5

Serbia

SRB

4.6

4.8

4.7

1.6

5.9

4.1

5.2

2.3

3.9

4.2

69

1.7

Timor-Leste

TLS

3.6

0.2

2.1

4.9

5.0

5.0

6.8

7.2

7.0

4.2

69

4.2

Bosnia and Herzegovina

BIH

4.2

3.4

3.8

2.3

5.3

4.0

6.1

2.4

4.5

4.1

74

2.5

China

CHN

7.9

5.5

6.9

1.8

3.6

2.7

3.9

3.5

3.7

4.1

74

2.3

Jordan

JOR

3.9

1.5

2.8

3.8

7.7

6.1

5.4

2.6

4.1

4.1

74

2.3

Peru

PER

7.0

2.5

5.2

2.3

3.3

2.8

4.8

4.3

4.6

4.1

74

1.0

Togo

TGO

1.6

2.2

1.9

5.1

4.0

4.6

8.2

7.6

7.9

4.1

74

1.2

Zambia

ZMB

2.4

1.8

2.1

5.2

5.3

5.3

4.8

7.1

6.1

4.1

74

2.3

Bolivia

BOL

3.8

5.4

4.6

3.4

1.7

2.6

6.0

4.8

5.4

4.0

80

2.2

Equatorial Guinea

GNQ

1.5

4.5

3.1

3.8

1.9

2.9

8.0

6.3

7.2

4.0

80

3.2

Thailand

THA

6.4

4.3

5.4

2.1

3.8

3.0

5.0

2.9

4.0

4.0

80

2.2

Gabon

GAB

1.9

5.0

3.6

3.0

2.6

2.8

6.6

5.4

6.0

3.9

83

1.8

Georgia

GEO

4.5

2.8

3.7

2.9

5.9

4.6

4.6

2.0

3.4

3.9

83

2.4

Morocco

MAR

4.9

4.1

4.5

3.3

1.9

2.6

5.8

4.1

5.0

3.9

83

2.8

Tuvalu

TUV

2.6

1.0

1.8

7.3

3.7

5.8

6.9

4.3

5.8

3.9

83

5.5

Vanuatu

VUT

4.2

0.0

2.3

5.0

3.2

4.2

5.8

6.4

6.1

3.9

83

3.5

Marshall Islands

MHL

2.1

1.1

1.6

7.3

2.0

5.2

7.7

5.2

6.6

3.8

88

5.3

Mongolia

MNG

3.6

3.6

3.6

2.6

3.6

3.1

5.6

4.5

5.1

3.8

88

2.0

Sri Lanka

LKA

4.9

2.3

3.7

2.7

4.4

3.6

4.6

3.6

4.1

3.8

88

1.6

Armenia

ARM

4.4

2.4

3.5

2.1

3.7

2.9

6.7

2.4

4.9

3.7

91

2.1

Comoros

COM

2.6

0.6

1.7

5.8

2.6

4.4

7.9

5.8

7.0

3.7

91

4.1

Micronesia

FSM

3.0

0.1

1.7

6.5

3.9

5.3

6.0

5.3

5.7

3.7

91

4.5

Namibia

NAM

4.1

0.4

2.4

4.5

3.5

4.0

4.6

5.9

5.3

3.7

91

2.7

Gambia

GMB

2.2

1.1

1.7

6.3

3.5

5.1

4.9

6.0

5.5

3.6

95

1.9

Ghana

GHA

2.4

2.6

2.5

4.0

3.0

3.5

4.4

6.1

5.3

3.6

95

2.1

Kiribati

KIR

3.0

0.1

1.7

6.1

2.7

4.6

6.2

5.8

6.0

3.6

95

4.3

Kyrgyzstan

KGZ

5.9

3.2

4.7

3.0

1.0

2.1

5.4

3.4

4.5

3.5

98

1.6

78

COUNTRY

ISO3NaturalHuman

Hazard & Exposure

Social-Economics Vulnerability

Vulnerable Groups

Vulnerability

Institutional

Infrastructure

Lack of Coping Capacity

INFORM 2017

RANK

Lack of Reliability Index

Viet Nam

VNM

7.2

2.8

5.4

2.6

0.9

1.8

5.1

3.4

4.3

3.5

98

1.8

Brazil

BRA

3.7

5.5

4.7

2.5

1.6

2.1

5.1

3.0

4.1

3.4 100 1.7

Dominican Republic

DOM

5.7

2.6

4.3

2.6

1.3

2.0

5.5

3.6

4.6

3.4 100 0.6

Guyana

GUY

3.1

0.4

1.8

4.2

3.6

3.9

6.3

4.6

5.5

3.4 100 2.6

Malaysia

MYS

4.8

3.6

4.2

2.3

3.7

3.0

3.3

2.9

3.1

3.4 100 3.1

Swaziland

SWZ

2.0

1.8

1.9

4.5

3.3

3.9

5.2

5.8

5.5

3.4 100 2.8

Belize

BLZ

5.2

0.1

3.0

3.0

1.5

2.3

6.3

4.0

5.3

3.3 105 2.4

Panama

PAN

4.9

0.1

2.8

2.9

2.9

2.9

4.8

3.3

4.1

3.2 106 1.9

Turkmenistan

TKM

4.6

1.1

3.0

2.7

0.9

1.8

7.5

4.6

6.3

3.2 106 4.9

Fiji

FJI

3.8

0.8

2.4

3.7

3.3

3.5

2.9

4.4

3.7

3.1 108 3.2

Saudi Arabia

SAU

2.6 10.0 8.0

1.8

0.4

1.1

4.7

2.1

3.5

3.1 108 3.5

Tunisia

TUN

4.5

4.3

4.4

2.1

0.7

1.4

6.1

3.3

4.9

3.1 108 2.3

United States of America

USA

6.9

4.3

5.8

1.2

3.3

2.3

2.7

1.6

2.2

3.1 108 2.5

Uzbekistan

UZB

6.1

5.0

5.6

1.9

0.7

1.3

4.9

3.3

4.1

3.1 108 5.1

Dominica

DMA

3.6

0.0

2.0

4.1

3.2

3.7

4.6

2.9

3.8

3.0 113 5.6

Bhutan

BTN

3.2

0.1

1.8

4.2

1.2

2.8

4.3

5.1

4.7

2.9 114 2.1

Botswana

BWA

2.7

0.1

1.5

4.1

2.9

3.5

4.9

4.6

4.8

2.9 114 2.0

Chile

CHL

6.6

1.3

4.5

2.3

1.5

1.9

3.1

2.6

2.9

2.9 114 1.8

Costa Rica

CRI

6.3

0.1

3.8

2.8

1.7

2.3

3.0

2.6

2.8

2.9 114 1.8

Palau

PLW

3.1

0.1

1.7

4.5

0.8

2.9

6.1

3.4

4.9

2.9 114 5.0

Paraguay

PRY

1.9

2.5

2.2

3.7

0.9

2.4

5.4

3.6

4.6

2.9 114 1.6

Albania

ALB

5.8

0.1

3.5

2.0

0.7

1.4

5.8

2.7

4.4

2.8 120 2.2

Cyprus

CYP

3.0

0.6

1.9

1.3

6.6

4.5

3.3

1.6

2.5

2.8 120 2.3

Israel

ISR

4.3

4.5

4.4

1.2

2.9

2.1

3.3

1.1

2.3

2.8 120 2.5

Nauru

NRU

1.4

0.2

0.8

5.6

3.1

4.5

7.2

4.3

5.9

2.8 120 6.4

Oman

OMN

6.2

0.4

3.9

2.1

0.9

1.5

5.0

2.6

3.9

2.8 120 2.2

Samoa

WSM

2.7

0.1

1.5

5.5

0.4

3.4

4.6

3.6

4.1

2.8 120 3.3

Greece

GRC

4.7

2.9

3.9

1.5

2.8

2.2

3.6

1.0

2.4

2.7 126 2.1

Moldova Republic of

MDA

3.9

0.4

2.3

2.5

1.3

1.9

6.3

2.5

4.7

2.7 126 1.8

Suriname

SUR

3.5

0.1

2.0

2.8

0.8

1.9

5.9

3.8

4.9

2.7 126 2.3

The former Yugoslav
Republic of Macedonia

MKD

3.6

1.8

2.7

2.4

1.5

2.0

4.6

2.4

3.6

2.7 126 2.5

Tonga

TON

2.2

0.1

1.2

5.8

0.9

3.7

5.5

3.6

4.6

2.7 126 4.0

Bulgaria

BGR

3.4

1.6

2.5

2.0

2.6

2.3

4.3

1.7

3.1

2.6 131 2.8

79Natural

Human

Hazard & Exposure

Social-Economics Vulnerability

Vulnerable Groups

Vulnerability

Institutional

Infrastructure

Lack of Coping Capacity

INFORM 2017

CUB

5.6

1.1

3.7

2.7

0.2

1.5

3.9

2.2

3.1

2.6 131 3.0

Italy

ITA

5.0

1.3

3.4

1.0

3.3

2.2

3.7

0.9

2.4

2.6 131 1.7

Romania

ROU

4.7

1.5

3.3

1.8

1.3

1.6

4.6

2.3

3.5

2.6 131 1.9

Argentina

ARG

3.5

2.4

3.0

1.6

1.1

1.4

5.0

2.2

3.7

2.5 135 3.3

Cabo Verde

CPV

1.9

0.1

1.0

6.1

1.0

4.0

4.1

4.0

4.1

2.5 135 2.0

Canada

CAN

4.9

0.6

3.0

0.8

3.5

2.3

2.2

2.3

2.3

2.5 135 2.9

Jamaica

JAM

3.7

1.1

2.5

2.5

0.9

1.7

4.3

3.5

3.9

2.5 135 3.1

France

FRA

3.8

1.5

2.7

0.9

3.9

2.5

2.8

1.1

2.0

2.4 139 1.9Montenegro

MNE

4.0

0.2

2.3

1.9

1.7

1.8

4.6

2.0

3.4

2.4 139 2.4

Australia

AUS

6.0

0.1

3.6

0.6

2.6

1.7

2.2

1.9

2.1

2.3 141 2.9

Croatia

HRV

5.0

0.7

3.1

1.5

0.9

1.2

4.4

1.4

3.0

2.2 142 2.2Saint Kitts and Nevis

KNA

1.7

0.0

0.9

5.3

0.5

3.3

4.6

2.3

3.5

2.2 142 4.4

Seychelles

SYC

2.5

0.0

1.3

3.3

0.9

2.2

4.4

2.6

3.6

2.2 142 3.9

Antigua and Barbuda

ATG

2.7

0.6

1.7

1.8

0.9

1.4

5.3

2.0

3.8

2.1 145 4.2

Bahamas

BHS

3.6

0.0

2.0

2.3

0.9

1.6

3.3

2.5

2.9

2.1 145 3.2

Belgium

BEL

1.6

5.1

3.5

0.6

2.7

1.7

2.3

0.7

1.5

2.1 145 2.2

Hungary

HUN

3.6

1.8

2.7

1.5

1.7

1.6

2.9

1.1

2.0

2.1 145 1.9

Kazakhstan

KAZ

4.3

1.5

3.0

1.2

0.4

0.8

5.0

2.4

3.8

2.1 145 2.7

Maldives

MDV

3.1

0.1

1.7

2.1

0.7

1.4

5.8

1.5

4.0

2.1 145 4.3

Mauritius

MUS

3.4

0.1

1.9

2.9

0.6

1.8

3.5

2.0

2.8

2.1 145 1.6

Spain

ESP

4.5

2.8

3.7

1.1

1.4

1.3

2.9

0.7

1.9

2.1 145 1.6

Belarus

BLR

2.3

2.0

2.2

1.0

1.3

1.2

4.6

1.4

3.2

2.0 153 3.3

Japan

JPN

8.3

0.6

5.7

0.9

0.8

0.9

2.0

1.0

1.5

2.0 153 3.3

Kuwait

KWT

2.3

0.4

1.4

2.4

0.9

1.7

5.2

1.4

3.5

2.0 153 2.0

Trinidad and Tobago

TTO

2.3

0.2

1.3

2.0

1.3

1.7

4.9

1.8

3.5

2.0 153 3.3

United Arab Emirates

ARE

6.0

0.0

3.6

1.7

0.7

1.2

2.3

1.5

1.9

2.0 153 2.9

United Kingdom

GBR

2.1

2.4

2.3

0.9

3.3

2.2

2.0

0.9

1.5

2.0 153 1.8

Poland

POL

2.4

0.4

1.5

1.3

1.9

1.6

4.0

1.3

2.8

1.9 159 1.6

Qatar

QAT

1.0

2.9

2.0

2.5

0.7

1.6

3.9

0.4

2.3

1.9 159 2.7

Bahrain

BHR

0.1

2.5

1.4

1.8

0.9

1.4

4.1

1.2

2.8

1.8 161 3.3

Malta

MLT

2.1

0.0

1.1

1.6

2.8

2.2

3.7

0.8

2.4

1.8 161 2.4

New Zealand

NZL

5.2

0.0

3.0

0.8

1.0

0.9

1.9

2.1

2.0

1.8 161 3.1

Austria

AUT

2.4

0.0

1.3

0.8

3.5

2.3

2.1

1.0

1.6

1.7 164 2.1

80

Lack of Reliability Index

ISO3

Cuba

RANK

COUNTRY

Natural

Human

Hazard & Exposure

Social-Economics Vulnerability

Vulnerable Groups

Vulnerability

Institutional

Infrastructure

Lack of Coping Capacity

INFORM 2017

BRN

2.3

0.1

1.3

1.0

0.6

0.8

4.7

4.2

4.5

1.7 164 4.1

Latvia

LVA

2.3

0.2

1.3

1.6

0.9

1.3

3.8

1.6

2.8

1.7 164 2.3

Saint Lucia

LCA

1.8

0.3

1.1

1.9

0.5

1.2

4.6

2.7

3.7

1.7 164 3.4Saint Vincent and the
Grenadines

VCT

1.0

0.6

0.8

2.7

0.6

1.7

4.1

3.0

3.6

1.7 164 3.6

Slovakia

SVK

3.4

0.1

1.9

1.1

0.9

1.0

3.8

1.1

2.6

1.7 164 1.8

Barbados

BRB

2.5

0.0

1.3

1.9

0.5

1.2

2.7

2.1

2.4

1.6 170 2.7

Germany

DEU

2.2

0.1

1.2

0.5

4.0

2.4

2.2

0.7

1.5

1.6 170 2.8

Korea Republic of

KOR

5.2

1.8

3.7

0.8

0.4

0.6

2.5

1.3

1.9

1.6 170 2.8

Portugal

PRT

3.6

0.0

2.0

1.5

0.7

1.1

3.0

0.9

2.0

1.6 170 2.1

Uruguay

URY

1.3

0.0

0.7

2.4

1.1

1.8

3.7

1.8

2.8

1.5 174 1.9

Czech Republic

CZE

2.1

0.1

1.2

0.8

1.4

1.1

3.1

1.0

2.1

1.4 175 2.0

Lithuania

LTU

1.8

0.0

0.9

1.4

1.0

1.2

3.5

1.1

2.4

1.4 175 2.3

Netherlands

NLD

1.9

0.0

1.0

0.4

3.5

2.1

1.5

0.9

1.2

1.4 175 2.2

Slovenia

SVN

3.8

0.0

2.1

0.6

0.9

0.8

2.2

1.2

1.7

1.4 175 1.7

Grenada

GRD

0.5

0.1

0.3

3.1

0.5

1.9

5.0

2.4

3.8

1.3 179 3.7

Ireland

IRL

2.0

0.0

1.0

0.7

1.7

1.2

2.2

1.3

1.8

1.3 179 1.9Sweden

SWE

1.1

0.0

0.6

0.5

4.4

2.7

1.9

0.9

1.4

1.3 179 1.9

Sao Tome and Principe

STP

0.1

0.1

0.1

4.8

1.4

3.3

6.3

4.7

5.6

1.2 182 3.3

Switzerland

CHE

1.9

0.1

1.0

0.4

3.6

2.1

1.0

0.6

0.8

1.2 182 1.9

Denmark

DNK

1.0

0.0

0.5

0.4

2.9

1.7

2.0

0.8

1.4

1.1 184 2.0

Liechtenstein

LIE

1.3

0.5

0.9

0.5

1.4

1.0

1.6

0.9

1.3

1.1 184 4.1

Estonia

EST

0.9

0.1

0.5

1.2

1.0

1.1

3.0

1.0

2.1

1.0 186 2.2

Iceland

ISL

1.3

0.0

0.7

0.6

0.9

0.8

2.1

1.7

1.9

1.0 186 2.6

Norway

NOR

0.2

0.0

0.1

0.2

3.5

2.0

1.9

1.3

1.6

0.7 188 1.8Finland

FIN

0.1

0.0

0.1

0.7

2.3

1.5

1.6

0.9

1.3

0.6 189 2.2

Luxembourg

LUX

0.4

0.0

0.2

0.9

1.3

1.1

1.8

0.6

1.2

0.6 189 2.2

Singapore

SGP

0.1

0.1

0.1

0.6

0.3

0.5

1.2

0.9

1.1

0.4 191 3.3

81

Lack of Reliability Index

ISO3

Brunei Darussalam

RANK

COUNTRY

Annex 5. Maps of the indexes

82

83

84

85

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