Chapter 1: Executive Summary The Need for a Statistical...

DRAFT FOR CONSULTATION – please do not quote or reference

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Asia-Pacific Expert Group on Disaster-related Statistics

DRSF DRAFT 2.0 (2nd consultation draft)

DRAFT FOR CONSULTATION – Please Do Not Reference or Quote

Chapter 1: Executive Summary

The Need for a Statistical Framework

1. The purpose of the DRSF is to help national statistical systems, particularly the national disaster

management agencies and national statistics offices, provide statistical information for informed

disaster risk reduction policies to achieve the goals and targets in the Sendai Framework on

Disaster Risk Reduction and the 2030 Agenda for Sustainable Development. Disasters pose direct

threats to sustainable development and while many hazards, like earthquakes and floods, are, to

some extent, unavoidable, many lives can be saved and huge damages can be avoided through

evidence-based disaster risk reduction, response, and recovery.

2. ESCAP Resolution E/ESCAP/RES/70/2 on “Disaster-related Statistics in Asia and the Pacific”,

established a regional expert group and requested the development of a framework for a basic

range of disaster-related statistics along with guidance for implementation. The Resolution 70/2

recognized better use of disaggregated data as a challenge for evidence-based disaster risk

management policy,

3. The demand for improvements to the quality and accessibility of basic statistics on disasters has

been acknowledged extensively elsewhere as well, for example in many reports on disaster risk

surveys of current data availability and national capacities. Research (e.g. World Bank, 2017) has

suggested, in the past, effects of disasters have been underestimated. The Report of the OECD

titled “Joint Expert Meeting on Disaster Loss Data: Improving the Evidence Base on the Costs of

Disasters: Key Findings from an OECD Survey” outlined some critical problems and limited

availability of internationally comparable statistics for many types of analyses on disasters,

including for measuring economic loss and for monitoring activities in disaster response and risk

reduction. The introductory paragraph of this report states:

“The rationale for the work on improving the evidence base on the cost of disasters

is grounded in the evidence that recent shocks from natural and man-made disasters

continue to cause significant social and economic losses across OECD countries. The

increase in damages is widely considered to outpace national investments in disaster

risk reduction, but this claim is more intuitive than supported by evidence. Indeed, there

is hardly any comparable data available on national expenditure for disaster risk

management and data on disaster losses is generally incomplete and thought to be

underestimated. Such estimates of the comprehensive costs of disasters are necessary to


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analyse the benefits of past and future risk management policies. In particular, this

information is helpful to inform decision making and to develop cost effective strategies

and measures to prevent or reduce the negative impacts of disasters and threats. Policy

makers, at present, possess usually scattered and incomplete data resources, which are

not comparable across countries. To design policies to reduce losses from disasters we

need to know how such economic losses are counted.”- OECD, 2016

4. The Hyogo Framework for Action 2005-2015, predecessor to the Sendai Framework, emphasized

the importance to: “Develop systems of indicators of disaster risk and vulnerability at national and

sub-national scales that will enable decision-makers to assess the impact of disasters on social,

economic and environmental conditions and disseminate the results to decision-makers, the public

and population at risk.” (UN, 2005, p.9).

5. Demands for comparable statistics for international analyses of disaster risk has been updated and

given increased attention with the adoption of the Sendai Framework and SDG indicators.

Indicators in the international databases managed by the United Nations and other organizations

are produced based on the official statistics of the national statistical systems. Requirements for

these systems include comparability of concepts and methods for measurement across disaster

occurrences. Thus, the systems depend heavily on coordination and consistency, which is

accomplished via the adoption and application (at national and local levels) of a commonly agreed

measurement framework.

6. As development of centralized databases to a basic range of disaster-related statistics is a new

endeavour in nearly all countries, there is a strong demand for technical guidance and sharing of

tools and good practices internationally.

7. There are growing challenges to predicting disaster risk due to climate change and other factors of

the modern globalized world. However, from a technical perspective, there are also many

enhanced opportunities, like free availability of software and methodologies for making use of

new data sources, such as remote sensing, mobile phone datasets, and so on. The World Bank’s

Global Facility for Disaster Reduction and Recovery (GFDRR) stressed that “these advances and

innovations create a need for better standards and transparency, which would enable replicating

risk results by other actors, reporting on modelling assumptions and uncertainty, and so forth.” A

statistical framework and common set of conventions and sample metadata can help with

greater transparency and replicability for the statistical inputs.

8. A crucial part of the Expert Group’s approach in developing this guidance was extensive study of

existing practices within leading national agencies in their production and use of statistics.

Disaster statistics is a unique domain in several ways. Each hazard or disaster is different,

random and creates significant changes to the social and economic context for affected regions.

Disaster risk is unevenly dispersed within countries, across the world and over time. To identify

authentic trends, rather than random fluctuations or effects of extreme values, much of the

analyses of disaster related statistics requires a very long time series. This puts an exceptionally

high value for longitudinal coherence of measurement for disaster statistics.

9. Statistics provide the context and a broad vision for comparisons and for a deeper understanding

of risk across individual and multiple hazards. Harmonized statistics is used to inform


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international support and boost solidarity, not only for responding to major disasters but also for

addressing risks on a continuous basis and with support from international cooperation.

Roles & Responsibilities

10. The main users of this framework are expected to be national disaster management agencies and

national statistics offices, but there are a diverse range of other national agencies involved in

relevant data collections, such as ministries of environment, ministries of finance, ministries of

health, economic and social development policy makers, meteorological organizations, and so on.

Implementation of a statistical framework should help national agencies to define and implement

clear requirements, roles and responsibilities across government regarding collection and

application of data , and how it is made accessible for policy-relevant research and monitoring

purposes.

11. A statistical framework is a tool to identify the opportunities to utilize existing data sources within

the national statistical system (NSS). In some cases adaptions to the sources or to the way that

data are shared between agencies will be need to fit the purposes for disaster risk reduction

statistical analysis. It is usually more efficient to adapt and reuse existing streams of data than to

establish new ones in response to each new question or indicator.

12. Through implementation of DRSF it will be possible to: (i) improve production of statistics from

existing databases and (ii) bridge the representations of the realm of disasters and risk reduction

on the one hand, with the socio-economic statistics on the other. The bridge between the two

domains of statistical information is essential for producing indicators. This bridge requires

strong partnership between disaster management agencies, national statistical offices, and other

official sources of relevant data and a strong mutual understanding of road concepts and the

methods for applying these concepts to practice for producing coherent statistics.

Figure 1: Statisical data and Policy Planning


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13. The scope for demands on official disaster-related statistics and indicators rests within a broader

context, which includes operational databases that are used for emergency response.

Implementation of DRSF will allow governments to produce coherent information and to make

use of the same instruments and collections of data for multiple purposes.

Figure 2: Uses of disaster-related data collections

Data Collection

Infrastructure Development Risk Assement Exposure

Resilience of Communities Post Disaster Assesment Hazard

Land use planning Indicators/Monitoring Vulnerability

Poverty Reduction Empirical Research Coping Capacity

Economic Development Planning Disaster Impact

DRR Activity

Operational Uses

Emergency Response

Evacuations

Early Warning Systems

Disaster Risk Management Planning

Summary & Time Series Statistics

Integrated Sustainable

Development Policy

14. The ideal scenario for disaster-related statistics, as described within the Sendai Framework, is

that, with improved availability of statistics, disaster risk reduction becomes an integrated part of

the broader sustainable development planning of the country at national and local levels. Some

examples are integrating disaster risk assessments into land use planning and urban zoning and

building resilience to disasters as a part of the broader strategy against multi-dimensional poverty.

15. The risk management cycle is a useful concept for understanding the demands for statistics in

relationship to various perspectives of decision-makers. While there are some overlapping

statistical requirements to support decision-making across the different phases of the cycle of

disaster risk management, there are also important differences.

16. During an emergency, responding agencies have special and relatively extreme requirements in

terms of timeliness and level of geographic detail required for the information to serve operational

purposes of an efficient and well-coordinated emergency response. The priority is to save lives

and minimize other damaging effects on the population, rather than on accuracy, comparability

between sources, or other qualitative characteristics of the figures.

17. In contrast, the reliability and comparability of statistics becomes crucial for risk assessment and

for designing prevention and preparedness programmes after disasters, especially when there is

demand for comparisons over time. Table 1 provides an overview of issues faced by decision-

makers and a sample of the demand for stastisic in each phase of the risk management cycle.

Figure 3: Cycle of Disaster Risk Management


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Reference: this diagram adapted from Thailand Department of Disaster Prevention and Mitigation (DDPM)

Table 1: Statistics in Disaster-risk reduction decision making

Typical issues in the different phases of disaster risk

management

Typical decisions and plans to be made

Sample of use of statistics

Peace time: Risk Assessment Disaster risks can be estimated but

are not known

Development investments should be

informed by risk profiles

Use of best available knowledge so

that development does not

exacerbate existing ( and or create

new) disaster risks

Prioritizing investments in

risk reduction

How to invest in development

while avoiding new risks

Dynamic hazard profiles

(magnitude, temporal and spatial

distribution)

Vulnerability and baseline of

exposure: (demographic and,

socioeconomic statistics) and

baseline of exposure in areas prone

to hazards Learning from experience of past

disasters, e.g. effectiveness of early

warning systems

Integrating historical disaster

impacts statistics to update hazard

profiles and vulnerability

assessments


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Peace time: risk reduction, mitigation and

preparedness Risk Profiles are changing as new

information becomes available and

development in potentially

vulnerable areas takes place

Early warning systems and other

monitoring systems, where

available, are continuous delivering

information on risks and possibilities

for mitigating impacts

Introduction of new measures

to reduce disaster risk

Introduction of mechanisms

to improve or ensure

sufficient early warning and

adequate preparedness

How to invest in risk

reduction measures as an

integrated part of the broader

poverty reduction and

sustainable development

initiatives

Whether and how to

discourage development in

hazardous areas

Scale, locations and other

characteristic of investment in

disaster risk reduction

Signals of slowly developing risks

approaching thresholds to a

potential disaster

Level of awareness, preparedness,

and investment against disasters by

households, businesses, and

communities

Identifying factors that cause and or

exacerbate disaster risks

Response Imperative is to act quickly and

efficiently to save lives and mitigate

unnecessary suffering

Sufficient resources to put crisis

under control

Urgent demand to meet

overwhelming needs for places

where vital systems and delivery of

basic resources were affected

Determine the magnitude of

the disaster and prioritize

needs for emergency relief

How to make the response the

most efficient

How to manage needs given

impacts to local supplies of

goods and services (how to

address temporary

interference to local services

supply)

How to mount emergency

response while also putting in

place requirements for

medium and long term

recovery

Disaster occurrence, including

temporal, and spatial spread of the

event

Disaster type and characteristics of

impacts, e.g. rapid or slow onset,

intensive or extensive impacts, etc.

Immediate indication of impacts on

population, damage, losses, and

disruption of basic services

Recovery needs, which potentially

could be increasing

Disaster response: who, what,

where, when, and how much

Medium and long term recovery Unaddressed humanitarian needs

Risk that fragile communities could

regress into a new emergency crisis

if recovery needs are not met

Less spotlight on initial response

may translate to less resources for

recovery

Often a normal development policy-

planning cycle resumes with many

requirements but, due to disaster,

less available resources

How to prioritize recovery of

economic sectors and

determination of appropriate

scale of re-building effort in

affected location

How to determine appropriate

level of investment required

for complete to recovery from

impacts for disasters:

Returning to consideration of

future risk identification and

mitigation (see above)

Comprehensive and credible post-

disaster accounting for damage,

losses, and disruption of functions /

services

Magnitude of requirements for

economic recovery (e.g. scope of

direct and economic impacts)

Assessing effectiveness of coping

mechanisms of communities,

localities and sectors

Identification of new vulnerabilities

created by the disaster

Components of A Basic Range of Disaster Related Statistics

18. The DRSF provides recommendations on methodologies for how to apply internationally agreed

concepts and terminologies for disaster risk reduction in relation to production of official

statistics. This includes technical recommendations on estimation for a basic range of disaster-

related statistics used for multiple purposes, including calculation of indicators.

19. A disaster is “a serious disruption of the functioning of a community or a society at any scale due

to hazardous events interacting with conditions of exposure, vulnerability and capacity, leading to


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one or more of the following: human, material, economic and environmental losses and impacts.”

(UNGA, 2015)

20. Presently, countries have different practices with regards to applying this definition for compiling

data and preparing statistical tables, which makes it difficult to make comparisons between

countries or conduct time series analyses over time and across multiple disasters. This handbook

can be utilized to address challenges for creating coherence across data sources and to

incorporate statistics related to all types of disaster events (regardless of scale) in alignment with

the UN General Assembly definition, towards a nationally centralized and internationally-

coherent basic range of disaster-related statistics.

21. Since governments are approaching challenges of improving their statistics and developed

centralized disaster-related compilations from different baseline capacities for nationally

harmonized disaster-related statistics, a tiered system of prioritization of statistical variables and

related practices have been developed for DRSF to help support a strategic implementation of the

guidelines.

22. Figure 1 shows the main components for the basic range of disaster-related statistics. Indirect

economic impacts are estimated by using other statistics from the basic range into applications

like modelled scenarios for long-term impacts to economies, or other types of analysis, Thus

indirect impacts estimation is one of the many applications (rather than a core component) of the

basic range of disaster-related statistics. All other elements of Figure 4 can potentially be

measured or estimated from direct observations and incorporated into a centralized database of

disaster-related statistics.

Fig.4: Components of the DRSF


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23. Figure 1 can be read like a timeline from left to right. First, there are statistics on disaster risk,

before the hazard occurrence. A threshold is passed at the moment of a call for emergency, at

which point data begin to be collected on the disaster occurrence and especially its impacts on

people, infrastructure, and the economy. Disaster risk reduction activities occur on a continuous

basis (like other activities of an economy). Indirect impacts, generally are experienced and

estimated during a period of time after the emergency response needs have already been met.

24. This handbook describes conventions and technical guidance for applying the agreed international

concepts and definitions of disaster risk reduction into the practice of statistics collection and

reporting. This includes, for example, guidance on measurement units, classifications, and other

conventions for compilers of statistics to produce coherent statistics on disaster, risk, occurrences,

and impacts, over time and across countries.

25. Case studies of development of compilations of summary statistics, aggregated across multiple

disaster occurrences are presented as examples and to share experiences, with an aim towards

providing illustrations of the concepts and sample outputs, and rationale for recommendations

provided in the text.

26. The statistics in this framework must be derived from a wide variety of sources. Important data

sources for compiling a basic range of disaster-related statistics are: population and housing

census, household surveys, monitoring data from geophysical, meteorological and geographic

organizations, the national accounts and its sources, disaster management agency assessments

and monitoring, ministry of environment, administrative records of health and safety institutions,

administrative records from emergency response and recovery operations, and (where possible)

specialized surveys targeting disaster-affected households and businesses.

27. Background statistics, such as GDP, basic demographic statistics, indicators of poverty,

environmental condition, are essential information for providing context to statistics on disaster

impacts, or the risk of impacts, as meaningful indicators for making comparisons and tracking

progress.

Relationships with other Frameworks and Applications

28. Implementation of DRSF involves interaction with a wide range of existing guidelines and

international standards adopted by the UN Statistical Commission, including recommendations

for population censuses, a classifications and other standards for economic statistics, including the

SNA and the System for Environmental-Economic accounts (SEEA). The current precedent in

the Statistics Commission for disaster-related statistics comes from the Framework for the

Development of Environment Statistics (FDES), which defined a component for “extreme events

and disasters”. For development of this handbook, the Asia Pacific Expert Group on Disaster-

related statistics consulted with a broad spectrum of disaster risk reduction and statistical

expertise and with established groups and forums, including: thet UNECE Task Force on Extreme

Events and Disasters, UN Expert Group on Statistical Classifications, the Advisory Expert Group


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on National Accounts, UN Expert Group on Environment Statistics, an the UN Committee of

Experts on Global Geospatial Information Management (UN-GGIM).

29. Key applications for disaster-related statistics are risk assessment and post-disaster impacts

assessments. Risk assessment is a continuous process because risks are dynamic. Moreover,

outcomes of disaster impacts assessments often will be important new information for future risk

assessment. Therefore variables used for vulnerability and for disaggregation of impacts statistics

will often mirror each other and risk assessment mechanisms need to be flexible for inputting new

data as it becomes available and, in particular, after a new disaster.

30. Usually, disaster risk assessment is primarily a responsibility of disaster management agencies

(or other related institutions). However, a lot of the data used for describing core drivers of

disaster risk (particularly exposure and vulnerability) are derived from established social and

economic statistics systems managed by national statistics offices. Also, data inputs used to

describe and predict hazards are derived from various other ministries and by the meteorological,

geological, and other geographic authorities.

31. Post-Disaster Needs Assessments (PDNAs) are conducted by the governments of affected

countries in collaboration with international agencies, particularly the World Bank. Guidelines

for conducting post disaster assessments and for using these assessments for developing disaster

recovery plans have been developed and published by the World Bank’s Global Facility for

Disaster Risk Reduction (GFDRR), in collaboration with the European Commission and the UN

Development Programme. The basic framework for PDNA studies derived the Damage and Loss

Assessment (DALA) Handbook (ECLAC, 2003). The DALA Handbook provides a globally

recognized conceptual framework for assessment studies, organized according to the different

components or sectors in the economy. The DALA methodology “focuses on the conceptual and

methodological aspects of measuring or estimating the damage caused by disasters to capital

stocks and losses in the production flows of goods and services, as well as any temporary effects

on the main macroeconomic variables.” (UNECLAC, 2003).

32. Assessment studies, including the PDNAs, are among the main applications for the basic range of

disaster-related statistics and the major sources of estimats of indirect impacts of disasters. DRSF

is built, where feasible, upon the existing data sources and standards in the national statistical

systems. Therefore, implementation of DRSF can lead to increased availability and comparability

of statistical inputs for use in the assessments and an improved alignment between PDNAs and

the regular outputs of official statistical systems, such as the System of National Accounts (SNA).

33. PDNAs, following DALA methodology, are usually only conducted after very large scale disaster

events such as hurricane Yolanda in the Philippines, Thailand’s 2011 floods, and Cyclone Evan

that caused major economic destruction in Fiji and Samoa. The World Bank’s GFDRRR website

currently hosts post-disaster assessment reports for 49 disasters in 40 countries, including 15

cyclones and multiple droughts, floods, earthquakes, tropical storms, and 1 volcanic eruption

(Cape Verde 2014-15).

34. In addition to published outputs rom PDNAs, several international compilations of statistics or

reporting tools are available for public access and were utilized by the expert group as important

references to develop guidance in this handbook included: UNISDR Global Assessment Report

(GAR) Risk Data Platform, DesInventar (Disaster Information Management System), and the UN

Environment Global Resource Information Database (GRID) network, and Munich Re Natural

catastrophe statistics online (NatCatSERVICE).


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35. DRSF complements these international reporting tools and databases by supporting improved

comparability of official statistics at the national (or regional) levels through application of

harmonized approaches to measurement.

Sendai Framework and SDG International Indicators

36. In 2015, global leaders adopted landmark agreements, establishing new international goals and

targets, in the forms of the Sendai Framework for Disaster Risk Reduction 2015-2030 and the

Sustainable Development Goals (SDGs).

37. The 2030 Agenda for Sustainable Development established 17 Goals and 169 targets for the

eradication of poverty and the achievement of sustainable development. In March 2016, the 47th

Session of the United Nations Statistical Commission (UNSC) agreed to a Global Indicator

Framework, specifying 230 indicators for measuring progress towards the Sustainable

Development Goals. In the SDGs, there are 11 disaster-related targets, spanning several of the 17

goals, and covered by 5 indicators (see Annex). By decision of the inter-agency expert group

(IAEG) on SDG indicators, the definitions for these indicators are aligned with indicators adopted

for the Sendai Framework.

38. The Sendai Framework for Disaster Risk Reduction was adopted at the Third UN World

Conference in Sendai, Japan, in March 2015. It is the outcome of stakeholder consultations

initiated in March 2012 and inter-governmental negotiations from July 2014 to March 2015,

supported by the United Nations Office for Disaster Risk Reduction at the request of the UN

General Assembly. Furthermore, after adoption of the Sendai Framework, an intergovernmental

process was established to reach agreement on terminologies and indicators for monitoring the

targets of the Sendai Framework. This intergovernmental process completed in December, 2016

with a report1 endorsed by the UN General Assembly. In order to help ensure cohesion between

national compilations of official statistics with demands for global indicators, the terminologies in

the DRSF are aligned with the Sendai Framework Report.

39. The Sendai Framework establishes four priorities for action: (1) Understanding disaster risk, (2)

Strengthening disaster risk governance to manage disaster risk, (3) Investing in disaster risk

reduction for resilience, and (4) Enhancing disaster preparedness for effective response and to

“Build Back Better” in recovery, rehabilitation and reconstruction. The Sendai framework

contains a statement of outcome, for the next 15 years, which is to achieve a substantial reduction

of disaster risk and losses, to lives, livelihoods and health and to the economic, physical, social,

cultural, environmental assets of persons, businesses, communities and countries. The proposed

targets for monitoring progress in the framework are:

1 A/71/644: “Report of the open-ended intergovernmental expert working group on indicators and

terminology relating to disaster risk reduction”


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1. Reduce global disaster mortality

2. Reduce the number of affected people

3. Reduce direct disaster economic loss

4. Reduce disaster damage to critical infrastructure and disruption of basic services, among them

health and educational facilities

5. Increase the number of countries with national and local disaster risk reduction strategies

6. Enhance international cooperation

7. Increase the availability of and access to multi-hazard early warning systems and disaster risk

information

40. A collection of 27 independent (excluding composite) indicators were adopted for international

monitoring of all seven Sendai Framework targets.2 Monitoring the 7 targets in the Sendai

Framework requires, as a minimum, good quality basic statistics on disaster risk, disaster

occurrences, direct impacts and commitments to interventions for reducing risks. These basic

requirements, in terms of a system of compilation of statistics draws from multiple data sources

across multiple governmental agencies and should cover, in principle a complete range of

different types of disasters relevant to the country.

41. The specifications for the Sendai Framework and SDG Indicators provide the common baseline

reference on the scope and prioritization for the high-level international demands for statistics.

However, all countries are starting from very different contexts in terms of the nature (e.g. extent

and intensity) of their baseline disaster risk factors. Thus, implementation of DRSF is a tool to

support national agencies with their reporting of aggregated indicators and also with development

of statically compilations with a broader scope and broader range of applications, as required for

decision-making at the national and local levels.

42. Diversity in current practices combined with the demand for international comparisons and time

series indicators creates the need for clear guidance on practical measures and, in some cases,

simplifying conventions for harmonization of measurement. Improved coherence and

transparency of approaches to measurement of basic disaster statistics is necessary for analyses of

the critical drivers of differences and trends in the internationally-adopted indicators, including

differences in underlying risk factors faced by different countries and communities.

Harmonization statistics is also needed for analyses that can distinguish between authentic

examples of progress from random variations in the time series.

43. Statistical databases are summaries of broader collections of raw data gathered from a number of

sources, including the operational databases, surveys, censuses, monitoring systems, and

administrative records. Indicators are designed to provide limited and targeted information to

policy-makers and to the general public to help inform disaster risk reduction policy frameworks

and to identify if and where progress is being made. Where possible, indicators should also be to

identify and encourage positive actions towards sustainable development pre-emptively, before

disasters.

44. DRSF rests in the middle of the theoretical information pyramid. The production of statistical

tables inevitably involve some degree of aggregation and summary of basic microdata, but the

statistics framework also needs to be relatively complete and flexible for calculating a broad

range of indicators, as well as for facilitating other types of analyses.

2 See complete list in Annex


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Figure 4: Information pyramid for disaster risk reduction

Chapter 2: Main Concepts for Measurement

Identifying and counting disaster occurrences and magnitude

1. A disaster is:

“A serious disruption of the functioning of a community or a society due to hazardous events

interacting with conditions of exposure, vulnerability and capacity, leading to one or more of

the following: human, material, economic and environmental losses and impacts.” -The United Nations International Strategy for Disaster Reduction (UNISDR), adopted by the UN General

Assembly (December, 2016)

2. DRSF applies the above definition but elaborates some criteria for producing harmonized statistics

on occurrences and direct impacts of disasters. For each disaster occurrence, there are at least four

characteristics of the event that should be recorded in centralized disaster statistics databases.

These characteristics of disasters are used for making connecting with other variables, including

the statistics on disaster impacts. The four characteristics are:

a. Timing (date, year, time and duration of emergency period)

b. Location (region(s)/province(s)/country(ies) and affected area raster or shapefile)

c. Hazard type (e.g. geological, meteorological, etc.)

d. Scale (Large, moderate, small)

3. In addition each disaster occurrence is given a unique identifier code (e.g. a numeric code) for

ease of reference and querying within a multi-disaster event database.

4. There are international initiatives for unique naming and coding of hazards, which can be utilized,

where applicable, by the national agencies, such as (e.g.) the GLobal IDEntifier number (GLIDE)

initiative promoted by promoted by the Centre for Research on the Epidemiology of Disasters

Indicators

Summary statistics (DRSF)

Sources of basic data (censuses, surveys, admin. records, data

used for operartional response)


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(CRED) of the University of Louvain in Brussels (Belgium), OCHA/ReliefWeb, OCHA/FSCC,

ISDR, UNDP, WMO, IFRC, OFDA-USAID, FAO, La Red and the World Bank.3

5. From the international definition of a disaster, two basic criteria can be derived for measurement

purposes (see figure 5): observation of significant impacts (“human, material, economic and

environmental losses and impacts”) and an emergency declaration (“A serious disruption of the

functioning of a community or a society”).

Figure 5: Criteria and Statistical Requirements for Disaster Occurrences

6. An emergency declaration (at local, regional or national level) is the signal of an abnormal

disruption. Emergency declarations are called by officially responsible agencies and are the

catalysts that spur collection of data. Emergency declarations can take various forms depending

on the type of hazard and laws and administrative policies of the responsible government. The

differences in laws and administrative polices across countries are prerogatives of the governing

authorities and, generally, do not significantly affect the statistics.

7. Sometimes, e.g. for slowing evolving risks leading to disaster, the emergency response may take

the form of initiating collection of data for monitoring the situation, followed by implementation

of a series of preventative measures (such as evacuations or other responses to boost coping

capacity and minimize impacts).

3 http://www.glidenumber.net/glide/public/about.jsp


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8. Other emergencies, especially sudden or unexpected hazards, are more explicitly represented by a

formal and public declaration and request to mobilize resources for response. The scale of

emergency declaration local, regional, national or international) is a useful indication for

assessing and categorizing the scale of the disaster.

9. The statistical requirements at the bottom of Figure should be developed and maintained in

centralized databases for each country and for each identified disaster occurrence. This is a

minimum requirement to identify disasters and describe their basic characteristics.

10. While the more common statistical demands in relation to individual occurrences of disasters is

information on impacts, counting and describing disaster occurrences according to their basic

characteristics has some limited but important analytical applications as well. Counts of

occurrences provide the context for analyzing disaster impacts statistics or for reviewing the

trends in occurrences over a very long time period (e.g. 50-100 year trends), which can be used as

inputs for risk assessment. Counts of disaster occurrences also provide the basis for calculating

statistics on intensity of impacts from disaster occurrences over time.

11. It is of central importance that the counts and descriptive characteristics of disaster occurrences

are done consistently over time (i.e. across individual events). If the scope for incorporating

disaster occurrences into outputs of official statistics, than there will be fundamental

inconsistencies in the scope of impacts statistics over time.

12. Such inconsistencies are common in the current national and international compilations for

disaster occurrences. A comparison of simple counts of disaster occurrences by hazard types for

any given country from different databases (e.g. a comparison from international sources versus

the records of an official national agency) reveals large differences in the numbers of events that

are recognized as the basis for statistics like number of deaths or economic impacts. Sometimes

inconsistencies are caused by errors but there can also be valid conceptual differences in scope of

measurement between databases, which will be improved through implementation of a common

framework.

13. There will also way be borderline cases and small differences in interpretations for special cases,

but a goal disaster occurrence statistics is to minimize the inconsistencies. There are two primary

sources for conceptual inconsistencies for counting disasters (and their impacts) in the current

national and international practices. The first source is a different scope of the hazards that are

accounted as a disaster. The second source is use of a minimum scale of impact threshold.

14. Impact thresholds are an application of basic statistcs on disaster for analysis and comparisons.

Thresholds are used as a practical tool to put practical limits on the scope for disaster impacts

statistics and for time series or multi-country analyses. For example, within the CRED EMDAT

databse, minimum threshold criteria were defined so that the compilations focus primarily on

moderate to large-scale emergencies. For the primary sources and in the official national

databases, there is no need to define a minimum scale of impact threshold, prior to analysis. For

databases, compilations need only to apply the criteria for a disaster occurrence in diagram 1, i.e.

at least some impact was recorded. Regardless of how minor the impacts, there must be at least

some objectively observed social-economic impact to qualify as a disaster.4 In this way, the

relatively small-scale disaster events are, in principle, included within the statistical databases and

it is up to users of these statistics (including CRED and others) to define thresholds or other

criteria, as needed, to match their own needs. In general, producers of official statistics should

4 Ground-shaking from earthquake with no impacts is a hazard, but it becomes a disaster occurrence at the

moment that impacts, however large or small, could be identified.


15

avoid introducing analytical criteria or other potential biases into the primary database so that a

broader range of potential applications of the statistics will be feasible from the official data

sources, through the subsequent grouping, threshold filtering, or other analyses of the basic data.

15. In principle, collection of statistics related to disasters are applicable for disasters of any scale.

Paragraph 15 of the Sendai Framework states that it applies “to the risk of small-scale and large-

scale, frequent and infrequent, sudden and slow-onset disasters caused by natural or man-made

hazards, as well as related environmental, technological and biological hazards and risks. It aims

to guide the multi-hazard management of disaster risk in development at all levels as well as

within and across all sectors.” Thus, there is a clear demand for a nationally coherent

measurement framework for application at different scales.

Hazards types

16. Current practices for scope of coverage of hazard types are extremely variable. Many countries

have an officially adopted list of hazard types and definitions inscribed into the national laws for

disaster responses. In these cases, the scope of official data collections (and metadata) usually

should be aligned with the scope and terminologies from the legal text. For all cases, a formal list

and glossary of the hazards should be published as part of the core metadata alongside the

statistics.

17. As with the case of the impacts threshold, it is only at the stage of analyses and production of

indicators from the databases that filtering or limiting the selection of hazard types will become

applicable, depending on the particular requirements of the study or reporting. Statistics for all

hazard types recognized within the country could be compiled in accordance with DRSF.

18. However, as a general recommendation towards increased consistency in scope of disaster-related

statistics, national agencies are encouraged to follow the scope of hazards defined for

international monitoring for the Sendai Framework and SDGs global monitoring according to

UNGA (2016) and the subsequent UNISDR Methodological Guidance for indicators. This

recommendation is to report nationally aggregated statistics according to the overall scope of

coverage of the IRDR Peril Classification and Hazard Glossary (IRDR, 2014) and for two

additional categories of hazards: environmental hazards and technological hazards.

19. For organization of the presentation of statistics on disaster occurrences into categories of hazard

types, the main perspective is time series analysis. One of the important examples of aggregated

category that should be derivable from an agreed classification of hazards is climate-related

disasters. These are hazards in the meteorological and hydrological hazard families as defined by

IRDR (2014).5

20. Climate is “the synthesis of weather conditions in a given area, characterized by long-term

statistics (mean values, variances, probabilities of extreme values, etc.) of the meteorological

elements in that area.” (WMO, 2017)

21. The Intergovernmental Panel on Climate Change (IPCC) has indicated a strong likelihood that

climate change will lead to increases in frequency and severity of related hazards, thus reducing

overall predictability of such hazards based on historical records (see, e.g., IPCC, 2012 and

5 Allignment with meteorological and hydrological families of IRDR can be used as the broad scope for

measurement of climate-related disasters. However, some special distinctions may be needed in the details, for example to distinguish between fires that are accidents caused directly by human activities in urban area as compared to wildfires that are consequences of extreme climate conditions (dry heat).


16

Birkman, 2013). Trends will be different and unevenly distributed across the globe. Statistics are

needed for assessing how climate change may be impacting disaster risk for different countries or

different regions over time.

22. Another aggregated category of hazards mentioned in the Sendai Framework are “man-made

disasters”. Although the term “natural disasters” is no longer used, man-mad hazards refers

especially to environmental and technological hazards, which are not covered by IRDR (2014).

23. In UNGA (2016), technological hazards “originate from technological or industrial conditions,

dangerous procedures, infrastructure failures or specific human activities. Examples include

industrial pollution, nuclear radiation, toxic wastes, dam failures, transport accidents, factory

explosions, fires and chemical spills. Technological hazards also may arise directly as a result of

the impacts of a natural hazard event.”

24. Also from UNGA (2016), environmental hazards: “may include chemical, natural and

biological hazards. They can be created by environmental degradation or physical or chemical

pollution in the air, water and soil. However, many of the processes and phenomena that fall into

this category may be termed drivers of hazard and risk rather than hazards in themselves, such as

soil degradation, deforestation, loss of biodiversity, salinization and sea-level rise.”

25. Other hazards not covered in the scope of the 2014 IRDR publication are violent conflicts,

including civil war and the associate human crises, e.g. refugee crises. The OECD estimates that

approximately 80% of international transfers of humanitarian aid goes to conflict-related

settings.6. UNGA (2016) excludes "the occurrence or risk of armed conflicts and other situations

of social instability or tension which are subject to international humanitarian law and national

legislation" from its definition of a hazard for the purpose of Sendai Framework monitoring.

26. A cascading multiple-hazard disaster occurrence is a disaster occurrence in which one type of

hazard (such as a strong storm or a tropical cyclone) causes one or more additional hazards (e.g.

flooding or landslides), that create combined impacts to the population, infrastructure and the

environment (see further description in Chapter 3). In some cases (e.g. Indonesia), cascading

multi-hazard disasters can be reported as their own specialized category of hazard types, noting

also the original trigger hazard (e.g. storm), as well as the connected hazards (e.g. floods,

landslide). Cascading multiple-hazard are not simply events with proximate timing or locations by

coincidence. They are events that are explicitly linked to the same original trigger hazard, and

thus are part of a broader single disaster occurrence.

27. “A slow-onset disaster “emerges gradually over time. Slow-onset disasters could be associated

with, e.g., drought, desertification, sea level rise, epidemic disease.” (UNGA, 2016). Slow-onset

disasters emerge after a period of slowly evolving catastrophic risk, which, given available data

and the right monitoring conditions, can be identified early in order to develop preventative and

mitigation measures for minimizing impacts in advance of the emergency.

28. “A sudden-onset disaster is one triggered by a hazardous event that emerges quickly or

unexpectedly. Sudden-onset disasters could be associated with, e.g., earthquake, volcanic

eruption, flash flood, chemical explosion, critical infrastructure failure, and transport accident.”

(UNGA, 2016).

Scale

6 See statistics on humanitarian aid at stats.oecd.org


17

29. Scale of impacts is another important characteristic for organization and presentation of statistics.

Usually, large scale disasters are less frequent but also attract international attention and solidarity

for response and assistance. Smaller scale disasters have less extensive impacts, but may be more

frequent and the cumulative effect can be very significant but also more likely underrepresented

by current databases.

30. It is a common practice of disaster management agencies to categorize disaster occurrences

according to a 3-category scale (minor, moderate, and large scale occurrences). There are various

ways for classifying scale. The recommended (first tier) approach is to refer to the geographic

scale of the call for emergency and support, i.e.: national scale, regional, or local scale disasters.

The use of the geography of the call for emergency is useful as a generic proxy measure for the

scale of the impacts to society.

31. Large disasters are disasters in which the emergency is at a national (or higher) sale and have

special characteristics of interest for analysis because they are relatively rare but have extensive

and long-term effects on sustainable development. Large disasters are often also covered by post

disaster assessment studies, creating opportunities for more comprehensive and more detailed

compilations of statistics on direct and indirect impacts. The impacts of large disasters often cross

administrative boundaries, including international borders, and therefore recordings of statistics

for large scale events are usually applicable to multiple reporting regions. An example was

Cyclone Evan (2012), which caused major damages in Fiji and Samoa, spurring separate

internationally-funded post disaster assessment studies in both countries.

32. Medium and small scale disasters refer to emergencies at smaller than national geographic

scales, which usually result in relatively smaller values of impacts aggregated at the national scale

but with large shares of the total number of disaster occurrences for a country or region. This

distinction is related to the concept of intensive and extensive risk from disasters developed by

UNISDR (2015). “Extensive risk is used to describe the risk associated with low-severity, high-

frequency events, mainly associated with highly localized hazards. Intensive risk is used to

describe the risk associated to high-severity, mid to low-frequency events, mainly associated with

major hazards.”

Disaster Occurrences Time Series

33. Disasters occur randomly in space and over time, which makes analysis of their impacts also

highly sensitive to the time period. The current international standard for a baseline time series

analysis of disaster impacts statistics from the Sendai Framework and SDGs is the 16-year period

from 2015-2030. For some other analytical purposes, such as for risk assessments by hazard

types, a much longer time period is needed.

34. Since disasters occur randomly, trends are easier to identify over a relatively longer time period.

Although year to year variations in disaster impacts are highly susceptible to randomness of

disaster occurrences, compilations of annual statistics within the databases allow for flexibility by

users to modify their own selections of time periods for their analysis. Flexibility is important

because, in some extreme cases, inclusion (or exclusion) within the timer period of one particular

abnormal occurrence could dramatically change the analyses. Choices in relation to time periods

for dissemination and analyses of statistics vary depending on the special characteristics of hazard

types. For example, the time scale for occurrences of earthquakes and tsunamis is typically much

longer than certain types of floods or meteorological hazards.

2b) Disaster risk


18

Background

1. Improved utilization of official statistics for understanding disaster risk is a basic motivation

for development of DRSF and its implementation in national statistical systems. Improved

understanding of risk is also priority number one of the Sendai Framework.

2. Disaster risk “is the potential loss of life, injury, or destroyed or damaged assets which

could occur to a system, society or a community in a specific period of time, determined

probabilistically as a function of hazard, exposure, vulnerability and capacity.” (UNGA,

2016)

3. Disasters are the outcome of present conditions of risk, including exposure to a hazard and

the related patters of population and socioeconomic development. (UNGA, 2016) “Disaster

risk is geographically highly concentrated and very unevenly distributed” (Pelling, in UNU

2013). Measurement must account for extreme variability of risk with a broad coverage of

the land and population while also targeting relatively high-risk hotspots with disagregated

statistics.

4. Statistics on the underlying risk are the contextual information for analyzing statistics on

disaster impacts and for understanding how impacts from disasters can be reduced for the

future.

5. Paragraph 6 of the Sendai Framework, states:

“More dedicated action needs to be focused on tackling underlying disaster risk drivers,

such as the consequences of poverty and inequality, climate change and variability,

unplanned and rapid urbanization, poor land management and compounding factors such as

demographic change, weak institutional arrangements, non-risk-informed policies, lack of

regulation and incentives for private disaster risk reduction investment, complex supply

chains, limited availability of technology, unsustainable uses of natural resources, declining

ecosystems, pandemics and epidemics. Moreover, it is necessary to continue strengthening

good governance in disaster risk reduction strategies at the national, regional and global

levels and improving preparedness and national coordination for disaster response,

rehabilitation and reconstruction, and to use post-disaster recovery and reconstruction to

‘Build Back Better’, supported by strengthened modalities of international cooperation.”

6. Disaster risk is dynamic and its measurement is capture, in part, by common work of

national statistics offices and other providers of official statistics at the national level, such

as: demographic changes, poverty and inequality, structure of the economy, expenditure,

economic production, conditions of ecosystems, and land management.

7. The focus in DRSF is to clarify the role of official statistics as inputs, made as accessible as

possible, for risk assessments. In Birkman (2013), Mark Pelling describes two

complementary types of risk assessment internationally: risk indices and hotspots. UNDP

and UNEP-GRID have been among the leading international agencies developing global

disaster risk indices (or DRIs). DRIs can be developed for individual hazard types (e.g. for

floods or cyclones) or multi-hazard risk, i.e an index covering multiple hazard types.


19

8. The early DRI analyses were conducted mainly at a national scale (e.g. in comparison to

GDP and population density at the national scale) instead of as analyses of the areas exposed

to or directly affected by the hazards. The hotspots approach follows a similar model that

has been used in the domain of biodiversity, and focuses on applying analyses at a more

geographically detailed scale, utilizing key data that can indicate relatively high levels of

likelihood for hazards combined with exposure and vulnerabilities of the population. Many

interesting examples are emerging, for example in the disaster management agency of

Indonesia (BNPB), which is tracking statistical information on economic activities (derived,

e.g., from local tax revenue records) and on children (from administrative records on

enrolment in schools) in relation to the hazard areas of the country.

9. Modern versions of DRIs and other models that can be found in the literature now

incorporate both approaches through geographically disaggregated statistics and analysis

using geographic information systems (GIS) . An advantages of the GIS-based risk

production of statistics for assessment is the potential to apply the methods at different

levels of geographic scale, i.e. at the global, national or regional scales, or for hotspots.

Scope of measurement

10. In the literature and current practice of many disaster management agencies (e.g. the national

disaster management agency of Indonesia, BNPB), disaster risk is defined and measured

according to three core elements: exposure to hazards, vulnerability and coping capacity.

𝑅𝑖𝑠𝑘 = 𝑓(𝐻𝑎𝑧𝑎𝑟𝑑, 𝑉𝑢𝑙𝑛𝑒𝑟𝑎𝑏𝑖𝑙𝑖𝑡𝑦, 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦)

11. This basic definition for measurement of risk appears in many sources in the disaster risk

reduction literature, and has also been known as the PAR model (Birkman, 2013). Disasters

occur at the intersection of the hazard (e.g. an earthquake) and the human processes

generating exposure, vulnerability and coping capacity. Risk of impacts from a disaster is

not driven only by the scale of the hazard itself (e.g. force of energy of the earthquake or

category of storm) but equally so by social factors that create exposure, vulnerability and

coping capacity (UNISDR, 2015).

12. The three elements of exposure to hazards, vulnerability and coping capacity are not

independent factors of risk. This basic formula is useful as the conceptual basis for defining

the scope and organizing statistics on risk in DRSF. It should not to be taken literally as a

mathematical formula for econometrics.

Estimating exposure to hazards

13. There are two main elements to measuring hazard exposure; there is a probabilistic mapping

of the hazard on the one side and a complement mapping of the population, critical

infrastructure (and other objects of interest such as high nature value ecosystems) for the

exposure side.


20

Figure 6: Population exposed to hazards measurement

(Sources: Right Map: UN Environment-GRID’S frequency of flood hazard map. Left map: Population census

2015 from KOSTAT, resampled by UNESCAP to the DLR’s Global Urban Footprint.)

14. The mapped area meeting in the middle is the hazard exposure measurement. Producing

statistics that can be used for estimating the exposure element is one of primary

responsibilities of national statistics offices and census organizations (e.g. through the

regular population and housing census).

Hazard Mapping

15. For hazard mapping, many variables can be relevant, most of which are not normally a

domain for national statistics offices, but are often available from the official sources of

disaster management, meteorological and geographic information for a country (or region).

16. The BNPB Indonesia example (see annex) provides a good practice example of the types of

data inputs needed for hazard mapping, among which include:

a. knowledge of the distribution of soil-type to model the spatial variation of ground

acceleration from an earthquake,

b. values for surface roughness to define the distribution of wind speed from a tropical

cyclone;

c. a digital elevation model (DEM) to determine potential flood height or other hazard

features.

17. There are also software tools and other resources available for probabilistic hazard

modelling software, e.g.:

a. The Austalian Goverfnment’s Earthquake Risk Model

(http://www.ga.gov.au/scientific-

topics/hazards/earthquake/capabilties/modelling/eqrm)

b. BNPB Indonesia’s InARisk (http://inarisk.bnpb.go.id/)

c. CAPRA (http://www.ecapra.org/)

d. U.S. Environmental Protection Agency’s CAMEO (https://www.epa.gov/cameo)

18. A collection of the spatial, intensity, and temporal characteristics for events in an event set is

known as a hazard catalogue. Hazard catalogues and statistics on impacts from historical

http://www.ga.gov.au/scientific-topics/hazards/earthquake/capabilties/modelling/eqrm

http://www.ga.gov.au/scientific-topics/hazards/earthquake/capabilties/modelling/eqrm

http://www.ecapra.org/

https://www.epa.gov/cameo


21

events together with risk models can be used in a deterministic or probabilistic manner.

Deterministic risk models are used to assess the impact of specific events on exposure.

Typical scenarios for a deterministic analysis include renditions of past historical events,

worst-case scenarios, or possible events at different return periods. A probabilistic risk

model contains a compilation of all possible “impact scenarios” for a specific hazard and

geographical area. A goal for probabilistic hazard modelling is convergence of results and a

long time series of input data is usually necessary. For example, a simulation of 100 years of

hazard events is too short to determine the return period and random samples over a period

of 100 years of events could easily omit events, or include multiple events.

19. According to IPCC, three changes are likely to be observed for climate-related hazards for

some geographic regions as a result of rising global temperatures: increases in frequency,

severity, and decreased predictability of hazards. Thus, climate change has contributed to the

dynamic nature of hazards, as an input into the formula for assessing risk. Other risk factors

(exposure, vulnerability, capacity) are, for different reasons, also highly dynamic.

Exposure Statistics

20. For the exposure side, the objective is to measure people, infrastructure, housing, production

capacities and other assets located in hazard-prone areas.

21. Exposure statistics have dual purposes in disaster statistics. In addition to one of the three

basic metrics for disaster risk, exposure statistics are also useful as baseline statistics for

assessing (or estimating) impacts after a disaster.

22. An approach has been developed for DRSF (see annex), applying the available population

census data using GIS. A method was developed and pilot tested among countries in Asia

and the Pacific to demonstrate the possibilities for applying census statistics for estimating

population exposure to hazard at different scales, based on the available public access

population census counts by administrative region (which can be accessed from national

statistics offices at different scales, depending on the country). The methodology7 was

developed and tested among Expert Group countries during 2016 and 2017 and a complete

step-by-step manual describing the steps to replicate the output statistics for any country

using the available population data from census authorities.

23. The basic objective for this methodology is to provide national agencies with a simple,

reproducible and scalable approach to producing statistics on population exposure, i.e.

estimations of population density in areas exposed to natural hazards or disasters from

publically-accessible data sources.

24. The difference in geographic distribution of hazard areas as compared to the normal

dissemination of population data (i.e. administrative areas at sub-regional or district levels )

creates the requirement to re-allocate t distribution (down-scale) population data so that it

7 See full methodology descriptions at the Expert Group website (http://communities.unescap.org/asia-pacific-

expert-group-disaster-related-statistics)


22

can be overlaid with a reasonable degree of accuracy to the actual geographic areas of a

hazard or disaster. The basic requirement is assimilation of population statistics or other

exposure elements (e.g. critical infrastructure) with the hazard maps. The methodology

developed for DRSS\F uses a grid-based assimilation of census data in GIS.

Figure 7: Grid-based data assimilation

source: Jean-Louis Weber, CBD Technical Series 77, 2014

25. Generally, the lower the level of geographic detail of the population aggregates (e.g.

administrative regions 01, 02, 03), the more accurate the gridded estimates of population

density should be for producing statistics on hazard exposure.

26. So, for example, in cases such as Tonga, in the Pacific, where census data are accessible by

GPS coordinates, no modelled estimation is required as the census records effectively reveal

point locations for households and the number of people living there (among other relevant

data from the census). These statistics can be used for highly accurate and high-resolution

analyses of location of population with respect to other geographic elements8, including in

relation to hazard area. The most detailed level of geographic area for data collected by the

census organisations are geographic areas called census blocks, which are instruments for

organizing census collection operations and usually contain somewhere between 50-200

households, depending on the country and region. Most commonly, the census data that are

available to users is at the level of administrative region (e.g. provinces, municipalities or

administrative level 01).

27. Pilot studies for the population exposed to hazards estimation methodology revealed that,

with high quality data of built-up areas such as the DLR Global Urban Footprint (GUF)

produced from radar satellite images (accessible at https://urban-tep.eo.esa.int/#), it is

8 See the Pacific Community’s POPGIS tool (prism.spc.int)

Satellite

images

Hotspots,

Occurences

,

Monitoring

data, samples

Socio-

economic

statistics

Classify,

aggregate

& map

Extrapolate

Overlay

Data inputData assimilation

(e.g. within

1 ha or 1 km2 grids)

Statistics integration,

analysis & reporting

Ref. Geo-

DataCode, Name

Disaggregate

& map

Data QA/QC,

analysis &

processing

e.g. by

administrative

unitse.g. by river

catchments or risk

perimeters…

https://urban-tep.eo.esa.int/


23

possible to estimate location of population using a simple model with results that are at

least comparable with other existing international estimations (such as, e.g., by

Worldpop.org (http://maps.worldpop.org.uk/#/ or by Global Human Settlement Layer by

JRC http://ghslsys.jrc.ec.europa.eu/) based on census results produced for public use by

national statistics offices. Due to the method’s simplicity, transparency and the opportunity

for free access to high resolution GUF data, reproducing estimations for population to hazard

exposure is feasible at different scales according to the detail of population data available

and to varying policy requirements.

28. Hazard exposure statistics come in the form of maps that are also very simply converted into

standardized statistical tables. The figure below summarizes the basic inputs from the

hazard and the exposure side, which will have close relationships to the measurement of

vulnerability.

Figure 8: Hazard Exposure Model

Summary Statistics Table B1b: Population Exposure by Population Groups

http://maps.worldpop.org.uk/#/


24

0-45-60

60+M

aleFem

aleUrban

RuralDisabled

Poor

1Population

SDG 1.5.1,

Sendai A1,B1

SDG 1.5.1,

Sendai A1,B1

SDG 1.5.1,

Sendai A1,B1

SDG 1.5.1,

Sendai A1,B1

SDG 1.5.1,

Sendai A1,B1

SDG 1.5.1,

Sendai A1,B1

SDG 1.5.1,

Sendai A1,B1

SDG 1.5.1,

Sendai A1,B1

SDG 1.5.1,

Sendai A1,B1

SDG 1.5.1,

Sendai A1,B1

SDG 1.5.1,

Sendai A1,B1

SDG 1.5.1,

Sendai A1,B1

2Population in Hazard Areas

2.1Geophysical

2.1.1H

igh exposure

2.1.2M

oderate exposure

2.1.3Low

exposure

2.2Hydrological

2.2.1H

igh exposure

2.2.2M

oderate exposure

2.2.3Low

exposure

2.3Biological

2.3.1H

igh exposure

2.3.2M

oderate exposure

2.3.3Low

exposure

2.4M

eteorological & Clim

atalogical2.4.1

High exposure

2.4.2M

oderate exposure

2.4.3Low

exposure

2.5O

ther [specify]

2.5.1H

igh exposure

2.5.2M

oderate exposure

2.5.3Low

exposure

TOTAL

C2a4 - Specific

vulnerability groupsN

O

TOTAL

C2a1 - Age groupsTO

TALC2a2 - G

ender groupsTO

TAL

C2a3 - Urban/Rural

population


25

Vulnerability

29. The Sendai Framework recommendations adopted by the UN General Assembly in 2016

defined vulnerability as “the conditions determined by physical, social, economic and

environmental factors or processes which increase the susceptibility of an individual, a

community, assets or systems to the impacts of hazards.”

30. In some reports, terminologies such as susceptibility, exposure, sensitivity, fragility, and

coping capacity have been used interchangeably with vulnerability. Also the variables for

describing different type of risk factors are not always independent. However, from a

measurement perspective, vulnerability is a distinct and useful concept for organizing

statistics on the baseline conditions, as descriptions of the population and infrastructure, a

step beyond the simple overlapping of location with hazards (i.e. exposure).

31. Previous studies can suggest a potential short list for geographically disaggregated variables

for compilation to improve the availability of reference statistics for identifying potentially

vulnerable segments of the population, such as:

Median household disposable income

Education enrolment, by age group and level of achievement an by male and female

heads of households

Information on assets of households, such as type of dwelling

Other human development statistics, by age group, including evidence related to

nutrition and childhood health,

Type of employment, e.g. identifying households engaged in agriculture of fishing

Urban versus rural distribution of affected or exposed areas

32. All of the above are items for potential disaggregation of the exposed populations, where

available, and could be compiled into basic summary statistics on disaster risk, similar to

DRSF table B1b. The same information is also avaialable in the form of gridded maps and

could be disseminated at different scales of geographic disaggregation, as needed for the

risk assessment studies.

33. Vulnerability arises from a wide variety of causes. Children are more vulnerable than adults

for physiological reasons. Women could be more vulnerable as a result of social factors,

related to (e.g.) type of employment or economic status. Studies of vulnerabilities for ageing

populations have revealed location and type of residence can be a good reference for

assessing vulnerability for the elderly, especially in cities.

34. If the statistics used in vulnerability assessments are gathered and updated on a regular

basis by geographic regions and specifically for hazard areas within countries, than disaster

management agencies would have a priori information on extent and specific locations

(among other characteristics) of vulnerability for developing targeted disaster risk reduction

or response strategies at local and national levels, in alignment with the overarching

objective of Sustainable Development Goals and of not leaving anyone behind.


26

35. Vulnerability assessments for disasters cut across three traditional sustainable development

pillars (economic, social, and environmental) and measusrement goes beyond people or

households. For example, although pollution in water bodies is generally considered as an

environmental problem, in the context of disaster risk, pollution is also a social and

economic liability because it can lead to significantly worse impacts to human lives and

health and to the economic costs of recovery. Another example is vulnerability of assets (or

infrastructure), which is sometimes called “physical vulnerability”. The response of existing

structures to potential hazards is not only an engineering problem. In most cases, physical

vulnerability also stems from other social-economic or environmental problems. Relatively

poor households often have little choice but to accept relatively less resilient shelters in their

dwellings or work places. Poorer communities, such as slums or lower income areas of urban

sprawl, often are also the most e likely to be situated in areas with environmental

vulnerabilities affecting the degree of exposure to hazards.

36. The 2010 World Development Report (World Bank, 2010) stated that “natural systems,

when well-managed, can reduce human vulnerability”. Examining and supporting cases of

positive synergies between environmental protections, also called ‘pro poor environmental

policies’ is one of the objectives for the United Nations Poverty and Environment Initiative

(PEI). Wherever environments are heavily polluted or degraded, often it is the relatively

poor populations that are more likely to be disproportionately affected and, by extension,

more vulnerable in the event of a disaster.

37. Population density and geographic location are the basic dimensions of exposure

measurement, but they also can be factors for vulnerability. Many rural communities face

marginally higher vulnerabilities due to the generally poorer access to transportation, health

facilities, and other types of critical infrastructure or support services. The largest share of

people living in poverty also tends to be in rural areas in developing countries. On the other

hand, other facets of rural communities, such as informal community support systems, could

be notable sources of resilience.

38. The defining characteristic of the urban centres, particularly the megacities, many of which

are located in coastal zones or otherwise hazardous locations in Asia and Pacific, is extreme

population density. While there are social benefits to having large groups of people

concentrated within relatively small geographic areas, such conglomerations can be

inherently vulnerable to impacts from hazards. Also, the characteristics of urban slums9, as

defined by the United Nations Human Settlements Programme (UN-Habitat) are likely to be

key factors for vulnerability in those communities.

39. Economic-related vulnerabilities include structural factors that are specific to geographic

regions within countries. For example, tourism and agriculture both have characteristics that

can lead to increased vulnerability to impacts from a disaster as compared to other types of

economic activity. So, economies based on agriculture and other kinds of productive

activities that are space intensive and/or heavily dependent on meteorological and other

9 A slum household suffers: lack of access to improved water source, lack of access to improved sanitation

facilities, lack of sufficient living area, lack of housing durability or lack of security of tenure (UN-

Habitat,2016)


27

environmental conditions will, in most cases, be relatively more vulnerable to natural

hazards as compared to, for example, services-based economies. Thus, some of the

economic vulnerabilities to disasters are assessed through macroeconomic analysis on the

structure of economies for specific geographic areas exposed to hazards.

Coping capacity

40. The term “resilience” has been applied in reports on disaster risk reduction with various

meanings or descriptions. Commonly, resilience is mentioned almost interchangeably with

the concept of coping capacity. This is the ability for households or businesses or

infrastructure to withstand external shocks without sustaining major permanent negative

impacts, and instead guiding towards opportunities for improvements in the future (e.g..

“building back better”).

41. Birkman (2013) writes: “In contrast to vulnerability, resilience emphasizes that stressors and

crises in social-ecological systems also provide windows of opportunity for change and

innovation. Hence crises and destabilization processes are seen as important triggers for

renewal and learning.”

42. Many strategies for coping with disasters are informal and not managed by governments or

through regulations, and therefore their significance to understanding risk is difficult to

measure with statistics. For example, one of the coping mechanisms in the case of drought or

other types of climate or hydrological-related hazards is simply migration, either

permanently or temporarily, in search of a livelihood outside the worst affected areas.

Population displacement and other movements of the population that correspond in timing

with a disaster can sometimes be captured via statistics from population censuses or

population administrative records. More difficult is to attribute movements specifically to

hazards or a past disaster.

43. There also are coping mechanisms which are organized efforts that can be captured by

statistics for disaster risk assessments. Disaster preparedness is a good example. After major

earthquakes struck in the Canterbury province of New Zealand, population and housing

census results revealed significant increases in disaster preparedness of households (e.g.

rationing emergency food and water storage). Such information reveals a decrease in overall

risk, via increased coping capacities, and also direct benefits from learning and from

educational programmes enacted after the experience of previous disasters.

44. Basic statistics on coping capacities are an important input for understanding risk, but an

additional use for statistics on coping capacity is to show direct results from investments in

increased preparedness. Disaster management agencies utilize the best available risk

information to design and implement activities to reduce the impacts of disasters. The aim of

these activities is that they improve preparedness and strengthen the overall resilience of a

community before a hazard or disaster.

45. Disaster risk reduction-related activities (see Section 2e) are activities that boost the coping

capacities of society. In order to assess the direct results of these investments, governments


28

should also collect statistics for assessing how these investments affect coping capacities,

e.g. coverage of early warning systems and the basic knowledge and preparedness of

households.

46. People are not equally able to access the resources and opportunities (or knowledge and

information about hazards). The same social processes involved in the disadvantages of

poverty also can have a significant role in determining their level of preparedness and

access to information and knowledge. (Wisner et al., 2003). Thus, at the household level,

vulnerability and coping capacity are related measurements.

Summary Statistics Table B3: Coping Capacity Background Statistics

Geographic disaggregation

Geo Region

1

Geo Region

2

Geo Region

3… National

Measuremen

t Unit

Coping Capacity Table1 GDP SDG 1.5.2 SDG 1.5.2 SDG 1.5.2 SDG 1.5.2 SDG 1.5.2 Currency

2 GDP per capita Currency

3 Median Households disposable income Currency

3.1 Local currency (NAME...)Currency

3.2 US$ PPP US$ PPP

4 Number of dwellings with slum

designation

no. of units

5 Population living in areas with slum

designation

no. of people

No. of systems

6.1 Population covered Sendai G-3 Sendai G-3 Sendai G-3 Sendai G-3 Sendai G-3 %

6.2 Share of population in exposure areas covered %

6.3InvestmentExpenditure (also DRRE_A,

3.2) Currency

7.1 Share of households with emergency plan %

7.2 Share of households with backup storage of food and water %

7.3 Share of households with improved access to water and sanitation %

7.4 Other Preparedness (houehold level) %

8.1 Forest area sq km

8.2 Share of water bodies in good condition %

8.3 Other ecosystem condition measures

Currency

9.1 Disaster risk reduction characteristic transfers received Currency

9.2 Disaster Risk Prevention Currency

9.3 Disaster Risk Mitigation Currency

9.4 Disaster Management Currency

9.5 Disaster Recovery Currency

9.6 General Government, Research & Development, Education Expenditure Currency

Currency

6 Early Warning Systems

9 Risk Reduction Activity

10 DRRCA Transfers fom Central to local

government

7 Household Preparedness

8 Environmental Resilience


29

47. For producing, and utilizing in risk assessment, the many potentially relevant variables on

disaster risk, the key requirement is geographic disaggregation. Data assimilation in GIS

creates possibilities to apply the available data to produce and communicate statistics at

multiple scales. At a minimum, variables identified for vulnerability to disasters should be

compiled to the lowest available sub-national administrative regions (e.g. Administrative

region 02 or 03). In DRSF background statistic tables, all variables are organized according

to geographic regions used for statistics within the country. In reporting tables, geographic

disaggregation is predetermined by existing practices and requirements of users. However,

within GIS, geographic regions can be defined or adapted to the specific analysis.

48. Often it is useful to define homogenous regions --- e.g. urban and rural, residential and non-

residential, agricultural land, etc. One of the basic inputs for developing exposure statistics

are land cover and land use maps and, where available, the cadastres of municipalities. Land

cover and land use maps, among other kinds of geospatial information, serve an additional

purpose in DRSF by providing baseline information for defining specific geographic objects

of interest in risk assessment.

49. Risk statistics differ from impacts statistics in that they are baseline information about the

population or infrastructure compiled prior to a disaster whereas impacts statistics are

information for describing population affected by a specific and unique disaster occurrence.

Producing impacts statistics requires not only geographic data disaggregation but also

disaggregation according to the different types of demographic and social groups in the

affected area. The disaggregation of statistics for the affected population (see section 2d)

should, in many cases, mirror the groups that were identified in the vulnerability assessments

– e.g. children, the elderly and the income poor – and eventually the two types of

assessments should become mutually reinforcing to improve one another, built upon the

same basic initial data collections used for disaster risk measurement. For example, baseline

statistics on economic activity for areas exposed to hazards can be reused for estimating

costs of damages in impacts assessments.

50. Increasingly, traditional data sources of the national statistical system like household and

business registers, household and business surveys, population and housing censuses are

conducted with use of detailed geographic referencing. The geographic referencing may be

confidential at the level of individual records, but summary statistics can be disseminated for

use for comparisons for relative levels of risk at practically any scale. The quality and level

of detail of available data with geographic location referencing of households, businesses,

and other land uses, varies greatly between countries, and sometimes within countries (e.g.

between rural areas and urban centres). But the broad trend for official statistics has been a

rapid expansion in the possibilities, using affordable tools and the existing data, to greater

level of flexibility and level detail for geographic disaggregation of statistics on risk.

2c) Material Impacts and Economic Loss

Material Impacts


30

1) Direct material impacts encompass damages to assets, including critical infrastructure,

triggered by a hazard. Direct material impacts also constitute the source of direct economic

loss measurement, as defined for the Sendai Framework (see section on International

Indicators)

2) Initially, statistics on direct material impacts are produced by disaster management agencies

based on assessments conducted immediately after an emergency (UNGA, 2016). These

statistics are complemented by statistical information on the location and basic characteristics

of infrastructure in a disaster areas known prior to the hazard, i.e. estimates of exposed

infrastructure.

3) Background statistics on infrastructure serve a dual purpose of baseline or contextual

information for analyses of impacts data and as inputs to estimating risk prior to a disaster.

4) Also complementing assessments of material impacts by the disaster management agencies

are results of analysis of regular sources of time series statistics within the national statistical

system, such as the population and housing census, business surveys, and compilations of

other records of economic activity that are used to evaluated trends on a continuous basis, i.e.

before, during, and after disasters. In particular, comparisons for an affected area before and

after a disaster can be used to estimate the extent of materials impacts and their economic

costs.

5) Basic statistics on material impacts from a disaster are compiled, initially, in physical terms,

i.e. in terms of area (sq. m), or volume, number of people affected, or counts of units (or

buildings) that are damaged or destroyed. Defining measurement units (see discussion in

Chapter 6) is a crucial step for designing the collection and dissemination of a robust and

consistent compilation of material impacts statistics. The scope of meausurement is defined

according to the stocks of physical assets potentially exposed to hazards ( see classification of

material impacts in Chapter 5). Prioritization is given to especially important groups of assets,

such as the critical infrastructure and agricultural crops.

6) There are multiple possibilities, with rangin analytical relevance for measurement units and

other choices for compiling direct material impacts from historical disasters in physical units.

Ccollection of basic data on number of units (e.g. no of buildings) of the differtent categories

of critical infrastructure, see example below from the Philippines, is a good starting point. On

this basis, additional data – e.g. classes of hospitals damaged, length of roads, numbers of

people affected by disruptions, and so on, can be integrated for the production of statistics

needed for assessing the scale of the impacts and the recovery needs.

Sample Table 1: Damages to Critical Infrastructure in the Philippines, 2013-15


31

source: report from Philippines for DRSF Pilot Studies (2016); units: no. of buildings

7) Critical Infrastructure is “the physical structures, facilities, networks and other assets which

provide services that are essential to the social and economic functioning of a community or

society. ” (UNGA, 2016) A list of critical infrastructure is presented as a sub-group of the

broader classification of direct material impacts in Chapter 5.

8) Damages to dwellings create an explicit link between human and material impacts tables. In

the example below, impacts are measured again in terms of numbers of units, this time for the

case of Indonesia. Number of dwellings will be roughly equivalent to number of households

affected. In principle, the sameic source of this information could also be utsed to calculate

the number of persons affected by a damaged or destroyed dwellings (Sendai Framework

Indicators B-3 and B-4). There is also an opportunity, having identified and counted specific

dwelling affected, to collect data on characteristics (age, gender, poverty status, etc.) of

affected individuals for assessing the recovery challenges and as an input into updated risk

assessment.

Sample Table 2: Damages to Dwellings in Indonesia

Damaged Dwellings (#of units)

geophysical hydrological meteorological Climatological Other total

Aceh 9307 2026 201 0 11534

Bali 3 148 46 197

Bangka-Belitung 0 103 103

Banten 55 403 173 631

Bengkulu 321 178 112 611

Region I (Ilocos) 3 33 5

Region II (Cagayan Valley) 30 0 0 19 8

Region III (Central Luzon) 64 140 12

Region IV-A (Calabarzon) 12 0 0 5

Region IV-B (Mimaropa) 123 0 0

Region V (Bicol) 66 0 0 10 1

Region VI (Western Visayas) 36 0 1

Region VII (Central Visayas) 286 82 37 55 26 18

Region VIII (Negros Island Region) 347 0 0 24 4 0

Region IX (Zamboanga Peninsula) 0 0

Region X (Northern Mindanao) 0 0 0 3 3

Region XI (Davao Region) 0 0 18 3

Region XII (Soccsksargen) 0

Region XIII (Caraga) 0 0 39 6

National Capital Region (NCR) 8

Cordillera Administrative Region

(CAR) 1 20

Autonomous Region of Muslim

Mindanao (ARMM)

Nattional total

PH

ILIP

PIN

ES

Region

DAMAGES TO CRITICAL INFRASTRUCTURE

Hospitals/

Health

facil ities

Education

facil ities

Other critical

public

administration

buildings

Roads BridgesOther critical

infrastructures


32

Gorontalo 3 3 6

Irian Jaya Barat 0

Jakarta Raya 3 0 250 253

Jambi 47 148 162 0 357

Jawa Barat 1345 6969 1547 9861

Jawa Tengah 830 1285 4768 0 6883

Jawa Timur 612 576 3218 0 4406

Kalimantan Barat 90 158 248

Kalimantan Selatan 334 129 0 463

Kalimantan Tengah 1 0 1

Kalimantan Timur 47 1 39 0 87

Kalimantan Utara 1 0 1

Kepulauan Riau 4 49 111 0 164

Lampung 0 0 1023 1023

Maluku 620 83 703

Maluku Utara 146 23 169 Nusa Tenggara Barat 735 1454 129 2318 Nusa Tenggara Timur 11 151 32 194

Papua 1 0 1

Riau 9 30 343 0 382

Sulawesi Barat 76 31 107

Sulawesi Selatan 66 23 697 786

Sulawesi Tengah 27 3 0 30

Sulawesi Tenggara 8 114 122

Sulawesi Utara 37 145 6 188

Sumatera Barat 2688 504 281 0 3473

Sumatera Selatan 78 20 244 342

Sumatera Utara 27 30 2249 0 2306

Yogyakarta 22 18 61 101

Papua Barat 9 305 314

National Total 17023 15107 16235 0 0 48365

Damaged Dwellings, # of units (1900-present), accessed from Data Informasi Bencana Indonesia

(DIBI) http://dibi.bnpb.go.id, 2017

9) There are several purposes for accounting for direct material impacts (i.e. damages to assets)

after a disaster, including impoving knowledge of physical vulnerabilities to hazards,

estimating the value of economic loss from a disaster, and also for measuring disruptions of

basic service from a disaster, which is the focus of Sendai Framework Target D:

“substantially reduce disaster damage to critical infrastructure and disruption of basic

services, among them health and educational facilities, including through developing their

resilience by 2030.”

http://dibi.bnpb.go.id/


33

10) Statistics on disruptions to services from material impacts, can be presented according to

hazard types and/or according to geographic regions within the country. Ther are two

measurement units and common denominator used for statistics on disruprtions to basic

services: numbers of persons affected and length of time (number of days) for the

disruptions.

Table D2a Disruptions to Basic Services from a Disaster by Hazard Type

11) In addition to damages to critical infrastructure and other buildings, another important

component of direct material impacts is damages to the land and other natural resources,

especially to agricultural land, destruction of trees, and damages to the conditions of

important ecosystems.

12) In economic terms, impacts to agriculture are often among the most significant l impacts from

disasters. In part this is because, as a land intensive activity, agriculture faces a relatively

large exposure to hazards. Another reason is because thre are many forms of material impacts

to agricultural establishments. They are manifested as damages to the land itself, including

the soil (accelerated erosion, landslide impacts, salination...), land improvemnts (e.g.

irrigation systems), r constructed assets (building and equipment) as well as direct losses to

the growing (non-harvested) crops. Each of these components of damages can be measured

separately, in physical and monetary terms.

13) There are also material impacts to other natural resources, including unowned natural

ecosystems. Natural environments are critical inputs to resilience of proximate communities

to disaster, impacts to ecosystems can include significant changes to resilience, increasing

disaster risks after a disaster (e.g. risks of flash floods after deforestation by landslide or

volcano eruption), as well as negatively impacting various other quantifiable benefits of

ecosystems.

Economic Costs from Material Impacts

14) Direct economic loss is a composite indicator adopted for monitoring progress in the

Sustainable Development Goals and Sendai Framework for Disaster Risk Reduction. For the

purpose of the international indicators, direct economic loss is defined as “the monetary

value of total or partial destruction of physical assets existing in the affected area.” (UNGA,

2016). Production of this indicator depends mostly on the compilation of data, as completely

Disruptions to Basic services from a Disaster1 Health services Sendai D-7 Sendai D-7 Sendai D-7 Sendai D-7 Sendai D-7 Sendai D-7

1.1 No. of people

1.2 Length of time

2 Educational services Sendai D-6 Sendai D-6 Sendai D-6 Sendai D-6 Sendai D-6 Sendai D-6

2.1 No. of people

2.1 Length of time

3 Public administration services Sendai D-8 Sendai D-8 Sendai D-8 Sendai D-8 Sendai D-8 Sendai D-8

3.1 No. of people

3.2 Length of time

4 Water services

4.1 No. of people

4.2 Length of time

5 Other Basic Services

5.1 No. of people

5.2 Length of time

6 Total Disruptions Sendai D-5 Sendai D-5 Sendai D-5 Sendai D-5 Sendai D-5 Sendai D-5

6.1 No. of people

6.2 Length of time


34

as possible, on the estimated costs of reconstruction or replacement of damaged or destroyed

assets, as observed by disaster management agencies after an emergency.

15) UNISDR developed guidance for producing the direct economic loss indicators for

international monitoring of Target C in the Sendai Framework (UNISDR, 2016). However,

national agencies still face significant challenges for estimating the monetary value of direct

material impacs from disasters consistently across disasters and, as much as possible, with

established principles practices in economic stsatistics (e.g. the national accounts). There are

also a broader collection economic impacts, including the indirect economic impacts, that are

estimated as applications of basic statistics on material impacts. For consultation on this

draft, a working paper (see annex) was prepared on relationships between direct economic

loss measurement and the System of National Accounts (SNA) for the purpose of collecting

inputs and feedback from the Advisory Expert Group (AEG) on national accounts.

16) When it comes to estimating monetary value for direct economic loss, the challenge is to put

a value to the physical damages to assets observed by the disaster management agencies (and

other relevant authorities). These are negatives changes in volume to stocks of assets, to

which some estimation of economic value needs to be attached coherently, as much as

possible, across the full range of types of assets that were damaged or destroyed.

17) While there is a strong international demand for internationally comparable indicators of

direct economic loss, there is also an interest to produce multiple related figures, where

possible, in order to meet different purposes of economic analysis, including, subsequently,

for assessments of the indirect impacts of disasters. For the purpose of a fist-tier basic range

of disaster-related statistics, measurement of direct economic loss should follow, as much as

the definitions from UNGA (2016) and indicators guidance in UNISDR (2017.

18) For most cases, these are estimates for replacement costs of assets, as defined by the SNA

and in the classification of direct material impacts (chapter 5).

19) Conceptually, the replacement costs are value markers for changes to the stocks of assets, as

estimated according to the costs for recovery of pre-disaster assets. However, they are also

actual expenditures (recorded whenever the reconstruction activity takes place) and therefore

recorded as a contribution to overall national expenditure and GDP. In other words, while the

principle is to value changes in value of stocks of asset, the information for valuing the

changes is also found in activity (flow) accounts for production and expenditure.

Economic Impacts to Agriculture

20) Reconstruction or recovery costs for direct impacts to assets are not always available, even as

estimates and there is a need for exceptions for valuation of damages to certain types of

assets. A very important case for understanding the different types of scenarios for direct

impacts and their valuation in monetary terms s is the range of possible direct economic

impacts of disasters for agriculture, forestry and fisheries.

21) For agriculture, economic assets exposed to potential direct economic losses take a broad

range of forms: including land (or improvements to land, following the SNA definition) ,

machinery and equipment, and other resources like crops, livestock and plantations.

22) The measurement of the output of agriculture, forestry and fishing is complicated by the fact

that the process of production may extend over many months, or even years. Many


35

agricultural crops are annual with most costs incurred at the beginning of the season when the

crop is sown and again at the end when it is harvested. Value for assets and crops may depend

on their maturity and closeness to harvest. According to the accounting principles, the value

of the crop has to be spread over the year and treated as work-in-progress. Often the final

value of the crop will differ from the estimate made for the growing crop before harvest. In

such cases revisions to the early estimates are made to reflect the actual outcome. When the

crop is harvested, the cumulated value of work-in-progress is converted to inventories of

finished goods that is then run down as it is used by the producer, sold or is lost to vermin.

[SNA 6.137] Thus, in principle, the value of losses of crops to a disaster (catastrophic

losses) depends on the timing of the hazard with respect to the timing in relation to

harvesting. In practice, the measurement can be simplified by estimating average per-unit

values for each agricultural output for the region affected at the time of the disaster (or at the

time of harvesting closest to the date of the hazard).

23) When it comes to livestock, and fisheries and to forest cover (cultivated and non-cultivated

forests are recognized as assets in the SNA), the approach could differ slightly from the case

of single-yield crops by taking the value, according to the practice of asset accounting , at

the time of the disaster, which, in principle includes the potential decline in value over time

as the assets age and are used up (consumption of capital). However, for cases where

updated values for the assets are unavailable, average per unit value estimates is a practical

simplification of measurement for market prices for the lost asset. In principle, even the

average price based estimateds should approximate the value as defined according to the SNA

definition of assets (i.e the present value of future benefits to owners).

24) Thus, for the case of agriculture, there are at least 3 distinct types of direct impacts and

valuation. These different values can be aggregated for direct impacts without double-

counting: (i) estimated market price value of destroyed crops, livestock, and trees (as a proxy

for the loss of value to owners of assets/inventories), (ii) replacement costs for damaged or

destroyed buildings and equipment, and (iii) recovery costs for damages to restore

improvements to the land.

25) Impacts from disasters refer especially to direct impacts from disasters, as defined within the

framework and not all types of damages or losses to agricultural units. For example, the SNA

specifies that ‘incidental losses of animals due to occasional deaths from natural causes form

part of consumption of fixed capital. Consumption of fixed capital of an individual animal is

measured by the decline in its value as it gets older.” [SNA 10.94]. This is the basic

distinction, using the example of livestock, between the gradual consumption of capital (AkA

depreciation) and catastrophic losses (i.e. direct material impacts) from disasters.

2d) Human Impacts

1. In DRSF, there are two basic categories of statistics on impacts from disasters: the material

impacts (previous section) and human impacts. Some of the statistics relate to both categories

and therefore provide a bridge between the material and human impacts tables. For example,

in principle, the same data sources are used for accounting for damaged or destroyed


36

dwellings (an indicator in the Sendai Framework Target C for economic loss) should also be

applied for estimating the number of people whose houses were damaged due to hazardous

events (also an indicator for monitoring the Sendai Framework, under Target B for affected

population).

2. Human impacts include the components used as inputs for calculating indicators for

“Affected Population”, defined for monitoring the Sendai Framework and SDGs as a

composition of indicators on deaths, missing, injured, ill, disrupted or destroyed livelihood,

and otherwise affected (see annex for more information on indicators for international

monitoring).

3. The rows in the summary statistics tables on human impacts (see Table C2 below) provides a

summary of a basic range of statistical outputs compiled from various sources for describing

the human impacts of disasters.

DRSF Table C2: Summary of human impacts by hazards types and geographic regions


37

Geo-physical

Hydrological

Meteorological & Climatalogical

Biological

Other

Adjustment for multiple counting of occurneces

by types

TOTAL Region 1

1 - Su

mm

ary of H

um

an Im

pacts

Hu

man

, affected p

op

ulatio

n

1.1D

eaths o

r missin

gSD

G 1

.5.1

/Sen

dai A

-1SD

G 1

.5.1

/Sen

dai A

-1SD

G 1

.5.1

/Sen

dai A

-1SD

G 1

.5.1

/Sen

dai A

-1SD

G 1

.5.1

/Sen

dai A

-1SD

G 1

.5.1

/Sen

dai A

-1

1.1

.1D

eath

sSe

nd

ai A-2

Sen

dai A

-2Se

nd

ai A-2

Sen

dai A

-2Se

nd

ai A-2

Sen

dai A

-2

1.1

.2M

issing

Sen

dai A

-3Se

nd

ai A-3

Sen

dai A

-3Se

nd

ai A-3

Sen

dai A

-3Se

nd

ai A-3

1.2In

jured

or ill

Sen

dai B

-2Se

nd

ai B-2

Sen

dai B

-2Se

nd

ai B-2

Sen

dai B

-2Se

nd

ai B-2

1.2

.1 M

ajo

r inju

ries

1.2

.2 M

ino

r inju

ries

1.2

.3Iln

esses

1.3D

isplaced

1.3

.1P

erma

nen

t reloca

tion

s du

e to d

estroyed

dw

elling

Sen

dai B

-4Se

nd

ai B-4

Sen

dai B

-4Se

nd

ai B-4

Sen

dai B

-4Se

nd

ai B-4

1.3

.2O

ther D

ispla

ced

1.4D

wellin

gs Dam

aged

1.4

.1N

um

ber o

f peo

ple w

ho

se ho

uses w

ere da

ma

ged

du

e to

ha

zard

ou

s events

Sen

dai B

-3Se

nd

ai B-3

Sen

dai B

-3Se

nd

ai B-3

Sen

dai B

-3Se

nd

ai B-3

1.5Lo

ss of Jo

bs/o

ccup

ation

s1

.5.1

Direct lo

sses of jo

bs/o

ccup

atio

ns in

ind

ustry a

nd

servicesSe

nd

ai B-5

Sen

dai B

-5Se

nd

ai B-5

Sen

dai B

-5Se

nd

ai B-5

Sen

dai B

-5

1.5

.2D

irect losses o

f job

s/occu

pa

tion

s in a

gricultu

re

1.5

.3Lo

sses of d

ays o

f activity

1.5

.3.1

Direct lo

sses of d

ays o

f activity in

ag

ricultu

re

1.5

.3.2

Direct lo

sses of d

ays o

f activity in

ind

ustry a

nd

services

1.6N

um

ber o

f peo

ple evacu

ated o

r receiving aid

1.6

.1N

um

ber o

f peo

ple w

ho

receieved a

id. In

clud

ing

foo

d a

nd

no

n-fo

od

aid

du

ring

a d

isaster

1.6

.2Su

pp

orted

with

evacu

atio

n

1.6

.3N

on

-sup

po

rted eva

cua

tion

s

1.6

.4N

um

ber o

f peo

ple w

ho

receieved a

id a

fter a d

isaster

1.7O

therw

ise affected

1.8A

ffected P

op

ulatio

n (n

o o

f imp

acts)SD

G 1

.5.1

/Sen

dai B

-1SD

G 1

.5.1

/Sen

dai B

-1SD

G 1

.5.1

/Sen

dai B

-1SD

G 1

.5.1

/Sen

dai B

-1SD

G 1

.5.1

/Sen

dai B

-1SD

G 1

.5.1

/Sen

dai B

-1

1.9M

ultip

le cou

nts, in

divid

uals (m

inu

s)

1.10To

tal Hu

man

Imp

actas (no

of p

eop

le)

Ge

o R

egio

n 1


38

Disaggregation of human impacts statistics

4. When estimates for human impacts are initially recorded by disaster management agencies,

basic demographic and social information (such as age and gender) about the affected people

may not yet be known because compiling demographic or social information about the

affected population is not a priority during the emergency period. Therefore, disaggregation

impacts may be a secondary step involving estimation and linking between multiple data

sources.

5. Sometimes there are challenges in producing disaggregated demographic and social

information for describing affected populations. The sample table extracted from the

Philippines pilot study reporting shows an example of how available disaggregated statistics

can be utilized, even when the information is incomplete, by including a category for

“unidentified”.

6. For future disaster occurrences and through increased experience with compiling summary

statistics after disasters, it becomes possible, via linking datasets, to produce social and

demographically disaggregated statistics for a basic range of human impact statistics for

specific disaster or over a period of time and for regional and national levels.

Sample Table: demographic disaggregation of affected population statistics, extract from

Philippines

Dea

th

Year Age groups

TOTAL

Gender groups

TOTAL

0-4

5-60 60+ Unidentified

Male Female Unidentified

2013

46

423

246 5,899 6,614

887 864 4,863 6,614

2014

22

202 45 25 294

200 87 7 294

2015 12 95 18 10 135 94 41

135

Mis

sin

g

Year Age groups

TOTAL

Gender groups

TOTAL

0-4

5-60 60+ Unidentified

Male Female Unidentified

2013 4 42 1 1,038 1,085 91 28 966 1,085

2014 2 19 0 11 32 25 7 0 32

2015 0 13 0 13 26 20 2 4 26

Source: Philippines Department of National Defense and Philippines Statistics Authority, via DRSF Pilot Study, 2016

Deaths or Missing

7. Death or missing is a combined category of statistics because missing people are either found

or, unfortunately, eventually declared dead. The transition from missing to dead follows a


39

procedure and period of time, which varies according to national laws. The differences in

laws and practices in terms of the time period for missing persons do not affect the

measurement because, eventually, in all cases the total amount of fatalities includes the

missing and later declared dead in the final statistics.

8. However, rules for attribution for deaths or missing population to a disaster currently varies

internationally, which effects the comparability for scope of human impacts measurements.

Rules for attribution of deaths to a disaster cannot be standardized across all cases, but the

general framework for attribution is:

a. deaths occurring during an emergency period (or deaths caused by an injury or illness

sustained during an emergency) and believed to be caused by a disaster as defined in

Section 2a, and

b. indirect fatalities associated with a hazard, e.g. deaths from illnesses caused by

consequences (poor access to water and sanitation, exposure to unsanitary or unsafe

conditions), resulting from a hazard.

9. The usual source of official records for deaths and causes of death, where it could be

determined, are via civil registration authorities and the Ministry of Health, which is

responsible for maintaining and monitoring health information systems. However, in the

event of a disaster, records for deaths or missing is, in the short-term, more commonly a

responsibility of the national disaster management agency (or equivalent organisation) in

partnership with the Ministry of Health and others as part of the disaster response and the

broader compilation and assessment of data on impacts from the disaster. These figures are

reported by and to the different levels of local and national government and usually at some

stage are shared in official reports to the press and the general public. Commonly there is a

need to revise original reported counts on deaths (and other human impacts) following the

emergency and after sufficient time to assess the sources of data and account for all of the

cases. The revised figures, which may be different than initial reports to the public, must be

stored in the centralized compilations of disaster impacts statistics across occurences and

utilized for indicators.

10. A key consideration for the broader statistical system is ensuring that the final official counts

of deaths after a disaster are also incorporated into the broader official system of

administrative statistics (i.e. the civil registration system), which is also the source use for the

long-term and comprehensive official statistics on mortality and health of the population.

These administrative sources have many important uses, including for estimating the rate of

growth of populations and for investigating public health issues, such as trends in mortality

from different types of health challenges. Civil and health administrative records contain

confidential information, but can be utilized to produce broad summary statistics for

describing trends in the population without revealing private information about individuals.

Injured and ill

11. Besides deaths, the other two main physical impacts from disasters to humans are injuries

and illness. Injuries and illness have both direct and indirect costs for households. The relative


40

importance of injuries or illnesses will vary depending on the characteristics of the underlying

hazard as well as on social factors, especially the vulnerability factors of the population in an

affected area.

Sample Table: Illness/ Injuries in Bangladesh with demographic disaggregation, 2006-2015

Bangladesh

C2a1 - Age groups TOTAL

C2a2 - Gender groups TOTAL

0-4 5-60 60+ Male Female

Illness 330378 1472750 87605 1890733 990769 899966 1890735

Injuries 2324 25273 5309 32906 19126 13782 32908

Source: Bangladesh Disaster-related Statistics, Bangladesh Bureau of Statistics 201

12. In Bangladesh, for example, illness is a more frequently occurring impact from disasters

compared to injuries, overall. But, the relevance for injuries or illnesses varies by hazard type

and also depending on the age and gender of the exposed population.

Displaced Populations

13. One of the immediate and conspicuous ways in which lives and livelihoods can be impacted

after a disaster is though temporary or permanent displacement. Displacement statistics are

are organized according to two characteristics: length of time and whether or not the

displacement was arranged (or ordered or financed) by governing agencies.

14. For all types of movement of the population as a direct result of a hazard, including

evacuations and permanent relocations of people due to a disaster, the suggested term is

displacement.

15. In the adopted terminology for the Sendai Framework (UNGA, 2016), evacuation is defined

as: “Moving people and assets temporarily to safer places before, during or after the

occurrence of a hazardous event in order to protect them.” Evacuations are not considered

part of “affected population” according to the Sendai Framework indicators because

evacuation is also a method of disaster risk reduction.

16. Thus, counts of evacuations refer to temporary arrangements, usually according to evacuation

plans and other support by government agencies. Sometimes, however, there are also

voluntary evacuations, in which households temporarily relocate from a hazard area on their

own expense (e.g. temporarily residing with family in another part of the country). In this

case, use of household surveys, and/or estimation is required for estimating the counts of

individuals or households affected.

17. For cases where evacuations are carefully managed, basic social and demographic

characteristics of the evacuated population are collected as part of administration of the

evacuation plan by the responsible government authorizes (usually social welfare ministries).


41

18. The other common cause of displacement, in this case occurring after a disaster, is

displacement caused by a damaged or destroyed dwelling. In the extreme cases, dwellings are

completely destroyed, effectively leaving households homeless and in need of immediate

relocation to another site. Another possibility includes minor damages that could be repaired

but require a temporary relocation of the household for safety reasons. There are also cases

where the dwelling structure may have received negligible damages but due to the changes of

the circumstances (and knowledge of circumstances) regarding the location of the dwelling,

the area is deemed unsafe for continued residential occupation. For all cases, the statistics can

be summarized most broadly according to permanent or temporary displacement.

19. The tables below show some sample statistics on evacuations for Philippines and Indonesia

collected from national official sources. In the case of the Philippines, the term “displaced” is

used for numbers of people evacuated as result of a disaster.

Sample Table 3: Evacuations in the Philippines by Hazard Type and Geographic Region,

2013-15

Source: Philippines Department of National Defense and Philippines Statistics Authority, via DRSF Pilot Study, 2016 Sample Table: Number of people evacuated by region and hazard type in Indonesia (2015)

IND

ON

ESIA

Province

EVACUATED

Drought Earthquake Flood Flood and

Landslide Landslide

Tidal Wave/ Abrasion

Tornado

Aceh 0 0 36522 68 456 336 29491

Bali

0

0

Bangka Belitung

0 0 0

0

Banten

0 0

0

0

Bengkulu

0 0 0 0 0

geophysicalmeteorologi

caltotal

Region I (Ilocos) 567,177 567,177

Region II (Cagayan Valley) 724,559 724,559

Region III (Central Luzon) 2,227,691 2,227,691

Region IV-A (Calabarzon) 561,932 561,932

Region IV-B (Mimaropa) 44,183 44,183

Region V (Bicol) 2,131,495 2,131,495

Region VI (Western Visayas) 99 2,471,882 2,471,981

Region VII (Central Visayas) 465047 870,617 1,335,664

Region VIII (Negros Island Region) 1,949,110 1,949,110

Region IX (Zamboanga Peninsula) 3,600 3,600

Region X (Northern Mindanao) 73,003 73,003

Region XI (Davao Region) 207,057 207,057

Region XII (Soccsksargen) 129,368 129,368

Region XIII (Caraga) 536,806 536,806

National Capital Region (NCR) 264,323 264,323

Cordillera Administrative Region (CAR) 239,936 239,936

Autonomous Region of Muslim Mindanao (ARMM) 27,116 27,116

National total (unadjusted) 465146 13029855 13495001

DISPLACED

PH

ILIP

PIN

ES


42

Central Java 0 0 2833 25 1166

700

Central Kalimantan

0

0

0

Central Sulawesi

0 200 375 4 East Java 0 0 1040 0 760 0 5

East Kalimantan

0 10 0 5 0 12165

East Nusa Tenggara 0

85

1190

5439

Gorontalo

406

0

522

Jakarta

1762

5997

7419

Jambi

150

0

0

Lampung

0

0

0

Maluku 4 0 8 423 12

1069

North Kalimantan

2238

0

11

North Maluku

11796 0

0

North Sulawesi

4031

3672

583

North Sumatra

0 75 77 500

11113

Papua

0

0

0

Riau

0 55 0 0

86

Riau Islands

0

0

792

South Kalimantan

0 0 0

0

South Sulawesi

30 103

211 0 40

South Sumatra

0 0 0 0

0

Southeast Sulawesi

0 65

0

West Java

0 1577 65 11825 0 4154

West Kalimantan

51 0 1740

8

West Nusa Tenggara

0 600 0 2500

0

West Papua

0 West Sulawesi

0

0

0

West Sumatra

0 1854 0 8382

75

Yogyakarta

0

22

3

National Total 4 30 65461 1033 38442 336 73675

Source: Informasi Bencana Indonesia (DIBI): http://dibi.bnpb.go.id

Impacts to livelihood

34. Impacts (or disruptions) to livelihoods is a concept from the internationally adopted

recommendation for the Sendai Framework monitoring (UNGA, 2016). The concept is broad

and measurement for Sendai Framework indicators is deferred to national practices. UNISDR

guidance defines livelihoods as: “the capacities, productive assets (both living and material)

and activities required for securing a means of living, on a sustainable basis, with dignity.”

35. Many of the assets and capacities related to this definition for livelihood are covered in the

framework as material impacts (i.e. impacts to dwellings, impact to agricultural crops and

http://dibi.bnpb.go.id/


43

other assets), as disruptions to basic services (like utilities, health and education services), or

by other human impacts.

36. Impacts to employment are measured similarly with disruptions to basic services, i.e. in terms

of number of people affected and length of time. Utilizing a household Survey specially

designed for evaluating impacts from disasters, Bangladesh Bureau of Statistics reported

statistics on impacts to livelihoods as distributions, across the affected population, according

to ranges in the number of losses of days. In some case these disruptions will be correlated, or

directly related to other impacts, such as damages to dwellings or other infrastructure.

Sample Table: Number of Households experiencing disruptions to employment or in access to

water and sanitation due to disasters, Bangladesh 2009-2014

Division Disruptions to Employment

Disrupted access to water and sanitation

Barisal Division 4361261 108501

Chittagong Division 818137 77650

Dhaka Division 430540 139357

Khulna Division 931668 120061

Rajshahi Division 668873 56920

Rangpur Division 613704 55125

Sylhet Division 488564 55859

Bangladesh 409776 613474 Source: Bangladesh Disaster-related Statistics 2015

Sample Table: Number of Households missing work due to disasters by hazards and

distribution by number of days missed, 2009-2014

Division/District

Working days

Total Number of Days

1-7 8-15 16-30 31+ Total

Bangladesh 395088 400737 230251 52042 1078118

Barisal Division 170240 81965 7721 4888 264814

Chittagong Divition 69765 35149 10533 696 116143

Dhaka Division 54301 86973 57418 1639 200332

Khulna Division 19748 15277 28073 14027 77125

Rashahi Division 12524 25427 29323 24978 92251

Rangpur Division 44140 51930 30453 3217 129740

Sylhet Division 24370 104015 66731 2598 197713 Source: Bangladesh Disaster-related Statistics 2015

Table: Number of days of lost employment due to disaster by division, 2009-2014


44

Division/District

Working Days

Number of Days (Male) Number of Days (Female)

1-7 8-15 16-30 31+ Total 1-7 8-15 16-30 31+ Total

Bangladesh 205043 213385 118629 28053 565110 190045 187352 111622 23989 513008

Barisal Division 87008 45908 4494 2006 139416 83232 36057 3228 2882 125399

Chittagong Divition 35940 17932 4976 472 59320 33824 17218 5557 224 56823

Dhaka Division 27504 47048 30012 1006 105570 26798 39926 27406 634 94764

Khulna Division 11231 8328 13470 6838 39867 8517 6949 14602 7189 37257

Rashahi Division 6459 12634 15818 14535 49446 6065 12792 13505 10443 42805

Rangpur Division 24706 29925 15227 2017 71875 19434 22006 15226 1200 57866

Sylhet Division 12195 51610 34633 1179 99617 12175 52404 32098 1419 98096 Source: Bangladesh Disaster-related Statistics 2015

Table: Number of school days missed due to disaster by division, 2009-2014

Division/District

Total Children (Age 4-

17)

Cause of Non Attended School

Total Damage School

Reduced HH

Income

Communication Failure

Ruined School

Spoilt Books

Illness/ Injury

Others

Bangladesh 6097562 1078118 75361 26381 787045 15261 20314 112808 40948

Barisal Division 1110302 264814 43318 8699 162655 4880 7912 26573 10777

Chittagong Divition 734173 116143 8722 1687 87366 1620 4236 8888 3624

Dhaka Division 1369514 200332 6371 4626 149371 1429 2726 27197 8612

Khulna Division 718127 77125 4720 3213 57687 820 1052 3276 6356

Rashahi Division 712588 92251 3535 2721 71282 882 1373 10479 1978

Rangpur Division 649637 129740 1688 2430 98377 3004 1306 16907 6028

Sylhet Division 803220 197713 7007 3004 160307 2626 1710 19487 3574 Source: Bangladesh Disaster-related Statistics 2015

Table: Number of children did not attend school due to disaster by causes and division, 2009-2014

Disruptions to basic services

37. Disasters are defined as disruptions to the functioning of a community or a society (UNGA,

2016), and some particular types of disruptions can be estimated based on the available data

on material impacts from disasters.

38. Disruptions to services from material impacts, like all other impacts tables, can be presented

according to hazard types (as in Table D2a below) and/or according to geographic regions

within the country. These statistics are an extension of direct impacts to critical infrastructure

(Table D2).


45

Table D2a Disruptions to Basic Services from a Disaster by Hazard Type

Aggregated statistics on human impacts

39. There are many waysthat human impact statistics can be presented or aggregated in summary

tables. This is a choice of presentation for dissemination of statistics, rather than a conceptual

decision, but the structure of tables also can affect th eaggregations into combined counts of

multiples types of human impacts from disasters Databases can always be queried in multiple

ways for multiple purposes. In this chapter we have shown a few examples, drawn from real

data and case studies conducted as part of the development of this handbook. The presentation

and organized structure of human impacts tabulation will vary depending on the requests of

users – whether to calculate a time series for specific indicators (such as SDGs and Sendai

Framework international monitoring indicators) or for other purposes, like a post-disaster

needs assessment (PDNA). The figure below demonstrates one way (among several

possibilities) of structuring human impact variables with the scope of the basic range of

disaster-related statistics

Figure 9: Sample structure of basic range of human impacts statistics

Geo

-ph

ysic

al

Hyd

rolo

gica

l

Met

eoro

logi

cal &

Clim

atal

ogi

cal

Bio

logi

cal

Oth

er

TOTA

L

Disruptions to Basic services from a Disaster1 Health services Sendai D-7 Sendai D-7 Sendai D-7 Sendai D-7 Sendai D-7 Sendai D-7

1.1 No. of people

1.2 Length of time

2 Educational services Sendai D-6 Sendai D-6 Sendai D-6 Sendai D-6 Sendai D-6 Sendai D-6

2.1 No. of people

2.2 Length of time

3 Public administration services Sendai D-8 Sendai D-8 Sendai D-8 Sendai D-8 Sendai D-8 Sendai D-8

3.1 No. of people

3.2 Length of time

4 Transport services

4.1 No. of people

4.2 Length of time

5 Electricity and energy services

5.1 No. of people

5.2 Length of time

6 Water services

6.1 No. of people

6.2 Length of time

7 ICT services

7.1 No. of people

7.2 Length of time

8 Other basic services

8.1 No. of people

8.2 Length of time

9 Total disruptions Sendai D-5 Sendai D-5 Sendai D-5 Sendai D-5 Sendai D-5 Sendai D-5

Hazard types


46

34. There is a broad demand for aggregated counts of “affected population” after a disaster.

UNGA (2015). The UNISDR Guidance on international indicators for Sendai Framework

monitoring has ruled out, for their purposes, adjustments for multiple counts of the same

individual, which may be affected by the same disaster in several ways – for example an

individual experiences an injury, a damaged dwelling, and a temporary loss of employment.

This means the Sendai Framework “affected population” indicator is actually a count of

number of impacts, rather than of number of people.

35. For other purposes, besides the international indicators reporting, another possibility is

impacts to estimate an adjustment for multiple counts in order to produce an additional

aggregation variables 1.9 and 1.10 in Table C3, above) measured in terms of numbers of

people. Both aggregations, in terms of counts of people or counts of cases of impact across

the selected categories are relevant for users and should be possible to estimate utilizing the

same basic underlying sources of data.

36. A similar situation can be observed for several other areas of social statistics, such as (e.g.)

statistics on domestic violence or abuse or statistics on slum population. For statistics on

domestic violence, usually there are multiple categories of abuse reported (e.g. physical,

verbal/psychological, sexual, other) and sometimes, the same individuals may be affected by

multiple categories of abuse. Thus, there are two potential aggregated statistics among the

relevant populations: total number of people affected by abuse and total number of individual

cases of abuse across all categories. Another example arose with interesting in measurement

of population living in slums. Slum-dwelling households are defined according to a list of

Hu

man

Imp

acts

Physically Affected

Dead

Missing

Injured or Ill Displaced

Other Impacts to Livelihood

Damged or Destroyed Dwelling (1.1.4)

Loss of employment

Otherwise Affected

Received humanitarian assistance

(e.g. food or assistance with evacutionn)


47

either/or categories. The aggregated number, which is also another international SDG

indicator, is calculated as the number of households experiencing at least one or more of the

defining characteristics of slums, i.e. counts of slum dwelling households includes an

adjustment to subtract any cases of double-counting (similar to item 1.10 in table C3).

37. Since there are many categories of human impacts that are potentially included in a

compilation of basic statistics, there are multiple sets of double-counting adjustments for

consideration in each aggregation, multiplied by the number of categories that are non-

exclusive, e.g. injured/ill, displaced and otherwise affected.

38. The Venn diagram below is a visualization of the different types of multiple counts (a,b,c,d)

from a hypothetical example. In practice, measurement of counts for each individual case of

multiple counts may not be feasible because it requires matching identification of individuals

for different impacts (potentially recorded from different data sources). However, a general

estimate (N) for individual counts for situations a, b, c, an d is sufficient for making an

estimate adjustment from the number of impacts to counts of individuals. The adjustment is

equal to N f(a+b+c+d)-1.

Figure 10: Venn Diagram of cases of multiple counts for individuals impacted by a disaster.

2e) Disaster Risk Reduction Activities

1. The Sendai Framework describes disaster risk reduction (DRR) as a scope of work “aimed at

preventing new and reducing existing disaster risk and managing residual risk, all of which

h f e a

b c d

g


48

contributes to strengthening resilience. DRR encompasses all aspects of work including the

management of residual risk, i.e. managing risks that cannot be prevented nor reduced, and

are known to give rise to, or already, materialize into a disaster event.” (United Nations,

2015)

2. In order to make a case for increases or improvements in DRR, a sufficiently accurate

quantification of the existing activities is needed. Government and other entities allocate

budgets to DRR and information on these activities is needed to determine effective means,

within the different contexts of disaster risk, to identify new proejcts or investment

opportunities that could significantly raise reduce risk or prevent unacceptable risks of

impacts from a disaster.

3. Another important purpose for measuring and monitoring DRR activities and expenditures is

they can be critical inputs for estimating the economic costs from disasters, since a large part

of post-disaster recovery is support for basic needs of affected communities and the

reconstruction effort, good as overall indicators of economic impacts.

4. Often the publically-financed disaster risk reduction activities, particularly disaster recovery,

are transfers from budget from central government to local authorities, and/or international

transfers (e.g. ODA). These transfers can be tracked through balance of payments and

national accounts statistics, just as with other types of transfers and activities (i.e. production,

investment, employment) in the economy as long as the activities with a DRR purpose can be

specifically identified and isolated from the broader national figures.

5. Statistical information on DRR activities particularly transfers and expenditures, are also

critical inputs for estimating the economic costs from disasters. (see section 2c)

6. Often the publically-financed disaster risk reduction activities, particularly disaster recovery,

are transfers of budget from central government to local authorities, and/or international

transfers (e.g. ODA). These transfers can be tracked through national accounts and balance of

payments statistics, like with other types of transfers and activities (i.e. production,

investment, employment) in the economy as long as the activities with a DRR purpose can be

specifically identified and isolated, for measurement purposes, from the broader aggregated

values.

7. There are two complementary approaches that can be applied for isolating the relevant values

and producing statistics on DRR activities, particularly the quantifications, in monetary terms,

of DRR transfers and expenditures.

8. The first approach is to produce a focused analysis of transfers from relevant institutions and

to analyze transfers and expenditures on a particular geographic region and time period where

there is a large-scale disaster recovery underway. This is an application of the existing

statistics on government finance and statistics derived from administrative records or

outcomes of surveys or censuses on the activities of businesses and households. A second

approach is to develop a series of functional accounts and indicators that track all types of

transfers and expenditures in the economy with a specific DRR purpose.


49

9. The tool that statisticians use to produce the economic statistics in the latter approach is to

develop specific functional classifications in order to define the domain of interest. DRR-

characteristic activities are defined (in order to objectively identify shares of expenditures or

transfers with a DRR purpose) and classificaed in Chapter 5.

10. The provisional classification of DRRCA is developed (see detail in Chapter 5), starting from

the Sendai Framework and the recently adopted terminology adopted by the UN General

Assembly. (UNGA, 2016) Following the Sendai Framework definition for disaster risk

reduction quoted above, the scope of DRRCA. activities is:

1. Disaster Risk Prevention

2. Disaster Risk Mitigation

3. Disaster Management

4. Disaster Recovery

5. General Government, Research & Development, Education Expenditure

Disaster risk reduction characteristic transfers include:

6. Internal transfers between public government services

7. Risk transfers, insurance premiums and indemnities

8. Disaster related international transfers

9. Other transfers

11. The same approach is also utilized for several other important cross-cutting domains of

economies (e.g. health, tourism, education), often designed as “satellite accounts”, which

refers to their nature as specially designed extracts (or “satellites”) of the system of national

accounts (SNA).

12. Typical outputs from accounts of expenditures or transfers of DRR activity, following the

basic framework of the SNA, will include:

a. Total national expenditure with a DRR purpose

b. DRR expenditure by source of financing (e.g. central government, local government,

private sector)

c. DRR expenditures and transfer by beneficiaries

d. DRR expenditure by type of DRR activity (e.g. disaster preparedness, recovery and

reconstruction, early warning systems, etc. – see Chapter 3 for the complete proposed

list of categories DRR activity categories)

e. Values of transfers from central government to local authorities

f. Values of transfers from international donors – i.e. DRR-related overseas

development assistance (ODA).

13. While hazards and disasters are events happening randomly in terms of timing and in relation

to the society, DRR is a continuous activity needed to strengthen society’s resistance and

resilience and thus DRR statistics should be compiled on a continuous and periodic basis (e.g.

as annual accounts). In this way, DRR statistics become an integrated and relatively

conventional domain of statistics, as an extension to the existing national accounts.


50

14. However, as there may be special demands for analysis of DRR activities at certain periods,

such as after a large-scale disaster, regular compilations of accounts of DRR expenditures and

transfers are complemented by specially designed studies and statistics for analyses of

specific events or to improve the understanding of the effectiveness of DRR investments

made before or after a disaster.

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