Everyone Counts. March 2018 issue. Saving lives, changing minds.

Transcription

1 Everyone Counts March 2018 issue Saving lives, changing minds.

2 International Federation of Red Cross and Red Crescent Societies, Geneva, 2018 Any part of this publication may be cited, copied, translated into other languages or adapted to meet local needs without prior permission from the International Federation of Red Cross and Red Crescent Societies, provided that the source is clearly stated. Requests for commercial reproduction should be directed to the IFRC Secretariat at All photos used in this study are copyright of the IFRC unless otherwise indicated. P.O. Box 303 CH-1211 Geneva 19 Switzerland Telephone: Telefax: Web site:

3 Everyone Counts March 2018 issue Saving lives, changing minds.

4 2 Contents 1. About the FDRS 9 Where in the world did people receive help from National Societies in 2016? 10 The previous two FDRS reports 11 What the FDRS does 11 The FDRS team wants to hear from you 12 How to work with the FDRS data 12 The web application: overview 12 The web application: National Profiles 13 Limitations and things to consider when using and interpreting FDRS data 14 How to download and use the data National Society achievements in 2016 and over the past five years, through the lens of the FDRS 15 The main indicators in the FDRS 16 National Society achievements for 2016 in summary, Region by Region 17 National Society achievements since Additional data on numbers of people reached according to type of programme or service 18 Additional data on sources of National Society income 19 How complete is the data? 22 How complete is the data overall? 22 How complete is the disaggregated data for women and men? 22 How complete is the disaggregated data for different age groups? The number of people volunteering 25 Has the number of volunteers dropped since 2012? 27 Is the drop in National Society volunteer numbers due to a global decline in volunteering? 29 Spontaneous volunteers and unaffiliated volunteers: Do we have to keep changing our indicators to keep up with changing trends? Different countries, different National Societies 34 Using per million indicators as an alternative way to understand National Society coverage 35 Connections between the per million indicators 37 National Society per million indicators: how are they affected by Human Development Index and population? Predicting the number of National Society volunteers after a disaster Maps: The extraordinary National Society support network 47 FDRS network maps Breaking down the data by sex, age and other factors what does it tell us? 55 Why break down the data? 56 Women and men: same or different? 57 Old and young: same or different? 58 People reached by National Societies: direct and indirect contact 59 Breaking down the data in other ways How should the FDRS change in the future? Acknowledgements Data Sources and References 65 Data Sources 66 References 66

5 3 List of figures Figure 1: National Societies that reach out to people in their country via disaster response and early recovery programmes, long-term services and development programmes, or both 10 Figure 2: Screenshot of the FDRS web application showing a National Society profile 13 Figure 3: Screenshot of the FDRS web application showing a map of people volunteering their time 13 Figure 4: Map showing largest source of income for each National Society (2016) 21 Figure 5: Numbers of people volunteering in different National Societies around the world (2016) 26 Figure 6: Change in total National Society volunteer numbers since 2012: more accurate figures excluding National Societies which had changed their reporting system (red line) compared to original figures (grey line) 27 Figure 7: Change in volunteer numbers since 2012, comparing volunteers for National Societies with volunteers for all organisations according to the World Giving Index, excluding countries in which the National Society changed their reporting to FDRS 29 Figure 8: Change in median numbers of volunteers per National Society since Figure 9: Number of National Society volunteers per million of population, by Region * 36 Figure 10: Connections between key per million indicators for National Societies, and between them and socio-demographic indicators 37 Figure 11: How the National Societies are allocated to four quadrants according to low versus high HDI and low versus high population 39 Figure 12: Differences in median number of blood donors (per million of population) between National Societies in the four quadrants: low versus high HDI and low versus high population 40 Figure 13: Differences in median number of people trained in first aid (per million of population) between National Societies in the four quadrants: low versus high HDI and low versus high population 40 Figure 14: Differences in median number of local units (per million of population) between National Societies in the four quadrants: low versus high HDI and low versus high population 41 Figure 15: Differences in median National Society income (per million of population) between National Societies in the four quadrants: low versus high HDI and low versus high population 41 Figure 16: Differences in median number of people reached in disaster response and early recovery programmes (per million of population) between National Societies in the four quadrants: low versus high HDI and low versus high population 41 Figure 17: Differences in median number of people reached in long-term service programmes (per million of population) between National Societies in the four quadrants: low versus high HDI and low versus high population 42 Figure 18: Differences in median number of staff (per million of population) between National Societies in the four quadrants: low versus high HDI and low versus high population 42 Figure 19: Differences in median number of volunteers (per million of population) between National Societies in the four quadrants: low versus high HDI and low versus high population 42 Figure 20: The impact of severe floods in Argentina in 2013 on volunteer numbers, and on numbers of people reached. Numbers are per million of population 45 Figure 21: The impact of the Ebola epidemic in Sierra Leone in 2014 on volunteer numbers, and on numbers of people reached. Numbers are per million of population 45 Figure 22: The disaster timeline. How volunteer numbers increase in the year of a disaster and in the subsequent years 46 Figure 23: An example of the kind of interactive map provided at the FDRS web application 48 Figure 24: Support links in 2016 between National Societies 49 Figure 25: Support links between National Societies within Regions 50

6 4 List of tables List of boxes Table 1: 2016 total results for the main indicators in the FDRS, by Region 17 Table 2: National Societies achievements since Table 3: People reached directly in 2016, according to type of programme or service 19 Table 4: Percentage of the different sources of income in each National Society, averaged across Regions (2016) 19 Table 5: Numbers of National Societies supplying all main indicators to the FDRS, by Region, with percentages 22 Table 6: National Societies contributing at least one sex-disaggregated main indicator to the FDRS in 2016, with percentages of National Societies in each Region 23 Table 7: National Societies contributing at least one sex-disaggregated main indicator to the FDRS since Table 8: National Societies contributing at least one age disaggregated main indicator to the FDRS in 2016, with percentages of National Societies in each Region 24 Table 9: List of support links between National Societies 52 Table 10: National Societies achievements in 2016, per cent of women 57 Table 11: National Societies achievements in 2016, disaggregated by age 58 Supporting National Societies with data collection 12 What s it like working in the FDRS team? 12 The main indicators in the FDRS 16 New indicator on people trained in first aid 16 Behind the numbers: Turkish Red Crescent Society support for refugees 20 Data disaggregation: monitoring the Fundamental Principles 24 Indian Red Cross Society: From estimates to better data on volunteering 28 Bangladesh Red Crescent Society: Inclusion of youth volunteers 28 Spontaneous volunteers in Mexico 31 Spontaneous volunteers in Fiji 32 Volunteer numbers grow in response to a major flood in Argentina 44 Cooperation between National Societies responding to the crisis in Syria 51 Vietnam: making sure that elderly and disabled people are included 56 Ecuadorian Red Cross: The tools to improve data quality already exist 61 Leaving no one behind: IFRC s 2018 World Disasters Report 62

7 5 Abbreviations CHF DREF FDRS HDI ICRC IFRC ODK SDGs VCA WGI Swiss francs Disaster Relief Emergency Fund Federation-wide Databank and Reporting System Human Development Index International Committee of the Red Cross International Federation of Red Cross and Red Crescent Societies Open Data Kit Sustainable Development Goals Vulnerability Capacity Assessment World Giving Index

8 6 In the picture, migrants hosted in the Teobaldo Fenoglio centre where the Italian Red Cross provides them food, shelter, clothing, health care, first aid and psychosocial support. 11/08/2016 Settimo Torinese (Turin, Italy) Emiliano Albensi/ Italian Red Cross

9 7 Turning data into action Let my dataset change your mindset Hans Rosling, Global health expert and data visionary It is with great pleasure that the IFRC presents this new edition of Everyone Counts which examines key data from 190 National Red Cross and Red Crescent Societies. Since 2012, we have been collectively committed to increasing transparency and accountability towards our communities. Reporting and publishing National Societies key indicators and key documents to the Federationwide databank and reporting system on a yearly basis is one example of such commitment. This strong pledge from all our members has made it possible to build a comprehensive dataset. This report is an attempt to further explain some of the key numbers and trends experienced by our membership. Focused analyses were conducted on several substantial themes such as our extraordinary and unrelenting network of volunteers, the diversity and uniqueness of the contexts within which we operate, as well as the outstanding cooperation and support across our Movement. The broad picture presented in the report is promising. In 2016, National Societies health services reached directly 171 million people, while water, sanitation, and hygiene promotion activities reached 11 million people, and disaster risk reduction programmes reached 28 million people. The analyses provided in this report are just the beginning of what is possible with this rich data set. We hope they inspire readers to discuss the findings and the policy implications, and to dig further into the data with their own in-depth analyses. By providing analyses that inform our actions, this report is a step forward in improving data responsibility. As the IFRC is a large player among dataready humanitarian organizations, our obligation towards accountability urges us to provide open, transparent, and accurate data for our operations and activities. Moving forward, we will continue to improve the quality of our data to better illustrate the impact of our work and so that we can provide increased assistance to the most vulnerable and hardest to reach communities, including those left behind. And we will make sure that this focus on data responsibility feeds into IFRC s Strategy Elhadj As Sy, Secretary General

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11 ABOUT THE FDRS 9 1. About the FDRS In this introductory chapter, we introduce the Federation-wide Databank and Reporting System (FDRS). We look at what kinds of data are included and how to access and analyse it, report some recent successes and problems, and show how it can be used to answer some interesting questions about National Societies in a global perspective.

12 10 ABOUT THE FDRS Where in the world did people receive help from National Societies in 2016? Figure 1: National Societies that reach out to people in their country via disaster response and early recovery programmes, long-term services and development programmes, or both 1 That kind of question can be answered using the FDRS The Federation-wide Databank and Reporting System. The FDRS collects data from National Red Cross and Red Crescent Societies (National Societies) across the world about what they are doing and with what resources. National Societies have been contributing data to the FDRS since Even in the very first year, all except two National Societies (187 of IFRC s 189 member Societies in 2012) contributed at least some data; by 2013, all National Societies were already contributing at least some data. The coverage and quality of the data has been improving every year since then. 5 years Five years after its launch in 2012, the FDRS is firmly established as a unique database on humanitarian capacity and response worldwide. Disaster response Disaster response and long-term services Long-term services No data 1 The boundaries and the designations used on this map and all other maps in this publication do not imply the expression of any opinion on the part of the International Federation of Red Cross and Red Crescent Societies and are used for illustrative purposes only.

13 ABOUT THE FDRS 11 The FDRS data tells us that in 2016, of the 142 National Societies for which data is available, 116 reached people in their country with disaster response and early recovery programmes and 118 reached people with long-term services and development programmes; 92 National Societies did both. All National Societies that provided data are providing some kind of service to their populations. Forty-eight National Societies were not able to provide this data but most of these are certainly providing services too. So in most, if not all, of the countries in the world, National Societies were supporting people with at least some humanitarian services. The grey gaps on the map illustrate the need to have the most comprehensive data possible in the FDRS to be able to best capture and present National Society performance and successes. The FDRS can help answer many other questions too. Here are some that are addressed in this report. What did National Societies achieve in 2016? (page 15) How many National Society volunteers are there? Is this number growing or shrinking? (page 27) What factors such as the size or development of a country best explain the differences between National Societies on the main indicators? (page 38) Is there such a thing as a typical disaster timeline? If a major disaster hits a country, how do the numbers of volunteers change in the following years? (page 43) The previous two FDRS reports This is the third report on the FDRS. The first two can be found here: Everyone Counts Key data from 189 National Red Cross and Red Crescent Societies a baseline November 2014 Download the report in English Download the report in French Download the report in Spanish Download the report in Arabic Everyone Counts Progress 2015 Download the report in English Download the report in French Download the report in Spanish Download the report in Arabic What the FDRS does Provides National Societies, IFRC staff, partners, researchers and other stakeholders with immediate and engaging access to data about National Societies worldwide. Allows better understanding of the true scale and scope of Red Cross and Red Crescent humanitarian assistance. Promotes greater awareness of the capacities, services, and potential of National Societies, to boost their self-development and external profile. Facilitates monitoring and reporting of National Society performance in a consistent, transparent and accountable manner. The FDRS benefits National Societies, nationally and globally, by improving transparency and accountability, visibility, understanding and awareness. Annual reporting by all National Societies is a constitutional requirement and the FDRS has designed this around a small set of main indicators guided by IFRC s Strategy FDRS Iraqi indicators are smart and they are actually guiding us to collect and report programme-related data on a regular basis. They are also included in our standard reporting template and help us in collecting gender disaggregated data for better planning. Javed Ahmed, National PMER Manager for Pakistan Red Crescent Society Red Crescent Society finds the FDRS as a useful tool to communicate to the Red Cross Movement the various services that the National Society as been providing in Iraq since 2012 Husam Sabri, Head of International Relations Department for Iraqi Red Crescent Society

14 12 ABOUT THE FDRS Supporting National Societies with data collection Collecting data which is disaggregated e.g. by sex, can be very complicated. Many National Societies asked for help. The IFRC s Technical Note on Counting People Reached (2017) responds to this kind of problem, helping volunteers and staff to improve the way they capture and report disaggregated data. This Note was piloted in two phases in 2017 in Asia Pacific region (including Philippine Red Cross, Bangladesh Red Crescent, Australian Red Cross, Nepal Red Cross, Myanmar Red Cross and Singapore Red Cross). Specific challenges were identified such as harmonising paperbased forms across many branches. The Technical Note is being shared more broadly to achieve better quality data capturing and reporting in the National Societies. What s it like working in the FDRS team? An FDRS team member says: Keeping in touch with 190 different National Societies, helping them report annual data for a wide range of indicators, covering their resources, capacities, and reach, is even harder than you might think. The biggest challenge (and headache) was developing a standard list of indicators that includes clear guidance and definitions. We want National Societies to be able to accurately illustrate the work and services they provide. But there are 190 of them different, independent organisations operating in a wide variety of contexts. Even when it comes to some of the most straight-forward indicators, such as number of local units, we hit quite a few questions. National Societies have different organisational structures and operate differently within their national borders. Some National Societies have branches, others have chapters, or districts. To accommodate this variety, the FDRS team has decided to use a neutral term: local unit. That s just one example. Imagine what happens when we tried to agree on how to count the number of people reached! Then, when we ve received the first datasets from the National Societies, we must make sure the data is as accurate as possible, so we have to do data cleaning, triangulation, and analysis. This usually means we are constantly in touch with National Societies, as well as different technical and regional colleagues. I don t know if they ever get bored with our endless questions mostly I think they want to help to get the best and most accurate data possible. The FDRS team wants to hear from you The FDRS team consists of three full-time staff members in IFRC Headquarters and Regional Offices, as well as a number of data analysts who cover IFRC s five Regions. They are keen to hear from you with suggestions and ideas for the next FDRS report to ask more questions about National Society capacity and response for help with analysing data. The team are also happy to provide on request the original data files and statistical code files which produced all the tables, graphs and analyses. Contact the FDRS team at fdrs@ifrc.org with requests and ideas. How to work with the FDRS data The easiest way to find out more about National Societies worldwide or in a particular country is to use the FDRS web application. THE WEB APPLICATION: OVERVIEW The web application (data.ifrc.org/fdrs) was launched in February Users can visualize the data using maps and tables. So far, more than 8,000 users from almost every country in the world have used the FDRS website 2. 2 Data from 31 January 2018; users came from nearly every country in the world (193 countries)

15 ABOUT THE FDRS 13 Figure 2: Screenshot of the FDRS web application showing a National Society profile THE WEB APPLICATION: NATIONAL PROFILES The FDRS web application also shows data for each individual National Society. Users can go directly to data.ifrc. org/fdrs/societies. data.ifrc.org/fdrs The FDRS web application provides maps and tables for the most important National Society indicators and shows profiles for each National Society. Figure 3: Screenshot of the FDRS web application showing a map of people volunteering their time

16 14 ABOUT THE FDRS Limitations and things to consider when using and interpreting FDRS data FDRS is ambitious and wide-reaching. Data quality is improving all the time, but there will always be some problems and things to bear in mind when using and interpreting the data. Here are some key points. 1. Missing data: Data is not available for some indicators for some National Societies in some years. The National Societies that did provide data may not be typical for all National Societies and so analyses and summaries of this data may not be valid for all National Societies. This is particularly an issue with sex and age disaggregated data, which can be a challenge to collect, so the number of National Societies which do not provide them can be quite large. 2. Standardisation and regularity of reporting: Every National Society has its own ways of recording data, which also may change over time. It is a big achievement of the FDRS to provide a set of definitions of key data which are clear and not too difficult to use. But it has taken some years for National Societies to gradually adopt the FDRS definitions. When comparing countries or years, there are still some limitations on the comparability of data due to the different methodologies of data collection, sample sizes, definitions, and frequency of reporting. 3. Time lag: The availability of definitive figures on income and expenditure, are constrained by the availability of the most recent audited financial statements. This is due to varying fiscal years, and corresponding planning and reporting cycles of the National Societies. This means data for one year are usually published in the FDRS a year or more later. 4. Reporting bias: The data submitted through the FDRS is self-reported information by each National Society, which is the owner and gatekeeper, and is responsible for accuracy and updating. The FDRS team tries to triangulate the data provided by the National Societies with previous data and other data in the public domain, but still it is always possible that a National Society might try to provide data which presents itself in a better light. 5. Knowing the data: Context is important. Although the numbers in the FDRS are now quite reliable, it is still important to think carefully about the context of the specific National Society and the meaning of the indicators in each context before interpreting the data. How to download and use the data At data.ifrc.org/fdrs/data-download, users can download the complete FDRS dataset for free and without needing to register. It is provided as a CSV (Comma-separated values) file which can be saved on the user s computer and opened in a spreadsheet programme such as Microsoft Excel. Each column in the spreadsheet represents one indicator, together with identifying columns like Country, Year, etc. So most National Societies have several rows, one for each year. Other columns such as Population, which are not officially part of the FDRS, have been provided. These additional columns have been merged in to the spreadsheet by the FDRS team from other datasets like the World Bank; they can be very useful in helping to understand and use the FDRS data, for example Population and Human Development Index (HDI) are used in this report on page 38. The column headings in the spreadsheet are quite short and may be hard to understand, so a codebook is also provided, which explains what they mean in more detail. For those wanting to investigate the data and look at differences between countries, the FDRS team has some suggestions to make: Consider medians as well as means, and look at numbers per million of population as well as raw numbers. This helps ensure that all countries and National Societies are treated on an equal footing. Consider including the most important external variables such as number of disasters experienced in a country, Human Development Index, Gross Domestic Product (especially in the Purchasing Power Parity variant) and population size (even when looking at data which is already expressed per million of population). The FDRS team has found these variables to be particularly good at explaining differences between the main indicators for different National Societies. Download The complete FDRS data sets are available for download. They also include important socio-demographic indicators for each country, to help National Societies and other interested parties to investigate the data in more detail.

17 NATIONAL SOCIETY ACHIEVEMENTS IN 2016 AND OVER THE PAST FIVE YEARS, THROUGH THE LENS OF THE FDRS National Society achievements in 2016 and over the past five years, through the lens of the FDRS 2016 is the fifth year of FDRS data collection. In this chapter, as well as presenting the main FDRS indicators and some National Society achievements in 2016, we also look at how things are changing over time.

18 16 NATIONAL SOCIETY ACHIEVEMENTS IN 2016 AND OVER THE PAST FIVE YEARS, THROUGH THE LENS OF THE FDRS The main indicators in the FDRS National Societies send data annually to the FDRS team on a small set of main indicators. Here is the original set: The main indicators in the FDRS Number of people reached: all direct and indirect recipients and people benefiting from Red Cross and Red Crescent services. This is actually two scores: people reached with disaster response and early recovery programmes, and people reached with long-term services and development programmes Number of people volunteering their time: people who have volunteered at least four hours of their time during the reporting period. Number of paid staff: people who work with a National Society for a minimum of three months during the reporting year and are remunerated. Number of people donating blood: people who have donated blood at least once during the reporting period. Number of local units: local units are defined as National Society subdivisions that work with the community and can include local chapters, branches, regional and intermediate offices, and headquarters. Total income received: the fiscal value of money, material goods, and services a National Society receives during a fiscal year. Total expenditure: the fiscal value of money, material goods, and services a National Society spends during a fiscal year. Some of the key indicators (people reached, volunteers, staff, people donating blood) are also disaggregated in different ways: see page 22, page 23 and page 55. In 2016, the initial list of indicators was expanded to include one completely new indicator: New indicator on people trained in first aid Number of people trained in first aid: people who have completed at least one first aid course (at any level, either online or face to face) during the annual reporting period. Additionally, from 2016, some indicators are disaggregated further: Indicators for people (people reached, volunteers, staff, people donating blood) are now broken down by age group (the age groups are aligned with SPHERE s core standard 3). The indicators for people reached are now broken down by type of service/thematic area, (disaster risk reduction, shelter, livelihoods, health, water sanitation and hygiene, migration, cash transfer programming, and social inclusion and a culture of non-violence and peace). Total income is now broken down by source of income. Finally, a new category of additional indicators was introduced: Whether the National Society has accident insurance coverage for staff and volunteers. List of other National Societies giving support to the National Society or receiving support from it (page 52). Number of staff deaths on duty. as well as some additional financial information like whether the National Society accounts are audited. The FDRS includes a small set of main indicators like number of people volunteering as well as some additional indicators; some of the indicators are also broken down further, for example by sex.

19 NATIONAL SOCIETY ACHIEVEMENTS IN 2016 AND OVER THE PAST FIVE YEARS, THROUGH THE LENS OF THE FDRS 17 National Society achievements for 2016 in summary, Region by Region There are some notable differences between the Regions. African National Societies have the smallest total numbers of staff. National Societies in the Americas do not have many local units: they have around 11 staff members for every local unit, whereas in Africa there are more local units than staff. National Societies in Middle East and North Africa reach more than 14 million people with long-term services, more than in Europe and Central Asia or the Americas. In Africa and Middle East and North Africa, about 4-8 Swiss francs was spent for every person reached, whereas in Asia Pacific, about 285 Swiss francs was spent. In Africa, there are more than 180 volunteers for every staff member, whereas in Europe this figure is around 7. Africa Europe The profile of National Society achievements differs strongly from Region to Region. For example, in Africa, there are more than 180 volunteers for every staff member; in Europe there are around 7. Table 1: 2016 total results for the main indicators in the FDRS, by Region 3 AFRICA AMERICAS ASIA PACIFIC EUROPE AND CENTRAL ASIA MIDDLE EAST AND NORTH AFRICA GRAND TOTAL Local Units 12,902 3, ,284 17,989 4, ,822 People Volunteering 1,618, ,927 5,615,096 1,640,200 2,251,956 11,649,519 Paid Staff 8,893 43, , ,542 29, ,161 Blood Donors 303,971 3,125,080 11,648,242 3,902, ,986 19,384,589 People Trained in first aid 218,693 3,156,072 5,716,669 2,045, ,503 11,348,246 People Reached by Disaster Response and Early Recovery 13,498,219 4,432,732 13,190,986 13,829,830 4,603,041 49,554,808 People Reached by Long Term Services and Programme Development 31,783,969 11,048,723 37,398,662 11,951,965 14,281, ,464,621 Total Income (CHF) 230,950,708 3,134,753,279 14,387,284,159 5,466,971, ,402,182 23,400,362,119 Total Expenditure (CHF) 188,368,832 3,179,784,337 14,201,151,098 5,356,228, ,782,419 23,090,314,900 3 For the numbers of National Societies reporting, see Table 5, page 22.

20 18 NATIONAL SOCIETY ACHIEVEMENTS IN 2016 AND OVER THE PAST FIVE YEARS, THROUGH THE LENS OF THE FDRS National Society achievements since 2012 Since the FDRS already includes five years of data, it is possible to look at changes over time. Table 2: National Societies achievements since Number Local Units 164, , , , ,822 People Volunteering 16,501,334 16,028,877 14,542,563 14,216,795 11,649,519 Paid Staff 431, , , , ,161 Blood Donors 20,863,417 20,746,474 22,435,799 19,711,524 19,384,589 People Reached by Disaster Response and Early Recovery 85,637, ,848,792 57,931,563 67,928,784 49,554,808 However, this data is not yet comprehensive so is not shown in Table 2. Some key points from Table 3, page 19: National Societies make enormous contributions to people s health all over the world, with more than 170 million people reached directly, including nearly 100 million people in Africa. Health really dominates National Society services and programming in Africa Disaster risk reduction is a big focus in Europe and Central Asia but is much less of a focus in Asia Pacific. Cash transfer programming is already directly reaching more than 2 million people People Reached by Long Term Services and Programme Development 103,744, ,856, ,136, ,848, ,464,621 Total Income (CHF) 32,545,717,017 30,794,786,782 31,415,239,987 29,611,876,470 23,400,362,119 Total Expenditure (CHF) 32,196,192,428 30,394,278,665 31,653,295,058 29,542,773,315 23,090,314,900 One striking change here seems to be a fall in volunteer numbers but on Figure 7, page 29 we see that the story is not so simple. We can also see that the numbers of people reached varies a lot from year to year as funding comes and goes, while the infrastructure staff numbers and numbers of local units remains more stable. Staff numbers have gone up by almost 10 per cent in the last five years despite a big drop in income. What does this mean for efficiency? For effectiveness? For sustainability? Additional data on numbers of people reached according to type of programme or service Since 2016, additional information is gathered for the existing indicators on people reached which records the different type of programme or service involved (Table 3, page 19). National Societies are also asked to provide additional figures for the even larger numbers of people reached indirectly A58% More than 170 million people are reached globally by National Societies with health services every year; nearly 100 million of them (58 per cent) are in Africa.

21 NATIONAL SOCIETY ACHIEVEMENTS IN 2016 AND OVER THE PAST FIVE YEARS, THROUGH THE LENS OF THE FDRS 19 Table 3: People reached directly in 2016, according to type of programme or service 4 AFRICA AMERICAS ASIA PACIFIC EUROPE AND CENTRAL ASIA MIDDLE EAST AND NORTH AFRICA People Reached by Disaster Risk Reduction Services 6,917,659 40= 1,719,315 10= 2,519,752 15= 12,747,091 74= 4,479,405 26= 28,383, = People Reached by Shelter Services 247,961 1= 141,390 1= 202,553 1= 910,083 5= 1,743,604 10= 3,245,591 19= People Reached by Livelihood Services 1,372,488 8= 139,734 1= 365,268 2= 5,970,213 35= 8,777,079 51= 16,624,782 97= People Reached by Health Services 99,508, = 12,657,945 74= 46,197, = 7,618,567 44= 5,371,825 31= 171,354,333 1,000= People Reached by WASH Services 3,207,358 19= 446,143 3= 2,965,674 17= 2,754,272 16= 1,473,692 9= 10,847,139 63= People Reached by Migration Services 3,526,882 21= 132,124 1= 128,289 1= 1,093,600 6= 85,601 0= 4,966,496 29= People Reached by Cash Transfer Programming Services 510,948 3= 234,079 1= 950,554 6= 228,861 1= 83,869 0= 2,008,311 12= People Reached by Social Inclusion Services 1,069,396 6= 1,066,318 6= 4,246,613 25= 3,713,460 22= 2,439,852 14= 12,535,639 73= GRAND TOTAL Additional data on sources of National Society income In 2016, National Societies started to report more information about their sources of income. Table 4: Percentage of the different sources of income in each National Society, averaged across Regions (2016) AFRICA AMERICAS ASIA PACIFIC EUROPE AND CENTRAL ASIA MIDDLE EAST AND NORTH AFRICA Income from Home Government 7 105= = = = = = Income from Foreign Government = = 0.4 6= = 0 = = Income from Individuals = 3 45= = = = = Income from Corporations = = = = 1 15= 3 45= Income from Foundations 0.2 3= = 0.2 3= = = = Income from Pooled Funds = 0 = 0 0= 0.5 8= = = Income from UN Agencies and Other Multilateral Agencies = = = = = = Income from NGOs = 0.5 8= = = = 2 30= Service Income = = = = = = Income from Generating Activities Other National Societies = = = = = = = 9 135= = = = = Income from IFRC = = = = = = Income from ICRC = = = = = = Income from Other = = = = = = GRAND TOTAL 4 For the numbers of National Societies reporting, see Table 5, page 22.

22 20 NATIONAL SOCIETY ACHIEVEMENTS IN 2016 AND OVER THE PAST FIVE YEARS, THROUGH THE LENS OF THE FDRS The Table 4, page 19 shows the percentage of the different sources of income in each National Society, averaged across regions. It shows very clearly the different ways in which National Societies in different Regions typically finance their activities. In Middle East and North Africa, funds from Foundations are the most popular source of income a source which is almost unknown in other Regions. Service income and funding from home government, and from individuals, are particularly important in almost all Regions but much less so in Africa. Income from other National Societies, ICRC and from UN agencies is particularly important in Africa Income from IFRC is important in Africa but even more so in Asia. Figure 4, page 21 is one way to show this diversity across the globe. For each National Society, only the most important of all the various sources of income is shown. Each National Society is unique in the way it funds its activities; but some patterns emerge across Regions. For example, funding from home governments is very strong in all regions except Africa. What more could we learn from looking beyond this data to how sources of income have been changing over the last five years? Behind the numbers: Turkish Red Crescent Society support for refugees Behind every piece of data in the FDRS is at least one story. Let s look for example at People reached with long-term services and development programmes. One of the biggest numbers for this indicator comes from the Turkish Red Crescent Society: nearly 4 million refugees within the country were reached with different services in One big component of Turkish Red Crescent Society operations is partnering with the World Food Programme to deliver the Emergency Social Safety Net (ESSN), a multi-purpose cash transfer scheme which aims to provide monthly assistance through debit cards (KIZILAYKART) to 1.3 million of the most vulnerable refugees in Turkey. Those receiving assistance decide for themselves how to cover essential needs like rent, bills, food, and medicine and pay for them with the debit card (debit cards are widely used in Turkey even for very small purchases). The largest European Union humanitarian aid programme ever, ESSN is part of the 3 billion euro Facility for Refugees in Turkey. KIZILAYKART is also easy to link with other programmes and helps link humanitarian response with longer-term outcomes such as promoting sustainable livelihoods. ESSN selects those most in need, such as large families, the elderly, single females, single-headed households, and people with disabilities. Cash gives people choice with dignity. It empowers them to manage their families priorities with a degree of independence and allows them to stretch their budgets further. 5 This newest data for 2017 will be included in the FDRS during TRC IFRC/Turkish Red Crescent Society Gaziantep, Turkey, 2017 Shik Mohammedi s family is one of the 600,000 families who escaped to Turkey, now living in the border town Gaziantep. They are holding ìkizilaykartî, a pre-loaded cash card provided by the Turkish Red Crescent for asylum seekers and refugees. Each family member eligible for the cash support scheme is receiving a monthly payment of 100 Turkish lira (25 euro) via the card. People can buy whatever they need most with the card whether it is food, rent, school fees or mobile phone costs.

23 NATIONAL SOCIETY ACHIEVEMENTS IN 2016 AND OVER THE PAST FIVE YEARS, THROUGH THE LENS OF THE FDRS 21 Figure 4: Map showing largest source of income for each National Society (2016) Main Sources of Income Home Government Foreign Government Individuals Corporations Foundations UN and other multilateral agencies NGOs Income Generating Activities Service Income IFRC ICRC Other National Societies Others

24 88% 12A 74% 26A 77% 23A 72% 28A 72% 28A 88% 12A 8 20A 86% 14A 91% 9A 72% 28A 97% 3A 83% 17A 28A 72% 98% 2A 94% 6A 83% 17A 94% 6A 72% 28A 77% 23A 63% 37A 83% 17A 89% 11A 61% 39A 22 NATIONAL SOCIETY ACHIEVEMENTS IN 2016 AND OVER THE PAST FIVE YEARS, THROUGH THE LENS OF THE FDRS How complete is the data? HOW COMPLETE IS THE DATA OVERALL? Table 5: Numbers of National Societies supplying all main indicators to the FDRS, by Region, with percentages Africa Americas Asia Pacific Europe and Central Asia Middle East and North Africa Grand Total = = 100A = = = = = = = = = = = = = = = 100A = = = 47 73= = = = = 11 23A A 94+ 6A 91+ 9A A 77% 85% 94% 91% 77% = = 161 1,000= = = 147 In 2013, all National Societies were already providing data for at least one indicator, and 85 per cent provided all main indicators. In 2014 and 2015, this number increased, but it is lower again (77 per cent) in 2016, as National Societies adjust to the increased data disaggregation requirements for FDRS. 77 per cent of National Societies provide data on all the main FDRS indicators. 77% = HOW COMPLETE IS THE DISAGGREGATED DATA FOR WOMEN AND MEN? When collecting data about people, it is important to record whether they are men or women. As the situation of women and men is different in so many ways all over the world, keeping separate figures can not only help highlight inequalities but also help understand in more detail the situation of National Societies on the ground. To find this out, National Societies are now asked to provide their data broken down into figures for men and women.

25 34A 11A 9A 61A 20A NATIONAL SOCIETY ACHIEVEMENTS IN 2016 AND OVER THE PAST FIVE YEARS, THROUGH THE LENS OF THE FDRS 23 Table 6: National Societies contributing at least one sex-disaggregated main indicator to the FDRS in 2016, with percentages of National Societies in each Region AFRICA AMERICAS ASIA PACIFIC EUROPE AND CENTRAL ASIA MIDDLE EAST AND NORTH AFRICA GRAND TOTAL A % 66% 89% 91% 39% 8 In 2016, 80 per cent of National Societies were providing some sex-disaggregated data, which is better than in 2012, but there is still room for improvement. Some actual National Society results for the main indicators, broken down by sex, are reported in the special section on disaggregation, on page 55. HOW COMPLETE IS THE DISAGGREGATED DATA FOR DIFFERENT AGE GROUPS? Data disaggregation should be carried out not just by sex but also by age and, as far as possible, for any other factors which could be important for ensuring impartiality and for understanding how National Society services are delivered. 155= 42 85= = = 48 25= 7 1,000= 152 From the beginning, National Societies contributed sex-disaggregated data to FDRS. National Societies have worked hard to provide sex-disaggregated data, with the best results so far in Africa, Asia Pacific, and Europe and Central Asia. Table 7: National Societies contributing at least one sex-disaggregated main indicator to the FDRS since A A A A A 77% 78% 65% 63% 8 725= = = = = A8 80 per cent of National Societies provide at least some sex-disaggregated data.

26 43A 47A 40A 83A 44A 24 NATIONAL SOCIETY ACHIEVEMENTS IN 2016 AND OVER THE PAST FIVE YEARS, THROUGH THE LENS OF THE FDRS Data disaggregation: monitoring the Fundamental Principles The Fundamental Principles are an expression of the International Red Cross and Red Crescent Movement s values and practices, and they guide everyone in the Movement. All programmes and activities need to be aligned with the Fundamental Principles, including Impartiality, which states that National Societies provide humanitarian support without discrimination. To ensure that we uphold these principles, our Movement resources, and the numbers of people reached, must be recorded and reported appropriately to ensure there is transparency and accountability to affected people and to demonstrate that we are pro-active in targeting the most vulnerable and not discriminating or denying services to groups who need humanitarian support. FDRS data disaggregation responds to these demands. In 2016, National Societies started contributing age disaggregated data to FDRS, and already more than half of National Societies have responded. Table 8: National Societies contributing at least one age disaggregated main indicator to the FDRS in 2016, with percentages of National Societies in each Region AFRICA AMERICAS ASIA PACIFIC EUROPE AND CENTRAL ASIA MIDDLE EAST AND NORTH AFRICA Some actual National Society results, broken down by age, are reported on Table 11, page 58. GRAND TOTAL 33A % 57% 53% 6 17% 56% 320= = = = 32 30= 3 1,000= A56% 2016 was the first year in which National Societies were asked to provide age disaggregated data, and already more than half have done so.

27 THE NUMBER OF PEOPLE VOLUNTEERING The number of people volunteering In this chapter, we look at the numbers of National Society volunteers worldwide: is it true that fewer people are volunteering?

28 26 Volunteers are incredibly important to National Societies. Volunteers allow National Societies to efficiently serve communities from the inside with activities ranging from disaster relief operations to longer term community development services. THE NUMBER OF PEOPLE VOLUNTEERING Figure 5: Numbers of people volunteering in different National Societies around the world (2016) National Societies want to know how many volunteers they have and present this number to the public. IFRC presents itself as the world s largest humanitarian network. What numbers back up this statement? How reliable are they? One of the main FDRS indicators records the number of people volunteering at least four hours with the National Society in the previous 12 months. Of course, many people volunteer much more of their time. As the map shows, there are National Society volunteers all over the globe. > 50,000 5,000 50,000 < 5,000 No Data

29 THE NUMBER OF PEOPLE VOLUNTEERING 27 Has the number of volunteers dropped since 2012? In the previous chapter, we saw that the total number of National Society volunteers appears to have dropped between 2012 and In this section, we look more closely at these numbers. the way they reported the numbers (red line), compared to the original figures. (The grey line shows what the trend used to look like when those National Societies were still included.) So, while there is still a decrease in the total number of National Society volunteers globally between 2012 to 2016, the decrease is smaller than originally feared: around 16 per cent. The fall seems to have stabilised after Figure 6: Change in total National Society volunteer numbers since 2012: more accurate figures excluding National Societies which had changed their reporting system (red line) compared to original figures (grey line) Before the FDRS, it was only possible to make estimates of the total number of people volunteering for National Societies. Now, the FDRS is starting to provide hard data. However, in the first years of the FDRS, some National Societies did not yet have accurate figures for the number of volunteers according to the FDRS definition and had to use estimates based on other data instead. In 2016, many National Societies have been able to provide more accurate data. Mostly, this means that the revised figures for the numbers of volunteers in these particular National Societies are lower. This might seem like a disappointment but in the medium and longer term it will be much better to have more accurate numbers, nationally and globally. Where lower figures in more recent years are due to improvements in data quality, it is important not to think of the differences as a drop in volunteer numbers. So, if we exclude from the overall totals the National Societies for which we know there was a change in the way volunteer data was reported 6, what is the trend over time? Figure 6 shows the trend when we exclude data from National Societies which reported any volunteer numbers more than 25 per cent different from the previous year, and had changed Total Number of People Volunteering 16M 14M 12M 10M 8M 6M 4M 2M People Volunteering, controlling for change in reporting system People Volunteering National Societies in Nigeria, Tanzania, Uganda; Brazil, Dominican Republic, Jamaica; Cambodia, India, Maldives, Mongolia; Armenia, Cyprus, Russian Federation, Slovakia

30 28 THE NUMBER OF PEOPLE VOLUNTEERING Here are two examples that will give some insight into the challenges and rewards of having accurate data. Indian Red Cross Society: From estimates to better data on volunteering Until recently the Indian Red Cross Society used an estimated figure of 12 million as the total number of its volunteers and members. During the development of the new Indian Red Cross Strategy 2030, the need for more accurate data was recognised and the National Society carried out an extensive data collection exercise in Based on data received from more than 1,100 state and district branches, a total of 297,729 active volunteers were reported. At the same time the Indian Red Cross also collected data on its Junior and Youth Red Cross members, who are registered through schools and colleges and pay nominal membership fees. Their confirmed number reaches a remarkable total of 9,617,931 people. While many of these Junior and Youth Red members engage in volunteer activities, it is not possible to ascertain how many of them have volunteered for four hours or more and are therefore qualified as active volunteers. The FDRS entry for active volunteers is therefore kept at 297,729. The Indian Red Cross Society is currently piloting online volunteer registration and management tools, and it is expected that this will greatly enhance data collection and accuracy in the coming years. This example from Indian Red Cross Society showed how its volunteer numbers were revised downwards (but may yet be revised again upwards), whereas this next example from Bangladesh Red Crescent Society is rather different. Bangladesh Red Crescent Society: Inclusion of youth volunteers Bangladesh Red Crescent Society has been reporting to the FDRS since its inception. Recently, the National Society has revised its data collection to improve decision-making and external reporting as well as providing better data for the FDRS. It revised the definition of volunteer as per the FDRS guide, which means including branch committee and management members as well as youth volunteers both these categories were not included in the past. On top of substantial upward revisions in volunteer numbers due to improved data collection and adopting the FDRS definition, the Bangladesh Red Crescent Society is also seeing a real increase in volunteer numbers (when using the FDRS definition consistently) as it responds to substantial humanitarian needs in the country. Diana Coulter/IFRC Cox's Bazar, Bangladesh, 8 November 2017 Romana, 18, is a Bangladesh Red Crescent volunteer who designed puppets Meena and Raju to teach children in the makeshift settlements about healthy hygiene, including handwashing. A lack of clean water and toilets is causing health problems in the camps. Since 25 August 2017, more than 680,000 people have crossed the Myanmar-Bangladesh border seeking safety from violence in northern areas of Myanmar's Rakhine State. Most are women and children living in makeshift settlements in dire conditions. An apparent large drop in the total number of National Society volunteers globally was mainly due to changes in individual National Society reporting systems. When excluding these National Societies from the data, the decrease in the total number of National Society volunteers globally between 2012 to 2016 is smaller than was originally feared: about 16 per cent. Numbers have been stable since 2014.

31 THE NUMBER OF PEOPLE VOLUNTEERING 29 Is the drop in National Society volunteer numbers due to a global decline in volunteering? We have seen that there was a drop in the number of National Society volunteers globally, at least in , though the drop was smaller than originally feared. Is this drop due to fewer people volunteering for organisations, on average, all over the world, or is this something specific to National Societies? It is difficult to answer this question definitively. But we might get an indication if we compare the FDRS data with data from a survey of volunteering trends worldwide, published by Charities Aid Foundation as the World Giving Index 7 (WGI). The WGI data comes from representative samples of house-to-house surveys and asks about whether people have volunteered their time to an organisation in the past month. This question is a reasonably good comparison with the FDRS data: it asks about volunteering for any organisation, not just a National Society. So, if these figures are falling, this could be a reasonable explanation of why volunteer numbers are falling in National Societies in particular. 7 caf-world-giving-index-2017 Figure 7: People volunteering for National Societies 15M 10M 5M Change in volunteer numbers since 2012, comparing volunteers for National Societies with volunteers for all organisations according to the World Giving Index, excluding countries in which the National Society changed their reporting to FDRS People volunteering for National Societies Percentage of People Volunteering in each country: World Giving Index In Figure 7, the red line represents the total number of National Society volunteers (after removing those National Societies which changed their reporting system between 2012 and 2016, see Figure 6, page 27). The black line shows the average percentage of people who volunteered for any organisation (in the month before they were interviewed) in each year in the approximately 140 countries covered by the World Giving Index survey. The figure indicates the number of people volunteering for all kinds of organisations is slowly rising over this period but is falling for National Societies 8. This suggests that the decrease in National Society volunteering, especially , is something specific to National Societies, not a reflection of global trends (at least as recorded by the WGI). However this is only an overall view, and is not true for every country; it is influenced by a smaller number of 8 This was tested with several statistical analysis. In particular, the relative drop in National Society volunteers compared to the WGI trends is significant also when considering the National Society volunteer numbers as percentages of the population, country-by-country, as well as when considering total global numbers of volunteers. Contact the FDRS team for details: fdrs@ifrc.org Percentage of people volunteering in each country: World Giving Index

32 30 THE NUMBER OF PEOPLE VOLUNTEERING National Societies which had larger drops in volunteer numbers. Figure 8: Change in median numbers of volunteers per National Society since 2012 There are other ways to look at the popularity of volunteering for National Societies which put all of them on an equal footing. For example, we can look at the number of National Societies experiencing an increase compared to the number experiencing a decrease; and in fact the majority of National Societies have experienced an increase in volunteer numbers between 2012 and Another way is to look at the median number, which tells us how the typical National Society is performing; and in fact the median number is rising, which means that the typical National Society (in terms of volunteer numbers) has more volunteers in 2016 than in Median of the Total Number of People Volunteering in each National Society These analyses are an example of how the FDRS can be combined with other datasets to answer quite sophisticated questions. The FDRS data available for download at data.ifrc.org/fdrs/data-download already includes some key data (like national population) from other datasets.

33 THE NUMBER OF PEOPLE VOLUNTEERING 31 Spontaneous volunteers and unaffiliated volunteers: Do we have to keep changing our indicators to keep up with changing trends? Large numbers of spontaneous volunteers often mobilise themselves to try to help with larger threats such as floods and earthquakes, especially when there is substantial media coverage 9. National Societies have a variety of ways of working with them. Spontaneous volunteers in Mexico 10 In September 2013, Mexican Red Cross volunteers and staff sent aid to relieve the communities most heavily hit by hurricanes Ingrid and Manuel. The National Society sent thousands of volunteers and technicians to respond to the immediate needs of affected people in the states of Guerrero, Veracruz and Tamaulipas. The operation became difficult due to lack of access, and heavy rain slowed the relief efforts leading to the use of air support for delivering aid to the difficult to reach communities. Apart from the Mexican Red Cross volunteers, a lot of people from the community came together to provide assistance. These spontaneous volunteers worked together with the Red Cross, for example with packaging food and hygiene items. Should these spontaneous volunteers be counted as Red Cross volunteers? Mexican Red Cross Following the severe impact of hurricanes Ingrid and Manuel, Mexican Red Cross volunteers and staff have continued responding, sending aid to relieve the communities most heavily hit by the storms. Since the onset of the tropical storms, the Mexican Red Cross has sent more than 7,000 volunteers and technicians to respond to the immediate needs of people affected in the states of Guerrero, Veracruz and Tamaulipas. 9 International Federation of Red Cross and Red Crescent Societies Volunteering in emergencies: Practical guidelines for Red Cross and Red Crescent Societies managing volunteers in emergency situations p (2012). 10 Article on IFRC website: Spontaneous volunteers inspire Red Cross action in Mexico dated 27 September 2013; Article on IFRC website: Fiji Red Cross Headquarters Effective management of spontaneous volunteers during disasters (2016).

34 32 THE NUMBER OF PEOPLE VOLUNTEERING Spontaneous volunteers in Fiji In early 2016, Tropical Cyclone Winston was a category 5 tropical cyclone which made landfall in Fiji and caused huge devastation to the island. As many as 50,000 people were sheltered in more than a thousand evacuation centres. The Fiji Red Cross mobilised volunteers who had already been trained in disaster response. Many other people who were not connected to Fiji Red Cross either as a member or a volunteer turned up at the Should these spontaneous volunteers be counted as Red Cross volunteers? head office wanting to help. Fiji Red Cross managed the situation by offering orientation twice a day on a range of topics from the history of the Red Cross to disaster response processes and restoring family links. The spontaneous volunteers were advised to go back home and wait for the National Society to call when they were needed. Of the 450 registered spontaneous volunteers, more than half were called back to do volunteering activity. The current FDRS definition of volunteers is the number of people who worked freely to support the delivery of services of a National Society for at least four hours during the annual reporting period. The phrase to support is a good way to capture volunteers who are affiliated to IFRC, and it might include those volunteers in Fiji, where the National Society tried to manage these spontaneous volunteers by helping them to become affiliated volunteers: to volunteer formally for the National Society. So, at some point they may be included in National Society volunteer statistics. But the definition might not cover the volunteers in Mexico. There is increasing recognition of informal, non-affiliated forms of volunteering, and many people especially young people don t want to be members of an organisation, they just want to help out spontaneously where they can and whenever suits them. They volunteer for causes, not brands. How do National Societies respond? Many welcome unaffiliated volunteers who just drop in sometimes and interact with the National Society in limited ways. These National Societies are trying to become better at engaging (in a variety of ways) with unaffiliated volunteers without trying to recruit them. Yet most National Societies still want to maintain a big role for affiliated volunteers and especially focus on ensuring they have good training and skill levels, while perhaps accepting and managing a stagnation or even decline in numbers of affiliated volunteers, see page 27. Fiji, May The Fiji Red Cross is conducting an Emergenecy Response Training to train volunteers to all the aspects of an emergency response. Benoit Matsha-Carpentier/IFRC What does this all mean for the FDRS? How can National Societies count people who perhaps don t want to be counted? More generally, how should the FDRS address the challenge of trying to keep the indicators like number of people volunteering relevant as the world changes? Changes in the way National Societies deal with spontaneous and non-affiliated volunteers might require new approaches to recording volunteer numbers in the future.

35 THE NUMBER OF PEOPLE VOLUNTEERING 33 Will the FDRS have to complement the key indicator on numbers of affiliated volunteers with other data on spontaneous volunteering? How can we start to monitor and record work with non-affiliated volunteers? These possibilities are being thoroughly explored by the German Red Cross. Possible indicators might be: Is your National Society/branch prepared to work with volunteers who are not affiliated (and don t want to be)? Has your National Society/branch worked with volunteers who are not affiliated (and don t want to be) in the past year? Global concepts of formal volunteering (conducted within an organisation or other formal setting) interact with local notions of formal and informal volunteering (practiced independently from organisations as an expression of community, cultural participation or social or humanitarian conscience). While global conceptions can help standardise and simplify mechanisms for promoting and managing volunteering, doing so in the name of its universality risks obscuring and riding rough-shod over the diverse ways volunteering is practiced and experienced. In each context, volunteering is shaped by very particular social, cultural, political, economic and religious and other factors IFRC Global Review on Volunteering (2015) Iraq, Erbil: Iraqi Red Crescent staff and volunteers at work in the Khazer camp, where they are providing food, relief supplies and psychosocial support to families who have fled Mosul and surrounding towns. The camp can accommodate some 6000 families, or 30,000 people. Safin Ahmed/IRCS

36 34 DIFFERENT COUNTRIES, DIFFERENT NATIONAL SOCIETIES 4. Different countries, different National Societies In this chapter we ask whether particular FDRS indicators tend to go together, so that National Societies that are strong on one particular indicator tend to be strong on another. And we ask: are there any simple explanations for the differences between National Societies on these key indicators?

37 DIFFERENT COUNTRIES, DIFFERENT NATIONAL SOCIETIES 35 As one of the most generous countries in Europe, Sweden is home to many newly-resettled-refugees and asylum seekers. Among them are affected by torture, armed conflict and migration. The Swedish Red Cross opened its first treatment centre for this particular group for more than 30 years ago. Since then, tens of thousands of people have got treatment and care. Swedish Red Cross Using per million indicators as an alternative way to understand National Society coverage In 2016, Italian Red Cross had 42 staff, while Luxembourg Red Cross had 2,134. The Red Crescent Society of the Islamic Republic of Iran had more than 2 million volunteers, whereas the Red Cross in Liechtenstein, a very small country, had only nine. The FDRS data reveals a lot about the differences between National Societies all over the world on key indicators. Do particular indicators tend to go together, so that National Societies which are strong on one particular indicator tend to be strong on another? The short answer to this question is: of course. The trouble is, this answer isn t very interesting, because generally speaking all the big and/or rich countries have high scores on all the main FDRS indicators high income, high numbers of volunteers, high numbers of people trained in first aid, etc. So a high score on any one indicator tends to go together with a high score on all the others the bigger the country, the higher the scores. This doesn t tell us much about the different styles or types of National Society for example, do National Societies that have a large number of volunteers also have large numbers of staff, even taking into consideration the population of the country? We can answer this question by dividing the main FDRS National Society indicators such as number of blood donors, number of local units, etc., by the population for that country. So we get, for example, number of volunteers or number of local units per million people. For example, a figure of one local unit per million means just that: this National Society has one local unit for every million people in the population of the country. The Albanian Red Cross has 39 local units for a population of nearly 3 million people, so there are about 13 local units per million. Calculating the main indicators per million of population gives important information on how dense infrastructure and services are compared to the population; how good is the coverage. They are critical to understanding how National Society services are present in the field. Figure 9, page 36, simply presents the volunteers per million indicator, per region. Presenting volunteer numbers in this way highlights how even small countries like Burundi can have remarkable results. Would it be useful and constructive to produce top ten tables of the National Societies which perform best on each of the main indicators, compared to the size of their populations?

38 36 DIFFERENT COUNTRIES, DIFFERENT NATIONAL SOCIETIES Figure 9: Number of National Society volunteers per million of population, by Region* 60,000 40,000 20, ,000 40,000 20, ,000 40,000 20, ,000 40,000 20, ,000 40,000 20, ,000= Burundi Comores 162= Togo 149= Chad 77= Swaziland 66= Seychelles 66= Rwanda 59= Malawi 58= Congo, The Democratic Republic of 58= Cape Verde 57= Congo 45= Ghana 40= Central African Republic 36= Sudan 36= Lesotho 34= Senegal 34= Kenya 29= Namibia 25= Guinea 24= Sierra Leone 22= Gabon 22= Mauritania 20= Zimbabwe 18= Botswana 16= Sao Tome and Principe 13= Benin 11= Zambia 11= Somalia 10= Cameroon 9= Ethiopia 8= Mali 6= Equatorial Guinea 6= Mauritius 5= Djibouti 5= Angola 4= Madagascar 3= Cote d Ivoire 3= Gambia 3= Uganda 3= Burkina Faso 3= Nigeria 2= Tanzania 1= South Africa 0= Liberia 0= Mozambique 0= 257= Grenada Cuba 71= Antigua and Barbuda 48= Jamaica 48= Saint Kitts and Nevis 47= Bahamas 46= Guyana 39= Saint Lucia 33= Dominican Republic 32= Barbados 28= Saint Vincent and the Grenadines 24= Costa Rica 21= United States of America 17= Haiti 16= Dominica 14= Colombia 10= Suriname 9= Ecuador 8= Canada 7= Nicaragua 7= Belize 7= El Salvador 6= Chile 5= Honduras 4= Mexico 4= Argentina 4= Panama 3= Uruguay 3= Trinidad and Tobago 3= Paraguay 2= Guatemala 2= Venezuela 1= Bolivia 1= Peru 1= Samoa Japan 171= Vanuatu 97= Bangladesh 85= Korea, Democratic People s Republic 70= Vietnam 67= Nepal 62= Maldives 61= Palau 60= Timor-Leste 58= Korea, Republic of 45= Mongolia 36= Indonesia 32= New Zealand 29= China 22= Fiji 18= Philippines 18= Micronesia, Federated States of 18= Australia 16= Singapore 15= Tonga 14= Afghanistan 14= Cambodia 13= Thailand 9= AFRICA AMERICAS ASIA PACIFIC EUROPE AND CENTRAL ASIA MIDDLE EAST AND NORTH AFRICA 314= Brunei Darvussalam 9= Solomon Islands 8= Myanmar 5= Sri Lanka 4= India 4= Malaysia 3= Papua New Guinea 2= Pakistan 1= 274= Monaco Croatia 217= Norway 165= Serbia 146= Austria 145= Iceland 138= Switzerland 125= Slovenia 120= Denmark 100= Finland 94= Germany 85= FYRO Macedonia 83= Luxembourg 77= Spain 75= Cyprus 60= Sweden 56= Montenegro 48= Bosnia and Herzegovina 47= Bulgaria 42= Italy 42= Belarus 41= Georgia 40= San Marino 38= Belgium 38= Andorra 33= Hungary 32= Netherlands 29= Latvia 26= Uzbekistan 21= Slovakia 19= Tajikistan 18= Greece 17= France 17= Portugal 16= Poland 16= Armenia 15= Albania 15= Azerbaijan 15= Ireland 14= Russian Federation 9= Moldova, Republic of 8= Lithuania 7= Kyrgyzstan 6= United Kingdom 5= Romania 5= Turkmenistan 5= Czech Republic 5= Estonia 4= Liechtenstein 4= Malta 3= Turkey 3= Kazakhstan 1= Ukraine 0= 450= Iran, Islamic Republic of Israel 30= Palestinian Territory, Occupied 30= Tunisia 27= Libyan Arab Jamahiriya 22= Lebanon 22= Morocco 10= Yemen 9= Kuwait 7= Syrian Arab Republic 7= Egypt 6= Saudi Arabia 5= Iraq 3= Qatar 2= Jordan 1= Bahrain 1= * National Societies not reporting number of people volunteering in 2016 not included.

39 DIFFERENT COUNTRIES, DIFFERENT NATIONAL SOCIETIES 37 The Figure 9, page 36 shows those National Societies that reported the number of people volunteering their time in The more volunteers each National Society has per million people, the higher the National Society is shown on the figure. Some National Societies in every IFRC Region, and especially Burundi, stand out with very high volunteer coverage. Some countries such as Monaco, Grenada and Samoa stand out because a healthy number of volunteers is divided by a small population. Volunteer coverage is quite low in Middle East and North Africa, but the Islamic Republic of Iran is a clear exception. Per million indicators ensure achievements of smaller National Societies do not get lost in the data. Connections between the per million indicators But how are these per million indicators related to one another and how are they influenced by other factors such as the Human Development Index (HDI) of the country? The next figure is one answer to that question. Figure 10: Connections between key per million indicators for National Societies, and between them and socio-demographic indicators 12 Population First Aid Trained Income Staff HDI People reached: disasters Disasters Type of indicator Sociodemographic indicators National Society indicators Type of connection Negative Positive Local Units Volunteers People reached: long term All National Society indicators, and the disaster indicators, are per million population. Indicators are log transformed where appropriate. Note: thicker lines mean stronger connections. What really explains the number of staff a National Society has? Researchers are encouraged to use the FDRS data to answer specific questions like this. 12 Correlations smaller than.25, and also correlations between socio-economic indicators, are suppressed. All data is from The following National Society indicators are excluded because they did not have any strong enough correlations: National Society income per million, blood donors per million, age of National Society; and the socio-demographic indicator on refugees per million was also dropped for the same reason. All National Society indicators, and the disaster indicators, are per million population. Indicators are log transformed where appropriate.

40 38 DIFFERENT COUNTRIES, DIFFERENT NATIONAL SOCIETIES This figure is an innovative way to present the connections between, on the one hand, the main per million indicators in the FDRS (shown with hexagons) and on the other National Society per hand some key socio-demographic indicators (shown with million indicators: how circles). Thick lines mean strong connections between two National Societies with a high score on any indicators blue means positive, red means negative. one of the key field indicators, (number of are they affected by volunteers, local units, staff and people trained in first aid, measured per million), HDI and population? tend on average to have high scores on the others. We could just say that these National Societies have good coverage. First, we can see that four of the National Society indicators per million are strongly related: local units, first aid trained, staff, and volunteers. Number of volunteers per million is also related but more weakly to the number of people reached with long-term services and development programmes. This means that National Societies all over the world differ quite strongly on what we could call coverage: the number of people trained in first aid for each million people in the population the number of volunteers per million the number of local units per million the numbers of staff per million. National Societies with a high score on one indicator tend, on average, to have high scores on the others. For example, in 2016, Burundi Red Cross Society had the highest number of volunteers per million from all National Societies in the world, and the second highest number of local units per million, and is also 55th for numbers of staff per million. Croatia is 6th for the number of volunteers per million, 14th for numbers of people trained in first aid per million, 32nd for numbers of staff per million and 39th for numbers of local units per million. The figure also shows how these per million indicators are connected to socio-demographic statistics for each country, such as HDI. These are shown by circles. Where is the highest coverage of people reached by the National Society with disaster response and early recovery? in countries with low HDI, but a high number of disasters per head 13. Where is the highest coverage of National Society local units? in countries with low populations. Where does the National Society have the highest coverage of staff? in countries with high HDI. There s a lot of data in the FDRS and it can be bewildering to look at all the different ways of understanding the differences between National Societies. This section looks at one way of presenting some important differences. We will look again at how the per million indicators for each National Society are related to the HDI and population of each country. But we will do it in a very simple way. We will just divide the National Societies into four groups 14 : High HDI and high population (for example, Japan), Low HDI and high population (for example, Nigeria), High HDI and low population (for example, Iceland), Low HDI and low population (for example, Vanuatu) The Figure 11, page 39 shows these four quadrants with approximately equal numbers of National Societies. The number of volunteers per million is a central indicator because it is related to the other coverage indicators on the one hand but is also linked to the number of people reached (per million) with long-term services and development programmes. 13 Many other factors also influence these indicators. The ones mentioned here are just the ones with the strongest correlations as shown in the figure. 14 The National Societies were split at the median for each indicator, so that the four groups of National Societies are roughly equal.

41 DIFFERENT COUNTRIES, DIFFERENT NATIONAL SOCIETIES 39 Figure 11: How the National Societies are allocated to four quadrants according to low versus high HDI and low versus high population 1,000,000 India China Population (thousands) logarithmic scale 10, Niger Central African Republic United States of America Indonesia Nigeria Pakistan Brazil Bangladesh Russian Federation Philippines Mexico Japan Ethiopia Egypt Congo, The Democratic Republic of Viet Nam Turkey Iran, Islamic Republic of France Germany Thailand United Kingdom Tanzania Italy Myanmar South Africa Uganda Kenya Colombia Ukraine Korea, Republic of Algeria Argentina Spain Afghanistan Morocco Iraq Poland Canada Sudan Uzbekistan Saudi Arabia Mozambique Yemen Angola Nepal Peru Venezuela Malaysia Ghana Cote d'ivoire Madagascar Cameroon Australia Sri Lanka Burkina Faso Mali Malawi Syrian Arab Republic Romania Kazakhstan Netherlands Chad Senegal Cambodia Zambia Guatemala Ecuador Chile Guinea Zimbabwe Cuba Greece Belgium Burundi Benin Rwanda Tunisia Portugal Bolivia Dominican Republic Belarus Haiti Honduras Azerbaijan Sweden Tajikistan Jordan Hungary Czech Republic Israel Switzerland Sierra Leone Togo Papua New Guinea Kyrgyzstan Paraguay Libyan Arab Serbia Bulgaria United Arab Emirates Lao People s Democratic Republic Austria Nicaragua Jamahiriya Lebanon Turkmenistan Slovakia Finland Denmark Congo El Salvador Singapore Ireland Norway Liberia Mauritania Palestinian Territory, Occupied Bosnia and Herzegovina Costa Rica Panama Kuwait Croatia New Zealand Moldova, Republic of Mongolia Georgia Jamaica Albania Uruguay Lithuania Namibia Armenia Qatar Botswana Gambia Lesotho FYRO Macedonia Latvia Slovenia Guinea Bissau Gabon Swaziland Bahrain Equatorial Guinea Timo-Leste Trinidad and Tobago Mauritius Estonia Cyprus Djibouti Fiji Comores Guyana Solomon Island Montenegro Cape Verde Suriname Luxembourg Maldives Malta Brunei Darussalam Belize Bahamas Iceland Vanuatu Barbados Sao Tome and Principe Kiribati Samoa Saint Vincent and the Grenadines Micronesia, Federated States of Tonga Dominica HDI Saint Lucia Grenada Saint Kitts and Nevis Seychelles Palau Antigua and Barbuda Andorra Liechtenstein 100e 100e Africa Americas 100e 100e Asia Pacific Europe and Central Asia 100e Middle East and North Africa Quadrants Note: the red double lines divide the National Societies into four quadrants

42 40 DIFFERENT COUNTRIES, DIFFERENT NATIONAL SOCIETIES We now present a figure for each indicator showing the median 15 score for each of the four groups. The median score is the score of the National Society which is in the middle if the National Societies in that group are arranged from lowest to highest. Dividing the National Societies into four equal quadrants (low population and low HDI; high population and low HDI; low population and high HDI; and high population and high HDI) makes it easier to understand the impact of these two key socio-demographic factors on National Society profiles. Figure 12: Differences in median number of blood donors (per million of population) between National Societies in the four quadrants: low versus high HDI and low versus high population 16 Population High 77 Low Low HDI High HDI Figure 13: Differences in median number of people trained in first aid (per million of population) between National Societies in the four quadrants: low versus high HDI and low versus high population Population High 2 Low Low HDI High HDI The graphs reveal a number of really strong connections between HDI and population on the one hand and the per million indicators on the other. But the connections differ strongly from indicator to indicator. Also, there are strong interactions between HDI and population. That means that the influence of HDI on the indicator depends on the size of the population. If someone wants to understand how National Societies are present in the field with different infrastructure and services, they should have a good look at these graphs to see the substantial effect of HDI and population. 15 The median is used rather than the mean because in many cases there are one or two National Societies in each group with very high scores, which would distort the results. The big story with blood donors per million is that a lower population is associated with lower numbers the opposite to what we see with the other indicators. The quarter of National Societies in countries with low population and low HDI have almost no blood donors. There are major differences in coverage between National Societies in countries with low and high HDI and low and high population. For example, the majority of National Societies in countries with low population and low HDI have almost no blood donors. 16 The median numbers in this and subsequent figures are rounded to the nearest unit. Figure 13 shows median values for numbers of people trained in first aid (per million). HDI and population make substantial differences low population and high HDI each have a very strong link to the number of people trained. National Societies in the top half of HDI have around 15 times as many people trained per million as National Societies in the bottom half. High 15 Low 1 National Societies in the top half of HDI typically have around 15 times as many people trained in first aid per million as National Societies in the bottom half.

43 DIFFERENT COUNTRIES, DIFFERENT NATIONAL SOCIETIES 41 Figure 14: Differences in median number of local units (per million of population) between National Societies in the four quadrants: low versus high HDI and low versus high population Population High 19 Low Low HDI High HDI Figure 15: Differences in median National Society income (per million of population) between National Societies in the four quadrants: low versus high HDI and low versus high population Population High 6 Low , , ,895 1,485,518 Low HDI High HDI Figure 16: Differences in median number of people reached in disaster response and early recovery programmes (per million of population) between National Societies in the four quadrants: low versus high HDI and low versus high population Population High 55 Low , , Low HDI High HDI High HDI and low population are associated with high numbers of local units per million, and National Societies with low HDI and high population have a particularly low number of units. HDI and population size also affect the income of National Societies. National Societies in countries with both low HDI and high population have particularly low income. People reached with disaster response and early recovery programmes: here, higher HDI is associated with much lower numbers of people reached per head, especially in countries with lower populations. Why are more people reached in long term service programmes (per million of population) in higher-population countries?

44 42 DIFFERENT COUNTRIES, DIFFERENT NATIONAL SOCIETIES Figure 17: Differences in median number of people reached in long-term service programmes (per million of population) between National Societies in the four quadrants: low versus high HDI and low versus high population Population High 75 Low ,573 8, ,492 7,907 Low HDI High HDI Figure 18: Differences in median number of staff (per million of population) between National Societies in the four quadrants: low versus high HDI and low versus high population Population High 18 Low Low HDI High HDI Figure 19: Differences in median number of volunteers (per million of population) between National Societies in the four quadrants: low versus high HDI and low versus high population Population High 31 Low ,062 1,910 Low HDI High HDI People reached with long-term services: here, higher HDI is associated with much higher numbers of people reached per head, and more people are reached in countries with higher populations. Looking at the number of staff per million, again both HDI and population make a difference, but HDI makes a particularly strong difference in high population countries, where there are more than five times as many staff per million with high HDI than with low HDI. Finally, there are more volunteers per million in countries with higher HDI and lower population. Why are there far more volunteers (per million of population) in low-population countries, but far fewer blood donors per million?

45 PREDICTING THE NUMBER OF NATIONAL SOCIETY VOLUNTEERS AFTER A DISASTER Predicting the number of National Society volunteers after a disaster Volunteer numbers often go up in response to a disaster or a threat. But is this visible in the FDRS data across all disasters and all National Societies? And, if so, how long does the boost in volunteer numbers last?

46 44 PREDICTING THE NUMBER OF NATIONAL SOCIETY VOLUNTEERS AFTER A DISASTER We ve already seen (Figure 9, page 36) that there is a very big variation between National Societies in the number of volunteers both in absolute numbers and in coverage or volunteers per million. What explains these differences? Socio-demographic factors like HDI play a role. But what about disasters? It seems plausible that volunteer numbers go up in response to a disaster or a threat. But is this visible in the FDRS data? And, if so, how long does the boost in volunteer numbers last? Volunteer numbers grow in response to a major flood in Argentina On 3 April 2013, a severe local storm brought nearly 400mm of rainfall in a 24-hour period in La Plata, Buenos Aires province in Argentina, affecting up to a million people. This disaster was a turning point for the Argentine Red Cross, with many branches working together, as the disaster was too big to be addressed by only one branch. The Argentine Red Cross worked with government and other agencies and provided aid to more than 20,000 families 17. The National Society partnered with the private sector and implemented a cash transfer programme for the first time in the country. After this experience, the National Society became a reference point for cash transfer in the region. Nearly 300,000 Swiss francs were allocated from the IFRC Disaster Relief Emergency Fund (DREF) for this disaster. The relief operation lasted three months. The number of volunteers in the year of the disaster nearly doubled (from 4,572 in 2012 up to 7,847 in 2013). Staff numbers fluctuated less. Income and expenditure have increased steadily but not substantially. The number of people reached also increased. In the years following the emergency, the increase in numbers for volunteers and people reached continues: in 2015, Argentina was subject to heavy floods again, although less devastating than the ones in The Argentine Red Cross was able to keep the volunteers that joined in 2013 and has developed campaigns to keep adding volunteers, reaching 9,756 volunteers in Cruz Roja Argentina Argentina Red Cross vehicle on the way to provide assistance to people affected by floods in Argentina. Cruz Roja Argentina Argentina Red Cross volunteer filters water for the residents affected by floods in Argentina. 17 Memoria 2013 Cruz Roja Argentina

47 PREDICTING THE NUMBER OF NATIONAL SOCIETY VOLUNTEERS AFTER A DISASTER 45 Figure 20: The impact of severe floods in Argentina in 2013 on volunteer numbers, and on numbers of people reached. Numbers are per million of population 3500 People Reached: Disasters People Reached: Long Term Volunteers Figure 21: The impact of the Ebola epidemic in Sierra Leone in 2014 on volunteer numbers, and on numbers of people reached. Numbers are per million of population 9000 People Reached: Disasters People Reached: Long Term Volunteers Number of People (per million of population) Number of People (per million of population) Data for Sierra Leone tells a similar story. Here also, although there was a fall in volunteer numbers to 2013, it has been possible, at least up to 2016, to retain the boost in volunteers who came forward to help respond to the Ebola crisis in These two examples support the idea that volunteer numbers can be boosted by disasters and can even be maintained. Is it possible to make a synthesis of all such graphs to look at this idea globally? There are many challenges in doing this for instance, how to deal with the data when a National Society experiences more than one disaster. And it is desirable to take other factors like socio-demographic data into account. A more sophisticated statistical simulation was constructed to investigate this idea further. The analysis 18 included all National Societies that had experienced at least one disaster between and had received funds via IFRC for this disaster: it uses disaster data from both IFRC's DREF records and Emergency Appeals records. How can National Societies best ensure development services are sustained even while responding to a disaster, and in the years afterwards? 18 This analysis is based on an MSc thesis conducted during an internship by Olta Ndoja at IFRC: The Macro- Determinants of Red Cross and Red Crescent Volunteers: Cross-Country Panel Data Analysis. Internship Thesis (2017).

48 46 PREDICTING THE NUMBER OF NATIONAL SOCIETY VOLUNTEERS AFTER A DISASTER Figure 22: The disaster timeline. How volunteer numbers increase in the year of a disaster and in the subsequent years 125 and how much it is due to National Societies increased capacity to recruit volunteers, or a combination of the two or indeed other factors. % change in volunteers (after 1 disaster at time 0) ,20, ,95+5,45+25,35+30,30, ,85+12,40+25,35+30, ,80+15,35+25,33+30, % Confidence Interval % Confidence Interval Confidence Interval Prediction The results simulate the effect that one disaster has on volunteer numbers in the year of the disaster (year zero) and the subsequent years. Already in year zero, there is a positive impact on the number of people volunteering (the red line in Figure 22): this increases by more than 40 per cent. In the year after the disaster, there is still a positive impact of around 34 per cent; this effect decreases steadily and ultimately reaches 0 two to three years after the occurrence of YEARS AFTER DISASTER the disaster. This model simulates the effect of one additional disaster, so it also explains the typical increase in volunteer numbers, in a National Society that has to deal with one additional disaster alongside one or more others. The statistical significance of this effect is not firmly established, but it indicates a very interesting area for further research, which could also investigate how much this effect is due to an increase in people s willingness to volunteer Volunteer numbers are typically boosted when a disaster happens, by around 40 per cent; volunteer numbers typically remain raised for a while but return to the baseline two to three years later. But what about the money? When a country experiences a disaster, the National Society usually receives funds from IFRC. How do these funds relate to the increase in volunteer numbers that the National Society can typically expect? This increase gets even bigger as we combine the number of disasters experienced in one year with the amount of funds received from IFRC to respond to the emergency and is particularly relevant when a country experiences several disasters in one year. For example, when a country experiences four disasters, the increase in volunteer numbers depends strongly on the amount of funds received National Societies that received the smallest amounts of funds experienced a 12 per cent smaller increase in volunteer numbers than National Societies that received the largest amounts of funds, when everything else remains constant.

49 MAPS: THE EXTRAORDINARY NATIONAL SOCIETY SUPPORT NETWORK Maps: The extraordinary National Society support network In this chapter, we use maps to take a closer look at the FDRS data on support links between National Societies.

50 48 MAPS: THE EXTRAORDINARY NATIONAL SOCIETY SUPPORT NETWORK Maps are a great way to display and understand global data. For example, the FDRS web application provides interactive maps like this one: Figure 23: An example of the kind of interactive map provided at the FDRS web application data.ifrc.org/fdrs/overview/map Users can select different indicators and also look at results for different years. Maps for the main indicators can also be accessed directly: Total number of people volunteering Total number of paid staff Total number of people donating blood Total number of people trained in first aid Total number of local units Total number of people reached by disaster response and early recovery programmes Total number of people reached by long term services and development programmes Total number of people reached by disaster risk reduction Total number of people reached by shelter Total number of people reached by livelihoods Total number of people reached by health Total number of people reached by water, sanitation and hygiene Total number of people reached by migration Total number of people reached by cash transfer programming Total number of people reached by social inclusion and building a culture of non-violence and peace data.ifrc.org/fdrs/overview/ map/kpi_peoplevol_tot data.ifrc.org/fdrs/overview/ map/kpi_pstaff_tot data.ifrc.org/fdrs/overview/ map/kpi_donblood_tot data.ifrc.org/fdrs/overview/ map/kpi_trainfa_tot data.ifrc.org/fdrs/overview/ map/kpi_nolocalunits data.ifrc.org/fdrs/overview/ map/kpi_reachdrer_cpd data.ifrc.org/fdrs/overview/ map/kpi_reachltspd_cpd data.ifrc.org/fdrs/overview/ map/kpi_reachdrr_cpd data.ifrc.org/fdrs/overview/ map/kpi_reachs_cpd data.ifrc.org/fdrs/overview/ map/kpi_reachl_cpd data.ifrc.org/fdrs/overview/ map/kpi_reachh_cpd data.ifrc.org/fdrs/overview/ map/kpi_reachwash_cpd data.ifrc.org/fdrs/overview/ map/kpi_reachm_cpd data.ifrc.org/fdrs/overview/ map/kpi_reachctp_cpd data.ifrc.org/fdrs/overview/ map/kpi_reachsi_cpd FDRS network maps In 2016, there were more than 800 support links between National Societies within IFRC Regions and across the world. The FDRS dataset contains a wealth of data that can be used to investigate and present National Society activities across the world, beyond the simple maps provided in the web application. For example, each National Society is asked to list the other National Societies which provided support (whether financial, technical, or human) during the previous year. This allows us to look at the support links between National Societies, as givers and receivers of support. There are more than 800 such links, giving evidence of the extraordinary depth of the support network between National Societies across the world. These links are listed in Table 9, page 52, later in this document. In Figure 24, page 49, we visualise them on a map. Figure 24 shows links between National Societies across the world. The sizes of the circles representing each National Society are proportional to the number of other National Societies which that National Society supports. The lines are coloured according to the sending Region. The second map, Figure 25, page 50, focuses just on the links within Regions.

51 MAPS: THE EXTRAORDINARY NATIONAL SOCIETY SUPPORT NETWORK 49 Figure 24: Support links in 2016 between National Societies Africa Americas Asia Pacific Europe and Central Asia Middle East and North Africa Number of outgoing support links to other National Societies

52 50 MAPS: THE EXTRAORDINARY NATIONAL SOCIETY SUPPORT NETWORK Figure 25: Support links between National Societies within Regions Africa Americas Asia Pacific Europe and Central Asia Middle East and North Africa Number of outgoing support links to other National Societies

53 MAPS: THE EXTRAORDINARY NATIONAL SOCIETY SUPPORT NETWORK 51 What kind of support do National Societies give one another? There is a very wide variety, in a wide variety of contexts: support can be related to long-term services, disasters, emergencies, conflict, and so on. One notable example comes from the Middle East and North Africa region. Cooperation between National Societies responding to the crisis in Syria Since 2011, the crisis in Syria has caused a humanitarian emergency spilling over far beyond the borders of the country. By March 2017, 8 million Syrians had been displaced within the country and almost 5 million had fled to other countries as refugees 19. More than 4 million Syrian refugees have settled in the Middle East and North Africa region, further increasing the humanitarian load from previous waves of displacement. The International Red Cross and Red Crescent Movement has been providing humanitarian support to Syrians since the beginning of the crisis. This support has been delivered in Syria, Egypt, Lebanon, Iraq, Jordan, and Turkey by the National Societies in those countries and also by the Palestinian Red Crescent Society. It is captured in the Movement-Wide Reporting Mechanism (MWR) which provides even more detail than the FDRS on the Movement s collective response, to facilitate Movement positioning among other humanitarian actors and to attract additional resources to the ongoing work of National Societies. Apart from enormous cooperation within the Movement on direct humanitarian actions, mutual support was also provided for the development and implementation of the Movement Wide Reporting Mechanism between National Societies, IFRC and ICRC country offices and the IFRC Regional Offices in Middle East and North Africa and Europe. Finnish Red Cross provided technical support and partner National Societies in Middle East and North Africa Region as well as British Red Cross and Swedish Red Cross, financially supported the implementation. 19 data.unhcr.org/syrianrefugees/regional.php Stephen Ryan/IFRC As part of the Lebanese Red Cross assistance programme for Syrian refugees, hygiene promotion activities are carried out in a fun and engaging way for children. IFRC Secretary General Elhadj As Sy watches on during a visit to this informal settlement near Zahle in Lebanon's Bekaa valley region. There are over a million Syrian refugees registered in Lebanon, with some estimates indicating that hundreds of thousands more people may be living in the country without registration. Lebanese Red Cross has been providing assistance to those in need since the onset of the Syria crisis in 2011.

54 52 MAPS: THE EXTRAORDINARY NATIONAL SOCIETY SUPPORT NETWORK Table 9 shows the same information as in the maps, but in list form. The first column shows each National Society providing support to other National Societies, which are then shown in the second column. The colours show the Regions. Table 9: NATIONAL SOCIETY PROVIDING SUPPORT AFRICA Botswana Burundi Cape Verde Central African Republic Cote d Ivoire Ghana Guinea-Bissau Kenya List of support links between National Societies Mauritania Nigeria Senegal Sierra Leone South Africa Tanzania Togo AMERICAS Antigua and Barbuda Argentina Belize Canada NATIONAL SOCIETY RECEIVING SUPPORT Lesotho, Ecuador Cote d Ivoire, Afghanistan, Myanmar Spain France Burundi Nigeria Cape Verde Sierra Leone, Somalia, South Africa, Tanzania, Finland France Ghana, Liberia Cote d Ivoire Ghana Mozambique Malawi Central African Republic Bahamas, Dominica Brazil, Chile, Ecuador, Paraguay, Uruguay Antigua and Barbuda, Chile Angola, Congo, The Democratic Republic of, Ethiopia, Kenya, Liberia, Mali, Senegal, South Sudan, Dominica, Dominican Republic, Ecuador, Colombia Dominica Haiti Honduras Mexico Panama Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Suriname Trinidad and Tobago United States El Salvador, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Suriname, United States, Afghanistan, China, India, Indonesia, Maldives, Myanmar, Nepal, Pakistan, Philippines, Denmark, France, Germany, Greece, Netherlands, Spain, Lebanon, Palestinian Territory, Occupied, Syrian Arab Republic, Yemen Ecuador Haiti Mexico Colombia, El Salvador, Guatemala, Nicaragua Canada, Ecuador, Haiti, United States Costa Rica, Ecuador, Paraguay Dominica Dominica Dominica Dominica Dominica Benin, Guinea, Kenya, Liberia, Namibia, Rwanda, Sao Tome and Principe, Senegal, Sierra Leone, South Africa, Tanzania, Zambia, Zimbabwe, Antigua and Barbuda, Argentina, Bahamas, Belize, Canada, Colombia, Costa Rica, Dominica, Ecuador, El Salvador, Grenada, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Peru, Suriname, Australia, Bangladesh, China, Indonesia, Micronesia, Federated States of, Myanmar, Nepal, New Zealand, Philippines, Vietnam, Armenia, Belarus, France, Germany, Greece, Italy, Kazakhstan, Kyrgyzstan, Netherlands, Norway, Romania, Russian Federation, ASIA PACIFIC Australia Bangladesh China India Indonesia Japan Korea, Republic of Malaysia New Zealand San Marino, Spain, Switzerland, Ukraine, United Kingdom, Iraq Papua New Guinea, Bangladesh, Cambodia, China, Cook Islands, Fiji, Indonesia, Kiribati, Lao People s Democratic Republic, Maldives, Micronesia, Federated States of, Mongolia, Myanmar, Nepal, Pakistan, Palau, Philippines, Singapore, Timor-Leste, Tonga, Tuvalu, Vanuatu, Vietnam, Denmark, Samoa, Tajikistan, United Kingdom, Saudi Arabia Malaysia, Myanmar, Timor-Leste Ethiopia, Tanzania, Uganda, Canada, Ecuador, Haiti, Cambodia, Fiji, India, Indonesia, Japan, Korea, Democratic People s Republic, Lao People s Democratic Republic, Myanmar, Pakistan, Philippines, Sri Lanka, Thailand, Vietnam, Italy, Kazakhstan, Kyrgyzstan, FYRO Macedonia, Tajikistan, Ukraine Fiji Timor-Leste, Vietnam Angola, Kenya, Malawi, Namibia, Zambia, Bangladesh, Cambodia, China, India, Indonesia, Korea, Republic of, Mongolia, Myanmar, Nepal, Philippines, Timor-Leste, Tonga, Vietnam, Greece, Italy, Tajikistan, Ukraine, Jordan, Lebanon, Palestinian Territory, Occupied Equatorial Guinea, Haiti, Cambodia, Indonesia, Lao People s Democratic Republic, Malaysia, Mongolia, Myanmar, Nepal, Philippines, Thailand, Timor-Leste, Vietnam, Tajikistan Philippines Cook Islands, Fiji, Indonesia, Kiribati,

55 MAPS: THE EXTRAORDINARY NATIONAL SOCIETY SUPPORT NETWORK 53 Philippines Singapore Thailand Tonga Vanuatu Vietnam Micronesia, Federated States of, Singapore, Timor-Leste, Tonga, Tuvalu, Vanuatu, Vietnam, Italy, Tajikistan, Saudi Arabia Ecuador China, Indonesia, Japan, Nepal, Philippines, Sri Lanka, Vietnam, Italy Japan, Nepal, Sri Lanka Japan Fiji Japan, Lao People s Democratic Republic EUROPE AND CENTRAL ASIA Albania FYRO Macedonia Austria Ethiopia, Kenya, Rwanda, South Sudan, Suriname, Nepal, Armenia, Azerbaijan, Belarus, Bulgaria, Georgia, Kyrgyzstan, FYRO Macedonia, Montenegro, Russian Federation, Serbia, Switzerland, Ukraine, United Kingdom, Lebanon Belgium Benin, Botswana, Burkina Faso, Burundi, Congo, The Democratic Republic of, Gabon, Kenya, Lesotho, Mali, Uganda, Zambia, Zimbabwe, India, Nepal, Greece, Monaco, Russian Federation, Syrian Arab Republic Bulgaria Malawi, Costa Rica, Korea, Democratic People s Republic, Belarus, Greece, Italy, FYRO Macedonia, Ukraine Croatia Greece, FYRO Macedonia Czech Republic Nepal, Italy, Slovenia, Ukraine, Syrian Arab Republic Denmark Central African Republic, Chad, Guinea, Kenya, Liberia, Madagascar, Malawi, Mali, Mozambique, Niger, Nigeria, Seychelles, Togo, Uganda, Zimbabwe, El Salvador, Haiti, Suriname, United States, Afghanistan, Cambodia, Nepal, Pakistan, Philippines, Armenia, Belarus, Bulgaria, Finland France Germany Hungary Iceland Georgia, Germany, Italy, Kyrgyzstan, Montenegro, Russian Federation, Serbia, Switzerland, Ukraine, Jordan, Lebanon, Palestinian Territory, Occupied, Qatar Burundi, Cote d Ivoire, Ethiopia, Ghana, Kenya, Malawi, Niger, Senegal, Sierra Leone, Somalia, South Sudan, Swaziland, Uganda, Zimbabwe, Bolivia, El Salvador, Honduras, Suriname, Afghanistan, Cambodia, Korea, Democratic People s Republic, Mongolia, Myanmar, Nepal, Philippines, Thailand, Denmark, Greece, Lithuania, Romania, Slovakia, Tajikistan, Ukraine, United Kingdom, Iraq, Lebanon, Syrian Arab Republic, Yemen Benin, Central African Republic, Chad, Comores, Congo, Congo, The Democratic Republic of, Cote d Ivoire, Guinea, Mali, Mauritania, Mauritius, Niger, Senegal, Swaziland, Philippines, Vanuatu, Vietnam, Bulgaria, Denmark, Greece, Monaco, San Marino, Jordan, Lebanon, Saudi Arabia Burkina Faso, Chad, Kenya, Madagascar, Mozambique, Senegal, Seychelles, Uganda, Colombia, El Salvador, Peru, Bangladesh, Japan, Nepal, Nepal, Pakistan, Pakistan, Philippines, Thailand, Tonga, Vietnam, Albania, Armenia, Bulgaria, Croatia, Denmark, Greece, Italy, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, FYRO Macedonia, FYRO Macedonia, Monaco, Serbia, Switzerland, Ukraine, Ukraine, Jordan, Lebanon, Morocco, Palestinian Territory, Occupied Japan, Nepal, Croatia, Greece, Italy, FYRO Macedonia, Pakistan, Ukraine Malawi, Sierra Leone, Somalia, Dominica, Grenada, Armenia, Belarus, Cyprus, Ireland Italy Liechtenstein Luxembourg Monaco Netherlands Denmark, Georgia, Greece, Lebanon, Palestinian Territory, Occupied India, Indonesia, Nepal, Philippines, Greece, Serbia, Switzerland Kenya, Madagascar, Malawi, Sao Tome and Principe, Antigua and Barbuda, Bolivia, Brazil, El Salvador, Guatemala, Haiti, Honduras, Nicaragua, Panama, Peru, India, Indonesia, Japan, Korea, Democratic People s Republic, Pakistan, Thailand, Vietnam, Armenia, Bosnia and Herzegovina, Denmark, Georgia, Kyrgyzstan, FYRO Macedonia, Monaco, Montenegro, San Marino, Tajikistan, Palestinian Territory, Occupied Nepal Benin, Burkina Faso, Burundi, Mali, Nepal, Monaco, Montenegro, San Marino Burkina Faso, Burundi, Cote d Ivoire, Mali, Niger, Nepal, Armenia, Greece, Italy, Switzerland, United Kingdom Angola, Benin, Burundi, Central African Republic, Chad, Congo, Congo, The Democratic Republic of, Cote d Ivoire, Ethiopia, Kenya, Lesotho, Malawi, Mali, Mozambique, Namibia, Niger, Rwanda, Seychelles, Sierra Leone, Somalia, South Sudan, Sudan, Swaziland, Tanzania, Uganda, Zambia, Zimbabwe, Colombia, Ecuador, Guatemala, Haiti, Nicaragua, Suriname, Afghanistan, Bangladesh, Fiji, India, Indonesia, Korea, Democratic People s Republic, Korea, Republic of, Nepal, Philippines, Sri Lanka, Bosnia and Herzegovina, Bulgaria, Croatia, Germany, Greece, Hungary, Italy, FYRO Macedonia, Russian Federation, Serbia, Ukraine,

56 54 MAPS: THE EXTRAORDINARY NATIONAL SOCIETY SUPPORT NETWORK Norway Portugal San Marino Serbia Spain Sweden United Kingdom, Iraq, Jordan, Lebanon, Palestinian Territory, Occupied, Syrian Arab Republic Burundi, Kenya, Lesotho, Madagascar, Sao Tome and Principe, Senegal, Zimbabwe, Colombia, Cuba, El Salvador, Honduras, Suriname, Afghanistan, India, Korea, Democratic People s Republic, Myanmar, Nepal, Pakistan, Philippines, Tonga, Vietnam, Albania, Cyprus, Denmark, Georgia, Latvia, Moldova, Republic of, Romania, Russian Federation, Israel, Lebanon, Palestinian Territory, Occupied Sao Tome and Principe, Ecuador Bosnia and Herzegovina FYRO Macedonia Burkina Faso, Burundi, Cameroon, Central African Republic, Comores, Congo, The Democratic Republic of, Ethiopia, Gambia, Guinea-Bissau, Liberia, Mali, Mozambique, Namibia, Niger, Rwanda, Sao Tome and Principe, Senegal, Sierra Leone, Tanzania, Togo, Antigua and Barbuda, Argentina, Bolivia, Colombia, Ecuador, Guatemala, Haiti, Honduras, Peru, Nepal, Philippines, Vietnam, Bosnia and Herzegovina, Greece, Italy, Kyrgyzstan, Russian Federation, San Marino, Serbia, Lebanon, Palestinian Territory, Occupied, Syrian Arab Republic Angola, Congo, The Democratic Republic of, Cote d Ivoire, Kenya, Liberia, Senegal, Seychelles, Uganda, Suriname, Bangladesh, Denmark, Greece, Romania, Russian Federation, United Kingdom, Lebanon, Palestinian Territory, Occupied, Qatar Switzerland Turkey United Kingdom Chad, Ethiopia, Guinea, Malawi, Mali, Sao Tome and Principe, Seychelles, Bolivia, Ecuador, Honduras, Suriname, Bangladesh, Cambodia, Nepal, Philippines, Armenia, Austria, Belarus, Bulgaria, Denmark, Georgia, Germany, Greece, Italy, Kazakhstan, Kyrgyzstan, Lithuania, Serbia, United Kingdom, Lebanon, Syrian Arab Republic Cote d Ivoire, Djibouti, Somalia, Ecuador, Afghanistan, Bangladesh, Pakistan, Sri Lanka, Thailand, Albania, Georgia, Italy, Luxembourg, FYRO Macedonia, Iraq, Palestinian Territory, Occupied, Qatar, United Arab Emirates Central African Republic, Cote d Ivoire, Djibouti, Guinea, Kenya, Lesotho, Namibia, Senegal, Sierra Leone, South Africa, Uganda, Zimbabwe, Canada, El Salvador, Australia, Bangladesh, Indonesia, Mongolia, Nepal, Pakistan, Philippines, Armenia, Austria, Denmark, Finland, France, Germany, Ireland, Italy, Monaco, Norway, Portugal, San Marino, Slovakia, Spain, Switzerland, Tajikistan, Jordan, Lebanon, Palestinian Territory, Occupied MIDDLE EAST AND NORTH AFRICA Bahrain Philippines Iran, Islamic Republic of Senegal, Sri Lanka, Armenia, Azerbaijan, Lebanon Iraq Jordan, Lebanon Israel Ecuador, India, Nepal, Philippines Kuwait Jordan, Lebanon Libyan Arab Jamahiriya Spain Palestinian Territory, Occupied Qatar Saudi Arabia United Arab Emirates Lebanon Guinea, Guinea-Bissau, Kenya, Mauritania, Senegal, Afghanistan, Philippines, Kyrgyzstan, Jordan, Lebanon Kyrgyzstan Comores, Mali, Seychelles, Sri Lanka What can be done to promote stronger links within regions as well as between them?

57 BREAKING DOWN THE DATA BY SEX, AGE AND OTHER FACTORS WHAT DOES IT TELL US Breaking down the data by sex, age and other factors what does it tell us? In this chapter, we look at how recording and analysing disaggregated data can help National Societies understand and respond to their current situation and future threats, nationally and globally.

58 56 BREAKING DOWN THE DATA BY SEX, AGE AND OTHER FACTORS WHAT DOES IT TELL US Why break down the data? A National Society programme might count, for example, the number of people receiving first-aid training. But do they also record, for each person, their sex? What about their age or disability status? Disaggregating data in this way helps National Societies: report to donors understand more about their staff and volunteers find out where there are gaps in their programmes and services find out who exactly in the community is being served design programming to better serve the community, especially vulnerable people actively improve gender and diversity composition at all levels of the organisation, and reduce inequality demonstrate that they put a high level of importance on gender equity and equality, and respect for diversity. Collecting and processing disaggregated data can seem like a big challenge. On page 12 we saw how the new Technical Note on Counting People Reached is helping National Societies with this task. Any humanitarian assistance must be sensitive to gender, age and other socio-economic considerations, as well as being proportionate to the magnitude of the situation. Assistance must be provided first to the most vulnerable people and delivered in a way that respects their dignity. 20 Disaggregated data can help individual National Societies in their work. But it can be really useful in global comparison too. In the next sections we will look at the stories told by the disaggregated FDRS data globally. Vietnam: making sure that elderly and disabled people are included The Chairman of People s Committee Mai Hoa Commune, Trâ n Văn Giáo says that Mai Hoa in Tuyen Hoa district, Vietnam has a fairly slow economy. Most community members are farmers. The living conditions are not great in comparison to some neighbouring areas, natural disasters can be very tough on the community. In 2007 there was a massive flood, one of the worst in 60 years, and it caused extensive damage to the commune. Whenever natural disasters happen, the most seriously affected are mainly elderly people, children, some women, and people with disabilities Phȧm Thanh Hái lives in Nam Son Village. He recalled that the water levels were very high in the 2007 flood, flowing violently and rapidly, damaging houses and threatening people s lives. He is a person with a disability, however he does not see himself as a disabled person, especially in times of disaster, when he wants to support his community and himself. He regularly joins community meetings to understand, contribute and prepare for disasters. All over the world, National Societies sometimes hear people saying that disabled and elderly people don t count quite as much as others, and if they are affected by disaster this does not count in quite the same way. Viet Nam Red Cross now specifically includes disabled people in their Vulnerability Capacity Assessments. This helps make sure the most vulnerable people are really made visible, really participate, and are really reached by programmes. VCAs like this are being implemented all over Vietnam with support from International Red Cross and Red Crescent Movement partners. Explicitly addressing vulnerable groups in a VCA is one way to make sure that the most vulnerable people are visible, not hidden. It goes hand-in-hand with collecting disaggregated data for the FDRS. 20 International Federation of Red Cross and Red Crescent Societies Strategy 2020, p.13 (2010). Vietnam Red Cross Reaching affected people by boat by Viet Nam Red Cross staff in Nghe An province.

59 59A 51A 42A 43A 48A 50A 72A 60A 56A 52A 44A 33A 30A 73A 50A 44A 42A 43A 55A 68A 47A 57A 48A 45A 44A 35A 72A 52A 53A 47A BREAKING DOWN THE DATA BY SEX, AGE AND OTHER FACTORS WHAT DOES IT TELL US 57 Women and men: same or different? Sex-disaggregated data was included in the FDRS right from the start, as described on page 22. Table 10: National Societies achievements in 2016, per cent of women People Volunteering Paid Staff Blood Donors People Trained in First Aid People Reached by Disaster Response and Early Recovery People Reached by Long Term Services and Programme Development AFRICA AMERICAS ASIA PACIFIC A EUROPE AND CENTRAL ASIA MIDDLE EAST AND NORTH AFRICA GRAND TOTAL % 52% 56% 57% 55% 56% % 5 67% 7 45% 65% % 41% 28% 27% 32% 28% % 49% % 48% % 58% 44% 56% 43% 47% % 57% 48% 58% 52% 53% A A A A A Note: this data differs from Table 2, page 18 because it is based only on those National Societies which provided sex-disaggregated data. Table 10 shows how useful it can be to break data down by sex. It tells a lot of stories. For example: More women than men volunteer globally but the difference is not so strong in the Americas. There are nearly twice as many female as male staff globally but the picture is reversed in Africa and Middle East and North Africa. Globally, far more men than women give blood, though the difference is not so strong in Africa and the Americas A+65% of paid staff are women More women than men volunteer globally, and there are nearly twice as many female as male staff.

60 BREAKING DOWN THE DATA BY SEX, AGE AND OTHER FACTORS WHAT DOES IT TELL US Old and young: same or different? National Societies started reporting age disaggregated data to the FDRS in 2016, see Table 6, page 23. Table 11 displays this information. Table 11: National Societies achievements in 2016, disaggregated by age People Reached Over to to to to to 17 6 to 12 0 to 5 1% 3% 5% 5% 49% 13% 15% 9% AFRICA AMERICAS ASIA PACIFIC 5% 14% 37% 31% 6% 4% 1% 2% 3% 2 51% 6% 13% 5% 1% 4% 9% 42% 23% 2 EUROPE AND CENTRAL ASIA MIDDLE EAST AND NORTH AFRICA 1% 2% 4% 12% 49% 11% 14% 7% GRAND TOTAL Globally, National Societies reach people of all age groups but there are some big differences between Regions, with National Societies in the Americas having a special focus on people aged 50-59, whereas Europe and Central Asia, as well as Africa, have a relatively stronger focus on under 18s. People of all ages volunteer, including people under 12 and over 70. In Asia Pacific, well over half of all volunteers are aged In Africa, around one in five blood donors are under 18, whereas in Asia Pacific, very nearly half are aged Paid Staff People Volunteering Over to to to to to 17 6 to 12 Over to to to to 49 1% 5% 1 74% 8% 2% 1% 13% 86% 1% 1% 3% 33% 56% 4% 1% 1% 14% 3 55% 1% 4% 5% 16% 66% 8% 2% 18% 8 1% 9% 13% 67% 8% 1% 7% 29% 64% 1% 4% 1 55% 19% 11% 1% 3% 18% 78% 1% 5% 1 48% 31% 5% 6% 24% 7 Blood Donors Over to to to to to 17 1% 11% 69% 2 2% 8% 89% 6% 44% 5 3% 12% 85% 10 3% 15% 82% 50+50A+5 People of all ages volunteer, including people under 12 and over 70; in Asia Pacific, well over half of all National Society volunteers are aged People Trained in First Aid Over to to to to to 17 6 to 12 5% 9% 59% 24% 3% 2% 8% 74% 12% 4% 1% 5% 19% 64% 6% 5% 8% 66% 16% 1 6% 4 18% 35% Note: this data differs from Table 2, page 18, because it is based only on those National Societies which provided age disaggregated data. 2% 9% 68% 13% 7%

61 BREAKING DOWN THE DATA BY SEX, AGE AND OTHER FACTORS WHAT DOES IT TELL US 59 People reached by National Societies: direct and indirect contact If someone listens to a National Society radio jingle about epidemics, should they be counted in the number of people reached by that National Society? National Societies have been reporting the number of people reached by their programmes and services to FDRS since But this is a particularly difficult number to define and report. If someone listens to a National Society radio jingle about epidemics, have they been reached? FDRS has now started to distinguish 21 between people reached directly and people reached indirectly. This is crucial as numbers of people reached directly are nearly always more reliable: indirect data is heavily dependent on estimates. National Societies are now asked to separate their data on people reached directly and indirectly when reporting, to allow readers to fully understand the information and make good decisions based on it. Some programmes don t have direct data, such as when National Societies use technological tools like social media for awareness raising. For example, in recent years, almost all National Societies have turned to Facebook and YouTube 21 The Technical Note on Counting People Reached defines people directly reached as People directly reached (also known as direct recipients ) by IFRC Network services are countable service recipients by a Federation Network provider (or affiliate) present at the service delivery point. People indirectly reached (also known as «indirect recipients ) by IFRC Network services are estimate counts of service recipients when a IFRC Network provider (or affiliate) is not present at the service delivery point to verify service delivery. Delivery point refers to a location where a IFRC Network provider is physically present to record delivery of services provided directly to people. to deliver some messaging. With proper analytics to capture the number of viewer or downloads, National Societies can count them as indirect people reached. Many National Societies have created applications (apps) for smartphones (such as The South African Red Cross Society, Croatian Red Cross, Ethiopian Red Cross Society, Lebanese Red Cross, Kuwait Red Crescent Society, British Red Cross, Turkish Red Crescent Society, Kenya Red Cross Society, American Red Cross, The Canadian Red Cross Society, Indian Red Cross Society, Malaysian Red Crescent Society and many others). Community members can go onto their mobile device app store and download the app. The type of information provided varies from app to app. Some common subjects are first aid, memberships, volunteering, blood donations, disaster risks and the type of services that are being provided by the National Societies (health, social care, water and sanitation for example). Breaking down the data in other ways National Societies input disaggregated data into the FDRS on a yearly basis. Many National Societies disaggregate by additional criteria such as disability, ethnicity, migrant status, income source, location, household information and other programme-specific information. The FDRS team encourages this and recommends using the Washington Group Short Set of Questions in emergency programming to disaggregate data by disability. The FDRS team is working on integrating more of these additional indicators into the FDRS in the future. 12 year old Hortência Fundane, who was abandoned by her parents and lives with her grandmother, registers for a Red Cross food distribution in Mabalane district, Mozambique. Aurélie Marrier d Unienville/IFRC

62 60 HOW SHOULD THE FDRS CHANGE IN THE FUTURE 8. How should the FDRS change in the future? In this concluding section, we make some suggestions for the future development of the FDRS and National Society data collection.

63 HOW SHOULD THE FDRS CHANGE IN THE FUTURE 61 For reliable data on people reached by National Societies, the FDRS distinguishes between people reached directly (face-to-face) and indirectly. But how will this work in the future if more and more services are provided virtually? Ecuadorian Red Cross: The tools to improve data quality already exist In 16 April 2016, Ecuador suffered a 7.8 magnitude earthquake that caused devastation in the northern region of the country. As many as 673 people died, 20,849 people were injured and 30,223 people were left homeless. The Ecuadorian Red Cross deployed hundreds of search and rescue volunteers trained in Open Data Kit (ODK), an open-source set of tools, that uses Android phones to collect data such as text, numeric data, Global Positioning System (GPS), photo, video, barcodes and audio, which is submitted in real time to an online server. The use of ODK allowed volunteers to survey affected families in the field and provide appropriate help as well as carrying out follow up surveys. It also makes for faster, cleaner and more efficient data collection. Volunteers traditional and spontaneous can learn how to use the tools quickly. Can mobile data capture help provide realtime data for FDRS? Could it also help track the activities of spontaneous volunteers? Members of the Ecuatorian Red Cross (Cruz Roja Ecuatoriana) register kids using the ODK tool in schools of the Pedernales and Jama sectors. Vladimir Rodas/Cruz Roja Ecuatoriana Could National Societies that have trouble reporting on all the FDRS indicators benefit from some extra support to data literacy overall, on data collection or consolidation, or technology development? Many National Societies such as the British Red Cross, have developed smartphone apps which allow them to more easily register volunteers and/or members. Should these interface with FDRS, and if so, how? Can social media, smartphone apps, or other technologies help National Societies capture data even from non-affiliated volunteers who participate in National Society activities, making volunteer data more future-proof? In some countries, there are data regulations in place or soon to be implemented 22. How will these regulations affect how National Societies can collect and store data on individuals? The standard categories for people reached in FDRS do not currently include options for sex beyond male/female. This is because the great majority of National Societies do not currently collect data beyond these two categories and because the use of additional categories can be complex. However, National Societies are encouraged to further disaggregate their data by other sex and/or other gender categories where they are able to do so. This is a step towards inclusion 22 For instance, the European Union General Data Protection Regulation (GDPR) which comes into effect in May 2018 replaces the Data Protection Directive 95/46/EC and was designed to harmonize data privacy laws across Europe, to protect and empower all European Union citizens data privacy and to reshape the way organisations across the region approach data privacy ( of this data in the FDRS. Should the standard FDRS sex category be extended in the future to a gender category which has other as a third option? What other indicators could be added to the FDRS to make the data even more useful? Should the FDRS web application provide more sophisticated ways to analyse the data? What can the FDRS team do to encourage and facilitate the use of the data by researchers based in National Societies or universities? The current FDRS data collection process provides nationally consolidated data. Would it be useful to drill down one or more levels and have that same data per local unit? This would mean knowing where each local unit is located (GPS coordinates), what are its resources, what types of services are provided, and how many people are reached. During the 2017 General Assembly, National Societies committed to ensure that the actions of the IFRC network are non-discriminatory and promote gender equality, in particular in the governing bodies of the IFRC 23. Can the FDRS data collection process support the monitoring of this decision? Can National Societies report the composition of their governing bodies to the FDRS? 23 General Assembly 2017, decision number GA/17/25

64 62 HOW SHOULD THE FDRS CHANGE IN THE FUTURE Leaving no one behind: IFRC s 2018 World Disasters Report The upcoming World Disasters Report, due to be launched in September 2018, aims to explore the theme of leaving no one behind in humanitarian response. How do we identify groups who are left behind and perhaps have not been identified in the past? This is a big challenge for data collection and analysis, and a big challenge for FDRS. The concept of leaving no one behind is a key theme underlying the Agenda for Sustainable Development and the Sustainable Development Goals (SDGs), agreed in Recognising that previous development gains had not been equally shared, the Agenda calls for the SDGs to be met for all nations and peoples and for all segments of society and to reach the furthest behind first. IFRC seeks to delve deeper into the question of who is being left behind by the humanitarian sector specifically. The report will identify common factors leading to groups being left behind, such as geographical remoteness, individuals and groups who are not registered or mapped, groups that are unable to access assistance or receive the information they need, particular crises not being of geopolitical importance and therefore receiving inadequate funding, marginalised ethnic or political or other minorities, etc. The report also plans to look at trends in disasters and disaster response over the last decade, analysing what types of disasters are occurring and how has this changed, as well as looking at regions, category and scale. The report will use both internal and external sources Centre for Research on the Epidemiology of Disasters, Index for Risk Management, World Health Organisation, Health Observatory etc, supplementing and cross-checking this with our own data from FDRS, the IFRC GO Platform (source of the data for IFRC operations), and National Society annual reports. Collecting reliable, comprehensive and disaggregated data from FDRS and other platforms is really important in helping answer these questions. IFRC data, such as FDRS, will improve our understanding of the countless smaller crises that are dealt with nationally, don t lead to DREF allocations or UN appeals and rarely make the headlines, but which collectively result in significant disaster affected populations. Finally, the report will share IFRC insights and lessons learned around anticipation and early action, preparedness and resilience, as well as looking at relevant innovations in the fields of data and technology. A Red Cross volunteer delivers water, sanitation and hygiene promotion activities in the village of Gnambouasso, Ivory Coast. Remo Naegeli