00:00 To this first module
00:01 of the learning
00:03 from evidence on forced displacement training program.
00:06 This learning program is organized by the
00:09 FCDO UNHCR World Bank,
00:11 building the Evidence on forced displacement Program
00:14 in collaboration with the World Bank,
00:15 UNHCR Joint Data Center on Forced Displacement.
00:18 My name is Domenico Tavaso.
00:19 I'm a senior economist with the Joint Data Center.
00:22 And together with my colleague Paulaliche,
00:25 who's a policy specialist
00:28 in the impact evaluation specialist,
00:29 pardon me,
00:30 um,
00:31 in the building the evidence program,
00:33 uh,
00:33 will be facilitating this,
00:35 this module today.
00:37 Before we start,
00:38 I would like to remind us of a few housekeeping rules.
00:41 This meeting is being recorded.
00:44 Uh,
00:45 and,
00:45 uh,
00:45 and the recording will be made available,
00:48 uh,
00:48 publicly after the,
00:49 after the meeting.
00:50 We kindly ask you if you want to leave your camera on.
00:53 It's always nice for speakers to see some faces while they speak,
00:57 but we do kindly ask you to keep
00:59 your microphone muted,
01:00 um,
01:01 while,
01:01 we're not talking.
01:03 You can use the chat to ask questions,
01:05 or also toward the end of the session we uh we've,
01:08 we'd like to leave some space for a Q&A and it would be nice to,
01:12 you know,
01:12 to hear directly from you.
01:13 So just raise your
01:15 hand and,
01:15 and come in.
01:17 Uh,
01:18 you can also use the chat to introduce yourself to other,
01:21 to the other members of the audience should you wish to do so.
01:26 This is
01:27 Um,
01:27 this is,
01:28 uh,
01:28 a focus.
01:29 This is the first module
01:30 and it focuses on,
01:32 uh,
01:32 for the use of for socioeconomic data on,
01:35 on forced displacement.
01:38 Throughout this module,
01:39 our instructors will cover a multiplicity
01:42 of themes
01:44 related to to socioeconomic data.
01:46 They will talk about
01:48 demographic characteristics.
01:49 They will talk about poverty assessment.
01:50 They will talk about labor market outcomes.
01:53 All of these,
01:54 many of these
01:55 topics will then be covered more in depth
01:58 in the next 7 modules,
01:59 which will constitute this training program.
02:03 This training
02:04 is about evidence.
02:05 We will mostly focus on quantitative evidence,
02:08 but we'll also have modules that will
02:11 look more at examples coming from qualitative evidence
02:14 or descriptive evidence.
02:17 Much of the material that will be presented throughout this training
02:20 come from the building evidence on forced displacement program.
02:24 And therefore it is my great pleasure
02:26 to,
02:27 to host
02:28 today
02:29 Paulo Verma,
02:30 who is the lead economist at the World Bank.
02:33 Paulo has been leading
02:34 the
02:35 building evidence on forced displacement program,
02:38 and so I would like to invite him to.
02:40 with us
02:41 some reflection of how the program worked,
02:44 the
02:44 evidence that has been
02:46 gathered through these and produced through this program,
02:50 and its aims and its final targets.
02:53 Paulo,
02:54 without further ado,
02:55 over to you.
02:59 Thank you,
03:00 uh,
03:00 Domenico and,
03:01 uh,
03:02 Paula for putting together this program.
03:04 This was,
03:05 uh,
03:06 a long process and hard process,
03:08 so congratulations to you for putting together the program.
03:12 Uh,
03:12 I wanted to give you a little bit of background on,
03:15 uh,
03:16 what led the production of this program today.
03:20 Uh,
03:20 if we,
03:21 uh,
03:21 go back in time 10 years,
03:24 uh,
03:24 the time of the first cooperation between the World Bank and the UNHCR,
03:28 we're talking about 2013,
03:30 2014,
03:32 the midst of the Syrian refugee crisis.
03:36 Uh,
03:36 the World Bank and the UNHCR knew each other very little,
03:39 but we got together,
03:40 we decided to do a poverty assessment of Syrian refugees in Lebanon and Jordan,
03:45 which eventually led
03:46 to,
03:47 uh,
03:47 the 2016 book,
03:49 The Welfare of Syrian Refugees.
03:51 Now,
03:51 that was the first uh experience of its kind and made us realize,
03:55 uh,
03:56 different things.
03:57 One is that research and hardcore research on refugees and IDPs
04:02 was extremely sparse,
04:04 very few articles,
04:06 almost no articles in top economics journals,
04:09 and,
04:10 uh,
04:10 the reason for that was mainly because there were very,
04:13 very few data.
04:15 Microdata on refugees and IDPs
04:18 which could be used by researchers to do their own research
04:22 and uh at the same time,
04:24 there was a very little interest on the part of the
04:26 economics professions but also many other social scientists on the topic.
04:30 That completely changed with the Syrian refugee crisis
04:34 and then later on with the migration flows towards uh Europe.
04:39 Um,
04:41 fast forward the 5 years,
04:43 we have the Compact on Refugees,
04:45 a major milestone
04:48 for,
04:48 um,
04:49 refugees and IDPs.
04:51 This is where international actors got together
04:55 and decided that,
04:56 uh,
04:56 we needed more resources,
04:58 more data,
04:59 more evidence,
05:00 more of everything to tackle this global crisis.
05:03 And this is where,
05:04 uh,
05:05 the major stakeholders uh in the international organizations
05:09 really made a commitment to change,
05:11 uh,
05:12 uh,
05:12 the,
05:13 the way we would work with um
05:15 the refugees and IDPs.
05:17 Following the 2016 book,
05:20 the World Bank and the,
05:21 uh,
05:21 and the UNHCR decided to get together
05:25 and start a joint research program on uh uh refugees and
05:30 IDPs which was called The Building the Evidence of Forced Displacement.
05:34 Uh,
05:35 and,
05:35 um,
05:37 uh,
05:37 the program initially was designed for 4 years.
05:39 Eventually,
05:40 we,
05:40 we extended that to 7 years,
05:43 $16 million US dollars,
05:45 uh,
05:46 focusing on low and middle-income countries where
05:48 85% of the displaced population live.
05:52 And uh I will tell you more with the next slide
05:55 about the content of the program and what you could use
05:59 uh of uh the evidence that emerged from this program aside from everything that we,
06:04 you will learn during this course.
06:07 Uh,
06:07 the program,
06:08 uh,
06:09 um,
06:09 uh,
06:10 was structured,
06:10 uh,
06:11 uh,
06:11 initially in different pillars.
06:13 Uh,
06:14 we had a global pillar that really was focusing on global studies.
06:19 We did 4 sector global studies on health,
06:22 education,
06:22 social protection,
06:23 and jobs,
06:24 but also 2 thematic global studies,
06:26 one on gender and one on social cohesion.
06:29 Um,
06:30 the results of all these studies,
06:32 uh,
06:32 and,
06:33 uh,
06:33 the papers that came out and were published,
06:35 uh,
06:36 can be found on our,
06:37 uh,
06:38 website.
06:39 Uh,
06:40 a different pillar was focused on impact evaluations,
06:43 RCTs,
06:44 and other types of impact evaluations,
06:46 and,
06:47 uh,
06:47 we did 15 of them,
06:49 uh,
06:50 across,
06:50 uh,
06:50 3 continents.
06:52 And uh these were really designed to evaluate,
06:56 uh,
06:57 World Bank or UNHCR or government-led programs
07:00 that uh were specific on certain,
07:03 uh,
07:04 uh,
07:04 topics,
07:04 could have been a cash,
07:06 uh,
07:06 uh,
07:07 transfer or,
07:08 uh,
07:09 you know,
07:09 we were trying to address questions like
07:11 are forced disployment projects achieving the desired outcomes
07:15 or are there low-cost and scalable interventions
07:17 that can enhance the effectiveness of projects.
07:21 Uh,
07:21 we also had another pillar that,
07:23 um,
07:24 uh,
07:24 produced a number of,
07:25 uh,
07:26 focused paper.
07:27 We call them,
07:28 uh,
07:29 this,
07:29 under this pillar,
07:30 we also had,
07:31 uh,
07:31 a Young Fellows program
07:33 where we hired the,
07:34 uh,
07:35 24 young fellows for,
07:36 for a period of one year
07:38 to produce research on forced displacement but at the same time,
07:42 uh,
07:42 been embedded into World Bank or UNHCR unit and
07:45 therefore contributed to the work of these units.
07:49 Um,
07:51 then we had the microdata initiative,
07:53 uh,
07:53 that,
07:54 uh,
07:54 at the early stages supported the UNHCR in building
07:57 the microdata infrastructure that they needed to use their data
08:02 and that eventually was turned into what today is
08:05 called the World Bank UNHCR Joint Data Center.
08:08 So we designed the center
08:10 as a spin-off of the program
08:13 precisely because one of the main uh
08:16 Train and bottleneck to your research
08:19 was the lack of microdata.
08:21 Today,
08:21 we have,
08:22 uh,
08:22 a fully dedicated,
08:23 uh,
08:24 microdata library
08:26 for refugees at IDPs at the UNHCR
08:29 and,
08:29 uh,
08:30 a fully dedicated uh subset of the World Bank microdata library at the World Bank,
08:35 something that would have been unthinkable 10 years ago.
08:39 So today,
08:40 researchers have at their own disposals hundreds of microdata sets
08:44 which can be downloaded from these two sources.
08:48 I invite you to visit both the UNHCR Microdata Library
08:51 and the World Bank Microdata Library to learn more.
08:55 Um,
08:56 We also had the dissemination and uptake program through social media,
09:00 blogs,
09:01 uh,
09:02 uh,
09:02 we had a seminar series and many other initiatives to,
09:05 uh,
09:05 disseminate this work.
09:07 And this is really the,
09:08 uh,
09:08 this training program,
09:10 the culmination of,
09:11 uh,
09:12 this dissemination and optic strategy.
09:14 We,
09:16 what we really wanted to do was to put together everything we have learned,
09:19 structure this,
09:20 uh,
09:21 uh,
09:21 learning
09:22 and put it uh in formats that is available for people to learn in the future.
09:27 And we are talking about over 130 studies that we financed.
09:32 They're all published and available
09:34 on our website,
09:35 so you can consult every single one of them.
09:38 Uh,
09:39 and,
09:39 uh,
09:40 we have a very wide range.
09:42 We go from top economics journals,
09:45 uh,
09:46 papers that have been published in top economics journals,
09:49 all the way to World Bank,
09:51 UNHCR joint studies
09:53 to,
09:53 uh,
09:54 uh,
09:54 different kind of,
09:55 uh,
09:55 reports specific on certain topics.
09:57 So you can find everything again on,
09:59 on the web,
10:00 on the website.
10:01 So this training program is really the,
10:03 the end,
10:04 uh,
10:05 process of this dissemination uptake.
10:07 We really hope that uh you're going to enjoy the program,
10:10 but remember,
10:12 Uh,
10:12 what can be covered in this program will be some of the best evidence that,
10:17 uh,
10:17 we were able to put together and other people were able to
10:20 put together in this new domain of research on refugees and IDPs,
10:26 but there is a lot more out there,
10:27 so again,
10:29 go and check the World Bank and the UNHCR microdata libraries,
10:33 our websites,
10:33 and all the resources that will be,
10:36 uh,
10:36 made at your disposal during this course.
10:39 Thank you very much.
10:42 Thank you very much,
10:43 Paulo.
10:44 And effectively this during this training we will have the possibility
10:48 and the opportunity to hear from instructors coming from different
10:51 organizations who have collaborated with the building the evidence program.
10:54 So constructor from the World Bank,
10:56 UNHCR,
10:58 but also academia and other.
10:59 Research institutions.
11:01 Among the resources that I'd like you to visit is
11:03 also the the website of the Joint Data Center.
11:06 So www.jointdatacenter.org.
11:09 And in the interest of time,
11:10 I just invite you to visit that when you have a chance and
11:14 and subscribe to our newsletter.
11:17 Now,
11:17 this program
11:19 Is this,
11:20 this module today is about socioeconomic data
11:23 and its application to forced displacement.
11:25 So,
11:26 Before we start and before
11:28 I ask our instructors who
11:30 today are all from,
11:32 from,
11:32 from UNHCR,
11:33 and then we will hear again from the World Bank at the end of the session with Paula,
11:37 but before I ask our instructor instructors to tell us about
11:41 how we collect data
11:42 and then how we produce
11:44 data and how,
11:45 sorry,
11:45 how we produce evidence with this data and how we then serve this evidence.
11:49 To policymakers,
11:51 I would like to hear from,
11:52 from you.
11:53 Um,
11:53 I would like to understand.
11:55 We'd like to understand whether you see the importance of
11:58 working with socioeconomic evidence
12:01 on forced displacement
12:02 or whether,
12:03 you know,
12:03 you don't know,
12:04 find it irrelevant.
12:05 And if you do think it's important,
12:07 why?
12:07 So I invite you to visit this website,
12:10 slider.com.
12:11 You don't need to log in.
12:12 You don't.
12:12 To enter any details about yourself.
12:14 You just need to enter this number in the field at the top of your web page,
12:19 and you can enter the answer to this question.
12:22 Why do you think it is important to for
12:25 humanitarian and development institutions like the one running these programs
12:29 to,
12:30 to have evidence to produce evidence force displacement?
12:33 And again,
12:33 if you don't think it is important,
12:34 please let us know.
12:36 Your answer,
12:37 uh,
12:37 which I'm going to go through,
12:38 uh,
12:38 in a,
12:39 in a minute,
12:40 will,
12:40 uh,
12:40 will help us inform this module today
12:42 as well as also how we're working in the future.
12:46 While you do that,
12:47 let me introduce our
12:48 four instructors for today.
12:50 They,
12:50 as I said,
12:51 they come from,
12:52 from the World Bank.
12:53 We have Sebastian Stein Muller from the
12:54 Global Data Service who's going to talk about
12:57 how
12:58 the
12:59 Socioeconomic data are collected and used within UNHCR
13:03 and then we'll have two examples of
13:06 evidence
13:06 that has been produced to data.
13:08 And one example coming from Kenya being produced,
13:11 presented by one of our economists,
13:13 Florence Nimmo,
13:14 and our senior economist Teresa Beltramo will
13:18 give us an example from,
13:19 from Mozambique.
13:21 We then want to learn more about how we link this evidence to the policymaking,
13:26 how we do deal with policymakers when it comes to
13:29 using the evidence that can be produced by our institutions.
13:32 And in this respect,
13:33 we've invited our colleague Paulo Sergio Almeida
13:36 from the country office in Brazil
13:38 to tell us more about the Brazilian experience.
13:42 Before I give the floor to Sebastian,
13:44 let me,
13:45 let's go and see what happened,
13:46 what is happening in the,
13:48 in Slido.
13:49 Allow me to
13:51 Um
13:52 To move here to Slido to see.
13:55 If you ever enter any,
13:57 any answer,
13:58 and I do see some answer
13:59 in terms of why it is important,
14:01 well,
14:03 I can see that to better tackle the flow
14:06 of relevant
14:07 information
14:09 without data,
14:09 you are in the dark.
14:10 I love that because you could
14:12 support evidence-based and data-driven policymaking.
14:15 And
14:17 Better programming.
14:18 And I see that,
14:20 you know,
14:21 there is a lot of emphasis on programming policies,
14:24 and you can see there are some keywords that are popping up here.
14:29 So it seems that we are off a good start.
14:31 We're all kind of on the same page here,
14:33 at least from their answers that I'm able to to show here on on the screen.
14:37 And I'm going to leave this one.
14:39 Without that data,
14:40 you are in the dark.
14:41 I'm going to leave it here with this message.
14:43 I'm going to pass it on to
14:45 Sebastian,
14:46 who's going to tell us more about socioeconomic data
14:49 in uh
14:50 on forced displacement from the UNHCR perspective.
14:54 Without further ado,
14:55 Sebastian,
14:55 over to you.
15:01 Thank you very much,
15:02 Domenico.
15:02 And um in this first part,
15:04 we are gonna talk about
15:07 a little bit what we understand by socioeconomic data
15:10 in UNHCR
15:12 and how we and our um
15:14 partners work together to collect and use
15:18 socioeconomic data
15:20 in our institutional settings.
15:23 Um,
15:24 in 2019,
15:25 UNHCR published its,
15:27 uh,
15:27 data transformation strategy,
15:30 outlining for the following five years,
15:32 the
15:33 priorities,
15:35 um,
15:36 to,
15:36 um,
15:37 in the use and regeneration
15:39 of data.
15:41 And that says UNHCR is to become a trusted leader on data and information,
15:46 enabling actions that protect,
15:48 include,
15:48 and empower.
15:50 And we already see,
15:51 and we can go on to the next slide from here,
15:53 we already see,
15:55 um,
15:55 data and evidence in UNHCR
15:58 is certainly a means to an end,
16:00 and some of these ends are here on this slide.
16:03 Improve socioeconomic conditions for refugees and host communities.
16:07 Now,
16:08 this is from the global compact on refugees,
16:11 and I would certainly want to add stateless populations
16:14 and other displaced
16:16 communities such as IDPs.
16:18 Assess and address the impact of large refugee populations on host countries.
16:23 And importantly,
16:24 identify and plan appropriate
16:26 solutions.
16:28 So we really do want to build the pathway
16:30 from uh collecting data
16:33 and
16:33 generating
16:35 more and better evidence,
16:36 and to feed that into informed programming,
16:39 policy and advocacy
16:40 to the end
16:41 of uh improving living conditions
16:44 and finding better solutions,
16:46 moving on.
16:48 So in the last few years,
16:49 UNHCR has really taken a few key steps to
16:52 improve the use of evidence and operational response.
16:55 And first of all,
16:57 investing massively in the data agenda.
16:58 This included
17:00 the um creation of whole new entities dedicated to data,
17:04 most importantly,
17:05 in partnership with the World Bank,
17:07 the Joint Data Center,
17:08 of course.
17:09 And the division of resilience and Solutions
17:12 has been leading.
17:13 In the uh use and generation of evidence,
17:17 the creation of a whole new
17:18 entity,
17:19 global data service,
17:21 uh,
17:21 reporting directly to the executive Office of the High Commissioner.
17:24 And finally,
17:25 and not to forget the DEMAS,
17:27 the data centers in the regional bureaus,
17:30 um,
17:31 creating data,
17:32 uh,
17:32 collecting data,
17:33 creating evidence,
17:34 and
17:35 carrying the data work
17:36 into the regions.
17:38 Um,
17:39 building on that,
17:40 um,
17:40 we've been investing in different staff profile profiles,
17:44 many of the new
17:45 development officers,
17:46 very importantly,
17:47 economists,
17:48 and,
17:49 crucially to,
17:50 to improve partnerships with,
17:52 uh,
17:52 data actors
17:54 and also statisticians and data scientists,
17:57 uh,
17:57 to collect and analyze survey data and other data,
18:00 population data,
18:01 for example.
18:02 Um,
18:02 and,
18:03 uh,
18:03 these investments in,
18:05 uh,
18:05 staff profiles and new entities.
18:08 And
18:09 has really led to,
18:10 uh,
18:11 investments in research,
18:12 uh,
18:12 using innovative approaches such as,
18:14 uh,
18:15 poverty imputation methods
18:17 to better understand characteristics and needs of
18:19 forcibly displaced and
18:21 stateless population as well as,
18:23 uh,
18:23 host communities.
18:25 Moving on from here.
18:28 And here,
18:29 just a,
18:29 a list of
18:31 um topics of fields you would typically be collecting,
18:36 um,
18:36 socioeconomic
18:37 indicators on specifically in socioeconomic and demographic household surveys.
18:43 I'm not gonna go through all the lists,
18:44 many of you are gonna be very familiar with the indicators in each of these fields,
18:49 but I do want to point out,
18:50 uh,
18:50 consumption and expenditure modules at The very end in household surveys
18:55 deployed to um estimate
18:58 rate and death of uh monetary poverty among refugee populations
19:03 and then crucially be able to compare
19:06 um these estimates
19:07 to those of the uh national of the host populations,
19:11 which gives us really,
19:12 really valuable
19:13 programming and advocacy
19:15 and it's
19:16 moving on.
19:19 Now,
19:20 uh,
19:20 the people in UNHCR who would typically do these,
19:24 do these tasks,
19:25 there are many more staff cos involved,
19:26 but two typical ones,
19:27 economists and statisticians,
19:29 um,
19:29 obviously slightly different angles on these job profiles.
19:33 Economists,
19:34 uh,
19:34 we,
19:34 of course,
19:35 have many in HQ in the regions,
19:37 but also crucially,
19:38 we've got economists in the country operations,
19:41 and so they are particularly well suited to build partnerships,
19:44 for example,
19:45 with national governments,
19:46 with NSOs.
19:48 In the countries.
19:49 Statistics and data analysis offers us,
19:51 as we call our statisticians,
19:53 um,
19:54 typically
19:55 to be found in HQ in headquats and in the regions,
19:58 um,
20:00 working a lot,
20:00 obviously also outside socio-economic data in itself,
20:04 for example,
20:05 to develop,
20:06 um,
20:07 models for the analysis and projections of
20:10 population of democraphic data.
20:12 And to also on the international level and on the regional level,
20:16 coordinate uh with partners
20:18 in the development and the maintenance of statistical definitions and standards.
20:24 Moving on from here.
20:26 I,
20:27 uh,
20:27 briefly want to present,
20:29 uh,
20:30 three
20:30 core initiatives of UNHCR
20:33 that,
20:34 um,
20:34 help us and that,
20:36 uh,
20:36 are really important in the creation,
20:38 um,
20:39 of socio-economic evidence.
20:41 First of all,
20:42 theor Displacement surveys,
20:43 FDS,
20:44 UNHCR's new flagship household survey program.
20:48 Um,
20:48 the aim is really to,
20:49 uh,
20:50 collect high-quality,
20:52 timely data
20:53 on possibly displaced people.
20:55 Uh,
20:56 this is happening
20:57 within standardized and integrated multi-topic household surveys
21:02 aligned with international statistical standards,
21:05 allowing us,
21:05 for example,
21:06 to collect data on many of the SDG,
21:09 uh,
21:09 priority indicators for forced displacement.
21:12 Aim to be comparable across countries and over time.
21:17 The FDS aim to cover around 99% of the global refugee
21:20 and asylum seeker population in low and lower middle-income countries.
21:25 Uh,
21:25 we see here,
21:26 uh,
21:26 the map on the left,
21:28 which countries these are,
21:29 and we
21:30 Currently,
21:31 um,
21:31 uh,
21:32 data collection
21:33 underway in the first,
21:35 um,
21:36 force displacement survey in South Sudan,
21:39 and we are hoping to,
21:40 uh,
21:40 publish,
21:41 uh,
21:41 our first very high-level report
21:43 early next year.
21:45 Two more,
21:46 um,
21:46 FDS are currently planned
21:48 in Pakistan and Cameroon in the immediate future.
21:52 Moving on from here.
21:54 And this has been mentioned,
21:55 um,
21:56 before,
21:57 um,
21:57 by Paolo,
21:58 the microdata Library of UNHCR
22:00 externally facing public online library
22:03 for anonymized and curated microdata
22:07 for external researchers especially to use.
22:10 So we really want to
22:11 put the data sets we collect and we collect a lot of info,
22:14 a lot of data.
22:15 In UNHCR,
22:16 we want to put that out there for other people to use
22:19 and to hopefully,
22:20 to hopefully improve the evidence base
22:23 on forcibly displaced and
22:25 um.
22:26 And stateless populations.
22:28 Sorry,
22:29 if we could
22:29 just stay on the previous slide for a moment.
22:31 Thanks so much.
22:32 Um,
22:33 the MDL has been modeled,
22:35 um,
22:35 after other,
22:36 uh,
22:36 microdata libraries such as,
22:38 for example,
22:39 of course,
22:39 the World Bank microdata library.
22:41 As of summer 2023,
22:43 we had over 650 data sets available
22:46 on possibly displaced and status populations.
22:49 And,
22:50 these slides,
22:50 you can,
22:51 of course,
22:51 uh.
22:52 Look through
22:52 for yourself here,
22:53 just two examples of future data sets.
22:56 But also,
22:56 and importantly,
22:57 and this is really what we want to stress,
22:59 um,
23:00 here in the last bullet points,
23:02 the microdata,
23:03 uh,
23:03 sets have been used for external research,
23:07 um,
23:07 on poverty measurement,
23:08 for example,
23:09 uh,
23:10 among refugees in Jordan.
23:12 Moving on.
23:14 Last but not least,
23:16 a big,
23:16 big initiative,
23:17 um,
23:18 the Expert Group on refugee internally
23:19 displaced persons and statelessness statistics.
23:23 AR's
23:23 been going on for 7 years now,
23:26 and with a mandate by the,
23:28 uh,
23:28 UN Statistical Commission.
23:30 I'm not gonna go too much into,
23:31 into the details,
23:33 but
23:33 As many of you know,
23:35 3 main recommendations have been published so far
23:38 by the,
23:39 uh,
23:39 ERIS as a
23:41 country,
23:41 um,
23:42 driven,
23:43 um,
23:43 expert group to develop statistical standards,
23:45 the international recommendations on refugee statistics,
23:48 on IDP statistics,
23:50 and on statelessness statistics.
23:52 And this really brings us to the point,
23:54 you always want to make sure,
23:56 uh,
23:56 we eventually,
23:58 um,
23:59 aim to,
23:59 um,
24:01 We aim to include,
24:02 uh,
24:02 refugees,
24:04 possibly the other possibly displaced population and stateless populations
24:08 in national data collection exercises
24:10 and having these
24:12 country-driven
24:13 um standards,
24:14 uh,
24:14 really helps us do that.
24:16 Moving on.
24:20 So here really to summarize,
24:22 in UNHCR we're using socioeconomic data
24:25 to improve programming,
24:26 policy advocacy.
24:28 We want to stress the value of data evidence.
24:30 Uh,
24:31 this data is key for development,
24:32 active financing and engagement,
24:34 and we want to be able to share this data,
24:36 for example,
24:37 through initiatives
24:38 like the microdata Library.
24:41 Uh,
24:41 Joint analytical work and collaboration
24:44 with host governments,
24:45 with the World Bank on the generation of comparable socioeconomic data
24:49 has opened doors for the inclusion of possibly displaced and stateless persons
24:54 international statistics,
24:55 statistical exercises.
24:57 And again,
24:57 this is
24:58 where we want to be working towards.
24:59 This is why we've got ER,
25:01 um,
25:01 eventually.
25:02 We want to aim,
25:04 uh,
25:04 for the inclusion
25:06 of
25:07 populations UNHCR ourselves
25:09 in national data collection exercises.
25:12 Thank you so much for your,
25:13 uh,
25:13 attention until now,
25:15 and I'm giving back to Domenico
25:18 for the introduction of the next part.
25:21 Thank you.
25:23 Thank you very much,
25:24 Sebastian.
25:25 Uh,
25:25 before you go in for your break,
25:28 uh,
25:28 I understand there is a question in the,
25:30 from,
25:31 in the chat room.
25:32 Maybe Paula,
25:32 if you can help me with that,
25:34 we'll read it to Sebastian.
25:35 It's a
25:36 quick question that you should probably be able to answer,
25:39 uh,
25:40 straight away.
25:41 Thank you.
25:43 Exactly.
25:43 This is a clarification question,
25:45 and uh it is whether
25:48 Um,
25:48 this work,
25:50 um,
25:50 is being done in Latin America and the Caribbean too.
25:55 Um
25:57 Generally,
25:58 yes.
25:59 Uh,
25:59 I'm wondering to make my,
26:01 uh,
26:02 to make my answer a bit more concrete,
26:03 uh,
26:04 which of the works,
26:05 workstreams you mentioned.
26:06 If you talk about the creation of,
26:08 uh,
26:09 the collection of socio-economic data,
26:10 for example,
26:12 um,
26:13 ICRS,
26:13 uh,
26:14 certainly we have been having initiatives in the
26:17 Americas.
26:17 We obviously do have ADDEMA,
26:19 a Regional Data Center in UNHCR in the Americas,
26:21 as you,
26:22 uh,
26:22 certainly know.
26:24 Um,
26:24 FDS currently,
26:26 we don't have planned in,
26:28 uh,
26:28 the Americas as far as I know.
26:30 No,
26:30 we don't,
26:31 we don't.
26:32 Um,
26:33 but,
26:33 uh,
26:33 I do want to mention that,
26:35 uh,
26:35 the forest displacement survey isn't the only survey initiative we've got.
26:39 Uh,
26:39 we've got,
26:40 for example,
26:41 uh,
26:41 on a more internal level in UNHCR we've got the resources management service,
26:46 where we are also increasingly,
26:48 um,
26:49 Managing to,
26:50 um,
26:51 first of all,
26:51 standardize these surveys so we are able to,
26:54 um,
26:54 collect data and estimate,
26:56 um,
26:57 representative,
26:59 um,
26:59 figures on SDG indicators for these,
27:01 for example,
27:01 among,
27:02 um,
27:02 possibly displaced populations.
27:05 And,
27:05 uh,
27:06 we also,
27:07 uh,
27:07 aim to really um integrate these services so to make the,
27:11 uh,
27:11 tools,
27:12 uh,
27:12 we are using the questionnaires,
27:14 the,
27:14 uh,
27:15 cob collection forms,
27:16 uh,
27:17 to make them as standardized as we really can.
27:20 Hope this answers it.
27:21 Uh,
27:22 I,
27:22 I'm happy to,
27:23 to,
27:23 to,
27:24 uh,
27:24 to reply more concretely to that.
27:26 Thanks.
27:27 Thank you.
27:27 And,
27:28 uh,
27:28 so I invite whoever
27:30 wrote that question,
27:30 maybe needs,
27:31 uh,
27:32 further clarification to put them in the chat or,
27:34 uh,
27:34 raise their hand later.
27:36 Uh,
27:36 but I do see one,
27:38 raised hand.
27:39 I'm gonna then leave the floor to,
27:41 to Laura,
27:42 and,
27:42 uh,
27:42 and then after this question,
27:44 we'll then move on to the,
27:45 to,
27:45 to,
27:46 to the next,
27:46 uh,
27:47 uh,
27:47 block.
27:48 Over to you,
27:49 Laura.
27:49 Welcome.
27:50 Hi,
27:51 thank you.
27:52 Uh,
27:52 and thank you to Sebastian for this,
27:54 uh,
27:54 really useful overview.
27:56 Um,
27:57 I'm really happy to see the forest placement survey,
28:00 uh,
28:01 as I know there's,
28:01 there's several initiatives to do panel surveys
28:04 in different countries,
28:06 but,
28:06 uh,
28:06 you know,
28:07 they're not always using the same measurements,
28:09 so it's good to see that there will be something,
28:11 uh,
28:11 That will enable comparisons.
28:13 You mentioned the first country has uh almost finished data collection.
28:18 What's the plan for rolling it out in all of the 30,
28:21 33 countries over what period?
28:23 And
28:23 is this a one-off survey or are you planning multiple rounds?
28:28 Thank you.
28:28 Um,
28:29 thank you,
28:29 very,
28:30 very good questions.
28:31 Thanks.
28:31 Um,
28:32 so,
28:32 at
28:33 33 countries,
28:34 that would be an ambition.
28:35 These are big,
28:36 big surveys.
28:37 Um,
28:38 so realistically with the resources,
28:40 we've got
28:41 I don't want to really put a number of it,
28:42 but let's say,
28:43 uh,
28:44 45,
28:45 maybe 6 per year would be probably realistic in the,
28:48 in the medium term.
28:49 Um,
28:50 so we are hoping to obviously,
28:51 uh,
28:52 go through these countries,
28:53 but,
28:53 uh,
28:54 to get data on all 33 of them is gonna take a while.
28:57 Um,
28:57 certainly not a,
28:59 um,
28:59 one-off survey in the sense that,
29:02 um,
29:02 this is a standardized survey,
29:04 so we do want to,
29:05 um,
29:06 collect comparable data across these countries.
29:10 That said,
29:10 uh,
29:11 it is not a panel survey
29:12 necessarily per se.
29:14 So we are really in South Sudan,
29:16 for example,
29:17 uh,
29:17 collecting data
29:19 for this year.
29:20 And
29:21 in the immediate future,
29:22 uh,
29:23 a follow-up that might be,
29:25 I'm going to say a few
29:26 lines,
29:27 uh,
29:27 a few years down the line.
29:29 At the moment,
29:30 not necessarily,
29:31 um,
29:31 designed as a panel.
29:33 But obviously,
29:34 there might be scope uh to,
29:36 uh,
29:36 to,
29:37 to roll it out in the future,
29:39 a little bit depending on,
29:40 for example,
29:41 research
29:42 and needs and,
29:43 and feedback,
29:44 yeah.
29:46 Thank you very much,
29:47 Sebastian.
29:48 I understand there are more questions for you in the chat.
29:50 So if you want to have a look at there,
29:52 start answering them if you have the chance.
29:54 Otherwise,
29:55 again,
29:55 at the end or possibly even
29:57 afterwards,
29:58 we'll try to make sure that we can save the,
30:00 the question,
30:02 the questions.
30:03 Um,
30:03 as I said at the beginning,
30:04 uh,
30:05 we collect socioeconomic data,
30:07 but we want to create evidence from this socioeconomic data,
30:10 data,
30:11 evidence that can be
30:12 then
30:13 used
30:13 for informing programming and policies.
30:16 Uh,
30:17 we'd like,
30:17 I'd like now to,
30:18 therefore to ask our colleague Florence
30:21 from the
30:22 UNHCR Regional Bureau in Nairobi
30:25 to come in and provide us some,
30:26 some example of the type of work they have conducted
30:29 in order to use data and translate data into evidence
30:32 in,
30:33 in Kenya and in her region more more generally.
30:36 Florence,
30:37 the floor is yours.
30:39 Thank you so much.
30:39 Thank,
30:40 thank you,
30:40 Dominico.
30:41 So,
30:41 Um,
30:42 just building on what Sebastian has said,
30:44 once you collect the data,
30:46 what we do is to come up with that evidence,
30:48 as um Domiko mentioned,
30:50 and I'm going to show us one of the simplest way of doing this,
30:55 which may be very simple or may be lengthy,
30:57 but very useful.
30:58 And I'm going to use the work that we have done in Kenya
31:01 to show examples on this.
31:04 So,
31:04 um,
31:05 these socioeconomic surveys,
31:06 being its household surveys,
31:08 censors,
31:08 or microdata.
31:10 Um,
31:10 they are especially important because they are the primary building
31:13 block of any statistics on the population of interest.
31:16 For instance,
31:17 if you want to provide individual
31:19 and household objective and subjective measures,
31:21 this is the data that you might want to look at.
31:24 And if you want to also study correlations and causality,
31:27 that is looking at the relationship or association between two things.
31:31 Then you might want to use the,
31:32 the,
31:33 the socioeconomic surveys,
31:35 and they can also be used to evaluate programs.
31:39 And then normally,
31:40 these surveys that we do,
31:41 they are representative of the underlying population,
31:44 meaning that
31:45 the findings that you get from this study can be generalized
31:48 for the whole population that you would want to look at.
31:52 But then,
31:52 um,
31:53 as I said,
31:53 after the data collection,
31:54 you want to analyze the data.
31:56 And one of the ways of doing this is to
31:59 come up with a descriptive analysis or a descriptive report
32:02 that can be very informative.
32:05 So,
32:05 if you ask me what a descriptive analysis is,
32:08 I would say that it's simply allowing the data to talk
32:12 without doing any sophisticated uh analysis.
32:14 And this might be very lengthy,
32:16 looking at the reports that we have done with um,
32:19 jointly done with the World Bank.
32:20 Not less than 40 pages.
32:22 We might not have time to read through the whole report,
32:24 but then
32:24 there are inform
32:25 uh
32:26 important information that can be very useful.
32:29 The figures that we provide,
32:30 the comparison that we provide,
32:32 the trend and the correlation that we provide,
32:34 they may be very simple,
32:35 but these are very important things.
32:37 So,
32:37 I'm going to use the work that we have done in Kenya to show you
32:41 how descriptive um statistics can help you to give you a
32:45 a fundamental understanding of the population that you want to study.
32:49 So in Kenya since 2016,
32:51 UNHCR has been collaborating extensively with the World
32:54 Bank to build on data and evidence.
32:57 So we started in uh with the Kakuma as a marketplace and yes,
33:00 in my backyard in 2016,
33:03 which basically talks about the impact of refugees on their host communities.
33:07 Then from 2018,
33:08 we started with a socioeconomic series.
33:12 On refugees in camps and also in urban areas,
33:15 which allowed us to do a comparative analysis
33:18 that shows the differences between the camp-based refugees
33:21 and urban-based refugees in Kenya in 2020.
33:25 We also undertook a study
33:27 on the dense,
33:28 stateless
33:30 Shona population who live in urban Kenya to Understand the characteristics
33:34 of these um stateless population no longer state um stateless.
33:38 And then,
33:39 of course,
33:39 COVID set in in 2020.
33:41 So we collaborated with the Kenyan Statistical Office and also University of
33:45 California to track the impacts of COVID over time for refugees and nationals
33:50 and through their household um um frequency phone survey.
33:54 And our current
33:56 um work is the Kenya police,
33:58 um,
33:58 just before,
33:59 just one.
34:01 So our current work,
34:02 um,
34:03 which is the Kenya Analytical Program on Forced Displacement,
34:06 is,
34:06 um,
34:07 seeks to steady refugees and host communities over time.
34:10 And hopefully,
34:11 we might finish in 2024.
34:13 Next slide.
34:15 So as I said,
34:16 the figures that are produced through these studies can be very informative.
34:21 For instance,
34:21 the Kakoa as a marketplace,
34:23 we learned that
34:24 the market in Kakoa is worth $56 million
34:29 and out of this,
34:30 refugees contribute about 29% of it.
34:33 And we also learned that there are about 2000.
34:36 Businesses as of 2016 run by refugees and local Kenyans in the Chokana County,
34:41 Kakuma to be specific.
34:43 And then one of the studies,
34:44 through one of the study too,
34:45 we got to know that 7 out of 10 residents own a cell phone
34:49 in Kakoma.
34:50 So this,
34:52 this information may look very simple,
34:54 but
34:55 it shows the impact that refugees.
34:57 Refugees can have on the host communities and
34:59 even the contributions that refugees can have,
35:02 can have on the private sector.
35:03 This study actually attracted a lot of,
35:06 a lot of attention from the government and also the private sector
35:10 in Kakoma,
35:11 uh,
35:12 consequently leading to the development and a huge investment of that county.
35:17 Next slide.
35:19 Another useful thing that descriptive analysis can
35:23 provide us is comparison between two groups.
35:26 So in Kenya,
35:27 the work that we or the data that we have collected on refugees,
35:30 we try to align the questionnaire
35:32 with the questionnaire that is used by the
35:36 Kenya Statistical Office to collect information on nationals.
35:39 So the table that you see here shows the kind of
35:43 questions or the kind of data that we collect on.
35:45 And refugees and how they align with national surveys that were done in 2016,
35:50 2019.
35:51 This allows us to do an easy comparison
35:54 between refugees and then the host community.
35:58 Next slide.
36:00 So just to give you a snapshot of how we do the comparison um for
36:05 refugees and then um host communities in Kenya,
36:08 using the study that we did on Kalobeye in 2018,
36:12 the poverty,
36:12 one of the findings showed that um
36:15 the refugees and their host community are poorer than the average um Kenyan.
36:19 And,
36:20 but then if you compare the refugees and their host community,
36:24 the host communities are more likely to be poorer than the refugees.
36:28 And then if you look at the dependency ratio,
36:30 for instance,
36:32 refugees have higher dependency ratios compared to their host communities
36:36 and also the nationals.
36:37 And again,
36:38 looking at the employment rates,
36:40 refugees are less,
36:41 more,
36:41 less likely to Employed.
36:43 As of 2018,
36:45 37% of refugees in Kalobey were employed
36:48 compared to the 62% of their host communities
36:51 and then 72% of the,
36:53 of the nationals.
36:55 So,
36:55 um,
36:56 such comparison has been very useful,
36:58 especially for the governments of Kenya and also development um
37:01 practitioners.
37:02 So,
37:03 right now,
37:04 um,
37:05 such a steady.
37:06 That we have done have been used to build
37:08 a case of inclusion of refugees into the national system.
37:12 So,
37:12 um,
37:13 the government of Kenya is looking into shifting
37:16 the approach of refugee management from the uh from the camp setting into
37:22 a more inclusive um um settlement approach through the Sharika plan,
37:26 which we can read more online.
37:28 Next slide.
37:30 And another usefulness of descriptive um report if
37:34 you have like different ways of the data
37:36 is to look at the trend,
37:38 how a particular population is faring over time.
37:41 And this is what we can showcase from the COVID-19 survey that we did
37:46 um in 2020 for refugees and nationals.
37:50 And if you look at some of the findings from this survey,
37:53 um,
37:53 the labor force status,
37:55 you realize that even
37:56 Before,
37:56 like the refugees were less likely,
37:58 as I showed in the previous slide,
38:00 less likely to,
38:01 to be employed than
38:03 Kenyans.
38:04 But then on the onset of the COVID and even after that,
38:07 we see that employment rates as shown in the
38:10 red line is increasing,
38:12 increased over time for both populations,
38:14 but then
38:15 the rate at which it was increasing for the refugees was lower as compared to the
38:19 urban nationals.
38:22 Next slide.
38:23 And then one thing that we'd also want to
38:26 talk about a very important usefulness of descriptive analysis
38:30 is to show the correlation between two things.
38:33 That is like the relationship or the association between 22 things.
38:37 And the Shona case studies is a perfect example.
38:40 So here,
38:40 we are not just saying that maybe statelessness causes that,
38:43 but what we are trying to say is
38:44 that being stateless can affect your individual characteristics.
38:48 And with this study that we did in Shona,
38:50 we found out that,
38:51 please,
38:52 the next slide.
38:54 Even though the Shona households have a
38:56 higher employment rate than the urban Kenyans,
38:59 but then they are more likely to be employed in the informal sector.
39:03 So for instance,
39:04 78% of the Shona are self-employed compared to 30% of the nationals.
39:09 And if you look at wage employment,
39:11 only 24% of the Shona are
39:14 engaged in wage employment compared to 58%.
39:18 for the nationals.
39:19 And as at the time of the data collection
39:21 or the time that the report was being written,
39:24 the reason for their higher involvement in the
39:27 informal sector was explained by the lack of citizenship
39:31 and then also the lack of identity cards.
39:33 So,
39:34 uh,
39:34 mainly the women were doing basket weaving and the men were involved in carpentry.
39:40 But then if you look at the poverty rates,
39:43 Sorry.
39:44 If you look at the poverty rates,
39:45 the Shones are more likely to be poorer
39:48 than the urban nationals.
39:49 So as of 2019,
39:51 53% of the Shona lived below the national poverty rate of 60
39:56 $60 US dollars per month versus 29% for the nationals.
39:59 And if you want to eliminate poverty between these two population groups,
40:03 it will cost more
40:04 for the um Shona than for the national,
40:06 um,
40:07 than for the Kenyans.
40:08 Next slide.
40:10 And then if you look at the enrollment rates
40:12 for the Shona and then the urban Kenyan children
40:16 at the primary level,
40:17 you see that
40:19 we have similar rates,
40:21 81% for Shona children and 86% for
40:24 urban Kenyans.
40:25 But when you get to the secondary level,
40:27 there's a wide gap which in which urban
40:31 Kenyans were twice more likely to be enrolled
40:34 in secondary school than the Shona.
40:36 And one of the main reasons,
40:38 as at the time because the education.
40:39 The system in Kenya has changed right now.
40:42 As of the time of the data collection,
40:44 one of the explanations for this slow transition to secondary school
40:48 was partly explained by the requirement to present
40:51 birth certificates to start primary primary 8.
40:54 So this kind of study that we had done actually on the Shona
40:57 was very insightful as it gave us an insight on the characteristics of the
41:01 Shona population and it's also contributed
41:04 to the recognition of the stateless people
41:07 as nationals or to be Kenyans in Kenya.
41:09 This light
41:14 So,
41:15 you can also go a little bit further
41:18 from what I have spoken about by trying to
41:21 use um the data to understand
41:24 uh or identify,
41:25 isolate or explain a relationship between various characteristics.
41:29 So,
41:30 one thing that we did before you even move on to any sophisticated um analysis,
41:34 one thing that we did in the Kaloee study
41:37 was to look at the characteristics of the poor.
41:39 So,
41:40 we saw That poverty is mainly driven by employment status of the head,
41:45 household size,
41:45 and assets.
41:47 So,
41:47 as I said,
41:48 this may be something very simple,
41:50 but very informative and can give you the foundation
41:53 for understanding your data,
41:54 and then you can build on to do
41:56 a sophisticated,
41:58 using sophisticated statistical methods to analyze your data,
42:01 which
42:01 I believe one of our colleagues,
42:02 Theresa,
42:03 is going to talk about.
42:04 Thank you.
42:05 I think I'll stop here.
42:08 Thank you,
42:10 thank you very much,
42:10 Florence.
42:11 And uh as I
42:12 mentioned at the beginning,
42:13 you've touched upon a number of dimensions
42:16 from education to labor market to poverty,
42:19 dimension that we will be
42:21 uh digging into,
42:22 into more details in the,
42:23 in the modules to come.
42:24 But so thank you so much for this overview.
42:27 We're doing quite fine in terms of time.
42:29 So if there is any question,
42:30 if anyone would like to take the floor for one question for Florence,
42:33 please,
42:34 um,
42:35 do,
42:35 do it now.
42:36 Uh,
42:37 while I prepare the slide for the next,
42:39 uh,
42:39 next speaker.
42:41 Um,
42:43 I don't see any hand up,
42:44 but,
42:45 uh,
42:45 going up,
42:46 so.
42:47 Let me
42:48 take it from where Florence ended her,
42:50 her presentation.
42:51 She mentioned the fact that we can actually use data,
42:54 of course,
42:54 also in slightly more sophisticated way than just,
42:57 you know,
42:58 descriptive statistics,
42:59 although,
43:00 as she correctly pointed out,
43:01 descriptive statistics are important tools,
43:04 especially when it comes to,
43:06 uh,
43:06 you know,
43:06 produce evidence that can be
43:08 read,
43:08 be used quite readily
43:10 for informing policies and programming.
43:13 Uh,
43:14 we have asked Teresa Beltramo,
43:15 who's a senior economist and head of research
43:17 in the divisional resilience solution of UNHCR,
43:20 to tell us about another example,
43:23 uh,
43:23 in which socioeconomic data have been analyzed
43:26 and investigated in depth
43:28 in order to assess the impact of,
43:30 of a program.
43:32 So
43:33 Tessa,
43:33 um,
43:34 I'll leave the floor to you
43:35 to tell us about your work and the work of your quarters in Mozambique.
43:42 Yes,
43:42 thanks,
43:43 thanks,
43:43 Domenico.
43:44 You can hear me,
43:44 I,
43:44 I trust.
43:46 OK,
43:46 good.
43:47 Nice to be with you,
43:48 colleagues.
43:48 Um,
43:49 and thanks for,
43:50 for the opportunity to talk about this work.
43:52 Um,
43:52 so this,
43:53 this is,
43:54 um,
43:55 a recently concluded
43:57 program,
43:57 uh,
43:58 and evaluation,
43:59 impact evaluation of that program.
44:00 It's joint with,
44:01 uh,
44:01 Sandra Seguera at LSE,
44:03 Matt O'Brien,
44:04 who's also at LSE,
44:05 and then Florence,
44:06 who you've just met,
44:06 uh,
44:07 in the,
44:07 who's in our regional bureau
44:09 for,
44:10 uh,
44:11 Eastern,
44:11 uh.
44:12 Yeah,
44:12 Eastern Horn.
44:13 So,
44:14 um,
44:15 let me go to,
44:16 before I could tell you about it,
44:17 but,
44:17 uh,
44:18 luckily,
44:18 we,
44:18 I can just show you.
44:19 So let me give you kind of a,
44:20 a summary of the program,
44:22 and then I'll touch on some points,
44:24 uh,
44:24 with this video.
44:25 Over to you,
44:25 Dominico.
44:30 Thank you,
44:31 Teresa.
44:31 Uh,
44:33 yes,
44:33 we are going to show you a video that summarize this,
44:36 this research.
44:41 Kemara nosakaza
44:43 logu
44:44 noshkoassa muja pensarrukebammora kew
44:47 mus spa kipodiolenyagenti continaoavida.
45:21 Mozambique is hosting close to 29,000
45:26 refugees and asylum seekers.
45:28 Out of that,
45:30 approximately 9000 call
45:32 Maratani
45:34 settlement,
45:35 uh,
45:35 their home.
45:38 It is a place that is quite dynamic.
45:42 We envisage together with the government
45:46 to provide the needed basic essential services.
45:50 So there's a school providing
45:54 education from the basic level all the way to high school.
45:58 There is a clinic
45:59 that is providing help needed.
46:01 Healthcare services,
46:03 we have electricity
46:06 connected to the camp,
46:08 and we have many
46:11 young people and elderly people who are involved in very many different
46:17 vocational and livelihood activities
46:20 so that they can be able to also support themselves.
46:30 While refugees normally account for a very
46:32 small part of the host country's population,
46:34 less than 1%,
46:35 since they receive humanitarian aid,
46:37 their presence is highly salient to their hosts,
46:40 particularly in poor rural areas.
46:42 Host communities
46:43 often perceive this as being unfair,
46:45 which prevents them from fully accepting refugees.
46:59 This is the problem of social cohesion that translates into
47:01 refugees not being fully integrated into the host community.
47:04 So in,
47:05 in Mauritan,
47:05 UNHCR has been implemented,
47:07 the graduation approach,
47:09 which is a set of interventions
47:11 aimed at bringing people out of extreme poverty.
47:16 The program benefited both refugees and nationals and consisted
47:19 of providing a cash transfer of $1000 US dollars,
47:22 which is an equivalent to about 30 months of the average salary in the area.
47:27 The program further provided individual coaching on life skills,
47:31 skills trainings on language and financial literacy,
47:34 helped participants open a bank account,
47:36 and it helped participants find an apprenticeship
47:39 in the nearby town or start their own business in the refugee camp.
47:46 Two years after the beginning of the program,
47:48 participating refugees and host
47:50 increased their monthly savings by 600%,
47:53 their household income by 94%.
47:56 And overall financial security by 54%.
48:00 Overall,
48:00 we observed food security increase by 9%.
48:03 The program also increased trust in others by 21%,
48:07 and those Mozambicans benefiting from the program
48:10 indicate that they were 20% more willing to
48:12 share jobs and government support with refugees.
48:19 The munta pesole Maratanikisabimunta kweza kika past the
48:24 the fazagu makoza boa it is involveroa agency
48:28 e tenakel medu zen port in.
48:49 scare.
48:56 No
48:58 different
48:59 thannos apart.
49:25 Great,
49:25 thanks so much.
49:25 So,
49:26 um,
49:27 if,
49:27 yeah,
49:27 here comes the slide deck.
49:29 So thanks,
49:29 colleagues,
49:29 um,
49:31 next slide.
49:32 Yeah.
49:33 Uh,
49:33 thanks for,
49:34 uh,
49:34 we thought it might be more impactful just to show you the,
49:37 the
49:39 This,
49:39 this video,
49:40 so,
49:40 you know,
49:40 quickly just um.
49:43 Just as an overview,
49:44 uh,
49:45 this is a formal impact evaluation.
49:46 So we did a baseline survey and,
49:48 uh,
49:49 before the project really kicked off in August,
49:52 the December 2019,
49:54 the project,
49:55 uh,
49:55 then began officially,
49:57 you know,
49:57 in January.
49:58 Um,
50:00 there was as,
50:00 as you saw,
50:01 a suite of training that was offered,
50:03 uh,
50:04 on a whole host of different things,
50:05 including coaching,
50:06 this is a classic graduation program.
50:08 So I've had skills training,
50:09 language,
50:10 uh,
50:11 financial literacy.
50:12 Um
50:14 Uh,
50:14 you know,
50:15 in those,
50:15 depending on the,
50:16 the employment track,
50:17 there was different,
50:18 uh,
50:19 opportunities,
50:19 whether people received coaching on their business plan,
50:22 etc.
50:22 So,
50:23 the cash transfers were very close together.
50:24 They,
50:25 uh,
50:26 they were
50:27 made in August 2021.
50:29 We did a mid,
50:30 midline survey,
50:31 you know,
50:31 right after the cash transfer,
50:32 essentially.
50:33 And then again,
50:34 we did a uh another phone survey in December at the end of that year,
50:37 and then we did an inline
50:39 from a uh after the program ended
50:41 in,
50:41 um.
50:43 In December,
50:43 and so then we,
50:44 we did it in line a couple months later,
50:46 uh,
50:46 it's about 4 to 6 months later.
50:48 So,
50:48 we have a,
50:49 uh,
50:50 this is the 2nd,
50:51 uh,
50:52 Wave of the,
50:53 of the graduation program in Mozambique and Nampula and it has,
50:56 uh,
50:56 you know,
50:57 about 500 participants,
50:58 um,
50:59 and we had
51:00 very low attrition.
51:01 Uh,
51:02 yes,
51:02 next slide.
51:05 So quickly,
51:05 I mean,
51:06 you saw the highlights,
51:07 but,
51:07 um,
51:09 you know,
51:09 overall financial security increased by 54%,
51:12 you know,
51:13 there was a
51:14 much higher likelihood of having,
51:16 uh,
51:17 you know,
51:18 households had higher income in the treatment group than in the control group.
51:22 And,
51:22 um,
51:23 you know,
51:23 savings was,
51:24 which is non-existence in the control group,
51:27 is,
51:27 uh,
51:27 positive and on average of $14 per month at the end of the program.
51:32 Next slide,
51:32 um,
51:34 Just some other interesting facts that um
51:36 coming out of this evaluation showing that,
51:39 you know,
51:39 these cash transfers,
51:40 you know,
51:41 led to,
51:42 you know,
51:42 durable,
51:43 uh,
51:44 sort of good or,
51:45 or,
51:45 you know,
51:45 um,
51:46 housing stock investments,
51:48 which is,
51:48 you know,
51:49 for refugees is quite interesting,
51:50 right?
51:50 Uh,
51:51 um,
51:52 and,
51:52 you know,
51:53 as well as electricity connections.
51:54 So,
51:55 uh,
51:55 next slide.
51:57 One thing that we really looked at is,
51:59 uh,
52:00 because
52:00 this is one of,
52:01 uh,
52:02 there's another,
52:02 uh,
52:03 evaluation of a different type of program in Uganda that,
52:06 uh,
52:06 colleagues have led,
52:08 uh,
52:09 including,
52:09 uh,
52:10 Dean Karlin,
52:10 who,
52:10 uh,
52:11 is,
52:11 you know,
52:11 one of the,
52:11 uh,
52:12 the authors of the science article on the graduation program.
52:15 But,
52:15 uh,
52:16 you know,
52:16 we've,
52:17 I've been talking with Dean about our different differentiation,
52:19 and,
52:19 and,
52:19 you know,
52:20 so this work,
52:21 we really,
52:22 um,
52:22 focused on a couple of specific things and particularly trying to underline,
52:25 you know,
52:25 and assess the relationship between,
52:27 uh,
52:28 financial security and social cohesion.
52:30 And so,
52:30 in fact,
52:31 we do see a really positive and significant
52:34 increase in self-reported trust across the groups.
52:36 Um,
52:38 With an increase of 21% for those um.
52:41 In the,
52:42 uh,
52:42 you know,
52:43 in the treatment groups.
52:44 Um,
52:44 and then we also see a shift in social norms that,
52:47 uh,
52:47 participants,
52:48 um,
52:48 you know,
52:51 You know,
52:51 they feel that they can share jobs,
52:53 uh,
52:54 nationals feel that they can share jobs with refugees,
52:56 uh,
52:56 and there's,
52:57 um,
52:59 you know,
52:59 the,
52:59 the significant and positive improvement in belief
53:02 that both groups should be equally prioritized,
53:04 um,
53:05 you know,
53:05 for employment.
53:06 Um,
53:08 next slide.
53:10 So,
53:10 you know,
53:10 in short,
53:11 we,
53:11 you know,
53:12 we find that financial security does play a critical role in promoting
53:16 social cohesion and socioeconomic integration among refugees and hosts.
53:20 Um,
53:21 Uh,
53:22 you know,
53:22 another finding is that,
53:23 you know,
53:23 this is something that HCR and colleagues and partners are doing.
53:26 I mean,
53:27 we know
53:28 that,
53:28 um,
53:29 in fact,
53:30 from the,
53:30 uh,
53:31 building evidence,
53:33 global questions on social cohesion among refugees,
53:35 we know that one of the findings that we've certainly taken on board and we,
53:38 uh,
53:39 is that
53:40 You know,
53:41 um,
53:41 programs are,
53:42 um,
53:44 We should always prioritize hosts and refugees in the sense of that,
53:47 that yields better social cohesion.
53:49 In fact,
53:50 you know,
53:51 we could make a similar point here that,
53:52 you know,
53:53 ensuring that humanitarian aid
53:55 includes both,
53:56 you know,
53:56 in this case,
53:56 ultra poor members of both communities
53:59 is a vital strategy for promoting social cohesion,
54:01 alleviating tensions,
54:02 and facilitating the economic and social integration of refugees in this,
54:06 uh,
54:06 resource scarce environments.
54:08 So,
54:09 um,
54:10 I see a bunch of questions.
54:11 So,
54:11 but let me just quickly say,
54:13 um,
54:14 The,
54:14 uh,
54:15 policy brief is almost out,
54:17 I'm
54:18 hopefully very shortly.
54:19 We're working closely with the operation on that,
54:21 and then,
54:21 uh,
54:22 we are writing an econ paper,
54:24 uh,
54:24 which is also
54:26 very close,
54:26 uh,
54:27 to be out,
54:28 uh,
54:29 well,
54:29 at least into,
54:30 uh,
54:30 submission,
54:31 but we'll make a working paper as soon as we submit.
54:33 So,
54:34 uh,
54:34 and there's one piece of this work that I,
54:36 uh,
54:36 that was,
54:37 that was the final piece,
54:38 which is,
54:39 we actually measured the impact of Hurricane Gobe,
54:42 because we happen to have the Hurricane Gobe hit,
54:45 um,
54:45 I think March 11th,
54:46 if I'm not mistaken,
54:47 uh,
54:47 Mozambique and then Nampula region,
54:49 which was,
54:49 it was very,
54:50 uh,
54:50 unfortunately right
54:52 in the line of fire.
54:54 So,
54:55 Um,
54:55 we,
54:56 and we have survey data between
54:59 December and uh,
55:01 Are,
55:01 are we actually had our survey running in already in April,
55:04 so we have really um
55:07 We're able to measure the impact of Gombe on
55:11 these,
55:11 you know,
55:11 very,
55:12 obviously positive impacts of the graduation program.
55:14 And I think just as a teaser,
55:16 uh,
55:17 you know,
55:17 the financial security,
55:19 uh,
55:19 sus results sustained during,
55:21 um,
55:22 you know,
55:23 as a result of Gombe.
55:25 Uh,
55:25 uh,
55:25 I mean sorry,
55:26 despite Gombe,
55:27 uh,
55:27 the people who are in the graduation program are still more financially secure,
55:31 uh,
55:31 than those who were not,
55:33 and that,
55:33 that helps mitigate the,
55:34 uh,
55:35 the climate shock.
55:36 But,
55:37 uh,
55:37 the social cohesion results,
55:39 um,
55:39 drop.
55:40 Uh,
55:40 and so this is an interesting finding,
55:42 and this is,
55:42 anyway,
55:43 to,
55:43 to more to come.
55:44 But so yes,
55:44 I,
55:44 I,
55:45 um,
55:46 let me stop there and,
55:47 uh,
55:47 I'll back to you,
55:48 Dominique,
55:48 and I'll review these questions here in the chat.
55:52 Thank you,
55:53 thank you very much,
55:54 Teresa.
55:54 Um,
55:55 yes,
55:55 there are a number of questions for you,
55:57 for,
55:58 for you in the chat.
55:59 Uh,
55:59 I don't know if you would like to,
56:01 to answer.
56:01 There is a question that asks about
56:04 whether,
56:04 uh,
56:05 you know,
56:05 the,
56:05 the participation in the program was in somehow some
56:08 way linked to the legal status of these,
56:11 of these refugees or not.
56:13 Maybe you can answer.
56:14 This briefly.
56:15 And,
56:16 and,
56:16 but let me also,
56:17 because I've seen questions about the,
56:20 uh,
56:21 you know,
56:21 how,
56:21 how we can share resources and material about the,
56:24 the Mozambique case.
56:26 Uh,
56:27 we are
56:27 going to share with you a link,
56:29 uh,
56:30 an online link where we've gathered all the resources,
56:32 not just from Mozambique,
56:33 but all of the examples that are presented.
56:36 Today
56:37 and so at the end of the presentation,
56:38 you will be able to,
56:39 uh,
56:40 we put it also in the chat,
56:41 you will have the link to all these resources
56:44 and also,
56:45 you know,
56:45 uh,
56:45 our instructors will be able to feed more and more evidence as comes through.
56:50 So this is going to be a live,
56:51 uh,
56:51 a live page where more resources will come available in the next few days or weeks.
56:57 Um,
56:57 Theresa.
56:58 Don't know if you would like to,
56:59 to,
56:59 to talk about that aspect if you are in the position to answer the question,
57:03 that would be great before we move to the next one is this the question,
57:06 uh.
57:08 By Berlin,
57:08 is it a random selection?
57:09 Is this the one,
57:10 or is there another one on legal status?
57:12 Sorry,
57:12 um,
57:13 I saw a question from,
57:14 uh,
57:15 from,
57:15 uh,
57:16 to durable solution.
57:17 I see,
57:19 um.
57:20 So,
57:22 anyone could apply to
57:23 to be part of this program in the refugee and host community in Nampula,
57:27 uh,
57:27 but we did select,
57:29 I mean,
57:29 I think I can,
57:30 um,
57:32 You know,
57:32 I,
57:33 I,
57:33 I don't know if you're referring to the backlog of,
57:35 um,
57:36 of,
57:36 uh,
57:37 asylum seekers on the,
57:38 you know,
57:39 in the process.
57:40 I'm not sure if you're referring to that,
57:41 uh,
57:43 but there is a backlog for,
57:44 uh,
57:44 you know,
57:44 with the government in terms of asylum seeker processing,
57:48 um,
57:49 but,
57:49 um,
57:50 any refugee or asylum seeker in living in Nampula,
57:54 um.
57:55 Because there's obviously
57:57 different populations around the country,
57:58 but this is for the Nampula-based
58:00 refugee settlements
58:01 could apply for the program.
58:03 And uh the program,
58:04 you know,
58:04 as a classic graduation program does target the ultra poor.
58:07 So we used a um
58:10 Methodology that the government applies for their
58:12 social protection program in the region,
58:14 and adopted it slightly
58:16 just for the refugee setting,
58:17 but it's,
58:17 it's,
58:18 it's essentially like a 10 point,
58:20 uh,
58:21 you know,
58:21 uh,
58:23 Scale,
58:23 uh,
58:23 you know,
58:24 to look at,
58:25 um,
58:26 to determine,
58:28 I mean,
58:28 not 10,
58:28 but short
58:29 number of variable scale
58:31 to determine,
58:32 uh,
58:33 you know,
58:33 those who are,
58:34 are ultra ultra poor.
58:35 Hope that answers the question.
58:38 Thank you,
58:38 Theresa.
58:39 Thank you so much.
58:40 Uh,
58:40 I see more questions are coming in for you
58:42 in terms of how social cohesion is measured.
58:45 Um,
58:46 so maybe you can answer either,
58:47 uh,
58:48 the chat or maybe we can bring,
58:50 take some of these questions also at the,
58:52 at the very end if time permits.
58:53 Uh,
58:54 but you've mentioned
58:55 social cohesion.
58:57 Uh,
58:57 you've mentioned,
58:57 of course,
58:58 the fact that this is an
58:59 evaluation.
59:00 Let me do a bit of advertising,
59:02 advertising here for this training program.
59:04 We have dedicated modules on social cohesion and impact
59:07 evaluation coming up in the next few weeks.
59:09 So all the,
59:10 all those of you who find this
59:12 of interest,
59:13 please
59:14 think about joining us also for those,
59:15 for those modules.
59:17 Thank you so much.
59:18 There isa.
59:19 Um,
59:19 we now move on to the
59:21 To the,
59:22 to the last,
59:22 uh,
59:22 to the last speaker who is,
59:24 uh,
59:24 Paulo Sergio Meira,
59:26 uh,
59:26 and,
59:26 uh,
59:26 as I mentioned at the beginning,
59:28 what we have asked Paulo Sergio is to
59:31 tell us a bit more about the experience that they are,
59:34 uh,
59:34 having in Brazil
59:36 when it comes to,
59:37 uh,
59:37 liaise with policymakers on the basis of the evidence that is created by their,
59:42 by their office around forced displaced
59:44 forcibly displaced people and host communities,
59:47 um.
59:48 This is a relevant question also because as
59:52 as Paulo alluded to at the beginning of his of his presentation.
59:56 Uh,
59:57 the amount of research and evidence
59:59 that is becoming available around force displacement
1:00:03 is,
1:00:03 is,
1:00:03 is exceptional.
1:00:05 Uh,
1:00:06 if we look at,
1:00:07 for example,
1:00:07 just in the field of economics,
1:00:09 the amount of research,
1:00:10 the articles that have been published in the last
1:00:13 15 years really around force displacement,
1:00:16 you can see that this,
1:00:17 uh,
1:00:18 this has really exploded.
1:00:20 Then the question really is,
1:00:22 what are we going to do with this evidence if
1:00:24 can we
1:00:25 find a way to,
1:00:26 to,
1:00:26 to,
1:00:27 to bring this evidence to those who can then take the decision,
1:00:30 which,
1:00:31 which,
1:00:32 depending on which,
1:00:32 you know,
1:00:33 the,
1:00:33 the life of refugees,
1:00:35 IDPs,
1:00:36 stateless people,
1:00:36 and host communities will,
1:00:38 will effectively depend.
1:00:40 And therefore that's what I would like to,
1:00:42 to,
1:00:43 that's what we've asked,
1:00:44 tasked
1:00:45 Paulo Sergio with to tell us a bit more about what's going on in Brazil.
1:00:49 And,
1:00:49 uh,
1:00:49 and so
1:00:50 Paulo Sergio,
1:00:51 uh,
1:00:52 I leave the floor to you
1:00:54 and,
1:00:54 um,
1:00:55 and uh I ask you to,
1:00:56 to,
1:00:57 to,
1:00:57 to
1:00:58 give us uh your insights from,
1:01:00 from Brazil.
1:01:02 So thank you very much,
1:01:03 uh,
1:01:03 uh,
1:01:03 Domenico for,
1:01:04 uh,
1:01:05 inviting me to,
1:01:06 to be here with all of you
1:01:08 and share our experience here in,
1:01:11 in how we are,
1:01:13 um,
1:01:14 working with,
1:01:15 uh,
1:01:15 evidence for,
1:01:16 um,
1:01:17 to strengthen the,
1:01:18 the socio-economic inclusion
1:01:19 of,
1:01:20 um,
1:01:21 of the refugee population here
1:01:23 in Brazil when,
1:01:24 and I would like also to thank you very much the World Bank colleagues here because
1:01:28 here in Brazil,
1:01:29 it's,
1:01:29 um,
1:01:30 it's a very
1:01:31 Uh,
1:01:31 we have,
1:01:32 uh,
1:01:32 this very,
1:01:33 um,
1:01:33 uh,
1:01:33 good partnership with World Bank and the,
1:01:36 the foresters that we,
1:01:37 we,
1:01:37 I will talk about here
1:01:39 are all,
1:01:40 um,
1:01:40 uh,
1:01:41 jointly conducted by uh RCR and,
1:01:43 and,
1:01:44 and Wood Bank.
1:01:46 So,
1:01:46 uh,
1:01:46 if you can please go to the next slide.
1:01:51 So these are the,
1:01:52 the 4,
1:01:54 studies that I,
1:01:55 I've mentioned.
1:01:56 It's,
1:01:56 um,
1:01:57 they are very important for us because here in Brazil,
1:01:59 um,
1:02:00 there is this perception that the local integration of,
1:02:03 uh,
1:02:03 of the refugee population,
1:02:05 it's,
1:02:05 it's,
1:02:05 it's easy and,
1:02:07 and because,
1:02:08 uh,
1:02:08 there are,
1:02:09 uh,
1:02:09 we don't have,
1:02:10 uh,
1:02:10 uh,
1:02:11 legal restrictions for both asylum seekers and,
1:02:13 and refugees to access the formal labor markets,
1:02:16 education,
1:02:17 health,
1:02:17 social assistance.
1:02:19 Um,
1:02:19 and other,
1:02:20 uh,
1:02:20 rights and the public service,
1:02:21 but,
1:02:22 um,
1:02:22 um,
1:02:23 we know that in practice,
1:02:24 in practices,
1:02:25 it's not the case.
1:02:27 So,
1:02:27 uh,
1:02:28 these studies provided us very good evidences on how we are,
1:02:32 uh,
1:02:33 distant from,
1:02:34 uh,
1:02:34 what the,
1:02:35 the legal environment provides for.
1:02:38 So,
1:02:39 for example,
1:02:41 And,
1:02:41 um,
1:02:42 in,
1:02:42 in terms of local integration,
1:02:44 uh,
1:02:45 one of the studies,
1:02:46 um,
1:02:47 um,
1:02:47 highlights that,
1:02:48 um,
1:02:49 the Venezuelans,
1:02:50 the Venezuelan population that,
1:02:51 uh,
1:02:52 represents
1:02:53 uh more than 80% of uh the fossil fossil displaced population here in Brazil,
1:02:59 um,
1:02:59 Venezuelans are,
1:03:00 uh,
1:03:01 64% less likely to work in the formal labor market,
1:03:04 um.
1:03:06 Um,
1:03:08 and,
1:03:09 and also they are 30% less likely to have access to social
1:03:13 protection.
1:03:15 And 53% less likely to,
1:03:18 uh,
1:03:19 for children to be in the school.
1:03:21 So,
1:03:22 uh,
1:03:22 the main conclusion is that the,
1:03:24 this population is staying behind,
1:03:26 uh,
1:03:26 when compared to,
1:03:27 to the host communities.
1:03:29 So it was very important for us to have these,
1:03:31 um,
1:03:32 these numbers in order for us to,
1:03:34 uh,
1:03:34 to adjust our democracy with the government and,
1:03:36 and
1:03:37 And to,
1:03:39 you know,
1:03:39 uh,
1:03:40 to reinforce,
1:03:40 uh,
1:03:41 our messages for the government that they,
1:03:43 uh,
1:03:44 they,
1:03:44 they have to,
1:03:45 uh,
1:03:45 to,
1:03:46 uh,
1:03:46 you know,
1:03:46 to put forward more policies,
1:03:48 um,
1:03:49 um,
1:03:50 to
1:03:51 help refugees and not only,
1:03:53 uh,
1:03:53 refugees but also the host communities to,
1:03:55 uh,
1:03:56 um,
1:03:57 to be in a better position in terms of socio-economic,
1:03:59 uh,
1:03:59 um,
1:04:00 inclusion.
1:04:01 Um,
1:04:02 in,
1:04:02 in Brazil and,
1:04:04 um,
1:04:05 another important study is related to
1:04:09 Uh,
1:04:09 the fiscal impacts of,
1:04:11 uh,
1:04:11 of,
1:04:11 uh,
1:04:12 the Venezuelan influx to the,
1:04:14 to the,
1:04:14 to local
1:04:15 government of,
1:04:16 uh,
1:04:17 of the state,
1:04:18 uh,
1:04:18 that is in the border with,
1:04:20 uh,
1:04:20 with Venezuela.
1:04:21 The,
1:04:21 uh,
1:04:21 the name is Horaima.
1:04:23 So,
1:04:23 uh,
1:04:24 there,
1:04:24 there,
1:04:24 there is also a perception that Venezuelans are a burden for,
1:04:27 for this,
1:04:29 uh,
1:04:29 local government,
1:04:31 uh,
1:04:31 that they are imposing
1:04:33 additional costs for,
1:04:34 for the states and this,
1:04:36 uh,
1:04:37 this,
1:04:37 this.
1:04:38 Discuss,
1:04:38 uh,
1:04:39 the speech,
1:04:40 um,
1:04:41 are triggering,
1:04:42 uh,
1:04:42 xenophobic speech for,
1:04:44 for local politicians.
1:04:45 So it's,
1:04:46 it's for us it was very important to,
1:04:50 you know,
1:04:50 to take a look on,
1:04:51 on,
1:04:51 on this assumption
1:04:53 and,
1:04:54 and try to measure the,
1:04:55 the,
1:04:56 the,
1:04:56 the real fiscal impact of,
1:04:58 of this flow,
1:04:59 uh,
1:04:59 to,
1:05:00 to local,
1:05:01 uh,
1:05:02 to this local states.
1:05:03 So,
1:05:03 um,
1:05:04 the,
1:05:04 the study.
1:05:05 Uh,
1:05:06 showed that there are some,
1:05:08 there are uh many,
1:05:10 uh,
1:05:10 uh,
1:05:10 uh,
1:05:11 several positive impacts,
1:05:12 uh,
1:05:13 of,
1:05:14 um,
1:05:14 for the,
1:05:15 for the state economy,
1:05:17 uh,
1:05:17 uh,
1:05:17 connected to the,
1:05:18 to the influx of Venezuelans to,
1:05:20 uh,
1:05:21 to this area of the country.
1:05:22 For example,
1:05:23 the increase of tax,
1:05:25 uh,
1:05:25 collection and,
1:05:26 and,
1:05:27 and the state revenue.
1:05:29 Uh,
1:05:29 diversification of local,
1:05:31 uh,
1:05:31 economy and even the,
1:05:33 the increasing,
1:05:34 in the employment opportunities in,
1:05:36 in this
1:05:37 area.
1:05:38 So,
1:05:39 and this,
1:05:39 uh,
1:05:40 stage is,
1:05:41 uh,
1:05:41 one of the states that uh have the,
1:05:44 uh,
1:05:44 the GDP growing,
1:05:46 uh,
1:05:46 uh,
1:05:47 as,
1:05:47 as
1:05:48 much more faster than,
1:05:50 than others
1:05:51 in,
1:05:51 in,
1:05:51 in,
1:05:52 um,
1:05:53 in a much more,
1:05:54 uh,
1:05:55 the increased rate is,
1:05:56 is,
1:05:56 is higher.
1:05:57 So,
1:05:58 uh,
1:05:58 it's,
1:05:59 it's,
1:05:59 it was very,
1:06:00 uh,
1:06:00 important for us to,
1:06:02 to,
1:06:02 uh,
1:06:03 you know,
1:06:03 to have these,
1:06:04 uh,
1:06:04 evidences and also a very interesting,
1:06:07 um,
1:06:08 Um,
1:06:09 evidence is that,
1:06:10 uh,
1:06:10 uh,
1:06:10 when comparing,
1:06:11 uh,
1:06:12 Spain and,
1:06:13 and,
1:06:14 and the,
1:06:14 uh,
1:06:14 this additional revenue,
1:06:16 uh,
1:06:16 uh,
1:06:16 that stems from the,
1:06:19 from Venezuelans that are coming to Brazil,
1:06:21 the,
1:06:21 the,
1:06:21 uh,
1:06:22 there is a,
1:06:23 like a,
1:06:23 a zero net balance,
1:06:25 uh,
1:06:25 and it's,
1:06:26 it's very important to,
1:06:27 to,
1:06:28 you know,
1:06:29 uh,
1:06:29 uh,
1:06:29 to demonstrate for local government that it is not,
1:06:32 uh,
1:06:32 uh,
1:06:32 a burden as they are,
1:06:34 uh,
1:06:34 uh.
1:06:35 Uh,
1:06:35 saying,
1:06:36 uh,
1:06:36 uh,
1:06:36 on,
1:06:37 on,
1:06:37 on the other,
1:06:38 uh,
1:06:39 um,
1:06:40 it's,
1:06:40 it's just on the contrary,
1:06:42 the,
1:06:42 the,
1:06:42 the Venezuelans are boosting the,
1:06:44 the local economy and,
1:06:45 and,
1:06:45 and promoting much more,
1:06:47 uh,
1:06:47 uh,
1:06:47 opportunities for even for the local.
1:06:50 Uh,
1:06:50 uh,
1:06:51 population.
1:06:51 So,
1:06:52 uh,
1:06:52 this,
1:06:53 uh,
1:06:54 the,
1:06:54 the 4,
1:06:55 as I,
1:06:56 as I said,
1:06:56 4
1:06:57 studies,
1:06:58 one specifically for local,
1:07:01 for the local labor,
1:07:02 uh,
1:07:03 market.
1:07:03 So,
1:07:04 uh,
1:07:04 as I said,
1:07:05 there are some,
1:07:06 uh,
1:07:06 positive impacts in the,
1:07:08 on the local,
1:07:09 uh,
1:07:10 labor markets,
1:07:11 so.
1:07:13 Um,
1:07:14 the,
1:07:14 the,
1:07:14 the,
1:07:14 the market is bigger,
1:07:16 uh,
1:07:16 uh,
1:07:16 after the,
1:07:17 the arrival of,
1:07:17 uh,
1:07:18 of,
1:07:18 uh,
1:07:19 the refu the Venezuelan's,
1:07:20 uh,
1:07:21 population,
1:07:22 but there are some,
1:07:23 uh,
1:07:23 uh,
1:07:23 impacts that should be,
1:07:25 you know,
1:07:26 uh,
1:07:26 uh,
1:07:26 um,
1:07:27 tackled by,
1:07:28 uh,
1:07:29 uh,
1:07:29 uh,
1:07:29 the local government and also the from the,
1:07:32 the Brazilian federal government.
1:07:33 So,
1:07:35 Um,
1:07:35 if you can go to the next slide,
1:07:38 please.
1:07:39 So,
1:07:39 uh,
1:07:40 uh,
1:07:40 uh,
1:07:40 these,
1:07:40 these are also,
1:07:42 uh,
1:07:42 um,
1:07:43 important conclusions of,
1:07:45 uh,
1:07:45 that,
1:07:45 uh,
1:07:45 came up from this,
1:07:46 um,
1:07:47 for,
1:07:47 uh,
1:07:48 studies.
1:07:49 So,
1:07:49 for example,
1:07:50 uh,
1:07:51 one important to,
1:07:52 um,
1:07:53 uh,
1:07:54 uh,
1:07:54 finding is the higher occupational emotion.
1:07:57 Uh,
1:07:58 so people are,
1:07:59 uh,
1:07:59 working,
1:07:59 uh,
1:08:00 below their,
1:08:01 their,
1:08:01 their professional,
1:08:02 uh,
1:08:02 qualifications.
1:08:04 And
1:08:05 yeah,
1:08:05 there are some,
1:08:07 uh,
1:08:08 barriers for,
1:08:09 for the integration that is very obvious,
1:08:10 like,
1:08:11 for example,
1:08:12 the language
1:08:14 and,
1:08:15 and also this,
1:08:16 um,
1:08:17 uh,
1:08:18 mostly in the,
1:08:19 in the border area,
1:08:20 the,
1:08:20 the xenophobic uh uh speech are
1:08:23 one important issue that we should,
1:08:25 uh,
1:08:25 uh,
1:08:25 try to,
1:08:26 uh,
1:08:26 you know,
1:08:27 to,
1:08:27 uh,
1:08:27 to,
1:08:28 to,
1:08:29 to tackle.
1:08:30 And,
1:08:32 and yeah,
1:08:33 there are also Venezuelans are much more poorer than the host community,
1:08:38 so they are uh
1:08:40 uh they have to access the,
1:08:41 the,
1:08:42 the social assistance
1:08:43 program,
1:08:44 but they are um assessing uh in,
1:08:47 in,
1:08:48 in a lesser rate than the,
1:08:50 the host community.
1:08:51 Next one,
1:08:52 please.
1:08:53 So there are,
1:08:54 there are some uh
1:08:57 uh recommendations that are,
1:08:58 um,
1:08:58 that come up from this uh studies.
1:09:01 So,
1:09:02 I mean,
1:09:03 the,
1:09:03 the issue of valid valid validating credentials,
1:09:07 diploma,
1:09:08 diplomas and skills,
1:09:10 uh,
1:09:10 increasing Portuguese uh
1:09:12 classes,
1:09:13 um,
1:09:14 you know,
1:09:15 also to,
1:09:16 to have a more,
1:09:17 uh,
1:09:17 efficient labor,
1:09:19 uh,
1:09:19 marketing,
1:09:20 uh,
1:09:20 intermediation services and
1:09:23 With the issue of the school's capacity,
1:09:26 um,
1:09:27 and.
1:09:28 And,
1:09:29 and others,
1:09:30 uh,
1:09:30 important,
1:09:31 uh,
1:09:31 uh,
1:09:32 you know,
1:09:33 uh,
1:09:33 actions or,
1:09:35 uh,
1:09:35 recommendations that emerge from these,
1:09:38 um,
1:09:38 studies.
1:09:39 Uh,
1:09:39 next one,
1:09:40 please.
1:09:41 So,
1:09:41 uh,
1:09:42 what I think it's pretty much interesting,
1:09:45 uh,
1:09:45 is that all the studies we,
1:09:48 we,
1:09:48 we had,
1:09:48 um,
1:09:49 a strategy to engage the,
1:09:51 the Brazilian government,
1:09:52 so
1:09:53 Since the beginning,
1:09:54 uh,
1:09:55 we invite the Brazilian authorities to be,
1:09:58 uh,
1:09:58 uh,
1:09:59 to be part of the studies and to be aware of the studies,
1:10:02 their objectives,
1:10:03 methodology,
1:10:05 who will implement,
1:10:06 conduct the,
1:10:07 the sturgy,
1:10:08 and
1:10:09 And always,
1:10:10 uh,
1:10:10 the partnership with World Bank is very,
1:10:13 was very important because the World Bank in Brazil is a very,
1:10:16 is considered a very trustful,
1:10:17 uh,
1:10:18 organization and,
1:10:19 and so the,
1:10:20 the,
1:10:21 you know,
1:10:21 the evidence that,
1:10:22 uh,
1:10:23 emerged from the studies are,
1:10:24 are much more accepted by,
1:10:26 uh,
1:10:27 a local,
1:10:28 uh,
1:10:28 authority and also by the federal government here,
1:10:31 um,
1:10:32 here in Brazil and
1:10:34 And also the,
1:10:35 when we have the results of the studies,
1:10:37 we,
1:10:37 we presented these studies for uh the government authorities beforehand,
1:10:43 before uh we publicize uh these results,
1:10:46 uh,
1:10:47 ensuring their,
1:10:48 um,
1:10:49 you know,
1:10:49 engagement,
1:10:50 uh,
1:10:51 for them to,
1:10:52 to,
1:10:52 to make comments
1:10:54 and this,
1:10:55 uh,
1:10:56 not only avoid complaints of not knowing the studies,
1:10:59 um,
1:11:00 before,
1:11:00 but also
1:11:01 Uh,
1:11:02 the results
1:11:03 could be,
1:11:04 um,
1:11:05 um,
1:11:05 the,
1:11:05 the government could buy in the,
1:11:07 the,
1:11:07 um,
1:11:08 the results.
1:11:08 So
1:11:09 we are also,
1:11:10 uh,
1:11:11 all the studies were launched in public events,
1:11:14 so we,
1:11:15 this is also a strategy that we,
1:11:17 we have in order for,
1:11:19 uh,
1:11:19 the studies will be widely,
1:11:21 uh,
1:11:21 disseminated and known by the,
1:11:23 by the,
1:11:24 by the population and,
1:11:25 and,
1:11:26 and all segments of the,
1:11:27 of the population by the government.
1:11:29 So the press,
1:11:30 uh,
1:11:30 is also a very um
1:11:32 um good partnership,
1:11:34 uh,
1:11:35 uh,
1:11:35 on this dissemination.
1:11:37 And of course,
1:11:38 this will,
1:11:39 uh,
1:11:40 will put some strains in,
1:11:41 in,
1:11:41 in key actor,
1:11:42 not only government but also other
1:11:45 sectors of the,
1:11:46 of the Brazilian,
1:11:47 um,
1:11:48 um society.
1:11:49 For example,
1:11:50 this,
1:11:50 this,
1:11:51 it's,
1:11:51 um,
1:11:52 the results of this study has,
1:11:53 uh,
1:11:53 impacted,
1:11:54 uh,
1:11:55 um,
1:11:56 private sector and,
1:11:57 and
1:11:58 And was uh used by us to,
1:12:00 um,
1:12:01 you know,
1:12:01 to further our,
1:12:02 uh,
1:12:03 uh,
1:12:03 engagement strategy with um companies in Brazil that can
1:12:08 Also be more engaged in,
1:12:10 in offering
1:12:12 or,
1:12:12 or to including uh um refugees in their recruitment uh um process.
1:12:18 So this led to a more um uh possibilities for,
1:12:23 for refugees to access uh formal
1:12:26 jobs in,
1:12:26 in,
1:12:27 in Brazil.
1:12:28 So,
1:12:29 um,
1:12:30 also,
1:12:30 uh,
1:12:31 these studies contribute to the idea that,
1:12:33 um,
1:12:34 uh,
1:12:34 it's,
1:12:35 it's just to,
1:12:36 to,
1:12:36 to give them documents and it's OK and,
1:12:38 and naturally be,
1:12:40 they will naturally be integrated and
1:12:42 so the studies are also,
1:12:45 um,
1:12:46 uh,
1:12:47 led the government to now they are
1:12:49 working on,
1:12:50 uh,
1:12:50 uh,
1:12:51 uh,
1:12:51 a national policy for,
1:12:52 uh,
1:12:53 refugees,
1:12:54 migrants,
1:12:54 and stateless.
1:12:56 Next one,
1:12:56 please.
1:12:58 So,
1:12:58 um,
1:12:59 this is what I said,
1:13:01 uh,
1:13:01 uh,
1:13:01 now the government is,
1:13:03 uh,
1:13:03 drafting this national policy.
1:13:05 Um,
1:13:06 it's a,
1:13:07 it's a very,
1:13:07 um,
1:13:09 good opportunity for us to,
1:13:10 to have a
1:13:12 more comprehensive,
1:13:13 uh,
1:13:13 uh,
1:13:14 uh,
1:13:14 public policies to support local integration of,
1:13:17 uh,
1:13:17 of,
1:13:17 of,
1:13:18 of,
1:13:18 uh,
1:13:18 refugees,
1:13:19 asylum seekers,
1:13:20 and other,
1:13:21 uh,
1:13:21 foster displaced people here,
1:13:23 uh,
1:13:23 in Brazil and in this,
1:13:25 this chart,
1:13:26 we can see.
1:13:27 That,
1:13:28 um,
1:13:29 um,
1:13:30 the Venezuelans are increasingly entering the,
1:13:32 in the formal labor market.
1:13:34 So now we have uh
1:13:35 almost 100,000 Venezuelans are formally employed in Brazil.
1:13:39 It's not,
1:13:40 uh,
1:13:41 on the same rate in terms of accessing formal,
1:13:44 the formal labor market as Brazilians,
1:13:45 but it's,
1:13:46 uh,
1:13:46 it's increas and we think this evidence,
1:13:49 uh,
1:13:49 helped us to,
1:13:51 you know,
1:13:51 um,
1:13:52 to advocate for more,
1:13:53 uh,
1:13:53 inclusive,
1:13:54 uh,
1:13:54 um.
1:13:55 Uh,
1:13:56 job placement service,
1:13:57 uh,
1:13:57 services from the,
1:13:59 from the,
1:13:59 uh,
1:13:59 the government,
1:14:00 but,
1:14:00 but also to,
1:14:02 um,
1:14:02 to have more companies engaged with,
1:14:04 uh,
1:14:04 hiring,
1:14:04 uh,
1:14:05 um,
1:14:05 uh,
1:14:06 refugees
1:14:07 in the,
1:14:07 um,
1:14:08 uh,
1:14:08 in,
1:14:08 in,
1:14:09 informal,
1:14:09 uh,
1:14:10 jobs.
1:14:11 Uh,
1:14:11 next one,
1:14:12 please,
1:14:13 and just to,
1:14:15 uh,
1:14:15 this is also an example,
1:14:17 um,
1:14:18 after the,
1:14:19 um,
1:14:20 this studies,
1:14:20 the,
1:14:20 the Joraima,
1:14:21 Joraima is the,
1:14:22 as I said,
1:14:23 the bordering state of,
1:14:24 uh,
1:14:24 with Venezuela.
1:14:25 They established uh a special uh um
1:14:30 employment,
1:14:31 public employment agencies to work um with job placement for uh Venezuelans in,
1:14:36 in,
1:14:37 in this state because they saw that there is
1:14:39 uh opportunities for the state to,
1:14:41 to,
1:14:41 to receive,
1:14:43 uh,
1:14:43 people with um skills that the,
1:14:46 the state,
1:14:46 the,
1:14:47 the,
1:14:47 the local economy is,
1:14:48 is,
1:14:49 is needing.
1:14:50 So this is uh uh we,
1:14:53 we see this a result of this um study,
1:14:56 the studies.
1:14:57 The next one,
1:14:58 please.
1:15:00 So,
1:15:00 uh,
1:15:01 just to,
1:15:01 to close,
1:15:02 um,
1:15:03 now,
1:15:04 uh,
1:15:05 well,
1:15:05 um,
1:15:06 uh,
1:15:06 based on what we are doing in terms of evidence,
1:15:09 uh,
1:15:09 it,
1:15:10 it,
1:15:10 it,
1:15:10 uh,
1:15:11 helped us to,
1:15:12 uh,
1:15:12 you know,
1:15:12 to,
1:15:13 to,
1:15:13 to be closer to the National Statistic Bureau office here in,
1:15:17 in Brazil.
1:15:19 So,
1:15:20 uh,
1:15:20 we
1:15:21 last year we,
1:15:22 we had the,
1:15:22 the census,
1:15:23 the.
1:15:25 Um,
1:15:25 and,
1:15:26 and thanks to this,
1:15:28 um,
1:15:28 you know,
1:15:29 uh,
1:15:29 uh,
1:15:30 uh,
1:15:30 to be recognized,
1:15:31 uh,
1:15:32 uh,
1:15:32 along with the World Bank as a pro,
1:15:34 um,
1:15:35 uh,
1:15:35 produce,
1:15:36 producer of,
1:15:36 uh,
1:15:37 of evidence,
1:15:38 uh,
1:15:38 we,
1:15:38 we,
1:15:39 we managed to have,
1:15:40 to sign an MOU with the,
1:15:42 the NSO,
1:15:43 the National Statistics Office,
1:15:44 and,
1:15:45 and we,
1:15:47 uh,
1:15:47 helped,
1:15:47 um,
1:15:48 them to,
1:15:49 you know,
1:15:49 to conduct the,
1:15:50 the census,
1:15:51 uh,
1:15:51 uh,
1:15:52 uh,
1:15:52 the census.
1:15:53 Um,
1:15:55 uh,
1:15:55 you know,
1:15:55 data collection with our population of concern in order to,
1:15:59 to,
1:15:59 to have all,
1:16:00 uh,
1:16:01 included in the census here,
1:16:03 uh,
1:16:03 uh,
1:16:03 in Brazil.
1:16:04 So we,
1:16:06 we trained their staff,
1:16:07 uh,
1:16:08 we educated the,
1:16:09 uh,
1:16:09 you know,
1:16:10 uh,
1:16:10 the characters of our population,
1:16:13 and,
1:16:13 uh,
1:16:13 I'm pretty sure that this will.
1:16:16 Uh,
1:16:16 we don't have the,
1:16:17 the full,
1:16:17 um,
1:16:18 you know,
1:16:18 uh,
1:16:19 reports from the census.
1:16:20 They,
1:16:20 they are,
1:16:21 you know,
1:16:21 uh,
1:16:22 preparing it,
1:16:23 but we,
1:16:23 uh,
1:16:24 we,
1:16:25 we,
1:16:25 we are sure that they,
1:16:26 they will,
1:16:27 um,
1:16:28 we will have,
1:16:28 uh,
1:16:29 uh,
1:16:29 pretty much,
1:16:30 uh,
1:16:30 uh,
1:16:31 uh,
1:16:31 accurate picture of what is happening with our population when the,
1:16:34 the reports will
1:16:36 be,
1:16:36 uh,
1:16:37 are,
1:16:37 are released by the,
1:16:38 by the,
1:16:39 by the,
1:16:40 the,
1:16:40 the,
1:16:41 the National Statistics Office here in Brazil.
1:16:43 So that's it from my side.
1:16:44 Thank you very much.
1:16:50 Thank you very much,
1:16:51 Paulo Sergio,
1:16:51 and uh it's great that you're concluding your presentation
1:16:54 with a reference to the National Statistical Office,
1:16:56 which is a bit
1:16:57 where we started from with uh,
1:16:59 with some of Sebastian's remarks.
1:17:01 So we're kind of closing the loop here.
1:17:03 So that's,
1:17:03 that's,
1:17:04 that's great.
1:17:05 Um,
1:17:06 Thank you.
1:17:07 Thank you to all our instructors today and thank
1:17:10 you also for managing the time so well.
1:17:11 They made my life as a facilitator much,
1:17:13 much easier.
1:17:14 So thank you,
1:17:15 I'm very grateful.
1:17:16 Uh,
1:17:16 I will now leave the floor to Paula
1:17:19 for concluding remarks and maybe there we also have time for a very brief Q&A.
1:17:24 Uh,
1:17:24 that's it from my end.
1:17:26 Uh,
1:17:26 we leave you with the remark that without data and without evidence,
1:17:29 we are in the dark.
1:17:31 Paula,
1:17:31 over to you.
1:17:33 Thank you,
1:17:34 Domenico,
1:17:35 and thanks also on my behalf uh to all the speakers and presenters.
1:17:39 Thanks as well for doing a great job in answering questions in the chat.
1:17:44 I feel that like
1:17:45 uh all of them have been answered um
1:17:48 by now.
1:17:49 So I'd like to,
1:17:50 uh,
1:17:51 yeah,
1:17:51 ask people to,
1:17:52 to come in,
1:17:53 uh,
1:17:53 raise your hands,
1:17:54 uh,
1:17:54 in case of,
1:17:55 uh,
1:17:56 we have some time now,
1:17:58 uh,
1:17:58 for,
1:17:58 uh,
1:17:59 one or two questions.
1:18:13 I don't see hands raised.
1:18:17 So,
1:18:19 That is fine,
1:18:20 um.
1:18:22 So,
1:18:22 it is my
1:18:24 uh task today to,
1:18:25 to wrap up the session,
1:18:26 so we can uh move in with the wrap up and then
1:18:29 uh maybe come back to questions if there is uh
1:18:32 uh still time.
1:18:36 Um,
1:18:37 so,
1:18:37 uh,
1:18:38 we hope that today you've taken away,
1:18:40 uh,
1:18:40 a few things from the session.
1:18:42 So first of all,
1:18:43 we wanted to highlight,
1:18:44 uh,
1:18:44 how the UNHCR is invested in leading on the generation
1:18:47 of high-quality socioeconomic data on displaced populations and hosts.
1:18:51 And these with three main objectives to improve socioeconomic conditions,
1:18:56 to assess the impacts of displacement on host populations,
1:19:00 and to,
1:19:01 uh,
1:19:01 promote appropriate policy and programming options.
1:19:04 And so I,
1:19:04 we hope that we convey that message.
1:19:07 Um,
1:19:08 second,
1:19:08 we want to explain,
1:19:09 um,
1:19:10 what,
1:19:10 uh,
1:19:11 socioeconomic data is,
1:19:12 what we mean with the socioeconomic data,
1:19:14 and also where it can be found,
1:19:16 um.
1:19:17 And uh we highlighted,
1:19:18 for instance,
1:19:18 the UNHCR and World Bank microdata libraries uh that host uh uh metadata sets
1:19:24 uh
1:19:25 safely anonymized on displaced populations and hosts as well.
1:19:30 Uh,
1:19:31 third,
1:19:31 we mentioned that there are now dedicated data production support
1:19:34 initiatives such as the Joint Data Center of Voices Placement,
1:19:37 but also UNHCR's,
1:19:39 uh,
1:19:39 forces placement household surveys,
1:19:41 and also,
1:19:43 um,
1:19:43 dedicated data standards initiatives,
1:19:45 uh,
1:19:45 such as ERS.
1:19:47 And last,
1:19:48 we went through 3 examples of,
1:19:51 on how
1:19:52 the use of uh socioeconomic data has been applied.
1:19:56 Uh,
1:19:56 so we heard from Florence on how descriptive statistics,
1:19:59 simple,
1:20:00 descriptive statistics
1:20:01 have,
1:20:02 uh,
1:20:02 really,
1:20:02 um,
1:20:04 have had a lot of value in,
1:20:05 in Kenya in terms of understanding.
1:20:07 Um,
1:20:08 private sector potential in Kakuma,
1:20:10 but also,
1:20:11 um,
1:20:12 the,
1:20:13 the needs and the hurdles of the
1:20:16 stateless,
1:20:16 uh,
1:20:17 Shona population.
1:20:17 For example,
1:20:18 uh,
1:20:18 little
1:20:19 of,
1:20:20 uh,
1:20:20 was known before,
1:20:21 um,
1:20:21 this data was exploited.
1:20:23 Uh,
1:20:23 we've heard from Theresa about how data was collected to,
1:20:27 in order to evaluate,
1:20:28 uh,
1:20:28 a graduation program in Mozambique.
1:20:30 Um,
1:20:31 and particularly to,
1:20:33 uh,
1:20:33 look at,
1:20:34 uh,
1:20:34 how,
1:20:34 what it does to,
1:20:35 to social cohesion with the host populations and between,
1:20:38 uh,
1:20:39 host and displaced populations.
1:20:40 And finally,
1:20:41 we've heard,
1:20:41 uh,
1:20:42 very helpfully from,
1:20:43 uh,
1:20:43 Paulo Sergio,
1:20:44 uh,
1:20:45 on how multiple studies were,
1:20:47 were done,
1:20:47 uh,
1:20:48 looking at Venezuelans in Brazil and those findings were really
1:20:51 Used systematically in the UNHCR dialogue with the government,
1:20:56 uh,
1:20:56 and really in informing the,
1:20:58 um,
1:20:58 policymaking,
1:20:59 uh,
1:20:59 in Brazil when it comes to,
1:21:01 um,
1:21:02 displaced uh populations.
1:21:03 So this was all very helpful
1:21:05 and we hope,
1:21:06 uh,
1:21:06 that these examples were,
1:21:07 were insightful
1:21:08 to all participants.
1:21:12 I wanted to then pass on to explain what's coming next in this uh training course,
1:21:17 um,
1:21:18 especially for those of you that have signed up for multiple modules.
1:21:21 So we're going to look at how socioeconomic data,
1:21:24 uh,
1:21:24 is used,
1:21:25 uh,
1:21:25 and was used,
1:21:26 and,
1:21:26 uh,
1:21:27 we'll,
1:21:27 uh,
1:21:28 we roughly structure the,
1:21:29 the training by,
1:21:29 by topics.
1:21:31 So first,
1:21:31 uh,
1:21:32 so next week on Wednesday,
1:21:33 we'll have,
1:21:34 uh,
1:21:34 uh,
1:21:34 the gender module.
1:21:36 Um,
1:21:37 and this is based on findings from a
1:21:39 global study on gender that looked at uncovering,
1:21:42 uh,
1:21:42 what are the
1:21:43 Uh,
1:21:44 differences and the,
1:21:45 um,
1:21:46 specificities of displaced populations when it comes to,
1:21:49 uh,
1:21:50 to gender and,
1:21:51 uh,
1:21:51 in terms of livelihoods,
1:21:52 social norms,
1:21:53 uh,
1:21:53 gender-based violence,
1:21:55 and,
1:21:55 uh,
1:21:56 and poverty.
1:21:57 Um,
1:21:57 and this global study is based on 1,
1:21:59 11 empirical papers,
1:22:01 uh,
1:22:01 covering 17 countries
1:22:03 and,
1:22:03 uh,
1:22:03 5 of these papers are featured in a special issue
1:22:06 in the Journal of Development Studies that is forthcoming.
1:22:09 Um,
1:22:11 another module,
1:22:11 in another module we look at the health,
1:22:13 um,
1:22:14 again,
1:22:14 a global study,
1:22:15 uh,
1:22:16 that,
1:22:16 uh,
1:22:16 sought to,
1:22:17 uh,
1:22:18 understand what are the differences and similarities in
1:22:20 the health needs of host displaced populations,
1:22:23 what the coverage gaps are,
1:22:24 and also,
1:22:25 uh,
1:22:26 what,
1:22:26 uh,
1:22:27 has been learned in terms of optimal ways to identify,
1:22:30 prioritize,
1:22:31 and plan
1:22:32 for the delivery of health services in,
1:22:34 uh,
1:22:34 situations of displacement.
1:22:36 This study is based on an evidence review,
1:22:38 analysis of secondary data,
1:22:40 and also 4 country case studies.
1:22:42 Um,
1:22:43 and I've seen there was a question earlier on qualitative data
1:22:46 and,
1:22:47 um,
1:22:48 this,
1:22:48 this study and other studies the one on,
1:22:50 on education as well
1:22:51 and the modules will be actually based on a
1:22:54 lot of uh qualitative data and the social protection,
1:22:56 uh,
1:22:56 module too.
1:22:58 So,
1:22:58 um,
1:22:59 yes,
1:23:00 while the primary focus of the program and also the
1:23:02 scarcity when we started was on the quantitative side,
1:23:06 uh,
1:23:06 we also have,
1:23:07 uh,
1:23:08 um,
1:23:08 as,
1:23:08 um,
1:23:09 uh,
1:23:09 Teresa helpfully explained,
1:23:10 complemented,
1:23:11 uh,
1:23:11 the,
1:23:12 the,
1:23:13 the hard data,
1:23:13 say with the,
1:23:14 um,
1:23:15 the qualitative evidence as well.
1:23:17 And,
1:23:18 um,
1:23:19 uh,
1:23:19 finally,
1:23:19 just to highlight the social cohesion
1:23:21 module will uh explains that uh illustrate the findings um
1:23:26 from the social cohesion Global Study,
1:23:28 uh,
1:23:28 uh,
1:23:28 that looked at,
1:23:29 uh,
1:23:30 what factors affect social cohesion in contexts of displacement.
1:23:34 And also um
1:23:35 what works uh to,
1:23:37 to promote social cohesion.
1:23:38 And some of the papers are also coming as a special issue in,
1:23:41 uh,
1:23:42 inward development.
1:23:42 So this is all to say,
1:23:44 uh,
1:23:44 uh,
1:23:44 and I've not mentioned but also the,
1:23:46 the health papers are coming in the Journal of,
1:23:48 of migration and Health.
1:23:49 It just testify to
1:23:51 the quality,
1:23:52 uh,
1:23:52 research that was produced,
1:23:53 um,
1:23:53 and,
1:23:54 uh,
1:23:54 mostly using existing data but also through the collection of new
1:23:58 primary data such as the qualitative data which I've mentioned.
1:24:02 Um,
1:24:05 We're,
1:24:05 um,
1:24:07 yes,
1:24:07 we're ready now to,
1:24:08 to close in today's session.
1:24:11 And uh,
1:24:13 We'd like to,
1:24:15 yes,
1:24:15 to point you to our website,
1:24:17 uh,
1:24:17 where,
1:24:18 uh,
1:24:18 for each module we'll be storing all the presentations,
1:24:21 the background materials,
1:24:22 um,
1:24:22 the recordings as well.
1:24:24 And this is public,
1:24:25 uh,
1:24:25 to anyone,
1:24:26 so feel free to share with colleagues that couldn't attend today.
1:24:29 Um,
1:24:30 and,
1:24:31 uh,
1:24:31 also,
1:24:32 um,
1:24:34 we'd like to,
1:24:35 uh,
1:24:36 please post a link to our feedback survey.
1:24:38 I think it has already been posted in the chat.
1:24:41 Uh,
1:24:41 yes,
1:24:41 it'd be great to get your feedback.
1:24:42 This is the 1st session.
1:24:43 There's uh 7 more to come,
1:24:45 so we'd love to hear from you now on
1:24:47 how we can improve,
1:24:48 uh,
1:24:48 things going forward.
1:24:50 Um,
1:24:51 and,
1:24:51 uh,
1:24:52 that takes me to the next slide,
1:24:54 um,
1:24:55 uh,
1:24:56 that shows the overview of the,
1:24:58 the timeline for this,
1:24:59 uh,
1:24:59 training course.
1:25:00 Uh,
1:25:01 I hope to see many of you
1:25:03 in the,
1:25:04 um,
1:25:04 gender module next Wednesday at the same time,
1:25:06 uh,
1:25:07 still here in Zoom.
1:25:09 Um,
1:25:10 and,
1:25:10 um,
1:25:11 I think we're ready to close.
1:25:13 So,
1:25:13 uh,
1:25:13 thanks a lot for,
1:25:15 to everyone for the particicipation.
1:25:16 It's really encouraging to see.
1:25:18 All the questions in the chat and uh also the engagement.
1:25:21 Thanks a lot again to uh the speakers that um uh took all these questions
1:25:26 and um
1:25:28 Um,
1:25:30 See you next Wednesday.
1:25:31 Uh,
1:25:31 and,
1:25:32 uh,
1:25:32 good afternoon,
1:25:33 um,
1:25:34 good evening,
1:25:35 and,
1:25:35 uh,
1:25:36 good morning,
1:25:37 uh,
1:25:37 good continuation of your day to,
1:25:39 to everyone.
1:25:40 Thank you.
1:25:42 Thank you very much as well.
1:25:43 Thank you very much.
1:25:45 Bye.
1:25:46 Thank you,
1:25:47 bye-bye.
1:25:49 Mercyoku.
1:25:51 bye-bye.
1:25:51 Thank you,
1:25:51 bye.
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This introductory module to the Learning from the Evidence on Forced Displacement training program highlights how socioeconomic data and evidence can be crucial in informing policies and programming. Using examples from different countries affected by forced displacement, instructors guide the audience in learning about what type of data is collected to assess the socioeconomic status of refugees, internally displaced populations (IDPs) and stateless people, and their host communities.
Speakers:
Domenico Tabasso - Senior Economist, Joint Data Center on Forced Displacement
Sebastian Steinmuller - Statistics and Data Analysis Officer, UNHCR
Florence Nimoh - Associate Economist (EHAGL), UNHCR
Theresa Beltramo - Senior Economist and Head of Research, UNHCR
Paulo Sérgio Almeida - Livelihood & Economic Inclusion Officer, UNHCR