Good morning.
Good afternoon.
Good evening, everyone.
So my name is Tatiana Eremenko.
I work at the Spanish National University of Distance Education,
which was the Spanish partner of this project.
And I will be chairing the first session of our conference
after Rachael's explanation I won't be talking
too much about the project.
I think,
as she said, one of the objectives of our project
was to understand how the data revolution that we all hear about
the implications it has for professionals
in the international development sector.
In terms of training, the use of new skills, the use of new data sources,
how well maybe they've had to work with new collaborators.
Anyway, all these changes that this data revolution
might have resulted in.
And so some of these results from the expert
interviews that we conducted were already published just recently
in an article in the Journal of International Development.
But we really thought there were so much more elements to be discussed
that it should be part of the final conference today.
So it's really an honor to have a five the five speakers with us
who all work in the sector in different positions,
who have very different visions on what are these issues and
we'll start right away
with with their participation.
So the plan is the following.
Each of the speakers will talk for about
four, several minutes and then either you will react to
the other speakers right away or will see if there are questions
and comments from the audience.
Maybe we'll take those right away and then do that.
So I will present each of the speakers and then
in the order of appearance in the program, and then we'll have
the debate at the end.
So the first person to talk today will be Bill Anderson
who is an information architect
at the organization Development Initiatives in the UK.
He's interested in the political economy of data
and focused on the strengthening of national data
infrastructures, particularly in low income countries.
He has published two pieces
the data side of leaving no one behind and data disharmony.
How can donors better act on their commitments
that summarize his learnings over the past few years?
So, Bill, the floor is yours.
Thank you. Thanks.
Thanks very much.
And thank you for having me today.
The term data revolution is seems to be going out of fashion at the moment.
I'm definitely on Twitter.
It sort of is disappearing, which is a pity
because I'm very proud to call myself a data revolutionary.
And I think that, you know, there is very much this is an ongoing
revolution and there's a lot of work still to be done.
As I said, I'm going to focus mainly although I'm working
in Malaysia on SDGs in Malaysia at the moment.
Most of my works in low income countries
that are sort of what I'm going to say is it's very much around
my work in Africa and in in low income countries.
And it's slightly different, I think, for other countries
because, I mean,
what is replacing the data revolution now is a lot of talk about innovation
and about the fourth industrial revolution
and how we can leapfrog
and how even Africa supposedly can leapfrog ahead
and bypass
a number of the slower steps that have taken place to date.
Why I think that revolution is still so important
is that in low income countries, there's a lot of smoke and mirrors.
We all realized importance of administrative data.
We all realize the importance of new data sources.
But in fact and to be controversial, I say
I think the SDGs have a bit of responsibility for this.
We are still stuck in low income countries
with household surveys, which are small samples
because like too expensive to be done any any larger samples.
Household of these surveys run the show and they might provide
useful estimates for global databases but in terms of the data
that is needed at a subnational level to meet the SDGs
or to meet development programs, they are not very helpful
and that is where we are stuck at the moment.
And there is a continuing sort of investment that is going on in this.
I was doing some work in Bangladesh
a couple of years ago and one of one of the indicators
that we came across.
So when you looking at maternal mortality is obviously
a very key issue.
And because most low income countries do not have
decent information on on deaths and causes of deaths,
there is quite a complicated formula that is used to calculate
the maternal mortality rate, which includes a statistic
called skilled attendance of birth
in Bangladesh.
There are six different systems collecting data
on skilled attendance for surveys and to administrative systems.
All of these are funded by different donors
and they are all producing widely different results
So on the one hand, you can say, Well, yeah, we've got a lot of data now,
but it is kind of like a wasted effort, a duplication
competition between donors and producing something
where we are still stuck in this world of surveys primarily.
And that real sustainable data investments are still a problem.
Who is to blame for this?
One could say in my
piece we published recently
sort of looks at the last 20 years of the commitments
made by international agencies and donor institutions.
They've been talking for 20 years
about the harmonization of aid.
They've been talking about respecting country ownership, respecting
countries systems and commitments they're talking about making
now all the same commitments they were talking about 20 years ago.
Are they going to change?
Well, we can ask again, but to me, the more important thing
is to say that countries have to take ownership on this
and it is countries.
And if anyone working in development does not really respect
the country, ownership
I think there's a lot of stuff
that has been spoken in the last year about localization.
And in fact, a lot of that has been saying, oh,
what we need to do from from the developed world is to
ensure
that the way we implement stuff on the ground
or the way that things are managed on the ground is done at a local level.
But it's still our project.
We still own the process.
I can't stress enough
how I mean, how we have got such a long way still to travel
in terms of the transfer of ownership in these processes.
Sort of to say a couple of things
about innovation
in Uganda.
That was a fantastic project.
I Global Pulse is a small U.N.
agency, was a laboratory in Kampala and one in Indonesia.
And in Africa.
Local radio stations are still really important, as perhaps
the most important means by which people communicate.
And a pilot was set up where
a lot of local radio stations were recorded.
They use a lot of different languages.
Those are transcribed and natural language processing.
What was was passed through it.
And in that work, they predicted a cholera outbreak
two days before the health service did.
An amazing point example of innovation
and what innovation can do and the powers of A.I..
The problem with that story is that was a pilot and it went nowhere
because there was no means of operationalizing it.
Operationalizing meant it was the finance to scale it up
more importantly, had the political engagement been done
with the National Statistics Office or the other ministries to say
for them to be on board to say, Okay, we can now start accepting that data
and how it could then
be staffed to be used in some kind of form
even gradually, however, gradually that might be read
a die to death
Well,
there's been a number of attempts to measure poverty.
So Facebook has a relative wealth index, which
I think determines defines a human being as someone with a smartphone.
And given that only half people in Africa have a smartphone,
it doesn't say much about how you count people.
There have been attempts to measure poverty
by looking at satellite photography of night lights.
There has been a project going started originally
by Department International, developed in the UK
to look at said like photographs of roofs
and trying to identify poor people from the nature of the roofs
of their houses.
All these attempts at innovation
might be well-intentioned, but the idea that
when we get to some of the basic points of development
and to say that proxies are the way in which we are going to use data today
to to define our priorities, I think is a problem
The there was a fantastic book
written a couple years ago called Geek Heresy by Kentaro Toyama,
who was started off the Microsoft Research Lab in India some years back.
And he argued that
technology can only amplify what you've already got
And to me, if you're looking at the Leave No.
One Behind agenda, to say that we are really going to use
data, you know, within development, you
to cut people.
I mean, people have to count.
And for people to count, you really have to count people.
We cannot get too excited and jumped the gun
and think that proxy is going to replace everything else.
There's one awful one. One final comment.
An awful
step backwards that's happened in Africa.
Civil registration.
Birth registration is a huge problem.
There is very little information on the registration of this in Africa.
And there was a really good program set up five more than five years ago
by the African Union and have a very complicated
acceleration program for CBS
and what it was doing.
It was getting the Buy-In of governments of,
you know, of of ministries to build up this process.
What has happened in the last couple of years is in has come aid for Africa
which is a private sector led by great biometrics, is the future.
We need a good biometric ID because that is the way
we're going to advance the economies of our countries. The
that process the idea for Africa process.
I mean, there's an article story recently by Paddy de Haussler
who was the assistant general of South Africa,
just pointing out and saying that. How.
But by pushing in that integration process, it has set back
the administrative processes that are needed to make that work.
So I just wanted to get on the idea is that there's
this interrelationship between innovation
and making things work on the ground.
To me is the most important issue facing us in the data revolution today.
Well.
Thank you, Bill, for this great opening to the session.
And your final point, I'm sure
the other participants will have
things to add.
So let's welcome Rafaela
Dakotan Eguchi, who holds a Ph.D.
in public law which specializes Asian in migration law and a master
in international development since 2008.
And she's the team
leader on human mobility and the Belgian Development Agency.
And before this, she served as an expert on migration and development,
mainly for the European Commission and the IOM.
The floor is yours, Rafaela.
Thank you very much, Tatiana, and thank you all for having me here.
I obviously don't feel qualified enough to discuss with you,
but I hope to bring some ideas on the table.
And mostly after listening to Bill, I feel very much challenged as
Polley's former
policymaker and now a program maker in the area of
international development with a specific focus
on migration and human mobility, as you said.
Tatiana So these are the lens
with which I come to the table to give small contributions.
So where does where do data
and data sources come into the picture
for a development practitioner like myself?
While trying to summarize
I have identified three key moments of my daily life
and the life of a development agency where where we have
witness changes and I am not quite sure
we were prepared for those, nor that we are now.
But I think it's interesting to engage in a discussion with your first
feasibility and project design.
So feasibility analysis and project design where we are asked
the more and more by our
donors or administration.
I have been very quick in introducing myself.
I work for the public
bilateral cooperation agency for a development of Belgium.
Therefore, my main donor and administration is the Minister
for Foreign Affairs and International Cooperation.
We are asked more and more to provide data
and use innovative data sources to care to build.
The innovation is really the key word for the present and the future.
When assessing situation
coming up, feasibility studies and designing project and programs.
When I talk about projects and programs, we are talking here
about multiannual frameworks of bilateral cooperation
between Belgium and other partner countries.
So they are obviously, obviously multidimensional, obviously
multisectoral, obviously multi partner.
So the complexity comes immediately into the play.
What are we talking about when we talk about data?
What are we talking about when we talk about innovative data?
And of course, we were used on conducting
household surveys with small samples to justify the problem
analysis that we would make.
And then the solutions
that we would to bring on the table through our programs.
This is the less and less accepted by the donors
and the administration them and we are, the more and more asked
to count on big data and data coming, for instance, from
analysis of social networks.
Most of all when we talk about assessment
about movements and about
diaspora relations, transnational relations.
Second very important moment where we need data, where we are
pushed towards new ways of new relations between practitioners
and data makers and analysts
is the implementation of programs.
Because during the implementation of programs, at several times we
if we think that the cooperation until five,
ten years back, we would have have cartography and mapping
exercises
in the different areas we would implement project.
Now, all of this is old fashion, and we are the more
and more asked to connect with those who
produce data in a in a
in an attempt, as Bill also pointed, to
transfer the ownership in data collection and support
local authorities and notably the National Institute
and National Statistics Institute in our partner countries to be
the more and more able to perform what they are asked to.
But at the same time,
and this is always the dilemma in development projects,
you are asked to intervene and this long term
knowledge and ownership transfer
and strengthening or building or development of capacity
and on the other end to produce
results very quickly and the famous quick wins,
we are constantly reminded us and therefore we are kept
in this in this tension and in this dilemma. And
we end up
very often going back to the old fashioned
method of carrying out survey household surveys
and small sample studies in order to have information
for the implementation of the project.
And last bit of the programing
cycle and project cycle management where they are extremely
and the more and more important them
and it is here where I would like to focus is the assessment phase.
So monitoring and evaluation phase impact assessment phases.
I will focus now in the last three, four years of project
programing and implementation
on migration and development in particular, because
through the injection of an enormous amount of money
since the creation of the EU Trust Fund to address the root causes
of irregular migration and instability in Africa,
we are talking about €2 billion and even more if we count
some other contribution in a very short period of time,
four or five years maximum.
This has been
enormous in terms of economic investment from the Commission
and the Member States and therefore an enormous investment
has also been done in trying to monitor
the way these funds were used to assess and evaluate
the results, the outcomes of the different projects,
and to assess the impact of this intervention.
And for all of these three measuring moments
and assessment moments, we have as international community
and European International Community developed new tools and new ways
of collecting data, analyzing data and using data.
And here
I really
appreciate what has been said by the advice adding at the beginning,
how do we collect data where the data comes from?
How are they treated?
These are a question that I have the impression
we have a bit left aside and we have concentrated ourselves
in hiring private companies that would, in parallel
of the implementation of the project, carry out extremely
heavy and burdensome data collection exercises
in order to have three monthly ideas and updates of specific framework
for monitoring the the implementation
progress of the very many hundreds of projects
that have been funded only through the EGF for Africa
Then other
external actors involved in annual evaluation
and data collection exercise project by project
on top of this overall evaluation
of all the projects assessed with data collection.
And I want to end with one exercise in particular that is,
I think, very relevant for your discussion, for our discussion now,
which is a counterfactual impact evaluation that has been launched
beginning of last year by the Commission, specifically on ten, ten.
I'm sorry, could we
maybe come back to this point, the counter of some of these
so that we have time for everybody to present, but absolutely.
Thank you.
So let's move on to our next speaker, who is Inigo Macias,
who works at Oxfam in Germany in Spain, and he works on issues
related to development, inequality and international cooperation.
In different public
and private organizations, both in Spain and internationally.
And he has a master's
in development studies from the London School of Economics
and a bachelor's degree in Economics and Business Administration from the
university.
Inigo.
Let's hear what you have to say.
Thank you.
I hope you can hear me.
First thing, some things.
Thanks for the invitation.
And also, we should all welcome this initiative
because I guess that most of us here in this in this event,
we are always dealing with data and
and the struggle in our minds, how to deal with that data.
And this is this is something that we are
we are all in the same team.
I just wanted
I'm going to I'm going to be very, very brief because I think that maybe
it is going to be more most important.
It's going to be the conversation afterwards.
But I would like to raise
well, I would like to raise what would we understand
are the opportunities and challenges of this of this data revolution.
But first of all, let me let me start with the caveat.
I guess most of you know Oxfam.
Oxfam is a global NGO that's a confederation of different Ingos.
And I work as a research coordinator here in Spain.
And I support all the initiatives that we as Oxfam,
we deal regarding campaign influence in.
So I'm going to talk about building on this experience.
Right.
What the I mean, the opportunities.
I think that what be said, this idea of
that that technology can only amplify what we have.
I mean, this is something that we are seen every day
when it comes to analyzing different social phenomena.
Whether it is happening in the south, in the global
in in the global south or in the global north.
And this is something we are seeing and we have just a recent example
because of COVID, the probably the the
after the deaths of the people, the economic and social impact,
I mean, data has been the most important collateral
victim of COVID.
I mean, we have missed
different waves of household services health services.
So we
we haven't been able to really track what has happened
in lots of countries.
So I mean, and we have seen that in the south.
At the same time, we have we have important opportunities
because, I mean, this
this data revolution is basically helping us to generate
different venues, new venues to generate knowledge,
whether it be research or any other way of generating knowledge.
And and also what we are seeing is that these data
revolution is is allowing us to analyze different
socio economic and political phenomena in a different way more directly.
I mean, we were used to use different I mean, using different techniques,
using instrumental variables,
but not being able to basically to analyze and see
what was going on with that phenomena and this data revolution
that is basically helping us to to monitor that.
And I give you a couple of examples.
I know during during COVID, I mean,
we were working with some
people from the World Bank and through phone services,
through phone surveys, we were able to check what was happening
in different countries at the household level, what
how COVID, how that that restriction policies, how the economic impact,
how that COVID was impacting different households in different countries.
So and then through these households,
these these phone surveys, we were able to somehow
see what the impact was Also, I mean, we've been able, for example,
to portray
how social mobility or how inequality is affecting social mobility.
We are able to see how people from different neighbor neighborhoods
in different countries.
I mean, they don't they don't they don't mix with each other.
What I mean, with this, I mean, we see people
from poorer neighborhoods, not going to the same places, us
as the families and households from rich neighborhoods.
So and this is helping us to to portray different phenomena
that before before this sort of data revolution,
I mean, we were just able to capture the tip of the iceberg.
So these are these are important opportunities
and other important opportunities that in some cases
we are being able to monitor these different phenomena
in real time.
And this is this has happened
in basically in lots of like rich northern countries.
It is more difficult to see that in developing countries.
But in the case of COVID, right now, we have some data sets that basically
following what was going on, the bank accounts, they help us to see
how COVID has impacting wages and things like that.
So, I mean, this is something that it is
I mean, if they are in place and also building on that, that huge gap
that exists between the the global north and the global south.
But that helps us.
I mean, in our case for Oxfam, that we are basically I mean,
we are able to monitor these these phenomena in real time.
We are able to provide solutions, policy solutions.
We are able to show the people what's going on and then to
move these people towards whatever decision.
And this is this is another important opportunity
that that that we see this this data revolution is
is helping out in terms of challenges I mean,
there are new, new, new thousands of new providers of information.
We don't really know anything.
Bill was talking about about household surveys,
but we don't really know
exactly how they do these new phone services, these new providers of data
and what's behind the methodology they are using to survey
and to get the phenomena.
And this is something that we have so many different providers
and they are not
as transparent as we are used to when it comes to household surveys.
And this is the main challenge.
And that leads us to two to two important implications.
This that sometimes I have been on my own, this false sense of confidence.
I mean, I have the data
and then I take this data as portraying the reality.
But I don't know the details,
and I am eager to use that data because I don't know the details.
What are the limitations, what they are currently measuring,
what I can say because I mean, we have the data, but we need
I mean, there is a huge gap between data and information.
We have lots of data.
But I mean, at least in my case, I need to transform
that data into information and knowledge.
And this this is it is an important this is an important challenge for me.
And also something that that Rafaela said about most of these providers.
I mean, there is an increasing privatization of this data and hand
and I do know I mean, I really would like to have this sort of
like Real-Time Data of what's going on with the people when when COVID
considering the impact, the
economic and social impact of COVID in different households.
I really like to have that data publicly available.
And what we see is that is basically managed
by banks and other private institutions.
And it is really, really difficult for us to access that that info that
that data sets from that data and to info
and then basically help to define what in this case, which would be
the most appropriate policies to deal with with these situations.
Okay. Can I leave it here? Thank you.
Thank you.
And thank you already for reacting to
what was said by our previous speakers.
It's really great.
I will present our next speaker, who is somebody who is the director
of the Demographic and Social Statistics Department
of the Senegalese National Agency for Statistics and Demography.
He's trained at the Institute of Demographic Training and Research
in Cameroon, has a Ph.D.
in demography and he's in charge of one of the things he's organizing
the most recent population census in Senegal.
Mr. and I will be talking in French,
but you will be able to hear translation more or less.
Slept by Hole was at LA.
It was a way to say that she said to me, it
gives you
an idea about who's active and how.
We measure the best.
So that goes to show that to support us.
And then you can nationality just to go to assemble
the demography, a directive establishing demographic distance
measurements, and then the rest of the demographic
they delegate
represent.
I spoke with you this they're
sort of a new, you know,
literally utility
step.
Presentation I am going to give you we'll talk about the usefulness
of statistics.
Compared to
medicine.
And what statistics can do to contribute
to training and qualifications
for social sciences.
As a reminder, the National Agency of Statistics and Demographics
has as its key mission to gather
statistics on the population
about demographic and economic aspects.
Let us have measured that.
I'm going to put in.
We deal with operations
such as the population census, which Tatiana mentioned.
Conditionality diminish.
And also we have data concerning health and.
Households.
As part of our mission.
The fact that.
We have these operations, we deal
with these operations of population census.
And the mission here consists in gathering analyzing
and disseminating population related data.
What this involves
a certain degree of technicality member system.
Here in Senegal, for example, shaded
demographic specialists
who have qualified as such receive.
The ABC.
As the people who have a degree in mathematics,
geology, anthropology.
And a multidisciplinary and.
And so what I want to say is that training in this department
is multidisciplinary in order to gather the data
that we use This also involves a previous
analysis of data.
In families.
And it's also important to consider the social context
in which we are gathering these data statistics.
Are there operations we work with in the statistical department.
To zero
mortality.
Indicators on, for example, mortality rates, birth rates,
fertility Family planning,
And you can see
that these indicators
are not necessarily linked to purely
social aspects
Apologies, because the connection, I'm
afraid, has cut off temporarily.
I may have to sift of to be.
These statistics provide us with data concerning
poverty, living conditions.
And as we already mentioned.
This is it would be interpreted
as this specifically for metals
is that to seek out of that sort of application social
reason.
To interpret these statistics we have to
understand them in the context of social reality.
So it's important to say that today
statistics. Are.
Put together using computer tools
apologies from the translator
as the connection seems not to be working.
I don't have the problem to.
Solve.
The connection.
We I'm sorry, Doctor, and we seem to have a connection difficulties.
We want to problem. But
could you perhaps
start again from a little while back?
This national.
Yes, I was saying that in order
to put together this population census. And.
We rely on new technologies
and this is increasingly done in an electronic world,
in an electronic way.
Connectivity
and then.
We also rely on this technology
to gather and collect our data.
And I think that you do.
And it also makes
all of our tasks easier when we are working
on social development gathering these data.
In. Another dimension linked to data analysis,
the census
is that they can be used for social sciences.
They are useful for social sciences because statistics can be
put together to diagnose a situation.
It is important when planning projects and programs
and also when monitoring those projects and programs
or assessing
projects and programs that have already been put into place.
And so if we have the statistics, we need to be able to understand them.
And to do that, we have to link them to social
data.
And that's.
What we need to do.
It better.
If we if we have a given situation and we want to head
to a new situation, we have to bear in mind these social circumstances.
Tell us that.
In achieving our goals, if we already have projects underway,
statistics will provide us with indicators
and these indicators will tell us whether or not we will be able to
achieve our objectives.
So someone who can speak.
Regardless of the situation,
the social aspect, can help us to understand
what degree of fulfillment we have achieved.
And efficiency.
With these data.
If we want to make them truly comprehensible for the population.
So it will be important to bear in mind how they are linked
to social aspects.
That we seek to live in.
And. We, as those who work with social aspects, need to help population
understand that these statistics are relevant for them. To.
That they are relevant for their projects.
So all their relevant when it comes to the involvement
of a given public institution, put I'm sorry.
Dr. Angela, could I just interrupt you briefly.
And to show you who your cohort.
Because we're running quite low on time,
and I think we should be hearing from the next speaker
before we move on to the following point in the debate.
And we will hear from you again. Do you mind?
No, not at all. Go ahead.
For the last speaker, who's going to be
Emily Persky, who is a statistic addition at the office, asked
I'm sorry,
who is a statistician
at the Office for National Statistics in the UK
and she's in charge of setting up and managing a new branch
focused on using on skills to increase statistical capacity
in partner countries.
Previously she worked at the Department for
International Development in the UK as well,
and her work focuses on increasing statistical capacity building
and in countries such as Tanzania, Kenya, Yemen.
So Emily and now in Egypt, where she is
talking from the floor is yours.
Thank you.
Thank you for having me as part of this debate.
I'm similarly to Ben.
I've spent most of my time
working in low income countries around around the world.
I happen to be in Egypt at the moment where we're running a statistical
leadership training course,
but that's just where I happen to be at the moment.
So I work for a national statistics office.
Now I've previously, as you said, worked
in the development organization.
So I kind of see this debate
from multiple sides, which is really, really interesting.
So as previous speakers have have have alluded to,
the have said that
the potential benefits of the data revolution cannot be overstated.
We have the potential to improve data collection, data analysis,
to bring new insights for those of us working in development is massive,
and there are lots of examples of innovations.
And I'm really pleased to hear from some of some of the previous speakers
about some of those I'm I think
one of the things that I want to say is that it's the role
of the National Statistics Office
to bring together information from a range of sources
and try and use this to interpret which is which data
they can use for decision making and planning and monitoring.
Our organization was set up during the Second World War
by Winston Churchill
when he got fed up of hearing various information from a range of sources
and not being able to make sense of that.
And he said, I won't, you know,
I will one institution which tells me, which is the data I can rely on.
And I think that is that is still the mandate of the Office
for National Statistics and National Statistics offices around the world.
I think, as Bill said previously, for many decades
now, a lot of national statistics offices have become data
collection agencies rather than agencies pulling data
from a range of sources and trying to get to the truth underneath that
And I think that there needs
to be a shift in the way that national statistics offices
think about their role and position themselves within within the government
I think there's
a huge role to have for bringing together
the innovations and the traditional data sources
to really try and get that robust picture.
So a couple of examples of innovations that I wanted to focus on.
One from from international development and one from in the UK
and that I think really kind of illustrates this point.
So in the UK and when the 2008 financial downturn
hits the official GDP figures were too slow too.
So to predict that that shows that there was a recession
and that the country was moving into recession
and the National Bank was therefore too slow to take the decisions
that were needed to manage fiscal policy, monetary policy.
And the onus came under quite significant criticism for that.
But know
saying there's a lot of data out there,
why does it take so long to produce the quarterly GDP figures?
And since then there's been an investment
in innovation within OTAs and we're now looking at using proxies
from a range of sources to have early indicators
of the economy and the economic change And so we used data from
shipping,
data from from the shipping industry.
We use website states range to come up with a range of proxies
that can say very early in in the period
what how the economy is faring and that that will not in any way
replace the official measures of GDP
based on the robust data which come later on in slower time.
But it allows those policymakers to see
early if decisions need to be taken.
I think another example of this, which the kind of requirements
have the traditional and bring it together with the
with the new forms of data
and a project that I'm really inspired by is the Grade three program,
which many of you I'm sure have heard about.
And so this came from the Gates Foundation
running vaccine distribution in many countries.
I'm finding that when they got to the remote areas,
they either had too many vaccines or not enough
because the population numbers were out of date
were we're not accurate enough to use for detailed planning.
And so the grade three program pulls together data from a range
of different sources.
It uses cell phone signals, night lights, etc.
mashes that together with the official data
so it gets more UP-TO-DATE low level estimates of the population.
And these have been proved to be really efficient in
planning the vaccine rollouts and bed net rollouts and other things that
the Gates Foundation were particularly interested in at the moment.
These are done
by in an academic way by universities
but they rely heavily on the traditional data
and the surveys that we talked about, the smaller sample, but robust
well funded surveys and the censuses in order to
to make that link between the proxy
and the solid evidence that you've got
and that I think that needs to be done more and more in conjunction
with the National Statistics Office and the way that the UK is using this,
these early indicators of GDP, which we're still doing that
within the system and we're linking those early estimates.
So if they
if they're
the new innovative forms of data that can give you great disaggregation
they can give you quicker data, they can give you more timely data,
but they can't
replace the official data because they need to be benchmarks by something.
So I think again, Bill mentioned the work that's been done
on using tin roofs and straw reefs to estimate poverty
and that data can give you much more
frequent and lower level estimates than than the household survey.
I think if you have poverty, but only if you know the relationship
between tin roofs and poverty, and that relationship is going
to change over time and space.
And so you need those benchmark surveys
to be able
to give you that relationship, which you can then based the model on
I think one of the challenges
for the next period and right now is the fact that a lot
of that experimentation is being done by different organizations.
As previous speakers have said, it's hard to know which,
which of the ones that are good quality, that have a solid methodology
that you can use.
How do you understand some of the sampling error biases
if you're using data from cell phones and only half the population
has cell phones or even worse from social media,
which is biased towards particular population groups.
And so there's a lot of experimentation going on.
And I think I think the trick and the challenge going forward
is linking that those innovations and those experiments
with the official statistics and trying to make sure that you can
that that that the user can understand how those things
can complement and build together.
And we in the UN, we have we're very privileged that we have
this fantastic data science campus
doing a lot of that experimentation and also looking at experimentation
by others and seeing how that's relevant to the UK.
Not all national statistics offices have that resource
or are going to have that resource.
And if we did things more effectively and efficiently, it wouldn't
need to have that resource.
That is really important for all of us to think about how we can invest
in the right kind of innovation, in the right kind of experiment,
where we can identify where existing data
can help us to give a more granular picture, faster pitch in order to plan
and take those decisions really well, but also how that can be linked
and used more systematically and used to build overall
effectiveness of the system rather than pockets of experimentation
Okay.
Well, thank you very much, Emily.
I think it links in an interesting way to what was just said before
from the viewpoint of Senegal where you have similar
yeah.
Working on similar data sources, but also thinking about
how to bring in innovation first of that.