Inequality in Latin America and the Caribbean is not only high but also persistent and multi-dimensional, meaning it is expressed across multiple dimensions of well-being (income, education, health, employment, housing) simultaneously and tends to accumulate in households and population groups, reinforcing itself over the life cycle and limiting opportunities for social mobility; this requires comprehensive public policies that address all dimensions together rather than focusing on single areas.
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Sixth Regional Seminar on Social Development in Latin America and the Caribbean - Day 1
Added:as well as to the implementation of the European Union's um inclusive and digital uh equality and so and digital inclusion efforts especially the strengthening of social development from a regional perspective.
Our partnership with ELAC is based on shared objectives to share these global challenges and we share this with social and inclusive development uh societies in Latin America and the Caribbean.
Fighting inequality and poverty and instability is part of what we do to attain an equitable and digital inclusive region.
These aspirations are not theoretical nor are they abstract. They are ingrained in our social, economic and environmental areas in the region with a conviction that regional integration, cooperation and solidarity are not optional. They are of the essence especially in this geopolitical context marked by recent tensions and conflicts.
This shared conviction is part of our collaboration with EKLAC co-inanced by the European Union. We work jointly on regional programs linked to digitalization, fair and sustainable mining and social protection.
All topics and subjects that bring us together here to materialize the objective of not leaving anyone behind.
It is a very uh important condition to advance together. This is why the JZ seeks responses that are adapted to the realities of the region. We know that there are no single or unique solutions, but we firmly believe that the exchange of experiences can generate valuable learnings and may strengthen the design of public policies and the creation of a more inclusive institutionality that is also sustainable.
As partners, we deeply value the work done by Eklac >> and the possibility of sharing as we're doing in this seminar by promoting social development. We value ECLA's work as a one of the most influential think tanks of the region as a pillar of multilateral efforts for the formulation of public policies for the future. On behalf of the GIZ, I would like to sincerely thank everyone's participation and especially to the social development team of fundamental partner and our commitment to inclusive social development in the region. We are convinced that spaces such as this contribute to enrich the regional debate on social development.
We wish you a fruitful debate during these three days of the seminar. Thank you.
>> Fabian, >> thank you Fabian. We'd like to thank you for your support to the strategic partnership that we have with the GIS, especially in moving forward with social development protection in the region.
And we thus conclude this inaugural panel with Human Dion from uh Mexico and Central America of the Ford Foundation. You're online, please. You have the floors.
Okay, we can hear you now.
Good morning.
First of all, I'd like to greet Mr. Alvet Toenas, director of the social development division at EKLAC.
ambassador of the European Union, um your Excellency, the Ambassador of Korea, and um of course Klein from the German Cooperation. I'd like to thank Ecklac for the invitation to participate in the inauguration of the sixth regional seminar on social development in Latin America and the Caribbean focused on inequality and digital inclusion.
This is a philanthropic um organization where we fight inequalities and strengthen democracy and the rule of law.
And here together with EKLAC we have a very specific program on society.
We have carried out some work together with EKLAC focusing on excellence and putting at the center inequalities in the region with uh studies, conversations and high level aura.
In a time where there is quick digital advance, this is uh it's extremely important to have digital inclusion.
Dual inclusion includes basic services, education, work opportunities, and the idea is to bridge gender gaps, urban, rural, ethnic, and race gaps. It's also important to democratize knowledge.
However, it is also necessary to better understand and offer access to digital aspects, but also these digital ecosystems so that they can be sustainable. It not only has to do with equipment, has to do with skills, capacities, think processes and as ECLA has mentioned and our colleagues also uh stated digital inclusion is not going to bridge these gaps on its own.
It has to do with other things directly related to the low economic growth that we see in the region the last 30 years and also inequality the existing inequality. All this um is done by well automation automation of work has also contributed to greater levels of unemployment and we're trying to be efficient but now we need to see how we're going to have everyone adapt to these new technological realities. So it's important to have a conversation on digital inclusion especially thinking of the sectors of the more marginalized sectors but it cannot take place without a more structural conversation on the lack of economic growth and the impacts of neoliberal um aspects that have deepened these gaps. And this takes me to the last two points I'd like to mention. The first has to do with the difference between those who generate digital wealth and those who consume it. Latin America in general is a consumer of all things digital, not a product. And all technological advances are in the global north. And if we think about it, they can actually consume resources in the south without offering any benefits. Think of all the digital artificial intelligence that is going to consume water from the south without offering any benefits. And the second thing is digital governance and the strengthening of democratic systems.
The domain of technological companies on democratic weak democratic systems can lead us to co-opting and the use of technologies that are not useful for inequalities and to generate well-being.
I feel it is urgent to have more discussions on the importance of regulation of technological companies, digital governance and the future of labor in the context of rapid digital advances taking into account the inequalities not only within the countries but also in the different countries of the world. I hope the seminar can serve as an opportunity to discuss these very complex situations and that we can discuss also positive experiences that can help uh bring solidarity and support to our region.
Thank you.
>> Thank you Ha for your words and for the support of the Ford Foundation.
which has offered Ekle and our division u significant support on all things digital and uh we are now concluding the inaugural session and we thank you all for your work together with ELAC and of course for the region. So this sixth se regional seminar on social development is therefore inaugurated.
We'd like to greet the over 120 people that are in the room and 400 approximately 400 connected online. We'd like to greet Mr. Dennis Kavayaradarees, Minister of Education of Honduras. Thank you, Vice Minister. It's a pleasure to have you here. Victor Cruz, head of the operational division from the Mexican Institute of Social Security in Mexico and also Professor Jane Foster who is connected and or joining us online and will be in the afternoon panel on multi-dimensional inequality. So I'd like to say that in this seminar on the side of especially from the um social development division we have five publications. We will be presenting them uh in a parallel event today during lunchtime. It is called universal health systems comprehensive sustainable and resilient um health systems. a requirement to advance towards inclusive social development. We're going to launch this uh multi-dimensional inequality document and in the afternoon we're going to launch the digital experiences and land of childhood in Latin America and uh tomorrow afternoon we will have another series of social policies on education and equality and social de inclusive social development. And here is the document again. We'll do that towards the end of the day. And finally, there's a publication that will be launched on social cohesion and inclusive development on Thursday in the parallel uh event on social cohesion. So these are the publications and they are available outside at the stand so that you can pose them during the break. I'd like to offer the floor now to Mariana and we're going to offer the floor to Francisco Ferda, Ambassador or whoever would like to uh leave us may do so now. Thank you, Alberto. Good morning, everyone. For those who are joining us in person and virtually.
It is an honor to open the first uh presentation for this social development regional conference for ICLA. It is a tremendous pre privilege to have professor Francisco Fera here with us as his work has been a fundamental aspect in understanding inequality, poverty and social mobility in Latin America and the world.
Professor Fera is one of the most important economists at the international level in studying inequality and he is currently working as the professor of inequality studies and is director of the international inequality student of the London school of economics.
Along his activity, his research has been an important aspect of generating and reproducing inequalities in different generations, which is the role of opportunities in the creation of the destination of people and which are the challenges of our societies to be able to work in social mobility. In this opportunity, Professor Feda will be sharing the presentation inherited inequality in Latin America and the Caribbean, a very relevant topic for this seminar that is going to deal with the trap of low equality and the mobility of the region. Professor Fava will have approximately 4550 minutes for his presentation and later on and depending on the time frame available.
We will open some time for questions and comments. Professor Fava, thank you for being here with us and we want to welcome you here and we offer you the floor. I would like to please receive him with a round of applause.
your authorities. Ladies and gentlemen, good morning. I would like to start by thanking Mariana for such a generous presentation and Mr. Alberto Arenas for inviting us here in ELA. Yesterday we realized that I had been included.
So thank you very much.
>> You will be happy to know that I will not be speaking Porto and I will switch to English for this presentation and we will be uh this will be much easier for all of us. So my apologies for changing languages.
talk I want to give today inherited inequality in Latin America is is joint work with Paulo Bernardi Gido Nhoffer Pedro Salas Ro and Riu and it's an it's a chapter for a handbook uh Elvia handbook on economics of intergenerational mobility edited by Derlof and Mazund where we were asked to write the chapter on Latin America and I hope it will be of use uh to the to the seminar. So here's a brief outline of what I'll do. First I'll try to be a little more precise about what I mean by inherited inequality and then we'll do a review of what we know about intergenerational mobility and inequality of opportunity in Latin America followed by some new work on inequality of opportunity which I'm calling here the state-of-the-art uh for the region and I'll end with some comparisons and conclusions. Okay. Uh so to start with what do I mean by inherited inequality? Inherited inequality is about persistence and it's about persistence basically of social economic status from one generation to another. And it has been typically studied in either one of two broad literatures. The first one looks at the association in this joint distribution here.
Normally I would be standing up. You know I'm a teacher. So for me to be sitting down is is quite hard. But I'll be standing up, but I'll still do some pointing uh to keep you all all awake.
So it's there's this joint association in the two distribution say of income of parents and incomes of their children, right? And people who look at the association between those two look at things like the popular, the transition matrix or summary statistics like a correlation coefficient, a rank correlation, a regression coefficient.
And that literature is typically known as intergenerational mobility. Okay, persistence being the opposite of mobility. But there's another literature that I've participated in and many others including many colleagues here at SAPO which look at um inequality of opportunity measured as the amount of inequality that we see today that can be predicted by circumstances outside of people's controls. things like their race, their place of birth, their gender, their family background, and that too has been done in a number of different ways. The idea of in of inherited inequality is a very simple one. I'm not claiming there's anything profound here, but it does theoretically encompass both of these um ideas and empirically it sort of lies between them. Okay? And and the idea is simply that it seeks to measure deviation from a very simple idea of fairness which is that people's income and I say income it could be any outcome of value right it could be educational attainment or achievement it could be health status but let me say income for simplicity.
So it seeks geometric deviations from this idea where income is independent of a set of inherited factors. So suppose in Chile for example we decide look we'd like to have a society where income is not associated with race or gender or family background or who your parents were. Those things would then be the set age of inherited characteristics and if income were independently distributed from them then we would be in a fair and just society. Okay. uh and if on the other hand income is not independently distributed from them then just by the statistics of it those factors have predictive power over YC that is if you tell me who your parents were and where you were born I know something about the probability of the income you'll have you'll all recognize immediately that that is the reality that we live in right so we we try to measure deviations from that ideal of independence Okay. And we do that by looking at this simple equation here that says look if H has predictive power over Y, right? Then the then how much inequality in the in the total can we explain with these factors? [snorts] If I know your parents' education, your parents' occupation, whether they were born in Laswandes or in you know some poor area of of north of Chile or of the south of Chile and if they're men or women, if they're indigenous or white um how much of the inequality can I predict? So that's the idea of inherited inequality.
It's very simple one. Um, and you can see that it encompasses both of the ideas that I mentioned earlier. And and I don't think I have too many equations later, so forgive these equations here now. Um, but they're they're just uh they're just the very standard tools that we used to use right to measure uh for example in inequality of opportunity as a set of circumstances. If we use a linear prediction function, we used to say okay how much inequality can circumstances like race and place of birth and gender explain in income. That was how we used to measure inherited inequal inequality of opportunity rather. And this is a standard Galtonian regression right that calc that estimates the intergenerational elasticity is the regression of children's income and parents income.
And this is the intergenerational elasticity. Many people use the correlation coefficient instead. because it's just independent of inequality in the two margins. Correlation coefficient though if you square it you've got the R squar. So this is just reminding you of your basic econometrics that you all had basic statistics. The R squar is the share in the variance of this thing explained by the variance of this thing which is very much the same. It's the share in the inequality the total inequality of income that is explained or predicted by C. So they are the same kind of formulations.
So if we have that set of characteristics H that we say we want income to be independent of this if that tends to c if that tends to the full set of circumstances we are in inequality of opportunity. If we restrict Y if you restrict age to just parental income you are in the inequality in the intergenerational mobility world.
So this idea of inherited inequality as I say spans you know both those two concepts and will typically be somewhere in between. So with that as a little introduction let me then review the literature's on both of those things very briefly. So first the intergenerational mobility one and then the inequality of opportunity one. the literature and I and I in in the case of mobility the literature has focused on three things in particular for Latin America occupational mobility educational mobility and income mobility I will not say much about occupational mobility though there's a very important literature on that by sociologists primarily I'll focus on educational and income mobility which is the things that economists have studied more and so I'm just more familiar with them occupational mobility literatur is very important um but I will not have time to mention much of it today. So the literature on intergenerational mobility of education goes back 25 years or so to the papers listed in the beginning here include contributions by the well-known Chileian sociologist Florencia Torch whom you uh probably know many other people there and um typically what they found is that mobility was lower in Latin America than uh than in say the US and southern Europe for example. The beta here is that regression coefficient from over here. Okay. But where these are education. So it's a it's an intergenerational regression coefficient. The higher it is, the higher the persistence, the lower the mobility. Okay. So here's a table that I want you all to memorize before the end before the end of the thing. It has estimates of this beta by various different studies. Okay. So these are five different studies that try to compare these numbers across them. Let me just highlight a few features of them. If we look at the oldest of these studies, it has the highest numbers. The numbers range the numbers are incredibly high. I mean again in the US and and Spain for example, these numbers are of the order of.3 and here they are ranging from 64 in Chile actually to n5 in Brazil. They don't have to stop at one. They can be higher than one, but very rarely are they higher than these are not rows, they're papers. So they they can be higher than one, but they almost never are. So these basically, you know, a number one means that there is no real change in in people's positions. If your father or mother was here, you'll be exactly there. Okay? As these numbers approach zero, it means that there's less predictive. So these are incredibly high numbers. But look at the bottom here. These are very old cohorts, right?
These are cohorts that go back all the way to the 1920s. And as we'll see in a moment, that makes a difference. Let's look at some more recent cohorts beginning in the n people born in the 1940s. And then the numbers range from, you know, the lower numbers here in these two studies. They're slightly different numbers, but they are the lowest in Costa Rica and Venezuela where they're just below or at 4. Okay? But they range all the way to 6.7 even.8 eight in one study for Guatemala there and uh and and the two countries in red are Guatemala and El Salvador. So that there's hetrogenity within the region.
Obviously, as we all know, Latin America is not a monolith. There these are very big differences from point 4 to point A in intergenerational mobility of education. Right? Now, as the comparison with the Herz study that had younger cohort suggests, there's been some dynamics here, right? And what I show in this graph are series for 18 I think they're 18 yeah 18 countries in Latin America um and and the points on the horizontal axis are um cohorts birth years so these are people born in the 40s 50s60s7s 80s most people here will be at this end point right the colors are just the sources so the salmon is data from Latino barometro the more the darker purple is country specific data and and you see how these have evolved over time and in general not everywhere but in general you see declines in this beta.
Okay. So, declines in beta good thing or a bad thing? They're a good thing, right? Because beta is persistence. So, as beta falls here, we're saying look, as these countries um as younger people are being born, their dependence on their parents seems to be going down.
Okay.
However, you may remember one of my earlier equations that beta that regression coefficient actually has two parts. One is the true correlation, true measure of association. The other is the inequality in the children's distributions and the inequality in the parents distribution. Okay.
So if oops giving away my secrets. So if this beta is going down it could be because the true correlation is going down or it could be because inequality in the children's generation is lower than in the parents generation or inequality in the children's generation is falling and this inequality is rising. And indeed if we look at that separately here here's a a summary for the regression coefficient the beta and the correlation coefficient which is I want for the purposes of this talk I just want you to think of the correlation coefficient as the real measure of association where the other one has these margins moving around inequalities in the two margins. And so it turns out that the declines that we were seeing in the regression coefficient are not really there in the correlation coefficient which means that either this thing is falling the inequality in the children's years of schooling is falling or the inequality in the parental years of schooling is rising over this period.
Now that's in the data we can look at it. It turns out both things are happening. As we move from the birth cohort in 1940s to the birth cohort of the 1980s, the parental inequality in years of schooling is rising and the inequality in years of schooling for children is falling on average and the row is pretty much stable. So what we thought and many studies in the literature had claimed were quite good news about the trends and the dynamics of intergenerational persistence in education don't really hold unfortunately if we look at the correlation coefficient they are due to these dynamics. Now of course you know people who are parents today were children about roughly 30 years ago. So if we move that graph 30 years back, what you see is basically this very neat uh inverted U curve here which suggests that what we were seeing was really the dynamics of educational inequality some kind of curve if you like although I'm not claiming it's the mechanism but something perhaps mechanical about as education rises from very low years of schooling inequality kind of rises because some part of the distribution is taking off. There's a ceiling in years of schooling and even though we're not near that ceiling, after some point it starts declining again. So now we we don't claim we fully understand or explain you know the mechanisms behind this but it is this inverted U in the inequality of years of schooling that's explaining the declines in beta not true changes in association. So that's our main conclusion for mobility in education. What can we say about mobility in income? Well, here in Latin America, we have suffered from very severe data limitations because to you know these other studies have years of schooling of of parents and children that can be matched. But incomes of parents and children are not typically in the same service. And if they are, they are in surveys where the children live with the parents which is obviously a very selected sample. It's called core residency bias. And so we we typically have a class of estimates based on a clever methodology which I won't bore you with too much but for those of you who are interested the basic idea there is that you use two samples use a sample from the time of the parents who are were working and a sample from the children you use characteristics from the parents to predict their income predict their income into this new equation but two sample two stages le squares Those of you who know what it is know what it is.
If you don't, that's a method people use. But it's not ideal. It's something you do when you don't have the best possible data. So recently, people have begun using administrative data like social security data in places like Brazil, Uruguay, here in Chile, in Ecuador. Um, which has the advantage that you can match actual incomes of parents and children. It turns out, as I'll show you in a moment, that this upper category here with these betas ranging from 76 to 7 in these studies have very different answers. Okay?
However, note what I'm saying here. Most of these papers, if you're using administrative data, well, by definition, what's not in the administrative data? The informal set, right? Which is often half of our populations. So, they are better than these other studies in one way. They observe incomes for parents and children. But on the other, they observe, let me for simplicity say a very biased and unrepresentative half of the distribution.
So the studies then here are a few countries. Um what I want to show you is just the range of estimates for this country. So for example, Mexico or Chile. Okay. Um there's Argentina, there's Brazil. If you look at Chile or Mexico, there are very big um differences in the estimate of the intergenerational income elasticity.
Somewhat less difference in the rank rank correlation, which is another measure. Quite a bit of difference there. The main, you know, we could discuss this at great length. The main point I want you to take away from here is that the differences in color. So the darker color here it says observed.
These are the studies that have administrative data where the parental income is observed together with the children's income.
These other ones are the two sample two stage these squares where the parental income is predicted.
Okay? And it turns out as you can see they're very big differences and the the the dark blues are on the left. They're lower numbers, right? In fact, we we use some criteria uh you know, we use some criteria to look to to go from all of these estimates to our preferred estimates. We said well let's take children aged between 25 45 because that's when they are their most productive years. Let's take um younger cohorts, more recent cohorts. Let's take uh studies where income is observed as preferred to where they are predicted.
Select the oldest cohorts and for comparability let's use parental income defined as father's earning. Sometimes it's mother's earning, sometimes it's an average to make it more comparable father's earnings. Well, the range is just astonishing, right? I mean Guatemala 394 is almost unbelievable.
This is a this this is this an incredibly large number. Uh the numbers from two stain two to two sample to stasis squares are typically in the 0.5 to 6.7 range which is more or less what we used to know. But now we have this these uh new estimates here at around 23.26 that's Ecuadorian and Chile. So here you have two options now because this is improvement right we are now moving to administrative data so we're all very excited but it's missing the informal set so you've got now 2235 are the numbers you get in Norway okay Denmark um so you can either believe that Ecuador has the mobility of Norway or that missing the informal sector is a very big deal and um for the moment we're thinking it's the latter. Okay.
So, so, so there's been some progress, but ignoring the informal sector clearly doesn't doesn't solve all the problems.
So, sometimes as you make progress with new data, you actually realize that uh that still some other imperfections to be fixed.
Now the fact the very fact that that we didn't have data on parental income very well observed or where we do we missed the informal sector led to a creation of this of this uh literature on intergeneration on inequality of opportunity rather which is now the next part of my my review and and here uh The basic idea was to go back to that those equations and not use just parental income but use that vector C use a bunch of different circumstances and say to what extent can these circumstances race gender place of birth occupation of the father and mother education of the father to what extent can they predict their children's income mind you it's not your own education it's your parents education to what extent can it predict your and Oops.
And the ratios we found this is income per capita. This is consumption per capita. So if you look at for example for income the numbers used to range for these six countries here that we had from around uh I think what quarter in Colombia to 36% or so in Guatemala for consumption is even higher 35% to 50% um of uh sorry 26% to 50% um of of uh of inequality in today's income could be predicted by those by those things. Um this is uh work that was done on incomes. There's some work also done on education including by Fabio Wenberg who's sitting there uh uh and uh and these are the sort of numbers we came up with. We thought of them as lower bounds because we don't observe all the circumstances that matter. In fact, how did we pick circumstances? is an illeible table but um it's it's how we did in that one one old study and I I want to show it to you just because I want to show how we're doing better now.
Okay. So what we did is we said oh we can observe father's occupation but we couldn't partition all of the father's occupations otherwise we divide the sample too much so we divided them into oh your parents or agricultural workers or domestic workers or other for mothers and father's education we said we had three categories no education or unknown completed primary schooling or you know lower primary one to four or five or more these are the parents right so we divided the groups like that and we said if We partition the society into these groups by ethnicity, by father's occupation, by mothers and father's education, by birth region. What is the differences in the income of their children? That's what we're doing here.
So if you've got two categories there and two categories there and three categories there and three categories there. Anyway, you take the product of all of these. They're mothers and fathers. So it's 3 + 3. It's 2 * 2 * 3 3 it gets to 108. Okay. Or if the father's occupation is not observed to 54 groups.
So we were estimating what extent these circumstances were predictive of income just by dividing the population fairly coarsely into groups that shared the same background characteristics. 54 to 108 groups and you still got some pretty large numbers here. Okay.
So now let me move to the to the to the second part of the uh of the of the talk. Um trying to think of the time we were going. Oh I don't have a lot of time do I? So what what we um uh what we're trying to do is exactly the same thing as here but we're trying to do it better. Okay. So as I said before this inherited inequality is about estimating the share in total inequality that can be predicted by these factors. Okay.
What we observe is a joint distribution of the factors. How do we select the best function to predict it? The first question is is parental income a sufficient statistic? And there I think we're going to say no. We're going to look at all these other circumstances as well. But then there's this problem here. When there are multiple categorical circumstance variables, the number of interaction terms or partition cells, so that number 54, 108, if I had two more groups there, it would have gone to 216 and so on. That explodes, right? Consider for example the data set I'm going to show you results for in a moment, which is the data set I have for Bolivia. We had 4,200 observations in this sample for Bolivia. We don't observe a lot of circumstances. Two categories for sex, seven categories for ethnicity, 11 categories for occupation of the father, 11 for occupation of the mother, four categories for education of the father, four categories for education of the mother, number of potential types, 27,000.
Right? So, you can't estimate a model with 27,000 interactions on 5,000 observations. You can't divide 4,000 observations into 27,000 cells. You just the model is overfitted. So, without getting into into technicalities here, skipping over them, the sort of methodological challenge that this literature has been facing is that there are two sort of biases facing each other here. There's a trade-off between two biases. You include too few circumstances, you bias your estimate downwards. You include too many cells in the sample, you bias it upwards because of the sample problem. Okay. So we use these machine learning techniques from from data science which are designed to maximize out of sample prediction that is to choose the specification of the types here within the sample in a way that maximizes predictability out of sample. that is to specify the model in a way the data permits. Let me leave it at that. Hopefully, it makes sense.
Basically, if you keep increasing your model, it's going to be too much. These methods allow you to choose models that the data is comfortable with using if if you see what I mean. Maximize your predictive power out of sample given the sample size.
And in particular, we use this um conditional inference trees in random forest. And this is the last complicated slide. So ignore it. I'm just going to tell you what it does. And and then hopefully we can we can do fiddon calls the harvesting after the model and get some results. So how does this tree works? It has a bunch of circumstances.
It has those circumstances, right? That I showed you here. For example, the machine starts with that and then it says, okay, which of these is most most closely correlated with children's income with the the incomes of their children? Pick that one. Okay, then you say, okay, well, how do you divide it?
Let's divide it in a way that maximizes the the statistical significance of the split. For example, suppose you had parental education, right? Is it better?
You can only split it's a binary. You can only split into two. Should you do primary schooling versus the rest more or less as we had done arbitrarily before or should you do up to secondary schooling and tertiary before we guessed in that table I showed you the machine now runs every possible uh combination and chooses the one with the biggest statistical difference between so it's a a datadriven method to divide the society like that now you got two nodes nodes. You do it again. What is most closely correlated in this nodes? In this node, what's most closely correlated in this node? Pick those variables. How the split maximizes. And you keep going until it's no longer statistically significant. Okay. So, what does that mean? Well, uh oh, this is the data we use. It's the data from 200 almost representative household service. For Latin America, we had 28 household service for the period 2020 2017. Those are the variables. These are the inquestas. All of which are available and nicely harmonized here in SAPL. Um although we actually got them from uh satlus in this study in Argentina. Um but there's a tree. Okay, there's a tree for Bolivia. So Bolivia was the one where I showed you you could have had 27,000 types, which obviously makes no sense if you've got 4,000 observations. So the machine learning technique this conditional inference tree generated this split where you have at the bottom I think count three 3 6 9 10 only 10 time okay how does it do it well it's actually you know informative I think so the first split was in by father's occupation on the right here were occupations one two and three which are managers technicians and professionals. I think those people uh were further divided one more time by mother's occupation. Again, same occupations plus stay at home and others. You ended up with these two types together. At the bottom there's the population share. So that's 4% 6%.
So that's the 10% richest if you like the 10% most advantaged um Bolivians. is not exactly that because it's by by parents um characteristic but basically this is these are the the richest types there are 10% of Bolivians the other 90% of Bolivians are in this area here where they next get divided by barf area you know Bolivia that makes a lot of sense these are the people who were born in urban areas these are the people who were born in rural areas and they are split again by mother's education ethnicity, father's occupations. And in the end, you've got these 10 types. And you can find, for example, the poorest and the richest type. The types are these nodes at the bottom. You should think of them as people who share the same circumstances, the same family backgrounds, if you like, by the variables that the machine used. Who are the poorest in Bolivia? Well, they're all Katwa, and Barini. They were all born in rural areas. Their fathers worked in farming, forestry, fishery or elementary occupations and their mothers had no formal education. Who who is the richest group? Their fathers worked as managers, professionals and technicians.
And so did their mothers or they stay.
If you look just at these 10 types, just 10, the inequality between them is a genie coefficient of 28, which is 56% of Bolivia's genie coefficient.
So remember we were trying to to to to estimate the extent to which these inherited characteristics could predict uh uh could predict uh inequality today and explains 56% of inequality today.
So we did this for those 10 countries.
We did it for the the 28 surveys I think. But here I'm only showing you the latest survey we used. In some cases it's not actually very recent. And the reason is we have to have data on parental characteristics in this data.
And this is typically in these surveys only in special surveys. Right? So that's the number we were looking at here. Where's Bolivia?
Bolivia for the three 28%. Right?
What we Yeah. Sorry. A gen of 28. Not 28%. A genie of 28. There it is. A genie of 28.
56%.
Okay.
Chile it's a little bit less. Okay. Now what are these? These are forests. So again minor technical commercial. These trees are very good. They're known to be unbiased but they are high variance estimators. So statisticians want to reduce that variance. They take a whole bunch of trees. What do they call a whole bunch of trees?
A forest. Right? So it's a random forest. That's an estimator that's designed to lower the variance and increase the precision. So these are our favored estimate estimates here. And what you see if you look for example at the relative genie so the share of inequality that can be predicted by circumstances it is typically upward of 50% in Latin America with the exceptions of Argentina and Colombia and on average they are 55%.
Okay.
So I'm getting uh uh towards the end.
One thing we can do also is use something called a sharply decomposition to estimate the contribution of each of those circumstances. The circumstances are being used in the trees.
There's father's occupation there.
There's mother's occupation here, but mother's occupation is al always there as well. Mother's education is here.
It's there. Okay. How how much do they contribute on average? particularly in a forest you have to use a technique for that and you get um a table like like this one and all I'm going to to emphasize now is that in Latin America across these countries father's education and mother's education that is parental education in general are the most significant uh factors okay um for a few countries where we had a number of different uh uh surveys we could look a little bit dynamics. Chile is one of them there. Peru is the one for which we had most observations.
Ecuador and Guatemala as well. I show you two things here. I show you the levels of total inequality over this period and the levels of inherited inequality or IOP.
And as we know, you know, from this period of the 2000s to the 2015s, income inequality in Latin America was generally declining. You do see that here as well and on the whole you see that the inequality of opportunity or the inequality due to inherited characteristics is going in the same direction which suggests that that decline is certainly consistent with and could potentially be driving the decline in total income inequality uh during the 2000s. Okay. Um these are now the my last section last one or two slides.
These are now awesome comparisons. So I mentioned before that we have 196 I think it was household surveys from 72 countries in this global estimates of opportunity and mobility database uh that that we've assembled my my collaborators and uh they represent about twothirds of the world the world's population.
So here we've got the the 72 uh countries. For some of them the data is for consumption and that is obviously not comparable with the ones for income data. So we put them on this side here.
The columns represent regions. So this is the red here is Africa where almost all of the information is for consumption with one exception. Uh brown is Asia, yellow uh sorry green is Europe. Uh, North America is here just basically just the US and well in blue is Latin America and um, as you can see Latin America is very very consistently concentrated to the right to the highest ratios of inherited inequality. Here again this is you should think of this as the inheritability inequality the degree of persistence and transmission of inequality. There's always someone who does worse than us if you want to feel good and that's South Africa. Um but you know see why South Africa would be up there. Uh we're next. Okay. Uh we can plot this I think is my last slide before the conclusion. So so I'm getting there. So this is uh this here plots the share of inequality of opportunity. If I plotted the the genie coefficient, the absolute opportunity, there will be a mechanical association because this is total inequality and the other inequality is part of it. But I'm plotting the share there. There there's no reason why there should be a mechanical association. But there is countries with higher inequality tend to also have higher shares of inheritance.
Now, this should remind you of a famous curve that we've probably all heard of, which is the Great Gatsby curve, which has mobility on this axis that was made very popular in the US by Alan Krueger, who was a chief economic adviser to Obama. Um, it was actually calculated by Miles Corak. But it showed that countries with more inequality had lower mobility, more persistent. Okay. Why did this matter particularly in the US context? It mattered because for a long time people like Milton Friedman had said, "Yeah, the US may be a little more unequal than Europe, but that's the price we pay for being much more mobile, much more dynamic. People have a chance, the American dream." Well, it turns out statistically that's not how it works.
Countries with more inequality tend to have less mobility. As I said, mobility and inequality of opportunity are all, you know, expressions of inherited inequality. So it's unsurprising that when we put inequality of opportunity, you get the same thing. The countries that are more unequal also have greater persistence also have a greater share of their inequality being predicted by the past. As the Obama team put it, you know, economists are very bad at this, but politicians are good. They can summarize things. They say the far the farther along are the steps of the ladder, the farther along are the steps, the harder it is to climb.
Yeah, more inequality in outcomes, more inequality in opportunities. They are not opposing concepts. They're two sides of the same coin.
So, if that I'll conclude, that's my last slide. That's what the chapter does. In addition, in the chapter, we also So, here's the range in in gen coefficients of income 46 to 66%. in the chapter which I didn't have time to discuss here we do the same for variance in years of schooling quantity of schooling and in test scores quality of schooling and the numbers are a bit lower and that's interesting in itself but we won't have time for it today so our inequality exceptionalism the fact that together with Africa we are the two most unequal regions in the world extends to inheritability where it actually seems we are more unequal than Africa but again the consumption problem is is an issue So unfortunately our inequality is not only high is also durable and persistent. Thank you very much.
Foreign Would you please approach one of the microphones from the school of government of Catholic University question about the individual characteristics? This breakdown shows that gender and ethnicity do not play a major role. Is that because of the income definition indirect effects or those less relevant factors? Do you think >> this is a problem because uh he's an expert as well. This gentleman is an expert. So he's asking tough questions.
Excellent. Excellent question Rafael.
The issue of gender as Rafael knows very well but we should highlight it. Gender uh doesn't show very much in here and it's something that uh we should recognize adequately because it's not that it's not important but it depends on the type of variable that is used.
The variable used is the per capita income of that household. So whatever difference between men and women within the household is omitted automatically.
Had we done it with earnings with individual wages, it would have been completely different and we say that uh very carefully in our paper. So gender should not be interpreted in that way.
What it measures is differences between income or earnings of households that have more men and more women than than men.
weakness ethnia is difficult because it reveals a weakness of decomposition and what chaplain says is that given these characteristics which are the ones that are most correlated with income and of course we get education of the mother the father and their occupation is important But naturally this is not a structural model. This doesn't show the effect that ethnia could have had on the education of their parents. So since the age of slavery in Brazil, black people had less education for structural reasons of slavery etc. they will have different levels of education and part of the effect of ethnicity is this. So if it were structural, which it isn't, it could perhaps be more important.
That's why we put this here. It is merely descriptive. It's a situation that describe or reflects a shadow of the past.
Thank you. And we have two questions here in the chat. After that, we will offer a space for another question in the room.
>> According to your experience, what be would be the conditions of public policies to achieve social mobility of poverty for the middle class?
If informality is so important in the region, how could you estimate inequalities in these types of social inequalities?
>> Try to estimate any of these inequalities.
>> I'll begin with the last one. It is a little easier.
>> Informality I was talking about has to do with the fact that these parents and mothers uh are in the same database which is an advantage but it was absent before. But what we had done in the past was to we had looked at these relationships in the surveys and in surveys the informal sector is there.
This is why all the work that has been done or was done for so long in these institutions was fundamentally based on household surveys because household surveys go to a very representative sample of households and therefore doesn't have that problem.
So the ma the actual issue would be more specifically in terms of income mobility where we don't have the information what to do with informality. There are two ways to do it. One is to improve the surveys and you can make a bigger effort to collect income information. It's not easy because uh perhaps some parents couldn't remember certain age and another would be to combine data of surveys and other administrative records. There's a study librto at L study that I mentioned attempts to do so.
>> So there are two ways. One to improve the surveys or to integrate information coming from the surveys with information coming from administrative data. I think those would be the two paths. Another would be the um policies public intervention. I think it's part of the seminar will touch upon digital inclusion naturally the my colleague Helen's talk will be about that and what I would do is I would emphasize two points where there seems to be a lot of consensus which is the importance of childhood early childhood and primary education which is where things begin economists like uh Hecman and Kuna because human capital what we bring to the labor market which is paid this human capital has a production function and what does this mean in English from the beginning everything that happens matters very much. If the child comes at five years of age without stimulus without a vocabulary etc he will have much a much uh more difficult time in primary school. So at an early age there needs to be quality um nutrition education etc. Is it enough? Maybe not.
It would take a lot of time, but it is necessary. This is a path we need to follow. The question is, well, maybe we could be more ambitious than this. And I think we could be, but that is for another seminar. Thank you, professor.
One last question in the room. Does anyone have any questions? Favio?
Yes, Fabio from the Flumen University.
It's a port to port question. Shiko, if the conclusions of what you called state-of-the-art are very different from the previous techniques, for example, or is the organization of the country going to change? Would Brazil, you know, Argentina, Chile still be at the end and others be better? And there's also well it's also important but difficult to find the data.
Again excellent question I haven't looked at the correlation between results uh based on the previous methods and the current methods. That would be even better actually and easier to do.
What can what I can say is that the absolute levels are higher and this is because the technique is designed to maximize predictions the predictive power of variables. So with the data you have well that's the maximum you can predict with those characteristics.
Well, before we were doing it in an arbitrary fashion. So, this is a very interesting motivation.
We wanted to reach the optimum level given the data restrictions.
That was the first thing. What was the second question? Wealth.
Interesting. In the framework of this Latin American and Caribbean quality review that we did recently, there was a paper from the Perry School of Economics and their conclusion was that in Latin America they could say what the what parts of wealth were higher.
And it was four countries.
The rest was all simulations, data combinations, other less rigorous things. And for these countries where there are examples, it would be possible to make this analysis. And I honestly don't know. I don't um I haven't done it but it would be interesting.
Unfortunately, the time is up and there is a lot of interest on the professor's talk. So, please approach him during coffee and he will be with us at other points in the seminar.
Before inviting you to coffee, let's offer him a round of applause.
We now have a coffee break and we hope to see you again at 11 for the high level panel. Thank you.
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>> Good afternoon, good morning to everyone. It's a great pleasure to welcome you all to this highle panel during this sixth uh regional seminar on social development in Latin America and the Caribbean. I would like to begin by warmly greeting all those that comprise this high level panel. Uh that will be uh our Alberto Arena Mesa, director of social development division of EKLA who will be delivering an introductory presentation and I would also like to welcome all the authorities that will intervene subsequently and to whom we reiterate our greetings uh and our gratitude for accepting their uh invitation to participate. Maria Jesus Wolf, Minister of Social Development and Family of Chile. Mrs. Badric Cares Dango, Minister of Social Development of Panama, who will be connected online.
Mrs. Gloria Reyes, Minister of Women of the Dominican Republic. Mr. Osmar Ribero Delmea, Junior, Executive Secretary of the Ministry of Development and Social Welfare, Family and Struggle Against Hunger in Brazil, who will also be connected online. Mrs. Claudia Valenuela, the vice minister of policy planning and evaluation at the Ministry of Social Development of Guatemala, who will also be connected online. Mr. Dennis Caseres Vayares, Vice Minister of Education of the Ministry of Education of Honduras. Mr. Carlos Paris, Vice Minister of Social Policies of the Ministry of Social Development of Paraguay, who accompanies us online, and Mr. Federrico Garana, Vice Minister of Social Development of the Ministry of Social Development of Uruguay, who will also be connected online.
I would like to express our gratitude for accompanying us uh this morning. And I would also like to greet Mrs. Marenes, technical secretariat of the social cabinet of Panama, who is uh present with us here today. In this panel, we want to reflect on how to move towards a an inclusive social development policy in the region that will enable us to advance towards the eradication of hunger, poverty and the reduction of inequalities. And likewise we would like to reflect on how uh the transformations associated to the digital era can produce new opportunities uh tackling with its challenges to reduce inequality and uh in order to achieve social inclusion. I would like to begin by offering the floor to Alberto Messa who is the director of the social development division of ELAC so that he can deliver his presentation. Alberto please you have the floor.
Thank you very much Claudia and uh again very good morning to all of you.
I am not going to repeat my gratitude but I would like to especially thank uh the authorities present with us here today. Um during this panel discussion some time ago we were thinking about the issue that we would like to share with you during this panel discussion and we said well look we're going to continue with the work on inequality and that inequality when we discussed inequality we will explore with what happens with inequality and the social and the digital exclusion in an area which is digital when we're experiencing uh digital transformation and certainly the future will be increasingly dig digital and in that sense that it's a cross cutting. It involves all the different dimensions. The digital issue is reaching all those all all the different sectors and in that sense we will explore during this presentation what has been putting forward as its diagnosis and what to do with regards to the development of the region. We will look at inequality and we will conclude by talking about some aspects related to this digital era or the social um uh the digital era and uh social inclusion which is first of all related to inclusive social development uh a strategic approach in order to overcome development traps and when we talk about development traps we are basically talking about what ECLA has been uh talking about with different uh countries over the past few years and it has a diagnosis and what ECLA has put forward is that the region faces a development crisis which is expressed in three main traps. The trap on low capacity to grow and transform. The trap of high inequality, low social mobility and weaker social cohesion. The trap related to little institutional capacity and ineffective governance. And these are all linked. They they and they are a tremendous obstacle in order to create a more sustainable uh future. And this development crisis interact within an international context that has significantly changed in the last decade with a an accelerated digital transformation with an accelerated aging with geopolitical changes that basically create new and major challenges and uncertainties for countries in the region. and ETLAC are faced with that diagnosis has uh said well what is it that we can do and the ETLAC has put forward 11 fundamental transformations in order to escape those traps we're not going to talk about each one of these transformations because we will be delivering a a presentation far longer than 15 minutes but these 11 transformations seek to coordinate productive dimensions economic uh social environmental gen gender and digital dimensions and there you can see that the digital transformation is one of them You can see that the reduction of uh poverty and increasing mobility and social cohesion is again another important aspect and these are issues that we will be discussing during the this three-day seminar and after conducting a diagnosis. The question that we have discussed here in necklac is how to manage these transformations the issue of how and u we mentioned four areas that need to be strengthened. One has to do with the issue of uh the dimension of governance of public policies and the management of transformations requires an endogenous governance and that means that governance is present from the very onset of the uh in the design of policies. The second one has to do with institutional capacities to invest in institutional capacities. We talk about the top uh capacities technical operative political and prospective capacities. The third dimension has to do with investing in social dialogue in uh the uh relations within a social dialogue in order to achieve crosscutting technical agreements uh in the social sphere. And the fourth dimension has to do with the polit uh with economic uh uh policies. many transformations not failed not as a result of bad ideas but as a result of the management of those transformation especially of the management of uh um economic policies and now having said that here in ECLA we promote economic development uh social development in the region and within that develop social development we talk about inclusive social development and we mention this as fundamental in order to tackle the very heart of these issues by social by inclusive social development. This development strategy is a strategy that places people and the and people's rights at the very center and where basically it objective is to promote a life free of poverty, hunger and inequalities. And in that sense in order to achieve that level of welfare we say that we require robust institutional policies. We also require active social participation and certainly uh a high inclusive and sustainable economic growth. That among other things that economic uh growth must be able to guarantee uh the generation of uh resources for the financial sustainability of social protection. And that is important because the the the vertical column of this inclusive social development is universal, comprehensive, sustainable and resilient social protection. And in that sense we have mentioned that a more productive inclusive and sustainable future means that we have to move towards inclusive social development.
Understanding that development is a dynamic and continuous process where inclusive social development forms uh the fundamental part of that process and promotes six of the 11 transformations that I mentioned at the beginning. the promotion of labor inclusion, the reduction of inequality, the expansion of social protection, an effective education and wide access to professional training, gender equality and certainly a digital transformation.
The second issue that I would like to mention is uh to talk about this trap, the trap of high inequality in the region. Well, first of all, this is important because there is a magnitude of this inequality. There's a persistence of this inequality and this inequality is a multi-dimensional phenomenon. The magnitude and persistence of inequality can be illustrated in this graph that income inequality which is a structural characteristic of the region evidence is high levels. The red line is far higher than the green line which is the average of the OECD countries. And we're saying that at a persistent level during many decades there are 14 percentage points. We are 14 percentage points above uh the average uh income of OECD of the average inequality in OECD countries and not only at a social level but at the level of development and of the economy. Well, that leads or creates uh obstacles weakening social cohesion and uh economic and social development and likewise inequality as a concentration of income continues to be an issue in the region. In this graph, we see that 10% of the highest uh uh income captures 34% of total income whilst 10% of lower incomes only captures 1.7%.
Now there uh we can see the different countries and more complex measurements that add information from surveys, household surveys, taxation registries and national accounts that suggest an inequality which is even greater than that 34% which is concentrated in the 10% higher income uh population and it shows uh data that is over that over 50% of total income on average in the region.
Now with regards to what has happened in terms of uh income inequality between 2021 and 2024 we have a genie index on average that has dropped in the region by 3% as shown in that graph and uh basically this is due to the behavior of five countries that have helped this Brazil, Costa Rica, Ecuador, Honduras and Mexico employment revenue venue coming from wages and salaries are contributing to this res this reduction at least 6 uh to the left.9 is what has been uh caused as total change in the last uh four or five years and in that sense the promotion of labor inclusion policies and the link of social protection must be a priority in policy design because they are either rendering ing some results in terms of reducing uh uh income inequality measured according to the GD gen index.
Um half of the total reduction uh of uh income inequality has been dealt by it.
Uh it's a multi inequality is a multi-dimensional phenomena that goes beyond income disparities. When we think of uh inequality uh we think of a genie index, we think of income inequality but inequality goes way beyond uh income because there are multiple dimensions to it. Uh dimensions related to uh labor, education, healthcare and other dimensions. Sorry. There are different pillars of socioeconomic standing, residence area, gender, age groups, ethnicity. So there are uh dimensions that are different from income that speak about the the need to understand this historical and structural phenomenon which is multi-dimensional and that is why among other aspects from we are making an effort to obtain a multi-dimensional measurement of uh uh inequality. High inequality limits growth. Low growth creates conditions to keep that high inequality. That is to say there's a bi dimensional um birectional relationship.
And in that sense what we propose is that inclusive social development contributes or fights uh this uh bond or this link. For example, uh investing on social development creates a virt circle, creates uh which creates resources in the financial security of social protection. In that sense, this social protection creates a virt circle.
It builds all the foundations for economic development. but also as part of the developmental strategy that countries have in the region. And finally, the third topic means that this digital transformation, this uh uh present and future and digital transformation uh is basically how it relates to inequality. And there's a sentence here there are opportunities as well as risks for uh inclusive social development.
There are two sides of the same coin, not only one, nor is there a easy recipe to deal with it. And in that sense, uh digital transformation, digital transformation opens up opportunities for inclusive social development, but it can also deepen the existing gaps. Let's discuss some opportunities. opportunities that are associated to an active public policy.
Social protection, digital social protection changes, access to social services that creates mobility to connect.
Social protection that we used to have five years ago will not be the social protection that will exist in five more.
not only uh in terms of how comprehensive they have and they offer but in the way in the they the way connect with citizens.
Legit information will change that and that will mean that there will have to be investment made on social protection that is exclusively devoted to that contact with its citizens. uh a more uh customized uh education, more personalized education with new tools to support teaching and learning processes, more comprehensive and resilient health.
Um of course uh there is a significant insufficient connectivity I would also say very heterogeneous. Uh tomorrow when morning panel we'll have a panel dedicated to that.
Amaria Palma will discuss about digital um inequality and she will present or how we delve into the detail of these topics. I'm going to go through this very quickly to offer the floor to the authorities. Um reproduction and deepening of inequalities associated to digital divides digital transformation may increase those gaps that exist today. Reconfiguration and segmentation of labor markets driven by artificial intelligence. When we talk about automation, we talk about artificial intelligence. Basically, that's the impact on the labor markets. On a Thursday morning, we'll discuss um about the topic with a keynote presentation.
We will hear about the impact of AI on the labor market and then we will hold a panel to uh talk about uh uh that topic.
a high risk of exclusion for workers with lower digital skills and in that sense AI will have a significant effect on social protection systems and thus on inclusive social development strategies because of the backbone of the uh social protection systems and social protection will be impacted by artificial intelligence and digital transformation. it will have uh an impact on development strategies uh um artificial intelligence will have a significant effect on that.
When we talk about meaningful connectivity and that it becomes a right to also other social rights, we mean that digital transformation as a dimension will be linked to all other dimensions in the field of social development and from the point of view of development strategies. Therefore the right being linked uh the right of people should have social digital inclusion will become more and more relevant for the development strategies of the countries. of course um development of digital skills. Um the design and implementation of public policies for digital inclusion, discussing uh those that are displaced by automation and of course the importance of having regulatory and ethical policies that prevent biases and protect uh recipients data as well as a safe engagement uh with with uh uh technological transformation. Um And in that sense just in closing uh some some final thoughts. First inclusive social development in addition to being social objective I've repeated this is also a necessary condition to move towards a more productive inclusive and sustainable development. This is basic because at the end of the day social development just like economic development are absolutely necessary in a in a country development. So the social development is not only played in the field of the social world. It does impact the economic development capability that countries have and the development potential as a as a potential society's inequality in the region remains high, persistent and multi-dimensional. Conditioning the opportunities of people throughout their life cycle and of countries in their development strategies. In order to reduce such inequality among other factors, you need the design and implementation of comprehensive public policies. We will discuss this in the panel that we will uh discuss this afternoon. But basically if um if we're talking about a multi-dimensional inequality and public policies to address that inequality are not comprehensive. That is to say they do not address each one of the dimensions of it that impact inequality. Of course it's going to be very difficult for this inequality to be reduced uh significantly.
Digital transformation is not neutral.
It can be a very powerful tool for sure.
for inclusion and social mobility. But it can also uh deepen existing gaps if it is not properly managed. And its effects will depend in our in our opinion on public policies for inclusion um human capital and education also on institutional strengthening and on infrastructure which is in a comprehensive way will allow to guarantee the right to digital inclusion in an increasingly more digitized era.
Thank you very much.
Thank you Alberto for this presentation uh to uh frame the discussion that is coming up next. We have more than 440 people connected virtually now uh and also watching through YouTube. We'd like to thank them as well. And now we'll go to the high level panel of the authorities. Of course thanking all of you for your engagement. We've asked uh to address two topics or two questions.
First uh area is the challenges that are being faced and the actions that are being driven in different countries in order to reduce uh inequality and to progress towards a an inclusive social development strategy. At the same time we've asked them to refer to the challenges uh involved in digital transformation uh for policym and for the actions that are driven in each country in order to strengthen uh digital inclusion and to reduce uh inequalities in this field. We'll ask each one of our uh panel members to please um speak for eight minutes each and then after this these eight minutes and we'll see how we can progress but at least we we hope we can at least a couple of minutes at the end for final thought. I will ask you I will apologize in advance because I am the timekeeper.
So, for those of you who are here in the room, I'm going to raise my my sign so you know uh that you only have uh two minutes left. Um and those of you who are online, I will ask you uh to um shorten our remarks. Uh let me tell you that there is a simultaneous interpretation. uh so those of you who may need it for those of you who are online especially there's going to be interpretation into English and there's also into Portuguese as well. So thank you very much. Uh we'll start with uh Maria Jesus Wolf, Minister of Social Development and Family uh from Chile.
Minister, you have the floor.
>> Mr. Alberto Arenas uh director of social development division of uh Mrs. Claudia Robles uh dear ministers and authorities and panelists that are attending uh and those of you who are connected online I'd like to thank Clack for the invitation to this dialogue. I'd like to start by telling you the kind of conviction that organizes our work as the minister of social development and family. The bar with which we measure society. It's not only its wealth but how it deals with its most vulnerable members because the poverty and is concrete in the reality of a family that doesn't make ends meet. That is a true uh asset test of any social policy in Chile. Almost uh one out of six compatriots still lives in situation of poverty. One out of four children.
That is a figure that has been reduced and we value that. But behind that there's something that is more difficult to measure which is the lack of hope and the progress of organized crime that finds in the fragility of the families uh fertile ground.
Reducing poverty for us is not just an economic goal. It is to rebuild the fabric that supports the people in our country. That is why the administration of President Cast has tried to pave the way in terms of what is going to be our goal for the next four years.
Strengthening families. Uh we uh hope that at the end of this period that Julian families will have more resources uh capabilities uh and necessary conditions in order to take care, educate and support the well-being of all its members. Because the family is the first space of care, affection and human development. It is there where the bonds and values are created that then support the entire society and defines a way of understanding the state. a state that is not only limited to managing uh uh scarcity but strengthens autonomy and people's capabilities to drive their uh lives and recognizing them as protagonists and not as just mere recipients of help or aid that are measured in a fragmented way.
We believe this is about comprehensively looking at our beneficiaries and from that conviction we organized our action. First um all economic autonomy because of best policy against poverty is work and their own income.
Not only transfers that are sustained in time can actually become dependency.
Today at the lower inome households the subsidies of the state have moved on to represent twothirds of what they receive and that is what um brings us here. We need to work on dignified income and of course to take care of childhood. A child is better prepared and protected in a strengthened family than outside of it. And then families that are connected to networks and communities because social cohesion and mutual support are the best insurance in view of adversity. And this conviction is being translated into action to strengthen and make um family economies more robust. We have programs to support employment and entrepreneurship that we have at Fossis and a labor group that should reach those who need it most and as best possible to strengthen these links. We have the plan growing as a family and the idea is to do this within the household.
the robustness of these foster families and the childhood offices almost present in every district of the country. These all allow us to move forward. We have the plan Chile reasse that seeks to remove the economic barriers and care barriers that make it so difficult to form a family project.
And of course trying to find solutions to the low birth rate in the country.
And we rebuilt the bonds at a local level, advancing towards a national support and care system to offer support to caregivers. And all this implies transforming the social policy to make it more comprehensive to overcome the fragmentation we see in different areas.
Summoning civil society and the private sector based evidence base that is best done with justice and reaching those who need it the most.
And it is for this reason that digital transformation occupies a core role. Allow me to begin with a with good news. Chile has advanced enormously in access gaps. 96% of households have internet connections, one of the highest figures in the region. But access is no longer the true barrier to digital inclusion. It is youth, skills, and opportunities.
This connection must be translated into better services, learning, and employment for vulnerable households. And it shouldn't just be yet another gap.
For the digital inclusion to be a reality, we need to better understand the tools. It must be a tool to better know families and bring the state and those regions that are further away closer to our reality. This should allow us to make it more bureaucratic, installing better institutional capacities.
Also thinking about responsibility because these gaps can leave people out.
A true conviction seems essential.
The idea is to include everyone from the onset, not as a subsequent correction.
In Chile, one of every six people has some sort of disability. And what is designed as accessible benefits everyone. lower sidewalks that are sidewalk friendly for those who are pushing a baby carriage or older people who have difficulty walking. It helps everyone having silent spaces. Designing in an accessible manner is simply designing better. That is why Chile has made universal design a condition of the state through the national universal access plan that organizes and prioritizes the access agenda including the digital dimension.
Allow me to conclude with two priorities. The first or elderly older persons, they must be included in the digital world, showing them that it is not a technical thing. It is a matter of dignity and autonomy so that technological advances don't become a new form of loneliness.
We've advanced through the national um senior citizen service promoting digital literacy programs for the elderly and this from the use of a computer to the use of internet uh being able to make payments or other um documents online and we are working on it to bring digital tools to them as well as to people with disabilities. The idea is to offer them concrete opportunities and spaces for care that will accompany them every day. Then children connectivity that educates also exposes our children and adolescents to organized crime and other crimes that stunt their growth. That is why we have a digital uh a safe digital space plan so that when they navigate the internet they can be safe because digital inclusion means that no child be left alone in front of a screen as just as we wouldn't leave them alone on the street.
The same with PC. Digital inclusion will not be measured by the number of plans that we announce, but by something that is much closer. The peace of mind of a mother that knows that her child is safe with a screen at home and the hope of a family that believes that doing better is possible.
Allow me to conclude by mentioning those who are responsible for this. The Minister of Social Development in the region.
Inequality and digital gaps do not recognize borders. That is why I invite all ministers to work jointly with the interest of families at the center. When a family is strengthened in our region and we arrive on time with our public policies, all of Latin America and the Caribbean grows and becomes more cohesive. It is a task that none of our countries can do on its own and that together we can achieve.
This is the social development that we seek. One that brings back hope and dignity, beginning with those who are most fragile and that builds a more cohesive society where forming a family is possible and valued. Thank you.
>> Thank you, Minister Wolf.
We thank you for your presentation. I'd like to offer the floor to Vric Calderango, Minister of Social Development of Panama, who is joining us virtually. Minister, can you hear us?
>> Yes. Good morning, everyone. Thank you.
Claudia, you have the floor.
I'd like to specially greet Dr. Venas, director of the division of social development at ELAC, ministers present, the technical teams that accompany us today from different countries in the region, and everyone who participates in this space for reflection.
and participating in representation of Panama, a country with modest territorial extension but with a deep conviction.
Social development must reach all persons without any uh differentiation based on their circumstances.
This is only possible when public policies are built with a comprehensive vision articulated and focused on people. For this reason, we have taken on the challenge of formulating public social development public policies in 2026 and to 2045.
taking it as a long-term vision based on the coordination effective coordination of all of the public institutions under one same strategic horizon. We begin with a basic premise. This mechanism for social development, they're not independent processes. They are interoperable for the same national aspiration.
It does not make sense to speak of development when there are advances while there are other areas that experience difficulties in covering their basic needs.
True progress of a nation is reflected in the quality of this development of these opportunities.
One of these has been understanding that we cannot adequately protect those who we don't know.
For years, many social protection systems have operated with fragmented information, insufficient information, limiting the capacity of the state to better identify those who face maximum vulnerability conditions.
Being aware of this reality, there is a household register that is interoperable and focused on people.
a technological tool that represents a new model of social administration that seeks to better understand the living conditions of the population and to respond in a more pertinent and timely fashion to these needs. The objectives are clear. When a family needs support, the state will offer and have the n necessary information to act accordingly. This is the true promise of digital transformation for development having and putting technology at the service of the community.
It is along these lines that we are moving forward with a multi-dimensional and poverty and indigenous peoples understanding the multiple dimensions of poverty which affects indigenous people. This initiative recognizes that traditional um groups do do not always are not always incorporated into the system adequately. It is important to do so and also to look at the territory and including their cosmo vision and the way they organize themselves.
in order to build public policies that are more pertinent, respectful, and inclusive. Basically, here are some results, some specific concrete results. Recently, Panama began the second part of the monetary transfers with an investment of over $54 million.
This is directed at 165,000 families.
Since July last year, we have included 11,000 new beneficiaries and it's around 64% coverage. And beyond this, the figures of this program represent the possibility of an elder to have their medication that a mother can guarantee um food for their her children or a family having sustenance. This is the de the proper dimension of public policies, social policies.
together with the national secretariat of science, technology and innovation and the city of knowledge.
Hundreds of youth presented robotic innovations and this experience reaffirms a purpose that we share in Panama will allow the youth uh to become a plan and it is not it is rather a strategic decision-making process of the country and this specifically at a historical ult time. We have to fully use our demographic activity and to prepare the new generations for all challenges and transformation.
Nonetheless, we are responsible when acknowledges acknowledge that challenges are important. The first one is to guarantee that digital transformation will be translated into real inclusion.
Technology by itself does not generate different activities but there is training and the development of skills and effective access to economic opportunities especially for the younger people and rural population. The second challenge is the reduction of the territorial gap.
There are different realities and through the indigenousness or rural communities. Our approach combines technological approach with the institutional presence in the territory.
Digitization should be widening the activities and not become a new way of exclusion. The third challenge is to tackle all the risks that are related to inequality.
And it happens when technology does not consider all people equitably.
The digital systems are as inclusive as those that are feeding them. If the inclusion does not have biases, the public decision-making process can create inequalities. And it is for this reason that together with the national innovation groups at government level, we're working with the development of integration process and strengthening of social information under criteria of equality and the protection of rights. We are aware that digital transformation is establishing different questionings that are overcoming technology automation and the changes of the labor market are generating productivity but also there are risks of displacement as well. Our social protection systems should be prepared to be able to keep company to people in these transition processes. Ladies and gentlemen, as it has been expressed, social development is not a result in itself of the economic growth. It is a political decision-making process. It is a priority of the market of the state and the commitment to human dignity.
Technology can be an extraordinary tool to accelerate the process, but only if we define the purpose with which this is being used, which is to guarantee that nobody is left out. Panama makes available all the lessons learned from the creation of the social registry of households and the different public policies for development in 2026 through 2045. We are quite humble in presenting this but we are fully convinced that regional cooperation is of the utmost importance to create the social protection aspect that are inclusive and we are creating a social protection system that is fair smart and something that is trying to be anticipating to vulnerabilities to close historical gaps and to widen opportunities for all people. And we are fully convinced that the development occurs when the state is efficient and equal in those activities and there should not be anybody that is not being hurt and and thank you very much for allowing me to participate today.
Thank you very much. Thank you very much, Minister Catalyst.
It is a pleasure seeing you and hearing you. And now we will be hearing Miss Gloria Reyes presentation. Minister of Women's Affairs from the Dominican Republic here in the room. Minister, you have the floor. Thank you very much.
Thank you, Claudia, our moderator. And I want to greet Mr. Altoenas from the social division department, our colleagues in the room and those that are virtually here with us. First, I would like to thank Clack for opening this space of the social inclusion area and digital transformation. These are all challenges that are main elements in our country. from the Dominican Republic. We are sharing that social inclusion is not an an automatic condition to the economic uh activities but the idea is to be able to reach it as as best as possible and equality between men and women should be the central element of these public policies. So please allow me as a summary and hopefully I have time enough to express which are the main actions or activities and main challenges that we are establishing from the women's affairs department in Dominican Republic and to be able to move forward to an inclusive social uh agenda. These elements that I will be presenting is carried out in a context where Dominican Republic has carried out one of the most important investments in social development. We have strengthened the social protection system. We have improved the focus on different areas.
We have updated our social registry. We have created activities between the different institutions of social security. We have widened the coverage of social programs in the most vulnerable fam families and we're promoting the different people to leave these programs. Those that have been able to access employment or a certain income that will generate stability to them to leave the program. And by creating this social intervention and protection system to make it more comprehensive and more adaptive especially to those situations that are related to the different methodological uh activities related to climate activities in our country such as ours and to reduce poverty and inequality needs to combine growth with public policies that can protect people when there are persistent inequalities. It is in this context that Dominican women have moved forward tremendously. For example, in education, we represent 69% of those people that have graduated from higher education. But that progress is not translated in economic uh equality.
The salary gaps is between 18 and 22%.
One out of four women in cities and one out of three women in rural areas do not receive the same income as men. the bank activities of women. Women represent 45% but 60% of men and only 14% of the large companies have a woman as the highest authority. And behind these gaps or there are structural elements that is explaining what we have women which is the unequal distribution of unpaid care and it is where we are finding the most important challenges of the new generations of public policies. the social protection systems do not only respond to poverty or vulnerability but also have to deal with um gender issues.
Around 850,000 people in the Dominican Republic have unpaid care and out of them 98% is women. Therefore, one of the main priorities is to acknowledge care as a right but also as an area of economy that has a tremendous potential to create a large number of employment.
This allows us to see care as public investment that is widening capacities to reduce vulnerabilities and to promote employability and social inclusion. It is along these lines that the country has taken important steps. We recently have approved the standard of care and consiliation of public activities with the support of the public administration ministry and these alternatives are acknowledging that care is to be included as well as self-care within the public sector and it has established that the state is co-responsible about all these activities and there is an important area in the maternity believes there is a need for um breastfeeding rooms and other activities all together with the national strategy of the community of care that is promoting the household care and the creation of companies that should be providing these services for the state as well as for families be able to hire them directly.
We are moving forward in the legislation where in Congress there are many different uh elements to create uh activities, laws and others and we have four complimentary lines to be able to widen the economic autonomy of women. First is to make the state a real market for women by applying the reserves of around 30% for uhmemes and another percentage for those that are led by women. We have more than 31,000 women that have signed up for the uh states providers or vendors. This means more formalization, more income, and more opportunities. The second is access to fair housing through the women's bonds and we are forecasting an investment that is doublefolded compared to last year's to allow women that are under vulnerability. The third one is the equality seal that we have called Iwualando Re that is implemented through the UNDP and they promote specific plans to close the wage gaps and to have areas of free discrimination and the fourth and not as less as it's not it's it's important as well and we have here the identification of the resources resources to be able to close the gaps between men and women. Alto together in these actions with equality and a budget sensitive to equality are part of comprehensive strategy for the autonomy of women in Dominican Republic. And that autonomy is not a sectoral agenda but a condition that it is important for overcoming inequality, strengthening co social cohesion and to have an inclusive aspect. It is in this area that the country has been reducing the multi-dimensional poverty and very specifically monetary poverty. Last year monetary poverty went from a 19% to a 17.3%.
And now in the first quarter 2026 even though there is a crisis at international level we have the lowest rate of our history of monetary already of 15.4%.
And evidently this means that it is a stage that places us on the table with a new reality on how to ensure that this progress not only mitigates the most vulnerable ones but also accelerates the economic autonomy of families and women and that helps to contribute in closing these gaps that are still existing. But in this forum the we see a fundamental question which is the challenge that digital transformation represents for inclusive development. I would say that digital transformation we have mentioned this throughout this morning. It is establishing a paradox for equality policies that represent a historical opportunity to widen the different activities. had on the other hand if it is not tackled from the perspective of rights it can really deepen these inequalities it is important to express that digital gaps is not only in terms of access according to the national statistics office women have been able to progress tremendously in for example tablets or or smart phones or others but inequality still exists where it really matters which is the advanced digital opportunities and technological career s and innovation and the creation of digital solutions. And we have been able to advance our progress in articulating all this together with interoperability of the state and that tries to improve the creation of capacity in women and this is still the main challenge. And in summary, this social inclusion has a digital dimension that is very important expressed by Alberto in the beginning of the panel. And this is why the policies of care and economy and public procurement and housing is sensitive to gender. The inclusion interoperability cannot be seen as separate agendas. And this in essence is what we are bringing here together. But we have to see the whole discussion in an articulate fashion. They're all part of a strategy to widen capacity, generate opportunities, and to guarantee that women are also in the midst of development. And we reaffirm the commitment. And we continue working with Clark and the rest of the countries in the region to create a digital transformation that is fair, centered, and human. a transformation that widens rights and strengthens the opportunities of the state, ensuring that no woman, child or family is left behind. Thank you very much.
>> Thank you very much, Minister Gloria Rees. Thank you very much for your intervention. Now, we'd like to offer the floor to Mr. Mari Delmea Jr. who is executive secretary of the Ministry of Development and Social Welfare, Family and Fight against Hunger of Brazil, who is accompanying us online.
>> Miss, >> can you hear me? Executive Secretary.
>> The executive secretary will be speaking in Portuguese and for that purpose we have simultaneous interpretation.
Secretary.
Introduction.
Sora Dominican.
Guatemala Dennis Carlos Paraguay A great pleasure to participate in to share our advances in terms of social inclusion headed by President Lula. Brazil faces historical inequalities that are structural, multi-dimensional and are reproduced uh throughout uh the life cycle. These uh gaps uh involve cutbacks in income also in terms of gender, race, ethnicity and territories.
>> And to overcome these traps, we're implementing an inclusive social development strategy based on three uh aspects. uh the constitutional guarantee of social um guar rights uh the federative uh agreement that involves uh social inclusive social development and participation. And first of all we must know the the single registry which is the only tool for socioeconomic uh information and the planning of social policies. Today that uh instrument obtains information from more than 46% of the low-income Brazilian population mapping their vulnerabilities and real potential. It operates as a powerful social registry that uh leads to the selection and design of more than 40 social programs at a federal level. It is uh the backbone of uh the data that enables the Brazilian state to effectively reach and tackle urgent needs.
As from 2023, Brazil reconstructed its uh social policies um especially Bulsa Familia. In June 2026, we uh reached 19 million uh beneficiaries and uh more than 22 million families are in the protection network uh that enables a partial uh partial benefit for 12 month uh periods.
Social protection is not uh an end in itself but rather provides dignity as from 2023 4.5 million families have already left the program having consolidated their financial stability.
We supplement.
We also have a program called Acredita mobilizing 15 million realis for training purposes, micro credit and to promote entrepreneurship focusing especially on women that represent 70% of beneficiaries.
Now these coordinated efforts have led to speedy and strong uh results. Uh over the past few years uh the rate of poverty has dropped from 31.6% to 23%.
Extreme poverty uh has reached uh the lowest uh levels historically only 3.5% of the population.
And that means that u 17.5 million Brazilians have been able to overcome poverty, rescuing their dignity and uh rejoining a cycle of consumption and active citizenship hunger is the most urgent uh face of social inequality. We're very proud to say that in 2025, Brazil left uh the UN hunger map and since between 2022 and 2024, millions have been able to overcome severe extreme poverty >> and we promote comprehensive uh uh food policy. We do not distribute policy but we support a family agriculture in order to struggle against hunger creating climatic resilience.
These um efforts uh require multis sectoral coordination.
and Bula Familia provides 31 billion real.
This integration enables ush to reduce bureaucracy so that families can gain access to this program in an easier fashion.
An enormous amount has been invested in order to purchase from small producers.
Uh school meals are provided to 40 million students on a daily basis and this is not an isolated effort.
It is uh upheld by the participative democratic governance with a reestablishment of the national food security and nutritional uh security council reaffirming multilateralism.
And this expansion of the global alliance against hunger >> which involves more than more than 200 members. 32 um countries come from Latin America and the Caribbean and ELAC already forms an active part of this international effort.
In order to respond to that uh challenge, uh Brazil innovated by creating the National Integration Secretariat uh to coordinate electronic social platforms, Synaps and its mission is to promote and coordinate the digital transformation of social policies in this sphere [snorts] and it acts strategically in order to guarantee uh the operation of uh systems governance uh data ethics and the confidentiality of data integrating databases in a secure manner. The digital transformation is understood as a right and as a vector for equality and not merely as an an additional hindrance for the marginal marginalized Brazil defense economic equality, social justice and socio environmental responsibility and all that has to advance in perfect uh harmony.
Brazil's progress is close to the historical approval of a constitutional law amendment that authorizes the continuous allocation of resources for social assistance and also as a challenge we have a need to consolidate efficient axis in order to overcome poverty with productive incentives.
I thank you for your attention.
I'm sorry, but the we had very very poor audio for that interpretation.
We apologize to English speakers.
Claudia Valenuela.
>> Mrs. Claudia Valenuela who is vice minister of policy planning and evaluation from the Ministry of Social Development of Guatemala who will now deliver her presentation online. We would just like to confirm that she is present. No, I have been told that she has been unable to connect and so now would offer the floor to Mr. Dennis Caseres Vayares, Vice Minister of Education of the Ministry of Education of Honduras. May you be very well welcome the vice minister. Uh you have the floor and I'll be doing the timekeeping. Thank you. It's a great pleasure for me to be here at uh the main uh think tank for our countries. This is the second time that I am here and it's a great pleasure.
I will talk uh a little bit more about educational inequality.
uh and the situation in my own country and also in other countries in the quality in terms of academic performance uh the latest uh PISA studies and also OECD countries it means that we're in one of the most unequal countries when it comes to academic performance and uh therefore what what has happened uh and what has this study show well we have some students that in our country and in Latin America have uh a performance which is equivalent to that of OED countries. However, there are other students that are below level zero and there and we were saying that we have to build ladders in order to reach the zero level. Now this uh inequality uh needs or deserves to be mentioned because I'll very quickly explain the context. Normally in these tests in mathematics we find a better performance on the part of uh boys and yet uh girls perform better when it comes to to reading. However, when uh students face um a direct implication uh test in higher education, well, higher education is the main factor that explains a difference between a student that will study engineering or architecture and and then mathematics becomes very important. And therefore going back to what happens at the preschool level, what happens at a basic education level when these inequalities uh are perpetuated as from when these students are children and therefore we re they reach higher education with boys that gain easier access to engineering and so on. And yet girls uh choose uh um teacher uh teacher training, tourism and that and there's a major difference which is subsequently explained in u an equal access to uh better paid jobs and uh the life project of some students is uh interrupted.
>> This is what we've been doing in the face of this. We are building a curriculum that can correct some some systemic inequalities mostly of access to mathematics lessons. Because if we realize that many students uh have uh a better academic performance, they have more favorable variables regarding academic performance. That could mean we could find a solution. So this is where we see a direct relationship in order to lessen that inequality in the area of mathematics.
I will explain this briefly simply when we design the higher education system in Honduras the biggest factor that explains that variance is math because sometimes there are issues in social science and communications or in natural sciences. So an academic performance with a difference of 3 or 5% might be the trigger of a life project of a person or just be stagnant there. That's why we in our country we are pushing for for a reform that is more favorable to the most vulnerable and poorest children because at the end of the day in a direct competition in the higher education system they always face each other and in in the maths world arena that is a psychometric explanation but at the end of the day it does perpetuate these inequalities.
among uh children with more resources and those with less or fewer resources in the area of technology. Now, Honduras is pushing an educational policy about uh connectivity. A few weeks ago, many of us met in Latin America, including Chile representatives. In the end, the decision was that this is about an ecosystem, a learning ecosystem that has to do with educational platforms that should be adequate for our students.
It has to do with uh training and building digital skills in all teachers.
Uh access to uh devices that in the end may coexist in a learning co in a learning uh ecosystem. access to digital uh learning. Many countries are moving towards uh STEM reform. This reform is mostly focused on science, mathematics and technology. Looking for a direct connection between u the first and the second item. We realize that uh those students that uh have a curriculum more focused on STEM uh disciplines have high probability of being more successful both in higher education as well as in life. This led us to think or conclude that digital >> we are forced to transforming our educational system to a more digital perspective because uh in the end uh this uh digital skills that are developed in the classroom are some of those that are already used in the market in the labor market.
At the end of the day, uh both the curriculum policy as well as digital transformation moves us to a to becoming citizens of the world. It's a curricula that needs to move to uh developing boys, girls and youth that can move around Latin America and the world. I wanted to explain the academic enrollment. I hope I was able to contribute and it's always a pleasure to attend uh IKLAC and connect the topic of inequality that exist in the classroom which is seem a mere difference in terms of academic performance but then they become a perpetual sentence because of a three or 5% difference in a specific area or discipline. Thank you very much.
Thank you uh Ben Vice Minister Cassides for your remarks for sticking to the agenda. Thank you. We'll uh now uh try to connect again with Miss uh Mrs. Valenuela, Vice Minister of Policy, Planning and uh evaluation of the Ministry of Social Development in Guatemala. She is now online. Vice Minister, please go ahead.
May you be welcome. Good afternoon everyone. Thank you. Thank you. Mrs. Roi. Ironically, we are talking about digital transformation and digital gaps and I'm only 450 uh kilometers from headquarters and a few times I've lost my connection. That might give you uh in real life the the kind of status of our apps in terms of access to digital means. But anyway, thank you.
colleagues in the panel and the audience that has is attending the sixth regional seminar on social development in Latin America and the Caribbean Caribbean is is an honor for me to tell you a little bit about the Guatemalan experience in fighting inequality for us as a government reducing poverty is more than a statistical goal it's a country and an ethical commitment with the population and most of all it's our wager on human dignity for whatever attacking or tackling poverty is not only a development goal because it's a country in more than 57% of population live in a multi-dimensional poverty context it becomes a matter of dignity equality and legitimacy of our social contract poverty has persisted not because of lack of commitment but because of the fragmented responses that we coordination and the insufficient use of the evidence that have limited very frequently the scope of sustainability and the impact of our efforts. Uh Bernardo Revalos administration has taken a step forward in facilitating social policies that are based on evidence um as the core of their social agenda.
We are strengthening the use of administrative records, territorial diagnostics and uh outcome monitoring in order to design policies that will respond to the actual needs that prioritize uh the most vulnerable populations and that will allow allow us to adapt our interventions depending on what works and what doesn't. Evidence today is not a technical luxury. It's an ethical imperative when we're thinking of public resources as well and also human lives. In that context, water has driven the intersectoral strategy that is known as manuano hand in hand. This initi initiative gathers several institutions and ministries uh 11 uh to to be precise to address poverty comprehensively combining support to income, nutrition, health care, education, uh housing improvements and access to utilities. Uh Manuel Mano recognizes that poverty is a multi-dimensional and territorial issue and no program can actually address it in individually or in isolation. Before we had an household social register, our bonus sale program which is a monetary transfer uh had 82,000 beneficiaries and 2 years later after starting that uh registry we can say that we have reached more than 225,000 uh beneficiaries and we are striving for the goal of actually including more than half a million beneficiaries in a country in which more than a million households are still living in dirt floor. This registry allowed us to identify these vulnerable households and to build more than 75,000 um floors in an ambitious goal for us in order to close that housing gap by aligning social protection with healthcare, education, uh food security and local infrastructure. We're not only trying to uh reduce uh immediate scarcity, but also to build uh a resilience support system that will support Border Modern families throughout their lifpan is the one that actually strengthens the presence of the uh administration in excluded territories that that uh reinstates rights guaranteeing the public actor actually reaches out to the people in a coordinated fashion. However, challenges are huge. We cannot eradicate poverty only by providing social protection.
I've heard it from my predecessors. Uh if it is but it should be part of an economic model that is much it's it's exhausted. A model that creates growth without inclusion, productivity without decent jobs and wealth without shared opportunities. The challenge is to reconcile grow inclusive growth strategies with social protection systems that should be sound in order to reinforce each other instead of actually sub substituting each other and to always measure always measure again to know what hap what what works and what doesn't and adjusting the public policy actions in time. Uh we as states we should rethink how do we connect economic policy, labor markets, air systems and social protection in a single path for consistent development that actually places dignity and resilience and prosperity shared at the core. Uh people and the people at the center of everything we do. 10 years ago, our country used to measure poverty based on income. uh we knew that that vision was in or incomplete and uh a household may have a minimum wage but have significant scarcities in terms of housing, healthcare or utilities. So as of 2018, we took a step uh in order to create the multi-dimensional index in and of poverty in Guatemala. This it's was updated in 2024 with the latest um survey in 2023. And that meant we were able to update a kind of a snapshot a panoramic view uh showing the results from that snapshot are clear. 57.7% of the mala population lives in a multi-dimensional poverty condition. In those who are poverty, the intensity of the deprivation reaches 45.1%.
Which means that we are not talking only about a single issue but several of those are committed in the same household. Uh the most recurrent uh examples is the lack of schooling years, informal uh jobs, lack of enough adequate sanitation. And now going deeper into inequality, poverty in Guatemala has a a significant and ethical and territorial phase. In rural areas, incidents reaches 76.3% compared to 40.8% 8% in urban areas and in different departments or provinces as they are called such as Ala Verapas poverty reaches 87.2% 2% of the population among the indigenous population may uh three out of four people live in multi-dimensional poverty. This figure talks about historical inequalities that we must address by addressing uh culturally pertinent uh policies. The ministry of social development in Guatemala has driven a second step which is that uh household social regist registry which is fully digital. This is the compass for social policy because it allows to identify each uh household with updated information. The registry is not just a database. It's a bridge that connects evidence uh of the multi-dimensional uh poverty index with the concrete action of social programs in our policy. According to the principle of the 2030 agenda of not leaving anyone behind, the household registry looks through an inclusive and participatory process to encompass the totality of the households in the territory. The information as a whole that is broken down by populated area or municipality allows us to design and to direct more articulated interventions in order to address specific needs of different groups in society and to prioritize actions that will address the most vulnerable people. But they also contribute with relevant information in order to design policies that favor social mobility most of all. This has a special connotation for service provision because uh it removes policy regarding the beneficiaries themselves and uh equates uh offer supply and demand. So that that forces uh state institutions to operate synergistically and to be responsible for the right population. And what do we mean by that?
A single digital tool can make that different state institutions to be islands so that there are bridges that would connect them and allow them to simultaneously address the scarcities that house households experience. The household registry facilities that monitoring and assessment of social programs by allowing us to follow the situation of households throughout time.
we can assess the effectiveness of interventions and to conduct the necessary adjustments in an iterative um and adaptively. Uh thanks to this registry, we've been able to implement the intersectoral manual manual strategy that I mentioned before that coordinates the actions of the different ministries to address uh the specific situations.
Concrete example is the declaration of municipalities without dirt floors in households defending the dignity of the families.
That would be unthinkable without the ability of uh actually pinpointing the households that are still being affected by that. We need to close vice minister.
Two minutes more.
>> [laughter] >> So beyond describing what we've done, we would just like to stress the challenges we were asked about in the beginning.
>> Although we have already tried on a very important path, the statistics, a platform, the most important challenge now is consolidating what we've done. expand coverage in order to improve the use of the multi-dimensional overd disconnected.
Minister of Public Policy from Ministry of Social Development of Paraguay.
There you are. Also accompanying us this morning.
We have the floor. Minister, >> can you hear us?
like to especially greet all the ministers, vice ministers, Alberto. It's a pleasure to participate in this panel.
>> Before coming here, I was working with my team on the design, a new design that many of you know already. And this program is so fitting because we're talking about digital inclusion.
This is something that is at our top of mind every day. And by 2050 it would be very important for us.
>> There are new changes and it's so crosscutting for social inclusion. As Alberto mentioned in his presentation, >> he talked about a digitalized era. And I'd like to make a comment that could even seem funny when we arrived at the ministry 2 and a half years ago. One of the things that caught her attention was monetary transfers.
or um banking windows. But two and a half years later, our monetary transfer system has changed in a transcendental matter and we're talking about artificial intelligence.
So it is so important like to comment to you that our program reaches around 200 families all of them with monetary transfers electronic monetary transfers and it's been around 380,000 people that also receive monetary transfers made with bank cards.
We have another program for small entrepreneurs that have um joined our Tokuna program and reach 7,000 contributions a year. These are contributions that of people that receive these bank transfers as well and our annual subsidy to fishermen.
We have around 4,000 people receiving these subsidies. So in under two and a half years we have transformed what before or would have seemed um so difficult that today it seems our cake.
We have taken a leap into the 21st century with these monetary transfers.
And just as a reference, we've reinforced all of the core aspects of social inclusion. And this is also part of a financial system with all banking of participants in this program.
We have a positive program in so many other ways. This allows starting them in a financial system. This is how we work with the theme ministry. We try to work with some sort of oval of some system with the um hairdressers, uh mom and pop stores, electricians and all these by means of technology, digital technology and financial inclusion.
that brings about other work all of these things that make it fully electronic. So we are taking steps and the same these same monies that the communities where they are working um are seeking to move the economy in that direction. The use of a bank transfer or an ATM >> useful for people who are senior citizens and do not enjoy using this technology.
So it makes it easier for all of them.
And things that seem quite normal for us are not for older beneficiaries.
However, they they begin in this financial world. But there are other similar benefits.
And this uh productive inclusion is also important in order to be able to link it with micro um entrepreneurship.
So this zero hunger program that we have is a one of our stars.
Over one million children are fed every day with this and with the same scheme with the same work and uh electronic technification to call it somehow is the same way we had to go out and find ways to provide these uh systems to feed people and work with all the institutions of the state. We don't really have a lot of time but we started interacting and making all our systems with the most important institutions to work with each other. Today we're lucky enough that our integrated system is working and any institution such as MDS and the public governance are begun within this system. Entrepreneurs receive service orders via the system. They make the purchases via these individual systems and then the distribution is made to the schools.
>> The acceptance of these products is also done electronically.
It is done through the corresponding ministry and then payments also are made electronically. So you may ask yourself, is it that easy? No, it isn't that easy, but over 7,000 schools that we had um there were around 320ome that didn't have an internet connection. So many of them, the more vulnerable ones, indigenous um had to set a series of agreements and we have been able to connect to all these schools.
We also talked to Alberto about inclusion and the risks and opportunities that we should have.
I believe Claudia either I'm too slow or Claudia is in a rush. You have two minutes, Minister.
gap is very important. The technological demand is a basic uh step and without equitable access, this would lag behind. The lack of access to devices is also of the essence and maintenance services in a timely fashion.
And of course, the increase in digitalization increases risks to safety, cyber security, and learning. We're always running behind technological advances.
Everything we do fall short. So, this is extremely important. And we understand that we need to work on these digital platforms with participants, optimizing resources, reducing bureaucracy, all very important. We need to entertain um this robust digital system and the platforms. They need to be robust. We require a lot of technology and through the legal framework we would like to carry out a PPP and uh I thank you all for the opportunity and send a greeting to all participants.
Thank you vice minister. It's a pleasure to see you and I thank you for your intervention.
And it is now Graa's turn. um service of social development Uruguay. They are also connected virtually.
Vice Minister, can you hear?
>> Thank you very much. Thank you, Claudia.
Thank you to all the colleagues.
It is the second time that I participate in an activity organized by you and it's a pleasure for us. You know I find myselves in a very strange place. This is a ministry. But what is strange this was once headquarters of the manager of one of the banks that generated one of the greatest economic crises in 2002 and left Uruguay with 40% of Uruguayans under the poverty line. I was one of the university students that was on the streets trying to generate um change and um hanging on to hope. The two former presidents that I see behind me um and Moika and they generated in 2004 40% poverty and then 2014 we were already at 9.7.
So this was done by means of this ministry with the different emergency plans and equity plans and this work was also always done together with social development, economic development both jointly and it was >> the relationship between technology and technological development and your wife and um was in trying to bring the population out of this poverty line.
We had the seal plan in 2007 where most uh all of the children in primary school had access to a computer.
And then in 2010 this was expanded to high school and not only the schools had to be connected.
In 2011, 42% of households were connected and we generated unaloges with special rates for the vulnerable families. And today in well 2019 we reached 88 and now we're at 96% of the citizenship connected to the internet.
This development generated an energy matrix where there it was usually dependent on hydrocarbon and you know that we do not produce oil and there was high economic dependence of the energy matrix and there was a political agreement in 2010 of all parties to generate a state policy that would mean that today in Ara it used to be 62% of hydrocarbons now it's 98% of the energy matrix which is generated by renewable sources. In this scenario then we want to deal with development through economy and education as was expressed by other colleagues. It is in this area that there was a planned need not only of access and through the SA plan of education that is linked to technology but in the year 2014 from the technological university the first university that is deals with technology in URA related to robotics and technology. We used to not have those careers in URA but now we have over 2,000 youngsters that are studying in this university.
Why am I expressing all this? Not because I believe that Uruguay becomes an example in many areas, but it is a country that is a very small country and the discussion here that we are involved in has certain limitations as was very well expressed. the efforts of a country, especially a country such as Uruguay, will be impossible to deal with the important size of the technological transformation that we're seeing. Maybe part of the geopolitical situation or even certain speeches that are trying to attack multilateralism like they're trying to generate exits of all the h of all humankind is probably related to this transformation at technological level that is occurring very fast. Latin America has an important place in this discussion as Latin America has lithium, cold, rare earths, copper, aluminum, silver and others. So when we review these challenges and for those of us that are momentarily guiding the destination of our countries, we will see if Latin America will be repeating part of the process or will make use of this political situation.
So it's not to be just mere generators or maybe be the ones that are generating raw material for those that are being developed and not to use this raw material for our development. So in this scenario I believe that the challenge goes through what we are mentioning. We have an ethical challenge where artificial intelligence is generating these challenges from the material perspective and from the environmental [clears throat] perspective which are the opportunities for development.
Planet is a finite area as humankind. We have to jointly define those actions not just for a group of multi-millionaires uh are the ones that are generating what is to happen. So in this scenario, the second point that we would like to mention is how will we be discussing the earnings that could exist in certain areas as the challenges as mentioned earlier already.
The challenge we have today is technology that can be progressing at such a level from the ethical perspective that it presents a challenge as humankind.
And the idea is to try to determine which are the minimum common denominators. We should not all fully agree but we have to agree that there is an area where this technology or the development of this technology does not become the development of inequalities and clearly the opportunity of a society where we become as close as possible to uh equals and this is why I started with this review. I thought it would be better to talk about this and for the challenge here the idea would be to question ourselves. We have an opportunity here and that would be the topic. Thank you.
>> Thank you very much uh vice minister Mr. Federico. It is a pleasure having you this here this morning. And now given our time frame, we have an important effort to request from our authorities.
We only have one minute to close the this uh session. We will start with the same order of the participation. Miss Minister Marie Jesus Wolf, you'll have some time to close.
Thank you very much.
Thank you for those that have been able to present today. There are many topics that we can uh talk about the cooperation between the countries of the generation of the region. I think it is something we can think of the common challenges that we're sharing. I did not delve into the social household registry, but technology can really be an important path to shorten the gap that exists between the state and the perspective of the p uh or the vision of the population and what this population is going through in a day-to-day activity. to shorten that distance allows us to generate impact through the different programs and public policies that we are setting forth.
Implementation is a fundamental aspect.
Anything that allows us to improve that last mile will mean that families can really be better. And as Chilean government, our work, our effort is that through the knowledge of the different localities of the different actors that are working on the social policy can be perfecting it and that we become an important state where artificial intelligence, technology and digitization of the population can be prepared and make use of this new scenario that we seen that is moving very fast and where we have real urgency but always focusing on quality of life of people by reducing unfair in inequalities and to establishing those foundations that establish social relationship and strengthen our countries. I want to thank you for the invitation.
Thank you very much, Minister Maria Jesus Wolf. It is a pleasure having you here. We now offer the Miss Batrice Carlos Dano, Minister of Social Development of Panama that is here with us virtually. I don't know if she's connected.
>> Thank you very much. All presentations were wonderful. Social development really shortens the gap of inequality.
It is not a consequence of economic growth. It is a priority of the state.
Technology is a powerful tool but it does not have a value in itself. The value is provided by ourselves with criteria of equality and the protection of rights. The value that it guides our work uh is what we can work with. I want to thank Clack the Jed, Freud Foundation, European Union for the opportunity of participating in this initiative being aware that regional cooperation is a very important tool to create social protection systems of focused in inclusion and resilience. Thank you very much.
Thank you, Minister Bedriles. It's a pleasure to have you here this morning.
Now, it is the time of Miss Gloria Reyes, Minister of Women's Affairs in Dominican Republic. Well, thank you very much. The truth is that this has been a very important time quite rich and I would like to conclude that I am fully convinced that the important challenge of our generation will be to create models of development that are able to combine growth, inclusion, innovation and human dignity.
making an emphasis in human dignity models that allow us to only be consumers of technology and become creators of solutions, talent and value of our societies, but also means to have more uh states that are smarter, focused in people and that are able to use technology to uh place services and coordinate uh solutions by identifying risks and be working with the citizens on a timely basis. And I would like to close by saying that inclusive social development is a very focused in the digital aspects and in Dominican Republic we want to continue with these conversations from specific actions and fully convinced that there is no inclusive social development if there is no uh important activity between men and women.
Thank you very much Minister Gloria Reyes for being here with us this morning.
Now is the executive secretary, Mr. Jr.
The English booth apologizes, but we do not have interpretation into Spanish to be able to transfer it into English.
>> Interpreters regret to inform that we cannot interpret this the his words.
Thank you, Executive Secretary. It was a pleasure to have you here this morning.
We understand that Miss Vice Minister Claudia Valenuela is not connected here today and unfortunately she was not able to reconnect. We now offer the floor to Mr. Dennis Casares Vades, Vice Minister of Education from Honduras.
Thank you for this time frame and for 2 o'clock Latin America is seeing a crisis in learning but through the UNESCO platform it is possible to unite the people in Latin America where many of the c countries that are here some are have advanced in the digital transformation of the classroom or others are really dominating artificial intelligence within the classroom.
So the message that I would like to present is that through ICLA and UNESCO, we can really push forth education as a social mobility driver in our Latin American region and that we can all overcome the crisis of learning and to take a step in incorporating technology to the classroom as many countries can be supporting other countries that are already traveled this path. So thank you o for the invitation.
>> Thank you vice minister Minister Dennis Cassidus for your participation and for being here with us. We understand that the vice minister of social policy in Paraguay Mr. Paris is not connected and we want to offer the floor to Federico Graa and vice minister of social development from Uruguay. Vice minister are you connected?
Yes, I again want to thank you for this opportunity and these instances are very important not only for the exchange of different experiences but also to try to generate different agreements. Iglag has a very important role in the region and maybe they could be also playing that role in finding those multiple common denominators that allow us to agree from different perspectives and and to be able to defend the right of make our citizens happier people.
Thank you very much, Mr. Federico Graa, Vice Minister of Social Development. It was a pleasure having you here with us and for your words. We want to thank you. I would like to give me 30 seconds, please, for certain announcements and requests.
First and a round of applause for our panelists this morning and it has been a wonderful session that really reflects the richness the of our or the wealth of our continent related to the perspectives, policies, experiences that are all diverse and complimentary at the same time. We have a panorama of the different challenges that we have and we want to again repeat our thanks. You have probably received a survey. We want you for those here in the room to fill in the survey and virtually as well as it really allows us to improve our work. And I also would like to request that we now have a parallel event that will be held in Medina Rome on the universal health systems that sustainable and resilient a requirement to move forward in social inclusive development. When closing this parallel event, there's a coffee break and some time for each one of you to have lunch.
There are two areas we have uh two places where you can have lunch and also want to thank the more than 400 people that are still connected to us and we will return at 3 p.m. Good afternoon and thank you very much.
[laughter] Hello.
Hello.
Hello.
Perfect. Everything is okay. Thank you very much.
Hello, Professor Foster. You hear me?
Andres, perfect. Here is the Alberto and Francisco James.
Nice to see you. Thank you very much.
Hi.
>> Hi, J.
And Chico, you gave a great talk today.
>> Thank you. That's very kind of you.
Thank you.
>> Some interesting parts as you might imagine for me.
Fernando is Okay. Is it the civilian.
Good afternoon everyone.
>> All of you accompanying us after lunch both online and uh in this room. I believe that there are 200 participants online. In this first panel of the regional seminar on social development, this panel seeks to present an exercise to measure uh multi-dimensional social inequality in uh Latin America and the Caribbean. And in this panel, we're accompanied by Albertas Mesa, director of the social development division of EKLAC, who will deliver some opening remarks. After that, Andres Espec who is an economic affairs officer of the that same division who will deliver a presentation on the proposal for a regional multi-dimensional inequality index. And we have the honor of having with us a distinguished panelist who will uh make comments to this publication, Professor James Foster, uh Oliver Tikar, professor of international affairs and professor of economics at the George Washington University who will be connected online.
He was uh here uh in person last year with us also professor Francisco who was introduced to you all this morning.
Andrea Vigorito from the Institute of Economics of the Faculty of Economic Sciences and Administration of Univers Republica in Uruguay who will also be connected online and Fernando Marani the director of the justice inclusion and equality program at the center for international cooperation at New York University and of the global alliance against inequality who likewise will be connected online. And now I'd like to offer the floor to um Alberto for his opening remarks.
Thank you Daniela. May you all have a very good afternoon. May you all also be very welcome to this panel one entitled towards a multi-dimensional measurement of inequality and uh and special greeting for those that accompanies both in person and uh online and I would sincerely like to express my gratitude for those that are accompanying us during this panel discussion who will make comments about the publication that we launched. I would like to first of all greet uh professor James Foster who is connected online. Professor Foster is uh truly a great pleasure to again have his participation his presence uh in the fifth regional seminar 2025 was uh very it was very important to have his to have his presence his the contribution that he made was very important and his accompaniment to this process has been very very important. In fact, many of the um of what we discussed last year helped us uh to launch this publication that we're presenting now. We would like to especially thank also professor Franciscoa who is uh uh director of the inequality studies and director of the international inequalities institute. It was an honor to have him this morning here um listening um or delivering a keynote uh presentation.
Professor Ferda's um institute has been the state-of-the-art entity when we talk about inequality at a global level and for this collaboration is a tremendous opportunity in order to strengthen the links between um state-of-the-art research and the challenges faced by our region. Professor Fera, thank you very much for being with us here in Santiago and we hope that we will continue to enjoy your presence. Thank you very much for the support that you're providing to the seminar. Likewise, we would like to u express our gratitude for the participation of Fernando Marani of the global alliance uh against inequality who is connected online. Fernando has played a very important role in the promotion of the international agenda that places inequality among the main challenges for contemporary development.
And what I would also like to greet Andrea Vigorito, distinguished academic from university and who collaborates with in several u research uh projects on uh development.
I would like to highlight the seminar on inequality that will be held in the next few months in Uruguay under her leadership. This workshop um forms part of a wide-ranging effort undertaken by over the past few years in order to understand and face up to one of the main obstacles uh for development in Latin America and the Caribbean and that is the persistence of the high inequality trap uh uh the social the lack of social mobility and lack of social cohesion. This has become a priority for ECLA research and we have tackled this uh challenge in our period of sessions in the conference on the social development for Latin America and the Caribbean in our contributions to G20 and Brazil in 2024 in the debate of the second summit on social development held in Qatar in 2025 and more recently during or in the social panorama for Latin America and the Caribbean in version 2025. One of the lessons that we have obtained in this region is that inequality as discussed this morning is a historical phenomenon uh which is persistent structural and multi-dimensional in nature and therefore it cannot only be understood as from the analysis of income. The gaps that we see in our region are expressed both in income and simultaneously in terms of education, in terms of health, in terms of housing, in terms of employment, pensions and social security. Even more so, these inequalities tend to accumulate in households and in population groups, reinforcing themselves during the life cycle and limiting the possibilities that people have. And therefore advancing towards a multi-dimensional measurement of inequality does not only constitute a methodological challenge despite the fact that uh it is something it is a a a complex task but it's not only a methodological challenge but rather a fundamental tool tool in order to better understand the social reality of the region in order to strengthen the design of public policies that are more comprehensive and effective. And in this context we are today launching this publication which is a the series of social policies 2050 entitled a multi-dimensional social inequality towards a multi-dimensional a regional proposal together with Andres Bejo who is economic affairs officer of our division and he will be presenting this and also with Alberto Spindo PTO and Paulo Era uh also colleagues and consultants from the social development division of atlark. This exercise is an important step in the research agenda on inequality. The idea this is a pioneer application for the region that uses the methodology developed by professor Foster and his colleague Joshin published in a working paper in 2024 which helps us to analyze the joint analysis of various dimensions of well well-being. This is the key aspect. The idea is to analyze to jointly analyze different uh dimensions of uh of well-being and therefore to advance towards a more comprehensive analysis of uh inequality and this methodology has the possibility of capturing the association between the different dimensions. In other words, the interreationship between dimensions, the overlapping of different aspects which is a substantive a substantial progress compared to when it comes to analyzing gaps individually and that is a qualitative uh um jump uh forward. We had inequalities in the labor market, inequalities in the uh health market and so on. But this creates an inter relationship between these all these different relationships and that is indeed the leap forward that this methodology implies for Latin America and the Caribbean. But perhaps what is most important in this work is that it opens up a new stage geared towards deepening uh our studies about multi-dimensional inequality uh social mobility and the accumulation of disadvantages to have a the possibility of comparing regional realities and and new evidence for the com for the design of comprehensive public policies capable of um of um resolving the multiple inequalities in the region. the inherited inequality that was mentioned for example this morning by professor Ferrera and one for one final reflection uh the work around this paper is the first publication that measures multi-dimensional inequality in Latin America with data from 13 countries of the region and it is also the first publication at a global level that measures u multi-dimensional inequality in any region on on this planet again I would like to thank professor James Foster for all his report and this effort is just uh the beginning and we hope that this panel will indeed contribute to enriching the reflections the design and implementation of of comprehensive public policies to reduce inequality in our region. Thank you very much.
>> Thank you Alberto for those uh opening remarks. And now I'd like to offer the floor to Andres Espeo who will be presenting the study.
Good afternoon everyone.
Can you please uh show the presentation?
Well, first of all, I'd like to thank Professor Fera who is here in Santiago, Professor Foster, who has been uh an inspiration in terms of developing this methodology and this exercise that we have undertaken in the past year and also Andrea Vigorito and Fernando for accompanying us with their words that I believe will very adequately supplement.
As Alberto said, we're just beginning this research work. This is an exercise that we're conducting for several countries of the region 13 specifically.
But this is only just the beginning of an exercise. We still have a long way to go. This is a methodology whereby we try to make this methodology a regional uh measurement uh for the region in the future. Okay. We still don't have the presentation. So I'll I'll just uh continue talking. As Alberto said, the presentation is based on a document that we drafted together with Alberto Arenas with Jose Prito who is unable to accompany us with Pablo Rea and with Alberto Spindola from Clark. And what we tried to do was to begin to push ahead with a methodology that as Alberto said could bring together various dimensions of well-being, education, health, employment, housing in one single index.
What we evidenced in our region is that we had different sources of information and different types of surveys, but we had no single measurement that could summarize what was actually happening with inequality. when I finally see my presentation I'll be able to show you this but I still cannot see it but I would like to tell you that in general terms in our region several efforts are underway to try to measure inequality but normally these measurements have been uh have have been separated from each other. There are various various efforts to measure inequality but basically they tend to be unitary academic exercises. In some cases exercises are conducted in one country but when they try to extrapolate that to other countries of the region they run into many limitations both in terms of data or whenever they have sources of information such as household surveys they face problems with the variables present there and it's difficult to standardize that and therefore what we have tried to do in this paper with Alberto Po with Jose Wain and Ernesto is something that is uh novel we have tried to advance towards this new frontier with a number that is unitary. Working with a single source of information or with or with one common source of information to uh to to achieve this goal.
Okay, I believe I'm going to skip many of them. Um but what Alberto was saying, this is a publication that is already published. You can uh pick up a copy uh on your way in. Also uh you can see a QR code that you can use to download it from our website. The outline includes four areas in this presentation. The first one talks about the multi-dimensional inequality measures.
Uh what have we been working worldwide in order to develop some inequality measures? Then we're going to talk about the multi-dimensional inequality measure and how this exercise was specifically designed. We took Foster and Lodkkins uh ideas that were published back in 2024.
Third, we're going to see how this methodology is applied in 13 countries in the region. And fourth, since this is an ongoing process, we'll tell you about our next steps.
Something brief now we saw this this morning in Alberto's presentation when we measure according to Genie what we can show in the countries in the region is that in general inequality by income measured by Genie has been stable for the last 20 or 30 years although there was some setback in terms of uh figures or progress since 2000 until 2015 from then onwards uh figures have been stagnant but not only stagnant but also the gap has remained with the OECD countries for example in general our numbers are much higher we have one point of difference with the OECD countries in in spite of significant progress educationally than or access to education improvement [clears throat] of labor conditions of the population improvement of labor related income in general quality has not been reduced anyway so that uh opens up to following question what's going on in our region that although there are several socio economic factors that have improved in the last three decades when you look at inequality measured according to Genie it doesn't changed this is what makes us think that the inequality measure is multi-dimensional with several factors impacting and this is what we're going to discuss during this presentation first it is What they discussed is that inequality goes beyond uh revenue or in income employment uh housing and healthcare is not necessarily represented when you discuss income for the last 15 years at a cla we've been saying that inequality is a multi-dimensional phenomenon. It integrates a different uh wealth well-being uh dimensions that is part of our uh approach. But this look of understanding inequality in a multi-dimensional fashion allows us to understand that the response to address it is through comprehensive policy making. It's not only a focusing on the labor market or educational policy but you need a set of policies that we call them comprehensive to be able to reduce inequality. The whole analytical work that we use doesn't start from scratch.
It starts from work that has already been done in house. We start from the multi-dimensional poverty index that had been proposed alqaeda and foster years ago that is being applied in several countries in the region. We took we took those frameworks and we apply them to the inequality field. Is is there an echo right? Yes. Yeah. From echo from beyond I guess.
Yeah. Better. Is that better?
Well, there's an issue I guess.
Okay. So, uh we have um a starting point which was our multi-dimensional poverty but also a starting point that for more than 15 years 20 years uh from our social development division we have something that is called social inequality matrix.
What is that is to understand that this equality has multiple dimensions and also some um areas gender gen gender ethnicity residence and socioeconomic index.
What did we say in this inequality matrix is that the different dimensions um amplify inequality.
It is different being in a city certain educational level being or having lower income with limited education living in an urban area compared to living in a rural area that the inequalities they will face will be higher. Why? Because the different areas will be amplified.
Not only uh do we have this regulatory framework but we have an umbrella here which are the development traps. Our executive secretary and then Alberto confirmed that uh mentioned uh uh high inequality, low mobility and weak social cohesion. Our countries are limited in their development because of these traps and we need a whole research effort to reduce that high inequality trap.
What is the issue? Historically Clark and most of the countries here and in the world work on inequality by measuring income. There are different alternatives. We discussed this morning with professor Farera that there are some alternatives to measure. uh maybe we get different numbers yesterday professor said that depending on the methodology that we use that will give us a completely different figure so by looking and depending on the source of information we'll have a similar value so there are measurement challenges that are important what type of dimensions regarding well-being should be included in order to measure inequality how do we include those attributes how do We measure the distribution of those attributes and at the same time if you in the regional seminars if you remember those three years ago professor Gorbinon attended and he said that he thought that a good way of measuring in multi-dimensional inequality was through a dashboard having different sources of information and with these diversity of sources is was to understand reality through different dimensions. What was the major problem is that we do not work with a single source. Many times what would happen in a in a source did not correlate with others. It was difficult to find a causal effect among the different dimensions and uh it didn't make sense for us to work with the dashboard and we had tried before. So we wanted to achieve a single index and since then last year we had fost professor Foster showing us the methodology and we tried to make progress towards a single uh index measurement and we started checking literature what the literature tell us in general I believe you can see it better up there are different authors and different papers Sunumi lugo etc >> different author that I've tried to measure inequality multi-dimensionally but as I've said in general none of these methodologies coincided in terms of the number of dimensions included they worked with income some of them with education others were with health care uh to be able to measure that according to Genie entropy Atkinson depending on the type of measure that was used but I believe that the major limitation of all these measurements is that in general they were with single cases. There was an exercise for a specific country and then you could not generalize it. And why?
Because the source of the information when the methodology was developed was for a specific data source. Very difficult to extrapolate to other research cases.
And in fact, each one of these methodologies would weigh the different dimensions differently. There are methodologies that would consider 50% for the income as the explanation for inequality but if there were three dimensions it was onethird for each. So there was no consistency when deciding what was the best methodology to be applied and from it and then we tried to say okay what is the best methodology then and we found something interesting that Foster proposed uh I'm just paraphrasing and Foster is listening to me that is quite complex um if I make a mistake professor Foster please interrupt and correct me uh professor Foster uh mentioned the this the data were features that there were six features that a methodology should comply with for it to be useful. The first one is that it needs to be comprehensible and easy to describe. What we saw in most of these cases is that they were not easily understandable and very difficult to describe uh for the policy maker. They would give numbers that were hard to understand. In general, this then caused that this was not easy to understand by the population and by the policy makers.
When public policies being made, there were some values and numbers that then were hard to translate into concrete policies. On the other hand, most of the methodologies need to be uh adequate to the purpose. If we develop a inequality methodology, we need to be clear about the objective. Our goal is to have comprehensive uh policies is to face inequality and to reduce it with a range of policies that the countries are applying. It needs to be technically sound, robust statistically to meet the axioms that are proposed. It needs to be feasible operationally. We saw the result of different alternatives of measurements when we wanted to replicate it in other geographies were not necessarily feasible. Some variables were missing and more importantly needs to be easily replicable.
In that sense what we did was to focus on the Foster and Lodgekin methodology and why exactly because of this reason because the whole list of the desiraables that you you would want to have uh we we expected this measure had to be useful for public policy and this is an exercise a a totally concrete exercise.
We're not trying to explore all the measures. We want one that is actually useful, that makes sense for the policy maker.
Austrian logic, what they do is to propose an inequality measurement that is relatively simple but technically very sound. And why? Because that measure allows you to separate what comes from each dimension in inequality, but also what comes from the combination thereof. What Alberto mentioned at the beginning of the presentation. that what we need is not only to see each dimension separately knowing whether ineability in education is a number that could be Jenin jinny or Atkinson but what happens with the association among the different dimensions this overlapping um among the different dimensions that create inequality and for sure this is all guided by axioms that are quite clear I'm going to simplify the fosters methodology in a chart here.
Echo is kind of annoying.
>> I think there's a open microphone somewhere.
Okay, that's fine.
>> Well, very practical terms and simplify the pathology. Apologies, professor. We have four major stages if you wish. We need to measure. We need to have a source of information in general at 8:00 and we'll discuss that in detail. We have the household surveys and that's a very good source of information because they are multi-purpose and they have different dimensions of well-being. We have a source of information and we have different dimensions and with each one of the dimensions we have to come up with a compound score a value for each dimension. And second is to calculate a unit of measure. that unit of measure of the different dimensions. Uh what they will allow us is to say but this will allow us to say can you hear me now it now it's better we'll start all over again.
What matters is having all of the scores but all built into a single score. A standardized score, Zcore, whatever it is, but we'll have a number. And this number tells us how it behaves in every dimension.
>> Then we can break it down and have a multi-dimensional index. And it's made up of two stages. One is about having that average value in each specific dimension. So we'll have one for health, one for household etc. And then we'll have a component that captures how we combine all these dimensions.
And in the fourth [clears throat] place which is the not not the least important, it is calibration the waiting. How what weight is each one going to be assigned? and then we're going to discuss it in each stage with a practical exercise.
Here we have Fosters's equation the multi-dimensional measurement of inequality. Then we have component A which is the addition of all of the dimensions that are unit dimensional so different dimensions and then we have a weighted average and we have a correction factor R and we'll explain it when we look at the results and then a value that is a structural correction.
And all of these elements of the equation are going to be reviewed when we look at the results in a practical exercise for Latin America.
>> So the third item is the measurement exercise for Latin America.
With this exercise, we're going to work with 2023 as an average. Three countries of the region. These three countries have the video where we concentrate all the household surveys that's in here at ECLE and we're going to analyze households and in each survey we have the same variable or number of variables and this in general um is very restrictive when we carry out these exercises. is it's difficult for Argentina, Brazil, Chile, all these countries to have the same kind of variable or dimension to work on this.
And even if I'm if we're going to present the results for 2023, this will allow us to look at not only whether the multi-dimensional results have been reduced, but what it explains. So this inequality has explained why some dimensions have been reduced to a greater or lesser extent.
And for this we're also counting on five dimensions. education, um, income, household employment, and we normally base ourselves on multi-dimensional um, poverty or inequality. And that is what ECLE does. We've been working on this at a regional level.
In the income dimension, we're working with one single variable which is the per capita income and we have the genies here.
This is a genie you look at when you have inequality.
You can look at the variability in terms of inequality.
We have low genies and well low for the region of course Uruguay has point4 Dominican Republic is there has 38 and there are countries that have over.35 like Colombia and other countries and there is a heterogeneity in values in terms of the inequality per income this is the first dimension I'm not going to make a very detailed reduction But we have the education dimension.
And what are we going to work on here?
It's years of studies.
>> I don't want to dwell on the restrictions of the system, but we want to look at um inequality, the years we complete. Of course, there is quality pertinence.
There is a much more complex thing here and tomorrow we're going to present the uh multi-dimensional genie and there's going to be a parallel event in the afternoon tomorrow which I feel could um supplement with greater information.
Thank you Danella. The important thing here is that this is a first approach using the different dimensions. We know it's not the best but it allows us to advance in a multi-dimensional approach in different dimensions.
It allows us to look um in our region for example that we have a um mandatory education until sixth grade. So 12 years of age is the limit that allows people to um >> overcome poverty and uh inequality genies can be compared with these and that allows us to observe that inequality by education genie is uh different from income genie index. So if you look at 2023 and compare it to 20 um 2000 you will see that the genie index is very different.
The same thing happens with health. And here we have three main dimensions.
We know it's not complete either in terms of what we can measure. Inequality health. We had a parallel event just now that talked about this. But what we can see here is that in general with what we can measure in the household surveys is uh sanitation, access to water um and the health services. They're weighted by one/ird each.
They offer us these genies which are quite low. What do these low genies explain that many countries I'll go to the European case again. They have a very low genie. their access to health is universal.
Then you have different readings and you can look at whether the access to health is pertinent, timely etc. And it shows us that people in theory have access to health. And from that perspective, the health genie which is in this dimension is much lower because people can uh opt for an excess and we're trying to measure um overcrowding.
There is variability between the countries.
And um in some cases you have people sleeping in rooms that are to rest. And Mexico we in Bolivia we have higher values than others in the region. And in the last dimension which is employment and pensions. employment and pensions. We tried to base ourselves on um dimension by Kristen Venush and we spoke to her and tried to focus not only on access to work but something more complex. We tried to look at um the contributo conditions plus the work or labor conditions whether they contribute to the pension systems, their um work hours, work stability. And we tried to look at this in a way that could be a little more complex, basing ourselves on the quality index promoted by Kristen And um we saw that in general the access to employment shows us that genie that brings us income inequality that is what we have more values for and they're higher closer to 0.4 4 0.5 in some countries.
And this takes us to difficulties in the labor market. 50% in um informality and it's not only about being formal to have a minimum level.
The income don't allow them to leave the poverty line when you look at the level per capita.
So this takes us to the to having a higher genie in the labor dimension.
This is a compared um perspective of the different dimensions that we've been working on. The first is income.
The second is education, health, overcrowding, household situation. And the darker colors are in different extremes both um employment.
And so here we say perhaps we should only work with employment and income.
And in fact we didn't because each dimension contributes something to the equation.
So if we're going to drop in equality, it doesn't change the education or health of people. each component affects uh general inequality and that's what we want to take a look at in this exercise.
We're not so much worried at the of the exact value but we want the methodology to be robust to have solid results in order to have some sort of multi-dimensional measurement.
This is another way of looking at these dimensions. It's a classic uh Lawrence curve. We have perfect inequality in the red line, the closer that the curve is, inequality should be reduced.
And here the other thing we have and that we were able to see with the previous slide is that both income as well as employment show an unequal curves the curves that are more towards the outside. And this is interesting. We're not going to look at this now because we have the results, the publication, but we want to see the Lawrence curves for each year.
And we can see how each dimension has come closer to others. They're more similar, more equal in a way, but some differences have increased in other countries. That's part of the work that we're going to do. But it is dynamic. It is changing constantly.
And to conclude here with the exercise, we see a plus here of the Buskran lodkin um methodology. What they're trying to say, what their methodology is. All these dimensions were found high. They're all unequal. So we have a measurement that one is observed and the other is inequality.
So how far are they how how different are are they? How far are they apart in terms of their differences or inequalities? And these countries in general show a homogeneous structure.
What it says basically is it in general in most of the countries in our region. There are dimensions that impact significantly but in all of them there are inequalities that superpose that overlap.
And this is very rich information because to um fight inequality we need to look at these in a multi-dimensional manner.
So I'd like to conclude by saying that on one hand and this presentation with the other studies that we've carried out we see that inequality in Latin America and the Caribbean is still structural. We've seen this for 30 40 years. It hasn't decreased when we measure by income. But we also consider it multi-dimensional because it is expressed in different maps with data and when we observe it uh separately we see that this inequality in order to address it requires comprehensive policies and these policies um need to be articulated with social protection, with education, with health etc. And this approach, this multi-dimensional approach will identify how these advantages affect some divisions and how they are accumulated in certain households. Some households will be affected by more than one dimension or more than one inequality. And this is what allows us to say on one hand uh that one of the objectives that we had when we began these exercises was to prove that um methodology could be a solid tool for a region and offer an alternative.
But in this context and with the data we have they offer a solid and transparent tool that we can justify that approach these mathematical formulas and allow us to work with the traditional tools but also require some more information. We know that um the sources of information that we have need to improve to have more solid measures with a multi-dimensional poverty index.
What we saw was it began to be implemented by uh Foster.
Most of the surveys didn't allow capturing the multi-dimensional poverty index and then some countries began improving their tools and their surveys.
Some countries have been able to apply it and made efforts to com make it more complete than others. I think that is where it's going.
What this will allow us to discuss and talk about how to tackle inequality which requires different ministries, different organizations as put them together because inequality needs comprehensive policies. Thank you very much.
Thank you Andre for that complete presentation and as Andre mentioned the document is available online for anybody that would like to see the details. Now we would go to the comment section for each panelists. Please comment the questions that we talked about for around 10 minutes, no more than that.
And I would like to remind you that we have simultaneous interpretation here on the room as well as online interpretation for anybody that might need it.
Professor Foster, who I would like to offer the floor to, will present his comments in English. So you have been acknowledged, Professor Foster.
It's good to see Virginia discuss the paper that was sent to me which had rather a different uh picture of things but I'll uh go ahead and give general comments based on all of this. Um anyway, uh Alberto, you're very fortunate to have Chico Ferrer there to uh give a one to try and capture the multi-dimensional aspect.
call economic integ.
So the idea of this tool See, I just said on economic inequality.
The goal develop such a tool.
And to do so, we built upon the work to build the first stage being combining the different dimensions.
Looking at that vector, applying an inequality measure to obtain So must it?
all the core axioms and is in other fixed vector of coefficients that areual indicator for each person. Now this this looked at any uh degree of sophistication.
But it so happens this case So our proposal then was to have a vector of coefficients for aggregating across the multip now now inequality.
such as one another. It's the base.
say a country for the compare and get results that were more robust.
And it did result very nice way into across the various dimensions.
Minus if some are high. Some people have high is a lower level of inequality.
So you uh based on other sorts of analyses you have that are subtractive.
In fact, the highest is in income is given the highest. Housing given the highest in each of the other dimensions and all the way down.
some inequality is relaxed. What we see with this graph is that you can how important it is the kind of structural component is in bringing down inequality from that max. The >> finally the way we did it is to say well maybe we want to incorporate into this. If we did, a policy maker might see from that point on.
One thing that I should mention is that once you meaning attached to them.
So the authors on really an important step.
My paper um we used actually income education Today's discussion final word is an inter now there are two And then comparisons over time the country's results I didn't know if you're doing a distribution.
So off point that the re-ranking discussion that you have in the presentation in understanding the difference between In addition, you had an interesting discussion of now that meant that you would take in the initial year and you have 2013 2023.
So for 2000, >> I think that one does need to update the number of one another since they're different measures.
All right.
But given.
Let's carry on.
Thank you, Professor Foster. Again, your comments are always useful to improve um future research. Thanks a lot, Professor.
Again, I'm going to spare you my my portonol uh and and switch to English because some of the subject matter here is is quite complicated and I'm more confident of my ability to address it in English if I am able at all uh to to do so. But thanks thanks [snorts] to um Alberto and Andres again for the opportunity to comment on on this work and and very nice to see James again even if not in person. James and I had offices about two blocks from each other in Washington for many years but now um see each other only occasionally. I want to to congratulate the authors again of this work um because it's it's I think it's a thing of beauty and in a way it's a thing of double beauty because there's real beauty in the methodology and I say beauty people may find that strange but there's real beauty in the way that that James and and Michelin uh derive something that looks very simple but that require a lot of work to prove that it has the properties it's supposed to have. So underneath this simple weighted sum of inequalities of margins plus a term that measures ranking, there's a lot of work uh and and and James referred to it in passing with this theorem and so on. But you know there's a very important proof here that you needn't necessarily worry about but uh it's a very important proof that says if we want the basic axioms of inequality that is if you give something from somebody who has a lot to somebody who has a little inequality should not rise or it should fall. The generalization of that to a multi-dimensional context is this um uniform ma majorization theorem. So I'd like you all to use this in your next dinner party. You know say oh yes as you know the uniform majorization theorem is a generalization of goodton and you know doubt you'll impress your hosts. Um but that aum uh that aum was the thing that only works if you've got that linear structure or it's only guaranteed work if you have that linear structure as which is what James was was was referring to earlier and and and and that you know that's very powerful and it's very useful and it's very nice that it then generates this thing which illustrates you know like I I teach a course of economics of inequality to non-economists at the LSC And when I talk about uh multi-dimensional or multivaried inequality, I say just a very simple thing to them. I say when we look at multi-ar inequality, there are two things you got to remember. The inequalities in each margin, but then there's the association between them and here they are in exact terms like that.
There's a term there that captures that association which is this ranking term.
So that's very nice. So it's a thing of beauty from the methodological side and it meets the beauty of Sapal's uh Sapal's rich database and the the sort of empirical uh uh skills of of these of these authors in in bringing that data together in having these five dimensions in the same surveys for 13 countries able to to to generate a really rich picture of inequality in the region.
uh across all these different dimensions. And then now for the first time looking at how correlated they are and adjusting the measure for whether or not the same people are deprived in two different dimensions or are privileged in the same dimensions or whether those privileges and deprivations are occur for different people. Uh and we get a really interesting picture out of that.
So that's the first thing I wanted to say. Then I have two other points to make on the basis of the questions that I was uh that I was given. Uh and and you know I I I got the question that is always the worrying one for people who work on measurement like like like J James and me uh which is oh what's the policy implications of this? How it can be used for policy and that's very very dangerous. So I I now turn from the praise which was very heartfelt and meant to you know limitations or warnings that come in the label of this of this powerful and beautiful uh product that we've got. Um and and and one of them is that interpretation.
Suppose you asked yourself okay well how you know how important are these different inequalities or should we address policies to income because inequality is bigger in income than it is in education or in health or how should we think about the fact that the genies are much lower in some dimensions than in others uh and so on and and the the three little issues I want to make mention there and I'll do that quickly.
one is is the concern about double counting.
So suppose for example that housing the number of bedrooms that you want to have which is one dimension is influenced [snorts] or or partially determined by income in that richer people have more bedrooms per person. So you've got these two dimensions that are treated as separate but one of them is determined by the other to a large extent. That's not a problem in a descriptive sense.
the measurement is correct but in terms of interpreting what to do about housing uh is much less clear now perhaps you can affect housing by affecting income.
Yeah. So when whenever measures are whenever the margins whenever the dimensions are functionally or causally related interpretation of these decompositions has to be quite careful. Um uh another example is income and education. Suppose income is a convex function of education as it probably is right. So wages for example tend to rise with education. You know often we'll write wages are a function of education, education squared and experience and a bunch of other things. And so you could have a very limited amount of educational inequality and a lot of income inequality. It could be that the way to reduce the income inequality is actually to expand the education thereby lowering the returns to in to education and therefore lowering income inequality. So what to do about these things is something that you don't necessarily take out of these measures. Now you know this is not meant as a criticism of the measure. The measure is a measure is a descriptive tool. But asking for it to tell you what to do in terms of policy is like asking a hammer to chop down a tree. you know, it's not a chainsaw. It's a hammer. It's meant to do one thing and not another.
Um uh the final point on this uh uh uh on these warning labels is the the rearrangement term itself, right? The rearrangement term is part of the beauty of this measure. It tells you the overlap to the association. Now how to interpret that may also differ depending on the nature of the economic processes that generate the the the rearrangement.
Consider two societies. One in which two societies that have the same inequalities in income uh and education.
So the same margins but the associations are different. In one of them there's a lot of correlation.
a lot of correlation bad thing you know income the educated are rich the uneducated are poor in the other this is not true but it's not true because there's discrimination for example people are giving men larger salaries than women even though the women have more education or people are saying I don't care so much about your education you're my father's nephew or that will make you my my cousin anyway um you know I know you or you know you are the same race as I am or we grew up in the same place that will reank things from education and income it'll break it'll augment that epsilon term it'll it'll make this rearrangement term bigger but is that a good thing we don't know right so again interpretation or policy in particular needs to be very careful so You know there was a question which was about um whether this informed prior prioritization of interventions for me and I know I noted here in my notes and my conclusion on that is that the prioritization of interventions of policy interventions should not be derived from any descriptive measure this beautiful one or another without an understanding of mechanisms. Okay, so that's that's one thing. And then my my very final point is another question to me was how does this relate to stuff I was talking about earlier about inherited inequalities and there the only thing I want to say is that actually here there lies another very interesting measurement challenge and and and maybe James uh will will help us there too as as he has in so many um other occasions which is the following.
you know in in our measures of mobility or inequality of opportunity we were looking at the association between income across generations right between income today coming by parents generation or income today and previous circumstances.
This uh measure looks at also at associations and sort of the rearrangement term in particular at associations but associations across dimensions not across generations. So in fact in some sense if we wanted to understand intergenerational mobility in a multivariate index of this sort we might be looking for a correlation of correlations a correlation across generations of the correlation across dimensions. For example, you know, if we were interested in how this rearrangement term changed. So, is a society becoming more or less um uh what's it called? Colinear, right? More or less um uh correlated more or less strongly correlated across dimensions across generations. That's a correlation of correlations. I'm not aware of any work done on that. That could be quite interesting, be a a potentially interesting uh frontier. So, let let me end that. Thank you.
>> Professor, >> Professor Fera for those comments and contributions both in terms of your uh congratulations and also in terms of your warnings. Now for his comments, I'd like to offer the floor to Andrea Borito. Please Andrea, you have 10 minutes.
Hi, good afternoon.
Thank you also for the invitation.
Uh the issue is very interesting and uh and uh this has been a panel that I have learned a lot uh from. I don't know what what I could contribute but perhaps I could uh um try to answer some of the questions that I have been sent and mention some general aspects as well. I believe that it is quite right to work on this multi-dimensional inequality index because as has been said the multi-dimensional approaches have uh focused on poverty above all uh and uh have been very successful in the measurement of poverty and uh in the region. Yet we still have um few little work in terms of uh composite indices and we're still undergoing many global discussions in terms of inequality the damage created by inequality.
>> So I think it is uh very timely to discuss this. we have spent more than a decade in Latin America uh with uh uh income inequality stagnated and the need to think about a new round of policies.
But this is also interesting because uh applied social policies at least in many countries and especially in Uruguay during the growth period focused strongly on income and less on other dimensions uh in salary policies uh conditioned and non-conditioned uh transfers and so on both because we had to reflow to income and also because the others are much more costly.
We also have the expansion of official measurements of multi-dimensional poverty in many countries in Latin America, but they are not really integrated with monetary poverty in statistical systems. And the other thing that I also think is very important is that this is a clear methodology that can be interpreted um very different to the ones that were available in the past.
I and those were the more uh general uh questions.
We have an option here in the sense that the multi-dimensional inequality index is much more focused on the distribution of well-being. We could also think about a multi-dimensional inequality indicator much more associated to differences in economic power, the way in which democracy is impacted and so on. And that opens up a multi-dimensionality so to speak of uh uh that that that we would like uh to reflect on. And if you also mention some properties, there is uh the methodological work with the function that relates to the measures uh measurements that we could use.
And the other thing is uh the analysis unit that focuses on households. Perhaps we could also think that multi-dimensional measurements could bring to the to the four um generational for example differences within households and I also have some doubt about the period that you choose between 2019 and 2023. I believe that is a brief period um when uh we were hit by the pandemic and therefore it would be adequate to look before that to see how this index uh fluctuates within a longer period of time and uh during a period when uh income [clears throat] u distribution fell uh to see whether that shows something different or not and linked to that uh I would like to mention and some doubts about the interpretation in terms of the environment the aspects uh uh related to changes uh in the economy and policy changes I think it's complex to interpret that and so I think that to expand the period could also be useful so that we could uh look beyond the crisis with belong with regards empirical or statistical challenges for the implementation of these indicators in the region. I believe that the definition is relevant to the analysis unit, the composition the of households, how we take into consideration the different members of the households and this could be very sensitive for the type of configuration. But also >> something else that is used as an argument and the methodology stops standardizing as the other multi-dimensional methodologies do and also the one that is corrected by inequality keeping the variables also working with binary variables and my question was what happens precisely when we combine these variables that have different paths because I believe that is also O seen in the inequality levels that you are capturing in the different dimensions if there's anything else that can be done in that regard.
also for the measurement and for the systems to think about the need of having uh indicators that are sensitive to the short and long term especially Latin America because of the crisis and based on that also thinking of medium term and how periodically it should be calculated what does this illustrate And the statistical design statistical design challenges are mainly due to the fact that uh measurements are quite traditional. If you wanted to get out of there um we would have to introduce also uh modules periodically in order to enrich the variables that we use for work. We then have the the problem of quality of information and uh information capture discussing [clears throat] distribution. And for that we could think of having access to tax uh records to correct for that and and prove those measures with all the problems that um those corrections involve. thinking whether that could be applicable or not and also identifying external sources to use it in the case of non-monetary dimensions not only for correction purposes but also to uh broaden the dimensions that can be used I believe um base the dimensions on the fact that these are the most frequent dimensions and that are had also been used uh for the multi-dimensional poverty indices.
But it would be good maybe uh to work with that a little bit further and to see what comes out of consultation exercises for example I don't know be beyond what what can be done and there are consultation exercises in several places in Latin America also systematic reviews about dimensions I'm taking one by Solis and others in terms of how to choose the dimensions and And the indicators that you take for uh multi dimensional inequality that are based on a household level, but they can vary depending on the life cycle as well. That could also be interesting to think about. Um you mentioned dimensions to introduce. Well, everyone wants to introduce more and more dimensions, but it all depends on what the focus is. if it is about access to economic and non-economic resources or if it's looking for other variables or other dimensions. Um there's some work that do some validation exercises to check for the correlation among the dimensions and um also avoiding those highly correlated dimensions if there's no conceptual argument for that. um the housing aspect, access to transportation and the en environment I believe it's important and it's part of some of the poverty indices in the Chile one and the Salvador one uh economic resources control um property and there's then you have the whole area of autonomy uh spare time and nonreunerated work and then recognition stigma discrimination or feelings of perception about safety or security. That's something that will be discussed tomorrow. I don't know if you all have they all have to be in this index but those are some of the aspects that could account for other dimensions of inequality. Something else that um is linked to the dimensions issue that called my attention and I believe the part of calibration and that does require further explanation in the document but if I understood it well half of or income weighs half of it and that the other five dimensions four dimensions weigh 12.5 so my question is why that option of 50% if such a high impact from an income um maybe Everything will be determined by the income since the other dimensions do not vary so much in the short term. You talk about a sensitivity exercise that would be good to see. The alternatives could be same waitings or the evaluation of surveys that you have sometimes you cannot base many of the indices from many countries on that but one could work with that. There's also work by mastos and guses lopees calva that is used for uh evaluation of poverty and you can see how the index uh varies the pen as to the dimensions income. I already mentioned the one about the potential corction beyond the issues in education the the option of mapping the number of years and 12 well it's not a poverty index inequality I think uh capping that in terms of higher education might be something relevant And by limiting and 12 they kind of truncate educational inequality. For example, here going into college or university although the whole system is public but it's a serious problem.
So um I would reconsider that limit in terms of number of years of schooling or education and housing. It would be good to think about those things that reflect access to service services and in healthcare I believe the problem is that the dimensions that exist overlap with housing and the strongest indicator is the access indicator that's why I is almost zero because there's almost universal uh access but here's there's a problem with the household service because they do not have good health related questions if we would have to try to push a little bit in that regard um uh to underestimate inability in this dimension that is very important.
Andrea, if you could please close. Yes, apologies. Yes, of course.
Well, I have a few slides I can send it to you. In terms of employment and pension, I believe there's something good in discussing non-reated work and to see what happens with uh unemployed people because there's very much focused on employment and uh and uh engineers.
Uh I'll stop here and apologies for uh apologies.
Apologies Andreas for interrupting because your comments are very useful.
There are some open questions uh but possibly in the exchange with Andres directly and then the uh publication its final version will be better answered and we thank you for your contribution and now we'd like to close the panel because uh there's side event so we'll offer the floor to our last speaker which is Fernando Marani. Thank you Fernando for your contribution. Thank you Danila and thank you I would like to thank Alberto and Andres for the invitation to take part of this panel as Andrea said it's very difficult to follow after professors Foster and Fareda so I will try to focus on other aspects as director of the center at the University of New York we have an initiative that for 10 years has been working on advancing in the implementation how we understand the uh goal number 16 how to create uh inclusive and peaceful societies and part of the work that we've been conducting is that inequality uh has a significant emphasis on uh the creation of better societies as part of our work in University of New York um uh with the secretary of global alliance against which is a coalition of uh countries, academia and civil society organizations that intend to keep this topic of reducing inequality at the center of the global discussions in the multilateral forum and also help uh reinforce coordination among the different stakeholders that are involved in this topic. Please allow me to start with a recognition. Although I've been in in New York for 11 years and more than 20 years outside Latin America, I believe I I I must recognize the contributions by the CLA historically in in inquiry aspects of centrally but also in terms of measuring in at the center of the development debate.
Even what Andres mentioned the social equity matrix of the social development division has changed the way in which the region understands the problem not only through income but also considering the gender ethnicity uh territories and life cycle.
Uh thank you for this initiative may have a regional multi-dimensional inequality index putting on the table.
It's not only technical but it's also a political decision that shows the continued leadership uh and that we also from our position we do uh appreciate it because this is a time in which the world needs new tools to understand the way in which we can measure inequality.
It is much more complex. What's valuable in this new index is not only because it introduces well-being, but it also allows us to see how many disadvantages accumulate on the same people and on the same households and that accumulation was turns this into structural and persistent inequality that are characteristics of the region.
I'd like to also add uh an element uh to this discussion because why isn't is it important in daring to present new initiatives and see how these can impact uh what we measure determines the political action. If we measure inequality poorly, we run the risk of underestimating it. And if we underestimate it, uh, we limit our capability of uh acting effectively. So I think it's not a surprise to anyone those of you who are attending the seminar. The idea is that an indicator uh is a political decision also about those who us who decide to pay attention to what's happening in us that is in any proposal to change the way in which we measure inequality is a proposal of how to guide the public action. These debates um are not easy to translate into political action. In our case, I'd like to uh bring a specific experience that can help think about the future of the of using uh this uh mechanism >> and with together with UN aid we elaborated a proposal to measure the way that the SDG10 is measured within the 2030 agenda. This objective is one of the ones that has had the least progress. And of course, it is about mobilizing political action and seeing how the governments decided to measure um SDG 10 of the 2030 agenda and focusing on the poorest 40% as a way to measure success.
and the problem of distribution.
>> In 2023, we presented a proposal by so that the SDG10 would be reviewed and there were many alternatives.
Of course, our proposal was to present the comparison of SG10 with the 40% the the the 40 richest 40% with the poorest 40% and it's a situation where the richest 10% does not concentrate the same amount of in the UN. The proposal was endorsed by many countries but it was not adopted by the commission.
Finally, there was this idea of a proposal that is still open that where are we mediating inequality in the correct manner and second is uh was about the responsibilities of measuring effectively and I think that this question remains open and international for there are many articles published with the need to review and an academic community that is not homogeneous and articulated in a specific way which affects the capacity at a global scale in terms of reaching some sort of action plan. uh did today we presented what we've heard um opportunity to bring this to the global arena for Latin America and and showing that multi-dimensional poverty inequality could be measured rigorously by the countries it would help supplement the report that we have today. It's also part of the uh frameworks that we have today capturing the disadvantaged populations in housing, health and other areas. We can truly discuss where we draw the line but we are learning about the experience of multi-dimensional poverty and trying to figure out how it can be best applied in the region. That is a true achievement of the report.
It's also important to mention the impact of advances in the digital area era.
and the use of technologies. They don't replace traditional ones but uh in some cases >> they replace existing to connect numbers and showing how much well-being is still uh missing and the dimensions and how these disadvantages are accumulated. All these factors are very present uh globally in New York and the UN context not only as part of the discussions of the global summit on social development last year or the negotiation process that ended up with the civil commitment. They are um topics that are of great interest.
To conclude and in the interest of the time that was allocated to me, there are three opportunities to advance in the implementation of this index.
In the first place, we need to include these indicators for the STGS in the national uh peer reviews, voluntary uh national reviews, VNRS and also uh social development networks and sending this information to assess whether the inequality reduction networks are actually being designed or not. And of course taking into account the multiple advantages simultaneously the index that you propose needs um to be looked at as a within the institution that needs to become a follow-up tool and accountability tool.
there is a historical context that not only has to do with the review of um SDG10 in the high level forum of the UN but also beginning conversations on the post2030 agenda which should begin to take place within the context of the United Nations and of course we would like to offer the collaboration and effort that global alliances could bring for the initiative through the partnership Ireland, Germany, Sierra Leon, Uruguay, Norway. Normally they seek to translate these technical advances into policies with the international agenda and by means of the full commitment.
I'd like to begin where we started.
Latin America and the Caribbean need to measure inequality. It's a factor.
continuously affects our societies in a structural and persistent manner. So again, I'd like to congratulate everyone that participated in assess in evaluating or elaborating this report.
I'd like to confirm to the world that this is a more complete way of looking at inequality. Thank you all very much.
Fernando, >> thank you. Thank you, Fernando, for your contributions. Very um much in line with our work with this. We are left with a panel that is rich in comments. Andreas has a lot of work left to answer some of his doubts. We have some comments and questions, but unfortunately we need to close now.
first day and it's important for us that you respond that you answer the survey on this seminar day on today's sessions and I'd like to invite you to a coffee so that you can then go to the next meeting Salam Medina on the eradication of poverty and old age and that will take place as I said at the Edina room that is the building that is towards the exit of ECLA. So you're most welcome to attend. If not, we'll see you tomorrow morning in this second and this seminar for the second day of very interesting sessions. Thank you.
>> Yeah.
Sorry.
>> See Serious.
Whatever.
>> Do we have
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