Translator: Sebastian Betti
Reviewer: Tomás Guarna There’s something
that happens to me very often and I imagine it probably
happens to you, as well. I go to “asados” with friends
and family, and suddenly an argument involving very deep convictions comes up. At first, it starts with a friendly tone, but as minutes go by the
bad-faith arguments come up, the far-fetched reasonings show up, or the ones using misleading data,
because we think arguments are something we must win at all costs and where it’s very hard to challenge our deepest beliefs. One of the persons I’ve argued with
the most during my life is my dad. We argue about anything, but above all
about politics and economics. And when I was just starting
my degree in Sociology was when we argued the most, and I got
really worked up during these arguments, I hated losing them, and in fact
I did lose most of them. However, after the argument ended,
I kept thinking – was it right, what I was saying? Was it crazy, what my dad had said? In many cases, I confirmed
that what my dad had said was indeed crazy, but in so many others I realized that my dad
was right in many points, just that halfway through the argument
it was hard to concede this because that would imply
questioning my deepest beliefs or, as it happened in a good
part of the cases, because it would hurt my personal pride. Let me tell you now two stories in which data helped me change my mind. The first has to do
with my doctoral thesis that I defended last March. Searching for a topic to research, I stumbled upon one that was a came up
frequently during arguments with my dad: Why are there rich and poor countries? This isn’t an original question,
in fact, it has decades of discussion in the social sciences. But I was interested in seeing if
I could contribute something of my own. My original idea was that
there was a direct relationship between industrialization
and economic development. So every country that
specialized in industry, i.e. any country that exported
industrialized products, would be necessarily a developed country. And conversely, my idea was that every
country specialized in natural resources, i.e. that exported commodities, would be necessary an underdeveloped one. Well, I started looking for data
and found lots of it. And I turned it into this data. Look, up there you’ll see countries
that are specialized in industry, i.e. that export industrial products,
and down there you’ll see countries specialized
in natural resources. Let’s see what happens
with developed countries. I didn’t put them all
so as to simplify things. See, most of them are
in the upper part, that is, they export industrial products. Which countries are up there? The United States, Japan,
Germany, Korea, France. However, there are three countries
that are highly developed and that they are in the
lower part of the graphic, and they mainly export natural resources. Which ones are they? Australia, Norway and New Zealand. Let’s see what happens
with the developing ones. In some way, the opposite happens. The majority of developing countries is in the lower part of the graphic, they’re specialized in natural resources. There we have Argentina,
South American countries, African countries, or Middle
Eastern ones, for example. However, there’s a bunch
of developing countries that are in the upper part
of the graphic, that export industrial products, such as Mexico, Thailand,
Philippines, or China. So, by using data, my original idea had to be at the very least nuanced. Now, could it be that there was
no better common denominator for economic development? Well, I kept playing with data
and found what I call the technological capacity index, which is calculated using the spending
in research and development as a percentage of GDP and the patents per capita
that each country has, apparently that correlates a bit better
with economic development rather with whether they export
natural resources or industry. Now we have a horizontal axis,
so countries in the right have high technological capacities and countries in the left have low technological capacities. Developed countries have
high technological capacities, the other way round happens
with the developing countries, regardless of them being in the
lower or upper part of the graphic. Well, this is a picture of today. What if we adopt a perspective
of the last 50 years, during the 60s? My idea at the beginning said there was
only one way to go towards development, that was to go from below to the left, that is, from the Southeast quadrant
to the upper right one, that is, to the Northeastern one. However, data showed me there’s
a diversity of possible paths, for example, Norway in the 60s was in the center of the diagram, started going up, then
found oil in the 70s, went down, and then right. New Zealand and Australia historically were in the Southern corridor of
the graphic, they went to the right. Japan, the USA or Germany were already
in the Northeast quadrant in the 60s, they went
more towards the right. Mexico started in the Southwest
quadrant, moved up to the left, stayed there stuck. Korea did this amazing journey
starting from the Southwest quadrant, and now it’s on the northeast quadrant. China did – it’s doing something similar to what Korea did with a 20-year delay. Look, there’s one that – I wonder
what happened – drunk all the Malbec and stayed there wandering
like a drunkard, Who’s that? Argentina. (Applause) The second story where data helped me changed my mind has to do with my brother Pablo. Pablo is 13 years older than me
and he taught me how to read when I was in kindergarten. When I was a kid, Pablo was my favorite
brother, he was very affectionate while at the same time he embodied
all the coolness of a rock journalist. When I turned 13, Pablo took
a very big turn in his life. He became a dad, and became
a Hare Krishna. From one day to the other
I had a deeply religious brother that I saw as unrecognizable,
and with whom I could hardly communicate. I hated religion when I was a teenager, I didn’t understand the role
it played in people’s lives, and felt it had abducted
my brother Pablo. After a long while, I was interested
in seeing the relationship between economic development of countries
and the happiness of their inhabitants; my point was that the higher
the economic development of a country, the happier its people are. So, I started to look for new data and to read specialized literature, and I found that said relationship,
even if it existed and was important, was much less powerful
than I had imagined at the beginning. An example for that is Latin America, that despite the enormous
violence problems, setbacks, and inequalities,
it’s a region whose inhabitants declare themselves among
the happiest in the world. And besides cultural causes, a possible reason behind it
might be that in Latin America we have warm social bonds
in a context of relative exercise of fundamental liberties. There’s much consensus among
specialists in relative wellbeing that warm social bonds
have a positive impact in the happiness of people. And by warm social bonds
I mean family ties, friendship bonds, religious bonds, or the feeling of being part
of a bigger whole such as the nation,
a neighborhood community, a religious community,
a political party, or a union. Knowing this data made me
question strongly the prejudice I had against religion. Religion comes from the Latin “religare”
which means to tie, to bind, strongly. In a world where it seems we’re
exceedingly individualized over time, religions bring us together, creates social bonds, and in that sense,
it has a positive social impact in the subjective wellbeing of people. It’s not as if I’ve become religious,
not even close, in fact, I’m still agnostic
and I’ve thousands of criticisms on the moral precepts of religion,
but getting to know these data let me understand the social role
that religion plays, and most importantly,
what’s the role religion plays in my brother’s life and from there strongly change
my way of relating to him. I don’t know if this happens to you, but when data goes with what
we already thought beforehand, it feels comfortable and gratifying,
it’s like seeing “You see, I was right.” And if data challenges us, what happens? Well, sometimes
a defense instinct come up and says “that data that
challenges me is wrong, it’s fake, and my daily life proves it.” It’s clear there might be fake data
or with serious methodological problems, but concluding that without
analyzing the data in detail isolate us and prevents us
from learning anything. Something else that often happens
when we discuss with data is that we torture data
until they confess what we want them to confess. (Laughter) In other word, we sweep under the carpet
the parts of data we don’t like and we do highlight the parts
of the data we do like. My proposal is simple:
let’s try to be more honest with data and with ourselves. And for that,
I’d like to make a few points. For example, data isn’t reality, it’s a simplification that enables us
to understand some aspects of reality, it’s a sort of "useful lie", so as to say. And the path that goes
from reality to data is what’s called methodology,
it’s the fine print any data comes with. The same way food is cooked and has nutritional information,
data has its own too, and we need to be aware of that. Furthermore, sometimes data requires
of previous social agreements for it to be constructed,
in other words, we need to agree on that fine print behind data. For example, in the case of poverty. Poverty means being behind
a minimum wellbeing threshold. Now, what’s that minimum threshold? Is it a food basket of 2000 calories,
of 2200 calories? With what sort of foods? Does it have anything else
in addition to food? What else? Clothing, sewers, a cellphone, a car? Depending on the methodological
decision we take, we can have poverty numbers
that are radically different, and it’s essential
that we understand that. In fact, in discussions we frequently compare our personal situation
with some variable over time or with other countries, and that’s great and we have to do it, but sometimes we’re not aware we’re comparing
apples and oranges, in other words, data built with
different small prints. We have to be very sure
we’re comparing apples to apples. For example, if a country defines as short anyone who’s
shorter than 1.50 m, and another country defines as short
anyone who’s shorter than 1.60, if someone is 1.55 m they’ll be short
in one country, but not in the other. The same thing happens with poverty, where the minimum wellbeing threshold are very different between countries. All in all, as I said before, if data goes with what
we already thought beforehand, it’s comfortable, it’s gratifying. What if data challenges us
and upon looking at it once and again, it keeps laughing in our faces,
what do we do? We have to finish eating the “asado” steak, we have to do some research, take all the time we need, and then maybe conclude that losing an argument
is also winning it. Thank you very much. (Applause)
