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)
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