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Which Nation Is the Best Lover? Ask Nanaya.

Para la versión en español por favor haga clic aquí.

Let’s get straight to the chase.

Most Romantic Nations

International Net Romance Scores.

Higher score, better lovers?

Ok, well it’s awfully hard to date a whole country but we can describe the populations of a country. So how can we say which country is “the best lover” or simply “most romantic?”

We took our database of global personalities, picked out the countries with the most data, and weighted traits that impact romance to come up with different romance scores.

We came up with three types of romantic scores and found out:

  • Mediterranean countries have the most romantic populations and Latin-American countries have the least. English speaking nations are in the middle.
  • Love might be a universal language, but national language is less important than geographic proximity and shared culture.
  • The more romantic a country is, the more divorces there are for each marriage.
  • The more romantic a country is the higher unemployment is – but Nanaya needs more data to confirm this relationship.

How Did We Do This?

Nanaya will be a service that can forecast your love and social life – but running the Nanaya algorithm needs a big database of personalities. Since mid-January, Nanaya has hosted personality tests to build that database. Unlike many personality tests online, this test was built on a foundation of psychology producing scientific, repeatable results.

In the past few weeks, we’ve had well over 15,000 users around the world take the main personality test. If you haven’t taken it, do so here.

We can take those numerical personality test results to determine what makes a good lover, whether it’s for a hot fling or a long-term, stable relationship:

  • Hot Fling Score. Hot flings are all about adventure and exploration, of the world and each other. A part of this is being social and charismatic. Without these traits, no one makes the first move. Thoughtfulness is important, that way you can read each other’s emotions and respond accordingly, but it trails the others in value.
  • Stable Relationship Score. Stable relationships are a different matter. Things like reliability and thoughtfulness begin to matter more to make a relationship last. To be clear, this is more of a descriptive term than something that’s been correlated.
  • Net Romance Score. The right relationship is a mix of a hot fling and stable relationship. I add and normalized the scores to come up with the ranking at the top.

No doubt what makes for a hot fling and a stable relationship are related – but they’re not entirely the same. I designed “hot fling” and “stable relationship” scores by weighting various personality traits differently. We then calculated these scores for the distributions of personalities in the countries we had the most data on. There’s a lot more countries in our database than those in this study, but there’s not enough for “significant” results.


Best Nations for Romance

Below is a bubble chart that shows the Hot Fling and Stable Relationship Scores for these countries.

Romance Scores Grouped by Language Spoken

Bubblechart of Romance Scores with grouping by language. Red is Spanish, green is Portuguese, blue is English, and grey is Greek. The size of the circle denotes scaled standard deviation of the national personality distribution.
Bubblechart of Romance Scores with grouping by language. Red is Spanish, green is Portuguese, blue is English, and grey is Greek. The size of the circle denotes scaled standard deviation of the national personality distribution.

We expect these scores to be somewhat related. The fact they’re on a line means they’re highly correlated which makes perfect sense based on how we designed the scores. That said, they’re not totally the same. The differences emerge if we rank them separately.

Ranking of Hot Fling & Stable Relationship Scores

Ranking of studied countries for Hot Fling and Stable Relationship.
Ranking of studied countries for Hot Fling and Stable Relationship.

We’re sorry to report this, Argentina. If it helps, this is only a number and love is not a number.

However, our first guess that language was a uniting cultural feature was wrong. Let’s try a different way of grouping. Note, scores don’t change!

Romance Scores by Cultural Grouping

Bubblechart of Romance Scores with grouping by “culture.” Red is Latin American, Blue is English speaking/Protestant, and grey is Mediterranean (yes, we know Portugal is on the other side of Gibraltar). The size of the circle denotes is again based on standard deviation of national personality distribution.
Bubblechart of Romance Scores with grouping by “culture.” Red is Latin American, Blue is English speaking/Protestant, and grey is Mediterranean (yes, we know Portugal is on the other side of Gibraltar). The size of the circle denotes is again based on standard deviation of national personality distribution.

This grouping methodologies gives us much a more sensible, tighter distribution.

Now is there something unique about Mediterranean countries that leads to better lovers? Greece is not a Catholic country whereas Spain and Portugal predominantly are. One uniting feature is that all have relatively high unemployment.

Well there’s a thought, do unemployment and romantic score have a relation?  We’ll just pick Hot Fling Score as there is a fundamental correlation between with Stable Relationship & Net Romance Scores.

Hot Fling Score and Unemployment

Is this a geometric correlation!? Maybe we just need more data. Notice US & UK are almost entirely overlapping.
Is this a geometric correlation!? Maybe we just need more data. Notice US & UK are almost entirely overlapping.

Yikes! So the more amorous and passionate a nation, the less likely they’ll be at work or to have the stable institutions in place? There’s a lot of history, recent events, and other metrics that require delving into to prove this. I’m skeptical but it is interesting.

Well if there is a correlation, it’s not a linear one! Maybe we’ll revisit this when we have more data. For the curious, we got our unemployment data based on January 2015 values from here.


What about Gender?

So for a given country and trait, what do these differences look like across men and women?

Typically, men and women will have very similar distributions for a given trait in a specific country. We consider the “thoughtfulness” of Greeks below.

A histogram of the trait of Thoughtfulness of Greeks. Here we see the Greeks are very thoughtful, with a distribution skewed toward 100, which is the most Thoughtful score. Note that men and women are nearly identical.
A histogram of the trait of Thoughtfulness of Greeks. Here we see the Greeks are very thoughtful, with a distribution skewed toward 100, which is the most Thoughtful score. Note that men and women are nearly identical.

But this can be contrasted by a few cases which skew results across men and women, such as in the two different romance scores. Here, we look at the charisma of Brazilians.

A histogram of the trait of Charisma of Brazilians. Here we see that Brazilian women test as more charismatic than their male counterparts.
A histogram of the trait of Charisma of Brazilians. Here we see that Brazilian women test as more charismatic than their male counterparts.

This is just a brief overview of the difference gender makes. Stay tuned to the Nanaya Blog for future posts discussing the impact and role of gender in psychology and sociology!


Romantic Score and Romantic Success

Now all of this is for entertainment if we don’t see an actual correlation between romantic score and reality. But what’s a good reality check?

We need a publicly available indicator that tells us that people are getting into long-term relationships while tracking how fast people are leaving them. My guess is that more romantic countries are, the more they’ll be able to sustain relationships with fewer divorces for every marriage. Conveniently, there’s a name for that indicator: ratio of divorce rate to marriage rate (data from here). Unfortunately, there’s no publicly available divorce rate from Argentina, but otherwise we have the below.

Hot Fling Score Vs Divorce-to-Marriage Rate Ratio

Hot Fling Score vs. Divorce-to-Marriage Rate Ratio.  R2 is 0.61, P=0.0368, T=2.83
Hot Fling Score vs. Divorce-to-Marriage Rate Ratio. R2 is 0.61, P=0.0368, T=2.83

Hmm…Greece here is an outlier. Why? There could be a lot of reasons, maybe to be covered in another blog post down the line. That said, looking at our P-test value we do see some linear correlation. If we had a good reason to take Greece out of the picture, like some unique cultural feature invisible in this level of data (e.g.  Orthodox Christianity), we would have the following:

Hot Fling Score Vs Divorce-to-Marriage Rates Ratio (no Greece)

Hot Fling Score vs. Divorce-to-Marriage Rate Ratio with Greece removed.  R2 is 0.99, P<0.00009, T=20.52
Hot Fling Score vs. Divorce-to-Marriage Rate Ratio with Greece removed. R2 is 0.99, P<0.00009, T=20.52

So it turns out my guess was wrong – completely wrong but in the best way possible. More national data would be great, but we simply had no idea that there’d be this strong of a linear correlation.

Even with Greece, while romance score is a predictor for long-term success, the more romantic the nation the more divorces there will be. More romance and more emotional exchanges may allow for more room for cracks in the relationship to grow and romantic opportunities beyond the relationship to open up. This even applies in Catholic countries!

We only have a few countries of statistically significant size, but hopefully we can redo this study later to see how this indicator may change. Even with Greece, our P-Test value still indicates correlation.


 Odds & Ends

There’s a sample bias going on across all countries that’s pretty uniform: they were interested enough to take a personality test on Nanaya. This crowd is a bit younger and more urban than the whole national population. Maybe a later post will discuss how we can statistically control Nanaya studies.

Don’t like what you see? Want to see your country included? Take the personality test and make data big. The more people take the personality test, the sooner Nanaya will be available.

If you want to complain, send invectives and curses to info@nanaya.co, we’ll still love you. Through the power of the internet, that very same email also works for positive feedback and questions.

Hot Fling, Stable Relationship, and Net Romantic Scores are synthetic metrics and aren’t a part of a Nanaya algorithm.

Data was pulled from the Nanaya dataset as of January 31, 2015 at around 15,200 users. It’s grown quite a bit since.

Bubble charts generously plotted by Jackie Wisniewski. Histograms & analysis were done in Mathematica.

What Are the Odds of Finding Love?

As I started to develop the algorithm behind Nanaya I wanted to make sure I wasn’t reinventing the wheel. I found all sorts of personal stories about people doing their own calculations of whether or not they’ll find love – but only one actually described what he did. Dr. Peter Backus, then a graduate student at University of Warwick studying economics, wrote a joke paper “Why Don’t I have a Girlfriend.” He used an equation near and dear to my heart to figure out the chances of finding love in the UK: the Drake Equation.

The Drake Equation has been around for a bit. It gives us an estimate of the chances that there are alien races populating the galaxy. In Backus’s version, he’s figuring out the chances there’s an ideal match he’ll run into in the UK. Maybe it’s another way of saying women really are from Venus?

So he has an equation of all these factors multiplied together which gives him the probability of finding a match. His factors are:

  • The rate of formation of people in the UK (i.e. population growth).
  • The fraction of people in the UK who are women
  • The fraction of women in the UK who live in London.
  • The fraction of the women in London who are age appropriate.
  • The fraction of age appropriate women in London with a university education.
  • The fraction of university educated, age appropriate women in London who I find physically attractive.
  • The length of time in years that I have been alive thus making an encounter with a potential girlfriend possible.

With some estimates and a few additional factors he comes to about 1/285,000. Is this reasonable?

The answer is no. Those few additional factors he assumes are “1 in 20 of the women find me attractive, half are single and I get along with 1 in 10.” Well that’s a big deal! In fact, that multiplier is bigger than any of the factors in his equation!

A part of what’s going on is that he assumes there’s linear independence involved in all of those answers. That is to say, there’s a relationship between “university educated women” and “women who find him attractive” so you can’t look at the odds of each one independently. For instance, women who are university educated, just by being in the same culture, will be more attractive to him and vice versa. By treating all these factors independently poor Peter is really making his odds worse for himself.

We can all do what Peter did for ourselves pretty quickly but it takes a lot of work to get the rest of the way. Figuring out what traits and cultural values are independent of others is the major problem. If you want to take it to the next level, the problem becomes how much that answer changes in time. The Nanaya algorithm solves both of these problems – no one else has done that.

Even if the chances seem bleak, one shouldn’t despair! Something like the birthday paradox comes around to help people. I’ll be writing on that later. Even at Peter’s self-reported odds of 1/285,000 he still found a wife. Congrats, Peter!

Why Am I Building Nanaya?

Whether it’s having a morning coffee, running to catch the bus, or getting to work – I always ask myself “Why am I doing this?” Even if I know why and even if there’s an answer, should I be satisfied? Am I living up to my own ideals? How do I even know if I’m doing the right thing?

When the idea for Nanaya came up it was as a curiosity. I had a theory for romantic decision analysis and I wanted to know if it would work. I had always used my skills to assess science and engineering – could I apply it to sociology?

When I first built the prototype, it actually seemed to work! But we all make mistakes so I asked colleagues and friends to review the Nanaya algorithm. Did I miss something big? Was my treatment of statistics right? Was anything wrong? Review after review, people were impressed. Everything seemed on target. Having a working, consistent algorithm and plenty of friends who were curious the question soon became “Do I really want to make this for everyone?”

I do have a philosophical issue with putting numbers to emotions. Love, fear, faith, passion, and all the feelings that make us human are so hard to express to other people let alone to put into numbers. I consider a problem in many relationships: what one person calls “love” and how they act it out might be different from their partner’s understanding of love. If two people in love, sharing the most intimate of moments, can’t share the meaning of a bond that brings them together – how can we even put numbers to it? The answer to this question will be the topic of future posts.

With this in mind, I want to be clear about intentions for Nanaya.

I wrote on the main page that Nanaya is not being developed to make a decision for the user but to be the beginning of a process. This is how we use similar tools in NASA, at the very beginning of designing a space mission. Whether you are single or in a relationship, Nanaya should be the springboard from which life decisions are contemplated, such as convincing yourself to be more social or where to move. Especially in a relationship, Nanaya should spur frank conversations that were being avoided or to affirm couples they are on the right track.  No matter what the output is, it is there to inform and affirm – not to make decisions for you.

I made Nanaya to encourage honesty and to bring people together.  While there are numbers that come out of Nanaya, I truly believe one of its greatest values is the very act of using it. Just filling out the information that the Nanaya algorithm needs forces you to put your romantic and life goals in perspective. Looking at your possible futures is like staring into a mirror – it forces self-awareness.

It is my deepest of hopes that it is used to bring honesty, self-awareness, and happiness to others. This is why I decided to build Nanaya for everyone.