World Cup = more tourism? Not for the BRICS

South Korean fans in Brazil cheering on their team at the 2014 World Cup. Credit: Korean Culture and Information Service via Flickr

Seems like hosting the football World Cup may not help tourism as much as governments think. An analysis of tourism data shows that the last two countries to host the World Cup didn’t see any impact on tourist spending after the tournament ended.

Incidentally, the last two countries to host the World Cup were South Africa in 2010 and Brazil in 2014, two of the BRICS (Brazil-Russia-India-China-South Africa) grouping of emerging economies, with a third, Russia, set to host it from this month.

Now at least 1.5 million people from around the world are expected to visit Russia during the World Cup, according to projections from Russian government officials. And economic activity around these football fans is expected to add $3 billion to the Russian economy, according to an analyst quoted in The Moscow Times.

The thinking is that with the World Cup boosting the profile of Russia, more people will be willing to make the country their next holiday destination in the years to come.

So could tourism revenues in Russia improve in the long run? Once the football is over and the hooplah has died down, will hosting the World Cup generate additional tourist spending in the future?

Not really, if the last two World Cups are anything to go by. We made use of data from the World Bank on tourism receipts, which is the amount spent by foreign tourists visiting a host country. We then compared South Africa, the host in 2010 and Brazil, the host in 2014, to hypothetical versions of the countries where they hadn't hosted the World Cup.

We found that South Africa and Brazil didn’t receive more tourist spending than their hypothetical counterparts in the years following the World Cup.


According to this analysis, in 2013, three years after the 2010 World Cup, South Africa earned around a billion dollars less than the hypothetical version that hadn’t hosted the World Cup. South Africa earned $10.5 billion from foreign tourists in 2013 while the hypothetical version, which we’ve called ‘Synthetic South Africa’ in the graphic, earned around $11.5 billion.

Similiarly, Brazil in 2015, the year following the World Cup, earned $6.2 billion from foreign tourists, while the hypothetical Brazil earned $7.1 billion that year (graphic below).


The actual amount by which South Africa and Brazil did worse than their hypothetical counterparts doesn't matter here. The point to note is that they did not do better.

(As for what these hypothetical countries are, why we need them, how we constructed them and other technical details, check the section after this story.)

Now if the World Cup actually had an impact on tourist spending, the graphics above would have looked a lot different. The line for the host, ie. the one showing the actual tourist spending would have been lying far above the line for the hypothetical versions.

And that is, in fact, the case for the graphics below. We conducted a similar exercise for the hosts of the three earlier World Cups, ie. France in 1998, Japan in 2002 (co-hosts with South Korea) and Germany in 2006, and found that the hosts performed much better than their hypothetical counterparts.


So what is going on here? Why have France, Japan and Germany experienced the benefits of hosting the World Cup while South Africa and Brazil haven’t?

Why Brazil and South Africa haven’t done well

The fall of tourist spending in Brazil can be partly explained by the outbreak of the Zika virus in 2015 but there seem to be larger, long-standing factors at play here too. Some idea of what these factors are can be found in the Travel & Tourism Competitiveness Report from the World Economic Forum.

Published every two years, it ranks over 130 countries on various indicators relevant to tourism such as safety, expenses, transport etc.

We took a look at how the five previous hosts and Russia, the current host, did in the latest edition of the report from 2017. With this, we can get an understanding of how Brazil and South Africa are different from France, Japan and Germany.

We found that South Africa and Brazil did poorest in three categories: 1) Safety and Security, 2) Prioritisation of Travel and Tourism and 3) International Openness (see table below).

In the ‘Safety and Security’ category, the report comes up with a rank after looking at various factors such as the incidence of homicide, terrorism and reliability of police services. South Africa has a homicide rate of 33 homicides in every 100,000 people, one of the worst in the world, while Brazil has a rate of 24 homicides per 100,000 people, according to the report.

In the category of ‘International Openness’, the report looks at issues such as how extensive the visa requirements are for tourists coming from abroad and the air connectivity between countries. An example of why Brazil did poorly can be seen in how it has treated Chinese citizens. China is the world’s largest source of tourists, but Brazil only last year extended the validity of its tourist visas for Chinese citizens from three months to five years. It's now easier for Chinese to make multiple visits to Brazil.

When it comes to the category ‘Prioritisation of Travel & Tourism’, the report looks at what percentage of the national budget is allocated for travel and tourism, the effectiveness of marketing campaigns and the country’s brand strategy, among other things. According to the report, Brazil only alloted around 3% of its budget to travel and tourism prior to 2017.

The thing to note is that Russia, the current World Cup hosts, does badly in many of the same categories that Brazil and South Africa do poorly in. Take the category of ‘International Openness’ for example.

There is a particular kind of agreement that countries sign called bilateral Air Service Agreement that determines what destinations another country's airlines can fly to, the frequency of those flights etc. In a way, it determines the quality of air connectivity between countries. These agreements with Russia have typically been very limited, with the tourism report ranking Russia 101st out of 136 when it comes to the openness of air service agreements.

Other factors that possibly held back people from visiting South Africa and Brazil, such as red tape when it comes to visas and a high incidence of crime, exist in Russia too. In a way, these emerging economies are absurdly both highly legalised and lawless at the same time. ¯\_(ツ)_/¯

So does all this mean that, like Brazil and South Africa, Russia is unlikely to maintain the surge in tourist spending after the World Cup? We’ll just have to wait and see. ◾

(The story actually ends above. Read on further only if you want to know how the analysis was done.)


So how were the hypothetical countries in the story created? They were constructed using a method called ‘synthetic control’. This is a method is typically used by academics and analysts for ‘impact evaluation’, ie. assessing whether a government policy or program has had any effect or not.

Now to figure out if hosting the World Cup leads to an increase or decrease in tourist spending, we need some kind of a counterfactual. Meaning, a way to let us know what would happen to the tourism figures of a country if it hadn’t hosted the World Cup.

Synthetic control helps us in constructing this counterfactual case, something that we can compare the actual figures to and make an assessment of how well a country has done.

We do this by constructing a synthetic country whose tourist spending figures are similar to that of the World Cup host country. This synthetic country is a weighted combination of countries similar to the host country.

To construct this synthetic case, we’ve chosen neighbouring countries whose citizens are, on average, as wealthy as the average person in the host country, ie. their per capita income levels aren’t that far apart.

(Typically, they’re in the same income group of countries as classified by the World Bank. Because South Africa just had two neighbours in the same income group, we relaxed the rule in this case to give us more countries to use. The reasoning was that the other countries would be similar enough to South Africa in several other respects to make up for not being in the same income group.)

This table below shows us the various countries that have been used to form the synthetic case for the past five world cups in this analysis:

For example, for Brazil, we constructed a ‘synthetic Brazil’ whose tourist spending figures are similar to that of the actual Brazil for three years prior to the World Cup in 2014, ie. 2011, 2012 and 2013. These three years represent a period of normalcy for the host country, a period before the World Cup has had an effect on tourist spending figures.

In this analysis, Synthetic Brazil is combination of five countries viz. Ecuador, Colombia, Paraguay, Venezuela and Peru. We would have chosen Argentina too if it hadn’t hosted the South American international football tournament, the Copa America in 2011. Because it hosted that tournament, its tourist spending figures for that year would have been higher than usual and skewed the figures for Synthetic Brazil.

(In fact, while selecting the countries used for constructing the synthetic case, it’s important to select countries that haven’t been through a ‘shock’ such as hosting a major tournament such as the Olympics or World Cup.)

So we take the tourist spending figures for the five South American countries for 2011, 2012 and 2013, find weightages/multipliers that we can apply to each of the countries, so that when the figures are added up, we get something close to the figures of actual Brazil.

We arrive at the weightages/multipliers to be used for each country by doing something called ‘constrained optimisation’ .

We then apply these weightages/multipliers to the tourist spending figures for these five countries for the years 2014 to 2016 and so arrive at the figures of Synthetic Brazil. By doing this, we get an idea of what the counterfactual would be, what the figures for Brazil would have looked like for all those years if it hadn’t hosted the World Cup.


The data for the story was taken from figures collated by the World Bank.

The data on tourism competitiveness of countries was taken from the World Travel & Tourism Competitiveness Report 2017 published by the World Economic Forum.

Got the idea for using the Synthetic Control method from this paper by Jorge Viana.

For an overview of synthetic control without much mathematical notation, check this paper from the British Medical Journal .

If you’re comfortable with mathematical notation and have some understanding of statistics or econometrics, I guess you could go for this overview of the method in the American Journal of Political Science.

For a summary of impact evaluation methods in general, this Harvard Business School (HBS) working paper is a good one.

The particular method I use here is a different version of synthetic control than is normally used.

The method I used was inspired by this paper by Guido Imbens (who seems to be an expert in the broader field of causal inference, he’s even written a textbook on it). Now this following sentence will only make sense to you if you’re familiar with the synthetic control method, but Imbens in his paper only uses outcome data and doesn’t use any covariates or predictor variables.

I don’t exactly use the method proposed by Imbens, the method I use is a simpler variant with -- bear with me as I get technical for a moment -- a non-negativity constraint on the coefficients for the outcome variables, while the intercept is allowed to be either positive or negative.

By allowing only for positive coefficients, this is closer to the 'traditional' synthetic control method. In a way, what I’m doing kind of lies between the Difference-in-Differences method (see the HBS paper for what that is) and the traditional synthetic control method.

To arrive at the weightages I used the nnls package in R. The code I used is available here (with the results commented out). Nnls stands for non-negative least squares. The package is usually used to do something called ‘constrained regression’ but I’ve used it here to do ‘constrained optimisation’.

There’s a bit of python code I used to create CSVs for the graphics, it's available in a jupyter notebook here. Could have done it in R, but it’s just easier for me to code in python.

If you want to do synthetic control the traditional way, academics usually use the ‘Synth’ package in R. If you want some sample R code to start you off, this blog post is useful.

There’s already been a lot, and I mean a lot, of work done on the impact of mega sporting events such as the World Cup and Olympics. I made a good-faith effort to see if someone had done anything similiar to this article which is a) based on synthetic control methods and b) which looks at tourism figures. Searched all the major journal databases, didn’t find any, so hopefully this article represents original work in some sense.

Now statistics can get very advanced and is very easy to get wrong. If you’re an econometrician, statistician or data scientist and think I should have done something differently in this analysis, do let me know! You can contact me on twitter at @shijith and by email at