Is there an East-West divide in the European Union?

From the way the media reports on European Union negotiations, it is easy to get the impression that there is a rift between East and West European member states, and that the enlargement of the EU  has compromised the EU’s decision-making capacity. In a text published at the EUROPP blog, I argue that there is no systematic evidence to support such claims:

“…in fact, against all odds, since 2004 the EU has managed to accommodate and integrate without much turbulence 13 new member states within its decision-making structures. This success is most remarkable and provides an important lesson for the future; a lesson that should not be overshadowed by the forthcoming exit of the United Kingdom from the EU.”

Read the whole thing; it has pretty pictures, too.

Is interpretation descriptive or explanatory?

One defining feature of interpretivist approaches to social science is the idea that the goal of analysis is to provide interpretations of social reality rather than law-based explanations. But of course nobody these days believes in law-based causality in the social world anyways, so the question whether interpretation is to be understood as purely descriptive or as explanatory remains. Here is what I wrote about this issue for an introductory chapter on research design in political science. The paragraph, however, will need to be removed from the text to make the chapter shorter, so I post it here instead. I will be glad to see opinions from scholars who actually work with interpretivist methodologies:

It is difficult to position interpretation (in the narrow sense of the type of work interpretivist political scientists engage in) between description and explanation. Clifford Geertz notes that (ethnographic) description is interpretive (Geertz 1973: 20), but that still leaves the question whether all interpretation is descriptive open. Bevir and Rhodes (2016) insist that intepretivists reject a ‘scientific concept of causation’, but suggest that we can explain actions as products of subjective reasons, meanings, and beliefs. In addition, intentionalist explanations are to be supported by ‘narrative explanations’. In my view, however, a ‘narrative’ that ‘explains’ by relating actions to beliefs situated in a historical context is conceptually and observationally indistinguishable from a ‘thick description’, and better regarded as such.

Olympic medals, economic power and population size

The 2016 Rio Olympic games being officially over, we can obsess as much as we like with the final medal table, without the distraction of having to actually watch any sports. One of the basic questions to ponder about the medal table is to what extent Olympic glory is determined by the wealth, economic power and population size of the countries.

Many news outlets quickly calculated the ratios of the 2016 medal count with economic power and population size per country and presented the rankings of ‘medals won per billion of GDP’ and ‘medals won per million of population’ (for example here and here). But while these rankings are fun, they give us little idea about the relationships between economic power and population size, on the one hand, and Olympic success, on the other. Obviously, there are no deterministic links, but there could still be systematic relationships. So let’s see.


I pulled from the Internet the total number of medals won at the 2016 Olympic games and assigned each country a score in the following way: each country got 5 points for a gold medal, 3 points for silver, and 1 point for bronze. (Different transformations of medals into points are of course possible.) To measure wealth and economic power, I got the GDP (at purchasing power parity) estimates for 2015 provided by the International Monetary Fund, complemented by data from the CIA Factbook (both sets of numbers available here). For population size, I used the Wikipedia list available here.

Olympic medals and economic power

The plot below shows how the total medal points (Y-axis) vary with GDP (X-axis). Each country is represented by a dot (ok, by a snowflake), and some countries are labeled. Clearly, and not very surprisingly, countries with higher GDP have won more medals in Rio. What is surprising however, is that the relationship is not too far from linear: the red line added to the plot is the OLS regression line, and it turns out that this line summarizes the relationship as well (or as badly) as other, more flexible alternatives (like the loess line shown on the plot in grey). The estimated linear positive relationship implies that, on average, each 1,000 billion of GDP bring about 16 more medal points (so ~315 billion earns you another gold medal).olymp1

The other thing to note from the plot is that the relationship is between medal points and total GDP, thus not GDP per capita. In fact, GDP per capita, which measures the relative wealth of a country, has a much weaker relationship with Olympic success with a number of very wealthy, and mostly very small, countries getting zero medals. The correlation of Olympic medal points with GDP is 0.80, while with GDP per capita is 0.21. So it is absolute and not relative wealth that matters more for Olympic glory. This would seem to make sense as it is not money but people who compete at the games, and you need a large pool of contenders to have a chance. But let’s examine more closely whether and how does population size matter.

Olympic medals and population size

The following plot shows how the number of 2016 Rio medal points earned by each country varies with population size. Overall, the relationship is positive, but it is not quite linear, and it is not very consistent (the correlation is 0.40). Some very populous countries, like India, Indonesia, and Pakistan have won very few medals, and some very small ones have won at least one. The implied effect of population size is also small in substantive terms: each 10 million people are associated with 1 more medal point (so, a bronze); for reference three quarters of the countries in the dataset have less than 25 million inhabitants.


Putting everything together

Now, we can put both GDP and population size in the same statistical model with the aim of summarizing the observed distribution of medal points as best as we can. In addition to these two predictors, we can add an interaction between the two, as well as different non-linear transformations of the individual predictors. In fact, the possibilities for modeling are quite a few even with only two predictors, so we have to pick a standard for selecting the best model. As the goal is to describe the distribution of medal points, it makes sense to use the sum of the errors (the absolute values of the differences between the actual and predicted medal score for each country) that the models make as a benchmark.

I find that two models describe the data almost equally well. Both use simple OLS linear regression. The first one features population size, GDP, and GDP squared. In this multivariate model, population size turns out to have a negative relationship with Olympic success, net of economic power. GDP has a positive relationship, but the quadratic term implies that the effect is not truly linear but declines in magnitude with higher values of GDP. The substantive interpretation of this model is something along these lines: Olympic success increases at a slightly declining rate with the economic power of a country, but given a certain level of economic power, less populous countries do better. The sum of errors of Model 1 is 1691 medal points.

The second model is similar, but instead of the squared term for GDP it features an interaction between GDP and population size. The interaction turns out to be negative. This implies that economically powerful but populous countries do less well than their level of GDP alone would suggest. This interpretation is a bit strange as population size is positively associated with GDP and seems to suggest that it is relative wealth (GDP per capita) that matters, but this turns out not to be the case, as any model that features GDP per capita has a bigger sum of errors than either Model 1 or Model 2.

Model 1 Model 2
Population size – 0.20 – 0.09
GDP + 0.04 + 0.03
GDP squared – 0.00000008 /
GDP*Population / -0.0000008
Sum of errors 1691 1678
Adjusted R-squared 0.83 0.81

Both models presented so far are linear which is not entirely appropriate given that the outcome variable – medal points – is constrained to be non-negative and is not normally distributed. The models actually predict that some countries, like Kenya, should get a negative number of medal points, which is clearly impossible. To remedy that, we can use statistical models specifically developed for non-negative (count) data: Poisson, negative binomial, or even hurdle or zero-inflated models that can account for the excess number of countries with no medal points at all. I spend a good deal of time experimenting with these models, but I didn’t find any that improved at all on the simple linear models described above (it is actually quite hard even evaluating the performance of these non-linear models). Let me know if you find a different model that does better than the ones reported here. (But please no geographical dummies or past Olympic performance measures; also, the Olympic delegation size would be a mediator so not a proper predictor).

The one model I can find that outperforms the simple OLS regressions is a generalized additive model (GAM) with a flexible form for the interaction. This model has a sum of errors of 1485, and the interaction surface looks like this:interactionGDPpop

In conclusion, do the population size, economic power and wealth of countries account for their success at the 2016 Olympic games? Yes, to a large extent. It is economic power and not relative wealth that matters more, and population size actually has a negative effect once economic power is taken into account. So the relationships are rather complex and, to remind, far from deterministic.


Here is the data (text file): olypm. Let me know if you interested in the R script for the analysis, and I will post it.
Finally, here is a ranking of the countries by the size of the model error (based on Model 2; negative predictions have been replaced with zero). This can be interpreted in the following way: the best way to summarize the distribution of medal points won at the 2016 Rio Olympic games as a function of population size and GDP is the model described above. This model implies a prediction for each country. The ones that outperform their model predictions have achieved more than their level of GDP and economic size imply. The ones with negative errors underperform in the sense that they have achieved less than their level of GDP and economic size imply.

country 2016 medals 2016 medal points predicted medal points model error
Great Britain 67 221 68 153
Russia 56 168 87 81
Australia 29 83 30 53
France 42 118 68 50
Kenya 13 49 0 49
New Zealand 18 52 4 48
Hungary 15 53 6 47
Netherlands 19 65 22 43
Jamaica 11 41 0 41
Croatia 10 36 2 34
Cuba 11 35 2 33
Azerbaijan 18 36 4 32
Germany 42 130 98 32
Uzbekistan 13 33 2 31
Italy 28 84 54 30
Kazakhstan 17 39 10 29
Denmark 15 35 7 28
Ukraine 11 29 5 24
Serbia 8 24 2 22
North Korea 7 21 0 21
Sweden 11 31 12 19
Belarus 9 21 4 17
Ethiopia 8 16 0 16
Georgia 7 17 1 16
South Korea 21 63 47 16
China 70 210 195 15
South Africa 10 30 15 15
Armenia 4 14 0 14
Greece 6 20 7 13
Slovakia 4 16 4 12
Spain 17 53 41 12
Colombia 8 24 14 10
Czech Republic 10 18 8 10
Slovenia 4 12 2 10
Switzerland 7 23 13 10
Bahamas 2 6 0 6
Bahrain 2 8 2 6
Ivory Coast 2 6 0 6
Belgium 6 18 13 5
Fiji 1 5 0 5
Kosovo 1 5 0 5
Tajikistan 1 5 0 5
Lithuania 4 6 2 4
Burundi 1 3 0 3
Grenada 1 3 0 3
Jordan 1 5 2 3
Mongolia 2 4 1 3
Niger 1 3 0 3
Puerto Rico 1 5 2 3
Bulgaria 3 5 3 2
Canada 22 44 43 1
Moldova 1 1 0 1
Romania 5 11 10 1
Vietnam 2 8 7 1
Afghanistan 0 0 0 0
American Samoa 0 0 0 0
Andorra 0 0 0 0
Antigua and Barbuda 0 0 0 0
Aruba 0 0 0 0
Barbados 0 0 0 0
Belize 0 0 0 0
Benin 0 0 0 0
Bermuda 0 0 0 0
Bhutan 0 0 0 0
British Virgin Islands 0 0 0 0
Burkina Faso 0 0 0 0
Cambodia 0 0 0 0
Cameroon 0 0 0 0
Cape Verde 0 0 0 0
Cayman slands 0 0 0 0
Central African Republic 0 0 0 0
Chad 0 0 0 0
Comoros 0 0 0 0
Congo 0 0 0 0
Cook Islands 0 0 0 0
Djibouti 0 0 0 0
Dominica 0 0 0 0
DR Congo 0 0 0 0
Eritrea 0 0 0 0
Estonia 1 1 1 0
Gambia 0 0 0 0
Guam 0 0 0 0
Guinea 0 0 0 0
Guinea-Bissau 0 0 0 0
Guyana 0 0 0 0
Haiti 0 0 0 0
Honduras 0 0 0 0
Iceland 0 0 0 0
Kiribati 0 0 0 0
Kyrgyzstan 0 0 0 0
Laos 0 0 0 0
Lesotho 0 0 0 0
Liberia 0 0 0 0
Liechtenstein 0 0 0 0
Madagascar 0 0 0 0
Malawi 0 0 0 0
Maldives 0 0 0 0
Mali 0 0 0 0
Malta 0 0 0 0
Marshall Islands 0 0 0 0
Mauritania 0 0 0 0
Micronesia 0 0 0 0
Monaco 0 0 0 0
Montenegro 0 0 0 0
Mozambique 0 0 0 0
Nauru 0 0 0 0
Nepal 0 0 0 0
Nicaragua 0 0 0 0
Palau 0 0 0 0
Palestine 0 0 0 0
Papua New Guinea 0 0 0 0
Poland 11 25 25 0
Rwanda 0 0 0 0
Saint Kitts and Nevis 0 0 0 0
Saint Lucia 0 0 0 0
Samoa 0 0 0 0
San Marino 0 0 0 0
Sao Tome and Principe 0 0 0 0
Senegal 0 0 0 0
Seychelles 0 0 0 0
Sierra Leone 0 0 0 0
Solomon Islands 0 0 0 0
Somalia 0 0 0 0
South Sudan 0 0 0 0
St Vincent and the Grenadines 0 0 0 0
Suriname 0 0 0 0
Swaziland 0 0 0 0
Tanzania 0 0 0 0
Timor-Leste 0 0 0 0
Togo 0 0 0 0
Tonga 0 0 0 0
Trinidad and Tobago 1 1 1 0
Tunisia 3 3 3 0
Tuvalu 0 0 0 0
Uganda 0 0 0 0
US Virgin Islands 0 0 0 0
Vanuatu 0 0 0 0
Yemen 0 0 0 0
Zambia 0 0 0 0
Zimbabwe 0 0 0 0
Albania 0 0 1 -1
Bangladesh 0 0 1 -1
Bolivia 0 0 1 -1
Bosnia and Herzegovina 0 0 1 -1
Botswana 0 0 1 -1
Brunei 0 0 1 -1
Cyprus 0 0 1 -1
El Salvador 0 0 1 -1
Equatorial Guinea 0 0 1 -1
FYR Macedonia 0 0 1 -1
Gabon 0 0 1 -1
Ghana 0 0 1 -1
Ireland 2 6 7 -1
Latvia 0 0 1 -1
Mauritius 0 0 1 -1
Namibia 0 0 1 -1
Paraguay 0 0 1 -1
Sudan 0 0 1 -1
Syria 0 0 1 -1
Costa Rica 0 0 2 -2
Dominican Rep. 1 1 3 -2
Guatemala 0 0 2 -2
Libya 0 0 2 -2
Luxembourg 0 0 2 -2
Panama 0 0 2 -2
Turkmenistan 0 0 2 -2
Uruguay 0 0 2 -2
Angola 0 0 3 -3
Lebanon 0 0 3 -3
Myanmar 0 0 3 -3
Ecuador 0 0 4 -4
Morocco 1 1 5 -4
Sri Lanka 0 0 4 -4
Argentina 4 18 23 -5
Finland 1 1 6 -5
Israel 2 2 7 -5
Oman 0 0 5 -5
Qatar 1 3 8 -5
Thailand 6 18 23 -5
Norway 4 4 10 -6
Portugal 1 1 7 -6
Algeria 2 6 13 -7
Brazil 19 59 66 -7
Malaysia 5 13 20 -7
Venezuela 3 5 12 -7
Iran 8 22 30 -8
Pakistan 0 0 8 -8
Peru 0 0 8 -8
Philippines 1 3 11 -8
Singapore 1 5 13 -8
Austria 1 1 11 -10
Chile 0 0 10 -10
Hong Kong 0 0 11 -11
Nigeria 1 1 13 -12
India 2 4 17 -13
Iraq 0 0 13 -13
Japan 41 105 119 -14
U.A.E. 1 1 18 -17
Egypt 3 3 21 -18
Turkey 8 18 37 -19
Chinese Taipei 3 7 29 -22
Mexico 5 11 49 -38
Indonesia 3 11 51 -40
Saudi Arabia 0 0 44 -44
United States 121 379 431 -52

The Commission’s plan for reforming EU asylum policy is very ambitious. But can it work?

Note: A 3,000-word analysis of reform plans that are probably never gonna see the light of day anyways, based on simple arithmetics and not-so-simple simulations. Also, an excuse to do graphs. Re-posted from Eurosearch


The European Commission announced last Wednesday a new package of proposals designed to reform the EU asylum system. The proposals include compulsory redistribution of asylum applications among the EU member states. This is called ‘corrective allocation mechanism’ or ‘fairness mechanism’.

Countries would be allocated ‘reference shares’ of asylum applications, and the moment a country’s reference share is exceeded by 50%, an automatic system will set it that will send the excess asylum applicants to countries that have not attained their reference shares yet. If member states do not cooperate, they will have to pay a ‘solidarity contribution’ of €250,000 for every asylum application they refuse to process.

The proposal for compulsory redistribution backed by the threat of financial penalties (sorry, solidarity contributions) is very ambitious. But can it work? And I mean, can it work in a strictly technical sense, provided that the EU musters the political support to adopt the proposals[1], manages to make the member states comply with the rules, ensures that they don’t game the national asylum statistics, and so on – additional problems that are by no means trivial to solve. Let’s also leave aside for the moment the normative issue whether such a compulsory redistribution system is fair, to asylum seekers and to the EU member states. For now, the simpler question – can the system work even in the best of political and bureaucratic circumstances?

How is the system supposed to work?

This is how the system is supposed to work, or at least how I think it’s supposed to work, based on the available explanations (see here, here, here and the actual draft regulation here):

(1) Each country gets a ‘reference key’ based on the relative size of its population and the relative size of its GDP (the two factors weighted equally) compared to the EU totals. For example, in 2014 Germany had a population of 81,174,000 (16% of the EU population of 508,191,116) and a GDP of €2,915,650 million (21% of the EU GDP of €13,959,741 million[2]), which, when combined, make up for a reference key of 18.5%.

(2) This reference key is translated into reference shares (indicative shares of the total number of asylum applications[3] made in the EU that each member state is expected to receive) by multiplying the reference key with the total numbers of asylum applications registered in the EU in the preceding 12 months. For example, according to Eurostat[4], between 1 January 2014 and 31 December 2014 the total number of asylum applications received in the EU was 653,885, so Germany’s reference share for January 2015 (covering the period 1 February 2014 – 31 January 2015) would be 18.5% of 653,885 which equals 120,969 applications.

(3) If a member state receives a number of applications that exceeds by 50% its reference share, the excess applications are to be redistributed to member states that have not attained their reference shares yet. For example, Germany would have to receive more than 181,453 (150% of 120,969) applications during the current and preceding 11 months to trigger the relocation mechanism.

(4) The reference totals and references shares are updated constantly and automatically.

How would the system work if it had to be implemented at the end of 2015 already?

Let’s first do the simple arithmetics to see how the system would work if it had to enter into force in the last month of 2015. We can plug in the total number of asylum applications registered in the preceding 12 months (hence, between 1 December 2014 and 30 November 2015) to calculate the country’s reference shares. We can then see who did more and who did less than their fair (reference) share, and we can estimate the number of transfers that would be necessary to balance the system.

According to Eurostat, the total number of asylum applications received in the reference period was 1,281,560, so this number is used in the calculations that follow. The figure below shows the reference shares (in black), 150% of the reference shares (in grey) and the actual numbers of applications received during the entire 2015 (in red). We can see that Hungary, Sweden, Germany, Austria, Finland, Bulgaria, and Cyprus would have exceeded 150% of their reference shares. And between them they would have had 449,821 ‘excess’ applications for redistribution. All the other member states are potential recipients (with the exception of Belgium, Denmark, and Malta which have received numbers of applications exceeding their reference shares but with less than 50%). The total number of available ‘slots’ for transfer is 592,469. The UK has 145,750, France has 106,227, Spain has 91,589, Italy has 67,689, Poland has 54,456, and so on. Even Greece would have to accept 8,577 more applications to achieve its fair share of registered asylum applications[5].

asylum_application_and quotas12

To sum up, if the ‘fairness mechanism’ was to enter into force in December 2015, it would require the relocation of almost half a million asylum seekers across the continent, with some member states having to receive more than 100,000 additional applications to balance the system. More than one-third of all applications received in the EU during the preceding 12 months would have to be relocated.

If the UK[6], for example, would refuse to accept the additional asylum applications to fill up its reference share, it would be expected to pay ‘solidarity contributions’ to a maximum of €36,437,500,000 (more than 36 billion euros). (For comparison, the total gross UK contribution to the EU budget in 2015 was around €16 billion). The total pool of asylum applications to be relocated would be worth  €112,455,250,000 (that is, more than 112 billion euros)! For comparison, the total budget of the EU for 2015 was € 141.2 billion.

To my mind, the scale of the potential fines (sorry, ‘solidarity contributions’) is so big as to make their application totally unrealistic. Of course, it is the threat of fines that is supposed to make the member states cooperate, but to do their work, fines still have to realistic enough.

To sum up the argument so far, things don’t look very bright for the solidarity mechanism. But one might object that 2015 was exceptional, and that it is precisely this type of imbalances observed at the end of 2015 that the fairness mechanism is designed to avoid. Yet, unless the system starts with a clean slate[7], the existing imbalances accumulated over the past months would have to be corrected somehow. The analysis above shows the enormous scale of the corrections needed, if the system would have entered into force five months ago.

How would the system handle the 2015 flow of asylum applications?

We can also try to simulate how the fairness mechanism with compulsory reallocation would have handled the flow of asylum applications that the EU experienced during 2015 (provided that the member states cooperated fully). That is, we start with the situation as observed in January 2015, we calculate the reference shares and apply the necessary transfers to balance the system, and then we move forward to February 2015 observing the actual numbers of applications received in reality, balance the system again with the necessary transfers, and so on until the end of the year. (The script for the analysis and the simulation (in R) is available upon request.)

Running the simulation for the entire course of 2015 delivers good news and bad news (for the architects of the proposed mechanism). The good news is that the redistribution system does not get ‘choked up’ – that is, it does not run out of capacity to redistribute asylum applications received by some member states in excess of 150% of their reference shares to member states that have yet to reach their reference shares. The bad news is that in order to get and stay balanced, the system applying the ‘fairness mechanism’ needs to make approximately 500,000 transfers (that makes 37% of all asylum applications received in the EU during the year).

The figure below shows the number of transfers (in black) that need to be made per month, together with the actual number of asylum applications received in the EU as a whole during this period (in grey). Approximately 157,000 transfers must be made in the first month of the simulation (January 2015) to balance the system initially, and the remaining 343,000 are needed to keep it in balance for the rest of the year. The peak is in August, when more than 50,000 transfers must be made. (The bars are not stacked upon each other but overlap).


The next figure shows the distribution of transfers per member state. The blue bars that go below the horizontal line at zero indicate that the member state is a net ‘exporter’ of asylum applications, and the red ones that rise above the zero line indicate that the member state is a net receiver of transferred applications during the year, according to the simulation. (The parts of the bars colored in light blue and light red show the transfers made in the first month of the simulation.)

It is clear from the figure that Hungary, Sweden, Germany and Austria export the greatest number of applications, while Poland, Italy, Spain, France and the UK are expected to receive the most transfers. Some countries actually change their status in the course of the year from exporters to receivers of additional asylum applications (Denmark) and vice versa (Finland). Even Sweden and Hungary – countries that are big net exporters for most of the year have to receive additional applications during one or two months (see here the detailed plot of the experience of individual countries over the 12 months of the simulation).asylum_application_transfers2015

Despite the huge amount of transfers, not all member states handle a completely proportional burden of the total EU pool of asylum applications throughout the year. While the monthly transfers correct for gross imbalances and ensure that no country deals with more than 150% of its reference share, the system still leaves potential for significant differences across the member states. The figure below demonstrates this fact by showing the simulated number of asylum applications in red (actual applications received and simulated transfers) and the references shares (in black and grey). While the two sets of bars are much closer now, and the red one does not exceed the grey one for any country, member states still vary from fulfilling 70% of their reference shares (for example, Croatia, Portugal, Romania, and Slovakia) to fulfilling close to 150% of their reference shares (Germany, Belgium and Austria).[8]

asylum_application_sims_and quotas


To sum up, the proposed compulsory redistribution of asylum applications among the EU member states can reduce the current imbalances, but only at the price of a huge amount of transfers between the member states. With the proposed parameters, the mechanism would be able to handle even a great influx of asylum seekers as the one observed during 2015. However, under the 2015 scenario, half a million applications would have to be redistributed to make it work. An enormous amount of transfers between member states would be necessary to balance the system initially (unless the mechanism starts with a clean slate), and as many as 50,000 applications per month might have to be redistributed later (under a scenario similar to the one that actually occurred in 2015).

The reference period of 12 months used for calculating the countries’ reference shares must be updated and moved forward every month to ensure that the system retains enough capacity for redistribution. Otherwise, the ‘cushion’ provided by the fact that countries only export applications once they receive 50% more than their reference shares might not be enough to guarantee that there are enough ‘free slots’ in other member states. For the reference period to be updated fast, reliable and almost instantaneous information about the flows of asylum seekers to all member states must be available. Currently, the latest month for which Eurostat has data on the asylum applications received in all EU member states is December 2015: that is,  at the moment, the reference shares can be updated with at least a 4-month lag. This might be too slow to accommodate the rapidly changing flows of asylum seekers to Europe and might quickly grind the fairness mechanism to a halt.

The disadvantage of a relatively short reference period of 12 month that is constantly updated is that some member states might have to receive transferred applications at one point of time and then be eligible to redistribute applications to other countries just a few months afterwards. Such moving around of asylum seekers across the continent is of course highly undesirable, and costly as well.

Although the system might be able to correct the gross imbalances, it might still allow significant differences in the asylum application burden that different EU countries carry to persist. This fact requires attention to the way the ‘excess’ applications are to be distributed among the eligible member states that have not achieved their reference shares yet (since, typically there will be more available slots than requests for redistribution).

Finally, given the scale of required transfers to make the fairness mechanisms work, the size of the proposed penalties (solidarity contributions) for refusing additional applications is so huge as to be completely unrealistic. If under this mechanism member states are potentially liable for amounts that exceed their total annual contributions to the EU budget, there is little chance they will agree to participate in the mechanism in the first place.

All in all, while in principle the proposed fairness mechanism can work to reduce significantly the imbalances in the distribution of asylum applications across the EU, once the member states realize the amount of additional applications they might have to deal with under this policy, it is highly unlikely they will approve it. And certainly not with the current parameters regarding the reference periods, the references shares or the financial penalties.


[1] The Polish foreign minister already called the proposals an ‘April Fool’s Day joke.’

[2] The population and GDP estimates are based on statistics provided by Eurostat. GDP is in current prices and comes from the ‘tec00001’ database, in particular.

[3] In addition to asylum applications, the system will also take in to account the number of resettled persons. Eurostat however does not provide monthly data on resettlement. And the (annual) numbers of resettlements relative to asylum applications are so low (less than 1%) that we can ignore them in the analysis without much harm.

[4] The monthly statistics on asylum applications are available in the ‘migr_asyappctzm’ database. The version used in the analysis has been last updated on 6  May 2016

[5] Wait, what? Greece would have to receive more asylum applications? That’s right. Although hundreds of thousands (if not millions) of migrants have arrived on Greek territory in the past year and a half, in 2015 the Greek state has registered as asylum seekers only a negligible proportion of them. So, according to the official statistics (that would be used to run the fairness asylum distribution mechanism), Greece would have to register more applications and would be eligible to receive transfers from other member states until it reaches its fair share. I will leave it to you to judge whether this is a feature or a bug of the proposed system.

[6] The UK and Ireland, by the way, are invited but are not required to join the proposed system, even if the rest of the member states approve it.

[7] From the available documents, it does not seem to be the case that the fairness mechanisms will start with a clean state; that is, with a reference period not extending 12 months back.

[8] Curiously, Hungary appears to have made more transfers than actual applications received during the year according to the simulation, due to the huge fluctuations in the monthly amount of applications registered (which average 20,000 in the first 9 months of the year, but then drop to less than a thousand in the last three) and the moving reference period for calculating the reference shares.

Key numbers

Austria AT 2,0% 88.160 25.631 62.529 49.713 -51.796
Belgium BE 2,5% 44.665 32.039 12.626 -3.393 0
Bulgaria BG 0,9% 20.375 11.534 8.841 3.074 -4.676
Croatia HR 0,6% 205 7.689 -7.484 -11.329 5.510
Cyprus CY 0,1% 2.265 1.282 983 343 -850
Czechia CZ 1,6% 1.515 20.505 -18.990 -29.242 14.694
Denmark DK 1,5% 20.940 19.223 1.717 -7.895 1.159
Estonia EE 0,2% 230 2.563 -2.333 -3.615 1.838
Finland FI 1,3% 32.345 16.660 15.685 7.355 -9.319
France FR 14,2% 75.755 181.982 -106.227 -197.217 94.098
Germany DE 18,4% 476.510 235.807 240.703 122.799 -120.770
Great Britain (UK) GB 14,4% 38.795 184.545 -145.750 -238.022 127.292
Greece GR 1,7% 13.210 21.787 -8.577 -19.470 7.408
Hungary HU 1,3% 177.130 16.660 160.470 152.140 -184.043
Ireland IE 1,1% 3.270 14.097 -10.827 -17.876 9.674
Italy IT 11,8% 83.535 151.224 -67.689 -143.301 56.460
Latvia LV 0,3% 335 3.845 -3.510 -5.432 2.756
Lithuania LT 0,4% 320 5.126 -4.806 -7.369 3.674
Luxembourg LU 0,2% 2.505 2.563 -58 -1.340 431
Malta MT 0,1% 1.850 1.282 568 -72 -519
The Netherlands NL 4,0% 44.975 51.262 -6.287 -31.919 9.498
Poland PL 5,2% 12.185 66.641 -54.456 -87.777 47.751
Portugal PT 1,6% 900 20.505 -19.605 -29.857 14.694
Romania RO 2,5% 1.255 32.039 -30.784 -46.803 22.958
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5 simple things to know about asylum policy in the European Union

Migration is quickly turning into the defining issue of our time. This might sound cliché, but is true. Not only does migration top the list of most important problems facing society, but it is also divisive in a way no other issue is. Unlike problems like inequality or the environment, immigration polarizes and divides opinions of ordinary people in a manner that cuts through social classes, education levels, age groups, and political affiliations. Divisions and bitter disagreements run even within families and close circles of friends. For no other issue do I see on my Facebook wall the full gamut of opinions ranging from strong rejection of migrants and refugees to their unconditional welcome and embrace. Most opinions of course fall somewhere in-between expressing, for example, support for `genuine’ refugees fleeing war but not for economic migrants, or for Christian but not for Muslim immigrants; yet, deep and important disagreements remain.

The current crisis with the influx of hundreds-of-thousands asylum-seekers in Europe in the summer of 2015 is only but the current episode of the unfolding migration drama. The crisis and the political responses to it bring powerful emotions in people: fear and compassion, anger and humility, empathy and contempt. Together with polarization, emotions further cloud the discussion of asylum policies and the right thing for the European countries to do in this situation. In response, I want to share five simple things I happen to know about asylum policies in the EU. I am by no means a specialist on the legal aspects of asylum or migration law. My expertise comes from two rather technical policy studies I have conducted on the aggregate patterns of asylum applications and country refugee recognition rates over the last decade, and on their relationships with the broader social and political contexts. (see here and here for the academic articles and here for a blogpost and visualization based on them)

1) The asylum policies of the countries members of the European Union still differ a lot. Despite a considerable body of EU legislation harmonizing national asylum policies, in effect these national policies have not converged to a common set of standards and rules. The differences concern the handling of asylum application procedures (e.g. their duration), the actual support provided by the state to the applicants during the procedure, the quality of the reception facilities, the rights and privileges gained after (and if) a refugee status is granted, the forms of alternative protection if a refugee status is not granted, and what happens to those who are refused any protection. Most importantly of all, however, the EU member states differ significantly with respect to their recognition rates (the share of applications that are granted the refugee status) even for applicants from the same country of origin. By implication, this means that different states apply rather different criteria when assessing the asylum applications.
These differences are crucial to understand why a joint common EU asylum application center does not seem politically feasible at the moment and is not even being discussed as an option to respond to the current crisis. Until such considerable differences exist, a truly single European policy on asylum would remain out of sight.

2) The strictness of national policies towards asylum-seekers matter relatively little for the asylum flows they receive. You can think that by tightening their asylum policies – making reception conditions worse, reducing support during the application and after, or lowering the recognition rate, countries can lessen the asylum application burden that they face. But in fact asylum flows tend to be relatively insensitive and unresponsive, at least in the short and medium terms, to the strictness of national asylum policies and to how low or high the national recognition rates are. So manipulating national policy is not an effective tool to divert (or attract) asylum application flows. The same goes for the effect of current economic conditions or the political climate in a country (for example, whether there is broad public and party support or opposition to migrants). Asylum flows are directed to a large degree by geographical convenience and existing transit networks, by hearsay and stereotypes to be affected by the details of national asylum policies or recognition rates. The implication of all that is that no single country can unilaterally isolate itself from the asylum flows coming to Europe. That being said, because asylum flows are highly clustered (see below), not being on what is at the moment the most convenient route to Western and Northern Europe can dramatically affect the number of asylum-seekers that pass through or end up in your country.

3) Asylum-seekers from the same nationality or region tend to cluster in particular places. Not only do asylum-seekers from the same region or country tend to travel on the same routes employing the same networks and middlemen, but they also tend to cluster when and where they choose a place to lodge an application and where they settle if allowed. These points are quite intuitive. Extended family ties and networks provide for crucial information about handling the asylum-application process and about the living and working conditions in the host country and city. They also provide support and protection, etc. So no wonder that new asylum-seekers and refugees try to go to where they family and friends already are.
What is important to recognize, however, are the not so obvious policy implications of the fact that asylum-seekers and refugees cluster in space. Because migrants would tend to congregate in few places, these places would be subject to a much greater asylum burden than others. This goes for countries, but also for cities and regions within countries.
That is why countries are reluctant to let asylum-seekers and refugees settle wherever they wish in the EU. Otherwise, the fear is that because of the attracting power of existing networks of relatives and compatriots, very few places will have to deal with the challenges of supporting and integrating a great proportion of the refugees. The call for mandatory country (and existing regional within-country) quotas are partly responding to these expectations.

4) Even when recognized as such, refugees do not enjoy a freedom of movement in the EU. As mentioned above, refugees (and asylum-seekers) are not allowed to move, reside and work freely within the EU, unlike citizens of its member states. Even though recognized refugees might have the rights to work and live in the country that has recognized their status and even benefit from the national social protection policies, they cannot choose to relocate to another member states. This is important in order to understand why it is so crucial for the asylum-seekers to reach the desired place in Europe before they lodge an application.
But it is also important to understand why the compulsory re-settlement based on country quotas that the European Commission proposes would likely not work. Even if adopted by the Council (which at the moment seems rather unlikely), the scheme would run into troubles the moment the refugees try to skip their imposed host countries and go to where their family and support networks are. And they will. The resettlement quote scheme would then have to be coupled with measures like compulsory self-reporting or tagging that would allow for tracing the location of refugees and asylum seekers. Such measures would not only by expensive, but morally objectionable as well.

5) Even when their requests for asylum are rejected, asylum seekers often stay in Europe. This is the dirty little secret of asylum policy in Europe. Even when an asylum application has been rejected, and even when other forms of alternative protection are not granted, the migrants are rarely sent back to their country of origin. They either disappear into illegality but never leave the continent or exist in a para-legal limbo where their presence is tolerated but no support is provided. European countries differ in the extent to which they allow this to happen, and it is hard to get precise numbers about the scale of the problem, but it is in any case huge. Alternatives are, however, hard to find as locating and sending people back to their country of origin is expensive, often impossible if the migrants lack proper documents, and, many would argue, morally objectionable. But this fact undermines the idea that asylum-seekers are a special group of migrants who are only allowed to stay in the country if they face serious threats for their lives and dignity at home. If those who are rejected are allowed or tolerated to stay anyways, the difference between an asylum-seeker and a migrant motivated by economic or other reasons is much hard to draw in the public mind. Note that I am not saying that people migrating for reasons others than fleeing wars and persecution should not be welcomed; only that many people have different attitudes and policy preferences with respect to different groups of migrants, and that blurring the boundaries between the groups can have negative consequences for people’s selective support of particular groups, like refugees.

 All in all, none of these five points suggest a comprehensive solution to the current asylum crisis or point to a clear way forward. What they do, hopefully, is to outline some of the facts and constraints that those in power must have in mind when designing responses to the situation and some arguments with which the judge existing proposals.

To put my cards on the table, I currently think that a combination of three policies can be preferable to the current system and to existing proposals:

  • A centralized single EU-managed system of asylum application centers at places along (and perhaps even outside) the borders of the EU, financed by the EU budged and staffed by European civil servants (support for the regions where the application centers are located would be needed to handle the flow of asylum-seekers during the time their applications are being assessed);
  • Free movement for recognized asylum seekers within the continent. This should include the rights to move, settle, and work, but not necessarily access to the social systems of the host countries. Places where refugees happen to cluster disproportionately would get support from a pot to which all countries contribute.
  • Strict control of the external borders of the EU to channel the applications through the official centers and strict `no access’ policy for people denied asylum or alternative forms of protection.

This would represent a rather drastic change from the system currently in place so it probably has low political feasibility. At the same time, current proposals do not seem to fare much better in the EU decision-making bodies, so the scale of required changes should be no reason to disregard the ideas.

Why political scientists should continue to (fail to) predict elections?

The results from the British elections last week already claimed the heads of three party leaders. But together with Labour, the Liberal Democrats and UKIP, there was another group that lost big time in the elections: pollsters and electoral prognosticators. Not only were polls and predictions way off the mark in terms of the actual vote shares and seats received by the different parties. Crucially, their major expectation of a hung parliament did not materialize as the Conservatives cruised into a small but comfortable majority of the seats. Even more remarkably, all polls and predictions were wrong, and they were all wrong pretty much in the same way. Not pretty.

This calls for reflection upon the exploding number of electoral forecasting models which sprung up during the build-up to the 2015 national elections in the UK. Many of these models were offered by political scientists and promoted by academic institutions (for example, here, here, and here). At some point, it became passé to be a major political science institution in the country and not have an electoral forecast. The field became so crowded that the elections were branded as ‘a nerd feast’ and the competition of predictions as ‘the battle of the nerds’. The feast is over and everyone lost. It is the time of the scavengers.

The massive failure of British polls and predictions has already led to a frenzy of often vicious attacks on the pollsters and prognosticators coming from politicians, journalists and pundits, in the UK and beyond. A formal inquiry has been launched. The unmistakable smell of schadenfreude is hanging in the air. Most disturbingly, some respected political scientists have voiced a hope that the failure puts a stop to the game of predicting voting results altogether and dismissed electoral predictions as unscientific.

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This is wrong. Political scientists should continue to build predictive models of elections. This work has scientific merit and it has public value. Moreover, political scientists have a mission to participate in the game of electoral forecasting. Their mission is to emphasize the large uncertainties surrounding all kinds of electoral predictions. They should not be in the game in order to win, but to correct on others’ too eager attempts to mislead the public with predictions offered with a false sense of precision and certainty.

The rising number of electoral forecasts done by political scientists has more than a little bit to do with a certain jealousy of Nate Silver – the American forecaster who gained international fame and recognition with his successful predictions of the US presidential elections. (By the way, this time round, Nate Silver got it just as wrong as the others). For once, there was something sexy about political science work, but the irony was, political scientists were not part of it. And if Nate, who is not a professional political scientist, can do it, so can we – academic experts with life-long experience in the study of voting and elections and hard-earned mastery of sophisticated statistical techniques. So the academia was drawn into this forecasting thing.

And that’s fine. Political scientists should be in the business of electoral forecasting because this business is important and because it is here to stay. News outlets have an insatiable appetite for election stories as voting day draws near, and the release of polls and forecasts provides a good excuse to indulge in punditry and sometimes even meaningful discussion. So predictions will continue to be offered and if political scientists move away somebody else will take their place. And the newcomers cannot be trusted to have the public interest at heart.

Election forecasts are important because they feed into the electoral campaign and into the strategic calculations of political parties and of individual voters. Voting is rarely an act of naïve expression of political preferences. Especially in an electoral system that is highly non-proportional, as the one in the UK, voters and parties have a strong incentive to behave strategically in view of the information that polls and forecasts provide. (By the way, ironically, the one prognosis that political scientists got relatively right – the exit poll – is the one that probably matters the least as it only serves to satisfy our impatience to wait a few more hours for the official electoral results.)

Hence, political scientists as servants of the public interest have a mission to offer impartial and professional electoral forecasts based on state of the art methodology and deep substantive knowledge. They must also discuss, correct and when appropriate trash the forecasts offered by others.

And they have one major point to make – all predictions have a much larger degree of uncertainty than what prognosticators want (us) to believe. It is a simple point that experience has been proven right times and again. But it is one that still needs to be pounded over and over as pollsters, forecasters and the media get easily carried away.

It is in this sense that commentators are right: predictions, if not properly bracketed by valid estimates of uncertainty, are unscientific and pure charlatanry.  And it is in this sense that most forecasts offered by political scientists at the latest British elections were a failure. They did not properly gauge the uncertainty of their estimates and as a result misled the public. That they didn’t predict the result is less damaging than the fact they pretended they could.

Since the bulk of the data doing the heavy-lifting in most electoral predictive models is poll data, the failure of prediction can be traced to a failure of polling. But pollsters cannot be blamed for the fact that prognosticators did not adjust the uncertainty estimates of their predictions. The tight sampling margins of error reported by pollsters might be appropriate to characterize the uncertainty of polling estimates (under certain assumptions) of public preferences at a point in time, but they are invariably too low when it comes to making predictions from these estimates. Predictions have other important sources of uncertainty in addition to sampling error and by not taking these into account prognosticators are fooling themselves and others. Another point forecasters should have known: combining different polls reduces sampling margins of error, but if all polls are biased (as they proved to be in the British case), the predictions could still be seriously off the mark.

Offering predictions with wide margins of uncertainty is not sexy. Correcting others for the illusory precision of their forecasts is tedious and risks being viewed as pedantic. But this is the role political scientists need to play in the game of electoral forecasting, and being tedious, pedantic and decidedly unsexy is the price they have to pay.