dataset.21 To calculate the dependent variable, I used the annual UNHCR
statistics of asylum applications and OECD data on population
developments. To arrive at the observations for my dependent variable for
each country and each year I divided the number of asylum applications
by the country’s population size and put this figure is relation to the total
number of asylum seekers divided by the total population of all countries
under investigation.22
Explanatory Variables
The explanatory variables are constructed in such a way as to allow for
the examination of the five above theories on key pull-factors for asylum
applications.
First, to test for economic pull factors, the paper analyses OECD data on
annual GDP growth (in percent) and the total number of registered
unemployed. The expectation is that a country's relative burden will be
positively correlated with its economic performance and negatively with
its numbers of unemployed.
Second, to test the importance of geographic factors, I determine the
average distance between the capital of a destination country and the
21 Asylum applications per population is the most commonly accepted way of analysing
relative burdens in this area. Controlling for GNP, instead of population size, leads to an
almost identical ranking order in terms of relative burdens. As this analysis here is
interested in explaining the distribution of relative asylum burdens over time, it does not
seek to assess the role of push-factors responsible for variations in absolute asylum-
applications.
ai,t
pi,t
22 Expressed formally: B =----,
i,t At
Pt
whereby the term B represents the relative number of asylum applications received in
country i in year t; a stands for the absolute number of asylum applications received in
country i in year t; p for the population of country i in year t; A for the sum total number
of asylum applications received across all OECD countries in the dataset in year t and P
represents the sum total population figure of all OECD countries in the dataset in year t.
17
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