| dc.description.abstract | Over the previous 50 years, economic geographers have observed some key changes in the
world. As globalisation has shaped our world, we now look at it from a more regional
perspective. We pay much attention to the regions that have succeeded, such as Silicon
Valley, but we also look at the regions that have fallen behind. We now often view the future
of peripheral regions in the first world as less hopeful than central regions in the third world
(Rodriguez-Pose, 2018). As many revitalisation projects have failed to make peripheral
regions catch up, policymakers believe it is the best strategy to put all eggs in one basket and
only focus on the biggest cities in the country. As a result, underperforming cities and
peripheral regions are left out of the development scope. These underrepresented regions
are ‘the places that don’t matter’ (Rodriguez-Pose, 2018). Populist parties are often popular
in these areas because they make the unrepresented feel heard (Rodriguez-Pose, 2020). As a
result, the support for populist parties has doubled in Europe over the last 20 years (Henley,
2020).
Through a literary study, we’ve established that Dunning’s (2008) OLI paradigm can help us
understand MNE’s FDI location choices. Ownership-, location- and internalization
advantages all play a role in determining if and where to invest. Important variables that play
a role are: formal and informal institutional quality, human capital, infrastructure, stability
and culture. In its turn, FDI’s can affect inequality and inequality can affect populism.
To test whether FDI’s also have a direct effect on populism, this researched proposed to
conduct spatial regression analysis in R. The research consisted of the entire European
Union, without former member the United Kingdom and without overseas territories on a
NUTS 3 scale level. FDI data was collected over a longer time period, namely 2003 till 2015.
All other variables were captured from 2015 till 2019 at the latest to see the effects of FDI’s.
To create the dataset, many different sources were used such as ESPON, Eurostat, the EQI
Index and the OECD database. Finally, to obtain voting data, the CLEA dataset as well as
many different internal affairs datasets were used.
The dataset consisted of 1157 regions and more than 20 variables. Although for some
variables not all regions were included, most variables still proved to be representative for
the total research population as the data was randomly collected and the sample size
allowed us to generalize with a 95% confidence interval. For some variables however, it
should be noted that the findings can’t be generalized to certain counties.
After analysing the dataset, we found that FDI’s increase both interregional- as intraregional
inequality. On its turn, these types of inequalities mostly significantly affected the four
different types of populism, although some relationships were positively related and others
were negatively related. As a result, no direct relationship between FDI’s and populism could
be established. Furthermore, there was no negative feedback loop detected between FDI’s
and populism. Some noteworthy relationships with populism that were found were the
positive relationship with unemployment and voting populist, Eurosceptic and far-left and
the positive relationship with high education levels and voting far-right, but negative
relationship with voting far-left. | |
