the waiting and the aggregation of the variables to get to the final index.
The first step includes the normalization of all variables.
which initially are measured in very different units.
Normalization also accounts for outliers in the data.
using the percentiles of the distribution.
The resulting scale ranges from 1, the minimum value, to 100, the maximum value.
Note that we normalize each variable over the panel and not over the cross-section.
has the advantage that it ensures comparability over time.
the past change when, for example, an additional year is added.
This is sometimes difficult to communicate to users of the index.
Here we see the distribution of the trade to GDP ratio.
a few countries have very high values.
Singapore, for example, has a trade to GDP ratio that exceeds 400%.
The distribution is thus highly right skewed.
we obtain a uniform distribution.
Each observation is assigned to a value from 1 to 100.
are looking at three countries in four years.
Germany, and Singapore, for the years 2004 to 2007.
the decentiles, where the smallest value is one and the highest value is ten.
because we have more than 100 observations.
Here we see the trade to GDP ratio for the different countries and years.
we obtain the following normalized values.
receives the maximum value of 10.
with the lowest ratio, receives the minimum value of 1.
The other observations are ranked in between according to the distribution.
the next step is to determine the weights with which each variable enters the index.
We apply principle component analysis, PCA, to do that.
bear more information about the overall variation in the data.
PCA is applied in three subsequent steps, one step for each level of the index.
each of the six subgroups, and each of the three dimensions of the index.
A caveat of this method is that weights are fixed over time.
This might pose a problem if the relative importance of variables change over time.
90s might not be that important anymore today.
For example, letters sent internationally have gradually been replaced by emails.
Ideally, we would have a procedure that allows for dynamic weights.
each variable to the final index.
using again the example of the trades to GDP ratio.
Actual Flows, with a weight of 22%.
sum up to the Economic Globalization, with a weight of 50% each.
Political Globalization together form the overall KOF Globalization Index.
The weight of Economic Globalization is 36%.
the KOF Globalization Index based on the individual variables.
assign higher weights to the remaining variables.
the subcategory is not published for that country.
based on the individual weighted variables.
we have calculated the KOF Globalization Index.
Savina will now present you the results from the 2016 vintage of our index.