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There is a Positive Relationship Between Peace and GDP per Capita – And Some Interesting Exceptions

This graph shows the relationship between global peace index- and GDP (nominal) per capita ranking for 145 countries, 2012.

A low peace index ranking is associated with more peace (Iceland has the lowest ranking and Somalia has the highest), whereas a low GDP per capita ranking means more wealth (Luxembourg is number one and Democratic Republic of Congo is bottommost).

Global_peace_index_rank_GDP_per_capita_rank_2012Global peace index rank source: Institute for Economics and Peace (IEP), it is based on 23 indicators. GDP per capita source: International Monetary Fund (IMF).

It can be seen that there is a positive relationship between peace and GDP per capita. This might be interpreted as an argument in favor of peace promoting activities or, depending on the direction of causality, a reason for additional focus on growth policy. Perhaps most likely these two variables reinforces each other back and forth.

Some notable deviations from the relationship exists. The following countries are poor but are still relatively peaceful. (I credit my friend Viktor Ostlund for coming up with the idea of making this comparison) Score is calculated as GDP per capita ranking + [lowest peace ranking=157 – peace ranking for country]; so a poor country with a low peace index (i.e. is peaceful) will score high.

It can be seen that Malawi, followed by Mozambique and Sierra Leone, has the highest score. The flip side version of this list is the wealthy countries with a high peace index ranking (i.e. are not very peaceful).


Israel, Russia and Libya top this list.

What factors, if any, do countries within these two groups have in common respectively? Are there distinct between group differences? Please make a comment if you have an explanation.


A VENSIM Simulation of the Solow Growth Model

This VENSIM model let you simulate a basic version of the Solow growth model.


A main benefit of using a simulation model is that it enables you observe the process during which the economy converges towards its “steady state” level of output per worker, given an initial level of capital per worker.

For saving rate = 20%, depreciation = 5 %, output = √capital (diminishing returns to capital) and intitial values of capital per worker = 20 and 200 respectively:


Steady state level of output (which is “4 units” of output per worker) does not depend on the intital level of capital but on the other parameters in the model. However the initial level do matter because it affect the adjustment period, as can be seen in the graph.

Data From Many Columns Into One Using MATLAB (Simple Code)

Sometimes you want to arrange data from many columns into one using MATLAB. Such a task should be easy to perform. However I had some difficulties and couldn’t find help online right away, so I descided to share a simple solution.

Start with (or any data):

data1 =

8 1 6
3 5 7
4 9 2

To achieve:

data2 =


This program will do the trick:

data1=magic(3) %or any data
a=[]; %storage vector
for i=1:length(data1(1,:))
a=[a b’];

Can You Absorb and Interpret Data Through Sounds (An Experiment)? It Turnes Out: Perhaps Not…

The conventional way to communicate data is to insert it into a graph. However humans do not only perceive through vision. We of course also utilize our other senses, for example hear things. So why not try to convey information using sounds.

This video let you hear “the sound of the Mexican economy” (annual GDP growth from 1961 to 2011). Sound speed is proportional to growth level.

The graph contains the same information for comparison.

Source: World Bank

Here the audio result is somewhat disappointing. But maybe the idea could be developed further. If you have done so, please let me know.

The Eurozone Drama – Chernoff Faces Tells the Story

Chernoff faces are used to display multivariate data in the shape of a human face. Most of the time these faces are constructed mechanically without any attempt to use the variables to create an emotion. Here desirable economical outcomes cause a positive face expression.

  • Size of face = Gross domestic product, current prices (billions of U.S. dollars)
  • Angle of eyebrows (relative to eyes) = Inflation, average consumer prices Percent change
  • Shape of mouth = Unemployment rate (percent of total labor force)
  • Vertical position of eyebrows = General government net debt (percent of GDP)

Data source is the International Monetary Fund (IMF). A larger face means higher GDP, “v-shaped” eyebrow means more inflation, smiling mouth means less unemployment, higher position of eyebrows means less debt. DE=Germany, ES=Spain, FI=Finland, FR=France, GR=Greece, IE=Ireland, IT=Italy and PT=Portugal.

The Eurozone drama (click on the pictures to see them clearly):

Story in year 1996

Year 2001Year 2006
Year 2011

Complete set of Chernoff faces

Demand for Money and the Interest Rate in Sweden Since 1987

Demand for money (M) is thought to be equal to GDP (current prices) times a function (L) which depends on the interest rate.

A higher interest rate should imply less demand for money — the cost of borrowing goes up and the payoff from a savings account is higher — so we would expect the relationship between money and the interest rate to be negative. (See Blanchard)

If equilibrium in financial markets is assumed, then money demand is equal to money supply.

The following graph plot the left-hand side together with the right-hand side of the (second) equation for Sweden since 1987. Money supply is measured by M3 and the interest rate is an average of 2Y, 5Y and 7Y government bonds.

Sources: Statistics Sweden, The Riksbank (of Sweden)

It can be seen that the expected negative relationship do in fact hold. Since the eighties the trend has been a higher ratio of money and a corresponding lower interest rate.

If instead yearly changes are displayed, a similar (however somewhat weaker) pattern appears:

Principal Component Analysis – There is an Interesting Pattern in My Stock Portfolio

Principal component analysis is a technique used to form new variables that are linear combinations of some original variables (see Sharma 1996).

I have used seven financial ratios as input variables: return on equity, RE; return on assets, RA; operating margin, OM; solidity, S; quick ratio (liquidity indicator), QR; asset turnover, AT; and sales per employee, SPE. These ratios have been calculated for the stocks in my portfolio (based on annual reports for 2011).

SAS 9.2 is applied to perform the analysis.

It can be seen that the first three principal components — that is the number of new variables — collectively account for 84 % of the variation in data.


The two most prominent ratios for each component are marked bold. Prin1 mostly dependent on profitability, Prin2 depend on the corporations’ ability to pay its debt in the short- and long run, and Prin3 depend on efficiency.

The following graph shows components scores for the stocks. I have only included the first two components (they account for 65% of the variation) to make interpretation straightforward. Stocks which have outperformed the OMXSPI (Stockholm) index, since the beginning of this year, have been given a green colour (otherwise red).


A pattern appears. Stocks with positive values on the first- and especielly second principal component (corporations with good returns ehich are capable of repaying their debts) have overall been a good investment for me 2012. Buying these kinds of stocks seems like a sound strategy as well…

It would be interesting to extend the analysis to a general population and compare outcomes during different parts of the business cycle. Perhaps this will be done in a later post.