• Aragorn’s Law: Of kings good and powerful

This post is an attempt to come up with a Law on the fundamental nature of power, which I call ‘Aragorn’s Law’. To begin with, we state three propositions. Proposition One: That Power is about control It’s possible to define the concept of power in a few different ways, but for the most part, when we think of

• Of car alarms, ugly facades & first aid courses – the wonderful world of ‘compound externalities’

Jargon is a nerd’s best friend Economists love to talk about ‘externalities’. ‘Externality’ is a wonderfully complex-sounding word that makes you feel more intelligent just by saying it. It is especially useful when trying to pull the wool over the eyes of a non-economist, as in: Community activist: “We’re outraged that our organisation has had its budget

• Soooo many people died in 2016, right? Wrong.

Should old acquaintance be forgot… New Year’s for most of us has been more a question of ringing out the Old Year, rather than ringing in the New. It’s more or less universally accepted that 2016 has been a crummy one, at least within my circle of acquaintances. One particular feature has been the remarkable

As with birth rates, we use data for 4 categories of countries from 1990 to 2015 (100 observations total). We have two explanatory variables, AGE and Y, where AGE is defined as the percentage of the population aged over 65 and Y is per capita GDP.

After eyeballing the scattergrams, we test the following functional form:

d = (minY^a)/Y^a * (1/AGE^g)

Where minY is the constant equal to the smallest value of Y in the series.

Logarithmic transformation gives:

ln(d) = ln(minY^a) – a*ln(Y) – g*ln(AGE)

which we test on the data using OLS. Here are the results:

Intercept coefficient: 7.37384
t-Stat: 20.4011

Y coefficient: -1.01444
t-Stat: -13.1059

AGE coefficient: 2.0097
t-Stat: 11.5208

The estimated intercept is a good, but not perfect, approximation of ln(minY^a)

Here are the fitted against actual values of the scattergram for death rate against per capita GDP: While the results are not as good as with the birth rates calculations, it is nevertheless a good enough fit and the explanatory variables have a strong enough confidence factor to be usable in our estimations.

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We begin by examining the scatter of data for 100 observations of per capita GDP and per capita emissions for 4 categories of countries, over 25 years (1990 – 2015).

The scatter suggests a cubic functional form, so we test:

GHG = a + b*Y + c*Y^2 + d*Y^3

where GHG are per capita emissions of GHG, and Y is per capita GDP.

The results from OLS regression are:

coefficient a: 1090
t-stat a: 3.06

coefficient b: 0.709310153
t-Stat b: 8.241453

coefficient c: -0.0000047025
t-Stat c: -1.01233

coefficient d: -0.000000000105314
t-Stat d: -1.47005

While the t-scores on the squared and cubed terms are low, the number of observations are also limited.

Here is the plot of the fitted against actual values: ×