My Review of Ken Follett’s “The Pillars of the Earth”

By Graham

The Pillars of the Earth (Kingsbridge, #1)The Pillars of the Earth by Ken Follett
My rating: 3 of 5 stars

Imagine an apple that has been drawn two different ways. The first is a mere illustration, an outline in black and white, one which lacks detail. Simple, yes. Yet it perfectly expresses the essence of the fruit – like the logo of a certain computer company.

The second drawing attempts to render the apple as true to life as possible, with colour, texture and background: all the details the human eye supplies to the brain when confronted with the real thing. But imagine the artist lacks the high degree of skill to deliver on this photographic promise. Let’s say the colour is wrong, the hue of the light rings false, the texture is not sufficiently apple-y.

When we put these two pictures side by side, we ‘see’ a better apple in the illustration. By providing us only with an idea, it allows our imagination to fill in the other details. The second, on the other hand, jars in our brains. It bothers us, despite the effort that might have gone into its crafting. Less is sometimes more.

In ‘The Pillars of the Earth’, Ken Follett has tried for more. A lot more. His portrayal of medieval life in the south west of England is very detailed. We eat, sleep, travel and even copulate with the stonemasons, monks and earls who populate his pages. It is obvious to the reader that he has meticulously researched his subject. But that is precisely the problem – the reader is too aware of the research. At times it feels like we are reading his notes and not his fiction. It is too studied, and therefore rings false.

There are also a number of structural problems, again due to the high level of ambition. It’s damn nigh impossible to sustain over three generations and as many cathedrals the strong plot dynamics for which Follett is known. There are lulls and the conclusion to one of the central plot threads is both contrived and unsatisfying. Follett also seems unsure whether to write Aliena, one of the central characters, as a fierce female protagonist, or as a weeping damsel in distress. In the end, he tries a bit of both, and for me it simply doesn’t work. This I found particularly disappointing, given how well he rendered the characters in the other novel of his I read (“Night Over Water”).

That said, the opening is strong enough; the protagonists likeable enough and the villains loathsome enough to keep the reader hooked.

It’s just a pity he didn’t stick to mere illustration.

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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:

Adjusted R square: 75.191

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.


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:

Adjusted R square: 0.980438073

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: