My review of John Buchan’s ‘The 39 Steps’

By Graham

The 39 Steps (Richard Hannay, #1)The 39 Steps by John Buchan
My rating: 4 of 5 stars

What is the ultimate homage one can pay to an author?

Surely it is to say that his or her work, when viewed through the lens of time, has lost some of its impact on the modern audience because it has become a genre-defining cliche – done and redone by copycats, some very talented, until the novelty fades. This kind of ‘victimhood of one’s own success’ can be said of the great Alfred Hitchcock. It can be said of the classic hip hop group Public Enemy.

And it can be said of John Buchan’s ‘The 39 Steps’. For the contemporary reader whose appetite for vicarious thrills has been fattened on the fast food of Jack Reacher, Jason Bourne, Jack Bauer – not to mention James Bond, the antics of Richard Hannay come across as a little hammy.

For one thing, his protagonist (Richard Hannay) has a first name which doesn’t begin with J. And then, many of the plot devices – the just-in-time escapes, the ‘ordinary man antihero’, the ratcheting up of the stakes as the plot reveals – all seem rather tired. That is, until you remember that Buchan’s character was penned in 1915, at a time when writing of this kind was largely non-existant. Richard Hannay was escaping from exploding buildings long before John McClane was even Born Hard, never mind the ‘Die’ bit.

Regarding the plot of this book itself, I won’t say too much, except to note the extent to which it is pregnant with the zeitgeist of a powerful Britain, caught in the midst of the Great War. The villification of the Germans and the rabid jingoism of the Empire are anacharonisms, which, when viewed in the tail lights of the muddy, blood-soaked trenches and sour colonial legacy of racist Western powers, retain appeal only insofar as they provide us with historical context.

Buchan called his books ‘shockers’, which in itself sounds silly and antiquated, until you think a little on the word ‘thriller’ and realise it’s actually no less silly-sounding. As a politician and a well-known biographer, he considered his shockers among the least important of his accomplishments. And yet even in his own lifetime it was clear that this is what he would be remembered for.

I give ‘The 39 Steps’ three stars for the story in its own right, and an additional star in recognition of the many Johns, Jacks, James’ and Jasons who followed in Richard Hannay’s wake.

View all my reviews

 Category: Uncategorised

Leave a Reply

Comments Protected by WP-SpamShield Anti-Spam

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: