Review of Daphne du Maurier’s “The Scapegoat”

By Graham

In many fields of human endeavour – cookery or ballet, perhaps – lightness is considered a quality to be striven for. Even as a description of one’s mood, the adjective ‘light’ trumps all others. Yet in literature, the quality of lightness is decidedly dysphemistic. We might say of the latest airport thriller, “it was a light read”; when what we really mean to say was that the book in question was a pile of rubbish. This, I think, is a great pity, for it arises from a fundamental misunderstanding of what constitutes literary quality.

Daphne du Maurier’s “The Scapegoat” is the quintessential light read, but it is far from being rubbish. True, her skillful plotting and razor sharp descriptions render characters and images with such stunning ease the reader find himself chauffer-driven straight into the French post-war village of St Gilles. In fact, it’s almost as if by the mere act of reading, we could step effortlessly into the clothes and skin of the local châtelain, the feckless and self-centred Count de Gué. Yet the lightness of style does not prevent du Maurier from delving deep into the well of the human soul, from which murky depths all great literature must draw its substance.

It’s not giving too much away of the plot to mention the key premise; that of Doppelgängers meeting by chance, allowing one man to enter into the life of another, whose complex and sinister history slowly reveals itself as the plot unfolds. This allows for much grimace-inducing comic light-heartedness, an opportunity du Maurier masterfully seizes throughout.

But it also allows for a deep exploration of the meaning of self. It asks the question whether any of us, if thrust into another’s world, with the full weight of that person’s past bearing upon us, could live his or her life any better. Are we, after all, victims of our circumstances? How much of the character we inhabit is the fruit of our free will, and how much sketched for us by the Great Novelist in the Sky?

These are deep questions indeed. And like all good philosophers, du Maurier is careful to treat them with delicacy, and without venturing too far down the road of the moral preacher. In this, one might say she takes on a very heavy subject. And does so with incredible lightness.

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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:

fitted-death-rates-against-actual-values

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:

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:

fitted-emissions-to-gdp-against-actual-values

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