The moment I became a writer

By Graham

The pub was crowded, as was usual for pub quiz night in our local Brussels boozer, an Irish pub by the name of ‘Michael Collins’. The quiz had reached that point where the organisers pause to tabulate the scores from the first four question rounds. Our team members all looked up from our scrawled notes and makeshift maps of African countries bordering the Congo, and began the ritual small talk. The latest movies and sports results, holiday plans, good places to eat.

“So,” Charlotte said, turning to me, “what is it you do?” Charlotte was new to our quiz team, and was still doing the introductory rounds.

“I’m an economist,” was my automatic response. I wanted to add, “but I’m really a writer. I’ve been writing all my life. The first draft of my novel is ready, waiting for me. It’s willing me to muster up the courage to nurture it fully to life.”

Instead I forced a self-deprecating smile and added, “Economics is boring stuff. You know, mostly just adjusting numbers in spreadsheets.”

That comment earned nothing more than a polite smile from Charlotte. Microsoft Excel might have pretty good mathematical functionality, but it’s a real conversation killer.

In my head, though, the conversation went on. As the quizmaster read out questions for rounds five through nine, and the team busily debated where the 1976 Olympics were hosted (Was it Munich?), and which country exported the most bananas (Costa Rica or Ecuador?) I found myself wondering why it was I was so shy about defining myself as a writer. By the time the final scores came in, and our team finished a lowly sixth place, I had come to a decision. If I was going to be a writer, I had to think of myself as a writer. And that would mean changing my priorities. The next day I told my boss I would be applying for a five month leave of absence.

Two weeks later I had packed my tent and sleeping bag, got on my bicycle and pedalled my way out of Brussels, out of Belgium, and right through the sausage-and-beer bloated midsection of Germany, until the Danube River bore me to Vienna. 1,400 km of cycling was enough to rid my mind of any recollection of the dreaded spreadsheets. I took a room in Vienna’s trendy Seventh District and spent the next four weeks poring over the text of my novel.

One evening, after rewriting a particularly satisfying chapter, I decided to break out of my hermit cell and found my way to a local beer garden, where I chanced to meet a Californian guy who had been an engineer and was now running workshops for alternative medicine and yoga.

“So what kiThe Hydrand of work do you do?” he asked me.

My glanced drifted momentarily to the string of lights suspended across the trellis at the edge of the garden.

“I’m a writer,” I answered.

And that was the moment I became a writer.

 Category: The Hydra

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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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