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How the Nobel Prizes Were Won

In between my research projects at work, I get curious when I come across datasets that evoke research questions. I came across the API (Application Programming Interface) which allowed me to download previous Nobel laureates. Nobelprize.org kindly shares up-to-date data of all of the prize winners so it can be explored further.

Using R, I downloaded the dataset and embarked on my quest to see whether there are any commonalities of age, gender, and country of origins of Nobel Prize laureates across chemistry, economics, literature, medicine, peace, and physics. The route that I’m taking is the common approach for Ehrenbergian researchers in Marketing Science – to observe any common patterns and empirical generalisation. Although this approach is often looked down by those favouring more complex modelling techniques, I would argue that descriptive analysis often excels in its simplicity and transparency. The results often arrive at a similar point to those derived from more complicated techniques – in a less oblique and a more understandable manner for non-academic audience.

Let’s get to it.

So, out of 989 winners, I focus my attention on the 959 non-organisational winners, so I can analyse the age when they won the Nobel Prize, their country of birth, and their gender. The analysis uncovers some interesting findings.

Nobel Prize Laureates are getting older

Unless you are active in establishing or maintaining peace, it’s unlikely that you will win a Nobel Prize in you’re not in your fifties or older. Dividing the period into decades, the average age for Nobel Prize winners upon the time of being awarded was 58 in 1901-1909 then dipped to reach the lowest around 1930-1939 (51). The average age for 2001-2009 is 62, followed by 63 in 2010-2019 – the average age of the winners in 2020-2022 is 66. Nobel Prize winners for Economics and Literature are older on average when the prize was given (63) – whereas Nobel Prize winners for Physics are generally younger when they received the prize (54). The overall average age for Nobel Prize laureates in the award year is 57.

The youngest ever Nobel Prize winner is Malala Yousafzai who won her Nobel Peace Prize in 2014 at the age of 13. Lawrence Bragg won the 1915 Nobel Prize in Physics at the age of 25 along with his father, William Bragg for “their services in the analysis of crystal structure by means of X-rays” (A side note: Lawrence Bragg was born in Adelaide, South Australia). In recent years, Nadia Murad from Iraq won the 2018 Nobel Peace Prize at the age of 25 for her “efforts to end the use of sexual violence as a weapon of war and armed conflict”. The oldest person who won the Nobel Prize so far is John Goodenough who won the 2019 Nobel Prize in Chemistry along with other researchers “for the development of lithium-ion batteries” at the age of 93. Arthur Askin also won the 2018 Nobel Peace Prize in Physics when he was 92 years old “for the optical tweezers and their application to biological systems”.

PS: It’s pretty neat as well that we can see the gaps when the World War I and World War II occurred in the chart above.

Nobel Prizes are increasingly shared or collaborative

In 1901-1909, the average number of Nobel Prize recipients within a category was 1.5, but this average increased to 2.6 in the last three years. Except for the Nobel Prize in Literature, winners are likely to be awarded as a collective – especially for Physics and Medicine. In fact, from 2000 onward, out of 272 Nobel Prizes, only 16% (43) of them were awarded to solo winners. Moreover, out of the 43 solo winners, two-thirds were awarded for Literature or the Peace Prize.

The United States, the United Kingdom, and Germany are Nobel Powerhouse

Based on their birth country, nearly half of the Nobel Prize winners were born in the US, UK, or Germany. If we consider the country of residence, this percentage is likely to be even higher – with many researchers working in the US. Only three out of ten Nobel Prize winners were born outside of the top-10 countries.

It’s a Man’s Man’s Man’s World

Only seven percent of Nobel Prize winners, across all categories, are female.

Let’s See The MADs

Lastly, we see whether certain demographic characteristics are more or less represented in different Nobel Prize categories. Again, we draw upon the Ehrenbergian tradition by looking at the Mean Absolute Deviations (MADs). MADs are the degrees to which a particular attribute for a category deviate from the average across all categories. I use 5pp difference from the average as a benchmark, to highlight any over- or under-representation.

The result shows that females would be more likely to be awarded the Nobel Prize for Literature or the Nobel Peace Prize. They are less likely to win the Nobel Prize for Economics or Physics. For the younger cohorts, winning the Nobel Prize for Physics would be more likely – but if you’ve built a lifelong career in Physics, it’s still possible for you to be awarded the Nobel Prize when you’re 80+ years old. Americans are much more likely to be awarded the Nobel Prize in Economics or Medicine — however, they are less likely to get the Nobel Prize for Literature or the Nobel Peace Prize. German researchers are more likely to be awarded a Nobel Prize in Chemistry – but are less likely to make a recognisable impact in Economics.

Finally, if you don’t live in any of the Top 10 countries, and have dedicated your life to the betterment of humankind, there’s a greater chance for you to win a Nobel Prize in Literature or the Nobel Peace Prize.

The analysis also uncovers that when we calculate the average of the MADs within each demographic variable, we see that Nobel Prize winners are likely to differ in their gender mix or age composition across different categories. However, they are more likely to be similar for their country of birth.

Final Words

My motivation in doing the analysis is not merely to see how the Nobel Prizes were won.

Firstly, beside quenching my curiosity, I did the exploration to highlight the transparency and the usefulness of descriptive analysis. Its transparency is often mistaken for being basic – in fact, I’d argue that it provides a solid foundation for more detailed and complicated analysis, if it is required. The onus is on us to provide the insights based on any generalised patterns we can observe, rather than solely relying on model outputs. We know what kind of an ‘animal’ it is, before we ask it to run!

Secondly, if you’re wondering what kind of specialised or sophisticated program I used – the answer is good old Microsoft Excel. A chef should be as comfortable cutting with knives as much as they do with top-of-the-line food processors.

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