Topic 2: The Value of Food
Are we spending too much on our pets?
This week we were hoping to create data visualisations related to food in class. The topic on pet food popped up when a few of my classmates were briefly bouncing ideas off one another. That led me to the latest total pet industry expenditure in 2017, the most recent figure from the American Pet Products Association (APPA), which added up to a sum of $69.51 billion in the US alone. Within that figure, pet food gets the lion’s share ofabout 41.8%.
Pets are Ohana. That is the shared sentiment amongst quite a number of pet owners surveyed by the APPA. To a certain extent, it does shed some light on why people are so willing to pour in money for their pets. On top of the necessities such as pet food and veterinary visits, people are willing to splurge on luxuries like pet care products, pet toys and furniture, travel accessories, grooming services, and in recent years, pet health insurance. The next question we may ask is whether this is the same in other countries. The answer is, not quite so. China, with a comparable market size and potential, did not spend nearly as much as that in the US. It is worthy to note that China may surpass US in the future with the rate in which its spending is increasing. Nonetheless, the US could still qualify as our pet’s go-to country for a better “quality of life” today.
I started to wonder whether the amount spent in the US pet industry alone is comparable to that spent in other countries on more pertinent industries like healthcare and education. However, with limited access to complete datasets, I turned to my next best alternative. From here on I will spend some time discussing on how I created my data visualisation for this week and share some of the insights I gained from the visualisation.
After creating my first data visualisation submission (Topic 1: Happiness “Do we have time to pursue happiness”) using excel last week, I decided to learn how to use Tableau for the submission this week. I wanted to find out whether too much was spent on pets in the US. To do that, I used three datasets: (1) total pet industry consumption expenditure in the US from 1994 to 2017 from Statista, and (2) the total consumption expenditure (sum of all household and government expenditure) by countries in 2017 and (3) total population by countries in 2017 from World Bank Open Data. Figure 1 below shows the data visualisation I created.
Figure 1 is a line graph representing the total consumption expenditure in the pet industry in US alone over the years. Each bubble on the chart represents a country’s total consumption expenditure in the year 2017. The size of the bubble represents the population size in that country in 2017. From Figure 1, we can understand, for example, that we spent a total amount of about $21 billion on our pets in 1998 which is approximately equivalent to the total spending in Zimbabwe in the year 2017 where they had 16.5 million people in the country. Admittedly, it would have been more relatable if the total spending in Zimbabwe in 2018, an even more recent figure, is available.
Before I continue analysing the data visualisation created, I would like to share with you what inspired me to use a combination of line graphs, bubble chart, and pegging data of the past to present to help viewers relate better.
Hans Rosling was one of Time magazine’s 100 most influential people in the world. It was in his book “Factfulness” where he shared his favourite graph (attached below in Figure 2). In summary, the line graph shows Sweden’s health and wealth over time. Each bubble in the chart represents one country’s health and wealth in 2017 with the size of the bubble showing the corresponding size of the country’s population. By pegging Sweden’s health and wealth position in the past to that in countries today made a simple line graph over time so much more powerful in communicating its information. With that, Sweden’s position in the past is now relatable and is not just a mere figure of the past on the chart. Rosling had successfully conveyed his message to his readers; Sweden’s health and wealth improved tremendously over the years.
I was really excited when this chart was shared in my class the other day by Dr. Charles Burke. Line graphs depicting changes over time are so commonly used. I had always thought of it as one of the simplest form of data visualisation we have. I had not realised that its ability to be impactful and meaningful to a wider audience could be limited. How good is Sweden’s health and wealth now? How terrible was it then? Was Sweden’s health and wealth improving at a fast rate? Without prior knowledge on the world’s health and wealth over time, a single line graph alone is unable to tell the complete story.
That was what inspired my choice of data visualisation this week. With reference back to my data visualisation in Figure 1, we can now better appreciate the extent to which money is splurge on pets in the US. At one point in the past in 2002, the total spending on pet in the US had already caught up with the total spending by the 0.6 million people in Luxembourg in 2017. Where we are at now is almost exceeding the total spending by the 2.6 million people in Qatar in 2017. Does this mean we are spending too much on our pets? Are we doing a valid comparison? Some of us might not want to jump to a conclusion straightaway at this point.
The Luxembourg example was surprising, or at least in my opinion, given the fact that it is one of the wealthy countries in the world. However, it is also good to realise that it had a much smaller population which may have resulted in a low total consumption expenditure. With this in mind, would it be better to compare the average consumption expenditure per pet in the US to the average consumption expenditure per capita in countries? I would say yes, it will better help us arrive at a conclusion on whether we are spending too much on our pets especially when we make comparisons to developing countries. This is one improvement that can be done to this data visualisation if we had access to the necessary statistics.
Of course, there are many other ways to improve this data visualisation to provide more insights and to enable certain questions to be better answered. We could peg the total pet food expenditure in the US to the total food consumption expenditure in countries today. This could help us understand food accessibility possibly in developing countries.
There are many questions that cannot be answered with this data visualisation alone. Nonetheless, we can still understand from Figure 1 that the spending on pets in the US at different point in time is equivalent to the total consumption expenditure in a certain country with a particular population size today. With that, the main purpose of my data visualisation for this week is satisfied.