McDonald's and Data Analytics: The Big Mac Index

After almost a month of nationwide closures, McDonald's is finally back!

We know the brand for its accessible fast food, but did you know that McDonald's also plays an important role in economics?

Created by The Economist in 1986, the Big Mac Index uses the prices of Big Macs to estimate Purchasing Power Parity (PPP) across the globe.

A big mac
The Big Mac plays an important role in estimating Purchasing Power Parity across different countries.

As a refresher, PPP compares currencies based on the prices of a fixed basket of goods.

It can be really difficult to choose this basket of goods, especially when cultural differences make certain goods more popular or prominent than others.

Although the Big Mac Index has its limitations, the Big Mac's ubiquity and relative consistency across countries make it easier for economists to compare PPP across different countries; 118 out of 195 countries, or 61% of all countries, have at least one McDonald's franchise.

In other words, an exchange rate is estimated based on the price of a Big Mac in two countries – a target and base country.

This is then compared to the actual exchange rate between the target and base currency; base currencies are typically the US dollar (USD), but have expanded to other major currencies (namely the Euro, British pound, Japanese yen and Chinese yuan).

Let's take the Singaporean dollar (SGD) as an example.

Comparing the price between a Big Mac in Singapore (S$5.90) and the United States (US$5.67), the exchange rate is estimated as 1 USD = 1.04 SGD.

However, as of Jan 2020, the exchange rate was 1 USD = 1.35 SGD. This means that in comparison to USD, SGD is priced less than it should be - specifically, 22.8% undervalued.

After accounting for GDP (i.e. factors like labour costs and import fees), SGD is found to be 23.7% undervalued compared to USD.

How is this linked to data analytics?

Think about the effort needed to calculate the Big Mac Index without data analytics technologies.

Economists would need to:

1. Find the prices of Big Macs in 118 countries (unfortunately, no easy way to do this yet!)

2. Manually calculate each country's exchange rate in comparison to a base currency

3. Repeat step 2 for each base currency (there's five in total)

4. Somehow visualise all the data into the chart above

This means that they will need to manually calculate a total of almost 600 exchange rates.

Imagine how time-consuming this would be – and remember, this excludes the time needed to calculate the GDP-adjusted index!

Yet, this was exactly how the Big Mac Index was calculated prior to 2018.

Thanks to technological advancements, economists can now rely on data analytics technologies to build models and codes that automate these calculations.

These also give them an easy and convenient way to add new metrics (such as GDP or other base currencies) and visualise data in the appropriate forms.

As of 2018, The Economist uses R to calculate the Big Mac Index.

R is a great platform for data visualisation, statistics, and data manipulation. Its packages significantly improve these features.

For instance, the data wrangling package dplyr allows easy manipulation of data, while data visualisation package ggplot2 allows easy visualisation of data in a clean, sleek and customisable manner.

Although the Big Mac Index started as a fun and easy way to estimate currencies, its impact in economics is undeniable.

It also teaches us an important lesson: Any type of data – even the prices of burgers – can be useful if you know how to manipulate and visualise it to produce insights.

In the digital age, exponential amounts of global data are produced daily, and data analytics technologies are rapidly improving.

Indeed, the possibilities are endless – ultimately, your ability to obtain the necessary insights from data will depend on your data analytics skills.

To find out more about the Big Mac Index or how it is calculated, please refer to the methodology and data published by the Economist (Fluency in the R or Python language is required).

Read more: Why Should You Invest in a Data Analytics Course?

Written by: Chloe Thio (Gen Infiniti Academy)