This repository contains the data behind The Economist’s Big Mac index, and code that shows how we calculate it. To download the data, go to the latest release, where you can download the index data in a CSV or Excel, or the code behind it.

In July 2022 we updated the Big Mac index to use a McDonalds-provided price for the United States (previously, we averaged the price from four major US cities). We also changed how we calculate the GDP-adjusted index. Instead of using the IMF's calculation of purchasing-power parity, we adjust the GDP per person by the difference in each country's Big Mac prices. The full history of the GDP-adjusted series will now be updated whenever the IMF’s historical GDP series are updated, which means the GDP series for a given year may change slightly over time as the IMF refines its measurements. The previously published versions of both indices are available in the releases.

Our source data are from several places. Big Mac prices are from McDonald’s directly and from reporting around the world; exchange rates are from Thomson Reuters (until January 2022) and Refinitiv Datastream (July 2022 on); GDP and population data used to calculate the euro area averages are from Eurostat and GDP per person data are from the IMF World Economic Outlook reports.

The script provides data in three files:

• big-mac-raw-index.csv contains values for the “raw” index
• big-mac-adjusted-index.csv contains values for the “adjusted” index
• big-mac-full-index.csv contains both

Each file also contains the source data used to calculate it.

This codebook largely applies to all three files. The exception is the variables suffixed "_raw" or "_adjusted"—these appear (with suffixes) in the "full" file but without suffixes in the respective ("raw" or "adjusted") files.

The code to calculate the index is provided as a Jupyter Notebook. The code itself is written in R, a programming language designed for data manipulation and statistics. You can view the notebook on github.

If you want to run the notebook, you’ll need to set up a few things:

You can refer to the installation instructions at the Hitchhiker’s Guide to Python

On a Mac, you already have Python 2.7 installed, but it does not come with Python’s package manager. We recommend using Python 3. To install it, we recommend using Homebrew. In terminal, install Homebrew:

$ /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

Then, use Homebrew to install Python 3.x:

$ brew install python3

On Ubuntu Linux you can use aptitude:

$ sudo apt-get update
$ sudo apt-get install python3.6

On Windows, instructions coming.

On Mac or Linux, you should now also have pip installed. pip is a package manager for Python. You can install Jupyter with pip:

$ python3 -m pip install jupyter

You’re all set. (If you are using Python 2, run python -m pip install jupyter.)

On Windows, instructions coming.

On a Mac, use Homebrew again. At a terminal prompt, run:

$ brew install R

On Ubuntu Linux, you’re recommended to add a new source to your aptitude setup to install R. Run:

$ sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9

Once you have added the key, add R repository (called CRAN):

$ sudo add-apt-repository 'deb [arch=amd64,i386] https://cran.rstudio.com/bin/linux/ubuntu xenial/'

Now, you can install R:

$ sudo apt-get update
$ sudo apt-get install r-base

On Windows, instructions coming.

IRKernel lets you run R code in Jupyter notebooks. This is the best way to work with R code (this is a truth not yet universally acknowledged). Installation instructions for IRKernel are here. In short:

At a terminal prompt, start R:

$ R
> install.packages(c('repr', 'IRdisplay', 'evaluate', 'crayon', 'pbdZMQ', 'devtools', 'uuid', 'digest'))
> devtools::install_github('IRkernel/IRkernel')
> IRkernel::installspec()

Congratulations, you can run R in Jupyter.

Finally, our R script uses a few R packages you’ll need to install. The tidyverse is a collection of useful packages for data science work in R. Data.table is a complicated but extremely useful alternative to R’s standard data frames for storing and manipulating data. At the R prompt from above, run:

> install.packages(c('tidyverse','data.table'))

You’re all set.

Navigate to the repository on the command line, and run:

$ jupyter notebook

You should see a browser window pop up on http://localhost:8888. Click on “Big Mac data generator” to launch the notebook.

To run the notebook, you can run the code cell by cell by clicking on the first cell and using shift+enter to run each cell in turn. Or you can run the whole thing by clicking on the “Cell” menu and selecting “Run All”.

We also include the calculation as a bare R script (data-generator.R) if you just want to run the code, but this doesn't explain what the code does or walk you through it. To run this, you'll only need to install R, tidyverse, and data.table; once those are installed, you can just run

$ R data-generator-v2.R

to calculate the index files. (The R script may generate numbers that are different at the last decimal place to those from the Python notebook—these differences are due to rounding errors and can be safely ignored.)

This software is published by The Economist under the MIT licence. The data generated by The Economist are available under the Creative Commons Attribution 4.0 International License.

The licences include only the data and the software authored by The Economist, and do not cover any Economist content or third-party data or content made available using the software. More information about licensing, syndication and the copyright of Economist content can be found here.