Here's how the average salary changes as more experience is gained.
Notes on methodology. To try and keep the data as scientific as possible, we’d like to add a few comments about data bias and limitations:
It wasn’t ideal to use a fresh data source to calculate US State GDP, but the World Bank GDP per capita dataset unfortunately doesn’t supply us with accurate GDP per capita information. For calculating GDP per capita within the USA, we used the 2014 figures from the US Department of Commerce data for GDP by state and divided this by the United States Census Bureau data for state populations (estimated totals as of 2014). Since we are only using this state GDP per capita for comparisons inside the US, this new dataset should be OK for what we need to calculate. We also felt this was preferable to trying to merge together two data sources and create our own GDP per capita calculations based on World Bank GDP information for the US as a whole and US census results.
We are not the greatest fans of GDP as a comparison metric between countries and appreciate that using industrial output as a measure of ‘success’ is somewhat flawed. It would have been preferable if we’d been able to use median household income, or even average salary, as a comparison figure, but unfortunately we weren't able to secure a reliable dataset from a single period in the last few years, that would give us reliable global data.
So we’ve settled for GDP for now, but its use should be viewed with a little caution. This international GDP data is from the World Bank’s GDP per capita (current LCU) dataset from 2013.
Our Google Sheet uses live currency conversion rates to turn local currency submissions into their current USD equivalents for easier comparison. Periodically, we export the data from the spreadsheet so that it can be shown on the minisite. So, while this article is correct at the time of publication, you can expect to see some variations if you’re checking out the data a few weeks or months later.