Initial r/Financial Independence 2018 survey results are in!  Here’s a quick and dirty dive into the survey results (we will go deeper in due time… don’t you worry ;)).  For those not familiar with Financial Independence, it’s a concept where you earn more, spend less, and invest the rest, while discovering and achieving your life goals! Usually this is achieved by saving a larger portion of your income, creating a nest egg that will hit a point at which withdrawing a certain amount per year will yield a livable income stream in perpetuity.

After downloading the survey raw file found here into Excel, I started to clean the data.  The structure of the survey, exported into a spreadsheet, was not extremely user friendly.

I only kept respondents in the United States (sorry non-U.S. readers!) and those who have not achieved financial independence yet.  Furthermore, I broke the vast majority of columns into two tables linked by an assigned Record ID, and created a data model for analysis (in case we wanted something from the second table).  I also bucketed a few answers for easy analysis, such as annual household income and net worth.  Yeah, yeah, blah blah blah get to the good stuff already!

We looked at 1496 respondents’ submissions after the initial sweep.

Here are a few highlights from the demographics portion.  I won’t include all of them, since there will be a lot of information…  If you want to play around in Excel, here is the OneDrive link to the workbook!  Download it into your local machine and start clicking away 🙂

Without further ado, here is some general demographic information!


  • 78% of respondents were male
  • 20% of respondents were female
  • The rest either declined to state or was non-binary

Highest Level of Education


Relationship Status


Full-Time Employment Status

  • 92.8% of respondents worked for an organization
  • Only 1.6% of respondents were self-employed!

Political Party

  • 51.0% of respondents were Democrats
  • 19.9% of respondents did not affiliate with a Political Party



The top 5 occupations were: Computer, Architecture/Engineering, Finance, Management, and Healthcare.  These five accounted for 66.6% of all occupations!

Age Groups


Children (Planned or Have)

  • 77% of respondents did not have and do not plan to have children!

Planned Retirement Age


Next, let’s get to the good stuff…

Let’s take a gander at the numbers; specifically, let’s look at annual household income, financial independence number, and percent to FI.

Here are the summary statistics for the three:


In a more visual representation, we see that there are quite a few suspected outliers outside of the inner fence (see here for an explanation on how to read box-and-whisker plots)


We can see that there’s quite a variance in what respondents earn and their net worth.  Not only that, but their idea of financial independence differs greatly, with some respondents only happy when they reach the multi-million dollar mark, and others when they break a few hundred thousand in net worth.

Let’s look at how annual household income is correlated to % to FI:


It looks like it’s weakly positively correlated.  However, this isn’t the best fit, with 86% of the variance in the model unexplained (r squared of .14, 1-.14 = .86).

Cleaning up the raw data to only include non-suspected outliers and outliers, we end up with this box-and-whisker plot:


It looks much cleaner; how does our Scatterplot look?


Our goodness of fit has decreased from 14.21% to 13.65%?! This perhaps intuitively makes sense, as someone in his late 40’s may be earning well into the six figures, but they only discovered FI a year or two ago, while a 28 year old college grad maxing out his 401k every year since graduation may think $400k is enough for lean FI, bringing his % to FI in the 20%’s!  Let’s take a look at some Pivot Tables to see if this is true:


It looks like the majority of respondents are still in the first half of their journey towards financial independence.  Let’s break this down by age buckets:


With a slicer, we can see only respondents in the <18 and 18-23 buckets.  It looks like the vast majority are in the 0%-10% bucket towards FI, but there are a few high achievers.  Keep in mind that their end goal can affect their coding (i.e. if they believe they will reach financial independence at $250,000, they will need $25,001 to be in the 10%-20% bucket, whereas if they believe they need $2,500,000, then they’ll need $250,001 to be in the same bucket).


At ages 24-28, we not only have more respondents in general, but also more respondents are inching towards the 10%-20% bucket.


When looking only at age 29-43 buckets, we see that a lot of the respondents are in the mid-game.  However, we also see that the largest two buckets are 0%-10% and 10%-20%!  Even if someone in this category is making six figures, they still have a decent chance of being in the 0%-20% buckets.  This could help explain why the Scatterplots were all over the place!

To be continued…

My next goal (after finals, yes I’m procrastinating) is to create a step-wise multiple logistic regression model to calculate the chance of being 0%-10%, 10%-20%, etc. towards FI based on a few statistically significant explanatory variables.  To make this interactive, I want to create a calculator in Excel to output the probability!

If you have other questions you want answered with the data, please do not hesitate and leave a comment!