Bernie Spends the Most Money, Creative Agencies Are the Real Winners of the 2016 Presidential Election, and Other Fun Discoveries from Campaign Expenditure Data

  • Democrats (3): Hillary Clinton, Bernie Sanders, Martin O’Malley
  • Republicans (12): Donald Trump, Ted Cruz, Marco Rubio, John Kasich, Ben Carson, Jeb Bush, Chris Christie, Rick Santorum, Rand Paul, Mike Huckabee, Carly Fiorina, Jim Gilmore
  • Third-party (2): Gary Johnson (Libertarian), Jill Stein (Green)

How much do candidates spend?

$840,000,000

Clinton and Sanders have spent by far the most

Total campaign spendings to date
Average monthly spendings

Comparing the frontrunners: Clinton outspends Trump in almost every category

Relative spending amount: Clinton versus Trump (this chart shows how much more Clinton has spent relative to Trump in each category — for example, she’s spent 3.9 times what Trump has spent on remote marketing, and 15.1 times what Trump has spent on payroll)

Spending amount is actually kind of correlated with popularity

Relationship between spending amount and popularity

What do candidates spend money on?

Comparing the frontrunners: remote marketing is the biggest expenditure for both Clinton and Trump, but other priorities differ

How each frontrunner is distributing their expenditures across categories

Creative agencies are the real winners of this election

  • Clinton and Sanders have spent the most money, by far.
  • Trump’s campaign is fantastic at either managing expenses or fudging expenses. They also really like making branded apparel.
  • Amount of spending is actually correlated with popularity.
  • Remote marketing is both presidential frontrunners’ most expensive category of spending.
  • This election has been especially financially rewarding for creative agencies.
  • We really need to standardize and tighten how candidates report their expenditures. The current process leaves too much room for inconsistency and inaccuracy.
  • If a campaign manager wants to cut costs, look into less expensive options for remote marketing, since that is currently the campaigns’ biggest line item.
  • If a creative agency (or private jet provider) wants to increase revenue, marketing to political candidates could be a good idea.
  • Why is Trump spending so much money (proportionally) on services?
  • What incentives exist today for candidates to report something as a campaign expense versus a non-campaign expense? This will help us understand why and whether a candidate might underreport or overreport campaign spendings.
  • How does spending amount relate to campaign budget? I would imagine how much money a campaign has impacts how much money that campaign spends.
Gems from the dataset
  • Why is Bernie paying people with ice cream and how common is it for political candidates to pay people “in kind” in general? I’m just imagining all those idealistic, bright-eyed interns working at campaign offices across the country, being paid in nothing but ice cream and office supplies and rent…
Yea I know this guy isn’t in the 2016 race but you know you wish he were
  • Writing Excel functions and SQL queries and making charts are really not the hard part of data analytics. They’re certainly not easy either, but what’s much more challenging is approaching a complex issue in the right way (or with the right “analytical framework,” as my instructor would say) and then telling a simple yet powerful story about the results. Learning to do that better is what I’ve enjoyed the most about my class. It is very much still a work in progress, and if you’ve read through this whole post and have feedback on how I can tell this data story better, I’m all ears.
  • Excel and SQL might not be the hardest part of data analytics, but they’re pretty darn hard to remember over the long haul in the absence of continual practice. For that reason, I’m challenging myself to take on a data analytics project on a regular basis. This election expenditure project was my first foray. There will be more to come. Can’t let those newly-built Excel and SQL muscles go flabby.
  • She who analyzes the data, has the power. I am blown away by how much sway the analyst has over major decisions and opinions. Most of the world population are reliant on other people to make sense of data for them. This is especially true when we’re talking about the increasingly massive datasets at our disposal, where tools like Excel and SQL and Python become crucial for sense-making and where the number of people who can do the sense-making becomes ever smaller. But as we saw earlier, datasets have flaws. They’re often not entirely accurate. They are vulnerable to individual analysts’ personal biases. Their results are dependent on what analytical framework gets chosen. All this makes it increasingly important for the general populace to have a foundational knowledge of how data works, and for all of us to be doubly critical when we are presented with data-driven results. It is simply too easy to lie with statistics.

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Annie L. Lin

Annie L. Lin

People & ops leader | data storyteller & nerd